But what my wife was imagining—a truly self-improving AI—is exactly what the world’s top labs are racing to build next.

Silicon Valley has a new buzzword: Recursive Self-Improvement (RSI). The concept—where an AI model autonomously, repeatedly improves its own intelligence—has become the ultimate goal for top labs racing toward Artificial Super Intelligence (ASI).

The timelines are moving fast. Anthropic has warned that AI models are nearing the capability to improve without human intervention, adding that they are even willing to slow down if risks escalate. OpenAI’s Sam Altman said in a recent blog that by March 2028, a significant portion of their AI research could be conducted by AI working alongside human researchers.

If achieved, this would be the holy grail of AI, the Technological Singularity. Popularized by Ray Kurzweil, this is the hypothetical tipping point where technological growth accelerates beyond human control or comprehension. The modern idea traces back to mathematician I.J. Good in 1965, who hypothesized that once an AI reaches human-level intelligence, it can rapidly redesign its own software and hardware, triggering an “intelligence explosion.”

A bunch of neolabs have emerged to explore this direction. Recursive Intelligence is a new research lab founded by former Salesforce Chief AI Scientist and You.com CEO Richard Socher. The company’s Chief Scientist Tian Yuandong recently explained their mission to Chinese media, which perfectly distills what RSI looks like in practice:

Recursive self-improvement means using AI to optimize certain steps of AI development to make the model stronger, and then continuing to iterate upward on that new foundation. Specifically, we want AI to handle tasks that still require manual human effort today—like scientists searching for new ideas, discovering new logic, or leveraging AI for reinforcement learning during the training process.

Our ultimate vision is to build a system where you plug in computational resources, and it outputs new knowledge, insights, and discoveries. As our website states, our goal is ‘maximizing the knowledge discovery rate’ to accelerate human progress.

Researchers in China are also closely following this trend. Lin Junyang, the former tech lead of Alibaba’s Qwen team, recently shared his thoughts on X.

Lin’s new lab, which has reportedly closed the first financing round at a $2 billion valuation, is rumored to explore this exact direction, alongside world models and embodied AI.

To be clear, today’s RSI isn’t a sci-fi superintelligence autonomously designing its next-gen successor from scratch. It’s still in an early stage like automating critical pieces of the AI research and development pipeline. Automated data generation for example has been for years a typical self-improving technique for models. AlphaZero, the go-playing AI invented by Google Deepmind, improved itself via self-play without relying on human data.

Before ChatGPT went viral, AutoML and Neural Architecture Search (NAS)—using AI to automate the design of neural networks—were incredibly hot topics. NAS eventually lost the spotlight because model architectures converged, and scaling data and compute became more important than micro-optimizing algorithms. However, looking back, NAS was absolutely an early form of self-improvement research.

This time is different, because the foundation models themselves are now incredibly powerful. They possess vast world knowledge, strong reasoning abilities, and most importantly the ability to write code and act in an agentic behavier. In top AI labs, AI is already writing 90% to 95% of the code. Therefore RSI is building on a highly capable foundation where the model itself possesses the tools to potentially modify its own behavior.

Furthermore, as AI agents mature, researchers are also studying self-improving agents. These systems can interact with and learn from their environments, autonomously extracting memories and skills rather than relying on human engineering, and iterating on what they learn.

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Self-Improving AI 101

One of the best ways to understand a rapidly emerging AI topic is to look for a systematic survey paper. Luckily, I found a great one, A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Superintelligence. This collaborative paper involves over 10 top global universities—including Princeton and Carnegie Mellon from the U.S., and Tsinghua, Shanghai Jiao Tong, and Fudan University from China—and is predominantly authored by Chinese researchers.

The paper argues that the core problem of today’s LLMs is their parameters are static. They don’t adapt to new tasks, knowledge, or changing environments. As we deploy LLMs in open-ended, interactive environments, this rigidity will become a bottleneck. To solve this, the survey puts self-improvement AI around four foundational questions:

• What to evolve? This focuses on modifying the models themselves, the context (like memory and prompts), the tools they use, and their underlying architecture.

• When to evolve? Evolution happens in two ways: intra-test-time (real-time adaptation while performing a task) and inter-test-time (learning between tasks by accumulating experience over time).

• How to evolve? The authors outline three major paradigms: reward-based evolution, imitation/demonstration learning, and population-based/evolutionary methods.

• Where to evolve? This spans general domains like memory mechanisms, model-agent co-evolution, and curriculum-driven training, as well as specialized industries like coding, GUI agents, finance, medicine, and education.

The paper also identifies five key metrics for evaluating self-evolving agents:

• Adaptivity: Can the model actually improve its performance?

• Retention: Does it remember past knowledge, or does it suffer from catastrophic forgetting?

• Generalization: Can it transfer its newly learned skills to entirely new tasks?

• Efficiency: At what computational and financial cost does this evolution happen?

• Safety: Can it improve without experiencing harmful drift or unintended behavioral alignment issues?

Inside the ICLR 2026 RSI Workshop

When I researched this topic, I found the Recursive Self-Improvement Workshop at ICLR 2026. Self-described as possibly the world’s first workshop dedicated to RSI, it was backed by Tencent and the Beijing Academy of Artificial Intelligence (BAAI).

What caught my attention was four out of 11 committee members are Chinese researchers representing ByteDance, Tencent, CUHK, and BAAI. It made me wonder where exactly China stands in the race for self-improving AI.

The primary organizer and contact for the workshop is Zhuge Mingchen. He earned his computer science PhD at KAUST under the mentorship of Jürgen Schmidhuber—the uncrowned godfather of AI. Zhuge recently moved to Silicon Valley to become a founding member of Recursive Intelligence. Looking back at his 2024 paper MetaGPT, he predicted that multi-agent frameworks would inevitably need continuous self-optimization and ability evolution to scale.

The accepted papers at the workshop point exactly to where the tech is going, from LLM agents training models during post-training, to frameworks that meta-learn memory designs to completely replace human engineering, and fully autonomous setups where multi-agent co-evolution removes the need for external data. Yet, despite the Chinese-heavy committee, there were few research submissions credited to mainland commercial AI labs or corporate giants.

So what are China’s frontier AI labs actually building behind the scenes?

Self-Improving AI Research from Chinese Labs

A recent South China Morning Post story revealed that several major Chinese players are already reporting internal progress.

Xiaomi’s AI lead Luo Fuli predicts fully self-evolving AI could arrive within just 1–2 years. MiniMax claims its M3 model autonomously reproduced an award-winning research paper in 12 hours and optimized complex GPU code in 24 hours. Alibaba said that its Qwen model completed a similar code-optimization task in 35 hours—roughly 10 times faster than human engineering teams. ByteDance and Tsinghua University published research showing a 100% speedup in hardware kernel optimization using AI research agents.

I dug in more academic papers coming out of these labs, which generally fall into three distinct strategies:

Zero Data

Much like AlphaZero mastered Go by playing against itself, Chinese researchers are using AI to generate its own training data from scratch.

• Tencent AI Lab in Seattle introduced R-Zero, a fully autonomous framework that builds training data with zero human input. It pairs a Challenger (designed to generate tasks that push the model to its absolute limits) with a Solver (the model finding the solution). This closed loop substantially improves reasoning capabilities across various backbone LLMs without needing pre-existing labels.

  

• Tsinghua University followed a similar direction with Absolute Zero Reasoner (AZR), except they condensed the loop into a single model playing both Proposer and Solver to generate and solve code-verified tasks.

Automating Policy and Credit Assignment

• Alibaba’s Tongyi Lab took the loop a step further with AgentEvolver (co-authored by Alibaba’s chief scientist Zhou Jingren). Instead of just generating data, it automates the learning policy itself using three core pillars: self-questioning (curiosity-driven task generation), self-navigating (experience reuse), and self-attributing (differentiated credit assignment). It beats out much larger baselines on benchmarks like AppWorld while remaining incredibly parameter-efficient.

  

• Shanghai AI Lab team runs massive reinforcement learning and agent programs. Their OS-agent research, including OS-Copilot, OS-Atlas, and OS-Genesis, is explicitly designed to build general, self-improving operating system agents.

AI-for-AI Research

• Last year, researchers from Shanghai Jiao Tong University, SII, Taptap, and GAIR made a bold claim that they have created the first Artificial Superintelligence for AI Research (ASI-ARCH). This autonomous multi-agent system ran over 1,773 experiments spanning 20,000 GPU hours. It completely bypassed human-curated spaces and discovered 106 SOTA linear-attention architectures that outperform Mamba2.

• On top of it, the same team is taking on a further challenge: Can AI automate the long-horizon, weakly supervised research loops that drive core AI progress? Their new framework, ASI-Evolve, introduces a closed “learn-design-experiment-analyze” loop. It enhances standard evolutionary agents with a cognition base (which injects human priors so the AI doesn’t start from scratch) and a dedicated analyzer (which distills chaotic experiment results into clean insights for the next round). The framework tries to discover breakthroughs across the three pillars of AI development:

  • Architectures: It discovered those 105 SOTA linear attention architectures mentioned earlier, with the best model beating human-designed DeltaNet by +0.97 points.

  • Data Curation: Its evolved data pipelines boosted average benchmark scores by +3.96 points, jumping a massive 18 points on MMLU.

  • Learning Algorithms: The algorithms it discovered beat out standard GRPO by up to +12.5 points on math benchmarks like AMC32.

Evolving Agent Skills

• Recently researchers have been exploring how to improve and refine LLM’s external knowledge and skills at scale. Google’s ReasoningBank and Microsoft’s SkillOpt are typical research works. Researchers from Alibaba’s Qwen team proposed Trace2Skill where the framework can turn model trajectories into skills in parallel. It uses inductive reasoning across the agent’s experiences—both failures and successes—to distill recurring lessons into a single SOP. What’s interesting is the skills extracted from small models can help improve a much larger model. For instance, skills evolved from Qwen3.5-35B trajectories successfully improved a massive Qwen3.5-122B agent by a 57.65 percentage points on the WikiTableQuestions benchmark.

DeepSeek hasn’t released a dedicated self-improving AI research yet, but in their blockbuster DeepSeek-R1 paper, R1-Zero proved that a model trained purely on reinforcement learning—without any initial human supervised fine-tuning (SFT)—naturally evolves emergent self-reflection, real-time verification, the famous “aha moment,” and an autonomous allocation of longer thinking time.

Furthermore, a Bloomberg report last year highlighted DeepSeek and Tsinghua’s work on a strategy called self-principled critique tuning. Rather than relying on human annotators to align the model, the AI uses an automated internal rubric to reward responses that are both accurate and understandable. This method outperforms standard alignment benchmarks while using a fraction of the compute.

Ultimately, based on the researchers I’ve spoken with and the latest ICLR workshop discussions, there is no major divergence between the US and China when it comes to the direction of self-improving AI. Researchers from both countries are focused on the similar playbook: optimizing loops to let models update their own weights, and engineering self-evolving agents that can interact with and adapt to the complex environments.

The real gap isn’t the strategy but the underlying foundation models. Because a self-improvement loop is only as smart as its foundation, whoever possesses the superior base foundation model inherently has a much higher probability of successfully upgrading it.

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When Anthropic abruptly suspended all access to its newly released Mythos 5 and Fable 5 models last Friday following an unexpected U.S. government ban on foreign nationals using these models, Chinese AI labs quickly capitalized on the opportunity. They released new open-weight models and sent a message: Their AI will remain open to the world.

For background, Mythos 5 and the safeguarded Fable 5 are Anthropic’s most powerful models to date, having just launched last week. Rumored to feature a staggering 10 trillion parameters, they achieved SOTA results on most benchmarks. However, Anthropic warned that the models pose cybersecurity risks—specifically the ability to autonomously find and exploit software vulnerabilities—which reportedly triggered the U.S. government’s sudden intervention.

Here is how Chinese labs are responding to the vacuum.

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MiniMax Open-Sources M3

First up is MiniMax. The Hong Kong-listed AI company introduced its latest flagship model, MiniMax M3, a week ago and on Friday opened its weights. As the company’s first multimodal flagship, M3 performs on par with GPT-5.5 in coding, agentic information retrieval, and physics-based reasoning.

Surprisingly, the model is incredibly small. It features only 428 billion total parameters, activating only 23 billion parameters per inference run. For context, DeepSeek-V4-Pro features over 1.6 trillion parameters.

The model supports a 1M-token context window, thanks to its new proposed attention mechanism called MiniMax Sparse Attention (MSA). The company said MSA can partition the KV into blocks more precisely compared to DeepSeek Sparse Attention (DSA).

Ryan Lee, Head of Developer Relations at MiniMax, tweeted about the architectural choice:

We’ve been discussing what parameter size works best for the community. While the M3 series boasts a larger parameter count compared to the M2 lineup, we’ve kept its scale deliberately restrained so local model enthusiasts can run it affordably. This time we settled on 428B, hoping it will be accessible to a wider audience.

The moment news of the Fable and Mythos restrictions broke, MiniMax quickly retweeted Anthropic, implying they would never lock out users.

Zhipu AI (Z.ai) Drops GLM-5.2

Next came Zhipu AI’s announcement of GLM-5.2. Friday night in the U.S. (Saturday afternoon in Beijing) is usually a terrible time to drop a flagship LLM, but Zhipu pushed ahead anyway.

The company said GLM-5.2 is already rolling out to its Coding Plan users, adding that intelligence should be open and accessible to empower developers everywhere.

Zhipu’s co-founder and Chief Scientist, Tang Jie, took a more direct stance on X:

A very interesting detail: According to Anthropic’s statement, they received the U.S. government’s legal directive to pull the plug on their models on Friday at 5:21 PM ET. Zhipu timed the rollout of GLM-5.2 to Coding Plan users at exactly 5:21 PM, Beijing Time, the next day, according to a popular Chinese AI influencer.

The model’s full rollout is targeted for later this week. I will update this post with technical details and pricing once it goes live. What we know so far is that GLM-5.2 supports a 1M-token context window and leads in long-horizon tasks (complex tasks executed over long periods). This remains a text-only model though, not a multimodal one.

Zhipu AI’s stock price surged as much as 48% today, as JP Morgan raised its target price to HK$1400 from HK$950.

Moonshot AI Releases Kimi-K2.7-Code

Moonshot AI also released a new specialized coding model called Kimi-K2.7-Code. The company boasted notable improvements over its predecessor, K2.6, in coding and agent performance: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite (it’s worth noting these are less widely recognized benchmarks).

The model’s pricing is at $0.95 per million input tokens and $4.00 per million output tokens.

The 1T-param model is 30% less verbose than K2.6. As token efficiency becomes an important metric for enterprise clients looking to lower AI inference costs, conciseness is becoming a major selling point. I plan to write a piece on this trend next week. K2.7-Code also features improvements in long-horizon coding tasks.

Alongside the model, Moonshot launched the Kimi Code Beta Program as a direct rival to Claude Code.

As geopolitical uncertainty rises and frontier AI development concentrates in the hands of just a few labs, the open-source card is allowing Chinese AI labs to claim the moral high ground.

Despite an overall performance lag behind the frontier, these Chinese models are accessible, cheap, and have crossed the threshold of being “usable” for complex end-to-end software engineering tasks. The abrupt closure of Anthropic’s Fable and Mythos models has handed these labs a massive marketing window.

And I do believe their commitment to open science is sincere, rather than just a marketing stunt. For example, most researchers at Zhipu come from strong academic backgrounds deeply involved in global research communities. Zhipu CEO Zhang Peng previously noted in an interview that receiving global recognition during the early days of the GLM series was what motivated them to keep pushing forward. They also rely heavily on the open-source community to iterate; recent versions of GLM openly adapt architectural innovations from other open models like DeepSeek and Kimi.

recently shared her observations in a Singapore AI conference:

Super cute. MiniMax and Z AI just told each other they’re huge fans of each other on the main stage of SuperAI. The vibes of the China AI ecosystem feels much more collegial.

To be frank, tech competition in China is notoriously brutal, and I don’t know how long this “lovey-dovey” honeymoon between rival labs will last. But for now, having multiple labs open-sourcing their research to benefit the global community—while still progressing commercially—is a clear win-win for everyone.

In a bigger picture, the divide between open and closed AI isn’t necessarily about who “cares” more about safety. Having followed Dario Amodei’s interviews and read Anthropic’s safety essays, I believe their commitment to the proprietary, closed-source model is rooted in a deeply considered effort to prevent AI from losing control. I have no intention of diminishing the gravity of that mission.

However, my perspective aligns more closely with the open-source approach: AI is not a nuclear weapon that should be locked. It is a computer science advancement that requires the collective scrutiny and ingenuity of the global community to build safeguards.

I am deeply skeptical of allowing one of the most important technologies in the human history to be concentrated within the boardrooms of just a few commercial companies. Over-concentration of power, without rigorous public regulation, is historically damaging. Rather than placing blind trust in a single commercial company, I believe in leveraging the diverse perspectives of researchers worldwide who can contribute without commercial or geopolitical agendas.

Moreover, we need a future where nations are not forced to beg for a piece of AI software from a few superpowers. Instead, we need a framework that empowers every country to develop their AI that respects their unique cultures, politics, and social fabrics—ensuring that the benefits of this technology are reclaimed by the people it is meant to serve. As posted on X, open source is the only path to any AI sovereignty.

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Today, SpaceX created the largest IPO in history, raising $75 billion at a $1.75 trillion valuation and trading on Nasdaq under the ticker SPCX. The stock rose 30% in its debut.

If you read SpaceX’s roadshow presentation, next to its “traveling to Mars” ambition sits another promise Elon Musk is selling—putting data centers in space.

A space data center is one of the sexiest stories from Wall Street’s perspective: shipping AI chips to orbit on cheap, reusable rockets, and letting them run on energy from sunlight and inside the free, near-infinite space, both of which are scarce and expensive on Earth.

What was a pitch slide a year ago is now becoming a reality. In November 2025, the Nvidia-backed startup Starcloud placed an H100 into low Earth orbit by using SpaceX’s rockets and ran a Google model on it. A Blackwell GPU is due around October 2026. Nvidia Founder and CEO Jensen Huang said “space computing, the final frontier, has arrived.”

But this is not just an American story. China has been flying the same wager through its own commercial companies and state-affiliated labs, and on at least two achievements—running a general-purpose model in orbit, and using a satellite to command a machine on the ground—China actually got there first.

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China is Already Up There

According to Zhang Shancong, president of the Beijing Astro-future Institute of Space Technology (BAIST), space computing is evolving across three tiers defined by scale, power, and thermal infrastructure:

• It begins with Edge Computing Satellite, which consists of small, low-power (<1kW) embedded boxes hosted as secondary payloads on satellites without complex cooling systems.

• The next tier is the Computing Satellite, a dedicated, medium-scale platform (1kW to 1MW) built specifically around data servers that requires independent radiator panels and fluid loops to manage heat across multiple orbital planes.

• The final and most advanced tier is the Space Data Center, which scales computing up to massive, mega-to-gigawatt infrastructure featuring full server racks. To handle the intense heat generated by this industrial-scale workload, it utilizes advanced radiator arrays and pumped two-phase cooling loops, and it is typically deployed in dawn-dusk sun-synchronous orbit, a specialized near-polar satellite trajectory, to maximize continuous solar energy.

Today, China’s orbital footprint is in the early pilot deployment phase—the edge computing satellite stage—driven by a mix of commercial firms and government-affiliated labs.

The flagship project is the Three-Body Computing Constellation, named after the famous Chinese sci-fi epic, built by the government-affiliated Zhejiang Lab alongside dozens of partners, including a Chengdu-based satellite company called ADA Space. In May 2025, the partnership put their first 12 satellites into low Earth orbit aboard a Long March rocket.

Rather than relaying raw data to the ground for handling, those satellites carry edge computing onboard, delivering a combined five POPS—peta-operations per second—and 30 terabytes of storage in their opening batch. The roadmap scales toward as many as 1,000 networked satellites and 1,000 POPS of pooled compute by 2032.

ADA Space, full name Chengdu Guoxing Aerospace Technology, is also pursuing a larger plan of its own: a decentralized “Star Compute” constellation of 2,800 satellites, 2,400 for inference and 400 for training, spread across sun-synchronous, dawn-dusk and low-inclination orbits.

The company has pulled off multiple technical experiments. In November 2025, it flashed Alibaba’s Qwen3 onto an operational satellite and ran end-to-end local inference in under two minutes, the first general-purpose LLM to run in orbit. In a separate demonstration with Shanghai Jiao Tong University, it used space-borne compute to directly command a ground robot, proving compute satellites can act as low-latency control nodes if terrestrial networks fail. Then, in March 2026, working with the Ministry of Industry and Information Technology (MIIT), it launched “Prometheus,” billed as the first space-based cloud platform aimed at commercial enterprise customers.

ADA Space has filed for an IPO on the Hong Kong Stock Exchange, with a pre-IPO valuation of RMB 11.5 billion (~$1.7 billion). The company reported 2025 revenue of 703 million yuan ($104 million) alongside a net loss of 256 million yuan ($38 million).

Meanwhile, Zhongke Tiansuan, or Comospace, is a deeper-pocketed commercial spin-off of the Chinese Academy of Sciences and Zhejiang Lab. The company is designing a near-Earth “10,000-processor super-intelligent cluster in orbit,” built from three upgradable modules:

• Energy Module (100 MW): Utilizing high-efficiency, flexible space photovoltaic arrays and modular energy storage systems to capture continuous solar exposure in dawn-dusk orbits.

• Communication Module (10 Tbps): Utilizing 100 separate 100 Gbps laser links to establish high-speed, dynamic space-to-ground and inter-satellite networks.

• Compute Module (10 EOPS): Integrating 10,000 high-performance computing cards optimized for the extreme thermal and vacuum environment of space.

Their premise is that ground-proven commercial chips can be adapted to the vacuum through software-defined resilience and environmental engineering, rather than the long design cycles of traditional, radiation-hardened space silicon.

Other key players include Beijing-backed Orbital Chenguang, which just closed its Pre-A1 funding and secured 57.7 billion yuan ($8.4 billion) in strategic credit lines in April. Shanghai Bailing Aerospace raised early-stage money toward a 100kW-class platform. Oriental Tiansuan, also based in Shanghai, partnering with the photonics startup Guangbenwei, claimed in May to be developing the world’s first space-based optical computing satellite.

The latest headline is on June 1, 2026, Beijing established its first space-computing innovation center in Haidian, jointly led by Beijing University of Posts and Telecommunications (BUPT) and built around six priority areas that include heat-resistant, radiation-tolerant chips made specifically for orbit. At the same, a space computing research institute was also built up in Beijing E-Town aimed at ramping up tech innovation.

Beijing’s Answer to Vertical Integration

The people building it are candid about China’s weakness. As Wang Shangguang, Dean of the School of Computer Science at BUPT said at the Beijing center’s launch:

We have to recognize that SpaceX, by itself, can pull the entire industry chain together—chips, rockets, satellites, payloads, applications—and then sell the resulting compute through that same network, earn money, and reinvest it, closing its own loop; domestically, by contrast, everyone is doing their own thing, and the state we are in is ‘small, weak, scattered, slow.

Digging into the research, I found Wang often delivered sharp quotes. For example, he said the core challenges facing China’s orbital computing ambitions in just twelve words: “网网不同、星星并存、算算失衡” (Fragmented networks, isolated satellites, and broken compute).

It means fragmented protocols disconnect space from Earth, isolated hardware architectures prevent satellites from collaborating, and a severe asymmetry in compute leaves orbit underpowered while ground response lags.

To prevent fragmented commercial development, the Chinese government is building a highly structured, top-down regulatory and technical framework.

• The China National Space Administration (CNSA), through its commercial space department, has opened a national feasibility study for a unified “space-based intelligent computing constellation.”

• The China Academy of Information and Communications Technology (CAICT), under the MIIT, has built a Space Computing Power Professional Committee to align chips, inter-satellite laser links, thermal management and space photovoltaics into commercial standards.

• The MIIT is drafting a full standards stack covering orbital hardware, operating systems, networking protocols and cybersecurity.

Beijing, Shanghai, Hangzhou, and Chengdu also lead the regional clusters. These centralized efforts are driving optimistic economic forecasts. Preliminary estimates from CAICT project that China’s domestic space-based computing power industry will surpass 250 billion yuan (~$36.6 billion) by 2030.

Engineering the Extremes

Operating high-density silicon processors in the vacuum of space presents brutal environmental challenges. Chinese engineers have been developing architectural workarounds to mitigate radiation, handle thermal dissipation, and establish high-speed communications.

Space radiation is deeply hostile to semiconductor hardware which would cause damage and hardware failure. To deploy powerful, commercial off-the-shelf (COTS) chips without relying on slow, outdated radiation-hardened components, Chinese engineers are turning to software-defined fault tolerance. For instance, Zhejiang Lab utilizes a “space-based distributed operating system” paired with a ground-based digital twin. This architecture simulates radiation anomalies before deploying AI models to orbit, using dynamic task migration and real-time error-correcting codes (ECC) to instantly reroute computing tasks away from degraded satellite nodes to healthy neighbors across the constellation.

Thermal management poses another hurdle in a vacuum, where the lack of air convection leaves radiation as the only mechanism for heat dissipation. To handle the intense thermal loads of high-density AI accelerators, Comospace developed a hybrid active-passive cooling architecture. This microgravity-safe system pumps liquid coolant directly across high-power processors and routes the heat to deployable, high-emissivity structural radiators. To maximize efficiency, these satellites are placed in 700-to-800 km dawn-dusk sun-synchronous orbits, keeping solar panels continuously powered while permanently pointing the radiators into the deep-cold shadow of space.

Finally, the viability of these space-based supercomputing networks hinges on ultra-high-bandwidth communications. The Chinese Academy of Sciences recently achieved a major breakthrough by establishing a stable satellite-to-ground laser link peaking at 120 Gbps over the Pamir Plateau. Remarkably, the engineering team doubled the system’s previous 60 Gbps capacity without changing any hardware; instead, they used in-orbit software reconfigurations to optimize beam-forming and modulation algorithms, successfully downlinking over 12 Terabits of data in a single 108-second window.

The US-China Gap

So, where does China lag? The most immediate bottleneck is hardware: domestic AI accelerators trail state-of-the-art U.S. chips by four to five years. To achieve equivalent system-level compute, China must bundle significantly more chips across more physical racks. This dramatically penalizes the system by inflating power consumption, weight, and thermal dissipation loads.

The second and perhaps more challenging shortfall is launch economics. As BUPT’s Wang Shangguang admitted:

The cost of reaching space—the maturity of reusable rockets—is where the United States is plainly ahead, and that directly governs how many compute satellites you can deploy and how quickly you can iterate... What worries me more is that much of their key technology is never made public, because a visible gap we can still chase, while the invisible one—disruptive technology evolving quietly in the dark—is what creates real pressure.

SpaceX’s future orbital data-center business relies on fully reusable rockets driving payload costs down to the low hundreds of dollars per kilogram—eventually targeting $183/kg. This will make throwing up modular, disposable compute clusters economically competitive with building data centers on land. In contrast, China still relies primarily on expendable launch vehicles like the Long March series.

China is aggressively pursuing reusable rocket technology. On June 1, 2026, China successfully completed the mainden flight of its 72-meter Long March 12B rocket—a new, reusable vehicle designed as a rival to SpaceX’s Falcon 9. The rocket flew in an expendable mode for its debut, delivering its first operational payloads for the Qianfan internet megaconstellation. According to the China Aerospace Science and Technology Corporation (CASC), the successful launch signals that “China has added a new commercial rocket to its fleet for building large-scale internet constellations.”

China has flown the GPUs, run the models in orbit, stood up the regulatory committees, and drawn the roadmaps. It is unequivocally aiming to build gigawatt-level data centers in space as AI continues to fuel an insatiable global appetite for compute.

SpaceX’s massive IPO will act as a global catalyst, stimulating an influx of capital, state support, and friendlier regulation across the industry. But China doesn’t have its SpaceX yet. Its top-down approach to uniting academia, industry, and government to build an orbital supercomputer is a great strategy, but catching up with SpaceX is going to be a long journey ahead.

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A week ago my dad called me with a joke. He said Huawei had just released a new semiconductor principle called 韬定律, the Tau Law. My first name is 君韬 (Juntao), and Tau 韬 sits right there in the middle of it. So by his logic, Huawei owes me a naming-rights fee. I told him I would settle for a Huawei Mate 80 smartphone.

The Tau Law was first unveiled by He Tingbo, who leads Huawei’s semiconductor business, at the IEEE ISCAS conference in Shanghai on the morning of May 25. Huawei also released the paper A Time Scaling Theory for Multi-Layer Electronic Systems that further explains this principle.

I am not a chip expert, but I have written multiple stories about China’s AI silicon, from the outside looking in. So when a new study shows up wearing my own name, creating new buzz, and immediately starts moving stock prices, I want to understand what it actually is. Here is what I learned.

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Personal update: Last week I launched Chinaidb.com, a one-stop intelligence source for China’s AI ecosystem, covering models, funding, applications, and news all in one place. I’d love for you to check it out and share your thoughts. Any feedback is greatly appreciated!

What is Tau Law

The core thesis is surprisingly simple. He Tingbo said Moore’s Law, the old rule of dimensional scaling, is slowing down beyond 7 nanometers, and the returns from simply making transistors smaller have flattened. So Huawei proposes a different yardstick. Instead of optimizing for size, optimize for time, the speed at which a signal travels through the whole electronic system, from a single device up to a gigawatt-scale AI data center.

That is where the name comes from. In physics and electrical engineering, τ (tau) is the standard symbol for the RC product, resistance times capacitance, which defines how long it takes a signal to propagate through a circuit.

Moore’s Law, named for Intel co-founder Gordon Moore, is the observation that the number of transistors on a chip doubles roughly every two years with little rise in cost. It was an empirical rule that hardened into an industry roadmap, and that roadmap is what powered the modern information age and nearly every device we touch. The computing power of today’s single integrated circuit is about 2 billion times what it was in 1960.

The catch is that you can only make things smaller until you can’t. Moore’s Law has been slowing for years. Former Intel CEO Pat Gelsinger has said the doubling now happens closer to every three years rather than every two, though he insisted Intel had strategies to claw the pace back. Huawei’s argument is that the industry needs a second engine for when the first one stalls. The Tau Law is its candidate.

Huawei’s Tau Law roadmap runs through 2031, and by then it claims to push past 400 million transistors per square millimeter, a density equivalent to a 1.4nm node. As Huawei does not have access to the lithography that the rest of the industry outside mainland China uses, the entire story is to explain how it intends to get there anyway.

One important difference from Moore’s Law: there is no universal rate like a doubling every two years across the board. Huawei predicts instead that under the Tau Law, performance improves at different annual rates depending on what the chip is for. It defines that annual improvement through a scaling factor it calls α (alpha):

• Mobile devices: roughly 1.3x per year, the modest pace you would expect for power-constrained hardware.

• Autonomous systems: roughly 1.5x per year for safety-critical applications.

• AI workloads: up to 10x per year, because in this sector raw throughput converts almost directly into economic value.

Why Huawei, of all companies

Every company faces the slowdown of dimensional scaling. Huawei faces something much more severe, and the Tau Law cannot be understood without it.

In 2019 and 2020, Huawei and its chip-design arm HiSilicon were placed on the U.S. Entity List. The Foreign Direct Product Rule was then rewritten so that any foundry, TSMC and Samsung included, was blocked from manufacturing a Huawei-designed chip if that foundry used U.S.-origin software or equipment. The same wave cut off the EDA giants. Synopsys, Cadence, and Siemens could no longer ship Huawei software updates, patches, support, or new licenses.

In October 2022 and again in 2023, Washington widened the aperture from individual firms to the entire country, restricting China’s access to advanced AI accelerators and to the tools used to build them, and barring U.S. persons from supporting advanced chip production at certain Chinese fabs without a license.

The keystone of the whole structure is EUV. Through multilateral pressure on the Netherlands (ASML) and Japan, the U.S. choked off the export of extreme ultraviolet lithography machines, the only practical way to geometrically print sub-7nm transistors, along with the most advanced DUV immersion systems.

But the blockade did not stop development. Huawei plows north of 20% of its revenue back into R&D, and a large share of that money has gone into a single strategic question: how do you keep advancing semiconductors when you are locked out of the equipment everyone else uses to advance? The Tau Law is the answer to that question.

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During President Trump’s recent state visit to China, NVIDIA’s H200 reportedly cleared the regulatory hurdles to enter the Chinese market, with ten Chinese companies, including ByteDance and Alibaba, permitted to buy. Days later, Trump told reporters that China had not actually bought any H200s yet.

Whether the H200 ever lands at scale in China is still an open question. What’s already happening is Chinese AI labs are increasingly training and serving models on domestic AI chips.

In May 2026, Baidu said a “key version” of its ERNIE 5.1 was trained on its in-house Kunlunxin chips. In April, Meituan’s trillion-parameter LongCat 2.0 was reportedly trained on domestic chips. Separately, two of China’s strongest LLMs, Zhipu’s GLM-5.1 and DeepSeek V4, were adapted to run inference on domestic AI chips.

And new hardware keeps coming. While I was writing this post, Alibaba announced its new AI chip, Zhenwu M890, which it claims is three times more powerful than its predecessor. The new processor features 144 GB of GPU memory and 800 GB/s inter-chip bandwidth. Alibaba also said it has already delivered 560,000 Zhenwu chips to more than 400 customers across 20 industries.

If you read only the headlines, you might conclude that China has reached domestic-chip parity at the frontier. The reality is more complicated. China’s domestic chips can now train flagship-scale models, but they cannot yet train the true frontier, and the gap between those two things is narrowing faster than the model launches alone would suggest.

Baidu ERNIE 5.1 and Kunlunxin

Right before its annual developer conference, Chinese search giant Baidu released its latest LLM ERNIE 5.1. Since the first generation of ERNIE in 2018, Baidu has always trained on NVIDIA GPUs. This release marks a difference. According to Shen Dou, head of Baidu AI Cloud:

Kunlunxin P800 has completed large-scale validation, and multiple ten-thousand-card clusters have been delivered since last year. On the fully domestic Kunlunxin cluster, the company successfully completed training of a key version of ERNIE 5.1. The overall effective training rate of the cluster reached 97%, while the linear scalability of the ten-thousand-card cluster surpassed 85%.

Read that wording closely: Baidu’s press release last week said Kunlunxin supported the training of “a key model in the ERNIE 5.1 series.” (Kudos to my former Baidu PR colleagues who are always precise with the wording.) It tells you that Kunlunxin was used somewhere in the training pipeline of an ERNIE 5.1 variant, maybe a smaller distillation model within the 5.1 family or an earlier version.

A few things to know about ERNIE 5.1: It was derived from ERNIE 5.0, a 2.4 trillion-parameter MoE model, at only ~6% of comparable pretraining cost thanks to a Once-For-All elastic training framework. ERNIE 5.1 has around 800 billion parameters, with active parameters roughly half of ERNIE 5.0’s. It scored 1,223 on the LMArena Search leaderboard, first among Chinese models.

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China’s robotic industry is in an unprecentend capital boom. Unitree Robotics is racing toward an A-share listing, AgiBot founded by a former Huawei prodigy is lining up a Hong Kong IPO, and a clutch of humanoid startups have crossed 10 billion yuan in valuation in a matter of months.

But just over a year ago, the industry’s first standout—a company once valued at 22.3 billion yuan and hailed as China’s leading robotics unicorn—quietly fell apart. The story of CloudMinds Robotics, and the founder who built it, is less an outlier than a preview: a window into the structural pressures that today’s better-funded successors have yet to escape.

This article is translate from a report originally published by Leiphone.

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One Year After CloudMinds’ Collapse: The Death of an Embodied-AI Unicorn

By Qi Chengyong | Edited by Lin Juemin

In the spring of 2026, China’s embodied AI sector is riding a wave of capital-market activity. Unitree Robotics is sprinting toward an A-share listing, AgiBot is preparing for a Hong Kong IPO, and Galbot and Fourier Intelligence have completed shareholding restructurings. Valuations at numerous embodied AI startups have already crossed 10 billion yuan. Lining up capital backers has become, it seems, the surest way for these companies to buy themselves a sense of security.

History suggests, however, that lofty valuations are no guarantee of stability. The most instructive case is that of a former embodied AI unicorn—CloudMinds Robotics (达闼科技).

Founder Bill Huang Xiaoqing spent a decade building the company into an embodied AI edifice once valued at more than 20 billion yuan ($2.9 billion). Just over a year ago, that edifice collapsed almost overnight. So who, exactly, “killed” this unicorn?

Start with a counterintuitive detail. The widely circulated narrative on social media holds that CloudMinds was felled by a snapped funding chain, employee lawsuits and tangled debts. In reality, as of March 2025, the total amount CloudMinds had been ordered by courts to pay stood at just 35.3 million yuan ($5.2 million). For a company founded in 2015, having completed seven funding rounds and once valued at as much as 22.3 billion yuan ($3.3 billion)—billed as “the leading unicorn of China’s robotics industry”—that is not a particularly large sum. The company’s real-estate holdings, including its Guangzhou branch building and an industrial park of several hundred mu, were owned outright. A simple sell-off could have pulled CloudMinds out of trouble. So why did it instead unravel into a scattered team and a founder who decamped to Hong Kong to start a new company?

The decisive trigger for the collapse of CloudMinds’ embodied AI empire lay not downstream, but upstream—in founder Bill Huang and the company’s shareholding structure.

The Limits of a First-Generation Embodied-AI Founder

China’s embodied AI entrepreneurs can be sorted, roughly, into three generations.

• The third generation—represented by Unitree’s Wang Xingxing, AgiBot’s Zhihui Jun and Robot Era’s Chen Jianyu—consists of engineers and scientists born after 1985. In their view, algorithms and models are the moat. A robot is not a piece of hardcoded control logic but a learned motion policy; hardware, by comparison, is secondary.

• The second generation, including UBTech’s Zhou Jian and Keenon Robotics’ Li Tong, were born in the latter half of the 1970s. They lean toward “traditional control plus limited intelligence,” treating the robot body as a “product” and “selling the equipment” as the business model.

• The first generation of robotics founders are mostly born in the 1960s. They carry, to a degree, the heavy imprint of their era. On one hand, they tended to see the robot as a mere actuator and underestimated the complexity of physical interaction. On the other, they leaned heavily on “relationship-driven” capital—government orders, state-owned funding. Bill Huang was emblematic of this generation.

Before founding CloudMinds, Huang’s CV was that of a polished technology executive.

After graduating, he built his career in electronic communications, spending five years at Bell Labs. There, in what was widely regarded as the cathedral of communications science, he led the development of a new system that compressed seven cumbersome subsystems into three—a single achievement that built his reputation inside the labs.

In 1995, he returned briefly to China, bringing a cohort of Bell Labs colleagues to UTStarcom, then shuttling for years between Silicon Valley and Hangzhou. As China’s telecoms industry inched into the 3G era, Huang, with a sharp technological instinct, spun out a new venture aimed at the 3G transition called Wacos. The vision was too far ahead of its time, and the “Wacos 3G” project ultimately failed.

In 2007, China Mobile offered Huang the position of president of its research institute. Over the next eight years, he watched the country shift from 3G to 4G.

In 2015, with the dawn of 5G, Huang again sensed opportunity and disruption. He had decided to start something of his own, though he was not yet sure what. He had spent his entire career in telecoms and had no prior experience in robotics.

By Huang’s own telling, he chose robotics because of a science-fiction dream. A fan of Star Trek, he was inspired by the show’s android “Data”—and from this, he combined cloud-based communications with robotics and coined the concept of the “cloud robot.”

Many former CloudMinds employees take a less elevated view. They argue that Huang was not particularly innovative; he simply traveled often between continents, kept his eyes open, and got early exposure to Carnegie Mellon professor James Kuffner’s “cloud robotics” idea, which he then imported to China—more “localization team” than originator.

A Robot Company with “Telecom DNA”

Trading on Huang’s telecoms pedigree and the buzz around “cloud intelligence,” CloudMinds quickly secured funding.

In 2016, it closed its first round, a $30 million investment co-led by SoftBank Group and Foxconn—Foxconn parent Hon Hai was then still run by Terry Gou. The following year, SoftBank doubled down, joining Zhongguancun Development Group in a $100 million round that set a record at the time for the cloud-intelligence space.

CloudMinds’ start was smooth: within a year it had become an industry unicorn.

Yet, for all the capital raised, the company made little real headway on product or technology. Its roadmap envisioned robots across many scenarios—the XR series of general-purpose humanoids (XR-1 through XR-4), low-cost reception and service robots branded Ginger Lite, unmanned-retail robots and more.

Huang at one point envisioned shipments of unmanned-retail robots in the tens of millions, with reception-service robots shipping at least 100,000 units a year. As of 2026, Galaxy Universal—a leader in unmanned-retail robotics—still has not reached the shipment numbers Huang projected back then.

More awkwardly, not long after raising its early rounds, CloudMinds began thinking in financial-engineering terms—turning up revenue in a hurry, with a U.S. listing in mind.

According to early CloudMinds employees, the company “didn’t really get anything off the ground in the early years. Instead, they made a lot of phones—including a secure phone designed for enterprise clients that was supposed to be sold to Foxconn, but the deal fell through.”

In 2017 and 2018, with his robotics business stalled, Huang was also weighing a Nasdaq listing. He needed orders to point to in front of investors and revenue figures to support a valuation, which pushed him into the phone business. After all, mobile communications was his home turf.

This chapter has since been quietly downplayed, but it left an imprint on CloudMinds’ DNA: telecom thinking. Huang grafted China Mobile’s “carrier” mindset onto the robotics industry, imagining himself as “the China Mobile of robots”—not selling robots, but selling “cloud-brain” services and charging by traffic.

Bill Huang, the Capital Operator

In 2019, CloudMinds set out to list in the U.S. The plan was Nasdaq, until the NYSE swooped in and waived tens of millions of yuan in fees. Huang switched venues without hesitation, planning to raise $500 million. After the prospectus was filed, investors were unconvinced—but the U.S. government took notice, and CloudMinds landed on the entity list.

The IPO was killed by sanctions, but Washington’s “endorsement” turned out to be a blessing in disguise. Forced back to China, CloudMinds was suddenly being courted for investment.

Not long after the failed NYSE listing, the Shenzhen and Shanghai municipal governments came calling. CloudMinds relocated to Shanghai, where it secured “well over a billion yuan,” 243 mu of land (162,000 square meters) and a generous package of incentives.

Two figures in the executive team played a pivotal role in this period: Yang Guanghua and Wang Bing. Both led the company’s government-facing business; Yang had worked alongside Huang since the UTStarcom days, while Wang had previously served as a vice president at Beijing Kingdee Software. Peers in the industry described them as “highly skilled operators of government-business relationships.”

This government-relations-driven model propelled CloudMinds’ cap-table to its peak between 2021 and 2023—but also left it highly fragmented: Shanghai Chengtou, the Zhuhai state-owned assets administration, Guangzhou Knowledge City, the Ganzhou Nankang District Urban Development Group, and on it went. Of the 67 shareholders identifiable in CloudMinds’ ownership structure today, 57 are corporate entities.

This shareholder pattern is hardly unique to CloudMinds. The embodied AI companies that closed large rounds in 2024 have cap tables that include Tencent, BYD, Hillhouse, Sequoia and local government funds. The list is longer, but the structure is the same: a “patchwork” cap table assembled in hopes of sustaining the next round of funding.

In China’s LP (limited partner) universe, government guidance funds occupy a central position. CloudMinds’ problem was that there was no “next LP” willing to take the baton. The fortunate position of today’s leading firms is that there is still a “next LP” looking on. But the due-diligence logic of the next LP is identical to the last one’s: orders, revenue, certainty.

In hindsight, the embodied AI leaders that locked in funding early in 2024 and rode the snowball higher are now, in 2026, facing the same tests of orders, revenue and certainty. For many CFOs at embodied AI startups, this year’s top KPI is nailing down mass-production figures and a clear revenue trajectory—because they, too, have reached the point of having to prove commercialization.

The Glory Years

2021 was CloudMinds’ peak.

The company had 12 project management offices, with the highest project count it would ever reach. Huang held regular project reviews—humanoid robots, the cloud brain, smart joints, digital humans, delivery robots, agricultural robots, security robots. Every project lead came with a deck, with data, with a promise that mass production was “right around the corner.”

At the time, CloudMinds even ran its own factory for motors, which gave it both cost control and customization. In 2021, CloudMinds was outshining a then-still-emerging Unitree.

“There were so many products and projects, and the sales playbooks they could support were just as varied,” a former PMO member recalled. He later joined another embodied AI company, where he found seven or eight PMOs, a founder running regular project reviews, and the walls plastered with project trackers—red for delays, yellow for risk—in roughly the same proportions as CloudMinds in 2021.

The six product lines CloudMinds maintained at the time have since been treated as a cautionary tale by later embodied AI entrepreneurs: “Don’t be greedy. Focus.” But the reality is that focus is both a risk and a luxury.

To focus is to go all in on one track, with the public and investors watching that one line. The risk is that you bet wrong. In a field as murky as embodied AI, picking the wrong path can send you tumbling out of the window of opportunity. And if the product lineup is too thin, it may not support the valuation.

Today’s leading embodied AI firms are quietly expanding theirs, too. From humanoid to quadruped, from industrial to home, from hardware to software, from robots to “embodied AI foundation models.” They don’t call it diversification—they call it “building an ecosystem.” How, exactly, does this differ from the “cloud robot ecosystem” CloudMinds was talking about in 2019?

Bill Huang’s Foresight

The founder’s vision was not behind the times.

In today’s embodied AI scene, many investors talk a great deal about founders’ “vision.” On that metric, Bill Huang was a founder with an unusually strong technical vision.

In 2020, Huang shared, on multiple occasions, his thinking on humanoid robots: “From a psychological standpoint, humans can’t accept a conversation with a table. If robots are going to share human space, they need to share human tools.”

No one really understood him at the time. In 2020, humanoid robots were still the stuff of science fiction. Boston Dynamics’ Atlas was doing backflips in the lab, and Unitree had only just sold its first quadruped.

Four years later, at the 2024 World AI Conference, the founder of an embodied AI company expressed a strikingly similar view in a comparable setting: “Humanoid is the ultimate form, because only the humanoid form can use every tool human beings have made.” The audience erupted in applause. The founder, nearly 30 years younger than Huang, wore the same black T-shirt and uttered the same words.

The difference: when Huang said it in 2020, CloudMinds’ humanoid existed only on paper; when this founder said it in 2024, his humanoid could already walk and jump, even if it could not yet do any real work.

Huang was ahead on ideas, too.

Beyond the cloud brain, he recognized early on the importance of bringing down the cost of robot joints. In a 2020 media interview, he said: “In the future, the number of joints will determine a robot’s performance. We need to bring joint costs down while maintaining high quality, and only then will the robot era truly begin.” Not long after, CloudMinds built a production and R&D base in Shanghai, aiming to push joint costs below 1,000 yuan ($150) a unit.

In 2025, many embodied AI startups began to realize that the path forward depended on bringing the price of joints and dexterous hands down. Huang had said the same thing five years earlier.

Huang thought too far ahead—far enough that the investors of 2020 couldn’t follow him, the state-owned LPs of 2021 didn’t dare invest, and Nanjing’s industrial guidance fund concluded in 2023 that his “commercialization progress fell short of expectations.” He stumbled on being “too early.” But “too early” is not, in itself, a sin: had he survived to 2024, he would have been hailed as a “prophet.”

In a sense, are today’s leaders not also “too early”?

What Huang could not deliver came down to two things: mass production never arrived, and the company’s shareholder structure denied it the time it needed to reach the point where its products could actually land.

The Fall

In the summer of 2023, at the WAIC conference, CloudMinds turned up to exhibit. In a hall where most peers were running demos, CloudMinds’ robot took center stage to attempt a basketball shot. It missed. A collective groan ran through the crowd. CloudMinds had hoped to reassert its veteran status. It made a spectacle of itself instead.

After that, the company rarely returned to the public eye in a favorable light. In early 2024, CloudMinds began missing wages.

“In late January 2024, everyone was busy preparing for the annual gala. Then on the 31st, the company suddenly couldn’t pay salaries,” recalled one former employee. The explanation given was a glitch in the finance system. On February 8—the day before Chinese New Year’s Eve—the company held an emergency all-hands on Feishu, announcing that any portion of an employee’s salary above 10,000 yuan would be paid at half value. Huang himself didn’t attend; only two members of management were present—the head of HR and the chief financial officer. After two months of running this scheme, salaries were stopped entirely in March and April. Headcount was frozen across the board. In May, after a one-off payment of 10,000 yuan to each employee, contributions to housing fund and social insurance also halted.

What was Huang doing at the time? By his own account, in that year he met with more investment institutions than in the previous nine years combined. He traveled to Hefei, Tianjin, Xiamen—and as far north as Jilin. With each one, he wasn’t dropping in briefly; he was sitting through endless meetings. “Whether they invested or not, I gave them a very important education session.”

But according to investors, the content Huang was pitching had barely changed since the 2019 U.S. IPO attempt: cloud brain, smart joints, humanoid robots, home services. Five years on, the slide layout had changed, but the core narrative had not.

“In 2024, the only money you could really raise on the market was government money managed by private-sector GPs.” That was Huang’s own conclusion. The unspoken subtext: private GPs had no dry powder, government money was too cautious to commit, and CloudMinds was caught precisely in that gap.

CloudMinds’ Series C was co-led by Guangzhou Knowledge City Group and Shanghai Guosheng Investment Group, for more than 1 billion yuan ($150 million). Just six months later, the funding chain snapped. Huang said an institution that was due to wire 300 million yuan ($44 million) in October 2023 ultimately balked.

He did not name names, but anyone familiar with CloudMinds’ funding history can guess: 300 million yuan is a check size typical of a local-government industrial fund. In 2023, CloudMinds had planned a Hong Kong IPO of $500 million, only to have approval delayed over its “commercialization progress falling short.” The institution behind that judgment was the Nanjing industrial guidance fund—a classic local-government industrial fund.

Watch the current embodied AI funding landscape and you’ll find today’s leaders raising from Tencent, Meituan and CICC Capital. They, too, are hunting for “money that can stomach uncertainty.”

But money in China—even from corporate venture arms like Tencent and Meituan—almost always has government guidance funds somewhere in the LP stack. CloudMinds’ predicament is not unique.

An Autopsy

To understand CloudMinds’ death, you have to go back to Huang’s CV—UTStarcom.

In the 1990s, Huang co-founded UTStarcom and served as CTO. The company was the dominant force in the PHS-handset era—”the king of Binjiang”—with a market capitalization of $10 billion, annual revenue in the tens of billions of yuan, and enough heft to put pressure on Huawei. UTStarcom’s collapse, though, came down to internal politics.

According to UTStarcom veterans, “company politics” took over: Zhou Shaoning, who would later become Google’s Greater China president, “had differences in approach” with Huang. Zhou was “good at making money, leading R&D and building products,” and the two ended up “in a power struggle.” After Zhou left, Huang took over, but “Bill is a relationship guy. He knows some tech, but it isn’t his strength.”

That “relationship-driven” management style transferred wholesale to CloudMinds.

An employee who joined CloudMinds in 2018 to run its middle-office said: “The chaos inside CloudMinds was always like that. Mini [Huang’s wife, CFO] really enjoyed it when people came to her with complaints about so-and-so; but if someone tried to put in a good word for someone else, they wouldn’t really listen. And they would take credit, constantly saying ‘thanks to me, this happened.’ Basically the credit went to them and the problems to everyone else.”

Huang’s “one-man rule” got more pronounced over time. “However senior you are, no matter how many people work for you, on a lot of issues I really do need to drill all the way down and understand every detail myself,” he said in an interview. To employees, the reality was: “Bill is involved in everything. He’d weigh in on trivial details. Working there was just exhausting.” Several former staff added that Huang berated people during meetings—”you bunch of [expletive],” “this stuff is so simple, [expletive]”—in language harsh enough that it stuck.

Beyond the micromanagement, investors offered their own read on the couple’s overall style: “Bill and Mini are both nice people, but their biggest problem is the lack of business sense. They’re both single-mindedly chasing the technical roadmap. Mini has improved a bit in the past couple of years, but Bill is still the same.”

More damaging still was the talent system. In its later years, CloudMinds brought in a wave of executives from Alibaba and ByteDance backgrounds—”not very pragmatic, fond of process for process’s sake and managing upward, generating endless weekly and daily reports, sometimes twice a day.” When problems arose, the response was not to fix them but to escalate them. “Everyone knew what the boss was like, so they buried him under reports on every conceivable detail. The boss never had the quiet space to think about what the company’s next strategic move should be.”

The experience of Xie Zheng is fairly representative of the CloudMinds atmosphere. A core figure behind UBTech’s Walker humanoid, Xie joined CloudMinds in 2022, only to find that “the technical roadmap and the architecture were all set by Bill, and no one could change them.” He left, then went on to co-found Yuanluo Tech with his high-school classmate Lian Wenzhao—formerly of Figure AI—and the company closed a sizable funding round in 2025.

A former CloudMinds staffer summed up the four kinds of people the company ended up with: first, those with a relatively high tolerance for the dysfunction, but who did real work and eventually got worn down by being pushed around; second, the very talented, who concluded “if you don’t want me here, plenty of others do,” and walked out; third, those who said “fine, say what you want, do what you want—but I have my principles”; and fourth, those who said “I don’t have my own ideas—do whatever makes you happy, I’m just here to serve you, and as for the people below me, I’ll leave them alone.” By the end, most of those who remained belonged to the fourth category.

Rebirth and Lessons

In April 2025, a video surfaced of Huang giving an interview to Hong Kong media in his new capacity as chairman of “Hong Kong Boy Robotics” (港仔机器人).

He was in his early sixties, hair gone gray, and he was still saying the same things: “home services,” “cloud brain,” “100,000-yuan robots.” The backdrop had changed—from a 243-mu base in Shanghai to a small office in a Hong Kong tower. The title had changed—from CloudMinds founder to chief scientist of Hong Kong Boy Robotics. But the story was the same.

Hong Kong Boy Robotics is a joint venture between CloudMinds and Guohua Group, a Hong Kong-listed company, with Guohua holding the controlling stake. Guohua has pledged to raise funding in the next year or two—on the condition that “the team gets the business, technology and products off the ground.” Word for word, this matched what investors had demanded of Huang back in 2015 when CloudMinds was first founded.

Meanwhile, the pitch decks of today’s well-funded embodied AI companies are still salted with phrases like “home services,” “cloud brain” and “100,000-yuan robots.” Their investors are the same crowd that backed Huang in his day—just operating under different fund names now.

The difference: Huang is in his sixties. That other founder is in his thirties.

CloudMinds’ death is not an endpoint, but a loop. The industry is repeating the same story: technology ahead of its time, sweeping scenarios, funding-driven growth, government relationships, expanding product lines, a strong-willed founder in total control, a snapped funding chain, missed wages, layoffs, death—rebirth, then death again.

Today’s leading firms face structural challenges strikingly similar to CloudMinds’. Their good fortune: CloudMinds died in 2024, and they made it to 2026. But what about after 2026?

So “who killed the former embodied AI unicorn”?

Not the technology or the roadmap—CloudMinds’ cloud brain, smart joints and humanoids were not behind the curve, and today’s leaders are still walking the same path. Not the market—humanoid robots saw their breakout moment in 2024 and 2025, demand is real, and today’s leaders are still telling the same story. And not, in the end, management—Huang’s “one-man rule” had its problems, but the founders of today’s leaders are almost all “technical autocrats,” cut from the same cloth as Huang.

The real reason is that the logic of the industry has not changed.

Technology ahead of its time, sweeping scenarios, funding-driven growth, government relationships—this same playbook made CloudMinds in 2015, broke it by 2019, made new unicorns in 2024, and will, at some point in the future, test them too.

CloudMinds was not a “loser.” It was a pioneer. The mistakes it made are being repeated. The road it walked is being walked again. The warning CloudMinds leaves for every embodied AI company operating today is this: be clear-eyed about how to find the variable that will let you live long enough for your products to actually land.

What is that variable? CloudMinds never found it. The leaders of 2026, perhaps, are still looking.

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Chinese AI companies just had their payday over the past week.

DeepSeek, the Chinese AI lab backed by quant trading giant HighFlyer that just released its latest LLM V4, is reportedly seeking its first-ever outside funding. They initially targeted $300 million at a $10 billion valuation, but investors’ demand were insatiable. The round has reportedly climbed to $7 billion at a $50 billion—if successful, this will be the largest single funding for a Chinese AI company in recent history.

Then it’s Moonshot AI, the developer of the Kimi model family, just raised $2 billion at a valuation of over $20 billion, led by Chinese delivery giant Meituan. This is the third round of their fundraising this year, totaling $3.9 billion in six months, according to Chinese media outlet LatePost.

Finally, StepFun, a Shanghai-headquartered AI company and developer of the Step model series, reportedly close to raising $2.5 billion. Its valuation target was $10 billion months ago.

To put that in context: the last time a Chinese private company raised $2 billion-plus in a single round was Shein in May 2023, at a cut valuation of $64 billion, down a third from its prior round. From roughly 2021 to 2025, China’s tech VC market was in prolonged contraction—regulatory crackdowns against technology companies, US-China tensions, rising exit difficulties, global rate hikes. Therefore the recent fundings signal something has shifted.

Still, the numbers are overshadowed by their Western counterparts. OpenAI closed a record $122 billion round, valuing it at $852 billion. Anthropic raised $30 billion in February at $380 billion, and is now in talks for a new round at a reported $900 billion valuation—which would put it ahead of OpenAI.

But the gap is less important than the trajectory. Chinese AI companies have figured out their commercialization story in the agentic era, and investors are fear missing out.

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DeepSeek

DeepSeek, founded in mid-2023, has never raised a single dollar before. Its backer, HighFlyer, is one of China’s largest quant trading firms, overseeing more than 70 billion yuan ($10 billion) with an average fund return of 56.6%, according to Bloomberg. That’s why DeepSeek could focus on research, commit to an open-source strategy, and ignore the pressure to commercialize or ship products on someone else’s timeline. Even after R1—its blockbuster reasoning model that made a global splash in early 2025—the company reportedly rejected funding offers from both Chinese and US investors.

What changed was talent retention. Competitors began aggressively poaching DeepSeek’s engineers and researchers. Luo Fuli, one of the DeepSeek V3 contributors, is now Xiaomi’s head of LLM team. Guo Daya, a core author of both DeepSeek V3 and R1, has joined ByteDance’s Seed lab. Without outside funding and a formal company valuation, researchers have no equity incentive, raising the cost of keeping the people who built the models.

There’s also a scale problem. V4 is nearly twice the size of V3, and the data used to train scales accordingly. DeepSeek is also allocating GPU resources toward unexplored research areas that require significant capital. Research at the frontier isn’t cheap, even for a company backed by a quant trading giant.

In April, DeepSeek reportedly started its first external funding round at a $10 billion valuation, seeking $300 million. Then China’s Big Fund—the state-backed semiconductor investment vehicle that also backs SMIC—is now in talks to lead the round at a $45 billion valuation, according to the Financial Times. Tencent is also said to be in discussions. The latest reporting from The Information is CEO Liang Wenfeng will personally invest 20 billion yuan ($3 billion), around 40% of the total amount, at a $50 billion valuation. DeepSeek is also expected to release V4.1 in June.

Moonshot AI

The Moonshot funding story was first broken by LatePost that the company was close to completing a new $2 billion round led by Meituan’s investment fund Longzhu, with China Mobile and CPE (previously known as CITIC Private Equity) also participating. LatePost also reported that Moonshot raised $1.9 billion across January and February, making the total $3.9 billion over six months (please note that the undisclosed round in February 2026 below was reported by LatePost; no other sources have surfaced details.)

According to Meituan Longzhu’s general partner, Moonshot’s annual recurring revenue (ARR)—mostly API revenue—reached $200 million in April. For comparison, Cursor’s ARR has surpassed $2 billion as of March 2026, and Cursor is in talks to raise $2 billion at a $50 billion valuation. Moonshot isn’t cheap at $20 billion, but it’s just starting to grow. And notably, Cursor’s models are in part trained on top of Kimi K series. Meanwhile, MiniMax and Zhipu, which both listed on the Hong Kong stock market, are valued between $35 to $70 billion. Against that backdrop, Moonshot at $20 billion looks like a discount.

Moonshot’s latest model, Kimi K2.6, is one of the best open-weight models available today. The trillion-parameter model features strong coding capability, long-horizon execution, and can handle up to 300 sub-agents simultaneously. A bigger and more powerful K3 is reportedly in development for later this year.

While Moonshot’s largest external shareholder should be Alibaba, which owns approximately 36% of the company after making its bet in early 2024, the fact that Meituan—currently locked in a fierce delivery war against Alibaba and JD.com—is Moonshot’s lead investor of this funding round signals an escalating AI arms race between China’s tech giants.

AI agents that order food and book hotel tickets are increasingly becoming the interface through which young people manage daily life. Alibaba is building its ecosystem around Qwen as the central agent for daily tasks. Meituan doesn’t want to be left behind. Moonshot’s coding and agentic specialization makes it a natural fit, from distribution via Kimi's consumer reach to agentic models powering Meituan's merchant and logistics ecosystem.

Meituan is also building its own AI models in-house. Its Longcat 2.0 model is reportedly trained on 50,000 to 60,000 Huawei Ascend chips—the largest training run on domestic Chinese compute infrastructure to date.

StepFun

Just one day after the Moonshot news, Shanghai-based StepFun was reported to be close to raising $2.5 billion.

For readers less familiar with the company: StepFun is one of the top five LLM startups in China, alongside DeepSeek, Zhipu, MiniMax, and Moonshot. Founded in April 2023 by former Microsoft corporate VP Jiang Daxin, its core team includes Zhang Xiangyu—former director of research at Megvii and co-author of ResNet, one of the foundational papers in modern computer vision—alongside other researchers from Microsoft Research Asia.

Their latest flagship model is Step-3: 321 billion total parameters, 38 billion active, with two architectural innovations that reduce KV-cache demands to roughly 22% of DeepSeek V3’s per-token cost. But language is just one dimension. StepFun has shipped 16 multimodal models—covering text-to-video, voice interaction, image understanding, and multimodal reasoning. The Step-Video-T2V model, co-released with Geely Auto in early 2025, was the largest open-source video generation model in the world by parameter count at the time of release.

The low-profile company made headlines earlier this year after announcing a 5 billion yuan funding round and a new chairman: Yin Qi, former co-founder and CEO of Megvii, one of China’s first-generation AI unicorns. Yin also chairs Qianli Technology, Geely’s autonomous driving subsidiary—a connection that goes to the heart of StepFun’s commercial strategy.

A differentiation from peers, StepFun bets on AI embedded in hardware. Their models are deployed on over 42 million shipped devices, reaching approximately 20 million daily active users—with partners accounting for roughly 60% of China’s leading smartphone brands, including OPPO and Honor. The business model is closer to ARM by charing licensing fees and revenue-sharing from device manufacturers.

On the automotive side, StepFun and Geely jointly developed Agent OS, an intelligent cockpit system integrating StepFun’s multimodal and voice models, explicitly positioned as a Chinese answer to Tesla’s in-car Grok. StepFun’s target is one million vehicle integrations by end of 2026.

The investor lineup of the funding round includes Huaqin and Longcheer, both of which are top global phone ODM manufacturers; OmniVision is an image sensor supplier upstream in the smartphone camera supply chain; ZTE has already deployed StepFun models in its Nubia Z80 Ultra.

StepFun has also dismantled its red-chip (VIE) structure and converted to a joint-stock company—the corporate form required for a Hong Kong H-share listing. The target is to file in Hong Kong before June 30, with listing expected by year-end. The Hong Kong Investment Corporation, the city’s sovereign fund, is reportedly among the pre-IPO investors—making StepFun its sole large model bet.

Why now

In my view, the recent funding craze has two catalysts.

The first is the successful IPOs of Zhipu AI and MiniMax. Between 2020 and 2025, Chinese AI startups faced a structural exit problem. US Nasdaq—once the default destination for Chinese tech listings—became increasingly difficult due to deteriorating geopolitics and valuation discounts driven by perceived regulatory risk. At the same time, China’s anti-monopoly regulation made it harder for domestic tech giants to acquire startups. With no clear exit, Chinese VCs grew more risk-averse.

Then Hong Kong plays an important role. The exchange’s average daily turnover (ADT) hit HK$249.8 billion ($32 billion) in 2025—a 90% jump year-over-year. Its liquidity continues to show robust growth in early 2026, with Q1 ADT reaching HK$276.7 billion ($35 billion), a 14% year-on-year increase. IPO equity funds raised on the Main Board reached HK$285.8 billion ($36.5 billion) in 2025, a 225% increase from 2024.

Into this market walked Zhipu AI and MiniMax in January 2026—and they gave every private AI investor a benchmark. Zhipu has surged over 600% since its listing, reaching a market cap of roughly $56 billion. MiniMax doubled on its debut day and has since climbed to over $33 billion. Two companies that raised a combined $1.2 billion in their IPOs are now worth multiples of that.

The second catalyst is commercial. These companies have figured out how to make money by pushing the limits of model capabilities. Coding agents and agentic AI can now handle complex, multi-step tasks autonomously. White-collar employees, developers, and entrepreneurs are willing to pay for tokens at scale. This is visible in Moonshot’s ARR trajectory and in Zhipu’s model-as-a-service revenue growth.

This moment is reminiscent of 2023, when ChatGPT’s release in November 2022 sent Chinese VCs racing to incubate the country’s own OpenAI. The companies became unicorns quickly, then investment lines flatlined as questions mounted about their ability to commercialize. In late 2025, Claude Code, OpenClaw, and the broader agentic wave is another ChatGPT-like moment. Chinese AI companies responded by investing heavily in LLM agentic and coding capabilities, and their ARR grew exponentially.

To be clear, all of this is still small against the spending of China’s tech giants. Alibaba committed $53 billion over three years (2025–2028). ByteDance has boosted its AI infrastructure capex this year to more than 200 billion yuan ($30 billion).

A closing thought

I recently read Nathan Lambert’s notes from his visits to Chinese AI labs. One line stuck with me:

In China, the LLM community feels far more like an ecosystem than battling tribes. Across many off the record conversations, it’s nothing but respect for peers. All of the Chinese labs fear Bytedance with their popular Doubao model, which is the only frontier closed lab in China. At the same time, all of the labs have massive respect for DeepSeek as the lab with the best research taste in execution. When you meet with lab members off the record in the States, sparks fly quickly.

The most striking part of the humility of Chinese researchers is how they also often shrug on the business side, saying it’s not their problem, where everyone in the U.S. seems to be obsessed with various ecosystem-level industrial trends, from data sellers to compute or fundraising.

Interconnects AI

Notes from inside China's AI labs

Staring out the window on a new, high-speed train from Hangzhou to Shanghai I’m gifted with views of dramatic ridgelines speckled with wind turbines that are silhouetted against the setting sun. The mountains cast a backdrop to a mix of spanning fields and clustered skyscrapers. I’m returning from China with great humility. It’s a very warming, human ex…

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2 months ago · 239 likes · 30 comments · Nathan Lambert

I have no doubt that in the near term, Chinese AI labs will continue this open and collaborative mindset. The market is large enough that aggressive internal competition isn’t yet necessary. Learning from each other through open releases is still the fastest way to improve. Ecosystem growth benefits everyone.

But as these companies continue to scale API revenue, bear increasingly high valuations, and face mounting commercialization pressure, the question is how long this friendly ecosystem can hold. At some point, open collaboration becomes competitive intelligence. When cut-throat competition arrives among Chinese AI labs—and it will—it will be just a matter of timing.

Until then, congratulations to all these companies that have overcome challenges and doubts to get here. The payday is well deserved.

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The long-anticipated DeepSeek-V4 has finally arrived, released in the same laast week as OpenAI’s GPT-5.5, Moonshot’s K2.6, and Tencent’s Hunyuan 3 preview, probably the most intense moment in the current model race.

The preview open-source releases include two versions:

• DeepSeek-V4-Pro features 1.6 trillion total parameters with 49 billion active parameters, making it even more sparse than DeepSeek-V3, pre-trained on 33 trillion tokens.

• DeepSeek-V4-Flash features 284 billion total parameters with 13 billion active parameters, a lighter model for the majority of developers who can’t deploy the gigantic Pro version. Pre-trained on 32 trillion tokens.

The selling point of the DeepSeek-V4 series is a one million token context window at an affordable price, laying the foundation for models handling long-horizon agentic tasks that require holding enormous amounts of information in memory without losing the thread. To get there, DeepSeek introduced new architectural innovations and incorporated techniques like mHC and the Muon optimizer.

On pricing, DeepSeek-V4 charges $1.74/$3.48 per million input/output tokens, while Claude Opus 4.6 charges $5/$25. The price advantage narrows against other Chinese open LLMs as Zhipu’s GLM 5.1 charges $1.4/$4.4 per million tokens, though the model doesn’t offer the 1M context window. DeepSeek also just slashed cache hit prices across its entire API to one-tenth of the original price, and the V4-Pro 75% off promotion runs through May 5, 2026.

On performance, DeepSeek-V4-Pro is strong at math (HMMT 2026 and IMOAnswerBench) and competitive programming (Codeforces and LiveCodeBench)—the kind of coding that solves well-defined problems with known solutions. On knowledge, DeepSeek also leads all open models by a wide margin, though Gemini 3.1 Pro is aheaad.

But it still lags top frontier models on real-world software engineering (SWE Pro) and agentic tasks (HLE with tools). The company admits in its technical report that its reasoning trails the top frontier models by roughly three to six months. On Chatbot Arena and Artificial Analysis, V4 ranks among the best open models but trails Kimi K2.6 and Xiaomi’s MiMo V2.5 Pro.

Also noteworthy: DeepSeek has optimized V4 for Huawei’s Ascend AI chips. Current serving throughput for V4-Pro is limited, but the company expects pricing to drop significantly in the second half of the year once Ascend 950 supernodes ship at scale.

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Architectural Innovations to Drive Down the Cost of 1M Tokens

While DeepSeek-V4 didn’t make as big a splash as V3, it is in my view the most architecturally ambitious model the lab has released. The backbone remains familiar—DeepSeek MoE and multi-token prediction—but nearly everything built on top of it is new.

In my earlier predictions, I thought V4 might just continue using DeepSeek Sparse Attention (DSA). What surprised me is that DeepSeek pushed further than DSA by proposing an entirely new hybrid attention architecture, the core innovation that makes cheap 1M token processing possible.

Standard attention scales quadratically with context length. At one million tokens, the compute and memory costs make it impractical to serve at scale. Breaking that bottleneck is the core problem DeepSeek V4 was built to solve.

DeepSeek designed two complementary attention mechanisms and interleaves them across the model’s 61 layers.

• CSA (Compressed Sparse Attention): Instead of attending to every token, CSA first compresses every group of 4 tokens into one condensed entry using learned weights — the model figures out during training what’s worth keeping. Then a fast lookup system called the “lightning indexer” identifies which compressed chunks are actually relevant to the current query, and the model attends only to those. A small sliding window of recent uncompressed tokens is also kept, so local context isn’t lost.

• HCA (Heavily Compressed Attention): More aggressive. It compresses every 128 tokens into a single entry and attends to all of them densely, without any sparse selection. Less precise, but extremely cheap.

The combination works because CSA handles nuanced, selective retrieval while HCA handles the broad sweep cheaply. Together they cover different retrieval needs at different computational costs. The paper reports that at one million tokens, V4-Pro needs only 27% of the inference compute and 10% of the KV cache—the memory used to store context—compared to V3.2.

There is a cost of course. The benchmark gaps on MRCR and CorpusQA—both long-context retrieval tests—versus Claude Opus 4.6 suggest some information loss from the compression. But for most tasks, what gets compressed away seems not to matter. The 10x memory saving is worth the marginal retrieval gap on the hardest benchmarks.

The first two layers of V4-Pro use HCA only, then the interleaved CSA-HCA pattern runs through the remaining layers, according to the paper.

On top of attention, mHC (Manifold-Constrained Hyper-Connections), which I explained in a previous post, is employed to strengthen residual connections between layers. Normally, information flows through a neural network layer by layer through a single residual stream—one pipe carrying signal from layer to layer. mHC widens that pipe to four parallel streams, giving each layer more flexibility in how it draws from and contributes to the information flow. The “manifold-constrained” part is the stability fix: without it, multiple streams stacked across dozens of layers cause signals to explode or vanish during training. DeepSeek’s innovation was to mathematically constrain the mixing between streams so the model stays stable without without giving up any of the intelligence gains. In plain terms: wider pipe, guardrails on the pipe.

Recode China AI

👀DeepSeek’s Next Move: What V4 Will Look Like

The post was originally written at Michael Spencer’s invitation and first published on AI Supremacy here. I have updated and edited it to reflect the latest developments and research…

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4 months ago · 17 likes · 1 comment · Tony Peng

The Muon optimizer is the third major innovation, and it comes with an interesting story. Muon was originated in the open-source research community and was notably scaled for LLM training by Moonshot AI for its Kimi models. DeepSeek adopted it for V4.

Most large models train using an optimizer called AdamW, which updates each weight independently based on its own history. Muon treats the weight matrices more holistically, making each update geometrically cleaner relative to the weight matrix as a whole, through a technique called Newton-Schulz iterations. The result is faster convergence and better training stability, both meaningful at a scale of 33 trillion training tokens.

The deeper point is that DeepSeek and Moonshot are rivals in AI race. Yet Moonshot has largely borrowed DeepSeek’s architecture since Kimi K2, and DeepSeek is now using Moonshot’s optimizer. This open-source practice as mutual adoption between competitors further benefits the whole ecosystem.

One thing I was hoping to see that didn’t make it: Engram, DeepSeek’s memory architecture, and multimodality. The paper is candid about why:

In pursuit of extreme long-context efficiency, DeepSeek-V4 series adopted a bold architectural design. To minimize risk, we retained many preliminarily validated components and tricks, which, while effective, made the architecture relatively complex. In future iterations, we will carry out more comprehensive and principled investigations to distill the architecture down to its most essential designs.

We are also working on incorporating multimodal capabilities to our models.

Infrastructure Improvement and Post-Training

Infrastructure is arguably where DeepSeek excels, and V4 is no exception.

On training efficiency, they proposed a fine-grained EP scheme that fuses communication and computation into a single pipelined kernel for communication-computation overlapping, achieving up to 1.96x speedup for latency-sensitive workloads like RL rollout, and validated it on both NVIDIA GPUs and Huawei Ascend NPUs.

They adopted TileLang, a domain-specific language for writing GPU kernels that lets researchers prototype operators quickly and optimize the same code for production without rewriting it from scratch.

On precision, they trained with FP4 quantization awareness from the start rather than applying it after the fact. The model runs faster and uses less memory at deployment without meaningful performance loss. The paper notes FP4 could be a further one-third more efficient on future hardware.

Finally, they designed a custom KV cache layout to handle the heterogeneous memory requirements of mixing CSA and HCA layers, and built on-disk KV cache storage so that shared-prefix requests—many users sending similar long prompts—only pay the expensive prefill cost once.

DeepSeek-V4’s post-training pipeline ran in two stages.

• Specialist training: they built separate expert models for each domain—math, coding, agents, instruction following—each fine-tuned on domain-specific data and then sharpened with reinforcement learning. They also trained three reasoning effort modes (Non-think, High, Max) by varying how much thinking budget each mode gets during RL.

• On-policy distillation: rather than merging specialists through weight averaging, which degrades performance, or mixed RL, which is unstable, they trained a single student model to learn from more than ten specialist teachers simultaneously by mimicking their output distributions on its own generated text. The key technical choice was full-vocabulary logit distillation rather than a token-level approximation, which produces more stable gradients and more faithful knowledge transfer.

V4 Doesn’t Have to Win to Matter

The lukewarm reception to V4, especially in Silicon Valley, is probably unsurprising. Anthropic and OpenAI are releasing powerful closed models, and the gap between the best open models and the best closed-source models may not be narrowing. Even among Chinese open LLMs, V4 is not a clear win.

But the implications of V4 may take time to show up. After V3 and R1, most major open LLMs adopted or borrowed DeepSeek’s architecture to make their models more efficient, and as a result open models in 2025 broadly improved without everyone having to independently experiment.

Entering 2026, against the backdrop of global compute shortage as agentic token usage skyrockets, V4’s memory and inference efficiency innovations could ease real constraints if adoption grows. It might take another three to six months to see V4’s architectural innovations diffuse across the open ecosystem.

What I find worth noting is that while every other major lab is racing to release models and eke out marginal leaderboard gains, DeepSeek-V4 still prioritized architectural exploration over benchmaxxing. New architectures are risky. They can destabilize training and waste compute. But the bet is consistent with a lab that believes the path to AGI runs through efficiency and democratization, not through scaling alone (partly also due to the export control on advanced chips).

DeepSeek is also navigating real pressures. It’s aiming to raise funding at a valuation exceeding $20 billion, with Alibaba and Tencent both said to be interested. The new capital would help the company access more compute for training and retain top talent. DeepSeek CEO Liang Wenfeng is also reportedly hiring more product managers and building new consumer products.

DeepSeek’s V4 release post closed with a line from the Chinese philosopher Xunzi:

不诱于誉，不恐于诽，率道而行，端然正己。Unseduced by praise, undaunted by slander; following the Way, standing upright in oneself.

(The English translation is attributed to .)

The road ahead for DeepSeek won’t be easy as the AI race intensifies. But a company this consistent in its long-termism—and this committed to keeping its research open—gives me more reason for optimism about where AI is heading.

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On the last day of 2025, Yang Zhilin, the CEO of Moonshot AI, said in an internal memo that his company’s goal is to become the world’s leading AGI company by surpassing frontier labs like Anthropic. He didn’t mention OpenAI.

Yang wasn’t alone. Yao Shunyu, chief AI scientist at Tencent and a former OpenAI researcher, made his preferences equally clear in a panel this January in Beijing alongside former Qwen lead Justin Lin and Zhipu AI’s chief scientist Tang Jie. When Yao discussed the companies he most respected in AI, Anthropic came first.

Recode China AI

🤯The Most Important AI Panel of 2026: Can China Lead the Next Paradigm?

At a high-profile AI event held on January 10, 2026, in Beijing, co-organized by Tsinghua University and Zhipu AI, a headline-making panel brought together leading voices from ind…

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5 months ago · 11 likes · 1 comment · Tony Peng

Three years ago, that conversation looked completely different. OpenAI was the north star. Ilya Sutskever was the most admired AI scientist in China’s research community (he still is). Sam Altman was something close to a AI evangelist. Nearly every Chinese chatbot launched between 2023 and 2024 looked like a ChatGPT clone.

Today, at least among China’s AI community, Anthropic and Claude have become the new reference point. A Chinese tech media called Anthropic “白月光 white moonlight”, which in local context refers to someone from your past who was perfect, pure, and just out of reach. Interviews with Dario Amodei—his curly hair and that particular brilliant unease, the head slightly bobbing, the eyes somewhere between present and elsewhere—are the new required reading.

And yet the most admired AI company in China is also the most hawkish one on China.

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The Pivot in the Models

The shift from OpenAI to Anthropic as a role model shows up not only public statements, but also in what Chinese labs are building and how they’re positioning it.

The latest models from frontier Chinese AI labs like Zhipu, MiniMax, Moonshot all focus on coding and agentic tasks, and all use Claude Opus 4.6 as their explicit benchmark competitor. AI models pivoting toward coding can drive massive productivity gains, move from content generation to autonomous action, and gain enterprise adoption.

GLM-5, Zhipu’s latest flagship, is built for agentic intelligence, advanced multi-step reasoning, and complex engineering tasks—specifically targeting long-horizon agentic workflows. The updated GLM-5.1, released this month, even claims the top spot on SWE-Bench Pro at 58.4 versus Claude Opus 4.6’s 57.3.

MiniMax is even more direct about its positioning. Their M2 launch announcement markets the model as available at “only 8% of the price of Claude Sonnet and twice the speed,” and lists Claude Code as one of the primary developer workflows M2 is designed for.

The iteration pace has been speeding up: in roughly four months, MiniMax shipped M2, M2.1, M2.5, and now M2.7. The latest version can build complex agent harnesses and complete elaborate productivity tasks autonomously. During M2.7’s own development, MiniMax let the model run 100+ rounds of scaffold optimization without human intervention, achieving a 30% performance gain on internal evaluations. They’re calling it “self-evolution.”

Moonshot’s Kimi series tells a similar story. Kimi K2.6, just released this week, featuring state-of-the-art coding, long-horizon execution, and agent swarm capabilities. And Moonshot earlier launched Kimi Code—a direct rival to Claude Code—letting developers use it through their terminals or integrated with VSCode, Cursor, and Zed.

Then It’s the business model. All three companies, along with Alibaba, ByteDance, and Baidu, have launched dedicated Coding Plans, a flat-rate monthly subscription designed specifically for developers using AI coding tools like Cursor, Cline, and Claude Code.

Before 2025, Zhipu’s revenue was dominated by on-premise government and enterprise deployments. By 2025, their model-as-a-service API platform hit 1.7 billion RMB in annual recurring revenue, a 60x year-on-year increase.

In their FY 2025 earnings call this April, CEO Zhang Peng said:

Anthropic is one of the most closely watched companies in global AI. Its growth logic is very clear: focus on delivering the strongest models to enterprises and developers through APIs, and let intelligence participate in creating economic value.

When the model is strong enough, the API itself is the best business model.”

Moonshot’s numbers are even more striking. After the K2.5 launch, the company reported that cumulative revenue in fewer than 20 days already exceeded its entire 2025 annual total.

As agent adoption has accelerated, these labs are finding that pricing power finally exists. Zhipu raised its API prices by 83 percent in Q1 2026 and saw call volumes rise anyway. That is a completely different market dynamic from two years ago, when Chinese LLM providers were engaged in a price war that pushed some API costs close to zero.

Why Anthropic

Learning from Anthropic is not surprising given what the company has achieved. Anthropic’s Claude Opus 4.6—now succeeded by Opus 4.7—is widely regarded as the best model available today, particularly for coding and agentic tasks. The Mythos model, recently previewed with cybersecurity applications, has raised expectations further.

The revenue growth has been without precedent in enterprise software: Anthropic went from $1 billion ARR in December 2024 to $9 billion at end-2025 to $30 billion in April 2026, surpassing OpenAI’s $25 billion for the first time.

Anthropic also has characteristics Chinese companies specifically admire. The company is extremely focused, prioritizing text generation and coding while largely staying away from multimodality. Its top talent has barely turned over. Its corporate culture resembles a religious conviction more than a startup, with an unshakable belief in the AGI mission as the organizing principle. OpenAI, by contrast, has been losing its shine. Multiple waves of talent exodus, the shutdown of Sora, and the controversies surrounding Sam Altman have all taken a toll.

“Anthropic has taste, and they keep delivering,” a Beijing-based AI infrastructure engineer told me.

A deeper reason is most Chinese AI labs face the same challenge Anthropic faced three years ago: they have almost no chance of winning the consumer superapp race. In China, ByteDance’s Doubao has (almost) won it. By late 2025, Doubao’s daily active users dwarfed the combined consumer bases of Moonshot, Zhipu, and MiniMax. In 2024, Moonshot reportedly spent hundreds of millions of yuan promoting Kimi as a consumer product, but it didn’t work. Neither did the similar pushes from MiniMax and Zhipu.

The DeepSeek moment in early 2025 was the wake-up call. It demonstrated that relentless model capability, not user acquisition spend, was the actual path. Anthropic’s enterprise revenue explosion provided the business model proof. You don’t need to beat the consumer superapp. You need to make the best model and sell access to it. That realization has redirected nearly every serious Chinese AI lab: away from the chatbot wars, toward model research, coding tools, and token sales.

By imitating Anthropic, Chinese labs have returned to their comfort zone—heads down, doing research. And the results are showing. In Q1 2026 alone, Chinese AI labs released probably more frontier-capable models than in all of 2025. Model iteration has accelerated from quarterly to nearly monthly.

It also means more aggressive globalization. Western developers and enterprises pay more for API access, and Chinese models priced at a fraction of Western alternatives are finding real traction. OpenRouter data from early 2026 showed Chinese open-source models accounting for over 60 percent of total token consumption on the platform.

The Anthropic playbook does have one complication as Chinese labs absorb it. As commercial pressure increases, open-sourcing the best models becomes harder to justify. For example, MiniMax just removed the commercial license from its M2.7 model. Financial Times also reported that Alibaba is shifting towards revenue over open-source models.

Open-source and open-weight releases remain important for Chinese LLMs—GLM-5.1 and Qwen 3.6 are both open—but the trend line is toward keeping the frontier models closer to the chest.

This is another page taken directly from the Anthropic playbook: Claude has never been open-sourced.

The Strange Relationship

Anthropic is not like OpenAI in how it has engaged with China. In 2023, Sam Altman gave interviews to Chinese media, spoke at a conference organized in Beijing, and spoke highly of China’s AI community, “China has some of the best AI talent in the world.”

Dario is among the most hawkish AI CEOs on China in the industry. In his famous essay Machines of Loving Grace, he proposed an “entente” strategy, a coalition of democratic nations using advanced AI in military applications to achieve a decisive advantage over adversaries, and described the US-China relationship as a new Cold War.

Twenty years ago US policymakers believed that free trade with China would cause it to liberalize as it became richer. That very much didn’t happen.”

At the Axios AI+ DC Summit in September 2025, he said “it is mortgaging our future as a country to sell these chips to China.” At Davos in January 2026, he compared shipping Nvidia H200s to China to handing nuclear weapons to North Korea. In February 2026, Anthropic accused DeepSeek, MiniMax, and Moonshot of using fraudulent accounts to generate millions of conversations with Claude to train its own models.

The irony is personal as well as geopolitical. Dario Amodei worked at Baidu from November 2014 to October 2015, before his time at Google Brain and then OpenAI. The man who spent a year inside one of China’s flagship AI companies is now the most vocal opponent of Chinese AI development among major Western AI executives. Chinese netizens have not missed this. The joke in Chinese tech circles is to wonder what Robin Li, Baidu co-founder and CEO, did to Dario during that year to turn him so decisively against China.

Anthropic has also translated its stance into policy and product. It prohibits users in China from accessing its services. It has recently added Know Your Customer controls to restrict who can access the API.

I don’t know what drives Dario’s position—whether it’s ideology, genuine security concern, or something else entirely. But whatever the motivation, the competitive reality is harder to separate from the principle. Anthropic is competing against Chinese AI companies globally. So far it has seen minimal business impact from Chinese open source models, according to Dario. But Cursor's latest model is built on Kimi K2.5, which means Chinese AI is already inside one of the most popular coding tools that developers use instead of Claude.

Anthropic recently removed Claude Code from its $20 Pro subscription plan. The company says it only affects a small number of new users, but the developer community is unhappy. In forums, frustrated subscribers said Anthropic was nudging them toward cheaper Chinese alternatives.

The Decoupling Problem

On one hand, the more successfully Chinese labs learn from the Anthropic playbook, the more capable they become as global competitors. On the other hand, Dario’s export controls are meant to slow Chinese AI down. The obstacle and the role model are the same company.

In the meantime, Dario’s worldview—AI as the new nuclear weapons, US and China in active technological Cold War—is increasingly becoming the assumption of US policy. In that narrative, it doesn’t matter how good the model is. Zhipu is already on the US entity list.

That narrative is spreading across Silicon Valley as well. In a recent interview on the Dwarkesh Podcast, host Dwarkesh Patel pressed Nvidia CEO Jensen Huang on whether selling advanced GPUs to China undermines US strategic interests—the kind of question that reflects exactly the framing Dario has helped normalize.

That is why Chinese researchers and executives have mixed feelings about Anthropic. They admire the models, the discipline, the revenue growth. They are learning from the business as fast as they can. But they cannot accept the premise—that China’s AI development is inherently dangerous, that containment is the right policy, that the race is zero-sum.

If AI safety is the genuine concern for Anthropic, the answer is collaboration: working with Chinese researchers, mapping shared risks, building toward a global AI safety council. Instead, Anthropic advocates for the kind of decoupling that could shut that conversation down entirely. You can believe in AI safety and still ask whether export controls advance it—or whether they mostly just advance Anthropic.

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Within four weeks this spring, two Chinese AI startups each raised $300 million to build video generation technology.

PixVerse’s developer AISphere closed a $300 million Series C in March led by CDH Investments. Days later, ShengShu Technology, the Beijing-based startup behind the Vidu video generator, secured a $290 million Series B led by Alibaba Cloud.

Both companies are betting that video generation is not the destination: it is the on-ramp to world models, many believe the next paradigm in AI. And both in different ways are entangled with the same backer and competitor, Alibaba.

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AISphere and Shengshu Technology

AISphere and its PixVerse platform are the more consumer-facing of the two. Founded in April 2023 by Wang Changhu, a former Microsoft Research Asia and ByteDance executive, AISphere launched PixVerse to global users in January 2024. The platform lets creators generate videos from text prompts or images, and it caught fire through viral templates, including a “Venom transformation” effect drew over one billion views in late 2024.

By the time of its Series C, PixVerse had surpassed 100 million users across 177 countries, with 16 million monthly active users and over $40 million in annual recurring revenue, according to the company.

The company calls PixVerse the “Canva for video generation.” Canva won by making design simple enough that non-designers stopped needing designers. PixVerse is making the same bet on video. Its latest C1 model claims film-grade quality and the capability to turn storyboards directly into video, and PixVerse V5.6 ranks among the top ten video generators on the Artificial Analysis leaderboard.

The outputs lean toward an animation rather than photorealism, but the motion quality and scene consistency are genuinely impressive. Some of their best generations deliver something harder to quantify: a creative and artistic quality that catches you off guard.

Its more ambitious model is R1, launched in January 2026. It is a real-time interactive world model. According to the company, users can input commands during video playback—changing lighting, replacing backgrounds, redirecting character—with a response latency of around two seconds and output in 1080P.

After R1 launched, the company’s co-founder said the majority of inbound enterprise interest came from the gaming industry. The investor composition of AISphere’s Series C also reflects this: the round included Ruyi Holdings, a film and TV content company, and 37 Interactive Entertainment, a games company.

Vidu, by contrast, is playing a more technical and enterprise-focused game. ShengShu was founded in March 2023 by Zhu Jun, a Tsinghua University professor who serves as its chief scientist. The company launched Vidu globally before OpenAI made its now-shuttered Sora widely available. Its latest model, Vidu Q3 Pro, supports up to 16 seconds of synchronized audio and video generation with multi-shot composition and camera control. The company reported more than tenfold growth in both users and revenue in 2025, though it declined to share specific figures.

Where AISphere’s R1 targets the intersection of video and gaming, ShengShu’s goals are more grounded in embodied intelligence. The company has developed Motus, an embodied AI model designed to enable robots to perform actions. The $290 million will fund a general world model that bridges generated video with real-world use cases like industrial automation and robotics. Founder Zhu Jun has described the goal as connecting perception and action, building AI that can model and predict real-world behavior consistently.

In my view, AIsphere loosely resembles MiniMax in its consumer-first, product-driven growth strategy, while ShengShu more closely mirrors Zhipu in its academic origins and enterprise focuses.

What is a world model, and why now?

Both AIsphere and ShengShu are betting on world models, though applying it toward different ends.

The simplest way to understand a world model is by contrast with what a LLM does. An LLM predicts the next token. A world model predicts what happens next in the world: How objects move, how physics behaves, how cause and effect unfold over time. This is why an AI-generated video clip currently lets a dog’s collar disappear when it runs behind a couch, or turns a love seat into a sofa mid-shot. The model has no stable internal representation of the scene. It is guessing frame by frame what is statistically plausible.

The world model thesis that video is the training ground for AI that understands physical reality has attracted serious believers. Yann LeCun left Meta to pursue it. Google DeepMind’s Genie 3 simulates real-time 3D worlds. Nvidia’s Cosmos platform trained on 20 million hours of real-world data to support physical AI development. Companies that have already spent years building video generation infrastructure are naturally positioned to make this leap.

Alibaba invests in everyone, competes against everyone

Here is where the story gets interesting. The lead investor in ShengShu’s $290 million round was Alibaba Cloud. Alibaba also led AIsphere’s $60 million Series B in September 2025. Which means Alibaba is the principal backer of both of the most well-funded video AI startups in China.

However, the e-commerce and cloud giant is simultaneously building and deploying its own video models internally. In the past week, the company’s newly formed AI unit Alibaba Token Hub, or ATH, released a video model called HappyHorse-1.0. The model debuted anonymously on the Artificial Analysis benchmark, climbed to the top of both text-to-video and image-to-video rankings, and triggered a wave of speculation about its origins before Alibaba confirmed its ownership.

The competition here is essentially Alibaba versus ByteDance. ByteDance’s Seedance 2.0 had been a dominant model on the video AI leaderboards. HappyHorse was the first model to challenge and displace it. My early review is HappyHorse’s photorealistic visual output is a clear step forward, but Seedance 2.0 has an edge in audio-visual consistency and multi-shot camera control. However, against the AI video startups Alibaba backs, HappyHorse outperforms both Vidu and PixVerse on the benchmarks.

This is similar to the ecosystem of Chinese LLMs, where Alibaba’s open-source Qwen family has consistently competed with ByteDance’s Seed series for developer mindshare, while simultaneously backing leading LLM startups—Zhipu AI, MiniMax, and Moonshot AI among them. In both cases, Alibaba’s strategy combines internal model development with external investment in the most promising labs, a dual-track approach that keeps it relevant at every layer of the stack regardless of which specific model wins.

Alibaba Cloud’s revenue grew 36% last quarter, driven by AI workloads, according to the company’s Q3 FY2026 earnings call in March. Video generation is among the most compute-intensive workloads. Every inference request, every API call from a PixVerse or Vidu user represents GPU-hours that, if those companies run on Alibaba Cloud, translate directly into revenue.

US-China race in video generation

If we examine the global AI video landscape, Runway raised $315 million in February 2026 at a $5.3 billion valuation. AIsphere raised $300 million in March at a reported $1 billion-plus valuation. Vidu raised $290 million in April at an undisclosed valuation. Three companies from the US and China, three rounds within eight weeks, all within $25 million of each other in size.

In LLMs, the gap between Chinese and US companies has been real, shaped by the first-mover advantages that OpenAI and Anthropic built through years of foundational research, US export restrictions on advanced chips, and different friendliness to subscription-based services. Video generation is a different story.

Chinese labs entered this race simultaneously with Western ones, and in some cases earlier. Vidu launched globally before Sora was widely available. Kling AI from Kuaishou was consistently competitive on benchmarks — it generated $150 million in full-year 2025 revenue and crossed $300 million ARR by January 2026.

Part of what drives this is structural advantage. China has the world’s most sophisticated short video ecosystem, hundreds of millions of daily active users who have been consuming, producing, and sharing short-form video for nearly a decade. That is both a massive training data advantage and a ready consumer base that is uniquely receptive to AI video tools. The regulatory environment helps too. Compared to the US and Europe, China’s looser approach to IP and copyright creates more room to train on existing content and generate derivative work without the legal friction that has slowed some Western labs.

The field is also growing more specialized. A year or two ago, frontier LLM companies on both sides such as OpenAI, Zhipu AI, MiniMax were hedging their bets by developing video generation models alongside their core LLM models. Most have since pulled back to concentrate on what they do best. The companies that stayed in video have sharpened considerably.

What remains to be seen is whether world models become the unifying layer that both sides have been racing toward, or whether the video and physical AI tracks diverge further, producing different winners for different applications.

Either way, China’s video AI companies have earned a seat at that table.

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DeepSeek is reportedly releasing its long-awaited DeepSeek V4 LLM this month. Its peer Moonshot AI is enjoying a strong moment of its own: a new funding round has catapulted the company’s valuation to $18 billion, buoyed by the successful rollout of Kimi K2.5.

These are the most watched frontier AI labs in China today—arguably in the world. Yet both remain secretive. Their founders rarely give interviews, and few people know what these organizations look like from the inside.

This week I read two excellent pieces about the two companies—by Chinese media outlets LatePost and Renwu—and couldn’t wait to share them. I used Claude to translate both, with minor edits.

DeepSeek, covered by LatePost, is the more journalistic of the two pieces. It surfaces newsworthy details about why V4 has been delayed, where the company’s research focus sits today, and what its workplace actually looks like. My quick takeaways include:

• DeepSeek is putting many efforts in adapting its models to Chinese-made AI chips—a strategic priority given the restricted access to high-end Nvidia hardware and China’s chip self-reliance ambition.

• The company is still committed to original, foundational research, while the outside world just wants the next R1 moment. Managing that gap is CEO Liang Wenfeng’s challenge right now.

• Singular focus remains DeepSeek’s uniqueness. They prioritize language model research above everything else, skipping trendy directions like multimodal generation and resisting pressure to chase every hot application. This is inseparable from Liang’s personal philosophy: do few things, do them completely.

• Surprisingly in a country known for its high-pressure workplace culture, DeepSeek has a no-overtime policy. Liang’s reasoning is exhaustion-induced poor judgment wastes precious compute.

• DeepSeek also faces a resource tension that is becoming harder to ignore. Frontier research is compute-hungry, and Liang is now working to establish a formal company valuation—partly to retain talent being courted with 2-3x competing offers, and partly to give the organization more runway for the kind of long-horizon research.

Moonshot AI, profiled by Renwu—the Chinese magazine known for its long-form, human-interest profiles—is a different kind of read. It portrays how a team of young geniuses works inside a non-traditional company structure. No KPIs, no titles, no formal reporting layers. That flatness accelerates creative collaboration, but also creates disorientation for people who need external structure to know whether they’re succeeding.

The piece is a delight to read, though I need to flag this is not a tech journalism article. The writer is more like an observer with a visible admiration for the people they are watching. If you’re looking for Moonshot AI’s research roadmap, growth metrics, or strategic mishaps, you’ll come away disappointed.

The two pieces share some commonalities. Both Liang Wenfeng and Moonshot AI CEO Yang Zhilin have built organizations that are direct reflections of their personal philosophies. Both have explicitly rejected the standard Chinese tech management—perhaps conventional structures are incompatible with frontier AI research.

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DeepSeek Before V4: Character, Organization, and Liang Wenfeng’s Singular Vision

By Cheng Manqi Edited by Song Wei

DeepSeek stands at an inflection point. Since the second half of 2025, the members who have definitively departed and landed somewhere new include:

• Wang Bingxuan, poached by Tencent’s Yao Shunyu late last year. A core author of DeepSeek LLM—the company’s first-generation LLM—he had remained involved in subsequent model training cycles.

• Wei Haoran, who left around the Lunar New Year. A core author of the DeepSeek-OCR series, he is expected to join a major tech company.

• Guo Daya, who recently made his departure official. A core author of DeepSeek-R1, he too is expected to join a major tech company.

• Ruan Chong, who left earlier in 2025 to effectively retire, then in January announced he was joining autonomous driving startup Yuanrong Qixing. Ruan was a veteran from the High-Flyer days and a core contributor to DeepSeek’s multimodal work, including Janus-Pro.

DeepSeek has never raised outside funding, and carries no formal valuation. As the market caps and valuations of rival AI companies have surged, Liang Wenfeng has been working to answer a pressing question from within his team: what is this company actually worth? The answer has direct bearing on what those employee equity agreements are actually valued at.

Since the fall of 2025, Liang has also been talking more about productization and commercialization. DeepSeek now has a product team of several dozen, though it has yet to move into hot application categories like AI coding or general-purpose agents—on the consumer side, it still offers only a conventional chatbot.

Then there is the challenge of managing scale. DeepSeek’s headcount has surpassed that of High-Flyer, making it the largest organization Liang has ever run.

Hanging over all of this: DeepSeek V4 has still not officially launched.

Around January of 2026, a smaller-parameter version of V4 was quietly circulated to some open-source framework communities for compatibility testing. Under more optimistic timelines, the full large-parameter V4 had been expected to ship and open-source around the Chinese New Year in mid-February. As things stand, V4 may arrive in April.

Some people have left. More have chosen to stay. DeepSeek is adapting—but much about it remains unchanged.

It is the only core AI lab in the world that doesn’t grind. While core AI developers at Google, OpenAI, xAI, ByteDance, and their Chinese and American peers routinely log 70 to 80 hours a week, most DeepSeek employees walk out the door around 6 or 7 p.m. There’s no clock-in requirement in the morning.

Liang Wenfeng’s view: it’s nearly impossible for a person to sustain more than six to eight hours of high-quality output in a day.

DeepSeek has no formal performance reviews, no hard deadlines. This lean, high-talent-density organization still runs on what it calls “natural division of labor”—researchers can freely form teams or pursue ideas independently.

“Beyond the main track, DeepSeek has people working on long-horizon research that might not yield results for a year,” said one person close to the company. “DeepSeek is the best place in China—arguably in the world—for someone who genuinely wants to do research.”

There is also this: DeepSeek is secretive. Especially since 2025, from Liang Wenfeng down, the team has gone collectively silent. On the social media platforms and communities where AI practitioners are most active, their voices are almost nowhere to be found.

This piece assembles what we’ve learned from multiple sources about DeepSeek’s defining traits, research priorities, organizational dynamics, and the changes unfolding inside a company of fewer than 200 people. All of it traces back to the singular goal Liang Wenfeng has set for DeepSeek.

Liang Wenfeng: Do Few Things. Do Them Completely.

Liang’s AI ambitions predate DeepSeek’s founding in 2023 by years.

In 2016, Demis Hassabis—the originator of the AGI concept and co-founder of DeepMind—assembled a quantitative trading team to generate revenue for DeepMind as it sought independence from Google. It didn’t make money.

That same year, Liang Wenfeng—a Zhejiang University alumnus with both bachelor’s and master’s degrees—had already been in quantitative investing for eight years. He had founded High-Flyer in 2015, started running deep learning strategies on GPUs in live trading in 2016, achieved full AI-driven trading strategies by late 2017, and in 2019 stood up High-Flyer’s first compute cluster: “Firefly No. 1,” with 1,100 GPUs.

Also in 2019, High-Flyer AI (High-Flyer Artificial Intelligence Fundamental Research Co., Ltd.) was formally incorporated. Luo Fuli, now heading AI at Xiaomi, and Ruan Chong, who recently joined Yuanrong Qixing, both joined High-Flyer after this, later transferring to DeepSeek in 2023.

Financially independent before 30, Liang leads a life that is both simple and opaque.

People who know him say he’ll wear the same clothes for days at a stretch. In Hangzhou, he lived in hotels for long stretches. In Beijing, where most of DeepSeek’s researchers are based, he rents an apartment. He’s slight and fit, known to keep a physical routine; the hobby most associated with him is hiking and outdoor activities.

Jensen Huang invites Nvidia employees to his home, pours drinks, chats about life, happily shows off his cars. Liang Wenfeng skips quarterly team-building events, rarely joins team dinners, and at the annual all-hands only shows up to give remarks—then leaves.

In 2022, a High-Flyer employee going by the pseudonym “An Ordinary Little Pig” made a personal donation of 138 million RMB to a charitable foundation. Many speculated that the little pig was Liang himself. High-Flyer’s response: “Employee donations are anonymous. The company itself doesn’t know the real identity of the little pig.”

Within the scope of work, Liang does few things. He doesn’t do many things typical startup CEOs do—like fundraising.

In 2023, Liang briefly met with a small number of investors. But we understand he put forward an unconventional condition: similar to OpenAI’s investment arrangement with Microsoft, he wanted potential backers to accept a capped return. None of the institutions that went through that process invested in DeepSeek.

In the two years since, Chinese LLM funding has been torrential, with multi-hundred-million-dollar rounds becoming routine. Liang stopped meeting investors entirely—not even to build relationships. Most founders, even outside an active fundraising window, wouldn’t turn down a coffee with a partner from a top fund. Liang has declined most such requests.

He has concentrated nearly all his time on the narrow set of things he believes deserve his focus—and pursued them with granular attention.

One of the keys to DeepSeek’s early success was what might be called singular focus: a clear prioritization of language models above all else, with no detours into hot areas like multimodal generation.

On the chosen track, Liang goes deep. He learns from team members across algorithms, architecture, infrastructure, and data; he participates directly in model and product discussions at the detail level.

People who have spent time with Liang consistently observe that he carries no CEO aura, no “genius energy”—he comes across more like a researcher. What he talks about most is specific technical problems.

Zhang Jinjian, founding partner of Oasis Capital, once shared a story in an essay: he asked MiniMax founder Yan Junjie whether there was anyone more focused than him.

Yan described arriving early for a first meeting with someone he’d never met—spotting a guy in a T-shirt and assuming he was an assistant. For half an hour, this person asked Yan detailed technical questions without introducing himself. Then Yan asked, “When is Liang coming?” The guy said: “I’m Liang Wenfeng.”

DeepSeek’s Organization: Flat, Cross-Functional, No Overtime

Consistent with Liang’s personal style, DeepSeek’s organization is radically flat, built on cross-functional collaboration, deliberately slow to expand, and free of overtime culture.

When Liang founded High-Flyer, he had co-founders. DeepSeek has no second-in-command—especially within the research team, where there are only two levels: Liang and everyone else. Liang makes major decisions and absorbs the most accountability.

The research team now numbers roughly 100 people and operates like a large academic lab. The DeepSeek researchers—most born around 2000—call Liang, born in 1985, “Liang Laoban” (Boss Liang). The relationship is closer to that of a dissertation advisor: he organizes research, coordinates resources, does hands-on research himself, and appears as corresponding author on shared outputs.

Liang’s deepest personal involvement is with the base model architecture team, where he joins detailed discussions to finalize the architecture of each new model generation. This team numbers in the low dozens and forms the core of pretraining.

Closely coupled with architecture are the infrastructure and data teams, each also in the low dozens. In many companies, infrastructure functions as an internal service provider that executes on algorithmic requirements. At DeepSeek, the infra team is in the room before model training begins—contributing to architectural decisions at the design stage.

The tight integration across these groups produces blurred organizational lines and what the company calls “cross-functional division of labor.” This happens to be the collaboration model best suited to how model training actually works: data selection and infrastructure tradeoffs have to be on the table during the experimental and finalization phases, not handed off afterward.

Liang serves as the connective tissue across all these modules. He attends each team’s weekly meeting, tracking progress and bottlenecks across the full picture. Most teams’ weeklies are open to members from other teams.

This kind of granular leadership and organically tight collaboration is hard to sustain at scale—which is why DeepSeek is deliberate about limiting the size of its core research team.

One of the things most distinctive about DeepSeek in the global AI landscape: No overtime, no clock-in, no formal performance reviews. Most employees leave around 6 or 7 p.m. The company subsidizes after-hours activities—sports classes, reimbursement for recreational facilities.

Liang’s logic: a person cannot sustain more than six to eight hours of high-quality work in a day. Exhaustion-induced poor judgment wastes precious compute. The cost outweighs any benefit.

On hiring, DeepSeek has historically avoided lateral recruitment almost entirely, relying instead on new graduates and converted interns. In early 2025, LatePost profiled the 172 researchers (including interns) who had contributed across DeepSeek’s first three model generations—LLM, V2, and V3/R1—and tracked down 172 of them, finding resumes for 84: more than 70 percent held bachelor’s or master’s degrees; more than 70 percent were under 30.

Before V3 and R1, DeepSeek was competing in the global LLM top tier with roughly one-tenth the headcount of major tech companies, approximately half the per-capita working hours, and extreme concentration of effort.

But as the frontier expands and the number of directions worth exploring multiplies, sustaining that organizational size, communication style, and research culture has become harder.

The Past 15 Months: DeepSeek Stays the Course While the World Shifts Fast

After V3 and R1 broke through in early 2025, DeepSeek didn’t press its advantage with a splashy follow-up. Instead, it continued down its chosen research track. The publicly disclosed work falls roughly into three categories:

First: efficiency optimization. Squeezing maximum intelligence out of every unit of compute. This includes the full suite of training and inference infrastructure released during DeepSeek’s open-source week in early 2025—covering inference kernels, communication libraries, matrix multiplication libraries, and data processing frameworks. (Note: a kernel is the lowest-level code executing on a GPU, implementing core operations like matrix multiplication.)

It also includes continued improvements to the attention mechanism: NSA (Native Sparse Attention) released in early 2025, followed by DSA (Dynamic Sparse Attention). Combined with MLA (Multi-head Latent Attention) introduced in V2, these share the same goal: processing longer context without proportionally increasing compute.

The September 2025 update to DeepSeek-V3.2 revealed something else: DeepSeek swapped its underlying operator library from the mainstream CUDA and Triton to TileLang—an open-source project initiated by Yang Zhi’s team at Peking University. CUDA is Nvidia’s lowest-level language; Triton was open-sourced by OpenAI; TileLang is a homegrown Chinese open-source alternative.

Second: architectural advances. These include mHC (Mixture of Hash Connections, or “flow-constrained hyperconnections”), released in early 2026 to improve stability in large-scale training, and Engram, which builds persistent long-term memory outside the model itself. Industry observers widely believe mHC will be incorporated into V4’s training.

Third: “non-mainstream” explorations. Among these, DeepSeek-OCR converts text into images before feeding them into the model—the intuition being that letting the model “read” text the way humans visually parse paragraphs and hierarchies improves comprehension of complex documents. Internally, more of these experiments are ongoing, including work on continual learning and autonomous learning.

Liang also brought on advisors with backgrounds in neuroscience and brain science in 2025, exploring learning mechanisms that more closely mirror the human brain.

Meanwhile, the external AI environment has shifted dramatically. Two competitive storylines have dominated attention:

The first is agentic models and applications built on coding capability. This has become the primary battleground between Anthropic and OpenAI, now playing out as Opus 4.6 vs. GPT-5.4 on the model side, and Claude Code vs. Codex on the product side. OpenClaw—the viral AI agent that swept China in early 2026—represents the latest expression of this trend.

The second is multimodal generation, which has repeatedly broken through the mainstream through what can only be called visual magic: OpenAI’s GPT-4o in the spring of 2025, Google’s NanoBanana in the fall, ByteDance’s Seedance 2.0 just before the 2026 Lunar New Year. Video generation is also tied to one of the most forward-looking frontiers: world models.

DeepSeek has made little investment in multimodal generation. Liang Wenfeng doesn’t consider it a core path to intelligence.

On the agent front, DeepSeek-V3.2 strengthened agent capabilities, but DeepSeek’s overall iteration cadence has been slower than rivals who grew deeply anxious in the wake of R1. Since early 2025, Zhipu, MiniMax, and Kimi have each shipped five, four, and three new model versions respectively—each targeting agents or coding.

According to OpenRouter data, over the 30 days from February 24 to March 26, DeepSeek-V3.2 ranked 12th in model token consumption among OpenClaw applications routed through OpenRouter—six of the top ten were Chinese models. (Note: OpenRouter reflects individual and small-developer usage and offers only a partial view of overall token consumption.)

DeepSeek’s Goal Isn’t the Mainstream One. Some Left. More Stayed.

DeepSeek’s distinctiveness connects directly to Liang Wenfeng’s conception of what AGI development actually requires. Beyond pushing the intelligence ceiling of LLMs, he sees two other critical workstreams:

First: building LLMs on a domestic Chinese tech stack.

DeepSeek invests in compatibility with domestically produced GPUs to reduce dependence on restricted high-performance compute supply. The V3.1 update last August, for instance, noted that DeepSeek’s adoption of UE8M0 FP8—a data compression format—was “designed for the next generation of domestic chips.” Switching from Triton to the domestically developed open-source TileLang, mentioned earlier, serves the same purpose: gaining more control at the foundation level.

In conversations with AI practitioners, Liang has also floated this question: “Could we achieve everything intelligence can do today using only the compute that already exists?”

Second: original innovation—pursuing directions that major tech companies or other startups won’t attempt or aren’t willing to try.

In the second half of 2024, DeepSeek launched the Janus series, experimenting with unified multimodal understanding and generation. It has also developed the Prover series, exploring formal mathematical proof. Then there’s the OCR work in 2025, and internally ongoing efforts in continual learning and brain-inspired architecture.

For Liang, what matters is not just model performance—it’s the more fundamental, original discoveries made in the pursuit of that performance.

But this doesn’t align with what some outside observers now expect from DeepSeek. Some want every DeepSeek release to be as seismic as R1. That’s an unreasonable demand, and it doesn’t reflect how technology actually develops.

Liang can ignore external expectations. Internal expectations are another matter.

For younger researchers, exploring the frontier means absorbing more uncertainty. The safer path is staying close to the industry’s strongest models, getting your name on technical reports that attract attention, and having access to abundant GPU resources to run experiments.

Beyond recognition and influence, the external pull on DeepSeek’s talent has a financial dimension. DeepSeek’s base compensation is strong, but competitors are offering more—significantly more. Headhunters told us that competing offers are “hard to say no to”—”doubling or tripling is no problem,” with some companies offering eight-figure RMB total comp packages including equity.

The competitive picture has also shifted. MiniMax and Zhipu have gone public with rising share prices; StepFun and Kimi are reportedly on IPO paths. This has made the equity agreements DeepSeek employees hold—with no clear price attached—feel increasingly uncertain.

Faced with the large offers, more people have stayed. They believe in Liang’s approach to AGI. They want to do research that isn’t driven by competitive pressure. And they’ve grown comfortable in DeepSeek’s comparatively relaxed, deliberate research environment.

Some recent rumors circulating externally are inaccurate. The team has seen some changes, but there has been no mass departure.

“The ones who stayed are, to some degree, still idealists,” said one person close to DeepSeek. Liang believes that beyond the main track of improving model efficiency and capability, it’s necessary to pursue directions whose near-term returns are unclear—because “the better-resourced labs abroad, like Google and OpenAI, are definitely trying all kinds of directions internally.”

To this day, DeepSeek’s relatively small team and the transparent, flat culture it has maintained since founding still allow members to divide work organically: sometimes a new research direction begins simply because three or five people all think an idea is worth pursuing—and so they do.

This echoes how Liang described it in a 2024 interview with Waves (a Chinese media outlet):

We generally don’t pre-assign roles. Every person brings their own distinct experiences and their own ideas. You don’t need to push them… But when an idea shows promise, we’ll also top-down reallocate resources.

“DeepSeek is the best place in China—arguably in the world—for someone who genuinely wants to do research,” said one person close to the company.

Changing the World, Being Changed by It

DeepSeek’s distinctive interpretation of what AGI requires is both its greatest strength and the source of its current internal tensions. The ecosystem-building and original exploration Liang prizes are aligned with—but not identical to—the industry’s dominant priority of simply staying strongest.

And as LLMs have matured, the definitions of both “strongest” and “most original” have grown murkier and more subjective.

Benchmark scores no longer fully capture model capability. Especially as the competition has shifted toward agentic models, product surface area—and the long-tail use cases and diverse data that come with it—has become increasingly important. That’s precisely the territory where DeepSeek, with its focus on core model research, hasn’t invested heavily.

The forthcoming V4 will almost certainly be the strongest open-source model at release—but it’s unlikely to be dominant by a crushing margin. Different developers and users in different contexts now hold increasingly varied standards for what “strong” even means.

As for what constitutes original, valuable new research: that has always been contested, determined by the experience, judgment, and intuition of individual researchers—what the field calls “technical taste.”

The way to validate taste is through experiments. And the number and scale of experiments are constrained by compute. Relative to its peers, DeepSeek doesn’t have that much of it.

Finally, whether it’s building the ecosystem foundations for domestic AI infrastructure, or exploring directions others won’t try—the payoffs of the work Liang values most are deeply uncertain.

Frontier research is supposed to absorb that uncertainty. But it sits in tension with limited compute resources, and with the expectation from outside that DeepSeek will continue to astonish—or even overwhelm—on a regular basis.

Liang sees the need to change. He has recently started working on establishing a company valuation and giving team members clearer financial expectations.

DeepSeek will also invest more in products. We reviewed every job posting published by a DeepSeek HR staffer on social media from December 2024 through today. In the most recent listings from mid-March 2025, DeepSeek for the first time named specific products in a posting—recruiting a “model strategy product manager” for agent work:

Continuously tracking industry developments, with hands-on experience using well-known agents such as Claude Code, OpenClaw, Manus…

More moves from DeepSeek on the agent product front are coming.

In early 2025, DeepSeek stunned China and the world with its open-source generosity and its achievement of doing more with far less—and changed the world in the process: pushing peers to invest more seriously in model fundamentals, directly inspiring models like Kimi K2 and K2-thinking, and seeding new teams, including MiroMind, backed by Chen Tianqiao.

A miracle is a miracle precisely because it doesn’t happen often. It is, by definition, a low-probability event. In China’s fiercely competitive, results-first environment, the very existence of a DeepSeek willing to pursue an unconventional goal is itself a pleasant anomaly.

Those who have spent time with Liang Wenfeng describe him this way: “He has an unusual immunity to noise.”

After R1 went viral in 2025, Liang showed little interest in the adulation. Now he faces a different kind of test: in an environment of intensifying external competition, distinguishing signal from noise—holding the line where it matters, and changing where change is called for.

“The people who keep their heads down may not be the ones still standing when the froth clears,” said one industry observer. “But the only way for Chinese technology to move from imitation to leadership is if more companies like DeepSeek exist.”

That is Liang Wenfeng’s work. And DeepSeek’s. For the many who have been moved by what this company has done, the ask is simple: set aside the hero narrative, and watch a company and its technology with something closer to equanimity.

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100 Hours Inside Moonshot AI

By Liu Mo, Edited by Jin Zha

Spring 2026 has been kind to Kimi. Revenue, fundraising, and valuation have broken records in rapid succession. A paper co-authored by a 17-year-old high school intern drew praise from Elon Musk and other Silicon Valley heavyweights. And Cursor—a U.S. company valued at $50 billion—publicly acknowledged that Kimi’s model powers the core of its product. In the span of weeks, Kimi completed what might be called a beautiful triple: capital, technology, and commercialization, all at once. This startup, founded just three years ago and now valued at over 120 billion RMB, is becoming visible on the global AI map.

And yet Moonshot AI—the company behind Kimi—remains, as ever, mysterious.

I was granted 100 hours of deep access inside the company’s headquarters. As an independent writer, I could interview any employee willing to speak, sit in on any meeting that didn’t touch trade secrets, and write without interference or compensation. That, it turns out, is exactly this company’s style.

Standing inside the company feels like being at the eye of a storm. Within the eye, everything is still. Workstations are quiet. Keyboard clicks are sparse. Laughter drifts over occasionally. The noise of the outside world—the rumors, the debates, the adulation and imitation—finds no echo here.

More than 300 people. Average age under 30. Each one carrying roughly 400 million RMB in implied valuation on their shoulders. About 80 percent of employees here identify as introverted—people sit side by side and default to typing rather than talking. Introversion isn’t a flaw here; it’s an organizational protocol.

I thought back to my first visit to this company in 2024, on a night when the storm was just beginning to gather. My first impression of Moonshot AI was not particularly good.

“DeepSeek Saved Us”

The evening of December 24, 2024 was a Christmas Eve most Chinese people wouldn’t have paid much attention to. For Julian, it was the darkest night of her life.

She was 26, two years out of Peking University, with no industry experience—and already one of Kimi’s earliest employees. This simultaneously young and senior woman sat at the long table in the Radiohead conference room, facing more than thirty colleagues, tears she couldn’t stop streaming down her face.

Julian had been unable to deliver a holiday marketing plan that met the co-founders’ standards. With the Chinese New Year still a month away, being asked to fundamentally overhaul a proposal already revised six times—and guarantee cross-team execution—was a low-probability event to begin with. But the company’s growth expectations for the 2025 Lunar New Year were enormous: it was the previous year’s New Year that had put Kimi on the map with its “2-million-character long-context” breakthrough, triggering a surge in consumer users and even spawning what the market called “Kimi concept stocks.”

The weekly meeting was long and dispiriting: twenty young colleagues with no more experience than Julian took turns presenting, covering everything from social media buying to user operations, domestic PR to overseas marketing, debated in full group, decided by the co-founders. Kimi was like an adolescent who didn’t know what to do with itself—even with a monthly marketing budget in the tens of millions of RMB, it was flailing against the oncoming competition.

The meeting finally ended before 4 a.m.

No one would find out whether Julian’s eventual plan succeeded. Because one month later, when the world first learned the name DeepSeek, none of it mattered anymore.

Hayley, from the growth team, went back to her hometown in Wenzhou for the holiday. Relatives and friends kept asking: have you heard of DeepSeek? Kimi suddenly sounded like a name from a previous era. Hayley had the hardest New Year of her life. “The silence inside the company,” she said, “was deafening.”

The annual all-hands meeting is typically held in March, after the holiday, and employees are encouraged to challenge leadership directly. That year, nearly every question was about DeepSeek. The sharpest came from the HR team, who with complete sincerity put into words what everyone was thinking:

“How do we answer the question candidates are asking us now: ‘DeepSeek gave me an offer too. Why should I come to Kimi?’”

Not everyone processed it that way. Alex, from the algorithm team, recalls that if he felt any strong emotion in the DeepSeek moment, it was one thing: excitement.

That excitement wasn’t just his—it reflected the whole algorithm team’s disposition. They saw a new possibility: lower-cost approaches, the open-source path, and a fact that no one had previously believed possible—that if the technology is strong enough and the model is solid enough, an obscure Chinese startup can earn the respect of the entire world.

The product team wasn’t panicking either. Kevin, among the earliest product hires, had a clear and steady confidence: DeepSeek broke through on its model. But once Kimi’s model capability caught up, he knew there would be far more they could do on the product side to build on that foundation.

No one knows exactly what discussions the co-founders had behind closed doors. But the company executed a strategic pivot and refocusing with remarkable speed, and reached genuine company-wide consensus. Ask any employee what the most important thing the company does is, and they’ll answer without hesitation: the model.

From that point on, you could feel a deep respect for DeepSeek spreading through the organization—on one level, the mutual recognition of peers; on another level, as Alex put it: “The truth is, DeepSeek saved us.”

Taste Is All You Need

“How can you wear shoes like that?”

Ezra blurted it out, and I was more surprised than she was. On her office floor, nearly everyone keeps a pair of slippers under their desk—comfortable clothes help people relax, focus, and think more creatively.

This is the dress code of smart people.

I’ve met many high achievers, but the “good students” here are unlike any I’ve encountered. When Ezra was in elementary school, she tried to hack her family computer simply because her parents refused to tell her the password. In middle school she developed an interest in Bitcoin—when a single coin cost just 300 RMB—and tried to persuade her mother to give her some pocket money to invest; her mother told her it was a scam. The first time she took a taxi in high school, she sketched out the product model for a ride-hailing app—if AI tools had existed then, she might have built it. In college, she finally had money of her own and went into the stock market, losing 90 percent of it on China’s A-share market.

The stock market disaster prompted a deep reflection on human limitations—which unlocked her interest in AI.

Her understanding of AGI is simple: create N Einsteins and solve all of humanity’s problems. From that point, she was determined to find a company exploring the limits of AGI—even after she had made back her losses in the A-share market.

With a strong academic background, she had offers from major companies everywhere. She chose Kimi for one reason: during the interview, founder Yang Zhilin’s deep technical understanding and careful attention to detail moved her. She felt he genuinely cared about the model. He had none of the restlessness you often find in very smart people, none of the transactional quality of a businessperson, and she didn’t learn he was the founder until the interview was over.

Karen was rebellious from childhood—argued with teachers, never listened to his parents, insisted on studying abroad when everyone expected him to stay, then insisted on starting a company after graduating, despairing at the stable, comfortable life that big tech companies offered. He didn’t want a life where he could see the end from the beginning.

I asked him: if you had to choose between a 100 percent chance of a 60-point outcome and a 1 percent chance of a 100-point outcome, which would you pick?

He chose the latter without hesitation. Not that he couldn’t live with 60. He just couldn’t stand the 100 percent.

This entrepreneurial DNA forms a collective undertone. By rough count, at least 50 people at Moonshot AI have founded or joined a startup.

Kimi, it seems, likes hiring CEOs.

More precisely: it shelters wave after wave of wandering geniuses. A genius isn’t necessarily a star student. What matters is that they have, in some dimension, a pair of eyes that can see through time.

Yannis’s CV isn’t impressive by elite Chinese university standards. But as far back as 2023—when AI model companies didn’t even have products yet—he had already foreseen in developer communities the rise of both DeepSeek and Kimi. A younger colleague spotted this foresight and introduced him to the company.

Karen puts it this way: too many smart people get trapped in the constraints of systems—family, school, workplace—and unconsciously conform to the collective, unable to see their own deepest needs. Only a few try to escape, and they’re often invisible to the world. One of Kimi’s missions, he says, is to see them.

Without that seeing, a 17-year-old high school student couldn’t have collaborated with the team as an intern and co-authored a paper that drew praise from Musk. The person who put him in the first-author position was Bob—the talent spotter who found him in the first place.

The line between genius and madman is thin. When a misunderstood madman arrives at Moonshot AI, they may suddenly become a world-changing genius; or those unfinished geniuses may only fully ignite in this environment. Bob told me: to some extent, a big ego isn’t a problem—it might even be an asset. Using ego as an internal drive, believing that one must be part of something great—that’s the truly mad kind of genius, and the kind they absolutely don’t want to miss.

Geniuses are obsessive.

In this team, training a top-tier AI model is called “alchemy”—and alchemy is fundamentally just debugging, endlessly. After launching a Flagship Run—a full-scale training of their most advanced model—Bob and his colleagues developed a habit they can’t break: the first thing they do every morning is refresh tens of thousands of internal monitoring metrics. Any curve on the screen that spikes abnormally triggers immediate alarm: is it an optimization error? An architectural flaw? A numerical precision mismatch?

They are as sharp as trained animals. Some team members filter through training data to find tokens with extreme values, print them out one by one, and interrogate each one: why are you fluctuating so violently?

Everyone who has truly participated in this “delivery” has lived through nights too tense to sleep—not from anxiety, but from curiosity in overdrive. That obsessive vigilance is what has pushed this model to the top of the industry.

Geniuses cluster.

Over the past year, more than 100 of Kimi’s new hires came through internal referrals—friends, or friends of friends. This recruitment model is called “person-to-person transmission” inside the company. Built on a network of relationships that are already deeply connected, trust becomes a natural organizational asset.

At its core, Kimi has transferred the difficulty of organizational management onto the hiring process. People who arrive through referral tend to be “the same frequency.” This echoes the keyword almost everyone at the company emphasizes: taste.

On a September 2025 evening, a few engineers casually kicked off an internal side project they named “Ensoul”—as in, to give something a soul. The name itself reads like a line of poetry: they wanted dormant code files to come alive, to become an intelligent assistant you could converse with in the command line.

This sensitivity to naming isn’t accidental. They once had a framework called “YAMAHA”—an acronym for “Yet Another Moonshot Agent.” Their core underlying layer was named “Kosong”—Malay for “empty,” drawn from the Buddhist expression “form is emptiness,” evoking a blank page that presupposes nothing yet contains every possibility.

It’s precisely this kind of taste that determines what the product looks like.

While everyone else was cramming chat windows into the command line, the team found it ugly: real programmers open a terminal to input commands, not to have a conversation. So Kimi CLI was designed to be more like a “smart shell”—it understands your commands but doesn’t try to turn itself into a dialogue window.

This minimalism extends to the code itself. The entire core logic is 400 lines of Python—like a short poem, stripped of all unnecessary decoration. Modules are cleanly decoupled so users can customize features or disassemble Kimi entirely and rebuild something of their own.

Even Kimi Agent in its early days once went by the internal name “OK Computer”—a name eventually forced to change because it was too niche to travel. But the person who named it didn’t seem to care about internet traffic maximization; they only answered to their own musical taste and linguistic fastidiousness.

Someone half-joked that if you ranked AI companies by the proportion of employees who play a musical instrument, Kimi might come in first.

Taste is the highest—and hardest to meet—hiring standard. It can’t be quantified, yet it’s everywhere.

Generalize, Then Evolve

You’ll probably never quite figure out what each person at Kimi is working on.

The company prefers the word “team” to describe how work is divided. Broadly, directions like algorithm, product and engineering, growth, strategy, and operations are roughly defined—but once you try to drill down into specific “departments” or individual responsibilities, no one can give you a clean answer.

Because what you’re dealing with is an organization with no departments, no seniority levels, no titles, no OKRs, and no KPIs. Even the reporting structure is almost comically simple.

For Brandon—who did his bachelor’s and master’s at Tsinghua, held management roles at Silicon Valley giants and Chinese tech majors, and built a billion-dollar startup—this was genuinely incomprehensible. Steeped in years of technical management, having led teams of nearly a thousand people, he arrived hoping to apply that experience and finally have room to operate—only to be told by co-founder Zhang Yutong that the company didn’t work that way, and that the team he’d be leading would have approximately two people.

Out of some intuition about the future, he wanted to learn more before deciding.

So in January 2025, on a long night full of doubt and unsettled feeling throughout the industry, Brandon met his junior Tsinghua classmate, founder Yang Zhilin. Brandon couldn’t have known then that Yang’s name would one day appear alongside Musk’s and Jensen Huang’s in media coverage as frequently as it now does. The only thing he remembered was that after the small talk, the first thing his junior classmate said was:

“RL—reinforcement learning—is the future.”

The conversation that followed was less a dialogue than Yang Zhilin thinking out loud—he was so deep inside his own reasoning that Brandon couldn’t follow much of the Chinese. But he couldn’t deny that for the first time, he felt the knowledge structures and mental frameworks he’d built over twenty years beginning to crack under the pressure of a revolution that was already underway—along with all his ego. As for what finally convinced him to join, he was slightly cryptic: Yang Zhilin might become a great prophet, because he has sufficient vision, and sufficient purity.

Later, when this title-averse company struggled to figure out how to formally slot him in, his response was unequivocal and deadpan: “Even if you make me clean the bathrooms, I’ll come. And I’ll clean them better than anyone.”

Not every seasoned manager or specialist from big tech thrives here. Phoebe is a post-2000 woman who transferred from the growth team to the product and engineering team and calls herself “a clueless young kid who knows nothing.” She told me in all seriousness that in this company, rich experience and an impressive resume can become liabilities. The AI industry is too new, changing too fast—a senior expert may not be able to learn and grow as quickly as someone like her.

She witnessed at least three mid-to-senior-level big tech arrivals fail to land successfully. One eventually decided to go to a completely different industry to live out the rest of his career. The reason: surrounded by people who were extremely young and extremely smart, he broke down after being outpaced repeatedly. He concluded this was not his era or his industry, and made peace with stepping back.

After the DeepSeek moment, Phoebe was hit with her own deep sense of crisis, decided to abandon her advertising entirely, and threw herself into learning everything she could about product and engineering—hundreds of hours of self-study, including livestreaming her learning sessions on Bilibili. What surprised her was that the company gave her the opportunity to switch roles from the very beginning, with complete ease.

In fact, among the thirty employees I interviewed, more than half had changed job responsibilities multiple times. Compared to what they were doing at their previous employer, the change in scope for most is probably 80 percent—meaning nearly everyone at Kimi is doing something entirely different from what they did before.

Kimi gravitates toward people with strong generalization ability.

In the language of AI, generalization refers to a model’s ability to perform in new scenarios outside its training data—not memorizing answers, but capturing underlying structural patterns. The big tech middle and senior managers, by contrast, have been trained too long inside a specific KPI system, a specific vocabulary for reporting up, a specific game of resource allocation. Their weights are overfitted to a local optimum. When the environment variables change completely, their capabilities break down in the face of a new distribution.

If traditional big tech employees are like specialized models, Moonshot AI’s ideal individual is a foundation model: learning basic rules through supervised fine-tuning, then self-playing across diverse tasks via reinforcement learning, ultimately acquiring the ability to transfer knowledge across domains.

James is a returnee from Silicon Valley, 26 years old, whose dream is “to put money in young people’s hands.” A fervent, almost devout believer in artificial intelligence, he considers his physical body little more than a sensor that collects information for agents. When playing League of Legends with friends, he simultaneously records audio and tracks biometric data—heart rate, pulse—to analyze how his teammates’ words affect his emotional state and in-game performance. His views are sharp to the point of provocation:

Anyone who starts learning an entirely new language after age 14 will never achieve native-level mastery. The same is true of AI.

Dan joined straight out of school. It was his first encounter with what real knowledge anxiety feels like. In school, he’d mostly tinkered with what he calls “toy-level” models—7-billion-parameter small models run for a few days on 32 GPUs. Now he was being asked to control a massive MoE architecture with hundreds of billions of parameters, confronting a data ocean of trillions of tokens. It was the difference between a small pond and the Pacific.

To tackle it, he went into a mode of self-inflicted deprivation: sleep schedule completely destroyed, working during Silicon Valley’s nights on Beijing daytime, then switching to Beijing’s nights when Silicon Valley was awake, staring at training monitoring screens for hundreds of hours at a stretch, like a trader who can’t look away from the ticker.

The real challenge wasn’t the workload. It was that he had to perform three jobs simultaneously: algorithm architect, designing optimal solutions through a labyrinthine model; systems engineer, tracing faults in a distributed computing environment like repairing a pipeline system spanning the globe; and data curator, “extracting gold” from a sea of data while being hard on benchmarks and gentle in conversation.

Midway through training, they sometimes had to perform what Dan calls “internal surgery”: key parameters stored in half-precision (BF16) were experiencing runaway numerical spikes, and the team made the call mid-training to switch to full precision (FP32) to stabilize—the equivalent of changing your running shoes in the middle of a marathon. Dan’s takeaway: someone who only knows algorithms, or only knows systems, or only knows data cleaning cannot build a top-tier model. No “that’s not my part” allowed. You have to fuse three completely different worlds simultaneously. This cross-dimensional tempering accelerates a person’s growth to a pace that might otherwise take years.

The screening for anyone hoping to join Kimi is accordingly brutal. There are no OKRs or KPIs. No office politics. No check-ins. But if you’re not “AI native,” if you can’t generalize, if you can’t do your own reinforcement learning as a human—you will find no meaning for your own existence here.

“No Hierarchy Smell Here”

Most brands want to have a story. But nearly every Kimi employee gently reminded me:

Don’t write about Pink Floyd in the article. Or the piano that sits at the company entrance. (Tony’s note: For context, these two details have been repeatedly included in almost every Moonshot AI feature story).

Those who already understand, they feel, already do. Those who don’t, don’t need to. From “Moonshot AI” to “Kimi,” neither name has anything to do with technology or AI. But if a company leans too hard on its connection to rock and art, it starts to seem performative. People here prefer to exist in a state of beauty without self-consciousness.

Win is a post-2000 escapee from big tech. He told me this place is bizarre: you can actually get work done without meetings.

At his previous company, daytime was consumed by meetings and actual work happened at night. He arrived at a simple conclusion: if most of your energy goes into coordinating the relations of production, there’s very little room left to improve the means of production.

This is a feature of AI-native organizations. More than ten employees explicitly told me they are increasingly uninterested in dealing with human colleagues and prefer to collaborate with AI—because AI is more reliable and simpler. This also connects to the company’s overall introverted character. One person used a more charming word: shy. Everyone can be socially effusive in a group chat; in person, they go quiet. Kimi doesn’t do many culture events—outside of the annual company gathering, the most recent one was an in-office group massage.

Introverted doesn’t mean closed off, or lacking energy. No one’s participation in my interviews was any kind of obligation. And yet I never received a single “no.” The internal group chats are flooded daily with information and discussion, and a rotating gallery of abstract emoji. No one’s message goes unanswered.

If you need someone’s help to get something done, the process is simple: ask directly. No need to go through a manager, no need for any approval, no coordination meeting, no “department wall” to break through. Kimi has no department walls—it barely has departments.

Yang Zhilin’s personal status line is four Chinese characters: 直接沟通—communicate directly.

But everyone acknowledges the company has been constantly changing since its founding. Some changes were chosen; others were forced—sometimes felt like being slapped in the face. From heavy advertising spend to focusing on the model. From insisting on closed-source to rapidly going open-source. From chatbot to Kimi Agent, Kimi Code, Kimi Claw. From consumer to enterprise and back to consumer. Not every change has held up to scrutiny.

In Ezra’s mind, one thread has never changed: respect for facts. All the changes, she says, have one cause and one purpose: making the company’s development more consistent with objective reality.

The company allows everyone to have an ego. But it doesn’t like hiring people who place themselves above facts. From the co-founders to every colleague, everyone here is persuadable. When the facts are clear enough, people are willing to acknowledge their own limitations—and so is the organization as a whole.

It’s precisely because of this extreme commitment to truth, Ezra says, that people dare to say true things. Because genuinely smart people don’t feel their self-worth threatened by honesty.

Another necessary condition for radical candor: no horse-racing between internal teams, no zero-sum game, no conflict of interest. Every member freely shares their research findings and technical details. Just as the company had its own community in the early days, it still advocates for a community culture. Sharing information and knowledge accelerates everyone’s collective learning and progress—and ultimately, everyone benefits.

As Win puts it: toxic culture is contagious. So is good culture.

Someone used the word “solidarity”—a word rarely used to describe a company these days—to describe this state. In fact, Kimi has always faced a harsh competitive environment: giants bearing down from outside, big tech squeezing from within, GPU resources constrained. But these pressures are intensifying the company’s cohesion. In the end, people are the only asset that matters.

Recently, Florence was approached by a peer company offering twice her salary. She declined without a moment’s hesitation. Her reason was simple: “There’s no hierarchy smell here.”

“I Don’t Know How She Got Through It”

When I first arrived to interview what is arguably one of the most intelligent, AI-literate groups of people on earth, I was extremely nervous: I’m a humanities person who has never worked in the tech industry, with only shallow knowledge of AI.

But when I actually sat down with the young experts from the algorithm and product teams, I found they were the nervous ones—terrified that I would be embarrassed by not understanding the jargon they use without thinking.

So they first translated the English terminology into Chinese, then translated the Chinese into Chinese I could actually understand.

That protectiveness was moving. It reminded me of the only thing the company had asked of me before I started: take care of everyone.

I tried, accordingly, to steer around questions that were too sensitive or might hit a nerve. Even so, Ty, on a phone call, let slip a nearly imperceptible tremor of emotion. When he first arrived and was going through the adjustment period, he struggled enormously—at one point he felt he couldn’t continue and thought about quitting. Then, in a weekly meeting, he saw Annie—a woman just two years out of school—who, after an unknown number of rounds of failure and internal doubt, had finally pushed a project to a genuinely meaningful milestone. He told himself he couldn’t give up either. He was older than her, had lived more, and yet his resilience was no match for hers. “I don’t know how she got through it,” he said.

He wasn’t the only one who had thought about leaving. Annie had considered it too. For a long stretch of time, she was tasked with building from scratch a presence in a certain overseas market—with no breakthroughs in sight. To make it worse, well-meaning colleagues from other teams told her directly to abandon “this meaningless effort.” She says she shed more tears for Kimi than she has shed for any other company—or for any ex-boyfriend.

She wasn’t short of options. She already had an offer from somewhere else with better terms. But she couldn’t convince herself to go work for someone else. She wanted to have one more conversation with co-founder Zhang Yutong.

After that conversation, she decided to stay. She didn’t tell me what was said. She only told me: “Yutong is the strongest, fastest-iterating, highest-ceiling boss I have ever met. Following her is the only way I can reach a higher limit.” And then she added: “I don’t know how she got through it.”

When you’ve collected enough data, you start noticing how often certain sentences repeat. The phrases that get repeated most tend to sketch out the shared qualities of the team.

Bob was pulled back to China to co-found the company by Yang Zhiming, giving up his PhD in the United States. He was there on day one—he represents perhaps the deepest institutional knowledge of anyone here. When asked the question everyone gets asked—what do you think is the most important quality in this team?—he paused for about two minutes, then answered in two words: resilience.

For a company only three years old, any emphasis on resilience might sound presumptuous. But it’s sincere. Bob believes that smart and brave are sometimes antonyms: the smarter you are, the easier it is to see the risks, and thus the easier it is to give up. But stupid persistence can’t succeed either. So only those who see clearly, calculate the probability of failure, and keep going anyway—those people deserve to be called resilient.

There’s a story that circulates internally, known as “entering the cave of reflection three times.”

In May 2023, Freddie and his colleagues received what looked like an impossible assignment: make the AI capable of reading 128K tokens of text in a single pass—equivalent to several hundred pages of a book—at a time when the industry could generally handle only 4K. He quickly designed MoBA v0.5, but because it required rewriting the core training framework while the main model was already half-trained, the cost was too high. The project was shelved. That was his first trip into the cave.

Six months later he returned with a v1 approach—revised so it could continue training on top of the existing model. The small-model validation worked. But on the large model, it hit a loss spike that no amount of debugging could fix. The project was sent back to the cave for a second time, for another six months—long enough to miss the company’s milestone of releasing a 200,000-character context capability. But the team was not disbanded. The company launched what it called a “saturation rescue”—mobilizing technical talent across the organization to collectively tackle the problem, rewriting the underlying logic, and finally achieving a stable pass on the “needle in a haystack” test with v2.

Just as launch seemed imminent, the third blow arrived: during the supervised fine-tuning stage, long-document summarization performed poorly because training signals were too sparse. At that point the project had absorbed enormous cost. But the engineers retreated to the cave one more time, and ultimately resolved the problem by adjusting the attention mechanism in the final few layers.

Three retreats. Three returns. At the end of the interview, I put the final question to Freddie: how would you describe this company?

He also answered in two words: lunar landing.

Why lunar landing? He quoted the famous speech:

“We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard.”

Genius Swarm

I ultimately chose not to approach or intrude on any of the co-founders. Externally, they are nearly invisible—they don’t seek interviews, they have no interest in public fame—but internally, they are everywhere.

In a radically flat structure, you need superminds at the center, or the energy becomes chaos. With no middle management layer, each co-founder is interfacing directly with 40 to 50 people, staying close to the technical and operational frontline, ensuring that decisions and execution remain tightly aligned.

All five co-founders graduated from Tsinghua. But even superminds are bounded by the limits of human attention and management reach. As the company’s valuation has reached 120 billion RMB and its headcount has grown past 300, even these superminds are running hot.

The overload isn’t just on the co-founders. This is an infinite game of self-driven ambition: each team member is expected to carry roughly 400 million RMB in implied valuation on their back—delivering value at a per-person rate far beyond what most companies have ever imagined.

The transformative variable is tools. Kimi’s working hours are not punishing. Employees are allowed to sleep in, and no one is expected to work until midnight. Leo, on the product team, says he commands a full army. Imagine this:

10 a.m., Leo wakes up and walks into the office. His task: consolidate user feedback from five global markets in the past 24 hours and determine iteration priorities for the week. Once, this would have taken three people two days. Leo activates three agents. A strategy agent filters 3,000 feedback entries for high-priority issues related to “long-context interruption.” A translation agent parses Japanese dialects and Korean formalities in real time, tagging true emotional intensity. A competitive intelligence agent simultaneously captures the day’s updates from Cursor and ChatGPT and generates a technical comparison. Leo does three things: overrides a sarcastic comment that was misclassified as genuine feedback, flags a screenshot containing unreleased UI, and confirms the top-three priorities the agent recommended. By 11:30 a.m., the product requirements document is done. And his coding agent has already auto-generated 70 percent of the base framework from those requirements, ready for the afternoon discussion with human engineers on the creative decisions.

Humans set the rules. Silicon-based systems execute the rules. The organization becomes a container for algorithms. Fluency in using agents and weaving them deeply into work is simply what it means to be an AI-native company.

The model is both the goal and the tool. Whether directly empowering productivity at the technical level or fundamentally reshaping organizational dynamics, AI’s DNA has been written into the bones of this company, whether people like it or not. Just as they develop something they call Agent Swarm—clusters of AI agents operating in parallel—the whole team is, in essence, a Genius Swarm: each genius working independently in parallel, seamlessly coordinating with the others.

And yet an organization this flat carries structural fragility. When asked whether this model is sustainable as the company scales from 300 to 3,000 people, most respondents were cautious. History is instructive: radically flat organizational experiments—Holacracy—have tended to hit decision-making bottlenecks once headcount passes 500. When there are too many information nodes, “communicate directly” becomes information overload.

The more immediate pain is the sense of weightlessness at the individual level. Without the buffer of seniority levels, directional ambiguity propagates directly to each person. One former employee who eventually returned to big tech was blunt: without top-down OKRs and KPIs, some mornings you walk in and genuinely don’t know what you’re supposed to be doing—and no one proactively tells you how you’re performing. That absence of feedback creates an insecurity that makes some people suddenly nostalgic for the big tech reporting lines, clear review cycles, and quantifiable deliverables they once found suffocating.

Because those apparently cumbersome structures actually provide an individual’s baseline certainty: where is the goal, what does done look like, how is performance evaluated—all of it clearly visible. That’s not Stockholm syndrome. That’s basic organizational physics.

If Alibaba resembles a finely calibrated promotion pipeline, ByteDance a mission-driven combat division, and Tencent a higher-tolerance professional institute, then Moonshot AI is a primeval forest: geniuses may find their hunting paths, but ordinary people may wander lost in the fog.

The Necessary Dimensional Weapon

No departments, no levels, no performance reviews—the AI-native organizational paradigm is anti-institutional, unstructured. Large tech companies can no longer rotate to accommodate it. Small companies missed the window for self-inflation. This is an asymmetric war.

In The Three-Body Problem, the Singer civilization casually deploys a higher-dimensional weapon—the “dark forest” strike—collapsing the solar system from three dimensions into two, flattening planets, stars, and humanity into a picture with no depth, destroying the Earth in the process.

Moonshot AI is voluntarily deploying this dimensional weapon on its own organization. Not to destroy competitors, but to push organizational efficiency to its absolute limit: no hierarchical depth, no departmental walls, none of the three-dimensional entanglement of office politics—only “model” and “intelligence,” in the most direct and orthogonal alignment possible.

In the force field of the AI era, every startup is being compelled to fire this weapon at itself. The proliferation of one-person companies is, at its core, a generational explosion of AI-native talent: when technology compresses organizational capability down to the individual as its quantum unit, all the middle management buffers are instantly vaporized. The organization flattens. There is no depth left to maneuver within. Every person is forced to confront the problem directly.

This is the iron law governing the evolution of organizational paradigms across the entire business world: everyone will be folded flat.

When people are exposed on the same plane, one mind radiating outward to fifty people is no longer a management anomaly—it becomes organizational normal. The distance from center to edge is redefined. Elites who depended on hierarchy and the OKR coordinate system will suffocate immediately. Geniuses, meanwhile, are violently dismantling intelligence on this exposed plane—while their guardians clear the path of all entropy and noise, and not without humility, call themselves pioneers at the expanding edge of human civilization.

But the journey from three dimensions to two cannot be paused—and cannot be reversed.

From here, the Kimis of this world cannot go back. Every strategic adjustment is a high-risk chaotic iteration. Competitors can still slowly turn around inside their labyrinth. If Moonshot AI tries to rapidly scale its organizational mass, it will only generate structural fractures from within. And all this self-dimensional compression serves a single purpose: to complete one more insane dimensional leap upward.

The endpoint of organizational compression is the elevation of intelligence.

Only when model intelligence breaks through its inflection point—rising to a height sufficient to escape the gravitational well of all carbon-based organization—can Moonshot AI flatten all its competitors’ organizational advantages in one move, and give ultimate justification to this irreversible act of dimensional risk-taking.

At that point, discussing management radius or organizational architecture will become meaningless—just as the Singer civilization doesn’t concern itself with which dimension it inhabits, because the sophistication of the dimensional weapon itself has already defined the new rules of war.

Then, “the dark side of the moon” will shift from metaphor to reality: they will become the high-dimensional light source illuminating the dark side of the intelligence universe, and all the organizational pain that came before will have been nothing more than the ablative heat shield burning away as the lunar module punched through the atmosphere.

Either ascend to a higher dimension and become legend.

Or collapse inward and be preserved in amber.

There is no third path.

The same week OpenAI quietly killed its AI video app Sora, a series of AI-generated absurdist comedy videos was racking up 5 billion views on Chinese social media.

The video series is called “Saving a Fox on a Snowy Mountain.” It’s 90 seconds long, rendered in the style of a low-budget 1970s Chinese martial arts film, and it follows a classic folk tale setup:

A wandering woodsman rescues a fox in the snow by leaving a braised dusk, and by genre convention, the fox should later transform into a beautiful woman and repay his kindness.

Instead, when the hero opens his door expecting his destined reward, he finds not the grateful fox spirit but the braised duck he’d left behind as food. The duck, also apparently now sentient, has other plans.

“I am the braised duck you abandoned,” it says. Revenge has arrived.

The video was made by a 4-person team at a food company selling spicy marinated duck. It cost about ¥4,000 to ¥5,000 to produce in 5 hours, and was purely to promote their food.

（I couldn’t find the original version on YouTube, and this one is the closest alternative.)

That’s the whole joke, and it went viral with a speed that no one anticipated. Thousands of user-created spin-offs were spawned by swapping the duck for mountains, wood, and bean juice. Local government accounts borrowed the format for anti-fraud PSAs.

From face-swaps to full pipelines

To understand where Chinese AI video is right now, you need to understand where it came from. The starting point is a regulatory quirk that made face-swapping technology mandatory in Chinese film production.

China’s entertainment regulator maintains an informal but powerful blacklist of artists with bad conduct records. When a performer gets caught in a scandal—tax evasion, drug use, moral violations as defined by the relevant authorities—they will be banned from screens overnight.

For studios that have already completed production with a blacklisted actor, the only options are to shelve the project entirely or digitally replace every frame of that actor’s face with someone else’s. This has happened enough times, with high-profile cases involving major stars, that face-swapping became a standard post-production capability.

By 2025, AI had moved beyond that narrow use case into conventional post-production: crowd scenes, digital environments, stunt sequences in dramas. For example, the 2026 hit drama Swords Into Plowshares (太平年) used Kling AI from Kuaishou to generate a scene of crows eating grain, a shot that would have been expensive or impractical to production.

Today the conversation is completely different. ByteDance’s SeeDance 2.0, released in February 2026, becomes the latest SOTA video generator model. According to the company, the model achieves roughly 90% generation usability, meaning 9 out of 10 clips it produces are workable. The prior industry average was around 20%.

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🙌ByteDance’s 'Gemini 3.0 Moment': Meet Seedance 2.0 and Seed2.0

Gemini 3.0 released in November 2025 was seen as a key milestone for Google to take the lead in the AI race. Now ByteDance is attempting to orchestrate its own Gemini 3.0 moment with a coordinated release of new models, including the video generation model Seedance 2.0 and the competitive multimodal foundation model Seed2.0…

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4 months ago · 17 likes · Tony Peng

Such AI generation technology might not be ready to produce Hollywood-level film or dramas, end-to-end, but it is a natural fit for short dramas, a type of 1–3 minute, high-intensity vertical videos built for smartphones that emerged from China’s Internet platforms. According to DataEye-ADX industry tracking data, in January 2026 alone, 14,634 AI-generated short dramas went live in China, one new title every 90 seconds.

The reaction inside the industry was also brutal. SeeDance 2.0 made content creators unable to sleep. Teams returning after the Chinese New Year holiday were working overtime just to test the new model—average queue times on the platform exceeded six hours during peak periods. In response, a Chinese production studio began working at 3 am to use SeeDance 2.0 during off-peak hours and skip the daytime queues.

Comic dramas gain traction

Over the past week, I noticed my wife watching an AI-generated animated drama on her phone for several evenings running. She is not usually a short drama or cartoon person, so I paid attention.

The show she was watching, on Hongguo (红果, Red Fruit), ByteDance’s dedicated short drama app, is set during a catastrophic cold event that has made most of the earth uninhabitable. A small community tries to survive an endless winter storm.

The animation is not good by any conventional standard. The character movement is limited, the rendering is sometimes inconsistent, and in any other context she would have found it easy to dismiss. She kept watching because the story was interesting enough that the visual shortcomings stopped mattering after the first few episodes.

What she was watching is called Manju(漫剧), literally comic drama, and it has quietly become one of the fastest-growing entertainment formats in China, thanks to AI.

The term comic drama was originally coined in 2021. It converts static illustrated panels—sourced from web novels, existing comics, or AI-generated art—into short video through voice acting, limited character animation, and sound design. Episodes run one to three minutes. Series typically run 60 to 120 episodes. The visuals are semi-static, much closer to a comic panel that has been given breath than to fluid animation, which is precisely why AI can produce them at cost. The format is quite close to motion comics, sitting between comics, audio drama, and animation without being any of them.

There are three tiers of comic dramas, each with different economics.

• At the bottom, meme and emoji comics cost ¥400–600 ($58–87) per finished minute. Competition has squeezed margins below 10%.

• The middle tier, 2D/3D comic drama, is currently the platform traffic mainstay, carrying profit margins of around 30–40%.

• At the top, AI humanlike drama pushes costs to ¥30,000 ($4,350) per minute but delivers profit margins above 60% for teams that can maintain the quality, though output is constrained to roughly one title per 10-person team per month.

To understand what makes it work, look at the biggest hit of 2025, Below the Immortal Execution Platform, I Shocked the Gods (斩仙台下，我震惊了诸神), which reached 1 billion views on Douyin.

The setup would be impossible to film affordably as live action: a lowly court servant in the celestial bureaucracy, accused of conspiring with demons and slaughtering gods, is brought before a divine tribunal for judgment. As the gods interrogate him, his true identity is actually a disciple of Sun Wukong, also known as the Monkey King, and he dismantles the moral hypocrisy of each deity who claims to judge him. The story is built on two mythologies every Chinese viewer knows—Journey to the West and Investiture of the Gods—and reframes them as a story about corrupt institutions protecting their own.

With AI, non-human characters, magical transformations, alien worlds, thousand-soldier battle scenes—all are as cheap to generate as a prompt. As one producer put it simply: “Comic dramas can creaate non-human characters. That’s the biggest difference from live-action short drama.”

Grace Shao’s latest piece on AI-generated microdramas, which I highly recommend, offers a framing that clarifies why AI found its commercial footing here. A conventional studio requires a massive crew to create a masterpiece. An AI content factory tests, iterates, acquires traffic, and monetizes retention. It is optimized not for Emmys but for thumb-stopping cliffhangers. As Shao writes:

The product is not pretending to be prestige storytelling, nor is it even attempting to be jaw-dropping cinematography; it is shamelessly positioned as narrative dopamine.

AI Proem

China’s AI microdrama factories are supercharged by ByteDance and Kuaishou

When ByteDance launched Seedance 2.0 on February 12, Hollywood did not like it. Disney and Paramount quickly sent cease-and-desist letters accusing ByteDance of infringing copyrighted characters and likenesses, and Reuters later reported that ByteDance paused the global rollout after the disputes escalated…

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3 months ago · 19 likes · 4 comments · Grace Shao

China’s comic dramas generated an estimated ¥16.8 billion (roughly $2.3 billion) in revenue in 2025, with 70 billion cumulative views and more than 60,000 titles produced on Douyin alone, according to DataEye Research Institute’s 2025 annual report. A separate estimate from iMedia Research put the figure at ¥18.98 billion with year-on-year growth of 276%.

Soy Sauce Animation, one of the industry’s most prominent production studios, describes its operation as 100% AI-generated with no traditional animation staff, produces 60-70 titles per month, and generates approximately ¥50 million ($7.2 million) in monthly revenue.

While western platforms monetize AI through subscriptions, cloud services, and enterprise software, Chinese companies monetize still primarily through advertising—and advertising revenue is directly proportional to time spent on platform. This creates an incentive to invest in content that maximizes user’s screen time. AI-generated comic drams, with its binge-optimized episode structure, daily release cadence, and cliffhanger-every-90-seconds rhythm, is among the most effective time-on-platform vehicles ever built.

The foundation is China’s web novel ecosystem, which collectively host tens of millions of works with years of readership data already validating which stories have audiences. Before comic dramas, traditional animation cost ¥50,000-100,000 per finished minute of footage, which meant only a small fraction of those novels could justify visualization. With AI-driven comic dramas now lowering costs, that entire long tail becomes economically viable. Thousands of novels that would never have been filmed now have a visual form.

The distribution infrastructure was already built. China had 1.164 billion monthly active short video users as of December 2025, according to QuestMobile. The algorithmic recommendation systems on Douyin and Kuaishou are designed precisely for such type of content. According to ByteDance’s data, comic drama achieves a test-out rate of 50-75%, meaning more than half of new titles successfully gain algorithmic recommendation.

Take ByteDance as an example. Its Tomato Novel app provides 67,000 novel IPs available for visual adaptation. Jimeng, ByteDance’s AI generation platform for images and videos, supplies the production tools. Douyin handles distribution. Hongguo Comic Drama, a dedicated comic drama app launched in November 2025, reached 8.54 million monthly active users in its first month. Ocean Engine manages advertising monetization. The company controls the source novel, the production tool, the distribution platform, and the ad revenue in a single stack. Tencent, Kuaishou, Baidu, and Bilibili have all launched competing apps and subsidy programs.

It’s worth noting that comic drama’s growth happened against a backdrop of the broader short drama industry contracting. Live-action short drama had expanded recklessly in 2023-2024. Production costs were rising as the market demanded higher quality, new titles were being uploaded faster than audiences could absorb them, and platforms began pulling back guaranteed stipends in early 2026. For producers caught in that squeeze, AI comic dramas offered a back up plan.

The regulator is also watching cautiously. China’s media and films watchdog National Radio and Television Administration brought comic drama under formal regulatory review in late 2025. The industry had already received a warning: One title that crossed 100 million views, Failed the College Entrance Exam, Tricked Classmates into a Ghost University (高考落榜，忽悠同学上冥牌大学), was pulled from all platforms for promoting superstition.

But the regulatory posture so far is not hostile. Local governments are simultaneously offering subsidies to attract comic drama production talents, and state-owned broadcast CCTV produced its own official AI comic drama in January 2026.

The part that’s harder to watch

The same AI tools producing comic dramas are also threatening the livelihoods of Chinese actors.

On March 18, 2026, a hashtag called “actors replaced by AI” trended to the top of Weibo, China’s equivalent of X. Reports circulated that major production studios and platforms were planning to replace background actors with AI. The same day, a production company called Youhug Media launched two AI actors, gave them social media accounts, and announced their first dramas. The two AI actors were immediately noted to resemble well-known actresses.

The backlash was immediate enough that state-owned People’s Daily weighed in within days. Its commentary column published a piece on March 21 arguing that people resist AI actors because they lack liveliness. The editorial warned that companies rushing to deploy AI simply to cut headcount without creating new value are using tactical efficiency to mask strategic failure. “The advantage is not doing old things faster, but doing new things in new ways.”

It doesn’t mean AI actors will be regulated away. China’s regulatory priorities in this space remain focused on content labeling and stability rather than labor protection. But it signals that the displacement anxiety is being taken seriously.

Recode China AI

😱AI Anxiety Is Spreading Among People in China

People in China are generally regarded as optimistic about AI. According to a KPMG survey across 47 countries, 69% of people in China said AI’s benefits outweighed its risks, compared to just 35% of Americans…

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3 months ago · 63 likes · 7 comments · Tony Peng

At one major industry conference, ChinaEquity Group founder and CEO Wang Ran said background performers and stunt double professionals will essentially disappear because of AI. Mid-tier actor demand will be massively compressed. Even top actors will see their upfront fees dramatically shrink. Hengdian, the filming base that has been the working home of tens of thousands of Chinese actors, saw short drama production starts fall roughly 75% in the first quarter of 2026 compared to last year.

Chinese tech media 36Kr profiled one actress who spent three years building a career playing supporting roles in short dramas, from the cheerful best friend, to the arrogant young miss. After the 2026 Chinese New Year, she voluntarily dropped her quoted rate by a third. Production companies were coming back with offers at half her previous day rate. She accepted. “Before you played the female lead, now they ask you to play second female lead. Now they ask you to play featured extra. Take it or leave it.”

There is no Chinese labor union representing actors. Unpaid short drama actors will post social media and hope public embarrassment forces a response. Sometimes it works.

Displacement and new opportunities co-exist

The conventional AI displacement story is about AI coming for existing jobs, actors losing work, the industry shrinking. That’s not quite what’s happening here.

Comic dramas didn’t destroy traditional animation. It created an entirely new market segment, at a cost and volume that was impossible before generative AI, serving content that could not have been produced otherwise.

And it is starting to move beyond China.

Chinese short videos and dramas are already a global phenomenon. Platforms like ReelShort and DramaBox, both backed by Chinese companies, have built meaningful audiences in the US and Southeast Asia. TikTok is replicating the entire Chinese short drama playbook in the US through a dedicated in-app section and a standalone app called PineDrama. The global short drama market hit $2 billion in overseas app revenue in 2025, according to Sensor Tower, and projections from TikTok put the eventual global market at $10 billion with 200-300 million monthly users.

AI comic drama is also moving. China Literature Online, one of the largest IP holders, has already produced original overseas comic dramas with localized art styles—a vampire drama rendered in an American comic art style, distributed on the overseas platform Sereal+. Industry insiders say the format’s global test is just beginning, with a six-to-twelve month window before anyone knows whether it can replicate the domestic model abroad.

But the Hengdian silence is also real. The actress who spent three years playing supporting roles is taking gigs below her usual rate, trying not to think about whether the category she built a life around will still exist in two years.

Both of these things are true simultaneously. The rest of the world will find out soon enough whether any of this translates.

Hangzhou-based Unitree Robotics just filed for an IPO on Shanghai's STAR Board, aiming to raise ¥4.2 billion ($630 million).

The proceeds will be used for advancing research into AI models, developing the robot body hardware itself, building out new product lines, and expanding manufacturing capacity. Unitree aims to reach an annual production capacity of 190,000 robots.

Founded in 2016 with just ¥100,000 ($13,800) in registered capital, Unitree is China’s shiniest robotics company at the moment. Its humanoid robots danced and performed backflips during the Spring Festival Gala, the most-watched television broadcast on earth. Founder and CEO Wang Xingxing recently welcomed German Chancellor Friedrich Merz, and sat alongside some of China’s most prominent business leaders during a meeting hosted by President Xi Jinping.

Now the prospectus is out. And the numbers look surprisingly good.

The un-techy financials

A typical high-tech company heading toward IPO is money-losing, carries a high R&D cost ratio, and runs on investor patience. Unitree doesn’t fit that profile.

In 2025, the company generated ¥1.71 billion ($257 million) in revenue, up 335% year-over-year. Adjusted net income reached ¥600 million ($90 million), up 674%. Operating cash flow tripled to ¥672 million ($101 million).

The gross margin sat at 60.27% for 2025. In comparison, Apple’s latest twelve-month gross margin is 47.3%, while Louis Vuitton runs around 65-70%. One of the reasons is full-stack vertical integration: Unitree designs its own core components like motors, decelerators, robohands, and lidars rather than buying them off the shelf. They have also developed distinctive motion control algorithms that deliver high performance without requiring the most expensive compute.

That said, these numbers will almost certainly come under pressure as competition between humanoid robot companies intensifies and pricing wars could force the whole industry toward thinner margins.

R&D as a share of revenue fell from 31.4% in 2023 to 17.8% in 2024 to 7.7% in 2025. While that trend line looks alarming, in context, absolute R&D spending kept growing from ¥30 million ($4.5 million) to ¥70 million ($10.5 million) to roughly ¥90 million ($13.5 million) annualized.

With 480 employees at year end, Unitree generated roughly ¥3.56 million ($534,000) in revenue per employee. Most robotics firms at a comparable stage would have ten times the headcount.

One noteworthy detail in the prospectus is Unitree spent just ¥22.57 million ($3.4 million) on advertising in the first nine months of 2025. For a company at this level of global visibility, that number is almost nothing. When your robots are dancing on China’s biggest stage, you don’t need to run many ads.

The humanoid robot gap

In 2025, Unitree shipped more than 5,500 pure humanoid units, claiming the top spot globally. For comparison, the prospectus states that Figure AI and Agility Robotics each shipped approximately 150 units that year. Tesla has not publicly sold Optimus; it remains in internal testing. Unitree’s volume advantage over its closest Western competitors is roughly 37:1.

The production-to-sales ratio for humanoid robots was 95.95% in 2025, suggesting demand is absorbing supply almost entirely.

Pricing has moved aggressively downward. The average selling price of a Unitree humanoid was ¥593,400 ($89,010) in 2023, when only five units were sold. By 2024 it had dropped to ¥260,700 ($39,105). By the first three quarters of 2025, it had fallen further to ¥167,600 ($25,140). A 72% price decline in two years, with gross margins that simultaneously expanded. The G1 base model now starts at ¥85,000 ($12,750); the smaller R1 Air starts at ¥29,900 ($4,485).

But the price requires a closer look. The G1 standard edition comes with no developer access—no SDK, no open interface. It cannot be reprogrammed. For universities, research labs, and developers who need to actually build on top of the platform, the relevant product is the G1 EDU edition, which runs ¥200,000 to ¥400,000 ($30,000 to $60,000).

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People in China are generally regarded as optimistic about AI. According to a KPMG survey across 47 countries, 69% of people in China said AI’s benefits outweighed its risks, compared to just 35% of Americans.

But something is shifting. Over the past few weeks, the phrase “AI anxiety” kept showing up in the podcasts, articles, and videos I came across, picking up pace especially after the OpenClaw mania swept the country. This change in sentiment, from excitement to unease, has been building quietly beneath the surface.

I searched “AI anxiety” (AI焦虑) on WeChat Index. There was a large spike starting mid-February, peaking on March 10 at over 2 million, against a usual baseline of around 20,000. The term also briefly climbed to Weibo’s trending topics last week.

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The clearest trigger is OpenClaw, an open-source, local-first AI agent platform that acts as a 24/7 proactive personal or team assistant. It runs on your own computer, connecting LLMs via API to local files, messaging apps like WhatsApp and Discord, and tools that automate tasks, schedule jobs, and manage digital workflows.

OpenClaw was developed by Austrian programmer Peter Steinberger. Originally launched in November 2025 as “Clawdbot,” it was later rebranded to “Moltbot” and then to OpenClaw in early 2026 following trademark concerns from Anthropic. In February 2026, OpenAI announced that Steinberger would be joining the company to lead development on “personal agents.” OpenClaw became one of the fastest-growing open-source projects in history, accumulating over 200,000 GitHub stars within a few months.

There are already strong reports of how China’s OpenClaw frenzy unfolded. MIT Technology Review and Wired both have great stories, as does ’s analysis. For those less familiar, here’s a quick recap:

• In February, as OpenClaw began going viral, Chinese developers and early adopters started sharing demos on Bilibili, Zhihu, and X. Tech influencer and entrepreneur Fu Sheng hosted a livestream demonstrating OpenClaw's capabilities, drawing 20,000 views. Meituan co-founder Wang Huiwen said he will invest in startup projects in the OpenClaw track.

• On March 6, nearly 1,000 people lined up outside Tencent’s Shenzhen headquarters for a free installation session hosted by Tencent Cloud engineers. The crowd was a mix of amateur developers, retired engineers, housewives, students, and AI enthusiasts. Tencent founder and CEO Pony Ma said on WeChat that he wasn’t expecting the wild popularity of OpenClaw.

• On March 7, Shenzhen’s Longgang district government released a draft policy proposing financial support of up to 2 million yuan for approved OpenClaw projects and up to 10 million yuan for larger ones, along with free compute credits and discounted office space.

• Some crypto entrepreneurs hosted the largest OpenClaw event in Shenzhen, drawing more than 1,000 people, with many unable to find a seat.

• The corporate pile-on followed quickly. ByteDance’s cloud unit Volcano Engine unveiled ArkClaw, a browser-based version that removed the need for local setup. Tencent launched a suite of OpenClaw-based products compatible with WeChat. Alibaba launched a mobile app called JVS Claw to help users install and deploy OpenClaw. Baidu, Zhipu.ai, Moonshot, and others all joined in.

• Markets responded in kind. Hangzhou Shunwang Technology soared 21% after claiming to embed OpenClaw in its cloud infrastructure. Talkweb Information System jumped 12%, and Beijing Vrv Software rose 15%, all while the broader CSI 300 gained just 0.9%. Tencent’s stock rose 8.9% in a week. MiniMax surged 27.4%, now up over 600% from its IPO.

• The frenzy started to wane down last week as the China’s central government warned state-run enterprises and agencies against installing OpenClaw on office computers, citing risks of data leakage and unauthorized access.

• By mid-March, social media platforms flooded with paid services offering to uninstall OpenClaw, from the same people who had charged to install it weeks earlier. One Shanghai-based seller charged 299 yuan to remove it and had already completed more than 10 transactions.

The incentives behind the corporate bandwagon are worth understanding. For cloud companies like Tencent, Baidu, and ByteDance, OpenClaw runs best connected to a cloud backend, LLMs, APIs, and compute. Helping users install it is a customer acquisition funnel dressed up as community support.

For LLM companies like MiniMax, Moonshot, and Z.ai, OpenClaw needs a model to run on, and whichever model becomes the default picks up token usage at a scale no chatbot can match. For local governments, backing OpenClaw-based projects shows pro-innovation governance and attracts startups. OpenClaw-based applications represent a practical form of AI that fits neatly into industrial policy goals.

My friend told me that China probably only has hundreds of thousands of people who actually installed OpenClaw on their computers, yet it became a national topic. Everyone was talking about “raising a lobster” (a reference to the word Claw).

This kind of collect rush around an AI product is not new in China. When ChatGPT went viral in late 2022, with no Chinese equivalent available, people were selling accounts and offering VPN access to ChatGPT. When Meta’s Llama came out in 2023, nearly every Chinese cloud company rushed to offer API access and local deployment. OpenAI’s video generator Sora also swept Chinese social media in early 2024. Not to mention the DeepSeek moment in early 2025.

What is different this time is the mood. In those earlier waves, the mainstream mood was excitement, awe, and curiosity. This time, more and more people are expressing anxiety, fear, and concern.

The most obvious reason of that anxiety is job insecurity. For most ordinary people in China, AI still means chatbots like Doubao or Qwen. Claude Code or Codex is not available. There is no household AI agent with real penetration. Then all of a sudden, media reports are claiming OpenClaw can handle a wide range of tasks autonomously. The gap between what people knew and what they were being told deepened the sense of being left behind.

The job market context makes this worse. According to Bloomberg, a Peking University study analyzing over a million Chinese job postings between 2018 and 2024, which found that sectors with higher AI exposure, including accounting, editing, sales, and programming, saw larger declines in recruitment. The Financial Times reported that China’s government plans to build an “employment-friendly growth model,” as a record-breaking 12.7 million university graduates enter the market in 2026.

A second catalyst came from an unexpected direction: On February 22, independent investment research firm Citrini Research published The 2028 Global Intelligence Crisis. Written as a fictional macro memo from June 2028, it argued that human intelligence has historically been the scarce input in economic history, and that AI is now causing the unwind of that premium. In the scenario they constructed, the S&P 500 falls 38%, unemployment hits 10.2%, and a deflationary spiral sets in as white-collar jobs are hollowed out faster than the economy can absorb.

Citrini Research

THE 2028 GLOBAL INTELLIGENCE CRISIS

Preface…

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4 months ago · 8373 likes · 90 comments · Citrini and Alap Shah

Two days later, a Chinese version appeared. Bob Chen, an economist-turned-VC at BroadVision Fund (博华资本) and former macro economist at the Chinese Academy of Social Sciences, published his response on his WeChat blog, Jolly Maker (嬉笑创客). His argument is China would not experience the same crisis, and for structural reasons that might look like weaknesses on the surface. SaaS never took off in China, leaving a trail of broken investors and failed products. If SaaS couldn’t reshape China’s information economy, AI won’t displace workers through the same mechanism. China’s model was always built on heavy staffing, offline relationships, and customized solutions, with leaders making the final call and workers functioning as atomized information processors relying on Excel. That structure, Chen argued, is better suited to AI-driven efficiency gains than AI replacement.

The East is Read

THE 2028 CHINESE INTELLIGENCE CRISIS

The 2028 Global Intelligence Crisis, a speculative macro memo published on 22 February 2026 by U.S. research firm Citrini Research on its Substack platform, has caused major tech and financial firms’ share prices to tumble, sparking heated debate among economists and strategists over the realism of its scenario…

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4 months ago · 28 likes · 7 comments · Junyan Zhao and Yuxuan JIA

It is a reassuring thesis, but the anxiety about keeping up with AI runs deeper than job loss alone. I saw a post on RedNote captured the feeling well. The user wrote:

I think I have AI anxiety. I haven’t figured out Claude Code, and Skills is already out. I haven’t understood Skills, and Clawdbot is out. I haven’t installed Clawdbot, and the Mac Mini price is already up. Throughout February, Chinese AI companies released new models in rapid succession. The pace itself became a source of stress.

Nvidia CEO Jensen Huang's quote has circulated widely in China: “You're not going to lose your job to AI, but you're going to lose your job to someone who uses AI.” People are worried they are not using AI aggressively enough, and they do not know how to use it well. They scroll past endless content showing people without technical backgrounds earning millions by launching an app, or making smarter investments using the latest AI tools, which reinforce the anxiety. OpenClaw is the latest version of that pressure. On top of this, workplace expectations are shifting. The abovementioned Fu Sheng said publicly that every employee of his company is required to use AI in their work, or risk falling behind.

There is also a small but vocal group that exaggerated OpenClaw’s capabilities, spread anxiety, and cashed in on the confusion. Many people paid a few hundred yuan to have the software installed, only to find they did not know how to use it, or that the product had real limitations and security concerns. One Chinese tech influencer said on Weibo:

If you need to pay someone a few hundred yuan to install OpenClaw for you, you probably should not be using the product.

Finally there is a dimension that has received less attention: AI in warfare. The use of AI in U.S. military operations during the recent conflict with Iran, along with the very public saga between Anthropic and the U.S. Department of War, has made many people in China reconsider what AI actually is.

Despite China’s stated policy of civil-military infusion, AI has largely been experienced by ordinary Chinese people as a civilian technology, something distant from military operations. The realization that the same chatbot technology could potentially assist in targeting or military decision-making has shifted that perception. This is becoming a global conversation, but for people in China who had kept a clear mental separation between AI and violence, it changes something fundamental.

Will the Chinese government step in if AI disruption starts threatening employment stability? Or will it continue backing development in the name of national competitiveness? I do not have an answer. But it is worth stopping treating people in China as uniformly optimistic about AI. The anxiety is real, and it will keep growing.

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The day after Alibaba released a new series of Qwen3.5 small models that turned heads across the AI community, the man who led the effort announced he was leaving.

Lin Junyang, tech lead of Alibaba’s Qwen, posted on X that he was stepping down. According to LatePost, one of China’s most reliable tech media, Lin submitted his resignation letter yesterday on March 3, and it came as a surprise to the company’s leadership.

My source told me Lin hasn’t left the company yet, but according to a disclosed all-hands meeting within Alibaba’s Tongyi AI Lab, Lin has little chance to come back.

Under his post, hundreds of people from across the AI world poured in with appreciation and good wishes. Other Qwen team members posted their own messages: “Qwen is nothing without its people.”

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Who is Lin Junyang

Lin is 32 years old, making him Alibaba’s youngest P10 executive — the company’s senior leadership tier. He joined Alibaba in 2019 fresh out of Peking University, where he studied computer science and linguistics. Thin, bespectacled, warm, and deeply ambitious, he was a key contributor to some of Alibaba’s most important early AI work, including the M6 and OFA models. But his defining achievement was turning Qwen from an unknown side project into the world’s most influential open-source LLM series.

As of January 2026, the Qwen model family had surpassed 700 million cumulative downloads on Hugging Face. Alibaba has open-sourced nearly 400 Qwen models, which the community has used to create over 180,000 fine-tuned derivatives.

Lin wasn’t the only one from the Qwen team who’s leaving the company, and he may not be the last.

Yu Bowen, who led Qwen’s post-training efforts, also left. His replacement is Zhou Hao, a former senior staff DeepMind researcher and a Gemini 3.0 contributor who was recruited by Zhou Jingren, CTO of Alibaba Cloud and head of Tongyi Lab. Zhou Hao will report directly to Zhou Jingren and lead Qwen’s post-training RL research.

Hui Binyuan, the lead of Qwen Code, reportedly left in January 2026 and joined Meta. Lin Kaixin, a contributor to Qwen 3.5, VL, and Coder, also tweeted his own departure.

Why Lin’s stepping down

No single reason has been confirmed, but several media reports point in the same direction.

• Alibaba is reorganizing its Qwen team and the broader Tongyi AI Lab, moving away from what was a vertically integrated, startup-like operation toward a more fragmented structure that separates pre-training, post-training, text, and multimodality into distinct teams. That kind of reorganization would directly limit Lin’s management scope, and reportedly doesn’t align with his philosophy of how AI development should work.

• Adding to that, the arrival of Zhou Hao created tension. Zhou was personally recruited by Zhou Jingren with a DeepMind background — a similar playbook to what ByteDance and Tencent have run, bringing in researchers from Silicon Valley to strengthen their AI teams. The intent was clearly to deepen Qwen’s research capabilities. But Lin reportedly did not accept the new arrangement easily.

• There were also scope conflicts. Over the past year, Lin had expanded Qwen’s work into image generation, voice models, infrastructure, and robotics — areas that overlapped with other teams in the Tongyi Lab.

• On the model performance side, Qwen’s latest releases haven’t dominated the way they once did. Qwen 3.5-Plus, which I covered in my last issue, was an ambitious model integrating multiple innovations, but it wasn’t the best open model out there. Over the past six months, Moonshot AI, MiniMax and Z.ai have been challenging Qwen on benchmarks and leaderboards. That kind of pressure compounds quickly.

• There’s one more detail worth noting. In a January panel, Lin said publicly that China has less than a 20% chance of winning the AI race, along with other pointed criticisms. I don’t think that directly caused his exit, but statements like that don’t go unnoticed internally, and the resulting PR pressure doesn’t make anyone’s position easier.

For those following Alibaba closely, this kind of talent exodus isn’t new.

Back in 2022, Alibaba’s DAMO research institute laid off around 30% of its staff and was pushed toward break-even. The departures were significant — Associate Dean Jin Rong, NLP chief Si Luo, the head of the City Brain project, the XR Lab lead, the autonomous driving chief, and Yang Hongxia, the architect behind M6. That was the first wave.

In early 2025, as Qwen gained momentum and China’s AI talent wars intensified, Alibaba lost another round of key names, including Zhou Chang, Qwen’s former tech lead. That was the second.

This is the third.

Recode China AI

💪🏻AI Makes Alibaba Great Again

“Make Alibaba Great Again” has been circulating inside the company for more than a year—and the numbers suggest it’s happening…

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8 months ago · 30 likes · 3 comments · Tony Peng

Alibaba’s ‘Unofficial’ Responses

An all-hands meeting within Tongyi Lab today was essentially damage control. Leadership’s message is the restructuring of Qwen and Tongyi Lab is an expansion. Qwen is the Alibaba’s top priority, more talent is needed, and organizational change is inevitable.

Alibaba CEO Eddie Wu apologized for poor communication around compute resource restraints but insisted Qwen was always his first priority. Zhou Jingren admitted resources have been tight and, strikingly, suggested he too has been marginalized internally.

On whether Junyang could return, the chief HR officer shut it down bluntly: no one gets put on a pedestal, and the company won’t make exceptions at any cost.

My Two Cents

Reorg usually happens when a team is in trouble. But Qwen isn’t in trouble. The models are good, maybe not SOTA, but strong across multiple benchmarks. The Qwen app has closed some of the gap against Doubao. Company morale is back, riding the “Make Alibaba Great Again” wave. There’s no obvious reason to push out one of your most talented AI leads. Top AI talent is scarce everywhere. That’s exactly why LatePost described Lin’s departure as sudden.

So what’s really going on? Qwen was never supposed to be this big. Tongyi Qianwen, the predecessor of Qwen, was a closed-source model, and Qwen started as a side project. It broke through starting in late 2023 with fast-improving performance and explosive community growth, and now it’s arguably Alibaba’s most valuable AI asset, along with cloud and chips.

But that success raised the bar. The old KPIs, which could be Hugging Face downloads and the number of derivative models, no longer cut it. The new expectations are Qwen should support Alibaba’s commercial success in both enterprise market and consumer apps, and that kind of pressure on a research team with a open-source priority can be brutal and destabilizing.

Commercially, ByteDance has been eating into its AI cloud business, and despite spending 3 billion RMB promoting its Qwen app in mainland China during the Chinese New Year, Alibaba has failed to match Doubao.

Jia Yangqing, former Alibaba executive and now VP at Nvidia following the acquisition of Lepton.ai, put it plainly:

For companies, balancing open source and business is genuinely hard. We’ve seen wins like Databricks and Redis Labs, and we’ve seen cautionary tales like RethinkDB, a beloved open-source database that shut down in 2016 despite technical excellence. Was there friction between open-source vision and business priorities? Pure speculation, but if there wasn’t, that would be the exception, not the rule.

Then there’s the resource angle. Ilya’s exit from OpenAI is the most famous example as he couldn’t get enough compute. If Lin ends up leading just one slice of a restructured Qwen, his access to GPU resources shrinks accordingly.

On top of that, Alibaba is pushing hard to scale its Qwen app in mainland China, which means more compute going to inference rather than research. That squeeze will be felt.

What It Means for China’s Open-Source AI

Lin was more than a team lead. He was the face of Qwen, and by extension, China’s open-source AI movement globally. His departure is a real blow to that intiative. Some in the community are already calling it the end of an era.

I don’t think Alibaba will pivot to closed-source immediately. But they will almost certainly invest less in open-source efforts going forward. What makes that matter beyond Alibaba is that as the leader of the open-source ecosystem in China, whatever Alibaba does sets the tone for other labs.

In the U.S., almost every major American AI lab has drifted toward closed-source, driven by the pressure to monetize massive infrastructure investments. Open-source builds goodwill and community, but it doesn’t pay the bills the way API-driven, paid-access models do. Chinese AI labs will face the same dilemma if the competition intensifies.

The one wild card is DeepSeek. As long as the well-funded DeepSeek keeps releasing open-weight models and contributing to the global community, it puts pressure on other Chinese AI labs to follow. A closed model from a second-tier lab means nothing when DeepSeek is giving it away. That dynamic may be the strongest check against a broader retreat from openness, at least for now.

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The post was originally written at ’s invitation and first published on here. I have updated and edited it to reflect the latest developments and research.

AI Supremacy

DeepSeek’s Next Move: What V4 Will Look Like 👀

Good Morning…

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4 months ago · 57 likes · 4 comments · Michael Spencer and Tony Peng

The widely anticipated DeepSeek V4 model is expected to meet the public this week. V4, DeepSeek’s upcoming generation base model, is supposed to be the largest challenge of open-weight models to the proprietary models, and the most anticipated releases in the AI space for early 2026.

According to Reuters and The Information, DeepSeek V4 is optimized primarily for coding and long-context software engineering tasks. Internal tests (per reporting) suggest V4 could outperform Claude and ChatGPT on long-context coding tasks.

The Financial Times reported that DeepSeek V4 will be a native multimodal model with “picture, video and text-generating functions.” The release would mark a major upgrade from DeepSeek’s previous text-only models, though it comes as little surprise as Chinese AI peers—including Moonshot, Alibaba’s Qwen and ByteDance’s Seed—have already embraced multimodalities in their flagship models.

Tang Jie, co-founder and chief scientist of Chinese AI firm Z.ai, said in an X reply that DeepSeek V4 will likely outperform all existing Chinese open-source LLMs.

Yet from a geopolitical perspective, DeepSeek reportedly withheld its V4 model from U.S. chipmakers including Nvidia and AMD for optimization, instead granting early access to domestic suppliers such as Huawei and Cambricon. The chip access decision reflects a deliberate effort to deepen ties with China’s domestic hardware ecosystem

Last week, Anthropic accused DeepSeek of conducting “distillation attacks” along with two other Chinese AI labs, Moonshot and MiniMax. OpenAI issued a similar accusation against DeepSeek in February. But both accusations drew backlash from the AI community, given that Anthropic, OpenAI, and most of their peers are themselves defendants in copyright and training data lawsuits.

2025 was a watershed moment for DeepSeek and the following open-source movement fueled by Chinese AI labs. The release of DeepSeek V3 base model and R1 reasoning model upended multiple AI narratives: that only spending hundreds of millions could produce a frontier LLM, that only Silicon Valley companies had talents to train competitive models, that the U.S.-China gap in AI was widening due to chip shortages.

Chinese AI labs have also been quick to capitalize on DeepSeek’s openness, adopting its training recipes to slash their own trial-and-error costs while pushing their open models further in knowledge, reasoning, and agentic coding.

Throughout the rest of 2025, DeepSeek continued churning out models with incremental improvements, including DeepSeek-V3.2-Thinking and DeepSeek-Math-V2, which won the International Olympiad in Informatics. Yet the widely anticipated DeepSeek-V4 and DeepSeek-R2, which were reportedly slated for release in the first half of 2025, were delayed. According to reports, DeepSeek CEO Liang Wenfeng was dissatisfied with the results and chose to delay the launch.

The Financial Times offered an alternative explanation: DeepSeek initially attempted to train R2 using Huawei’s Ascend AI chips rather than Western silicon like Nvidia’s GPUs, partly due to pressure from the Chinese government to reduce reliance on U.S.-made hardware. The training runs encountered repeated failures and performance issues stemming from stability problems, slow chip-to-chip interconnect speeds, and immature software tooling for Huawei’s chips. Ultimately, DeepSeek had to revert to Nvidia hardware for training while relegating Huawei chips to inference tasks only. This back-and-forth process and subsequent re-engineering significantly delayed the timeline.

Sparsity Through Iteration

DeepSeek’s architectural evolution has been driven by a consistent principle: Sparsity.

While computation can be scaled relatively easily by adding more data, more parameters of the model, and more chips, sparsity is the most straightforward way DeepSeek can scale intelligence under constraints, including compute, memory bandwidth, and chips. These constraints shaped every iteration of their model architecture, from DeepSeekMoE’s initial Mixture of Experts (MoE) through the attention optimizations in V2, V3, and V3.2.

In a Transformer, you have two main computational blocks: Attention and Network (specifically Feed-Forward Network). DeepSeek started by making the network layers sparse using MoE. Instead of every token going through the entire network, they route each token to only a small, relevant subset of parameters as experts.

• DeepSeekMoE laid the groundwork with a MoE architecture containing 64 specialized experts and 2 shared experts. For each input, the model would route it to the 6 most relevant experts (topk=6).

• DeepSeek-V2 expanded the expert pool to 160 specialized experts while keeping 2 shared experts and the same topk=6 routing, with improvements focused on distribution and efficiency.

• DeepSeek-V3 scaled further to 256 experts with 1 shared expert, and increased routing to topk=8. The architecture became more sophisticated in how it managed experts, and introduced a new communication system called DeepEP that makes expert coordination more efficient.

Once the network sparsity was well-optimized, attention became the next target for improvement. As the core module of the Transformer architecture, attention is where each token in a sequence analyzes and weights the importance of every other token to understand context and relationships. Attention presents a different challenge as computational complexity exponentially grows along with the increasing context window.

• Multi-head Latent Attention (MLA) was DeepSeek’s first major attention innovation introduced in V2. Instead of storing complete key-value information for every token (as standard Multi-Head Attention does), MLA compresses this information into a smaller representation. This compression reduces how much data needs to be moved in and out of memory.

• Then in a February 2025 paper, DeepSeek introduced Native Sparse Attention (NSA) with optimized design for modern hardware. Tokens are processed through three attention paths: compressed coarse-grained tokens, selectively retained fine-grained tokens, and sliding windows for local contextual information.

• DeepSeek Sparse Attention (DSA), introduced in V3.1 and V3.2, streamlined the NSA design. Instead of selecting blocks of tokens, DSA selects individual tokens. It uses a lightweight indexer model to identify the 2,048 most relevant tokens from the full context. This indexer is trained through a process where it learns to mimic the full attention pattern—the model first trains with full attention, then the indexer learns to predict which tokens the full attention would focus on. DSA can work with MLA.

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Multimodality Signals in DeepSeek OCR 2

The Financial Times’ reporting on DeepSeek V4’s multimodalities reminds me of DeepSeek’s important research in visual understanding, notably DeepSeek OCR.

Last year, I wrote about DeepSeek OCR, an innovative vision-language model that compresses long texts into visual tokens by 7 to 20 times, potentially enabling LLMs to process lengthy documents more efficiently. DeepSeek OCR consists of two main components:

• DeepEncoder is a custom encoder that combines SAM-base (window attention) with CLIP-large (dense global attention), plus a 16× token compressor. It offers controllable resolution modes (Tiny/Small/Base/Large/Gundam/Gundam-M).

• The LLM is a 3B MoE model with ~570M activated parameters at inference (6 out of 64 routed experts plus 2 shared experts).

Recode China AI

👀DeepSeek and Z.AI Bet on Visual Compression to Solve Long-Context Problem

Chinese AI labs are testing an interesting idea to reduce computational costs for processing long context documents: render long text into images and compress it with a vision-language model (VLM). The expected result is fewer tokens without much loss in accuracy…

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8 months ago · 10 likes · 1 comment · Tony Peng

In January, DeepSeek upgraded the model to DeepSeek OCR 2. The core improvement in DeepSeek OCR 2 is a new visual encoder called DeepEncoder V2, which introduces a concept called visual causal flow.

• Traditional vision-language models read images in a fixed raster order (top-left → bottom-right).

• DeepEncoder V2 instead reorders visual tokens based on semantic structure, mimicking how humans scan documents.

• This enables better reasoning over complex layouts such as multi-column papers or tables.

It’s worth noting that in the paper, DeepSeek researchers said

DeepEncoder V2 provides initial validation of the LLM-style encoder’s viability for visual tasks. More importantly, this architecture enjoys the potential to evolve into a unified omni-modal encoder: a single encoder with shared 𝑊𝑘,𝑊𝑣 projections, attention mechanisms, and FFNs can process multiple modalities through modality-specific learnable query embeddings. Such an encoder could compress text, extract speech features, and reorganize visual content within the same parameter space, differing only in the learned weights of their query embeddings. DeepSeek-OCR’s optical compression represents an initial exploration toward native multimodality, while we believe DeepSeek-OCR 2’s LLM-style encoder architecture marks our further step in this direction. We will also continue exploring the integration of additional modalities through this shared encoder framework in the future

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DSA+mHC+Engram

With the foundation of DSA established in V3.2, DeepSeek appears ready to push optimization further in V4. The evidence points to two other papers that DeepSeek released over the past few months.

Manifold-Constrained Hyper-Connections (mHC)

mHC represents a rethinking of how information flows through deep neural networks. While traditional networks pass information sequentially from one layer to the next, mHC introduces richer connectivity patterns between layers—essentially creating multiple pathways for information to flow across the model’s depth.

To understand mHC’s, we must first revisit residual connections, the backbone of modern deep neural networks regardless of architecture (CNN or Transformer). Proposed in the landmark 2015 paper Deep Residual Learning for Image Recognition, residual connections add the input (x) of a block directly to its output F(x), typically formatted as y = F(x) + x.

This simple shortcut proved revolutionary. By allowing gradients to bypass layers, residual connections solved the vanishing gradient problem that had plagued deep networks, enabling effective training of architectures with hundreds or even thousands of layers.

Then in 2024, ByteDance researchers proposed hyper-connections (HC) as an alternative approach. Rather than simple additive shortcuts, HC creates richer connectivity patterns that allow information to flow more freely between non-adjacent layers. This architectural flexibility offers advantages over standard residual connections, particularly in avoiding the gradient-representation tradeoffs.

But HC introduced its own challenge. As training scales increase and connectivity grows richer, the risk of training instability rises. More pathways for information flow means more opportunities for gradients to either explode (grow uncontrollably large) or vanish (shrink to near-zero) during training.

This is where DeepSeek mHC’s innovation emerges. The approach treats the model’s parameter space as existing on a high-dimensional geometric structure, a manifold. By imposing mathematical constraints based on this manifold geometry, mHC creates a guardrail system that maintains stable information flow even as connectivity increases.

Think of it as a multi-lane highway with intersections. Vehicles can change lanes, merge, or split—increasing routing flexibility. But the total traffic flow must remain constant and balanced. Information can take different paths through the network, but the overall flow is constrained to prevent either congestion (gradient explosion) or emptiness (gradient vanishing).

The mathematical formulation ensures that information transformations remain well-behaved across the manifold. This prevents the instabilities that would otherwise emerge from unconstrained hyper-connections, enabling deeper and more flexible architectures while maintaining training stability.

For V4, mHC could appear strategic. As DeepSeek pushes toward increasingly sparse and selective processing through DSA, Engram, and other mechanisms, having stable hyper-connections allows these components to interact more effectively across layers.

Engram

Engram, detailed in DeepSeek’s January 2026 paper Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models, introduces a new dimension to model architecture by adding conditional memory to the Transformer through efficient lookup mechanisms.

Traditional Transformer-based LLMs compress all learned knowledge into neural network weights. Whether answering a simple factual query like “Barack Obama was a U.S. president” or solving a complex mathematical proof, the model must route every computation through the same expensive neural processing.

Consider the phrase “New York City.” A standard Transformer must learn that “New,” “York,” and “City” together form a specific entity, then rebuild that relationship through attention computation every single time. This is static knowledge that never changes, yet the model treats it like novel information requiring full neural processing each time.

Engram’s premise is elegantly simple: not all knowledge requires neural computation. Static facts and established patterns can be stored in a complementary memory system and retrieved efficiently when needed. This mirrors biological memory: You don’t re-calculate that 2+2=4 each time; you simply recall it.

Engram implements this through a modernized N-gram lookup module operating in constant time (O(1))—retrieval speed remains constant regardless of how much information is stored. This creates a fundamental architectural separation:

• Neural computation (attention and MoE): Complex reasoning, novel synthesis, context-dependent processing

• Memory lookup (Engram): Static knowledge, established patterns, factual recall

The architecture optimizes the balance using a U-shaped scaling law—a mathematical framework determining ideal parameter allocation between neural computation and memory lookup at different model scales. This is crucial because the tradeoff isn’t straightforward: too much reliance on lookup tables risks brittleness; too much neural computation wastes resources on static knowledge. The U-shaped law identifies the sweet spot where both systems work synergistically.

Memory efficiency improves dramatically because Engram offloads static knowledge from expensive GPU memory to host CPU memory. Empirical results demonstrate that offloading a 100B-parameter lookup table to host memory incurs negligible overhead (less than 3%). This enables massive knowledge bases without proportional GPU memory costs, while supporting much longer effective context windows.

For reasoning and knowledge-intensive tasks, Engram delivers measurable improvements over comparable MoE-only architectures. Benchmarks show particular gains in multi-hop reasoning and long-context understanding—tasks that benefit from quick access to established knowledge while applying neural computation to novel reasoning steps.

Just before the release of this blog, DeepSeek, Peking University, and Tsinghua University introduced DualPath. The system aims to solve a massive I/O bottleneck in long-context agentic inference. Since decoding engines often sit idle while prefill engines are overwhelmed moving KVcache from storage, DualPath uses those idle engines to load the cache and transfer it to the prefill engines via RDMA. DualPath improves offline inference throughput by up to 1.87x and online inference by 1.96x. I see no reason why DualPath wouldn’t be incorporated into DeepSeek V4’s inference.

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Model1: New Clues to DeepSeek V4?

Just over a year after DeepSeek-R1 became the most-liked model on Hugging Face, sharp-eyed developers have spotted a mysterious “Model1” in recent code updates to the FlashMLA library. The timing is suggestive—could Model1 be the codename for DeepSeek V4?

Analysis of recent commits reveals several architectural signatures suggesting Model1 is an entirely new flagship model:

• The 512-Dimensional Shift: Model1 switches from V3.2’s 576-dimensional configuration to 512 dimensions, likely optimizing for NVIDIA’s Blackwell (SM100) architecture where power-of-2 dimensions align better with hardware.

• Blackwell GPU Optimization: New SM100-specific interfaces, CUDA 12.9 requirement, and performance benchmarks showing 350 TFlops on B200 for sparse MLA operations represent deep integration with next-generation hardware.

• Token-Level Sparse MLA: Separate test scripts for sparse and dense decoding indicate parallel processing pathways. The implementation uses FP8 for storing KV cache and bfloat16 for matrix multiplication, suggesting design for extreme long-context scenarios.

• Value Vector Position Awareness (VVPA): This new VVPA mechanism likely addresses a known weakness in traditional MLA—positional information decay over long contexts. As sequences extend into hundreds of thousands of tokens, compressed representations can lose fine-grained positional details. VVPA appears designed to preserve this spatial information even under aggressive compression.

• Engram Integration: References to Engram throughout the codebase suggest deep integration into Model1’s architecture.

I believe V4 and R2 will remain among the best open-source LLMs available, potentially even narrowing the gap with leading proprietary models. However, since DeepSeek R1’s release over a year ago, the competitive race to push LLM capabilities forward has only intensified. The “DeepSeek effect” has motivated several other Chinese AI labs—including Moonshot AI, MiniMax, and Zhipu—to redouble their efforts in releasing top-tier LLMs. This doesn’t even account for tech giants Alibaba and ByteDance, both of which are producing frontier models while simultaneously expanding into chatbots, AI cloud services, chips, and hardware.

Given this rapidly evolving landscape, expecting another watershed “DeepSeek moment” like the one in early 2025 seems nearly impossible.

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Usually, the weeks between the New Year and Chinese New Year follow a familiar rhythm for Chinese companies—recap the past year, let employees rest, and quietly plan for the year ahead. The AI race doesn’t allow for that luxury anymore.

In the weeks leading up to Chinese New Year, which began on February 17th, nearly every frontier Chinese AI lab—except DeepSeek—rushed out their latest flagship models. The timing wasn’t coincidental. With DeepSeek-V4 rumored to drop before the holiday, no lab could afford to sit still and wait to be made irrelevant. These models have a shelf life of months at best, and every lab knows that being first to claim a benchmark, a capability, or a narrative matters enormously for how developers and enterprises perceive you on the world stage.

And it wasn’t just Chinese AI labs. Anthropic, OpenAI, and Google each dropped major releases of their own—Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.3 Codex—in the same compressed window. If you zoom out and look at February 2026 as a whole, it may well go down as the most consequential single month in the history of AI development. I genuinely hope this pace of progress continues, as dizzying as it is to keep up with.

I’ll do my best to walk you through all of these releases and what they mean—but let’s start with the four Chinese open-weight models that I think deserve the most attention right now.

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Qwen3.5-Plus: The Most Ambitious One

Qwen3.5-Plus isn’t the strongest open model right now, but it might be the most ambitious, and I mean that as a genuine compliment.

Native multimodality, finally. Alibaba’s previous Qwen series separated vision and language into dedicated model lines, Qwen and Qwen-VL. Qwen3.5-Plus breaks from that tradition with a native multimodal foundation model trained from the ground up on a significantly large corpus of visual-text tokens, enriched with Chinese, English, multilingual, STEM, and reasoning data. This isn’t a text model with vision added on, and the difference shows in tasks like visual reasoning, visual coding, and visual agentic workflows.

Extreme sparsity. The model has 397 billion total parameters, but only 17 billion are activated per forward pass. It’s built on the Qwen3-Next architecture, which introduces higher-sparsity MoE, a Gated DeltaNet + Gated Attention hybrid attention mechanism, stability optimizations, and multi-token prediction.

• A quick note on what those attention mechanisms actually mean: Gated DeltaNet is a form of linear recurrent attention that uses gating to selectively update a memory matrix, letting the model efficiently track information across long sequences without the quadratic cost of standard attention. Gated Attention layers are interspersed with these linear layers to preserve the model’s ability to do precise, content-based retrieval when it needs to. Together, the hybrid avoids the bottlenecks of both pure transformers and pure linear models.

The throughput gains from this architecture are striking. Under a 32K context, Qwen3.5-Plus runs at 8.6× the decoding speed of Qwen3-Max; under 256K context, that jumps to 19×. It supports up to a 1 million token context window, built-in tools, and adaptive tool use. It currently ranks third on the Artificial Analysis Intelligence Index, behind GLM-5 and K2.5.

On pricing, Qwen3.5-Plus charges ¥0.8 per million input tokens (~$0.12) and ¥4.8 per million output tokens on Alibaba Cloud in mainland China, which Alibaba positions as roughly 1/18th the cost of Gemini 3 Pro at comparable performance. On OpenRouter, it’s $0.40 input and $2.40 output.

GLM-5: The Best Open-Weight Model

After GLM-4.7 signaled that Z.ai was back in the conversation among top-tier Chinese AI labs, GLM-5 makes the argument definitively. It currently sits at the top of the Artificial Analysis Intelligence Index, the best open-weight model you can run today.

The model’s focus is concrete: AI agents that can autonomously plan, implement, debug, and iterate on real-world software tasks. And I want to call out something I genuinely appreciated: Z.ai clearly learned from DeepSeek’s approach of writing accessible, readable research papers. GLM-5’s technical report is one of the cleaner reads in this space.

Architecture. The model has 744 billion total parameters with 40 billion activated per forward pass. Pre-training data expanded from 23 trillion to 28.5 trillion tokens, with a 200K context window. Z.ai adopted several of DeepSeek’s architectural choices, including Multi-head Latent Attention (MLA), multi-token prediction, and most notably DeepSeek Sparse Attention (DSA). DSA replaces the standard dense O(L²) attention with dynamic token selection, reducing long-context compute cost by 1.5–2× without sacrificing quality.

Post-training is where Z.ai really distinguished itself. Not all agent tasks are created equal. Some finish in a few steps; others require long, sprawling rollouts. Traditional synchronous RL infrastructure couples generation with training, which means fast samples sit idle waiting for slow ones, resulting in GPU idle, lower throughput, and a training bottleneck.

Z.ai’s solution is an Asynchronous RL Infrastructure that fully decouples generation from training. Fast tasks update model parameters immediately; slow tasks keep running until completion. But this introduces a new challenge: rollouts generated by older model versions become off-policy data. To handle this, they developed a suite of asynchronous RL algorithms—Token-in-Token-Out (TITO), Double-sided importance sampling, and Off-policy filtering—that together stabilize training on this kind of stale data.

Its technical paper, Z.ai researchers highlighted that GLM-5 has been optimized to adapt major domestic Chinese AI chips such as Huawei Ascend Cambricon, and Baidu’s Kunlunxin.

K2.5: Visual Coding and Agentic Search

Like Qwen3.5-Plus, K2.5 is Moonshot AI’s first native multimodal foundation model. It was trained on approximately 15 trillion mixed visual and text tokens—vision and language learned jointly from day one. The result is notably stronger cross-modal reasoning across images, videos, and text.

K2.5 builds on the Kimi K2 foundation model: a trillion-parameter MoE with 1.04T total parameters and ~32B activated per token. Where K2.5 stands out is in two specific areas: front-end visual coding (it’s particularly strong at creative website design, and can generate functional code directly from video walkthroughs) and agentic search, where it reportedly outperforms even proprietary models—a genuine achievement in deep research workflows.

The most novel contribution in the paper is Agent Swarm, which introduces a learned framework for parallel multi-agent execution. Rather than a single agent working through tasks sequentially, K2.5’s PARL (Parallel-Agent Reinforcement Learning) paradigm works like this:

• A trainable orchestrator learns when and how to spawn sub-agents. The sub-agents themselves are frozen during training—deliberately not co-trained—to avoid unstable credit assignment across a dynamic swarm. The orchestrator is then trained via reinforcement learning to schedule parallel work and balance efficiency against complexity.

The results are compelling: inference latency reduced by up to 4.5× compared to sequential agents, with meaningful gains in task completion quality and F1 scores. The agent swarm isn’t just a throughput trick—it actually improves how well tasks get decomposed and executed.

M2.5: The Office Specialist

M2.5 is a more incremental release—an upgrade over M2.1, likely timed to respond to the momentum of other models (and possibly DeepSeek V4 specifically). The architecture is presumably close to M2.1’s, so I won’t spend too much time on the model itself.

What MiniMax focused on this cycle is coding and office productivity. M2.5 achieved then-SOTA results on multiple coding benchmarks and, interestingly, demonstrated the ability to generate professional deliverables in Word, Excel, and PowerPoint—not just raw text. It wins approximately 59% of head-to-head comparisons against peer models on productivity benchmarks.

The more interesting story is MiniMax’s post-training infrastructure, Forge—their in-house agent-native RL framework that, like Z.ai’s asynchronous system, decouples training from inference. Forge trains across 200,000+ real-world environments spanning coding, search, tool interaction, and workspace automation. The key stabilization algorithm they use is CISPO, which helps maintain training stability and improves long-horizon performance on complex agentic tasks.

One number from MiniMax’s deployment data is worth lingering on: 30% of all tasks across the company’s daily operations—R&D, product, sales, HR, finance—are now autonomously completed by M2.5. And M2.5-generated code accounts for 80% of newly committed code. That’s not a benchmark. That’s a live claim about internal operations, and it’s either the most confident thing said in this release cycle or the most audacious.

Three Patterns Worth Taking Seriously

Looking across all four models, three themes emerge that I think have real implications for the broader industry.

1. Sparsity is the new default

MoE has been the dominant architecture since DeepSeek-V3 and R1, and these new models push sparsity further than any previous generation without giving up performance. But it’s not just network sparsity anymore. Frontier open-weight models are now also adopting sparse attention mechanisms—GLM-5 with DeepSeek’s DSA, Qwen3.5-Plus with its hybrid Gated DeltaNet and Gated Attention layers.

The practical consequence is cheaper inference, and that’s where Chinese open-weight models are carving out a real competitive identity. Alibaba claimed its model costs 1/18th-of-Gemini-Pro, and MiniMax’s “intelligence too cheap to meter” narrative also captures broad attention. For developers who are cost-sensitive, or for agentic workloads where token consumption can be 10–100× higher than standard queries, this pricing gap is not marginal. It’s structural. And in China, where AI commercialization paths remain unclear and most consumer chatbots are still offered for free, cheaper models can save companies not millions but billions in compute costs.

2. Native multimodality has arrived in open weights

A year ago, the leading open-weight models were overwhelmingly text-only, including Qwen3 and Kimi K2. Many researchers believed native multimodality wasn’t necessary for core intelligence. Then Gemini 3.0 happened, and its staggering benchmark performance shifted the conversation.

Chinese AI labs responded fast. Qwen3.5-Plus and K2.5 are both the first native multimodal flagship models from their respective labs. Add ERNIE 5.0 from Baidu and Seed2.0 from ByteDance, and you have a clear generational inflection: open-weight LLMs are no longer text-only. Training vision and language jointly from scratch—rather than layering vision on top—produces qualitatively better cross-modal reasoning. Both Qwen3.5-Plus and K2.5 can build functional websites directly from video input. That’s a capability class that didn’t exist in this model tier twelve months ago.

3. Agentic capabilities in real world

Previous-generation Chinese open-weight models added tool use and basic agent scaffolding. This generation is different—these models are credibly competing with top proprietary systems on complex, real-world workflows.­­­­

Almost every lab in this release cycle rebuilt their post-training RL recipe specifically for agentic tasks. Z.ai and MiniMax both developed asynchronous RL systems that decouple inference from training. K2.5 introduced Agent Swarm with a learned orchestrator. The common thread: standard synchronous RL wasn’t designed for the variance in rollout length that agentic tasks produce, and each lab had to engineer around that constraint independently.

The business implications are significant. Agentic models that actually work—that can replace portions of employee workflows—consume tokens at a fundamentally different scale than chatbots. Z.ai seems to understand this: days after releasing GLM-5, they raised the price of their GLM Coding Plan subscription by 30%, announced expansions to their chip partner network, and watched their stock surge over 40% in a single day. Their market cap is now approaching Baidu, which reported over $18 billion in revenue in 2024.

Three years ago, Chinese AI labs open-sourced their models largely to raise awareness and goodwill within the developer community. Contributing to the open-source ecosystem was the goal; competing internationally was not really on the table. That framing has changed meaningfully heading into 2026.

Look at how these labs are presenting themselves now. The release blogs, the benchmark visuals, the example demos, the design polish—all of it is clearly aimed beyond domestic audiences. These labs are active on X, writing in fluent English, and positioning their models explicitly against GPT, Gemini, and Claude.

The early commercial signals are modest but real. Moonshot AI, which is reportedly seeking a valuation of $10 billion in an ongoing funding expansion, disclosed that its overseas API revenue has quadrupled since November 2025. These models are finding their first footholds through open-weight platforms like OpenRouter, a natural entry point for cost-sensitive developers, and the expectation is that enterprise and consumer markets follow.

What makes this moment historically significant is: for the first time, China’s leading technology companies and AI startups are competing head-to-head against America’s best in one of the most consequential technologies of our era—AI, where the stakes may be higher than any of those. The race is still early, the outcome is genuinely uncertain, and that is precisely what makes February 2026 feel like a turning point worth paying attention to.

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Gemini 3.0 released in November 2025 was seen as a key milestone for Google to take the lead in the AI race. Now ByteDance is attempting to orchestrate its own Gemini 3.0 moment with a coordinated release of new models, including the video generation model Seedance 2.0 and the competitive multimodal foundation model Seed2.0.

ByteDance has long been recognized for exceptional AI applications—its chatbot app Doubao boasts over 100 million daily active users—but their AI models have lingered in the second tier. That perception may have just shifted.

The strategy mirrors Google’s Gemini 3.0 playbook: release across modalities simultaneously, demonstrate breadth of capability, and leverage existing distribution advantages to convert model strength into market dominance.

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Seedance 2.0: SOTA Video Generator

Seedance 2.0 is ByteDance’s latest AI video generator, now available across ByteDance’s AI apps such as Jimeng and Doubao as well as video editing apps Jianying/CapCut. The model has gone viral over the past week and shows no signs of slowing down. Elon Musk called it “it’s happening fast,” and Jia Zhangke, the Cannes-winning Chinese director, just made a film with it.

Multimodal Input: Seedance 2.0 can process multimodal input, including text prompts, images, videos, and audio clips. All four modalities can be mixed at once to guide the output. You can add as many as 9 reference images, 3 videos, and 3 audio clips, then guide Seedance 2.0 with prompts to make generation more controllable.

Motion Stability: Seedance 2.0 shows marked improvements in physics-based motion and character consistency. The model handles complex human motion and object interactions with notably fewer artifacts than previous generation tools—a critical advancement for practical use cases.

Native Audio-Visual Synthesis: Like Sora 2, Seedance 2.0 generates synchronized audio and video in a single pass. Sound effects, dialogue, and music align natively with visuals, eliminating post-production alignment steps.

However, this also raises deepfake and portrait rights concerns, as the model can generate photorealistic celebrity likenesses with matching voice characteristics from minimal prompting.

Dynamic Cinematography: Seedance 2.0 introduces sophisticated camera movement control. Unlike earlier models constrained to static or simplistic angles, Seedance 2.0 can generate complex camera work comparable to professional cinematography. The New York Times called the model’s output “more cinematic than anything so far.”

Prompt Adherence: Seedance 2.0 demonstrates significant improvements in following user instructions precisely. The model generates content that matches prompts with substantially higher accuracy—reducing the unpredictable, lottery-like outputs that plagued earlier tools. When you specify performance details, lighting conditions, shadow direction, or camera behavior, the model respects these constraints rather than interpreting them loosely or ignoring them entirely.

Video generators have advanced rapidly over the past three years—just look at clips like “Smith eating spaghetti”—but they still haven’t unlocked the kind of commercial value that LLMs have. The latest example is Sora 2. While Sora 2 launched to significant fanfare last year, hitting #1 on the Apple App Store with over 1 million downloads, its daily active user base has now reportedly dropped below 1 million, with App Store rankings falling to the 70s-100s range.

Here’s where ByteDance’s strategic position becomes clear. ByteDance doesn’t have this problem. Seedance 2.0 can seemlessly integrate directly into:

• Douyin/TikTok: The largest short-form video platform globally, where AI-generated content can be consumed within the same ecosystem it’s created. It will continue lowering the barriers to video production, ushering in an era where anyone can create. while more precisely matching each video to its ideal audience. Seedance 2.0 won’t disrupt Hollywood, but it will make short video apps even more addictive.

• CapCut/Jianying: With over 800 million users globally, this video editing platform provides the practical creation environment where Seedance 2.0 can become part of standard creative workflows. For creators already paying for features like multilingual subtitles and AI script generation, AI video generation will soon become a must-have premium feature.

Inevitably, the model has drawn criticism from Hollywood studios alleging training on copyrighted content, followed by the Japanese government launching an investigation over potential Copyright Law violations. Starting February 9, ByteDance disabled real human images or videos as primary references, a significant capability restriction that suggests both regulatory pressure and the challenge of managing deepfake risks at scale. The company told South China Morning Post that “We are taking steps to strengthen current safeguards as we work to prevent the unauthorised use of intellectual property and likeness by users.”

Seed2.0: A Production-Oriented Multimodal Foundation Model

While Seedance 2.0 captured social media attention, Seed2.0 represents ByteDance’s core play for foundation model credibility.

Positioned as a “production-oriented” multimodal model, Seed2.0 emphasizes real-world complexity over benchmark optimization alone. In the Seed2.0 model card, researchers from Seed, ByteDance’s AI lab, began by analyzing how AI is actually used in China. The data revealed that unstructured information processing and analysis dominates authentic model usage, representing the largest single share. In development workflows, frontend development and bug fixing substantially dominate agentic coding requests, each far exceeding their respective category alternatives.

Therefore, the model prioritizes visual and multimodal understanding, fast and flexible inference, and reliable complex instruction execution.

Seed2.0 launches in three variants optimized for different deployment scenarios: Seed2.0 Pro for research and complex reasoning, Seed2.0 Lite for enterprise MaaS adoption, and Seed2.0 Mini for scaled applications where cost efficiency dominates.

As of February 16, 2026, Seed ranks 6th on the LMSYS Chatbot Arena Text Arena (Overall) leaderboard and 3rd on the Vision Arena leaderboard.

Multimodal Understanding: The model demonstrates strong performance across 50+ image benchmarks and 24+ video benchmarks. Seed2.0 Pro shows particular strength in visual reasoning tasks like MathVision, LogicVista, and VisuLogic, as well as spatial understanding benchmarks including DA-2K 3D spatial and RefSpatialBench.

The model excels at document extraction on long-form documents (DUDE benchmark) and demonstrates exceptional motion perception capabilities—notably surpassing human baseline performance on VideoReasonBench. Long-form visual context processing represents another core strength, enabling the model to maintain coherent understanding across extended visual sequences.

Mathematical and Formal Reasoning: This is where Seed2.0 Pro shows frontier-class performance. The model achieves gold-level performance on both IMO 2025 and CMO 2025, demonstrating olympiad-level mathematical reasoning capability.

With a Codeforces Elo of 3020, Seed2.0 Pro shows strong competitive coding ability that extends beyond pure mathematics into algorithmic problem-solving. The model also delivers strong performance on AIME, HMMT, and Putnam-200 benchmarks, positioning it as competitive with Gemini 3 Pro on math and reasoning tasks across the board. Seed2.0 also pursues tackling tasks with genuine research-level complexity, such as open Erdős problems and scientific coding.

Coding Capabilities: Seed2.0 demonstrates strong coding performance across multiple evaluation dimensions. The model shows competitive results on LiveCodeBench and handles repository-level tasks effectively on NL2Repo-Bench, indicating capability beyond isolated function generation.

Debugging and refactoring scenarios, which are critical real-world workflows often underrepresented in benchmarks, represent explicit areas of focus. The model also shows particular strength in Vue.js-heavy frontend development, reflecting optimization for frameworks dominant in the Chinese market. The model card explicitly frames coding agents and multi-step task execution as design priorities, not afterthoughts, signaling ByteDance’s emphasis on practical engineering workflows over pure benchmark optimization.

Agentic Workflows: Seed2.0 emphasizes end-to-end task completion rather than single-turn interactions. Seed researchers attribute this to the nature of real-world tasks, which typically span longer time frames and involve multiple stages. Existing LLM agents struggle to independently construct efficient workflows and gain experience over extended periods, while real-world knowledge has significant domain barriers and follows a long-tail distribution. In response, the model strengthens its ability to follow complex instructions, handle long-chain tasks, and master long-tail knowledge.

The model shows strong performance as a search agent, handling complex retrieval tasks effectively. Coding agents represent a core capability, with the model demonstrating repository-level generation that goes beyond snippet completion. GUI agents, deep research workflows, and long-horizon multi-step instruction following are all explicitly supported design patterns.

Costs: While GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens, Seed2.0 Pro costs only $0.47/$2.37 per million input/output tokens and Seed2.0 Mini cuts costs to $0.03/$0.31 per million input/output tokens.

At 5-10x cheaper than frontier alternatives while maintaining competitive performance on many tasks, Seed2.0 targets the enterprise MaaS (Model-as-a-Service) market where cost efficiency enables entirely different use cases at scale. This aggressive pricing strategy isn't surprising—ByteDance initiated China's LLM pricing war back in April 2024, and has consistently positioned cost advantage as a core competitive moat.

However, Seed researchers also acknowledged specific gaps:

• Coding performance still trails Claude on certain SWE benchmarks

• Retrieval-heavy long-context tasks show headroom versus competitors

• Some long-tail knowledge gaps compared to Gemini on specific QA benchmarks

• Video reasoning on the hardest evaluation sets remains below human performance

Make Models Useful

The real test of AI capability isn’t found in benchmark leaderboards—it’s found in whether ordinary people can actually use it to solve their problems.

For example, my wife is a Douyin vlogger who typically spends 2-3 hours editing each video, wrestling with hour-long raw footage to add subtitles, voiceover, and special effects. She doesn’t follow AI research. She doesn’t care about benchmark scores or parameter counts. She has one question: Can AI actually save her time?

This is the audience ByteDance understands intimately. While AI researchers debate frontier capabilities and academics parse benchmark methodologies, hundreds of millions of creators on Douyin and CapCut are asking the same practical question my wife asks: Will this work for me?

When I saw Seed2.0’s model card demonstrate video editing capabilities in CapCut, I immediately shared it with her. Not because it achieved impressive scores on academic benchmarks, but because it promised to do exactly what she needs.

This is ByteDance’s strategic advantage. They’ve built their models not primarily for researchers to evaluate, but for real users to deploy. The company emphasizes real-world effectiveness over benchmark performance throughout Seed2.0’s documentation. Both Seedance 2.0 and Seed2.0 are engineered specifically to strengthen ByteDance’s existing applications from Douyin/TikTok to CapCut/Jianying and AI apps such as Doubao and Trae.

Unlike most Chinese tech companies rushing to open-source models for community credibility, ByteDance keeps both Seedance 2.0 and Seed2.0 proprietary, available only through their apps and cloud platforms. They’re optimizing for the number of videos actually created, the hours actually saved, the workflows actually improved.

Is this ByteDance’s Gemini 3.0 Moment? Not quite yet. Gemini 3.0, as a native multimodal model, shows exceptional capabilities across knowledge, coding, and multimodal understanding—powerful in nearly every aspect.

Seed2.0 is similar in architecture and ambition to Gemini 3.0, but more specialized. It prioritizes specific capabilities over comprehensive excellence. The Seed researchers themselves acknowledge this gap.

Seed2.0 marks the first major project since Wu Yonghui, former Google DeepMind Vice President and now head of Seed, joined ByteDance. According to Latepost, it features one trillion parameters, probably ByteDance’s largest model ever. This represents a remarkable achievement for a company that entered the LLM race late, beginning serious training only in 2023, without the robust infrastructure foundation of Google or OpenAI.

Wu is attempting to build Seed into a world-class research lab. Seedance 2.0 and Seed2.0 represent a major step in that direction—not yet matching Gemini 3.0’s comprehensive dominance, but demonstrating that ByteDance can compete at the frontier on the capabilities that matter most for its billion-user platforms.

Recode China AI

👀TikTok’s Parent ByteDance Quietly Builds Its AI Empire

When people outside mainland China think of ByteDance, they think of TikTok—an app that propelled short video into omnipresent popularity and placed ByteDance at the heart of U.S.-China tensions…

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a year ago · 27 likes · 4 comments · Tony Peng

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China’s AI race is unfolding among the country’s three largest technology giants: ByteDance, Alibaba, and Tencent. As we enter 2026, these tech behemoths are going all-in on what many see as a war that will determine their futures.

ByteDance operates China’s most popular AI chatbot, Alibaba has built what many consider the country’s—and possibly the world’s—leading open-source LLM, and Tencent, though late to arrive, wields an ecosystem of apps serving over 1 billion users and is racing to catch up.

LatePost, my favorite Chinese business media (reminiscent of The Information), recently published a viral piece documenting the latest AI developments at these three companies.

Below is the full translation, assisted with Claude’s assistance and reviewed for accuracy.

Head-to-Head Fight At Chinese New Year

In November 2025, Yao Shunyu appeared at an internal Tencent meeting wearing casual shorts and flip-flops. This 27-year-old former OpenAI researcher and technical genius who proposed the ReAct paradigm had just been recruited by Tencent with a substantial offer.

After joining, one of his key tasks was to help Tencent identify why the Hunyuan LLM had been underperforming over the long term, and report the situation directly to Tencent President Martin Lau. Yao Shunyu meticulously examined every link in the chain, frequently exchanging ideas with colleagues and interns until midnight—things his predecessors rarely did. He quickly became the top figure for Tencent’s large language models (LLMs).

“Hunyuan’s evaluation has major problems,” said one person present at the meeting, paraphrasing Yao Shunyu’s remarks. The gist was that the model was overly focused on chasing leaderboard rankings, incorporating benchmark data into the training set, which led to data contamination. Although the model excelled at answering questions, its performance in real-world scenarios was unstable. He hoped the team would stop chasing leaderboards and stop organizing work around rankings. At the meeting, Hunyuan’s relevant leaders also mentioned past problems with data, pre-training, and infrastructure.

Over the past two years, this Chinese internet company with the highest market capitalization and the largest traffic gateway has been relatively cautious about AI—whether in terms of investment intensity or organizational and product advancement speed, it lagged behind Alibaba and ByteDance. Until 2025, this state began to change: offering high salaries to attract technical talent, large-scale reorganization of model and AI product teams, and continuously tilting resources toward “Yuanbao (Tencent’s AI chatbot).” Yao Shunyu’s arrival marked the clearest turning point in this series of changes.

“Completely disrupting the previous pace and inertia is the first step back to the right track,” said one person from the Hunyuan LLM team.

Tencent is rallying its forces, while Alibaba is attempting to define the AI narrative.

Alibaba has proposed a new concept: “Tong通-Yun云-Ge哥”—Tongyi Laboratory, Alibaba Cloud (云yun means cloud in Chinese), and Pingtou Ge (平头哥T-Head), representing the trinity of AI, cloud computing, and chips. Alibaba considers itself one of the few technology companies in China with full-stack AI capabilities—from chips and AI models to cloud services and products. This is also Google’s story.

Among the three major internet giants, Alibaba’s core business has the lowest profit efficiency. According to reports, ByteDance’s net profit in the first three quarters of this year was around $40 billion; during the same period, Tencent and Alibaba’s net profits were around $30 billion and $10 billion respectively (calendar year basis). But this hasn’t affected its determination to invest.

According to a source familiar with the matter, Alibaba is considering increasing its investment in AI infrastructure and cloud computing over the next three years from 380 billion yuan (~$55 billion) to 480 billion yuan (~$69 billion). Domestically, Alibaba has its self-developed chip Zhenwu 810E; overseas, it is “using truckloads to transport purchased GPUs,” said one insider. At its most aggressive, “even consumer-grade graphics cards like the RTX 4090 were purchased in large quantities to build inference clusters and supplement inference throughput.”

In December 2025, the daily user acquisition spending for Qwen App, Ant Group’s Lingguang and Afu, each exceeded 10 million yuan (~$1.4 million); Qwen App’s single-day spending peak once reached 15 million yuan (~2.2 million).

For ByteDance, “AI is an opportunity that can affect the entire world,” said someone close to ByteDance’s senior management. From TopBuzz and TikTok to TikTok Shop, since its founding, the company has been searching for such opportunities. “Only things closer to the center of the world have greater exploratory value.”

Compared to Tencent and Alibaba, which have weaknesses in models and products respectively, ByteDance’s capabilities are more comprehensive. By the end of 2025, Doubao became the first AI product in China to break 100 million daily active users; Doubao’s AI models (including their LLMs and other multimodal models) processed an average of 63 trillion tokens daily, growing over 200% in six months.

In 2023, ByteDance founder Zhang Yiming said that the operating system-level opportunity of this era is AI + computing.

In 2026, ByteDance will fully accelerate the globalization of its AI business, with regions like Southeast Asia as priorities; it won’t enter the United States for now. According to reports, ByteDance’s goal is to become at least the third-largest player globally in generative AI. By the end of 2025, Doubao’s overseas version Dola had surpassed 10 million daily active users globally.

China’s current wave of AI enthusiasm began in 2023. In the early stages when star startups attracted the most attention, internet giants weren’t particularly prominent. The three chose different paths: Tencent emphasized AI application implementation and therefore waited relatively quietly for model capabilities to mature; Alibaba pushed its AI model toward open source, betting on building a larger ecosystem to open up incremental space for its cloud business; ByteDance started late and could only make up for technical shortcomings through saturated investment as quickly as possible.

Until early 2025, DeepSeek redrew a starting line for the entire industry, and the giants became active—the battlefield began to smell of gunpowder.

The 2026 Spring Festival (Chinese New Year) became the flashpoint of this war.

According to reports, ByteDance secured the Spring Festival Gala partnership at the highest price—its Volcano Engine became the AI cloud partner for the Spring Festival Gala, and Doubao will also launch various interactive features during the gala.

Tencent hasn’t sponsored any gala-type programs since 2015, but it clearly understands the value of the Spring Festival. Just a month ago, Tencent CEO Pony Ma inquired with the Yuanbao team, concerned about whether they had sufficient GPU resources. “He wanted to ensure that Yuanbao wouldn’t be constrained by computing power at this critical moment during the Spring Festival, affecting its performance.”

The two companies that missed the Spring Festival Gala decided to take the initiative on the product side.

Tencent Yuanbao prepared 1 billion yuan in cash red envelope incentives and a brand new AI social function called “Yuanbao Pai” for battle. At the group employee meeting on January 26, 2026, Pony Ma said they would give the marketing budget saved to users, letting everyone relive the joy of grabbing red envelopes from years past, and hoping to recreate the glorious moment of WeChat red envelopes in 2015.

Next to the facial recognition entrance on the first floor of Building C4 in Alibaba’s Xixi Park, every Monday morning and Friday evening are filled with suitcases belonging to Qwen App employees. They fly in from Guangzhou, Beijing, and other places for closed development, then fly away like migratory birds on weekends. This situation continued at least until the Spring Festival.

Alibaba sources told us that Qwen App will send red envelope benefits to users during the Spring Festival (turns out it’s 3 billion yuan, or $431 million). No company controlling traffic wants to give up the battle for gateways. Baidu also offered 500 million yuan in Spring Festival red envelopes, and Wenxin’s AI social features appeared on the Spring Festival battlefield.

Over the past twenty years, China’s three major internet giants have fought almost every key battle in the internet space—from e-commerce and lifestyle services to long and short videos, social media, gaming, mobile payments, and enterprise services.

Previously, they fought local battles—losing one card, the game could still continue. This time it’s more like the “Battle of Midway”—a turning point in the overall war. Once lost, they might lose the entire future.

After DeepSeek, the War Truly Began

Among all of China’s major tech companies, ByteDance was one of the later starters in LLMs. Before OpenAI’s ChatGPT launched at the end of 2022, Baidu, Huawei, and Alibaba (in order of release) had all released large language models, but ByteDance had not.

Starting mid-2023, ByteDance began accelerating its catch-up, quickly addressing shortcomings in infrastructure, LLM R&D, hardware and software products, and talent; by late the following year, Doubao became the AI product with the largest user base.

In January 2025, High-Flyer’s reasoning model DeepSeek-R1 emerged, directly driving an explosion on the product side: within less than a month of DeepSeek Chatbot’s launch, its daily active users broke 10 million, immediately surpassing Doubao.

This reasoning model wasn’t a novelty. After OpenAI’s o1 model preview appeared in September 2024, ByteDance paid attention to this direction and attempted to train its own reasoning model. Three months later, the results weren’t ideal. Later, in multiple settings, Zhu Wenjia, then head of ByteDance’s AI model department Seed, said “it was my mistake,” according to someone close to ByteDance’s senior management.

ByteDance’s model and product teams urgently convened to discuss countermeasures. The initial approach was to implement the capability in the frontend product first—that is, not making a breakthrough in the AI model from scratch, but first training/fine-tuning a smaller reasoning model for Doubao to quickly catch up—on one hand doing supervised fine-tuning with synthetic/annotated samples that included reasoning steps, and on the other hand trying DeepSeek’s data. However, the final results were poor, and they decided not to rush it but to carefully refine the foundation model.

After the Spring Festival, more and more internal ByteDance products considered integrating DeepSeek. At a product requirements meeting for Jimeng (ByteDance’s Sora-like video generator app), everyone assessed feasibility. People close to management told us that the legal and compliance teams had the strongest objections, believing that ByteDance’s products shouldn’t promote other companies’ models. The discussion yielded no conclusion, and finally the business leader made the call to continue integration.

In early February, former Google DeepMind Vice President Wu Yonghui joined ByteDance to lead fundamental theoretical research for AI models in the Seed department. Several algorithm and technical leaders who previously reported to Zhu Wenjia began reporting to Wu.

The person close to ByteDance’s senior management told us this was a long-planned adjustment unrelated to DeepSeek. ByteDance knows what kind of people to use at what time. Zhu Wenjia wasn’t a native AI talent, but in the early stages when ByteDance developed AI models and lacked sufficient influence to attract top practitioners, he was indeed the most suitable choice—understanding both technology and products, and having served as business number one; more importantly, he had sufficient rapport with group management.

When ByteDance’s senior management initially approached Zhu Wenjia to lead AI models, both sides had a consensus: ultimately someone who better understood AI should take the top position. “Wenjia had long been prepared to hand over at any time; it just happened that at this juncture, Yonghui arrived.”

Wu Yonghui largely continued ByteDance’s previous technical route, but after taking office, he led the breaking down of barriers between model departments and groups, achieving data sharing across various stages and teams. At a Seed all-hands meeting, Wu Yonghui emphasized the importance of long-term research, making it clear they would explore longer-cycle, uncertain, and bold topics.

Someone close to ByteDance’s senior management said ByteDance’s current infrastructure (engineering capability) is already stronger than any domestic company. But compared globally, the biggest problem is the lack of people like those at OpenAI who can propose directions and conduct frontier exploration, such as GPT-4o and Sora. “China hasn’t had real corporate research institutes in the past because private enterprises were too poor; now we can finally try.”

Unlike ByteDance, DeepSeek’s emergence allowed Tencent to see new opportunities.

Starting in 2023, Tencent’s investment in AI models was relatively cautious—it didn’t recruit AI talent on a large scale, nor did it actively stockpile computing power. It consistently emphasized externally that it valued AI’s practical applications more.

However, by the end of 2024, ByteDance’s Doubao daily active users had climbed to a high of 20 million, while Tencent Yuanbao only had a few hundred thousand, which made Tencent nervous.

Yuanbao not only launched a year later than Doubao, their initial positioning was also vastly different—Doubao’s goal was always to be an independent all-knowing, all-capable assistant; Yuanbao was Tencent’s product for testing Hunyuan LLM’s technology. So for a long time after its birth, Yuanbao was in a state of “seeking cooperation with various businesses within the company,” said an early Yuanbao strategy person.

At Tencent’s Binhai Headquarters Tower in Shenzhen, senior management discussed how to respond. Jiang Jie, Tencent Vice President and head of the Hunyuan LLM, issued a military order: “We must catch up to Doubao within six months.” After the meeting, Jiang Jie began recruiting a new number one for Yuanbao and expanding the team to rally forces.

Before joining Tencent, Jiang Jie was Alipay’s BI Chief Architect. At Tencent, he was responsible for Tencent’s big data platform and advertising technology system, but had never led a consumer-facing product; the Technology and Engineering Group (TEG) where Yuanbao belongs had never incubated a consumer product. Ultimately, Tencent decided to look for candidates elsewhere within the company.

A source told us that Wu Zurong, head of Tencent Meeting, and Zhang Xiaochao, head of QQ, were the two candidates senior management considered most suitable.

Both had experience building products from zero to one. The former had fought tough battles—during the pandemic, with a rhythm of stress testing at dawn and iterating during the day, he pushed Tencent Meeting to break out of its niche; the latter focused on social products, going from QQ to WeChat and back to QQ, and was also the first head of Video Accounts.

In large companies, innovative businesses are opportunities but also mean enormous risk. Finally, with the support of Dowson Tong, head of Tencent’s Cloud and Smart Industries Group (CSIG), Wu Zurong took on this task.

Wu Zurong’s appointment came at just the right time. A month later, during the Chinese New Year, a huge growth opportunity arrived. After the DeepSeek-R1 model appeared, Tencent became the most active large company in embracing it. Pony Ma personally pushed all businesses to integrate it. At the same time, Tencent began ordering GPUs to supplement computing power, ensuring DeepSeek could run smoothly within Tencent’s products.

After Yuanbao integrated the DeepSeek model, it leveraged the momentum to launch promotions, even advertising in county towns and rural areas. The result was meteoric—in one week, daily active users grew tenfold, approaching 2.6 million, later gradually climbing past 10 million, currently ranking among the top three domestically.

Alibaba appears to be the giant least affected by DeepSeek. Before DeepSeek emerged, its Qwen LLM was already one of the most popular models in the open-source community, even rivaling Meta’s Llama 3—in terms of downloads on distribution platforms like Hugging Face and developer ecosystem activity, it has long been stable in the global first tier.

As one of the few players in China with “full-stack capabilities,” the improvement in intelligence still allowed Alibaba to see a new future: AI models could serve as a trump card for Alibaba Cloud, using self-developed models to keep enterprise customers on the cloud and drive consumption and revenue from computing power and platform services—a path validated in the United States.

The self-developed chip business also has an opportunity to accelerate its entry into the market. According to our understanding, Alibaba’s PPU (Parallel Processing Unit) Zhenwu 810E has become one of the main chips in China’s new AI computing power market, finally receiving its first external major customer order in 2025. On January 22, 2026, Alibaba decided to support its chip company Pingtou Ge (T-Head) in pursuing an independent IPO in the future.

An Alibaba source revealed that in 2025, Alibaba’s senior management set the tone at an internal meeting that the new year would no longer prioritize e-commerce GMV growth as the top goal. Strategically, they decided to temporarily scale back categories like consumer electronics and Maotai that “can drive volume but have thin margins,” and invest more resources in categories like cosmetics and apparel that can better contribute revenue and profit. An Alibaba source analyzed that the underlying logic is—prioritize thickening revenue and profit, and invest the money earned more intensively in AI.

When AI finally has the opportunity to become infrastructure, the giants’ competition is no longer just about comparing models themselves, but quickly shifts toward the race for super gateways.

Doubao’s Ferocity: The Formation of a Super Gateway

Among the three giants, Tencent has a stable social moat and the world’s number one gaming business; Alibaba has rapidly growing cloud business beyond e-commerce. ByteDance’s foundation is almost tied to Douyin—this is its most core source of traffic and revenue, with advertising, e-commerce, and lifestyle services all relying on this product.

In 2023, the AI wave arrived. Old products like Toutiao and Douyin all launched new explorations. Zhang Nan, former head of Douyin, led the team to incubate the AI creation platform Jimeng, with her goal being to become the Douyin of the AI era. However, the product that first gave ByteDance hope of succeeding Douyin wasn’t them, but an AI assistant called Doubao.

Over the past two years, the landscape of AI super gateways has been constantly changing: in 2024, Moonshot’s Kimi initially showed its edge; in early 2025, the obscure DeepSeek made great strides to catch up, with downloads once topping the charts, and Tencent Yuanbao also squeezed into the top three; at the end of 2025, Alibaba’s Tongyi, renamed Qwen, made a renewed push. But throughout this process, Doubao has always been number one. By the end of 2025, it became China’s first AI product to break 100 million daily active users.

A ByteDance person said few people know that Doubao is a strategic-level product with relatively restrained advertising spending in ByteDance’s history. The choice stemmed from two reasons: early LLM capabilities weren’t strong enough, and after large-scale user acquisition, user retention wasn’t good.

Jimeng was similarly conservative in early advertising spending, with new users mainly from organic traffic. The team at most placed some cheap brand ads on Xiaohongshu and Bilibili. A person responsible for Jimeng’s growth told us that Jimeng’s advertising strategy wasn’t to first calculate “how much money a new user will make me in the future, how long they’ll stay,” but more like drawing a red line first: the advertising cost for acquiring a new user can’t exceed a certain amount—keep buying if below this line, stop if above.

Second, the larger the user base of AI products, the higher the cost, and current Chinese AI products have no clear commercialization path.

Since launching in 2023, Doubao’s strategy has undergone multiple transformations. ByteDance once tried introducing “supply side” and “consumption side” concepts in Doubao, guiding users to customize more bots (chatbots), then importing them to the recommendation page and distributing them to other users. But later they found that except for the official bot “Doubao,” other bots didn’t significantly impact the data. Later, the focus shifted to building it as an efficiency tool.

In early 2024, Moonshot’s Kimi became popular for its ultra-long text processing capability. At the same time, Kimi was also heavily advertising on Bilibili and Xiaohongshu. “We were also on the road, but didn’t expect to be preempted by a startup,” said someone responsible for Doubao’s long-text direction.

In fact, Kimi’s cost-effectiveness in buying users on these two community platforms was extremely low—dozens of yuan for one user, with customer acquisition costs even higher than some financial products, burning hundreds of thousands in a day. Doubao also began accelerating, with the algorithm team compressing the model iteration cycle to one version every three days.

Doubao’s leader is Zhu Jun (Alex), who once captured changes in young people’s social patterns on a train and later created Musical.ly, a short video product that became popular in the United States. People familiar with him describe him as an “internet poet.” He often writes his current state of mind in his Feishu signature—sometimes comparing himself to the “Nanke Prefect,” using allusions to express feelings about fame being like a dream; sometimes casually sharing a novel he recently read. Zhu Jun also has many romantic imaginations about AI—when ChatGPT seemed to define the AI assistant product form, he insisted that AI should be more “anthropomorphic” and “human.”

“We also joked about creating a Bot universe similar to the Marvel universe in Doubao, hoping users could find various companionship in it, jokingly calling it the ‘Xiaoning Universe’ at the time,” said a Doubao employee. Xiaoning is a Doubao Bot focused on emotional companionship, with the full name “Xiaoning Who Loves Chatting.”

At the end of 2024, the industry trend leans toward multimodality. Doubao launched the Seedream 2.0 model to strengthen text-to-image capabilities, video dialogue capabilities, and video generation capabilities. The previous year, it also launched real-time voice call functionality, with “emotion” as a key refinement direction—they traveled across the country to collect dialects, with accent granularity refined to district and county levels within cities; deliberately named Doubao’s default voice “Taozi,” which was also the voice actor’s online name; and did a series of stylized dialogue training on the model to give it a distinct personality.

In early 2025, videos of users creatively “voice-training” Doubao suddenly went viral on Douyin; a few months later, gameplay around Doubao for photo editing, group photos, and background changes became popular on Xiaohongshu. In half a year, Doubao pulled users’ imagination of AI from “deep conversation” back to more daily use. “It was so similar to Douyin back then—a fun feature appears, a group of creators and young people make it popular, ultimately forming viral spread,” said a Douyin person.

“We were all stunned because this wasn’t decided at all,” said a Doubao person. These gameplay features began bringing millions of new users to Doubao daily.

“The battlefield returned to Alex’s area of expertise,” said a Doubao person. Doubao tasted success and thus upgraded its strategy, beginning to accelerate “building a matrix”—because they weren’t sure which scenario would explode first, they had to try every scenario, gameplay, and feature. They knew that even though many feature points couldn’t withstand scrutiny and users would stop using them after playing for a while, they could slowly accumulate mindshare.

Zhu Jun and the team spent more effort recruiting product strategists, whose core job was to seek scenarios, gameplay, and features more proactively.

A Doubao business leader once provided a talent requirement document to the recruitment team, opening with a world-famous painting. “We just wanted to find people who could resonate with that painting, understand that painting,” said a person responsible for Doubao recruitment.

In the second half of 2025, as Doubao’s user numbers continued growing, the proportion of non-AI core users was also increasing. They had a characteristic: after opening Doubao, they rarely asked questions proactively, mostly clicking on preset questions that came with the system or simply chatting a few sentences. The team needed to accurately judge from the overall data which features truly had growth potential and whether users were satisfied with the generation results.

Doubao decided to adjust its user acquisition and growth pace, spending time accommodating new users and clarifying user needs. In the first round of competition, scale is always greater than efficiency, and user numbers are the only standard. But entering the second round, they gradually realized the importance of user quality.

“Doubao’s real challenge actually arrives after breaking 100 million daily active users,” said a Doubao person.

Yuanbao’s Catch-Up: From Engineering Debt to Product Rhythm

During the 2025 Spring Festival, Yuanbao completed a beautiful explosion. After the heat subsided, Tencent accelerated its catch-up.

After Yuanbao leader Wu Zurong took office, he pushed two things. First was team expansion—not only drawing core staff from Tencent Meeting, which he previously led, but also recruiting intensively from outside with a standard of “directly doubling salaries.” He personally appeared for important candidates, scheduling another meeting if one wasn’t enough, with extremely fast rhythm; second, he clearly benchmarked against leading competitors, first catching up to ChatGPT and Doubao in basic capabilities, not talking about differentiation but first filling shortcomings.

But organizational adjustments didn’t automatically clear historical problems. The engineering and data debt that Yuanbao accumulated in the Technology and Engineering Group (TEG) was carried into the Cloud and Smart Industries Group (CSIG).

Someone close to Yuanbao told us that in the past, TEG’s data cleaning and annotation in small model training wasn’t standardized. Taking the LBS intent recognition model as an example: when users ask “What’s good to eat nearby?”, this is clear intent; while “Any recommendations for good food?” is ambiguous intent.

If these two types of samples are mixed together in the training set, the model easily “learns incorrectly,” increasing the misclassification rate. The new team’s first step after taking over wasn’t to add models or stack parameters, but to return to the most basic engineering work—unify annotation caliber, rebuild evaluation sets, remove ambiguous samples, and then retrain all related small models according to the new data system.

After integrating DeepSeek, Yuanbao’s capabilities improved significantly. For example, when users search for “Yu Hua’s novels,” the team’s vision was for Yuanbao to pop up four reading cards while answering, allowing users to jump to WeChat Reading with one click; keywords in the answer body should also support underlining and link jumping. “Hunyuan’s two models often failed, but DeepSeek could perform stably,” said the Yuanbao person above.

But DeepSeek is after all an external model, making it difficult to help Yuanbao do specialized specific training. Tencent’s own Hunyuan large model has relatively weak capabilities and has its own technical improvement goals, also needing to chase leaderboard rankings. This resulted in the Yuanbao team being unable to validate some ideas on the product, with many desired features unable to be implemented.

A Yuanbao person gave an example: if Hunyuan’s new version tightened answer rules with the purpose of making the model “talk nonsense” less, this is often the correct choice in technical evaluation; but when it comes to a frontend product like Yuanbao, users might feel “the answers became colder, the experience actually got worse.”

Search is another challenge. Chatbot products don’t just compare whether models can accurately understand user intent, but also test whether they can quickly and clearly provide the right answer. Yuanbao once conducted internal evaluations showing Doubao’s search accuracy was considerably higher than Yuanbao’s. “ByteDance has years of accumulation in algorithmic recommendations; this is its advantage,” said the person above.

At the end of December 2025, with senior management coordination, Tencent transferred the Model Application Center and Search Algorithm Center, originally under the AI model team, to the Cloud and Smart Industries Group (CSIG) where Yuanbao belongs. Hunyuan and Yuanbao established joint design, cross-deployment, code review and sharing mechanisms to reduce mutual attrition.

But Yuanbao really came to the table too late. The team has calculated that Doubao currently has over forty basic feature points, and in a year, “we’ve only caught up to thirty-plus.”

They set a “three-step annual” rhythm for Yuanbao: first catch up to Doubao in a few advantageous scenarios, then achieve local superiority, and finally complete overtaking through innovation points. The North Star metric is very clear—the frequency of users inputting prompts, followed by the number of high-quality prompts. Only when these two metrics work can product innovation be meaningful. At the same time, Tencent is also accelerating the embedding of Yuanbao’s AI capabilities into national-level products like WeChat, QQ, Docs, and Meeting to amplify synergy effects.

Compared to Doubao’s “chaotic punches beating the master” approach, Yuanbao appears more restrained. It chooses to cut in from clear, controllable vertical scenarios—education, image generation, office, shopping. “The temperament is like an engineering man,” described a Yuanbao algorithm person. Tencent’s product approach is very classical: not aggressive, emphasizing experience, with each feature required to reach at least 80 points before being allowed to launch. This is an advantage; but the cost is also obvious—”AI needs to let imagination fly and even make many trial-and-error attempts to touch the technical boundaries and realize the value of technology, and this is precisely where Tencent is most cautious.”

“Ultimately it’s that Tencent’s traffic anxiety is far lower than ByteDance’s,” evaluated a ByteDance AI product manager. The latter seemingly has traffic engines like Douyin, Fanqie, and Toutiao, but the user profiles inside are very homogeneous, leaning toward entertainment attributes, with limited space for cross-business traffic. Meanwhile Tencent has leading products in almost every track—WeChat, QQ, Tencent Video, Tencent Meeting, QQ Browser, QQ Music—and can find whatever kind of users it wants. This is its confidence.

“Doubao and Yuanbao are at two extremes of the spectrum,” said a ByteDance person. One extremely free-flying, one extremely cautious—both could succeed.

“Doubao’s opportunity lies in running fast enough to maintain absolute leadership in speed; Tencent’s opportunity lies in having strong enough judgment to do the right thing at the right time.”

This is more like a public intelligence war. A Yuanbao person said: “We discuss a feature today, and Doubao can know about it the next day.” Doubao is also under pressure. “They’ve basically poached our people all over.” Sometimes, the winning move isn’t in a single feature point but in each group’s resource mobilization: Douyin’s mature stickers, effects and other content assets continuously contribute to Doubao’s image and video generation; Yuanbao turned to ecosystem collaboration, and after cooperating with “Honor of Kings,” activity clearly rose.

Compared to native AI applications like Doubao and Yuanbao, the transformation of older products like Alibaba’s Quark and Tencent’s QQ Browser is even more difficult.

A Quark person told us that the industry previously had a consensus: after Chatbot appeared, search scenarios would be the first to be disrupted. Based on this judgment, Alibaba believed that transforming the search experience starting from Quark browser was the smoothest path toward AI product forms. But in fact, Quark’s user mindshare is already very fixed—cloud storage, photo-based problem search, web browsing.

In 2025, Quark launched the concept of an AI super box, but the final result was that the vast majority of people using Quark’s AI capabilities were new users who came to try it after seeing promotions, while the original loyal users still used Quark’s old features according to past habits.

QQ Browser’s user profile is older and more downmarket than Quark’s, with even more solidified mindshare. They also discovered a new problem: QQ Browser launched an AI web assistant feature, hoping users could use it to interpret web content at any time while browsing. However, “high-quality content on web pages is extremely scarce, and the proportion and frequency of content needing interpretation are very low, far lower than WeChat Official Accounts,” said a QQ Browser person.

The transformation paths of the two browsers led to the same destination, ultimately having to seek other routes.

QQ Browser had planned to create a more lightweight, exploratory new browser product; at this time, another team within the department was also exploring an independent note-taking product—this was also one of the hottest AI product directions in Silicon Valley at the time. Ultimately, they decided to merge three capabilities—AI web assistant, lightweight AI browser, and AI note-taking product—into one product, thus giving birth to the intelligent office platform ima.

Under the push of Alibaba’s senior management, Qwen App (renamed from Tongyi App) replaced Quark as Alibaba’s core role in competing for the super gateway.

In earlier years, when this product was still in Tongyi Laboratory, it wasn’t the most watched product but was more like a technical testing ground: used to verify new capabilities, run evaluations, and make demos. Compared to building up its user base, the Tongyi team cared more about building up the ModelScope open-source community.

Some team members had tried many consumer-facing products, such as digital humans and image/video generation mini-programs, “but couldn’t get support,” said a former Tongyi Laboratory person. Even after being transferred to Alibaba Intelligent Information Business Group, its status was still inferior to Quark browser.

In November 2025, Qwen App finally ushered in a turning point in its fate. It began chasing Doubao and Yuanbao, with various businesses within the Alibaba system, including Ele.me Fresh and Fliggy, actively developing relevant capabilities for Qwen. In less than two months, Qwen App iterated more than a dozen times, maintaining an ultra-high frequency of 2-3 updates per week, with some requirements taking only 1-3 days from design to launch.

The Invisible Battlefield: Organization, Collaboration, and Internal Gaming

Giants’ wars affect the whole body when one part is pulled. Core AI departments lead the charge, but how far they can go depends to a certain extent on the support and cooperation of other business departments within the company.

At Tencent, WeChat’s traffic and ecosystem are the most critical resources. When the QQ Browser team was making the AI web assistant, they discovered that high-quality content on PC websites was too scarce, and users had no need to use AI to interpret content, so they partnered with WeChat to obtain Official Account API interfaces, allowing users to forward Official Account articles to the browser with one click for content analysis—this was the product’s biggest moat at the time.

As a new product, Yuanbao also needs to borrow power from Tencent’s other businesses, such as obtaining resources from WeChat, Music, Gaming, and Video; but sometimes, it also needs to provide its own value.

Tencent News was very proactive about cooperation with Yuanbao. A Tencent News product manager told us that after integrating Yuanbao, they found users really liked @ing Yuanbao in the comment section and liked having Yuanbao help interpret content, with activity greatly improved, so they began frequently urging Yuanbao to update and iterate quickly, saying they could provide any support needed.

But interests between departments always have inconsistencies: new businesses value gateways, resources, and growth speed; old businesses controlling traffic and budgets need both to defend interests and prove they can transform.

Doubao’s success is inseparable from Douyin. This is a super traffic gateway with over 800 million daily active users and is also the most efficient advertising platform for user acquisition and growth domestically. A Douyin person revealed that ByteDance’s internal products buying traffic on Douyin go through an internal settlement system, with advantages in both cost and efficiency.

But Douyin doesn’t always keep its doors wide open to Doubao. In 2024, Doubao hoped to obtain a more direct product gateway in Douyin, but ultimately wasn’t approved. The surface reason was: Douyin’s volume is too large, and Doubao needs to figure out itself which users to select and how large a scale to test. The two sides repeatedly discussed for several months without forming consensus. Finally, Douyin’s suggestion was—first try in products with smaller volume, like Kesong, a community product benchmarked against Xiaohongshu.

This ambiguous, restrained attitude reflects the complex competitive-cooperative relationship within ByteDance. On one hand, Douyin hopes to firmly grasp AI capabilities and key gateways related to AI within the app; on the other hand, this also means higher uncertainty.

Management style sometimes also determines the collaboration mode between different teams.

A Tongyi Laboratory person said the Qwen team grew up in a corner with almost no attention, but with less interruption and less pulling, the team could focus energy on the model itself’s iteration. This also gave the team stronger motivation for cross-boundary exploration. “Job scope is one thing, what you can actually do is another,” said the person above.

In 2025, the Qwen model team formed an embodied intelligence group; at the same time, some were also advancing directions like voice and text-to-image, while Tongyi Laboratory already had teams doing similar research internally. Team boundaries became more blurred.

According to our understanding, Qwen is also recruiting infrastructure talent responsible for engineering-related affairs. In infrastructure division of labor, Qwen has been collaborating with Alibaba Cloud’s artificial intelligence platform PAI: Qwen does more agile development within a unified framework, while PAI focuses on usability and platformization integration. “But both sides have their own leaders and pursue their own indicators, making it very difficult to truly work as one,” said a Qwen large model team person.

“Qwen appears to be silently swallowing more businesses,” said the former Tongyi Laboratory person. In 2025, after merging from DAMO Academy into Tongyi Laboratory, multiple technical leaders successively left, including Huang Fei, former head of natural language processing direction at Tongyi Laboratory, Yan Zhijie, former head of the speech team, and Bo Liefeng, former head of the applied vision team.

A Tencent person told us that Hunyuan’s current thinking is similar to Qwen’s. Yao Shunyu mentioned the Co-design (joint design) R&D model at a recent internal meeting: model R&D shouldn’t only pursue efficiency at the algorithm level but should connect infrastructure, algorithms, and the product side to form an integrated development process, thereby shortening iteration cycles and reducing internal friction.

Unlike Alibaba’s Qwen team which leans more toward “bottom-up” exploration of infrastructure and algorithm linkage, Tencent directly placed the AI Infra department under Yao Shunyu’s management system.

“Relying on self-consciousness for collaboration over time will turn into pulling, requiring a new variable to break the deadlock,” said a Tencent person. “Yao Shunyu is this external force.”

Talent Arms Race: “Are There Still Positions?”

In the AI race, talent is a key resource, and no giant hides its desire for them.

We mentioned in our article “Revealing ByteDance’s HR System: The Rise and Fall of China’s Internet’s Most Extreme Talent Factory” that one of ByteDance’s habits in recruitment is creating talent maps for fresh graduates—inventorying all core majors at the top dozens of universities nationwide, then obtaining contact information for all undergraduate, master’s, and doctoral fresh graduates from these schools and majors through various channels. The hard requirement for frontline HR is that outreach rates for key majors at key universities must be above 80% for undergraduates and above 90% for master’s students.

In the early catch-up phase, ByteDance would relatively loosely issue offers in batches when recruiting AI talent. “Using this method to build an organization may not guarantee the ceiling, but at least can align basic capabilities neatly,” said a ByteDance recruitment team person.

After the business got on track, they focused on the most excellent talent, with vision extended to the global scope—recruiting at least 10-20 of the best graduates globally each year. They also had a new standard for “excellent”—people who have the opportunity to squeeze into OpenAI’s top 40.

They also launched a plan to recruit entrepreneurs. “The internal judgment is that the vast majority of AI startups will fail,” said someone familiar with the plan, so whether it’s the company’s top management or the HR department, they will continuously communicate with excellent entrepreneurs, persuading them to join ByteDance.

Another question ByteDance repeatedly ponders is how to escape the gravity of an organization with hundreds of thousands of people.

ByteDance began discussing doing AI business in the manner of startups in early 2024—the organization should be more independent relative to the old system, and compensation should also be closed-loop.

According to our information, fresh graduates entering Seed may directly obtain higher job levels, with compensation standards possibly higher than traditional businesses; the AI department’s performance evaluation is every six months, with Seed Edge focusing on frontier scientific research having even longer evaluation cycles and no strict mid-process evaluations. They also specially designed Doubao shares—a virtual stock incentive system built around Doubao and related AI businesses, enhancing core talent retention through granting Doubao shares and supporting option buyback mechanisms.

But over the past two years, quite a few technical talents have still left Seed. They have some commonalities—mostly scientists with strong academic abilities but insufficient engineering capabilities, and relatively sensitive to organizational environment. A ByteDance employee said their biggest challenge is that group of young people in the company who have energy, desire, and want to climb up—this high-intensity atmosphere forces many technical talents to leave.

Zhou Chang, former technical head of Alibaba’s Qwen, is the most stable among this group of technical talents recruited by ByteDance. He currently leads multimodal interaction and world models at Seed. “Because Zhou Chang isn’t a scientist, but simultaneously has many pursuits for technology that exceed current practice,” said the ByteDance employee above. In 2025, Zhou Chang also began managing the vision foundation team and vision multimodal team.

This atmosphere creates poaching opportunities for opponents. “In most cases, technical bigwigs in large companies are hard to poach, but you can observe their organizational state. For example, when a company’s horse-racing culture is particularly severe, someone will definitely feel uncomfortable, and that’s the opportunity to make a move,” said a large company recruitment team person.

In 2025, Tencent became the biggest talent catcher among internet companies. According to our statistics, Tencent recruited over a dozen technical talents from Microsoft, Alibaba, ByteDance, Moonshot, OpenAI, DeepSeek and other companies in one year, completely changing its previously cautious and conservative image.

For Tencent, insufficient AI talent density is a long-standing problem.

A Tencent employee told us that Tencent in the past emphasized product engineering more, lacking native AI research teams, and even more lacking overall AI research. “Doing AI without a research team is like making products without product managers—you’ll ultimately lose direction.” Under this configuration, teams easily move toward “safe follow-up” and leaderboard-only results.

Hunyuan’s 3D model is a reverse example. A Tencent Hunyuan employee told us that its leader personally has clear technical judgment, has the ability to convince management to approve projects, and doesn’t have too strong performance catch-up pressure, ultimately delivering a good answer sheet. As of now, the Hunyuan 3D model has over 3 million community downloads, making it the world’s most popular 3D open-source model.

Since 2024, the technical recruitment team has frequently appeared at various top academic conferences, seeking the world’s top AI talent; departed executives from overseas major companies are also key targets they’ve locked onto. When there are suitable candidates, Tencent President Martin Lau will also personally meet with them to communicate and persuade them to join Tencent.

They initially met Yao Shunyu at a top academic conference in the United States and established contact. Yao had no intention of leaving OpenAI at the time, but the two sides maintained communication for over a year afterward. In 2025, Yao Shunyu decided to return to China. “He proactively contacted Martin (Martin Lau), and both sides hit it off immediately,” said the person above.

In mid-2025, Tencent reorganized its AI Lab (AI Laboratory), recruiting several technical talents in the AI field, forming an organization model centered on “researchers.” An AI Lab person told us that each researcher focuses on a direction related to AGI and ASI, leading teams to explore topics with longer cycles and higher uncertainty. They have no hard assessment indicators, and research doesn’t need to be bound to Hunyuan model or Tencent’s businesses, pursuing more technical influence. This is similar to ByteDance’s AI research department Seed Edge.

A person familiar with both ByteDance and Tencent’s recruitment systems said ByteDance first grabs people in, and as for what they’ll do after joining, they match later. But talents aren’t fools either—many times their calculation is simple, just five words: are there positions available. For example, when Yao Shunyu left OpenAI, Tencent happened to have a position, but at ByteDance and Alibaba it would be very difficult to find space to play.

“Recruiting a good talent requires the right time, place, and people,” said the person above.

Rebuilding the Tower of Babel: Will AI Make the World More Open or More Closed?

In 2010, Wired magazine editor-in-chief Chris Anderson and author Michael Wolff wrote a judgment for the era: “The Web is Dead.” At that time, people were leaving the open web and turning to increasingly independent and powerful Apps. For Google, this was almost a fatal prophecy—“That’s a world Google’s crawlers can’t reach, a world no longer ruled by HTML.”

But later facts proved that technological change doesn’t necessarily overthrow the old order. Not only did it not shake Google’s dominant position, it even forced the company to develop even stronger dominance. Google used Chrome to remake the browser into a system-level mobile gateway, making the web browsing experience approach native Apps as much as possible; Android helped it seize mobile distribution rights, extending search, browser, and advertising advantages to the mobile era.

Six years later, Chrome’s monthly active users reached 1 billion, and Wired magazine had to overturn its own conclusion: “Wait! The Web Isn’t Dead After All. Google Made Sure of It” Today, Google’s market value is close to $4 trillion, making it the world’s second-largest commercial company after Nvidia, and AI will most likely further consolidate or even amplify the advantages it has accumulated in the past.

In China, the AI war among giants is erupting exactly at the turning point of paradigm reshaping. On one hand, traditional internet innovation is weak with no new stories to tell. Among non-AI internet applications launched in the past five years, only two have broken 100 million daily active users: ByteDance’s Fanqie and Hongguo. And their success is essentially still an extension of the “Douyin methodology.” Giants more urgently hope to find new breakthrough.

On the other hand, large companies’ dominance has further solidified in the stock competition of recent years. Douyin’s traffic siphoning effect is increasingly obvious, Hongguo has formed nuclear deterrence against long video platforms, and Douyin E-commerce has ended the chaos of live-streaming sales. In the early AI era, giants’ leading advantage over other companies is greater than in the early mobile internet era.

In 2024, a LLM star entrepreneur felt pressure when meeting with a giant company founder. The two were competing head-to-head in the same track, and the other’s demand was also very direct—hoping he would give up entrepreneurship and join their camp. “Even if you have a 20% chance of success, I would choose to support you,” said that giant company founder.

Many originally believed that LLMs were as important as recommendation algorithms once were, but unlike Taobao and Douyin each guarding their own closed-source algorithms, Alibaba and DeepSeek have open-sourced LLMs, and entrepreneurs are only a few lines of code away from the most advanced intelligence. But reality presents another scene.

An AI entrepreneur calculated an account for us: ByteDance, Alibaba, and Tencent all have GPU scales basically above 100,000 cards. For startups, an 8-card H100 server’s monthly rent is about $10,000; by the conservative caliber of $10,000 annual cost per card, 100,000 cards would require about $1 billion investment per year—and this is only the minimum threshold to stay at the table when competing with giants.

The gap in data accumulation is even more significant. “Of all the data in the world, only 3% can be found on public clouds; the remaining 97% is either offline cold data or held in private enterprises,” said a ByteDance employee. “The reason ByteDance and Kuaishou are so strong in video models is precisely because they have data.”

From a scenario perspective, WeChat has a huge mini-program ecosystem, with large numbers of developers and merchants providing services, and even 70% of people’s government services can be completed on WeChat—this is WeChat’s natural advantage for Agent-ization. “These ecosystems most likely have little motivation to go to a new platform, sign a new set of agreements and build a new system,” said a WeChat emoloyee.

“Why doesn’t ByteDance invest much externally? Because it feels there’s nothing it can’t do,” said an investor at a leading angel investment fund.

Three years after the U.S. market was ignited by ChatGPT, hundreds of startups were established, raised funding, and explored new products, but so far there haven’t been enough good applications to bring AI close to everyone. On the contrary, giant Google’s Gemini, and ChatGPT deeply bound to giants, are swallowing more possibilities.

Over the past thirty-plus years, the scientific community and tech companies have continuously created new technologies that seemingly can deliver beautiful visions, but they still inevitably become alienated in commercial competition—the internet’s original intention was efficient interconnection, but in fact it cut the world more fragmented. Various platforms formed their own independent account, content, and recommendation systems, with algorithms continuously subdividing interests, ultimately pushing people into information cocoons and circles.

In December 2025, ByteDance released the Doubao Phone Assistant preview version, deeply embedding the Doubao large model into the phone system, bypassing various Apps, allowing users to complete operations that originally required repeated clicking with voice or light taps; a month later, Qwen App, which had been online for less than two months, announced high-profile integration with Taobao Flash Sale, Alipay, Fliggy, and Gaode. Its “task assistant” function can complete multi-step tasks for users, such as ordering food and making calls, organizing reports, processing financial documents, and even developing websites.

A ByteDance employee said that when users mainly obtain information or complete tasks through a dialogue box, on the surface it seems freer to do things with one sentence, but there are actually fewer control points determining what they can see, what they can do, and in what order various services appear, with more platform-determined rules emerging.

“Humans always try to unite everyone with a unified, grand plan, but perhaps evolution and development naturally resist singular order,” said the person above. “We may now be in the process of building another Tower of Babel.” In legend, the Tower of Babel was humanity’s “unification project” attempting to reach heaven using the same language, but God made people’s languages split and no longer understand each other, causing the project to collapse.

Anthropic CEO Dario Amodei wrote in “Machines of Loving Grace” that AI might bring humanity to a more humane, more abundant future. But he is equally vigilant: some people discussing AI risks in public discourse—not to mention AI company leaders—often describe the arrival of Artificial General Intelligence (AGI) like a “personal mission,” as if they want to lead humanity to salvation single-handedly like prophets.

“It’s dangerous to view companies as unilaterally shaping the world,” Dario Amodei said.

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At a high-profile AI event held on January 10, 2026, in Beijing, co-organized by Tsinghua University and Zhipu AI, a headline-making panel brought together leading voices from industry and academia.

The discussion featured Professor Tang Jie, chief scientist and co-founder of Zhipu AI; Professor Yang Qiang of HKUST; Lin Junyang, technical lead of Alibaba’s Qwen team; and Yao Shunyu, Tencent’s newly appointed chief AI scientist who was formerly an OpenAI researcher.

It was a rare moment in which several of China’s top AI figures appeared together in an open, public conversation. Li Guangmi, the CEO of Shixiang Technology, is one of China’s most active commentators and advocates for AI. I highly recommend his quarterly review of generative AI technologies with Benita Zhang Xiaojun.

The panel discussed how Chinese large (language) models can differentiate, the next paradigm for AGI such as autonomous learning, agent strategies, and the future direction of China’s AI ecosystem. For people interested in China’s AI industry, the discussion offered an unusually candid window into the current dynamics.

Of course the most headline-grabbing remarks came from Lin Junyang, who said Chinese companies have less than a 20% chance of becoming the most leading AI players in the next three to five years. In a separate speech at the event, Tang Jie added that the gap between the U.S. and China in AI is, in fact, continuing to widen.

Below is a full translation of the panel using Claude. The original transcript is sourced from Zhipu AI.

Li Guangmi (Moderator): I’m Guangmi, the moderator for the next panel. I was listening from the audience just now and had a few impressions.

First, Professor Tang has strong appeal—Tsinghua’s talent pool is excellent, not just domestically but overseas too; the proportion of Tsinghua alumni is very high.

Second, my impression from listening to several talks is: “not just following, not just open source”—everyone is exploring their own next paradigm, and not just coding; everyone is exploring their own product forms.

This timing is particularly interesting. 2025 is actually the year when Chinese open-source models “shine brightly.” The “Four Open-Source Masters开源四杰” (DeepSeek, Alibaba’s Qwen, Moonshot AI’s Kimi, Zhipu AI’s GLM) have achieved tremendous success globally, and coding has seen 10-20x growth over the past year. Even overseas, people are asking where Scaling has reached and whether new paradigms have emerged. So today’s event, and this panel discussing “where do we go from here,” is especially interesting.

1. How Will Chinese Large Models Differentiate?

(In China, people generally refer to large-size generative AI models, regardless of whether they are text, speech, video, or 3D, as large models大模型).

Li Guangmi (Moderator): Let’s start with the first interesting topic: differentiation. Silicon Valley companies are clearly differentiating. I think we can start the conversation with this “differentiation” theme.

Anthropic has been a huge inspiration for Chinese model companies. Despite fierce competition in Silicon Valley, they didn’t follow everyone else entirely but focused on enterprise, on coding, on agentic systems. So I’ve been thinking: what directions will Chinese models differentiate toward? I think differentiation is quite an interesting theme. I see Shunyu is online—Shunyu, why don’t you start by telling everyone what you’ve been busy with lately?

Yao Shunyu: Hello everyone. Am I appearing as a giant face at the venue right now? (Audience laughs) Sorry, I couldn’t make it to Beijing today in person, but I’m happy to participate in this event. Recently I’ve been busy building models and products—I think that’s just the normal state of doing AI. Coming back to China feels pretty good; the food is much better.

Li Guangmi (Moderator): Shunyu, can you expand on your thoughts about “model differentiation”? Silicon Valley is also differentiating—for example, Anthropic focused on coding, many Chinese models went open source, coding capabilities have also grown rapidly, and Google Gemini didn’t do everything either; it first excelled at multimodality. Your former employer focuses on consumer-facing products. Since you’ve experienced both China and the US, can you share your perspective? How are you thinking about differentiation going forward, whether for yourself or for other companies?

Yao Shunyu: I have two major observations. One is that To C and To B have clearly differentiated. The other is that the “vertical integration” path and the “model and application layer separation” path are also beginning to diverge.

Let me address the first point. Clearly, when people think of AI super apps now, they think of two: ChatGPT and Claude Code. You could consider them exemplars of To C and To B respectively. But what’s interesting is that when we use ChatGPT today versus last year, for most people most of the time, the perceived changes aren’t that dramatic anymore. But conversely, with Claude Code, the coding revolution perhaps hadn’t started a year ago, but this year—to put it somewhat dramatically—it’s already reshaping how the entire computer industry works. People are no longer writing code; they’re communicating with computers in English.

The key point is that for To C, most people most of the time don’t actually need such strong intelligence. Today’s ChatGPT compared to last year might be better at abstract algebra or solving Galois theory, but most people can’t feel it. Most people, especially in China, are still using it more like an enhanced search engine. Often you don’t know how to use it to activate its intelligence.

But for To B, it’s very clear that higher intelligence often means higher productivity, which means you can earn more money—everything is connected. Another obvious point for To B is that most of the time, many people are willing to use the strongest model. Maybe one model costs $200 per month, while the second-best or slightly worse model costs $50 or $20 per month. We’re finding that many people, at least in America, are willing to pay that premium for the best model. Because maybe their annual salary is $200,000, they have 10 tasks to do each day, and a very strong model like Opus 4.5 might get 8 or 9 out of 10 tasks right, while a weaker model might only get 5 or 6 right. The problem is, when you don’t know which 5 or 6 those are, you have to spend a lot of extra effort monitoring things. So in the To B market, the differentiation between strong models and slightly weaker ones will become increasingly pronounced. That’s the first observation.

The second observation is the difference between vertical integration and model-application layer separation. A good example might be ChatGPT Agent versus using Claude or Gemini with an application-layer product like Manus. In the past, people believed that if you had vertical integration capability, you’d definitely do better, but at least looking at today, that’s not necessarily true. First, the capabilities needed at the model layer and application layer are quite different. Especially for To B or productivity scenarios, larger pre-training is still a crucial thing, and this is indeed difficult for product companies to do. But to make good use of a really good model, or to enable such a model to have spillover capabilities, actually requires doing a lot of corresponding work at the application side or the environment side. So we’re finding that for To C applications, vertical integration still holds. Whether it’s ChatGPT or Doubao, the model and product are very tightly coupled for iterative development. But for To B, the trend seems to be the opposite. Models are becoming stronger, but there will also be more application-layer things wanting to leverage these good models to play a role in different productivity segments. These are my two observations.

Li Guangmi (Moderator): Let me follow up with Shunyu on one question. Since you have a new identity (Tencent), in the Chinese market going forward, what are your bets or priorities? What distinctive features or keywords can you share with everyone?

Yao Shunyu: Yes, I think Tencent is definitely a company with stronger To C DNA. So we’re thinking about how today’s large models or AI development can provide more value to users. But there’s a core consideration: we’ve found that many times our bottleneck on the To C end isn’t a larger model, or stronger reinforcement learning, or a stronger reward model—many times it’s additional context and environment.

An example I often give recently: suppose I want to ask “what should I eat today?” Whether you ask ChatGPT today, last year, or tomorrow, the experience will probably be poor. Because to make it better, you don’t need a larger model or stronger pre-training. The bottleneck for this question might be that you need more additional input, or context. For example, if it knows “ah, I’m particularly cold today, I need something warm,” and “I’m in this area today,” and maybe “my wife is somewhere else, what does she want to eat,” etc. Actually, answering such questions has more of a bottleneck in additional context. For instance, if I’ve chatted with my wife for many days, we can actually forward chat records from WeChat to Yuanbao, or if we can make good use of these additional inputs, it would actually bring a lot of extra value to users. This is our thinking on To C.

Then doing To B in China is indeed very difficult. The productivity revolution, including many Chinese companies today making coding agents, actually have to target overseas markets. In this regard, we’ll think about how to serve ourselves well first. One difference between startups doing coding and large companies doing coding is that large companies already have many application scenarios and various places where productivity needs to improve. If our model can do better in these places, not only will the model have its unique advantages, but more importantly, capturing data from more diverse real-world scenarios will be a very interesting thing. For example, Anthropic as a startup wanting to get more coding agent data needs to go through data vendors to label data. These data vendors need to use software engineers to figure out “what kind of data should I label?” The bottleneck there is that there are only so many data companies, only so many people, and diversity is limited. But if you’re a company with 100,000 people, there might be some interesting attempts at truly leveraging real-world data, rather than just relying on annotation vendors or distillation.

Li Guangmi (Moderator): Thanks, Shunyu. Let me cue Junyang next. How do you see Qwen’s future ecological niche or differentiation bet? Because you focused on the multimodal direction later. Previously, Alibaba Cloud was very strong in To B, so going forward, you also mentioned multimodality might be more To C-focused. How are you thinking about this?

Lin Junyang: Technically I can’t comment on the company’s strategy. But I don’t think companies necessarily have that many genetic divisions—each generation of people might shape these companies.

I also want to inject some of our own understanding of AGI into the next statement. Because I think today, whether To B or To C, we’re actually serving real humans. So the question we’re thinking about is how to make the human world better. Even if you make To C products, they’ll still differentiate. For example, OpenAI has become more like a platform today, but if you’re To C, ultimately who are the real users you want to serve?

Today there may be many AIs that lean more toward medical, more toward law, but it might form naturally. I’m willing to believe Anthropic probably didn’t say “today I think coding is really great, so I’ll bet on it.” Because I know they communicate with business very frequently. This might be something we ourselves haven’t done well enough, although we have huge advantages. Of course, it’s also possible that China’s SaaS market is indeed quite different from America’s—they communicate with customers very frequently and can easily discover these huge opportunities.

When I talk to many API vendors in America today, none of them expected coding token consumption to be this large. In China, it’s really not that large, at least from what I see. But in America, it’s basically all coding. I don’t think everyone could have bet on this. Today Anthropic is doing more finance-related things, which I think are also opportunities they saw while communicating with customers. So I think everyone’s differentiation might be natural differentiation. So I’m more willing to believe AGI should do what AGI should do, and let things develop naturally.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang, how do you view the differentiation question?

Yang Qiang: Regarding differentiation, I actually want to discuss the differentiation between industry and academia more. This spans both America and China. Academia has always been an observer, while industry has been leading and racing ahead madly. This has led many academics to also do industry work, like Professor Tang Jie. This is a good thing—just like how astrophysics initially started with observation, Galileo’s telescope, before Newton emerged. So in the next phase, when we have numerous stable large models entering a steady state, academia should catch up. What problems should we solve when we catch up? Issues that industry perhaps hasn’t had time to solve yet. This is also something I’ve been considering—where is the upper bound of intelligence? For example, given certain resources, computing resources or energy resources, how good can you do?

We can be more specific. For example, how do we allocate these resources? How much goes to training, how much to inference? I’ve been doing AI since the early 1990s and did a small experiment: if we have certain investment in memory, how much can that memory help reasoning? And will this help become counterproductive—for example, if we remember too much noise, will it actually interfere with reasoning? Is there a balance point? These questions are still applicable today and are fundamental issues.

Recently I’ve also been thinking about another problem. Those who studied computer science have all taken theory courses—there’s an important theorem called Gödel’s incompleteness theorem. Roughly speaking, a system, like our large model, cannot prove its own consistency; there must be some hallucinations that are impossible to eliminate. Maybe with more resources, it can eliminate more. The scientific question comes: how many resources can buy how much reduction in hallucinations? This is a balance point, very much like the risk-return balance in economics. So we also call it the “no free lunch theorem.” These things are particularly suitable for mathematics, algorithms, and industry to research together today, which harbors huge breakthroughs.

Professor Tang also mentioned continual learning just now. I think continual learning is a particularly good problem. It has a concept of time. In the continual learning process, you’ll find that if you chain different agents together, and each agent can’t achieve 100%, then after N agents, capability decreases exponentially.

How do we ensure it doesn’t decrease? I think humans are a sample. For example, the first day is learning, the second day you learn on top of the first day’s noise, and your capability will decline like a large model. But humans have a method to solve the decline: sleeping.

I recommend everyone read a book called “Why We Sleep.” It says sleeping every night is actually clearing noise, so the next day you can continue to improve accuracy rather than accumulating errors. These theoretical studies harbor a new computing paradigm. Today we might focus more on Transformer and Agentic Computing, but I think it’s necessary to do some new explorations. This is my answer—industry and academia need to align.

Li Guangmi (Moderator): Thank you, Professor Yang. Professor Tang, from an external perspective, Zhipu today looks more like it’s following the Anthropic path—coding is very strong, ranking high on leaderboards, including the Long Horizon agents you mentioned. How do you view this differentiation theme?

Tang Jie: I think we should return to the most fundamental question. In the early days, the most fundamental thing was indeed the foundation model and the upper bound of intelligence. In 2023, we were the first to launch Chat. The first thought at the time was to get it online quickly. Of course, later there were relevant unified regulations from the government, so we waited until August-September when everyone launched together. When everyone launched together, my first feeling was that about ten large models came online, and each one didn’t have that many users. Of course, today the differentiation is more severe.

After a year of thinking, I actually feel this might not truly solve the problem. My first prediction was that it would replace search. Today many people use models to replace search, but they haven’t replaced Google. Google actually revolutionized its own search instead. From this perspective, this battle has been over since DeepSeek came out.

So after DeepSeek came out, we should think about what the next bet is. What’s the next battle? I think the next battle must be to make AI do something. But what that something is can indeed be a bet. At that time, Guangmi even came to our place to exchange ideas. Later our team debated for many nights, and ultimately—you can call it our luck, but on the other hand, we also bet on coding. Later, we put all our energy into coding.

2. The Next Paradigm for AGI

Li Guangmi (Moderator): I think betting is a particularly interesting thing. Over the past year, China has not only been strong in open source, but everyone has made their own bets. Next comes a second interesting question. Pre-training has gone on for three years, and people say it’s reached 70-80% of its returns. RL reinforcement learning today has become consensus and may have reached 40-50% of its potential. Today Silicon Valley is also discussing the next new paradigm—Professor Tang just mentioned autonomous learning and self-learning. Let’s start with Shunyu.

You worked at OpenAI—how do you think about the next paradigm? OpenAI is a company that has advanced the first two paradigms for humanity. Regarding the third paradigm, from your perspective, can you share something with everyone?

Yao Shunyu: Autonomous learning is extremely hot in Silicon Valley right now—on Silicon Valley streets, in coffee shops, everyone is talking about it; it’s almost become consensus. But from my observation, each person’s view on this might be different. I’ll make two points:

First, I think the bottleneck for this isn’t methodology, but data or tasks. When we talk about autonomous learning, under what scenarios and based on what reward functions are we doing it? For example, when chatting, AI becoming personalized is a type of autonomous learning; when writing code, familiarizing with the company environment is autonomous learning; exploring new science, in the process becoming an expert from knowing nothing, like a PhD student, is also autonomous learning. Each type of autonomous learning might have different challenges or methodologies.

Second, I don’t know if this is non-consensus, but this is actually already happening. ChatGPT using user data to fit conversation styles, making the feeling better and better—isn’t that a kind of self-learning? Today Claude Code has already written 95% of Claude Code’s own project code, helping itself become better—isn’t that self-learning?

I remember when we were doing SWE-agent (Software Agent) in 2022-23, I went to AGI House to promote it, and the first slide in the introduction said the most important point of ASI is autonomous learning. Today’s AI systems essentially have two parts: one is the Neural Network (model); the other is the codebase: how you use this model, whether for reasoning or for agents.

Looking at the Claude Code system, it essentially has two parts: one part is the Opus Neural Network, the other part is a bunch of corresponding code for how to use this Network—whether it’s Kernel GPU, deployment environment, or frontend. If we’re doing Software Agents, if one day it can improve its own repo, isn’t that a kind of AGI? I think today Claude Code is already doing this on a large scale; people just don’t realize it, or it’s limited to specific scenarios. I think this is already happening; it might be more like a gradual change rather than a sudden change.

Li Guangmi (Moderator): Follow-up question. Some people are quite optimistic about autonomous learning, thinking we can see signals in 2026. What actual breakthroughs do you think are still needed? For example, Long Context, or model parallel sampling? From your perspective, what other key conditions need to be in place before these signals occur?

Yao Shunyu: Many people say we can only see these signals in 2026, but I think there were already some signals in 2025. For example, Cursor—their auto-complete model learns with the latest user data every few hours. Including the new Composer Model, it’s actually using real-environment data for training. Of course, people feel these things aren’t particularly earth-shattering yet—this is limited by them not having pre-training capability, so effects aren’t as good as Opus—but I think this is obviously already a signal.

I think the biggest bottleneck is imagination. For reinforcement learning or reasoning, we can easily imagine what implementation looks like—for example, O1 going from 10 points to 80 points on math problems. But if a paradigm happens in 2026 or 2027, announcing autonomous learning has been achieved, what task should we use to verify it? Is it becoming a money-making trading system, like an extension of Trading Bench? Or solving scientific problems? We need to first imagine what the blog post would look like at that time.

Li Guangmi (Moderator): Shunyu, OpenAI has already led two paradigm innovations. If there’s a new paradigm in 2026-27, globally, which company do you think has the highest probability of leading this innovative paradigm?

Yao Shunyu: Maybe OpenAI’s probability is still higher. Although its innovation genes have been weakened due to commercialization and other changes, I think it’s still the place most likely to give birth to a new paradigm.

Li Guangmi (Moderator): Junyang, what’s your expansion on the next paradigm, on 2026?

Lin Junyang: If we speak more practically, the paradigm just mentioned is still in early stages. RL compute hasn’t scaled that sufficiently, so a lot of potential hasn’t been realized—today we still see many infrastructure problems happening. Globally, similar problems still exist everywhere.

If we talk about the next generation paradigm, one is autonomous learning. We discussed earlier that “humans can’t make AI stronger”: if you continuously interact with AI, it will only make the context longer and dumber. Whether test-time scaling can truly happen, I think is really worth thinking about. Can it become stronger through more tokens? I think at least the O series has achieved this to some extent. Whether it’s possible, as Anthropic said, to “work for 30 hours” and truly accomplish very difficult tasks—I think what people are doing today with AI Scientists is actually quite meaningful, challenging some very difficult things, even things humans haven’t achieved. Can this be realized through test-time scaling? From this perspective, AI definitely needs autonomous evolution, but whether you need to update parameters—that’s debatable; everyone might have different technical means to achieve this.

I think there’s a second point: can AI achieve stronger initiative? That is, can the environment become my input signal? For example, this AI now must have humans prompt it before you can activate it. Is it possible that the environment itself can prompt it? It can think autonomously and do things.

But this raises a new problem: safety. I’m very worried about the safety problem—I’m not really worried about it saying things it shouldn’t say today; what I’m most worried about is it doing things it shouldn’t do. For example, if today it spontaneously generates ideas like throwing a bomb into this venue. We definitely don’t want these unsafe things to happen. But just like raising children, we might need to inject some correct direction. But active learning might also be quite an important paradigm.

Li Guangmi (Moderator): Yes. Junyang raised “active (learning),” which could also be a very critical bet for 2026.

Let me follow up with Junyang: If autonomous learning shows signals in 2026, what tasks do you expect to see it in first? Will it be “models training models,” where the strongest model can improve itself? Or automated AI researchers? Where do you expect to see it first?

Lin Junyang: I think automated AI researchers might not even need autonomous learning that much. I think “AI training AI” might be realized very soon. Looking at what our team members do every day, I think Claude Code could replace them very soon.

But I think it might be more about continuously understanding users—for example, personalization is actually quite important. In the past when we did recommendation systems, user information was continuously input, which made the entire system stronger. Although the algorithm was actually very simple, today in the AI era, can it understand you better? Can your information input become the best... We used to talk about Copilot, but actually even Copilot hasn’t been achieved today—the question is whether it can truly become my Copilot.

So I think if we talk about autonomous learning, it might happen in interaction with people. For example, personalization might be achievable, but what metric to use for measurement is a bit hard to say. Because in the recommendation era, the better your personalization, the more people might click and buy. But in the AI era, when it covers all aspects of human life, we don’t really know what the true personalization measurement metric is. So today I feel the bigger technical challenge might be: we don’t know how to do evaluation today. This might be a problem more worth researching.

Li Guangmi (Moderator): Junyang, since you mentioned active (learning) and personalization, do you think if we achieve “memory,” can we see a major technical breakthrough in 2026?

Lin Junyang: My personal view is that a lot of so-called “breakthroughs” in technology are actually observation problems—they’re actually linear developments; it’s just that human perception of them is very strong. Including the emergence of ChatGPT, for those of us working on large models, it was actually linear growth.

Now everyone is working on memory. Is the technical solution right or wrong? I think many solutions don’t really have right or wrong. But the effects produced—at least let me embarrass ourselves: our own memory seems to know what I did in the past, but it just recalls past things and calls my name every time, which doesn’t actually make you seem smart.

But can your memory reach a certain critical point where people feel that, combined with your memory, you can truly be like people in real life—remember how everyone talked about the movie “Her”? It’s truly like a person. Understanding your memory might be that moment when human perception suddenly feels it burst forth. That might be that moment.

I think it will take at least a year. Many times I actually think technology doesn’t develop that fast; it’s just that everyone is quite competitive and feels there are new things every day, but actually technology is developing linearly. We might just be in an exponential rise stage from an observation perspective. For example, a slight improvement in coding ability might bring a lot of production value, so people might feel AI is developing very fast. But from a technical progress perspective, we might have just done a little bit more. Looking at what we do every day—those bugs we fix—are really quite crude; we’re too embarrassed to share them with everyone. Very ugly.

If we can already achieve such results doing this, then I think if algorithms and infrastructure combine better in the future, there’s much more potential.

Li Guangmi (Moderator): Thanks. Professor Yang.

Yang Qiang: I’ve always been doing federated learning. The main idea of federated learning is “multiple centers, everyone collaborates.”

I’m increasingly seeing that many local resources are insufficient, but local data has many privacy and security requirements. So we can imagine that now large models are becoming more capable—how can these general-purpose large models collaborate with local specialized small models or domain expert models?

I think this kind of collaboration is becoming increasingly possible. Like in America, I see Zoom—what Xuedong Huang and his team did is called Federated AI system. He made a large base that everyone can plug into. Then he can work in a decentralized state, able to both protect privacy and effectively communicate and collaborate with general large models.

I think this open-source model is particularly good—one is knowledge open source, another is code open source. So I think especially in scenarios like healthcare and finance, we’ll increasingly see this phenomenon occur.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: I’m actually quite positive about significant paradigm innovation this year. I won’t go into too much detail because those points I... as I mentioned earlier, including continual learning, memory, even model architecture, even multimodality—I think all could see new paradigm changes.

But I think there’s a major trend. Let me talk about why such a paradigm would emerge. I think originally industry was running far faster than academia. I remember last year and the year before, when I returned to Tsinghua and chatted with many professors about whether they could do large models, many professors first had no GPUs... not that they had no GPUs, but the number of GPUs was almost zero. Industry had 10,000 cards, schools had 0 or 1 card—that’s a 10,000x multiple.

But now, many schools already have many cards, and many professors have started doing a lot of large model-related research, including many professors in Silicon Valley who have started researching model architecture, continual learning, and related topics. So it’s no longer... we used to always feel industry was dominating these things, but actually today I think at the end of 2025 to early 2026, this phenomenon no longer exists. There might still be a 10x difference—10,000 cards there, 1,000 cards here—but seeds have already been incubated. I think academia has this innovative gene, this possibility now. That’s the first point.

Second, I think for an innovation to emerge, there must be massive investment in something, and its efficiency becomes the bottleneck. Now in the entire large model space, investment is already huge, but efficiency isn’t high. That is, if we continue scaling, is there return? There’s definitely return. For example, our data originally might have been 10T in early 2025, now 30T, and we can even scale to 100T. But after you scale to 100T, how much is your return? And what’s your computing cost? This becomes a problem. If you don’t innovate, this might mean you spend 1 or 2 billion dollars, but the return is very small—not worth it.

On the other hand, for new intelligence innovation, if each time we have to retrain a foundation model, then retrain a lot of RL... Like when RL came out in 2024, everyone thought they’d just continue training, and the return was relatively good. But today if you continue crazily doing RL, there is return, but it’s not that significant anymore—still a return efficiency problem.

So in the future we might set a standard: on one hand, we need Scaling Up—I actually said on stage just now, “the dumbest method is Scaling,” because with Scaling we definitely have returns. This is a typical engineering approach. Scaling will definitely bring improvement to the upper bound of intelligence; there’s no doubt as long as you get more data. But the second method, I think we should set something called Intelligence Efficiency—that is, the efficiency of obtaining intelligence, how much investment we need to obtain intelligence increments. If we can use less investment to obtain increments, and we’ve now become a bottleneck—if we can use a new paradigm to obtain the same intelligence improvement, this is a bottleneck-type thing. So I feel 2026 will definitely have such a paradigm emerge.

Of course, we’re also betting, we’re also working hard, we hope this happens to us, but it might not.

Agent Strategy

Li Guangmi (Moderator): Right, I’m as optimistic as Professor Tang. Because actually each leading model company’s annual compute has compound growth of about 10x each year. Everyone has more computing resources, more and more talent is flowing in, everyone has more cards and does more experiments—it’s actually an experimental engineering project; some point might emerge.

Professor Tang also discussed a point about how to measure intelligence level. I think thirdly we can discuss Agent strategy together. Recently I’ve talked with many researchers, and everyone mentioned a big expectation for 2026: today Agents can reason in the background for 3-5 hours, doing 1-2 days of human work; everyone expects that in 2026 they can do a normal human’s work for a week to two weeks.

This is also a very big change because it’s no longer just a tool—as Professor Tang mentioned, not just Chat—but truly automating your entire day or even week’s task flow. So 2026 might really be the key year when Agents “create economic value.”

So we can expand on this Agent question. Everyone also mentioned “vertical integration” that Shunyu brought up—having both models and Agent products. Including seeing several companies in Silicon Valley also doing end-to-end from model to Agent.

Shunyu, you spent a lot of time on Agent research. For 2026’s Agents, like Long Horizon Agents that can truly automate one to two weeks of human work—this Agent strategy, including from a model company’s starting point, how would you think about this issue?

Yao Shunyu: I think it’s still like what I said earlier—it might be different for To C and To B.

Currently it looks like the To B situation has reached a continuously rising curve with no signs of slowing down. Anthropic is a very interesting company—it basically doesn’t do much innovation. It just thinks: model pre-training gets bigger, then honestly do RL well. As long as pre-training keeps getting bigger and post-training keeps doing real-world tasks well, it will get smarter and smarter, bringing more and more value.

I think what’s particularly interesting about Anthropic is that from a certain perspective, doing To B actually means all goals are more aligned. The higher your model intelligence, the more tasks you solve; the more tasks you solve, the greater the revenue in To B. This is great.

The problem with To C is that we all know DAU or product metrics are actually often unrelated to model intelligence, even opposite. I think this is another important reason Anthropic can focus: as long as they truly make the model better and better, revenue gets higher and higher—everything is completely aligned.

Currently I think productivity Agents are just beginning. Now besides models there might be two bottlenecks: one is the environment issue, or deployment issue.

Before OpenAI, I interned at a company called Sierra, a To B customer service company. Working at a To B company had many gains. The biggest gain was that I feel even if models today stopped improving, all model training completely stopped, but we deploy these models to companies all over the world, it could probably bring 10x or 100x today’s returns, or could have a 5-10% impact on GDP. But today I think its impact is far from 1%.

The second point is that education is very important. I observe that the gap between people is widening—it’s not that AI will replace people’s jobs, but rather that people who can use these tools are replacing those who can’t. Just like when computers were first invented, if you turned around to learn programming versus still using slide rules and abacuses, that’s a huge difference. I think the most meaningful thing China can do now is actually better education—teaching everyone how to better use products like Claude Code or ChatGPT. Of course Claude Code might not be usable in China, but we can use domestic models like Kimi or Zhipu.

Li Guangmi (Moderator): Thanks, Shunyu. Junyang, your thoughts on Agents, including because Qwen also has an ecosystem—Qwen doing Agents itself and supporting ecosystem general Agents—you can expand on this.

Lin Junyang: This involves a product philosophy question. Of course Manus is indeed very successful, but whether “wrapper” approaches are the future is itself a topic. I think at this timing today, I actually quite agree with your view: “model as product.”

I talked with TML people—they call it “Research as a Product.” I actually quite like this. Including looking at OpenAI, quite a few researchers themselves can become product managers to do things end-to-end. Today our own internal researchers all want to do more things facing the real world.

I’m actually willing to believe that Agents going forward can achieve what was just mentioned, and it has quite strong relationships with the self-evolution and active learning mentioned earlier. For example, if it can work for so long, it actually has to evolve in the process, and it has to decide what to do, because the instruction it receives is a very general task. So our Agents now are actually starting to become more “trusteeship-style” Agents, rather than the form where I have to constantly interact back and forth with you.

From this perspective, the requirements for models are actually very high—the model is the Agent, the Agent is the product itself. If they’re integrated, then doing foundation models today is actually doing this product. If we continuously improve the model’s capability ceiling, including if test-time scaling can improve, we can indeed achieve this.

But I think there’s another point related to environmental interaction. The environments we’re interacting with now aren’t very complex; they’re all computer environments. I have friends doing things more related to AI for Science. For example, today if you do AlphaFold, it’s not actually at that step yet. For drug development, even if you use today’s AI, it might not help you that much. Because you have to do wet lab experiments, you have to do these things to get feedback.

Is it possible that in the future AI’s environment complexity could be the real human world environment, commanding robots to do wet experiments to accelerate efficiency? Otherwise, according to current human efficiency, it’s actually very low. We even have to hire many outsourcers to do experiments in lab environments. If we can reach this point, that might be what I imagine Agents being able to do human work for a long time, rather than just writing files on computers.

I think these things might be completed very soon this year. But I think in the next three to five years, this might be more interesting. Then this would have to combine with embodied intelligence.

Li Guangmi (Moderator): I want to follow up with Junyang with a sharper question: From your perspective, is the opportunity for general Agents for entrepreneurs? Or is it a matter of time before model companies do general Agents well?

Lin Junyang: I can’t become a startup mentor just because I do foundation models; I can’t do that. So I can only borrow what Peak (Manus CTO) said—he said: what’s most interesting about general Agents is the long tail; that’s what’s more worth paying attention to, or what’s AI’s greater charm today is in the long tail.

If there’s a Matthew effect, the head stuff is actually quite easy to solve. When we did recommendations back then, we saw recommendations were very concentrated in head products. But we wanted to push the tail, but I was really miserable doing it then. As someone doing NLP and multimodality, encountering recommendation systems to solve the Matthew effect was basically heading toward a dead end.

But I think today’s so-called AGI is actually solving this problem—whether you’re doing general Agents, can you solve the long tail problem? Today as a user, I’ve searched everywhere and can’t find anyone who can help me solve this problem. But in that moment, I felt AI’s capability—anywhere in the world, I’ve searched everywhere and can’t find it, but you can help me solve it. That might be AI’s greatest charm.

So should you do general Agents? I think it depends. If you think you’re a “wrapper expert” who can wrap better than model companies, I think you can do it. But if you don’t have that confidence, this might be left for model companies to do “model as product,” because when they encounter problems, I just need to train the model a bit, burn some cards, and the problem might be solved. So it depends.

Li Guangmi (Moderator): Actually solving long tail problems, it seems model companies solving them with compute and data is also quite fast, right?

Lin Junyang: What’s most interesting about RL today, I think, is we found fixing problems is easier than before. It was very hard to fix problems before. I’ll give an example of a B-side customer situation—they said they wanted to do SFT themselves, can you tell us how to mix general data? Each time we were very troubled. And we felt the other party wasn’t very good at SFT; their data was very garbage, but they might think it’s very useful.

But today with RL, you might really only need a very small data point, not even needing annotation—as long as you have Query and Reward, train it a bit, merge it together—it’s actually very easy. That might be today’s technological charm.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang.

Yang Qiang: I think Agents should have four stages.

One is goal definition—is it defined by humans or automatically?

Second is planning—the actions in between. Planning can also be defined by humans or automatically by AI.

So this naturally divides into four stages. I think we’re now at a very elementary stage where goals are defined by humans and planning is done by humans. So current Agent definition systems are basically a higher-level Very High-level Programming Language.

But I predict in the future there will emerge large models observing human work, especially using human process data. Ultimately goals can also be defined by large models, planning can also be defined by large models. So Agents should be a native system endogenous to large models.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: I think indeed several factors determine Agents’ future direction.

First, does the Agent itself solve human problems? Is this thing valuable? How valuable? For example, when GPTs came out, many Agents were made. At that time you’d find Agents were all very simple, and ultimately found prompts solved it. Most of those Agents gradually died. So the first is how valuable is solving this thing, can it help people.

Second, what’s the cost of doing this? If the cost is particularly high, that’s also a problem. As Junyang said, maybe calling an API can solve it. But conversely, if calling an API can solve it, then that API itself might think this thing is very valuable and incorporate it. This is a contradiction—foundation and application are always contradictory.

The last dimension is application speed. If you say I have a time window, I can open a six-month time window, quickly meet the application, six months later either iterate or whatever, anyway can move forward. To put it bluntly, in the large model era until now, more is competing on speed, competing on time.

Maybe a decision is correct—like you just said maybe we bet on code correctly, then maybe we’ll go further in this regard. But maybe if the bet fails then it’s half a year, half a year is gone. So this year we only bet a little on Coding and Agents, and now our Coding call volume is quite good. So I think it’s more of a bet. Doing Agents in the future might also be a bet.

The Future of China AI

Li Guangmi (Moderator): Thanks. Because in the past model companies had to chase general capabilities, so priority-wise they didn’t spend that much energy exploring. Actually after general capabilities catch up, we’re also more expecting Zhipu and Qwen to have more of their own Claude Code moments and Manus moments in 2026. I think this is very worth anticipating.

The fourth question, also the last question, I think is quite interesting. Because this event, this timing, is more worth looking forward to the future.

I actually really want to ask everyone a question: three to five years from now, what’s the probability that the world’s leading AI company is a Chinese team? From today’s followers to future leaders, what culture and key conditions are still needed?

Shunyu, since you’ve experienced both Silicon Valley and China, what’s your judgment on this probability and what key conditions are needed?

Yao Shunyu: I think the probability is quite high. I’m still quite optimistic, because currently it seems that anything, once discovered, in China will quickly catch up or replicate, then do better locally in many areas. Including previous manufacturing, electric vehicles—such examples keep happening.

I think there might be several critical points. One might be whether China’s lithography machines can break through. If ultimately compute becomes the bottleneck, can we solve this compute problem? Currently we have very good electricity advantages, good infrastructure advantages. The main bottleneck might be production capacity, including lithography machines and software ecosystems. Solving this problem would be a big help.

Another question is whether, besides To C, we can have a more mature or better To B market, or whether there are opportunities to compete in the international business environment. Today we see many models doing productivity or To B are still born in America because payment willingness is stronger and To B culture is better. Doing this domestically is very difficult, so everyone chooses to go overseas or internationalize.

But I think what’s more important is the subjective concept. I recently talked with many people, and my intuitive feeling is: China actually has a lot of very strong talent. Anything, as long as it’s proven doable, many people will very actively try and even want to do better.

But it seems today in China, people who want to break through new paradigms or do very risky things might not be enough. This might have factors related to economic environment, business environment, and culture. But if we increase it a bit, can we subjectively have more people with entrepreneurial spirit or adventurous spirit who truly want to do frontier exploration or new paradigm breakthrough things?

Because currently, once a paradigm happens, we can use very few cards and high efficiency to catch up or even do better locally. But can we lead new paradigms? I think this might be today the only problem China needs to solve—in a sense the only problem to solve. Because everything else, whether business, product design, or this kind of catch-up engineering, we’ve already done better than America in some sense.

Li Guangmi (Moderator): Let me follow up with Shunyu: Do you have any calls regarding research culture in Chinese labs? You’ve also experienced OpenAI or Bay Area DeepMind research culture. What’s the difference between China and America? What fundamental impacts does this research culture have on being an AI Native company?

Yao Shunyu: I think research culture is very different everywhere. Differences between different labs in America might be bigger than differences between China and America. Same in China. I personally think there are two points:

First, in China people still prefer to do safer things. For example, today pre-training has been proven doable. This is actually also very difficult to do, with many technical problems to solve. But once proven doable, we’re all confident that in a few months or some time we can figure it out. But if today you ask someone to explore something like long-term memory or continual learning, everyone doesn’t know how to do it, whether it can be done. That I think is still quite difficult.

Of course, it’s not just that everyone prefers to do certain things. Very importantly, cultural accumulation and overall cognition is something that needs time to settle. Maybe at OpenAI doing RL started in 2022. Domestically maybe started in 2023, and understanding of this will differ. Or it seems China hasn’t scaled up RL this much. I think a lot is time issues. When you have deeper accumulated culture or foundation, it subtly influences people’s way of doing things.

Second, I think in China people look more heavily at leaderboards or numbers. On this point, overseas Anthropic does relatively well. Including DeepSeek also does well on this point—they might not care that much about leaderboard numbers; they might focus more on: first what’s the right thing, second what can you experience yourself as good or bad.

I think this is quite interesting. You see, Claude models might not be highest on many programming or software engineering leaderboards, but everyone knows this thing is the best to use. I think people still need to break free from leaderboard constraints and stick to what they think is correct or what is good.

Li Guangmi (Moderator): Thanks. Junyang, talk about China’s probability and conditions for winning.

Lin Junyang: Your question itself is a dangerous question. Technically in this setting you can’t pour cold water. But if we talk about probability, I want to mention the China-US differences I’ve felt.

For example, America’s compute might overall be 1-2 orders of magnitude larger than ours. But I see whether OpenAI or Anthropic, a large amount of their compute is actually just invested in next-generation research. We today are relatively stretched; just delivery might already occupy most compute. This will be a relatively big difference.

This might be a historical problem: does innovation happen in rich people’s hands or poor people’s hands?

Poor people aren’t without opportunities because we feel these “rich folks” really waste cards—they do so much ablation, maybe trained a lot that’s useless. But today if you’re poor, you’ll think about things like so-called algorithm-infrastructure joint optimization. Actually if you’re truly rich, you really don’t have much motivation to do this.

I think going further, Shunyu also mentioned the lithography machine problem. In the future there might also be a point: if from a software-hardware integration perspective, is it really possible to do it end-to-end? For example, our next generation model structure and chips might actually both be made together.

I particularly remember in 2021 when we were doing large models, Alibaba made chips and came to ask me: can you predict whether three years from now the model is still Transformer? Three years from now is the model multimodal? Why three years? He said we need three years to tape out. At that time I answered: three years from now, I don’t even know if I’ll still be at Alibaba. But ultimately today I’m still at Alibaba, and it really is still Transformer, still multimodal, and I really regretted why I didn’t push him to do it back then.

But our communication at that time was really “talking past each other”—he talked a bunch I didn’t understand, I talked and he didn’t know what we were doing, so we missed the opportunity. But could this opportunity come again? Although we’re a group of poor people, might poverty lead to change? The opportunity for innovation might happen here.

But I think what we might need to change is education. I feel, for example, I belong to the earlier 90s generation, Shunyu belongs to the later 90s, the team has many post-2000s—I feel everyone’s adventurous spirit is getting stronger and stronger. Americans naturally have very strong adventurous spirit. A typical example is when electric cars first came out, even with leaky roofs and possible accidental death while driving, many wealthy people were still willing to do this. But in China, wealthy people wouldn’t do this; everyone does safer things.

But today, everyone’s adventurous spirit is starting to get better, China’s business environment is also getting better—I think it’s possible to bring innovation. The probability isn’t that high, but it’s really possible.

Li Guangmi (Moderator): If we look at a number?

Lin Junyang: You mean what percentage?

Li Guangmi (Moderator): Yes, three to five years from now, the probability that the most leading company is a Chinese company.

Lin Junyang: I think below 20%. I think 20% is already very optimistic because there are really many historical accumulation reasons.

Li Guangmi (Moderator): Let me follow up with another question: is your inner fear of the gap widening strong? Some places are catching up, while other places compute is widening.

Lin Junyang: Today if you’re in this field you can’t be fearful; you must have a very chill mindset. From our mentality, being able to work in this field is already very good; being able to do large models is already very fortunate.

I think it still depends on what your original intention is. Shunyu mentioned a point earlier: your model might not necessarily be that strong, but in the C-side it’s actually OK. So I might switch to another perspective: what value does our model bring to human society? As long as I believe this thing can bring sufficient value to human society, can help humanity, even if it’s not the strongest, I’m willing to accept it.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang, you’ve experienced many AI cycles and seen many Chinese AI companies become world-class. What’s your judgment?

Yang Qiang: Reviewing internet development, it also started from America, but China caught up quickly, and applications like WeChat are world number one. I think AI is an enabling technology; it’s not an end product. But we in China have a lot of intelligence and talent that will bring products to their extremes, whether To B or To C. But I might be more optimistic about To C because a hundred flowers bloom; Chinese people pool wisdom.

But To B might have some specific limitations, like payment willingness, enterprise culture, etc.—these might also be changing. But I’ve also particularly observed some business aspects recently. For example, America has a company called Palantir. One of their concepts is: no matter what stage AI develops to, I can always discover some good things in AI to apply to enterprises. There’s definitely a gap in between that we need to bridge.

They have a method called “ontology,” using the ontology method. Actually I observed—the general idea is transfer learning we used to do, applying a general solution to specific practice, using an ontology for knowledge transfer. This method is very clever. It’s solved through an engineering method called FDE (Forward Deployed Engineer).

Anyway, I think this kind of thing is very worth learning. I think Chinese enterprises, AI Native companies, should develop such To B solutions. I believe they will. So I think To C will definitely bloom in diversity; To B might also catch up quickly.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: First I think we must admit that between China and America, whether in research, especially in enterprise AI labs, there is a gap. That’s the first point.

But I think China’s future is slowly getting better and better, especially this generation of post-90s and post-2000s rising up—it’s really far better than before. I once said at a YOCSEF meeting: our generation is the most unfortunate. Why? Look, the previous generation is still here, we’re still working now, haven’t had our day yet. Then very unfortunately the next generation has already emerged; the world has been handed to the next generation, skipping our generation seamlessly. Actually joking.

But I think what’s most interesting is that China’s opportunities in the future might be:

First, a group of smart people truly dare to do particularly risky things. I think this exists now—post-2000s, post-90s generation, including Junyang, Kimi, Shunyu—all are willing and very daring to take risks to do such big adventurous things.

Second, I think indeed the environment needs to be better. Whether from the national environment, like competition between large and small enterprises, issues between startups, including our business environment. Like Junyang said, still have to do delivery. I think building the environment better, letting a group of smart people who dare to take risks have more time to do innovative things. For example, letting people like Junyang have more time for innovation. That’s the second point, perhaps something our government including the nation can help improve.

Third, returning to each of us, is whether we can persist. Can we dare to do it, dare to take risks on one path, and the environment is also decent? I think the environment definitely won’t be the best; never expect the environment to be the best. But I think we’re perhaps also lucky—we’re experiencing an era where the environment is gradually improving from perhaps not being that good before. We’re experiencers, perhaps the people who harvest the most from this wealth. If we stupidly persist, perhaps those who make it to the end will be us. Thank you all.

Conclusion

Li Guangmi (Moderator): Thank you, Professor Tang. So we also really want to call for more resources and funding to be invested in China’s AGI industry. With more compute, letting more young AI researchers “burn cards,” maybe after three to five years of burning, China will also have 3-5 of our own Ilyas, right? This is what we very much look forward to in the next 3-5 years.

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