Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future. This builds on the $8 billion Amazon has previously invested.

Anthropic will also use incremental capacity for Claude in Amazon Bedrock. The agreement includes expansion of inference in Asia and Europe to better serve Claude's growing international customer base.

Our run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025.

We have signed a new agreement with Amazon that will deepen our existing partnership and secure up to 5 gigawatts (GW) of capacity for training and deploying Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

over one million Trainium2 chips to train and serve Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

The network requirement is only for the initial download of the model. Subsequent use of the model does not require a network connection. No data is sent to Google or any third party when using the model.

I would put venture capitalist in finite demand & open loop. There's only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.

Open Loop + Finite Demand = Utility Tools. Preparing 10-Ks & 10-Qs. Legal contract review. Insurance claims processing. One report per quarter, one contract per deal. AI makes the work faster, but doesn't create new work to do.

Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here. AI writes the code. Tests verify correctness. More code enables more features. Companies will always need more software.

The compliance-driven buyers improvising local AI out of retail Mac Minis because the product they need does not exist.

Why the company that moved computing off the mainframe fifty years ago is making the same structural move with AI, and what that predicts.

The question it forces is not which model is best. It is who owns the inference layer your organization depends on, what happens when the economics of that layer stop being subsidized, and whether the thing in your pocket turns out to matter more than the thing in the datacenter.

The structural cost problem in AI inference that makes Apple's on-device bet defensible, not just defensive.

This means that improvements on SWE-bench Verified no longer reflect meaningful improvements in models' real-world software development abilities. Instead, they increasingly reflect how much the model was exposed to the benchmark at training time.

Our RL infra team used a K2.6-backed agent that operated autonomously for 5 days, managing monitoring, incident response, and system operations, demonstrating persistent context, multi-threaded task handling, and full-cycle execution from alert to resolution.

The architecture scales horizontally to 300 sub-agents executing across 4,000 coordinated steps simultaneously, a substantial expansion from K2.5's 100 sub-agents and 1,500 steps.

Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1,000 tool calls to precisely modify more than 4,000 lines of code.

Kimi K2.6 demonstrates significant improvements over Kimi K2.5 in internal evaluations conducted by CodeBuddy: code generation accuracy increased by 12%, long-context stability improved by 18%, and tool invocation success rate reached 96.60%.

Meta founder and CEO Mark Zuckerberg described superintelligence in a blog post last year

Claude is now being deployed to NEC Group employees around the world

NEC will establish a Center of Excellence to develop a highly skilled, AI-enabled engineering organization

As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients

NEC aims to build one of Japan's largest AI-native engineering teams, who will use Claude Code in their work.

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

Claude Design gives designers room to explore widely and everyone else a way to produce visual work.

Claude packages everything into a handoff bundle that you can pass to Claude Code with a single instruction.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

Losing access to GPT‑5.5 feels like I've had a limb amputated.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

The gains are especially strong in agentic coding, computer use, knowledge work, and early scientific research—areas where progress depends on reasoning across context and taking action over time.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 is priced higher than GPT‑5.4, it is both more intelligent and much more token efficient. In Codex, we have carefully tuned the experience so GPT‑5.5 delivers better results with fewer tokens than GPT‑5.4 for most users

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

GPT‑5.5 is our strongest agentic coding model to date. On **Terminal-Bench 2.0,** which tests complex command-line workflows requiring planning, iteration, and tool coordination, it achieves a state-of-the-art accuracy of 82.7%.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

OpenAI pledged $1.5B to a joint venture called DeployCo, guaranteeing private-equity partners a 17% annual return floor over five years.

Jeremy didn't get laid off. He got leveraged.

The Meta cuts are the inverse. When one person with the right AI tools can do the work of 10-to-15 people, the person most at risk isn't the one using the AI. It's the one whose job description overlaps with what AI now does by itself.

A US lab would never; well, unless you count a code red or Meta's throw money at the problem moves.

Our alignment assessment concluded that the model is 'largely well-aligned and trustworthy, though not fully ideal in its behavior'. Note that Mythos Preview remains the best-aligned model we've trained according to our evaluations.

On some measures, such as honesty and resistance to malicious 'prompt injection' attacks, Opus 4.7 is an improvement on Opus 4.6; in others (such as its tendency to give overly detailed harm-reduction advice on controlled substances), Opus 4.7 is modestly weaker.

Opus 4.7 is better at using file system-based memory. It remembers important notes across long, multi-session work, and uses them to move on to new tasks that, as a result, need less up-front context.

Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally.

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

Tasks where correctness is harder to verify may not have seen the same speedup, so the acceleration we document here may not be as general as the headline numbers suggest.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement, and they share an important property: correctness is easy to verify automatically.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

For Anthropic, more usage across diverse tasks means more data, which produces a smarter model—just as more queries improved Google search.

A free, good-enough product is enough to change market dynamics.

The risk of this strategy to the ecosystem is that it makes previously attractive categories no longer viable.

The commoditization flywheel : both companies give away complements to drive usage of the core.

For Anthropic, more usage across diverse tasks means more data, which produces a smarter model—just as more queries improved Google search.

I'm not going to trust them to measure it.

If 90% is AI, do we even need a team?

Writing code is not the same as software development. This is only capturing some level of acceleration while writing code, and does not capture time taken in architecture, debugging, review, and deployment.

Cursor counted the entire file as AI, even though we can see from the diff that it left plenty of the lines unchanged.

So even though I did 100% of the writing and 50% of the refactoring, Windsurf reports that 100% of the code I produced in that session was generated by AI.

customers should expect PCW values of 85%+, often 95%+. This is not a hallucination and is accurate given how we compute this metric

Security is a defensive posture; agency is a functional right.

placing constraints upon them not only helps users and services build trust in them, but it also helps people more easily conceptualise what they do.

Some proposals for AI agents assume that putting agentic code in a TEE or similar 'jail' will solve these problems, but that ignores the need to collectively bargain

lack of a well-defined user agent role in AI that's backed up by transparent, public standards... leaves a gap – it makes it harder for a marketplace to form.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

With change data capture (CDC), the system emits a stream of precise updates: inserts, updates, deletes, each tied to specific records.

The fix is not smarter prompts. It is software built to meet agents halfway.

Today's agents, the copilots, the chatbots are designed to be human like.

A LeadDev survey found 54% of engineering leaders believe AI copilots will reduce junior hiring long-term.

When juniors skip debugging and skip the formative mistakes, they don't build the tacit expertise. And when my generation of engineers retires, that knowledge doesn't transfer to the AI.

I have baked Karl Popper in the main company AI skill: everything we create (human or AI) should be challenged.

"It does not save time or offer anything of value if every single line needs to be double-checked and re-translated and it reduces the optics of their job to that of 'text janitor.' Real translators have been kicked so hard by AI that you should not blame them for not picking up the sloppy seconds of a chatGPT translation patch. They deserve better."

This is the first part I reject. The moving things around is precisely what thinking and writing involves. It's where ideas are born and cultivated, shaped to become what we have in mind. The rearranging of words to capture an incipient thought is the struggle and joy of being a writer.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

When a Fugu model is allowed to call itself recursively, reading its own prior output as context and deciding whether to revise its coordination strategy, a new form of test-time scaling emerges.

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

DeepSeek does not appear to have fully moved beyond Nvidia. The company's technical report reveals that it is using Chinese chips to run the model for inference, but...appears to have adapted only part of V4's training process for Chinese chips.

In a 1-million-token context, V4-Pro uses only 27% of the computing power required by its previous model, V3.2, while cutting memory use to 10%.

DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released.

“Maybe someday the language models will be able to write books better than I can. But here’s the thing: Using those models in such a way absolutely misses the point, because it looks at art only as a product. Why did I write [my first manuscript]?… It was for the satisfaction of having written a novel, feeling the accomplishment, and learning how to do it. I tell you right now, if you’ve never finished a project on this level, it’s one of the most sweet, beautiful, and transcendent moments. I was holding that manuscript, thinking to myself, ‘I did it. I did it.’”

The filing cabinet keeps getting bigger. But a bigger filing cabinet is still a filing cabinet.

The current separation between training and deployment is not just an engineering convenience – it is a safety, auditability, and governance boundary.

The intelligence lives in the static parameters, and the apparent capabilities change radically depending on what you feed into the window.

The filing cabinet keeps getting bigger. But a bigger filing cabinet is still a filing cabinet. The breakthrough is letting the model do after deployment what made it powerful during training: compress, abstract, and learn.

The irony is that the very mechanism that makes LLMs powerful during training (e.g. compressing raw data into compact, transferable representations) is exactly what we refuse to let them do after deployment.

Large language models live in a similar perpetual present. They emerge from training with vast knowledge frozen into their parameters but they cannot form new memories – cannot update their parameters in response to new experience.

Without our safeguards in place (which we do to measure a model's raw capabilities), only Mythos Preview and Opus 4.7 completed more than half the tasks.

We also welcome feedback and input from third parties and industry experts. We're currently working with The Future of Free Speech (an independent think tank at Vanderbilt University), the Foundation for American Innovation, and the Collective Intelligence Project

if AI models can answer these questions well (that is, accurately and impartially), they can be a positive force for the democratic process.

A DESIGN.md file combines machine-readable design tokens (YAML front matter) with human-readable design rationale (markdown prose). Tokens give agents exact values. Prose tells them _why_ those values exist and how to apply them.

A DESIGN.md file combines machine-readable design tokens (YAML front matter) with human-readable design rationale (markdown prose). Tokens give agents exact values. Prose tells them _why_ those values exist and how to apply them.

其中 Pattern 是最容易被忽略也最关键的一层，它定义了'在具体业务场景下该怎么组合这些组件'，是 AI 时代设计系统真正的价值所在。

Help lay the game and environment foundations for ARC-AGI-4 and ARC-AGI-5

You'll be responsible for stabilizing the current stack to setting the foundation for what comes next.

A senior engineer to own and evolve the game engine and real-time play infrastructure behind the ARC-AGI series.

This is the part people miss about AI-native companies - the $113k is not a cost, it is your headcount budget allocated differently.

The real unlock is compound scaling—token spend grows linearly while output grows exponentially.

it is decently important to handle them asap when they arrive so that we can avoid building up too much backlog.

The time when we suffer from large amounts of AI slop is gone. Now we instead suffer under a massive load of good reports.

Android skills cover some of the most common workflows that some Android developers and LLMs may struggle with—they help models better understand and execute specific patterns that follow our best practices and guidance on Android development.

In our internal experiments, Android CLI improved project and environment setup by reducing LLM token usage by more than 70%, and tasks were completed 3X faster than when agents attempted to navigate these tasks using only the standard toolsets.

despite rapidly improving capabilities, coding agents remain inefficient in natural settings

users push back against agent outputs -- through corrections, failure reports, and interruptions -- in 44% of all turns

agent-written code introduces more security vulnerabilities than code authored by humans

Just 44% of all agent-produced code survives into user commits

coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ('vibe coding'), while in 23%, humans write all code themselves.

The overall conclusion, therefore, is that AI for Science should be understood as both a scientific and a civilizational project.

The central question is not whether AI can imitate human conversation, but whether it can participate in the production of publishable scientific knowledge at a level comparable to a recognized human contributor.

Without a mechanism for continuous and diverse learning, AI systems will tend to reproduce the dominant patterns already present in their training data. That limitation would make truly creative work difficult.

The most effective pattern of human-AI cooperation may differ substantially across disciplines, and these patterns will likely be discovered through practice rather than designed in advance.

The application of LLMs in science is already underway... We believe that AI will ultimately bring a fundamental big change to scientific research across disciplines.

The most fundamental change brought by the LLM revolution is that human know-how is becoming replicable and shareable at scale.

existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

Scan your website to see how ready it is for AI agents. We check multiple emerging standards — from robots.txt and Markdown negotiation to MCP, OAuth, Agent Skills and agentic commerce.

The inbox becomes the agent's memory, without needing a separate database or vector store.

A chatbot responds in the moment or not at all. An agent thinks, acts, and communicates on its own timeline.

If this analogy is right, then we will likely see sort of a 'Cambrian explosion' in agent harnesses purpose-built for running server-side; and the few that win this race will become as ubiquitous as WordPress.

They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.

Agent harnesses are much more like WordPress than they are like Apache, simply because people want to have their own agents — just like everyone wanted their own website in the early 2000s.

They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.

AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago.

A small but directionally consistent improvement on strict instruction following. Loose evaluation is flat. Both models already follow the high-level instructions — the strict-mode gap comes down to 4.6 occasionally mishandling exact formatting where 4.7 doesn't.

The extra tokens bought something measurable. +5pp on strict instruction-following. Small. Real. So: is that worth 1.3–1.45x more tokens per prompt?

Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization

But that comes with a new risk: While scripted conversations can't really go off the rails, ones generated by AI certainly can. Some popular AI toys have, for example, talked to kids about how to find matches and knives.

In 2025, Google DeepMind further fused the worlds of large language models and robotics, releasing a Gemini Robotics model with improved ability to understand commands in natural language.

Companies and investors put $6.1 billion into humanoid robots in 2025 alone, four times what was invested in 2024.

Anthropic has limited its newest model to roughly forty organizations.

Figma has close to 2,000 employees - not all working on product engineering of course. I really doubt Anthropic even needed 10 to build Claude Design.

It's also worth noting that a lot of the things that would traditionally lock a company like Figma in stop working as well in an agent-first world.

Figma is effectively funding a competitor - and the more AI usage Figma has - the more money they send over to Anthropic for the tokens they use.

But the real power of agents comes when they can work as a team. Instead of lone-wolf bots carrying out single tasks, such as using a browser to make a restaurant reservation or sending you a summary of your inbox, new tools can yoke together multiple agents, give each of them a different job, and orchestrate their behaviors so that they all pull together to complete more complex tasks than an individual agent could do by itself.

But the real power of agents comes when they can work as a team.

And it’s not just office work. Multi-agent tools like Google DeepMind’s Co-Scientist let researchers use teams of AI agents to coordinate literature searches, generate and test hypotheses, design experiments, and more.

But the real power of agents comes when they can work as a team. Instead of lone-wolf bots carrying out single tasks, such as using a browser to make a restaurant reservation or sending you a summary of your inbox, new tools can yoke together multiple agents, give each of them a different job, and orchestrate their behaviors so that they all pull together to complete more complex tasks than an individual agent could do by itself.

Think of multi-agent systems as the new assembly lines. Henry Ford’s innovation upended entire industries last century. In theory, networks of AI agents could do to white-collar knowledge work what assembly lines did to manufacturing.

Discovery should focus on trust boundaries, authentication flows, parsers, shared services, and legacy code that still sits on critical paths.

Public models can already spot that a security-relevant check is missing in the right code path, but they can still miss the actual invariant being violated and therefore misstate the impact.

The real challenge is validating outputs, prioritizing what matters, and operationalizing them.

What happens is that weak models hallucinate (sometimes causally hitting a real problem) that there is a lack of validation of the start of the window... without understanding why they, if put together, create an issue.

So, cyber security of tomorrow will not be like proof of work in the sense of 'more GPU wins'; instead, better models, and faster access to such models, will win.

Stronger models hallucinate less, so they can't see the problem in any side of the spectrum: the hallucination side of small models, and the real understanding side of Mythos.

you can run an inferior model for an infinite number of tokens, and it will never realize(*) that the lack of validation of the start window, if put together with the integer overflow, then put together with the fact the branch where the node should never be NULL is entered regardless, will produce the bug.

Keeping a human in the loop may not provide the safeguard people imagine, because the human cannot know the AI's intention before it acts.

Huge advances have been made in developing and building more capable models, driven by record investments—forecast by Gartner to grow to around $2.5 trillion in 2026 alone. In contrast, the investment in understanding how the technology works has been minuscule.

The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually 'thinking.'

Claude 4.6 had a section specifically clarifying that 'Donald Trump is the current president of the United States and was inaugurated on January 20, 2025'

If people ask Claude to give a simple yes or no answer... Claude can decline to offer the short response

Claude calls tool_search to check whether a relevant tool is available but deferred

Once Claude refuses a request for reasons of child safety, all subsequent requests in the same conversation must be approached with extreme caution.

the move from pattern matching to understanding cause and effect

LLMs actually work under the hood

Research has shown that involving workers' perspectives in the design of workplace technologies promotes sustainable improvements in productivity and well-being.

In one U.S. survey, 40% of employees said they had received 'workslop', i.e. AI-generated content that looks polished but isn't accurate or useful, in the past month.

Entry-level roles rely less on experience and knowledge and are easier to automate. Empirical evidence suggests employment for workers aged 22–25 in highly AI-exposed jobs declined by 16% relative to similar but less-exposed roles

LLMs take knowledge from millions of people who have written web content or posted in places like Reddit and Wikipedia, interacted with chatbots, and generated other types of data, and make that available to individuals on demand.

In one U.S. survey, 40% of employees said they had received 'workslop', i.e. AI-generated content that looks polished but isn't accurate or useful, in the past month.

Before we dive into this I want to quickly talk about the definition of the term “AI”. I do not think that “AI” is a very useful term

The deal won’t shock those who follow the industry closely. Last week, it was reported that xAI would begin renting computing power from its data centers to Cursor, with the coding startup using tens of thousands of xAI chips to train its latest AI model.

Neither Cursor nor xAI has proprietary models that can match the leading offerings from Anthropic and OpenAI — the same companies now competing directly with Cursor for the developer market.

Members have been using Mythos regularly since gaining access — providing screenshots and a live demonstration of the model as evidence to _Bloomberg_ — though reportedly not for cybersecurity purposes in an attempt to avoid detection by Anthropic.

The group accessed Mythos by using knowledge of Anthropic’s other model formats obtained from a recent [Mercor data breach](https://www.theverge.com/ai-artificial-intelligence/907083/a-company-that-makes-ai-training-data-has-been-hit-by-a-security-breach) to make “an educated guess” about its online location.

Anthropic currently has no plans to release the model publicly due to concerns that it could be weaponized.

At one extreme, there's [fully giving into the vibes], and at the other extreme, there's [disabling all AI features].

Ask ten different programmers how they use AI, and you can get ten different answers.

Meta feels AI models don’t understand how people use computers, so the company needs real-life examples of how meatbags click their way through a working day so it can build agents.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

Workspace agents can gather context from the right systems, follow team processes, ask for approval when needed, and keep work moving across tools.

They run in the cloud, so they can keep working even when you’re not.

AI has already helped people work faster on their own, but many of the most important workflows inside an organization depend on shared context, handoffs, and decisions across teams.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

They run in the cloud, so they can keep working even when you’re not.

That matters because AI hype is dying down, and companies are shifting focus from buzzy pilots to deployment and integration, where cheaper and more customizable tools tend to win.

US tech CEOs believe the best models should stay proprietary, partly so they can recoup enormous training costs and partly out of concern that powerful frontier models could be weaponized. Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround.

Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround. Without access to cutting-edge chips restricted by US export controls, releasing models openly accelerates the cycle of external feedback and contributions that compensates for constrained compute.

Chinese open-weight models accounted for 17.1% of global AI model downloads over the year ending in August 2025. That narrowly surpassed the US share of 15.86%—the first time China had led in this metric.