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Effectively all of MiniMax's 2026 downloads are of models above 70B, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%. The difference becomes clearer in total downloads. Moonshot’s frontier-only portfolio recorded 37M downloads over the year, while Qwen’s broader release strategy across model sizes reached 2

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attack. For most of that time, OpenAI was completely oblivious. On May 8, as part of a training process, OpenAI asked a model to work on a spreadsheet that included links to some Google Drive files. The model couldn’t complete the task because its sandbox blocked access to Google Drive. So the model began probing the boundaries of its digital prison cell. OpenAI’s sandbox had a proxy server that acted as a virtual prison guard; it provided access to software packages the models might need for t

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l has one striking feature: when an important, groundbreaking invention or new business gets backing from capital markets, it can rapidly establish a near-monopoly advantage. Take Google, or Meta—Facebook in social, Google in search—including Windows before that, in the desktop operating system space, and Apple’s iPhone. They each rapidly—in maybe three or four years—established a monopoly-like advantage in their respective fields. That advantage isn’t just technological—more importantly, it’s c

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across Africa are increasingly building on cheap, customizable Chinese models — including an Alibaba model reportedly handling Ugandan languages better than anything from Meta or Google — as Chinese firms expand cloud, telecom, and government AI partnerships across the continent. (NYT); A wide-ranging Economist briefing warns that the AI push Beijing is racing to promote is also the one most likely to displace its own workers — it opens in Zhengzhou, hub of China’s ¥100 billion ($15 billion), r

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to similar platforms like Health Data Nexus as examples of PhysioNet’s legacy. Although there are now more resources out there hosting similar electronic health data, according to Google DeepMind researcher Vivek Natarajan, both PhysioNet and MIMIC “set the standard,” he says, “and it’s still the standard right now.” That standard, according to those who use the platform, changed how research is conducted. Access to data should not be the determinant for which ideas are possible, according to Zi

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n you compare two cars you compare cars in the same price bracket; Anthropic is now ahead of OpenAI—is that durable? I don’t think it’s durable, it’s certainly a phase. OpenAI and Google will most likely keep trading places going forward; In the global division of labor in AI, the role Chinese companies are quite likely to play is still the largest producer. By ordinary logic, our production capacity is the largest, including chips—our capacity in chips may be the largest, and we have the most e

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agnitude less capital than any slice of the American AI ecosystem. The most direct comparisons are to OpenAI and Anthropic, who have slightly better public models. Others, such as Google and Meta have the largest cash flows in the history of business, and are behind on building models. As for American neolabs, the picture is even more competitive – Thinking Machines released their first model recently, Inkling , which is strong but not in the same class as Kimi K3. These American companies with

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atbots can grow into products and don’t need to be wrapped in software. Maybe one lab will start out-executing all the others and pull ahead on sheer product dynamism - Microsoft, Google, Facebook and Apple had to execute their way into a wining position before they had winner-takes-all efforts. Maybe something else will happen. In particular, we have not one but two potential dei ex machina - Trump and China. China is reportedly considering regulating open source and some people close to Trump

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nearly doubled, from 251 in 2025 to 472 in 2026. Huawei Ascend 910 rose 56%, from 137 to 213. Apple moved from 741 to 998, overtaking AMD to become the most-cited non-NVIDIA, non-Google accelerator in the open literature, which means consumer Macs now out-cite the leading silicon challenger. That likely reflects the spread of local inference and developer workflows rather than frontier training. TPUs, meanwhile, declined 7% in the open paper data, despite Google’s obvious importance to frontier

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51美元的token）。同样，AI系统并非能通过所有测试，运行成本也不总是低廉，但它们确实在以非常快的速度进步。在我自己的实验中，我发现Fable能够自主工作9小时，执行非常复杂的软件项目，而这些项目原本需要一个团队花一周多的时间才能完成。 到目前为止，我关注的是前沿模型，即那些“智能”最高的模型。它们由三家美国公司制造——Anthropic、OpenAI和Google（不过Google已经有一段时间没有发布新模型了）。但还有第二类接近前沿的AI模型，通常比前沿落后6至12个月，它们全部来自中国。这些是开放权重模型，意味着任何人在发布后都可以使用或修改它们（与专有的前沿模型相对）。这使它们的运行成本相当低廉。它们同样在沿着指数级改进曲线攀升，尽管落后于美国的闭源模型。你可以从我制作的一张图表中看到这一点，该图展示的是AI在一项名为AA-Briefcase的测试中的表现，该测试模拟一个复杂的、为期数周的咨询项目，AI必须进行多种分析。开放权重的中国模型（其他国家也生产开放权重模型，但没有一个接近前沿）处于自己的指数曲线上，落后于美国的闭源模型 但抽象的图表只能说明这么多，它们可能掩盖前沿

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xperience clicking over and watching it to learn rather than trying to proceed forward with questions there. In that sense, I’m basically using it like a very souped-up version of Google to zero in on the right human-written resource. What about you? You engage with these a lot. What’s the best way to use them? Dwarkesh Patel I think you put your finger on it. The most productive learning sessions I’ve had are when there’s some artifact that a human has produced—whether it’s an article, a book,

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ecent Mythos episode has woken up some policymakers, which may lead to increased interest in sovereign model training. Big Tech : For Big Tech companies, including Alibaba’s Qwen, Google’s Gemma, and, to some extent, NVIDIA, the motivations are more diverse. Alibaba uses model releases to upsell its closed models, while NVIDIA benefits from a flourishing open model ecosystem as it increases interest in and usage of its GPUs. This vested interest is different from the Llama era of open Western mo

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r is measured in the time it takes to stand up a cluster, not a career. This should sting, because Europeans invented much of modern AI. DeepMind was founded in London and sold to Google in 2014, and a great deal of the talent that followed now lives in California. Today Europe faces a company worth almost $1tn and American tech giants spending an estimated $450bn a year on AI infrastructure. Its answer has been the EU AI Act and a capital commitment that is a rounding error by comparison. A sin

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as concerning, given that the whole AI community, including Anthropic, has benefitted tremendously from open research — indeed, the AI revolution was kicked off by my former team (Google Brain) freely publishing the Transformers paper! Imagine if Microsoft’s terms of use barred anyone from using their tools to build competitive software, or if Google barred using it to search for information to work on competing search engines. Anthropic’s argument that it was unsafe for others to be able to mak

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losely-related problems,” they wrote. ( 444 ). Centaur math discovery: Researchers with the University of British Columbia, University of New South Wales, Stanford University, and Google DeepMind published a new math proof which was built in close collaboration with some AI-based math tools built at Google. “The proofs of the main results were discovered with very substantial input from Google Gemini and related tools,” they wrote. ( 441 ). If you squint, you could argue that this is a sign that

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bout 20%. W. Edwards Deming, decades before that, articulated it even more cleanly: 90 to 95% of an organization's productivity output is determined by the system, not the worker. Google has rediscovered the same thing more recently. When they studied 180 teams to figure out what made the best ones work, the composition of the team didn't matter. The norms did. The same individual could thrive on one team and struggle on another. The strongest predictor of performance is the system, the manager,

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load pattern. Gusto to calendar. Task: put every employee's birthday in our shared calendar. Required navigating Gusto's employee tab, extracting dates from the DOM, then creating Google Calendar events. Azure admin roles. Azure's admin portal is a pile of blades inside iframes. Raw CDP, via coordinate-level Input.dispatchMouseEvent, passes through at the compositor level. Try it Setup prompt for Claude Code or Codex: First person to find a task it fails on (not captcha/2FA) gets a Mac Mini. Ser

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dence, and their tradeoffs. You're also missing what's happening outside of them. We've already seen how fast the ground shifts. OpenAI closed the gap faster than anyone expected; Google entered with competitive performance and dramatically different pricing; Benchmarks have tightened to the point where the differences between top models are increasingly hard to separate Teams are also paying closer attention to headlines around supply chain risk and security incidents across providers, not as d

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好的界面是什么样子？ 一种选择是为特定工作或任务构建专门的界面。在所有专门的 AI 界面中，真正完整的只有编程类界面。这完全在预料之中：AI 实验室的员工是程序员，模型在代码上接受了大量训练，而构建这些工具的人往往是为自己而建。 我之前写过 Claude Code，这是 Anthropic 的编程智能体，可以自主工作数小时。OpenAI 的 Codex 和 Google 的 Antigravity 也能做类似的事情。我用 Claude Code 做过各种事情，从赚（少量）钱到做游戏，完全没碰过任何代码。我也发现 Codex 极其有用，能力水平相近。这些工具非常出色，但它们确实是为程序员打造的。它们假定你懂 Python 和 Git。它们的界面看起来像 1980 年代的计算机实验室。对于并非开发者的 99% 知识工作者来说，这些强大的 AI 工具并没有为他们优化。 Pomelli、Stitch 和 NotebookLM 在所有 AI 实验室中，Google 似乎在为其他职业构建专门界面方面试验得最多。它们都还有些粗糙，但展示了当 AI 工具为其他类型的知识专业人士构建时，未来可能是什么样

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cashflows from existing businesses to lean on. Of course, companies that do have all of that need to be able to disrupt themselves, but we’re well past the point that people said Google couldn’t do AI. The fourth problem is expressed in the quotes I used above. Mike Krieger and Kevin Weil made similar points last year: when you’re head of product at an AI lab, you don’t control your roadmap. You have very limited ability to set product strategy. You open your email in the morning and discover t

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