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ou’ll be deploying that many more PRs to production. This arises in the shipping of updates to native iOS and Android apps because every app update needs to go through Apple’s and Google’s manual approval processes which take hours or days to complete. Talking with Sulman Choudhry, Head of Engineering, ChatGPT, he explained how the app review bottleneck is affecting iteration speed. Sulman used to work at Facebook and remembers how the social media company sped up shipping mobile releases: “Back

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ey. OpenAI CEO Sam Altman said that embedding third-party evaluators is a solid step. SpaceX and Tesla founder Elon Musk echoed that oversight is long overdue. Demis Hassabis from Google DeepMind called the essay a step in the right direction. Microsoft CEO Satya Nadella emphasized that pursuing AI without keeping human benefit at the center isn’t worth doing. Tech investor Gavin Baker posted a great write-up below summarizing these industry responses Will Chinese labs follow the suit? Some soci

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aude. After years of using apps to track runs, I wanted something closer to an actual coach instead of just a collection of data points. I opened a Claude Project, connected it to Google Calendar, and gave it standing instructions with real guardrails. It has been my coach for the last six months and has helped me avoid injury while increasing my monthly mileage. It plans my week, logs every run from my watch data, catches when I’m ramping up too quickly, tracks how close my shoes are to retirem

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of the SDLC. Before Augment, Akshay spent several years at Uber advancing automated code review and repair systems, and conducted research across AWS’s Automated Reasoning Group, Google’s Android Static Analysis team, and UCLA. His work sits at the intersection of AI, software engineering, and programming-language theory. Related reading Factory AI vs Augment Cosmos: Which Agent Layer Do You Actually Need?; Software Factory vs DevOps: When Agents Do the Work; Software Factory Metrics: Measuring

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es rise to these off-balance sheet guarantees and backstops is the same one we described in The Front End Gets Crowded (institutional article). The Gigascalers (Amazon, Microsoft, Google, Meta, Oracle) have been gatekeeping the investment grade capital that the AI buildout runs on and have benefitted tremendously from the high spread between offtake price they are being charged, and spot/short-term price that they are selling at as well as services that sit on top. In response, Nvidia has been m

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or its XPU platform — a collection of different bits of intellectual property and access to semiconductor parts that allow it to build custom AI chips, the best-known of which are Google’s TPUs. It just signed a $30 billion deal with Apple to build “custom ASIC silicon products.” Apple was already a massive customer of Broadcom, which historically provided a good chunk of the wireless and radio frequency parts that you’d find inside iPhones and its other devices, representing at one point more t

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could see a scenario with substantial growth and modestly higher unemployment, or crazy growth and crazy unemployment. What people are saying: LSE economics professor Ben Moll and Google DeepMind’s director of AGI economics Alex Imas wrote an essay on Substack dismissing the predictions of double-digit GDP growth that Amodei and others have made. The economists think that such extreme forecasts would assume too much: Double-digit growth rests on several shaky assumptions, they write, including“m

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ing said, another example has emerged — Anthropic and OpenAI’s monstrous compute commitments, which now represent over $1.3 Trillion in revenue for hyperscalers and neoclouds like Google, Microsoft, Amazon, SpaceX, Hut8, SB Energy, Oracle, Cerebras, Nscale and Lambda. To be specific, per the Wall Street Journal, OpenAI projected to spend over $750 billion on compute through 2030 in July 2026 before it signed its deal with SB Energy (more info here), and per The Information’s research, Anthropic

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wen2.5 72B-Instruct 许可证或 Llama 许可证。DeepSeek 曾为 DeepSeek V3 使用自定义许可证，后来 R1 将其改为 MIT，这导致许多（中国）模型厂商在 2025 年采用 MIT 或 Apache 2.0 许可证。 2026 年，开放模型比以往任何时候都更具竞争力，这带来了两个有趣的发展：西方模型厂商采用开放许可证，Google 和 Meta 都转向了 Apache 2.0。然而，处于前沿的中国模型厂商正变得越来越严格：Kimi K3 附带的许可证要求提供推理或微调服务者签订商业协议，MiniMax M3 则要求超过收入门槛的协议，并规定了禁止使用的场景。 最新的例子是智谱的 GLM-5.3，它从 MIT（GLM-5.2 及更早版本）改为自定义许可证，对推理和微调服务提供商有如下条款： 如果被许可方或其任何关联方经营模型即服务（Model as a Service）业务，且被许可方及其关联方在任何连续 12 个月内的总收入超过 100 亿美元（或其他货币的等值金额），则被许可方在使用本软件或其衍生作品用于任何商业目的之前，必须通过 Z.AI 的

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circular financing to people, in the sense that some of the agreements are kind of clunky. NVIDIA funds OpenAI, who then spends that money to rent back NVIDIA GPUs from Microsoft, Google, Amazon, or CoreWeave, and then that money is used…to buy servers from Taiwanese ODMs (original design manufacturers) that build their servers, who then buy GPUs from NVIDIA to put in them. The reason it’s clunky is that people will, even if it’s not true, claim that there’s some indeterminately-large “other” su

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heaper and more abundant, demand for everything that goes well with it increases. This explains why companies will often “give away” things for “free”. Classic examples include 1) Google offering Chrome for free and open-sourcing Android, which increased access to the web and made search advertising more valuable and 2) Microsoft retaining the right to license MS-DOS to IBM’s competitors, which helped turn the PC into a commodity hardware platform. In Nvidia’s case, AI models and applications ar

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al London. Farage was, until Keir Starmer’s ouster, the bookies’ favorite to be the next UK Prime Minister. Meanwhile, in 2022, the largest tech companies in the world (Microsoft, Google, Meta, Amazon and NVIDIA) had hit a rough patch of flat-to-low growth after desperate measures had failed to help. While both the media and world governments were adept at making moves to sustain systemic thinking — that the system or systems know best and must be protected (we must prop up banks, we must make s

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ist and chief research officer at OpenAI; Dario Amodei, cofounder and CEO of Anthropic; Shengjia Zhao, chief scientist at Meta AI; Shane Legg, cofounder and chief AGI scientist at Google DeepMind; and John Schulman, the chief scientist at Thinking Machines. Looking at the METR report, it seems clear that AI-model capabilities have already advanced beyond our ability to understand and control them. Some of the signatories of “Pacing the Frontier” have noticed. And the challenge of aligning AI age

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I GPUs and associated hardware; Create demand for AI compute for its customers NVIDIA has succeeded in doing the first primarily by selling these GPUs to hyperscalers like Amazon, Google, Microsoft, Oracle, and Meta, who make up somewhere between 50% and 60% of its GPU sales depending on which analyst you ask. The rest comes from a mixture of unnamed “sovereign AI customers” and “neoclouds” — companies that exist to raise debt, buy NVIDIA GPUs, and put them in data centers to rent to theoretical

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er on revenue of $96 billion. CEO Jensen Huang projects 70% revenue growth for next year. Never has a company anywhere near that big grown anywhere near that fast. (By comparison, Google’s growth rate only once reached as high as 40% after it crossed the $20 billion annual revenue mark in 2008). And yet. The scale of Nvidia’s ambitions is also off the charts — and raises tricky questions for customers, investors, and policy-makers alike. Its $6 billion acquihire of much of the Poolside team, whi

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builder, and civic leader. And that wish led me to Clara Shih. Shih spent the past two decades building software for some of the world’s biggest tech firms. After early stints at Google and Salesforce, she founded and ran Hearsay Systems for a decade before returning to Salesforce to run Service Cloud. In 2023 she was named CEO of Salesforce AI, where she led the launch of the company’s agent platform, Agentforce. The next year she moved to Meta to build and run its business AI group, making ag

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s industry has become. Welcome to my Hater’s Manifesto. Want a great example of why everybody’s pissed off at technology? I just tried to resize the above heading, and in doing so Google Docs for no apparent reason decided to make the entire paragraph below the size of a header. Modern software is inherently broken, a convoluted mess of different menus, tech debt, and poor design choices driven by the Rot Economy’s growth-at-all-costs mindset which demands constant change at all times, none of w

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und its AI pivot, agreeing to sell gaming arm Lingxi for $1.5 billion. (Bloomberg); Alibaba said its open‑weight Qwen models have passed 3 billion downloads, overtaking Meta’s and Google’s open models. This is a milestone CNBC frames as a “tipping point” pulling investors back into China AI names. (Bloomberg/CNBC); China’s embodied‑AI sector raised ¥93.5 billion (roughly $13 billion) in the first half of 2026 alone, as the industry shifts from proof‑of‑concept demos toward scaled deployment. (虎嗅

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good enough prompt that it actually knows what to do in any given situation. Another thing is they're good enough at browser use, or computer use, that they can actually go into a Google Doc and make suggested changes, which is wild when you see it. Another thing is they're good enough now that I collected a dataset of 30,000 of her historical edits, used that to make a prompt, and then back-tested the prompt on all of the previous documents to hill-climb and make it better and better. Now we ha

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rosoft would be financing the chips. It’s unclear whether OpenAI would be on the hook for ongoing payments as Anthropic is under its $35 billion, private-credit funded deal to buy Google TPUs and then rent them back from Google; Per analyst notes from Wells Fargo, UBS and Barclays, OpenAI alone is expected to account for over $126 billion of Google, Amazon and Microsoft’s cloud revenues in the next year-and-a-half.The reason for the odd year-and-a-half designation is that Microsoft’s Fiscal Year

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