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Everyone believes CUDA is NVIDIA's moat, the reason we've maintained our dominance in the AI era. I've never believed that. NVIDIA has only one true moat: its people. And the most important of them is Jensen Huang. I've worked on CUDA compilers, libraries, and DSLs for eight years, from Volta to Rubin. I think I've earned the right to share my perspective on what NVIDIA's moat really is. I hope you'll bear with me. 1. What is CUDA? Many people think CUDA is just a programming language and its compiler, and that NVIDIA's moat comes from the massive amount of software written in CUDA, making it impossible for developers to leave NVIDIA GPUs. This is completely wrong. It's an incredibly naive understanding of CUDA. To me, CUDA is everything NVIDIA does. CUDA isn't just a language, software, or hardware architecture. It's a product defined by a unique customer experience. When you find GPU programming too difficult, CUDA gives you an easier way. When your GPU code isn't fast enough, NVIDIA engineers respond and help you solve it. When a handful of GPUs can no longer push your technology forward, NVIDIA has already scaled its computing platform from desktops to massive data centers. When you need more data centers than anyone else can supply, NVIDIA has already secured wafer and HBM capacity years in advance to meet your demand. That experience is CUDA. It's made possible by the relentless, day-to-day work of everyone at NVIDIA. And the person who brings all these people together, who gives them a shared vision and purpose, is Jensen Huang. 2. What made CUDA possible? Patience. Perseverance. An unwavering commitment to a vision. Around 2006, NVIDIA bet that the future would demand far more computing power, and began transforming GPUs from graphics processors into a platform for general-purpose computing. Initially, it was a commercial disaster. The added costs hurt margins, the market wasn't ready, and the strategy faced enormous skepticism and pressure, including questions from the board. But Jensen persisted. For roughly a decade, NVIDIA kept investing in CUDA, building its hardware, software, and developer ecosystem long before the commercial payoff became clear. Eventually, deep learning arrived. And NVIDIA was ready. Today, this sounds like an inspiring story. But for those who lived through it, it meant a decade of perseverance, doubt, and being misunderstood. Looking back at the history of technology, how many people could have done the same? This is what I've always loved most about NVIDIA. And now, Jensen has been pushed out. This won't kill NVIDIA. The company can still create tremendous commercial value. But it has killed NVIDIA's soul. And how far can a body go without its soul? If this is how Jensen Huang is treated today, how will those of us who built CUDA be treated tomorrow? I love NVIDIA deeply, but I have to say it: NVIDIA is dead.

48 hours after launching 1.0, Modern Web Guidance is the #1# trending repo on GitHub 📈 Thank you to the thousands of web developers, and our partners across Firefox, MDN, Edge, and community contributors, who are helping move AI coding agents onto modern, accessible, Baseline-supported web primitives. If you haven't tried 1.0 on your codebase yet: Install the agent skills: 'npx modern-web-guidance@latest install' Audit an existing codebase: 'npx modern-web-guidance@latest legacy-scan .' Check out the repo on Github →

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

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Jacek from DEGEN shares his perspective on how rapidly AI is changing software development and what that could mean for the future. “Jacek has seen the progress firsthand. Work that once required hiring separate developers to build an iOS app could now be handled by a single engineer using modern AI tools. The pace of improvement over the last 18 months has been remarkable.” “He believes AI could eventually become far more capable than humans across almost every type of work. Looking ahead, he sees two possible paths: humans adapting to a world led by advanced AI or integrating more closely with it. Either way, he believes this transformation is coming much sooner than many expect.”

BoxLite is a masterpiece. Most AI sandbox discussions focus on speed and pricing. But there’s another question: Where should your agents actually run? With BoxLite, developers can embed hardware-isolated microVMs directly into their applications. > Open source. > Local-first runtime. > Built for untrusted code execution. Not every agent needs another cloud dependency.

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This is the biggest irony of the whole Alice Huang / Nvidia saga: the Chinese government actually doesn't want Nvidia chips and is actively keeping them out of the country. Last year it even ordered Alibaba and ByteDance to cancel their Nvidia orders ( and sent customs teams to the ports to check every advanced chip coming in ( And right now 600,000 Nvidia H200s are stuck in Hong Kong because Beijing won’t let them into the country ( Nvidia also explicitly frames this as a threat to its business in its latest annual report to the SEC: "The Chinese government has encouraged customers to purchase from our China-based competitors and discouraged customers from purchasing, importing, or using our data center products" ( Why does China do that? Because every Nvidia chip a Chinese company buys is one that Huawei or Cambricon doesn’t sell, and domestic chips can only catch up if people actually buy them (which means money for R&D, etc.). Which means that the notion that Chinese intelligence was running honeytraps to get more Nvidia chips into China gets the whole thing exactly backwards. In fact, it looks like Megaspeed - Alice Huang's company - was a counter-reaction to official Chinese policy: a private sector workaround to these efforts by the Chinese government to curtail Nvidia. As was widely reported - including by Western media - Megaspeed built data centers in Southeast Asia and rented their computing power to Alibaba, which the New York Times called "a backdoor way of getting access" to Nvidia chips ( That is a backdoor not only - allegedly - around Washington’s export controls, but also around China's own efforts to wean its companies off Nvidia. I can’t believe the Chinese government was too thrilled about it: it'd make just about zero sense for it to run countless domestic measures to prevent Alibaba from using Nvidia chips, yet at the same time have its top spy agency help the same Alibaba get Nvidia chips through the back door in Southeast Asia 🤷 At the end of the day, as with all such stories, you always need to look at "cui bono?": who benefits? Who benefitted is pretty clear: - Nvidia itself: it's in the business of selling chips so a customer willing to spend billions of dollars on chips in Southeast Asia is a godsend. Again, as they themselves state in their SEC filings, not being able to tap into Chinese demand would have "a material and adverse impact on our business" ( - Chinese companies like Alibaba: facts are, 1) Chinese chips haven't yet caught up, 2) Chinese chip companies' production capabilities are still limited and 3) Chinese tech firms' historical tech stack runs on Nvidia’s CUDA ecosystem, and rewriting years of code for Huawei chips is painful. Renting Nvidia chips in Southeast Asia is the easy way to stay competitive and serve customer demand in the meantime. - The US, according to core members of the Trump administration: back in July last year, US Commerce Secretary Howard Lutnick explained that the point of letting Nvidia sell more chips to China was to get Chinese developers "addicted to the American technology stack" ( Who does NOT benefit, according to official Chinese policy, is the Chinese state, which has made tech self-reliance one of its foremost priorities. Which means that, to believe the spy story, you have to believe that a person - Alice Huang - was effectively a spy paid by her own government while simultaneously sabotaging its tech self-reliance drive, which it has spent years and trillions of dollars on - pretty senseless. Stranger things have happened, I guess, but it doesn't pass the smell test. And there is another absolutely major contradiction in the media story. According to a Financial Times article published yesterday ( "a person who knows [Alice Huang] said they had been asked by MSS for information about her earlier this year after news emerged that Singapore was investigating the company in relation to chip-smuggling allegations. The agency wanted to find out more information about Huang, including the nature of her business and connections, the person said." Which raises a damn obvious question: if she were indeed an MSS asset, why on earth would the MSS need to ask the people around her who she was and the nature of her business? It's a complete contradiction with the spy story - but very much not a contradiction with what I described above: a private businesswoman running a workaround to Beijing’s own policy, which the Chinese security apparatus would naturally want to look into. Especially since, around about the same time, Shanghai police detained her business partner, reportedly her husband, in a stock manipulation case ( also coherent with Chinese authorities looking into her network, and much harder to square with her being a prized MSS asset. And, to top it all, there is - to this day - not a shred of evidence that's been published that she is, in fact, a Chinese spy: just anonymous intelligence officials, repeated by journalists who never verified it, as I detailed here ( and here ( with one of the key FT journalists on this story actually admitting that he has no idea what evidence US intelligence actually has. So the claim went to print without anyone seeing the evidence, without anyone being able to check it, and without anyone knowing whether it even exists. And given everything above, from Beijing blocking the very chips she was supposedly recruited to help get into China, to the MSS asking around about her, to her partner being detained in Shanghai, my bet is that the evidence doesn’t exist at all. If anything, everything we can check points the other way.