搜索“NVIDIA”

evenue in its latest quarter. On a trailing-twelve-month basis, Broadcom is exceptional among its peers for how little it invests in R&D as a percentage of revenue, beaten only by NVIDIA, which has the excuse that it is the literal largest and most-profitable company on the US stock market. That’s because Broadcom doesn’t really do “innovation” or care about “being good to its customers, but by hoarding other people’s patents, iterating on their creations as little as necessary, and making it im

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AI code-review startup CodeRabbit raised US $143 million at a $1.5 billion valuation, while claiming it performs more than 2 million reviews a week for 17,000 customers, including Nvidia, Indeed, and BMW Group. The new era of code review will determine whether AI can ever provide code that is both faster and more reliable. It also has some software engineers thinking about the future of their profession: If entry-level engineers spend less time writing code themselves, how will they learn to jud

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I also just did a two part Hater's Guide To Circular Financing, covering the depths of NVIDIA's circular madness and the history of a very dangerous kind of "financial innovation." Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. This week's premium will be The Hater’s Guide To Broadcom, a company that has long ceased to innovate, and whose existence centers on buying successful companies and jacking up prices, and n

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ven beforehand); they would tell you to be scaling pilled. Well, we didn’t actually see that many of them throwing away everything to get more compute, more data, and investing in NVIDIA call options. Similarly, people talk about being AGI pilled. Well, if that’s your belief, that AGI will hit in the next year or two with the current transformer paradigm, why aren’t you buying as much land as possible, or folding your little neolab startup and begging one of the AGI-leading labs to let you in? I

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You know, sometimes it’s kind of hard to explain the “circular” part of 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

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COMPUTE As the financial results have come out from the major suppliers of server components, we are getting a sense – finally – of how much larger demand is than supply. And in Nvidia’s discussion of its second quarter of fiscal 2026 results, Nvidia gave us some hints in its fiscal 2027 and 2028 years for everyone to contemplate and build their models. Back in April, when Intel reported its financial results for the first quarter of this year, the company said that the supply shortfall for its

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venue, with reports that Tencent’s free cash flow also swung negative on AI compute prepayments. (Bloomberg/SCMP/CNBC/Bloomberg) The US is working to close a loophole that has let Nvidia’s best chips keep reaching China despite the existing export ban. Nvidia denied a separate report that it plans to roll out a China‑specific chip by year‑end. China, meanwhile, eased its own restrictions on importing Nvidia’s H200, a notable loosening given how tightly Beijing has otherwise policed foreign‑chip

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fixed hardware investment. A 27B model sits in a sweet spot: it’s small enough to run locally on a well-equipped computer (24 64GB of RAM) with premium consumer-grade GPUs such as Nvidia’s RTX 5090, but smart enough to handle a wide range of real-world tasks. The Qwen 27B series in particular has always been popular, which is why last Friday’s release of Qwen3.8-27B was hailed as massive news for builders. What surprised almost everyone was the benchmark hit: the 27B model scored a staggering 52

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, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2). I've even done a two part Hater's Guide to NVIDIA. If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal. I’m not trying to be a buzzkill here, but I have meaningful concerns about OpenAI’s ability to surv

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ard into permissive licensing. (SCMP); Ant’s AGI lab open-sourced Ling-3.0-tiny (7.9B total / 1.3B active parameters), designed to run agentic RL post-training locally on a single Nvidia DGX Spark rather than a cluster. (@AntLingAGI); MiniMax released MiniMax-Music3, an open-weights, production-ready music generation model. (@MiniMax AI); Enterprise AI costs hit a 2026 low, per new research, as price wars and Chinese open-source models compress what companies pay to deploy AI. (SCMP) Funding Bey

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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 2,045M (across repositories with declared parameter counts, 2,061M including all repositories) , a

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ond half of my career and take part in some of that. Zhang Xiaojun: As I was researching this chip history, I found that ‘97 also had a big event in the chip industry—in April ‘97 Nvidia launched its third-generation chip, the NV3, and only then, after the failure of its first two generations, did it finally get a firm footing. Liao Heng: Did you notice that company at the time? No, I didn’t—I think back then they were a nobody. Actually, I think if we’re talking about chip history in ‘97, there

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ing news, alongside a fresh DeepSeek raise. The chip-export standoff kept escalating even as regulators moved on quieter fronts. Washington is reviewing China’s offshore access to Nvidia chips following recent Chinese AI breakthroughs. (Bloomberg); China tightened chip-design IP protections and clarified ownership rights, aiming to spur domestic semiconductor innovation. (SCMP); The EU’s AI Act transparency rules formally took effect. (36氪); China’s AI soft power is showing up furthest from home

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d’s top two AI labs, which insist on a closed-source approach and reportedly lobby for policies that could constrain open models. The other is a coalition of tech companies led by Nvidia, Microsoft and Meta, which are strongly in favor of open-weight models. They signed a letter last week warning against “premature restrictions” on open-weight models and launched an open AI security alliance, an organization building tools to defend against cyber attacks from frontier models. Both sides have jus

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O—one of China’s largest ever—sent shockwaves through the global chip trade, hitting Micron and SK Hynix shares and reviving the “China chip breakthrough” narrative that’s rattled Nvidia investors all year. Founder Zhu Yiming pledged $5.6 billion in worker bonuses out of his own windfall, and semiconductor-sector profits in China are reportedly up 2,500% in the first half on the AI boom. Nomura flagged room for the stock to keep rallying; the FT cautioned the surge “isn’t the bubble signal it mi

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n model finetuning service is making hundreds of millions in revenue per year and they’re releasing the best open-weight models built in the U.S.A. — ahead of the early leaders in NVIDIA with Nemotron and Arcee’s Trilogy. On the other side of the ecosystem is the sustained pace from the Chinese labs, with newer entrants like Xiaomi still accumulating mindshare in the broader AI economy. Having predicted consolidation for a long time, it now seems like a safer bet is to predict continued adoption

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t “anxiety” and a narrowing US-China gap. US Treasury Secretary Bessent floated sanctioning Chinese models over alleged AI “theft,” and White House official said Moonshot accessed Nvidia chips despite the export ban. Not everyone’s buying the panic. Independent researchers pushed back on US claims that K3 was built via distillation, and Deutsche Bank told clients not to expect Chinese models to displace US rivals anytime soon. (Bloomberg/SCMP/The Information) Riding that momentum, Moonshot is ra

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l far from that, we haven’t scaled to anywhere near that level. And scaling here includes scaling of data, scaling of model size, and training cost 05 Domestic Chips and Ecosystem Nvidia’s CUDA moat is being dismantled fast. Part of that is that we now have AI, and with AI, building up an ecosystem is far easier than before, because AI can write code; The compute-chip market is already bigger than the gaming-chip market, so there’s no reason for the two to stay coupled. The trend now is that the

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K3, which it's calling the world's largest open-weight model, and framed it as closing the gap with—and in some benchmarks rivaling—OpenAI and Anthropic. The release landed hard: Nvidia and other US Big Tech names sold off on renewed competitive fears, and the shockwaves compounded a brutal week for chip stocks. (Bloomberg/CNBC) CXMT prices Asia’s biggest listing of the year. Beyond Kimi K3 (above), the agent race kept accelerating this week. Alibaba and Honor announced a partnership to build a

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2 nanometer chips are more expensive than 3 nanometer chips, and certainly are riskier to make. But someone has to drive the ramp to better yields and AMD is doing it now just as Nvidia did it with 4 nanometer TSMC processes a few years back. Wei reminded everyone that it takes five to seven years to build these fabs, by the way. So it does not happen overnight. Which is why the annual capex for TSMC is smaller than the commitments to spend. To give you some perspective, TSMC spent $29.8 billio

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