Jon Stokes (@jon_stokes) header
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Author. Coder. CTO. θηριομάχης. Building: https://xitter.cf/t.co/otXT4Wy6WR. Writing: https://xitter.cf/t.co/dBPBtyCIHw.

Austin, TX
Joined July 2008
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The FAA Telecommunications Infrastructure (FTI) is a nationwide, secure managed network managed that connects FAA facilities and supports daily air traffic operations across the U.S. National Airspace System. Why is it down? Some historical reasons below although the most popular ones among controllers messaging me are the new AI system is at fault or it’s a cyber attack.
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Jon Stokes retweeted
“All airspace is currently being sterilized and centers are preparing for ATC Zero operations. Airborne’s should NOT be majorly effected— i.e, at least for now, this is not a “9/11 type of event” where airbornes are being forced to land at the closest airport available. All airborne aircraft are being allowed to continue on their filed routes of flight.”
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Jon Stokes retweeted
Very good post from @lugaricano and @alexolegimas! "Exponential increases in capabilities requiring exponential increases in resources is at the heart of the original conceptualization of scaling laws. But the scaling laws are distinct from an intelligence explosion, which requires increasing returns to resources, i.e., fewer inputs to generate the same level of output." siliconcontinent.com/p/are-w…
Updated version!
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Jon Stokes retweeted
For context, all these Chinese AI training runs combined are order 1% of China's in-country AI compute, or equivalent to several weeks of Huawei's chip output. You could train all these models in parallel on maybe a 30MW Blackwell cluster. Call it 100MWs using Huawei. Chinese AI companies are incredibly compute efficient, because they have to be. I just don't see how export controls make much of a difference, to be honest. In the short term they're a small hassle for Chinese startups. In the long term they accelerate China's own AI hardware.
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 unemploymentlabs.com/checkpo…. 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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Jon Stokes retweeted
this is the exact issue. only a founder will take risks like this and absorb the pain it comes with they know that even they blow up, they've started from nothing once and they can do it again this is why companies die when the founder leaves
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Following his ouster as CEO of Nvidia Jensen Huang is reportedly considered for a high executive position at Mistral. Some experts, however, warn that for a high-profile position in France, two affairs might not be enough.
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Jon Stokes retweeted
so: - no succession plan. - 60 reports which means no obvious person with complete understanding of the co operates. or at least it’ll be insanely messy. potential for a lot of infighting. - a weak board suddenly tasked to manage a crazy crises w/ arguably the most important co of this era. - possible talent exodus since founder is leaving. - a ceo search that could take a while with no obvious outsider candidate at the top. - potentially more revelations. - jensen will have zero involvement going forward. no chairman role. - downstream impact on ai ecosystem from funding, support etc. this is not good. what am i missing?
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Jon Stokes retweeted
The real pressure on NVDA isn’t just the Jensen drama or macro noise, it’s Anthropic quietly driving the policy fight. Their incentives are structurally opposed. For Anthropic to win and justify a sky-high valuation, they need high-margin closed-source frontier models to dominate, with open-weight models heavily restricted or de facto banned (or at least sandboxed to the point they’re not competitive). Wide availability of strong open models collapses pricing power, enables local/self-hosted use, and turns inference into a commodity that anyone with GPUs can serve. For NVIDIA to keep compounding, the opposite is required: open models everywhere, running on as much hardware as possible. More open weights = more developers, more fine-tunes, more agents, more total tokens, more GPUs sold. Jensen’s public push for open weights and the Open Secure AI Alliance wasn’t charity, it was core to the business model. When one side needs the open ecosystem throttled and the other needs it to explode, the “safety” and “national security” arguments start looking a lot like competitive strategy. Show me the incentive and I’ll show you the outcome.
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This thread is astonishing. The most puzzling aspect yo me is not even the incompetence and paralysis of the autorities but the resignation and fatalism. It feels like reading some chronicle by a medieval peasant ...
There is an active outbreak of avian flu (H5 influenza) in dairy herds in Washington State, particularly the Yakima Valley. A number of dairy workers have been infected, with at least one death. Speaking with workers, we believe the official case numbers are a vast undercount. 1/
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I now assign some non-zero probability to the timeline where coding gets solved, solved swiftly, utterly, superexponentially, to the point that <30B models in Q2 2027 can zero-shot build operating systems – and then there's no "RSI" or singularity. basically Chollet's take
One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. "Future AI will have 10,000 IQ", that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like "making the tower taller", it's more like "making the ball rounder". At some point it's already pretty damn spherical and any improvement is marginal. Now of course smart humans aren't quite at the optimal bound yet on an individual level, and machines will have many advantages besides intelligence -- mostly the removal of biological bottlenecks: greater processing speed, unlimited working memory, unlimited memory with perfect recall... but these are mostly things humans can also access through externalized cognitive tools.
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I love that people want to assume that I formed my opinions on the viability of current generation open-weight LLMs for enabling low-skilled criminals to find useful zero-day exploits, based on having just never used an LLM at all. The first thing I did when Anthropic added me to their CVP was build an autonomous zero-day exploitation pipeline. I've since backported it to use open-weight models because the token cost was insane, so I have a fairly good feel for both current frontier and open-weight model capabilities. I'm well aware of what LLMs are capable of doing, and that they can, with the right steering or harness find zero-day exploits. Where we drift off course is with the claim that low-skilled criminals can just pick up something like GLM and find viable zero-day exploits. I think that: a) More than 90% of CVEs are functionally useless for threat actors. Threat actors have a very specific operational needs and "random issue in random software" does not fall into their scope. b) Many (quite possibly even the majority of) critical rated CVEs are not exploitable under real-world conditions in a way that is useful to threat actors. There's entire teams in China dedicated to n-day exploitation and watching them will give you a good feel for what percent of critical RCEs are actually useful. c) Threat actor with no exploit development background and minimal technical skills, even with access to a completely unrestricted open-weight LLMs, would struggle or completely fail to find useful zero-day exploits. d) People with very little experience in software exploitation, Large Language Models, and CTI, are watching professional vulnerability researchers working with next-gen frontier models and ending up with a wildly unrealistic beliefs of just how "easy" what they are doing is. e) There is an extremely noticeable lack of both zero-day exploitation and use of novel (non-public) n-day exploits by all threat actors, especially unskilled ones, and proponents of the "anyone can just pick up GLM and find useful zero-day exploits" theory continually refuse to even acknowledge that fact, let alone attempt to explain it. I'm not coming at this from the position of some dude who just logs on to social media to says uninformed shit. I'm coming at this from the perspective of a researcher who has spent thousands of hours since LLMs came out researching their capabilities, and has also spent their entire career tracking threat actors and their capabilities. I have a mountain of real-world data, because the low-skilled AI-powered threat actors have a high tendency to accidentally infect themselves with their own malware. This often results in them exfiltrate their entire hard disk, along with all their AI prompt logs, to a vibe-coded C2 with client-side authentication (the admin credentials are hardcoded into the javascript or something equally stupid, so anyone can log in and download it). My entire career I've been very pro "degrees and certs don't matter, everyone is welcome, and the best way to gain experience is hands-on practical work". But when I have some dude whose only experience is essentially just "downloaded abliterated GLM and scared himself with it", I feel like it's necessary to draw a line somewhere. It's not just the bad opinions, everyone has bad opinions on something, I have plenty. It's that resources are actively being diverted away from addressing real and pressing issues in cybersecurity, as a result of FUD being spread about imaginary GLM-powered zero-day wielding scriptkiddies.
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Huh
Reading both the Anthropic S-1 and Semianalysis research, the compute projections don't add up. S-1 gives $447.5B until 2030, which at $12bn per GW-year yields 5 GW at end-2026, 7 at 2027, and 13 by 2030. SemiAnalysis predicts 100+ GW of demand from Anthropic + OpenAI by 2030.
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Remind me, January 1st, 2028
Unfortunately, it seems like the intelligence explosion will be happening in 2027 after all. Most of this post analyzes the new METR report (though other sources of evidence are also important). If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario. I am scared, sad, and angry. blog.aifutures.org/p/q35-tim…
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A dead battery, flat tire, or getting stuck shouldn’t catch you unprepared. Here’s what I’d keep in the car: from roadside essentials and recovery gear to a couple of my favorite Wera tool sets.
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AI companies increasingly say they may soon reach recursive self-improvement (RSI). With RSI, AI capability progress could accelerate dramatically. This acceleration could happen inside AI companies before outsiders can notice. How close is RSI? Our new report reviews the evidence, turns key disagreements into forecasting questions, and gives initial LLM forecasts. 🧵
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Replying to @Jess_Riedel
Claude did do a clean install of Hermes subsequently, but only after giving me a homily about why one shouldn't use Sci-Hub. I don't think the default should be "Claude writes software that will only do things Claude thinks are morally and legally ok". None of our other tools work like that, and this kind of algorithmic enforcement predictably leads to significant overenforcement (cp earlier stuff with copyright law)
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Jon Stokes retweeted
The Jensen Huang saga reminds me of a story I recounted in my book about Neil Bush. During his divorce proceedings, he admitted to sleeping with Chinese women sent to his hotel rooms in Hong Kong: His wife’s lawyer said: “Mr. Bush, you have to admit it's a pretty remarkable thing for a man just to go to a hotel room door and open it and have a woman standing there and have sex with her.” Bush’s response: “It was very unusual.”
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ROSS DOUTHAT: In a previous lifetime. DONALD TRUMP: Yes. ROSS DOUTHAT: Do you feel you'd failed anyone, perhaps your ex- DONALD TRUMP: Kevin McAllister. ROSS DOUTHAT: Right, let's talk about Kevin. DONALD TRUMP: I pointed him to the lobby. ROSS DOUTHAT: Right, but Kevin had a rough time after that. DONALD TRUMP: Not in my hotel, his only issues in the hotel were a stomach ache from too much room service. But out there, in Dinkins's New York, it was a killing field.
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Jon Stokes retweeted
This community just lost: 1. A refurbished mall. At Rackspace they took over an old mall and it completely improved the local community. 2. A bunch of new business creation. 3. A ton of construction jobs and tax revenue. And a lot more. Sigh.
JUST IN: Maryland activist helps block a $5B AI data center planned for the site of an abandoned shopping mall that closed 24 years ago, after gathering over 25,000 signatures.
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Jon Stokes retweeted
Notably, von Neumann went on to point out that there’s no reason to think every behavior can be described precisely enough to do this, and that the simplest description of most objects may be the object itself.
how anthropic RL env engineers were moving in September 2026:
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