Simple, the Trump admin cannot roll out new restrictions/export controls targeting China because the Chinese can/will retaliate We are not in the H100 era, where the supply chain was largely concentrated in Taiwan/Korea/Japan Becuz of shortages, Nvidia & hyperscalers have been forced to qualify Chinese suppliers, especially in the PCB supply chain and electrical components like transformers China had a chokehold on optics (optical fibers and transceivers) from the beginning, and this is just getting amplified as optical content in DCs is increasing Coherent CEO went to China with the Trump delegation, asking for InP for lasers I want u guys to study the optical fiber preform supply chain, and who are the largest suppliers Btw, Chinese exposure is also spreading to other parts of the AI supply chain High end MLCCs use Dysprosium Oxide and China supplies most of it to Japanese producers Tungsten ban from China is causing the prices of WF6 gases to shoot up If PTFE is finalized for M9/M10 CCL, then Shengyi and Chinese PTFE suppliers will have a huge chokehold over Nvidia Google is in talks with Envicool for the supply of cooling components If diamond-copper composites are adopted as heat spreaders for GPUs, then China will establish a chokehold there as well, since most of the synthetic diamonds are produced there and China is at the cutting edge of this tech I haven't even talked about the use cases of gallium, germanium, tellurium, antimony, bismuth, fluorine, terbium, yttrium, ferrite cores in the AI supply chain, and how China has a chokehold there The Trump admin is constrained in a lot of ways and can't unilaterally export control stuff

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

Uhhh so incidentally, does anyone have a plan to prevent all the non-US citizen AI scientists from going to join foreign labs after they get bored of playing Wordle at work for a month, or are we just sort of planning on having the greatest counterproliferation failure since we deported Qian Xuesen in 1955 and gave Mao a rocket program?

This ugly reality is staring us right in the face: Americans will have walled AI gardens where we beg for access from a few East India companies, open source will get banned on national security grounds, and 6 billion people who aren't American, aka the rest of the world, will standardized on a Chinese AI stack. Bloated. Broken. Slow. Ugly. There's still time to change it but it's slipping through our fingers like fast running sand. Read this passage below from Bill Gurley because it's about to become our hideous reality if we don't change course quickly. "If a credible Western open frontier player does not emerge, the consequences cascade quickly. This is the inverse of the early Internet wave. In the 2000s and 2010s, Western companies — Google, Facebook, Amazon, Apple, Microsoft — dominated globally while China carved out its own walled garden. The AI version flips that dynamic on its head. Without a credible Western open frontier player, the only open models capable of running entire economies are made in China. If U.S. policy further restricts Chinese open-weight access on national-security grounds, the U.S. ends up with two or three closed Cathedrals serving the U.S. market — and the rest of the world picks the AI stack that is free, capable, self-hostable, and not embargoed. Europe, Africa, Southeast Asia, Latin America, India, the Middle East. Roughly six billion people. Chinese open models become the global default by 2030, and the United States ends up technologically isolated from the majority of the world’s AI users. We would have done it to ourselves." Fully essay in replies.

To all technical students and engineers in Europe. The US export control on LLMs is the first taste of what will become the new norm. Many people are calling for "radical measures" and that we need the equivalent of a Manhattan Project to create change in Europe. But this is not how change will happen. Change will never come top-down from a government. The state and EU can fund, but they cannot found it. That part is on us. You are the only ones who can change this. You are among the few people on this continent who actually know how to build foundational technology - LLMs, robotic AI, actuators from scratch, chip infrastructure, rocket engines, organoids. ETH, EPFL, TUM, École Polytechnique, KTH, Imperial and dozens more produce absurd talent every single year. And almost all of it talks itself out of building. We finish our degrees surrounded by such an incredible average that we're sure someone is always better at [your idea] - so who are we to start? I've seen so many friends at ETH think they need to "get more experience first" and take a job at Nvidia or Google and never do anything interesting again. Technology-driven companies aren't founded by the most qualified person. They're willed into existence by people who see what others do not and refuse to stop. The person who's "better than you" almost never does it. And as for experience, nothing will teach you how to build the thing like, well, just trying to build the thing. Our education is a chance most of the world will never have. There are people in Europe who have to worry about getting a job. We get to worry about finding our dream job. We're able to make bets that not many people can make or afford. It's nothing anybody expects you to do, but if you want a life filled with purpose, this is a unique kind of responsibility you can choose to step up to. So if you actually want to do something ambitious, how about changing a continent? If you really want change, you cannot wait for others. You are one of the few people who can create it. It starts with you.

When I struggle to structure my thoughts about what's happening I turn to writing. Today about the recent US Anthropic ban news, what it says about power and dependency, and what it should mean for Europeans and citizens of the world. It's a long one. lucumr.pocoo.org/2026/6/13/a…

Dear US government, Since you've just blocked Fable and Mythos on critical national security grounds, here are some other tools that pose a similar threat to the American people: - Microsoft Teams - SAP - Salesforce - Jira - Outlook Please do what you must to save America 🇺🇸

With all the controversy this week about silent degradation of Anthropic models, I think it's interesting that no one mentions that Google does something similar to prevent distillation

if you're still using frontier models as your workhorse models you're ngmi the majority of implementation can be done with composer 2.5 and chinese open weight models if you don't understand this by now you are simply behind, and you're going to be broke soon

Brilliant idea! Next up: Apple randomly reboots your Mac if you're building competing tech, Gmail silently edits your email if you mention rival platforms, and Tesla Autopilot swerves if it detects you're working on self-driving cars. All in the name of safety, of course. Because malicious actors controlling the world’s operating systems, inboxes and cars would be extremely dangerous!

#nature trolling all of us right now

NEW: malware developers added nuclear & biological weapons text to to their spyware. Goal? To trigger LLM safety refusals... so that their spyware wouldn't be analyzed by an AI security scanner. Cleanest practical example I can think of for why over-indexing on first order safety alignment is risky. When closed (and open) models ship with aggressive refusals, they will be sprinkled with second-order blindspots that attackers will discover...and exploit. We are only in the earliest days of attackers leveraging these features, and it wouldn't surprise me if users systems that need to handle complex cybersecurity issues demand that models be less safety-blunted. In the weeds: @SocketSecurity's post also shows why intention matters in how you design a malware analysis pipeline to avoid prompt manipulation. H/T to colleagues that shared this with me socket.dev/blog/mini-shai-hu…

Many of you forgot too fast the insane amount of shitty software we had to see and suffer in the pre-AI era.

The only people who believe any of this are non-coders. I tried to build a game (an area I’m an n00b in.) The results are amusingly disastrous - I never before coded a decent game. But I’ll crack out backend services w AI rapidly - because I coded dozens of them before…

Explainable AI may be pointing at the wrong features, systematically. When a machine learning model predicts that a patient is at high risk of a clinical event, and an explainability tool highlights "age" as a key driver, most practitioners will nod and move on. But what if age is not actually informative about the outcome — and the model is using it only to cancel out noise in a correlated variable that is informative? You would be reading a coherent, plausible explanation that is fundamentally wrong. This is not an edge case. Stefan Haufe and coauthors show why it is the expected behavior of most popular explainable AI (XAI) methods — including SHAP, LIME, integrated gradients, LRP, counterfactual explanations, and permutation feature importance. The core problem is what statisticians call suppressor variables: features that have no statistical association with the prediction target, yet improve model performance by removing irrelevant variance from features that are informative. A classic example — blood pressure depends on age, but age itself may not predict the disease. An optimal model may assign non-zero weight to age precisely to denoise the blood pressure signal. When XAI methods reduce to model weights, as the authors show analytically for linear models and empirically for non-linear ones, they will highlight the suppressor — not the informative feature. The authors introduce the Statistical Association Property (SAP) as a necessary condition for any XAI method to be fit for purpose: if a method flags a feature as important, that feature must have a genuine statistical association with the target. Testing a broad range of popular methods against this criterion, they find that nearly all fail it in the presence of correlated features — true of virtually every real-world dataset. The path forward is a shift from algorithm-first to problem-first development: formally define what an explanation should answer, derive correctness criteria, validate against synthetic ground-truth data, and only then assess robustness and usability. This has direct consequences for applied R&D teams. Explainability tools are increasingly used to shortlist candidate molecules, flag confounded biomarkers, or justify regulatory submissions. If those tools are suppressor attributors, the features they highlight may carry no real biological or physical signal — meaning that follow-up experiments, costly and time-consuming, could be chasing artifacts. Demanding formal SAP compliance from XAI tools used in applied pipelines is not academic caution; it is a prerequisite for reproducible, trustworthy science-in-the-loop workflows. Paper: Haufe et al., npj Artificial Intelligence (2026) — CC BY 4.0 | nature.com/articles/s44387-0…

Advice for PhD students in economics about using AI, from the brilliant Isaiah Andrews. This should probably be circulated to all PhD cohorts economics.mit.edu/sites/defa…