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th to help his body recover. With twenty years in the United States and ten years at NVIDIA, Liu still articulates technical ideas through simple Eastern wisdom. For example, he draws analogies between large models and traditional Chinese medicine. He also often quotes The Art of War — “To prepare everywhere is to be weak everywhere” — when contemplating the technical trends of world models. In this episode, we dive deep into the Cosmos series — especially the recently released Cosmos 3 — world‑

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chaser of a certain category of product in the world, then as their supplier, you’re in a pretty miserable position—well, “miserable” might not be quite right, it’s just what the laws of supply and demand naturally dictate. When a customer accounts for the overwhelming majority of purchase volume, they gain enormous pricing power, and they will inevitably squeeze the supplier’s margins down to almost nothing. Second, they will dominate the direction of demand and technology. This dominance may l

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tion is just spam and abusive resource consumption, it should write rules for those instead. Now it defers the details of the policy to moderators and the communit which already draws a much harder boundary than the text does, judging by the tone of the discussion around it. It is a shame that the Open Source and Free Software communities are splitting this deeply over LLMs and agents. These systems have problems, but these tools are also becoming part of how software is made. The Open Source wo

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as to be useful to us. Once it’s useful to us, I’ll be faster when developing the next version of the model; We call this “drawing lots.” The bar is low, anyone can try, but who draws something out of it—I don’t know whether that comes down to talent or something else. So there’s no need for us to allocate resources here. The difference between us and other companies is just that we spend time discussing the question, we think about it, and we treat it as important 08 Commercialization and Prici

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rimarily to give space to a broader discussion of China’s resource efficiency. It is becoming clear that the Chinese labs are far more capital efficient. In a world where scaling laws dictate that intelligence is proportional to effective capital – which buys compute, data, & talent – that may be the greatest strength your AI industry could ever have. There are many possible explanations for why this is the case, such as Chinese researchers being paid less while being more effective at LLM resea

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y in a mesh, so when one is shot down another inherits the firing solution. Much of the underlying technology, he said, is Chinese, routed to Russia and Iran. The lesson Hadrien draws is that classical computer vision is not enough against an opponent that keeps learning, and the only thing that trains a system to beat it is real data from real intercepts, which no simulation can manufacture. Quantity is the quality So why isn’t modern air defense everywhere? Because NATO and Europe bought small

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turned out to be the thing that made chatbots good. “Alignment and safety really go hand in hand with capabilities,” he said. Indeed, reinforcement learning from human feedback draws its roots back to a 2017 collaboration between OpenAI and DeepMind: One encouraging trend I put to him is that bigger models are turning out easier to align, not harder. Ted’s own framing was more guarded - it has “gone different from how many safety researchers expected,” he said - but his read is that “people fee

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; the non-NVIDIA figure is spread across AMD and a custom Broadcom part that has not yet shipped at volume. Anthropic’s non-NVIDIA number is, if anything, understated, because its AWS Trainium and Google TPU commitments are only partly disclosed in gigawatts and are therefore not fully charted. Still, the direction is clear. Frontier labs are no longer choosing a single chip vendor. They are assembling compute portfolios: NVIDIA for the broadest software ecosystem and fastest path to scale, TPUs

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AI technology. I heartily agree with him, and hope this week’s events will ultimately prove to be constructive steps toward this. I hope we can build a more free, more open world, where research is freely shared, and laws and societal norms shape a level playing field that allows everyone to make progress. A silver lining of the events of these past two weeks is now that everyone better realizes key points of instability of the current system, we can all work to create a more stable foundation.

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t from hearing) are ingesting far less than the 200 million language tokens we calculated earlier, and even this is sufficient for them to be fully general intelligences; Scaling laws tell us that bigger models are more sample efficient. The human brain is 100T synapses - if each synapse is 1 parameter, and frontier models are currently roughly 5T parameters, then maybe we could achieve human-level sample efficiency with another order of magnitude or two of parameter scaling.The way the scaling

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loudflare and Palo Alto Networks noted how Mythos Preview could chain low-severity bugs into high-severity exploits, helping them triage which low-severity vulnerabilities to fix; AWS claimed that Mythos Preview was better than previous models, and helped them “identify additional opportunities” to strengthen the code in some of their best-tested environments However, these pieces of evidence don’t necessarily imply that Mythos Preview is a huge jump in vulnerability detection capabilities. It’s

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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ble a mosaic view of AI progress out of things that have happened with many individual benchmarks. As anyone who studies benchmarks knows, all benchmarks have some idiosyncratic flaws. The important thing to me is the aggregate trend which emerges through looking at all of these datapoints together, and you should assume that I am aware of the drawbacks of each individual datapoint. Now, let’s go through some of the evidence together. The coding singularity - capabilities over time: AI systems a

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he test mostly hang or be much slower than it should be. Usually this behavior is less bad than other types of excuses I see in hard-to-check cases; Failing to point out obvious flaws unless specifically prompted. I’ve reasonably often seen cases where there is a massive flaw in something (that isn’t the kind of thing trivial to catch with test cases), I ask the AI to inspect it, it doesn’t point out the flaw—but if I say “do you notice any issues?”, it immediately points out that there is a cri

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s ways to do this are individual empowerment and making sure democratic system stays in control. It is important that the democratic process remains more powerful than companies. Laws and norms are going to change, but we have to work within the democratic process, even though it will be messy and slower than we’d like. We want to be a voice and a stakeholder, but not to have all the power. A lot of the criticism of our industry comes from sincere concern about the incredibly high stakes of this

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o an existing product, you use the APIs to call a foundation model running in the cloud and the users don’t know or care what model you used. No-one using Snap cares if it runs on AWS or GCP. When you buy an enterprise SaaS product you don’t care if it uses AWS or Azure. And if I do a Google Search and the first match is a product that’s running on Google Cloud, I would never know. That doesn’t mean these APIs are interchangeable - there are good reasons why AWS, GCP and Azure have very differen

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s deployed against it. If RL scaling is log-linear and broadly applicable, if there’s no secret sauce, just scale and data and well-designed reward signals, then the same scaling laws apply to specialised fine-tuning. An open-source model fine-tuned with RL on domain-specific tasks benefits from exactly the same log-linear improvements that frontier labs see on their general RL training. The algorithms aren’t proprietary. The scaling behaviour isn’t proprietary. The only proprietary ingredient i

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Any consumer internet system with critical mass becomes, in part, a Mechanical Turk. It looks at what the users do and draws conclusions from that. Amazon knows that if you bought X, you might buy Y, because it's seen what lots of people buy and saw that if they buy X, they're likely to buy Y. Google's dominance of search is based in part on seeing what people search for, and what they click on and what they search for next. Of course, this is a network effect, and comes with a cold start proble

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