All-In Summit: Microsoft CEO Satya Nadella -- The AI Doomer Slowdown -- Common Sense AI Guardrails -- What "Slowdown" Means for New AI Products -- Microsoft’s Master Plan -- Who Wins AI (0:00) @satyanadella joins The Besties! (0:55) Dario's blog, "pacing the frontier," common sense AI safety (6:28) The failure of AI CEO messaging, monitoring agents, what will a slowdown mean for new AI products? (14:22) Economic incentives for frontier lab doomerism, where the AI profits are (22:45) Microsoft's master plan for AI, how they are allocating capital (31:00) China's slow down, changing AI perception, data center benefits $MSFT

Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing. We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive. And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control. So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk. The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia. This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks. Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon.

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

Our own @johnschulman2 talks with Dwarkesh about where human judgment still matters as models improve and self-improve: teaching them to handle messy real-world tasks, applying taste to what works in the long run, and, above all, specifying what we actually want.

New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

Anthropic's economic scenario analysis is interesting. But this is not something you can ignore, this is the most important consideration! "the model cannot generate the negative feedback in which disruption depresses demand and amplifies its own labor-market consequences"

New from us: Anthropic just published scenarios for AI’s possible economic impacts, which range from minimal, to explosive GDP growth of 15% by 2030 as knowledge-worker unemployment hits 18%. I sat down with their co-founder Jack Clark to pick his brains on how they’re thinking about all of this.

what in the alien architecture is this

Fable is probably ~2-2.5T parameters, not 10T. Kimi K3 is 2.8T params, trained on maybe 20–30k Blackwell-equivalents. It lands within spitting distance of Fable 5 in terms of capabilities (5, not 5.1). Anthropic has far more compute than Moonshot, better rl environments, better architecture and better optimizers and all of that adds to capability per parameter. So if Fable is only slightly ahead of K3 with this in mind, it's almost certainly a smaller model. GPT-5.5 and 5.6 are smaller still (I'll say more on that later)

New post on the blog, featuring the excellent @ben_moll There’s been tons of discourse on how AI will contribute to economic growth, with many people closest to the technology predicting double digit increases. Are these forecasts likely? Probably not. The blog goes through the economics for why exploding improvements in capabilities (which technologists have been largely right about) may not translate to explosive growth. Ben’s thread covers this in detail, but gist is that: 1) there is nothing in economic growth models that prevents AI from leading to explosive growth but 2) this trajectory relies on a series of assumptions that are unlikely to hold in the real world. For example, one assumptions is likely to be violated because of a pretty counterintuitive feature of structural change: the sectors that become automated become smaller parts of the economy (because they’re cheaper, people become richer, and spending moves to non-automated parts of the economy). This, plus other features of the economy, is what will likely cause the trend of huge increases in capabilities coupled with “only” 4-5% growth (which is huge, btw) to continue. Here is the link: https://aleximas.substack.com/p/will-ai-soon-lead-to-double-digit Looking forward to hearing thoughts/feedback!