Sherlock

building

[Sherlock @sherlockpowder]

@PeterJ_Walker Opus 5.5 (used to be fable5.1) is our last shot with LLMs. If it can't crack these tasks, we're moving to other solutions—with more humans in the loop.

[Sherlock @sherlockpowder]

@collision @finkd @alexandr_wang 720p at most?

[Sherlock @sherlockpowder]

@SawyerMerritt can I offer my model y into robotaxi fleet in exchange of cybercab using? 🫤

[Sherlock @sherlockpowder]

@SawyerMerritt easy to scale with its own fab

[Sherlock @sherlockpowder]

@mntruell @milichab Demand: can they control any apps on my own Mac?

[Sherlock @sherlockpowder]

@claudeai Congrats! It asked me to update claude to use Opus5.5, but the app has no update choice, PTAL

[Sherlock @sherlockpowder]

@elonmusk Thanks Elon for the electrical engineering part, that’s exactly what we need. We’re struggling with Fable and Astra in this space.

[Sherlock @sherlockpowder]

@cb_doge Electrical engineering, that’s the amazing part

[Sherlock @sherlockpowder]

@IAmPascio wait what? You read yourself?

[Pascio @IAmPascio]

https://t.co/5b2m7xiDPx

[Sherlock @sherlockpowder]

@GavinSBaker Thanks for sharing, just need a small favor Gavin: it can be better if you can upload a high quality photo!

[John (Yueh-Han) Chen @jcyhc_ai]

Can AI autonomously align other AIs?

We built an agentic harness to wrap an AI (Claude Opus 4.8) as an automated alignment researcher and gave it 2 days to improve the alignment of other AIs.

It did research, proposed methods, then trained and tested the models all on its own. It worked really well.

More interesting findings in the🧵

[Sherlock @sherlockpowder]

@Alfred_Lin Wow, marked GOAT by Alfred, definitely should learn something from him

[Sherlock @sherlockpowder]

@SuJinyan6 highly respect that, but the things you feel sick are the things I feel SICK. Exactly same thing, but I like it so much.

[Sonya Huang 🐥 @sonyatweetybird]

Today we release my favorite episode of Training Data yet: the great Rich Sutton.

@RichardSSutton wrote the textbook, wrote The Bitter Lesson (and many other on-point essays like "Self-Verification, The Key to AI"), and trained a mafia of talented students who went on to change the AI landscape forever including David Silver, inventor of built AlphaGo.

@kjaved_ was Rich's PhD student at Alberta and wrote The Big World Hypothesis. They just left academia to start @oaklab

Their core argument:
(1) The Bitter Lesson: the world is massively more complex than any model of it, so anything trained on human-curated data has a ceiling
(2) Continual Learning: intelligence is continual by definition, and today's models stop learning the moment they ship.

The conversation covers:
— what The Bitter Lesson actually says, and what people get wrong
— why synthetic data is "just a big mistake," and the Big World Hypothesis behind it
— how LLMs are both a positive and a negative example of his own essay
— why no animal learns by supervised learning, and what squirrels can do that we can't
— the cure for catastrophic forgetting: per-weight step sizes and continual backprop
— why the biggest labs can't take a path where performance gets worse before it gets better
— a trillion parameters on 20 watts, and the Moore's Law math that makes it plausible
— why the endpoint isn't one mind but one design, running as many minds

It was both a fun generative idea- and debate-filled conversation, and a surprisingly human one too. Rich, thank you for beating cancer and changing the trajectory of AI. 💙

00:00 Introduction
02:10 An AI winter, a cancer diagnosis, and the move to Alberta
07:07 Writing "The Bitter Lesson," and what people get wrong
09:53 Are LLMs a positive or a negative example of it?
11:03 Synthetic data is "just a big mistake," and the Big World Hypothesis
18:01 AlphaGo, human priors, and why prior knowledge and learning should be friends
22:37 "Their weights never change": do LLM assistants actually learn?
26:09 Babies, squirrels, and why no animal learns by supervised learning
32:02 Rockets, imagination, and where paradigm shifts come from
36:42 The Alberta Plan and its 12 steps
38:53 Catastrophic forgetting and the cure
43:43 Oak's biggest ambition: a self-maintaining mind
47:56 Why the big labs are stuck in a local minimum
49:13 If everything goes right: LLMs, many minds, and hiring

The man who pioneered reinforcement learning thinks the rest of the field is weird, and lays it all out in today's episode. Together w/ @Alfred_Lin @sequoia

[Alfred Lin @Alfred_Lin]

.@RichardSSutton needs no introduction. He literally wrote the book on reinforcement learning, and many of the greatest minds in AI today have studied under him (including David Silver of DeepMind/AlphaGo fame and now @IneffableLabs).

My partner Sonya and I sat down with Rich and his co-founder @kjaved_ recently to discuss the state of AI, how to build agents that learn continuously from their own experience, why synthetic data is a big mistake, and how their new startup Oak Lab plans to build a 1 trillion parameter agent running on 20 watts - the equivalent of the human brain.

[Alfred Lin @Alfred_Lin]

'When you're playing a point, it has to be the most important thing in the world, and it is. But when it's behind you, it's behind you.' https://t.co/5fxXIIWgV0

[David Sacks @DavidSacks]

Some thoughts on Dario’s post:

1. Dario does not actually address Gavin Baker’s account of what he said – something he could easily deny if it were inaccurate.

2. Dario claims his critics live in a “bubble” where all regulation equals regulatory capture. He calls this an overly simplified view and notes that “Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people.” This argument is a straw man. Of course treating all regulation as capture would be overly simplified – but almost no one holds that view. I have repeatedly argued for strong antitrust enforcement to keep industries competitive, especially Big Tech. If Anthropic continues toward monopoly or duopoly status, I would be among the first to demand those rules apply.

3. Regulatory capture is not vague or in the eye of the beholder. Nobel laureate George Stigler defined it as regulation acquired by an industry and designed and operated primarily for its benefit. Stigler challenged the traditional view that government regulation arises from a benevolent state protecting the public from market failures. Rather, industry groups have concentrated stakes and pour resources into influencing regulators, whereas the public’s stake is diffuse and unorganized. The revolving door between companies and the agencies that regulate them compounds the problem. Anthropic understands these dynamics: it has hired multiple senior Biden AI-policy officials and built a substantial government-affairs operation plus a network of aligned organizations to push its preferred frameworks at state and federal levels.

4. Dario has consistently pushed for a new federal agency to review and approve frontier models prior to release – a proposal framed variously as an “FDA for AI,” an “FAA for AI,” and most recently a “FINRA for AI.” I call it a “DMV for AI” because a review process modeled on the FAA or FDA (which takes years) or FINRA (which issues rules for a staid industry widely seen as protecting incumbents) will create long queues as AI models wait for testing and approval. This process will only become more labyrinthine as rules accumulate to prevent theoretical harms. This would handicap the U.S. relative to China, which will not adopt the same constraints. It would also undermine Anthropic’s own business model, whose pricing power depends on remaining ahead of open models. Whatever Dario states today, it is difficult to believe the company would simply accept outcomes that erase that advantage.

5. Anthropic is on track to become one of the most valuable companies in history, with the resources to navigate any approval process and shape the rules while competitors wait. Dario wants open models under heavier scrutiny – he has called them dangerous in Senate testimony, criticized them for not being centrally monitored or withdrawn, and linked them to IP theft. He says he has never sought a ban, but he could achieve a similar result by insisting that identical rules apply to both open and closed models. The U.S. risks becoming an island of costly closed models while the rest of the world races ahead with broader choice.

6. Dario acknowledges that AI is structurally centralizing but attributes this mainly to chips and scaling laws. Access to compute matters, but the deeper risk is who decides which capabilities are available to whom. His preferred pre-deployment testing and FAA/FINRA-style oversight would place that gatekeeping power in a federal bureaucracy working hand-in-glove with a small number of frontier labs – reinforcing centralization rather than countering it.

7. The second part of Dario’s post assumes we have amnesia about Anthropic’s well-orchestrated campaigns hyping AI fears. His May 2025 claim that AI would wipe out 50 percent of entry-level knowledge jobs within five years still lacks supporting evidence fifteen months later. Similarly Anthropic breathlessly promoted its heavily contrived “blackmail” study on 60 Minutes. Yet Dario blames public negativity on a long-standing loss of trust in institutions rather than his own messaging.

8. These narratives have done more than anything to shape public fear. People are left asking the same question Mark Zuckerberg posed: why race to build a future you describe in such negative terms? Thomas Sowell’s "The Vision of the Anointed" captures the mindset – elite intellectuals convinced that only they are enlightened enough to control the outcome. As Zuckerberg notes, concentrating power in the hands of an enlightened few has rarely produced the promised results; the practitioners turn out to be less enlightened in practice than in self-conception.

9. Gavin Baker summarized the disagreement cleanly on our pod: Dario believes frontier AI is too powerful to distribute; we believe it is too powerful to centralize. Dario appears to believe, sincerely, that safety and progress are best served by centralizing authority in a marriage of corporate and state power. The weight of human history gives us reason to fear that outcome.

[Nicholas Fabiano, MD @NTFabiano]

You’re not depressed.

You just need to find your creative quest.

[Greg Brockman @gdb]

Black Hat talk from the team, with a detailed timeline of and takeaways from the OpenAI-Hugging Face Incident: https://t.co/GtXWiAZ2RQ