Epistemic status: Philosophical argument. I'm critiquing Hinton's maternal instinct metaphor and proposing relationship-building as a better framework for thinking about alignment. This is about shifting conceptual foundations, not technical implementations.

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Geoffery Hinton recently argued that since AI will become more intelligent than humans, traditional dominance-submission models won't work for alignment. Instead, he suggests we might try building "maternal instincts" into AI systems, so they develop genuine compassion and care for humans. He offers the mother-baby relationship as the only example we have of a more intelligent being "controlled" by a less intelligent one.

I don't buy this - for starters, it is not clear that mothers are always more intelligent than their babies, and it is also not clear that it is always the babies that control their mothers. And I'm just scratching the surface here.

Most AI alignment discourse still revolves around control mechanisms, oversight protocols, and [...]

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Epistemic status: I think you should interpret this as roughly something like “GenAI is not so powerful that it shows up in the most obvious way of analyzing the data, but maybe if someone did a more careful analysis which controlled for e.g. macroeconomic trends they would find that GenAI is indeed causing faster growth.”

1. Some people have asked me to consider going back to earning-to-give, and so I am considering founding another company. A major hesitation is that startups generally take 6 to 10 years to be acquired or go public. This is unfortunate for founders who believe that we are less than 10 years away from all human labor being automated.
2. It's possible that the advent of generative AI will make startups grow faster, which could provide a faster exit path. YCombinator CEO Garry Tan has publicly stated that recent YC batches are the fastest growing in their [...]

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Outline:

(03:26) Methodology

(04:01) Identifying GenAI startups

(04:55) Sources of error

(06:42) Results

(06:45) The most valuable companies 2 years after doing YC

(07:10) The most valuable companies 1 year after doing YC

(07:34) Average Growth

(08:25) Discussion

(08:29) Garry Tan's comments

(09:24) Has YCombinator just lost the mandate of heaven?

(10:52) Stripe data disagrees, showing that AI companies are growing revenue more quickly

(12:56) Carta's data agrees, showing that companies aren't growing faster

(14:12) You can still get rich quick though

(15:00) Further Research

(15:42) Conclusion

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(Crossposted from my Substack: https://taylorgordonlunt.substack.com/p/my-ai-predictions-for-2027)

I think a lot of blogging is reactive. You read other people's blogs and you're like, no, that's totally wrong. A part of what we want to do with this scenario is say something concrete and detailed enough that people will say no, that's totally wrong, and write their own thing.

--- Scott Alexander

I recently read the AI 2027 predictions[1] . I think they're way off. I was visualizing my self at Christmastime 2027, sipping eggnog and gloating about how right I was, but then I realized it doesn't count if I don't register my prediction publicly, so here it is.

This blog post is mostly about me trying to register my predictions than trying to convince anyone, but I've also included my justifications below, as well as what I think went wrong with the AI 2027 predictions (assuming [...]

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Outline:

(01:07) My predictions for AI by the end of 2027

(05:42) Justification

(06:01) Shallow vs. Deep Thinking

(07:10) Noticing Where LLMs Fail

(09:28) Why the Architecture of LLMs Makes Them Bad at Deep Thinking: Theyre Too Wide

(11:44) LLMs Are Also Too Linear

(15:49) Whats wrong with AI 2027

(17:29) The Takeoff Forecast is Based on Guesswork

(22:45) I Dont Take These Predictions Seriously

(24:25) The Presentation was Misleading

(26:23) Deep Thinking vs. Shallow Thinking For Making Predictions

(28:05) Was AI 2027 a Valuable Exercise?

(29:05) Conclusion

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Audio note: this article contains 42 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.

This post is an attempt to summarise and explain for the LW readership the contents of this paper: "Quantum Bayes' rule and Petz transpose map from the minimum change principle". It's a highly technical paper heavy on quantum mechanics formalism that took me a couple of days to unpack and digest a bit, but I think it may be important going forward. My work on it is far from done, but this is a quick introduction.

Epistemic status: I have a Physics PhD and spent about ten years working with computational quantum mechanics so hopefully I know what I'm talking about, but if anyone can peer review I'll be glad for the help.

The tagline of Astral Codex [...]

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Outline:

(05:19) Classical derivation

(07:37) Minimum change derivation of Bayes rule

(08:34) Quantum derivation

(10:38) Crunching some numbers

(12:35) Conclusion

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In 1787, Catherine the Great sailed down the Dnieper to inspect its banks. Her trusted advisor, Governor Potemkin, set out to present those war-torn lands to her in the best possible light. Legend has it[1] Potemkin set up painted facades along the riverbank, so that, from her barge, Catherine would see beautiful villages – each just a couple of inches thick.

The rise of AI agents makes the Potemkin problem commonplace. Research agents cite experiments that never took place. Coding agents often write fake tests and mock solutions as they cause catastrophes behind the scenes.

We're moving towards a world of Potemkin villages – where our understanding of reality drifts farther and farther from what is actually happening. At some point, we might stop catching our agents painting facades.

To avoid this, we need to understand AI agents and their effects on [...]

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https://fulcrumresearch.ai/2025/08/29/ai-agents-and-facades.html

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[CW: Partial nudity]

I'm a straight man. If you're a straight man who befriends other straight men, you will be occasionally have conversations that sound like this:

• A: I hate how Hollywood is pushing all of these unattractive actors lately. I want to see more people like [Celebrity Name].
• B: God, [Celebrity Name] is so bad. I would let [Celebrity Name] choke me to death with her thighs.
• C: Dude, I would shave my face off with a cheese grater just to sniff the feet of [Celebrity Name].
• D: [Celebrity Name] is kinda mid. But I would do that for [Celebrity Name 2] though.
• C: You're full of it. [Celebrity Name 2] is the one that's mid.

These conversations are fun, but I've always found them a bit weird. Not because I have different picks, or because I'm unfamiliar with the level of horny that would compel a [...]

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I often see people treating defensiveness as proof of guilt. The thought seems to go that if someone is defensive, it's because they know they’ve done something wrong. There are even proverbs around this, such as “a hit dog will holler” or “the lady doth protest too much”.

This has always felt false to me.

Now, it's certainly true that having done something wrong can be the cause of defensiveness. But that's just one out of many options!

Some situations that have made me defensive include times when someone has…

• … had a negative stereotype of some group that I belong to, and said something derogatory about it.
• … acted judgmentally about my choices without understanding my situation or wanting to hear my explanation.
• … dismissively rejected my input about a decision that I felt was important.

In none of these situations did I feel guilty. I may [...]

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I got this crazy idea; I wonder if anyone could try it. Let's make an online encyclopedia, similar to Wikipedia, with one difference: all articles would be edited by AIs.

Why? (I mean, other than "because it's possible and sounds kinda cool".)

First, because it's possible. If an AI can give you a report on a certain topic, it might as well create an encyclopedia article on the topic. But unlike asking the AI directly, when you read the encyclopedia you know that you are reading the same version everyone else is.[1] This avoids the problem of the AI telling you exactly what it thinks you want to hear. No more sycophancy - now the AI tells you what it believes.[2] Even if it lies, e.g. because the system prompt commands it to say certain things or avoid saying certain things, at least it lies the same way to [...]

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Outline:

(01:10) The first version

(03:00) More ideas

(06:18) Some problems

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PauseAI organised an open letter from UK lawmakers and civil society organisations to Demis Hassabis, CEO of Google DeepMind. PauseAI UK members emailed their MPs asking them to sign the letter.

Across-party group of 60 U.K. parliamentarians has accused Google DeepMind of violating international pledges to safely develop artificial intelligence, in an open letter shared exclusively with TIME ahead of publication. The letter, released on August 29 by activist group PauseAI U.K., says that Google's March release of Gemini 2.5 Pro without accompanying details on safety testing “sets a dangerous precedent.” The letter, whose signatories include digital rights campaigner Baroness Beeban Kidron and former Defence Secretary Des Browne, calls on Google to clarify its commitment.

Time confirmed for the first time that Google DeepMind did not provide the UK AISI with pre-deployment access to their Gemini 2.5 Pro model.

After previously failing to address a [...]

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https://time.com/7313320/google-deepmind-gemini-ai-safety-pledge/

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Last month we held a workshop on Post-AGI outcomes. This post is a list of all the talks, with short summaries, as well as my personal takeaways.

The first keynote was @Joe Carlsmith on “Can Goodness Compete?”. He asked: can anyone compete with “Locusts”: those who want to use all resources to replicate as fast as possible?

Longer version with transcript

The second keynote was @Richard_Ngo on “Flourishing in a highly unequal world”. He argued that future beings will vary greatly in power and intelligence, so we should aim for “healthy asymmetric" relations, analogous to that between parent and child.

Morgan MacInnes of U Toronto Political Science spoke on "The history of technologically provoked welfare erosion". His work with Allan Dafoe argued that competitive pressure sometimes forces states to treat their own citizens badly.

The next talk was a direct rebuttal to Morgan's [...]

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(04:39) My Takeaways:

(06:01) Whats next:

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