Séb Krier has 3 meta observations about the state of AI

Three meta-observations on the state of AI:

1. AI will continue to improve, get better integrated, and produce lots of value. This is true even if you think there's a bubble-y dynamic or an imminent correction. Lots of people genuinely believe the transformative prospects, but… pic.twitter.com/GGQMZ1F7oN

— Séb Krier (@sebkrier) August 8, 2026

Written out in full:

1. AI will continue to improve, get better integrated, and produce lots of value. This is true even if you think there's a bubble-y dynamic or an imminent correction. Lots of people genuinely believe the transformative prospects, but also lots of people have strong incentives to believe so AND for others to believe so too. So a lot of bulls are *honestly* bullish, whilst at the same time self-selecting into, and being driven by, discourse that happens to align well with their own interests.

2. Not exactly a revolutionary insight, but the very same facts will lead some people to think the exact opposite of what another group believes. The shape of recent progress will make some people think we are close to some sort of 'recursive self-improvement' dynamic (sometimes with unstated accompanying beliefs about speed of societal transformation). But another group, looking at the same results but indexing on other variables, will conclude we're seeing diminishing returns, jaggedness, and real but incremental progress (sometimes with unstated accompanying beliefs about the criticality of temporary failures).

3. A lot of public discussions on AI feel like they rest on a scaffold of leaky and highly imperfect abstractions. Too much is being written about models with reference to parables, metaphors, analogies, and stylized stories. Ofc this is somewhat unavoidable, but many jump to easy pattern matching and reason probabilistically *within* a particular causal story without adequately representing uncertainty over the story itself. There's so much noise that the correlations seem more explanatory than they actually are. Because the underlying understanding is itself so murky and uncertainty is uncomfortable, people go for easy familiar abstractions and are too quick to trust the data generating process itself. As a result of the above, the experts themselves are often more confused than one might expect, and so proper division of labour and deferral to authority is much harder in AI than in other established fields.

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A comment from me: Also, much of the discourse about AI is based on concepts that arose before, say, the ChatGPT breakthrough to the public or the AlexNet breakthrough within the industry. It's discourse based on possibility not proved actuality.