There's a meme that "nothing ever happens" that's popular among prediction market traders, with the idea being that the status quo changes less frequently and in ways less according-to-specific-priors than traders first-order expect. I think there's a similar principle that applies to reasoning about AI development and takeoff speeds: nothing ever happens (prior to the development of superintelligence). In non-meme form, I think people closely following AI development tend to systematically overestimate the likelihood and impactfulness of any particular event or change actually happening prior to the development of superintelligence, and this has some interesting implications.

For one, semiconductor, energy, and tech stocks are way up, and capital markets more broadly are roaring around AI, despite some geopolitical chaos. But the actual wider economic and societal impacts of AI so far seem surprisingly small, given how smart and easily accessible SoTA models and harnesses are.

If you showed a demo of Claude Code or Codex to someone in 2021 and mentioned that it was available to any business or individual for purchase at non-exorbitant rates^[1], I think a lot of people would be surprised at how little impact / transformation there has been given (a) how smart the models are and (b) how readily accessible / deploy-able they are.

This seems like a win for Eliezer's world model vs. Paul's, and a reason for pessimism about some iterative-deployment takes and plans are more broadly. It now seems more plausible that things will look and feel pretty normal ("nothing ever happens") for the vast majority of people, until they very suddenly don't.

For two, I think there's also a related story about a narrower impact on research specifically that goes something like: "we'll learn a bunch from iteration on earlier models, and also the world will look importantly different in ways that we'll be able to leverage to do or automate the most critical safety work before crunch time." Several people have written up plans and research agendas with various details and variations expanding on this basic plan, e.g. @ryan_greenblatt's recent posts: Plans A, B, C, and D for misalignment risk, Iterated Development and Study of Schemers (IDSS), and How do we (more) safely defer to AIs? are some recent examples in this genre.

But regardless of specifics, it looks less plausible that these kinds of plans get deployed in a world that looks relevantly and substantially different than the one we're in today, in part because "nothing ever happens". Even if things on the policy front improve somewhat, e.g. global agreements sufficient to slow race dynamics such that frontier labs have enough time to find and filter down to actually-good control and alignment techniques and then universally adopt + integrate them (a big if), they'll likely be integrating them in worlds that don't look dramatically different from the world we're in today.

And a marginally more interpretable / controllable / aligned (but non-superintelligent) model increasingly looks like it just doesn't change the gameboard much before actual superintelligence is developed, for the same sorts of reasons that having a plausibly-good-enough baseline level of Claude Cowork functionality and availability doesn't immediately result in automation of most white-collar work. Alignment researchers are quicker on the uptake in terms of adopting and adapting to new tools unlocked by non-superintelligent AI compared to the average white collar worker, but that's counterbalanced by the fact that frontier models are also apparently worse at alignment research than generic white collar work, and there are other bottlenecks (e.g. inference compute) as well.

So the upshot is that I think it's worth planning more for the case that many things during takeoff look pretty similar to the way they do today, and being somewhat skeptical of any plan that assumes a phase-changing uplift step during a gradual takeoff from non-super-intelligent AGI, and that this holds even if good things happen swiftly on the AI governance front.

1. ^{^}

   the $200 / mo subscriptions are kinda limited, but you can just pay-per-token and it's not crazy expensive for many use cases.