Pretraining scaling is starting to slow down, both in terms of compute and model size. The mad rush of 2025-2026 is done, and the models targeting 2027 hardware are probably already pretrained (though the work on their misalignment is then only getting started). In 2026-2028, scaling is about 2x slower than in 2025-2026, and then in 2028-2032 it gets 2x slower yet again. So "pacing" will be happening naturally in some sense. The problem is that the models of late 2026 are passing the level of human capabilities in many ways, so even slower scaling could get significantly harder to handle. And Meta, Google, and SpaceX currently have what seems to be well-made small models (somewhere between Sonnet and Opus scale-wise) and sufficient resources/ambition to plausibly catch up with OpenAI and Anthropic in 2027 at least in terms of model scale (with big models that are not so broken that it ruins their capabilities). To the extent scale is a source of misalignment difficulties that require operational excellence to sweep under the rug, they might have a hard time in 2027. And then slow-learning prosaic RSI of automated RL task/env creation in 2027-2028 probably introduces new misalignment/control issues, even if it doesn't manifest as acceleration or a step-change increase in capabilities, and merely fixes jaggedness by automatically filling in all ordinary capability gaps as they come up. Which is probably economically transformative, makes the LLM/RL paradigm more centrally AGI (by automating learning, in a very clunky way), enables useful humanoid robotics and opens up a path to a robot-industrial explosion, but isn't necessarily a raw capability jump per se. (Continual learning in a strong sense is another matter and probably quickly leads to strong architecture-rewriting RSI, but I don't know of a way to time its invention, other than to say that LLMs of 2027-2028 will be taking a stab at it. Like with math problems currently being slaughtered in droves, it could be "easy enough" in some sense for the LLM/RL paradigm to defeat quickly, but not necessarily.)

I think this post gestures at something important, but is quite confused in doing so. In particular, I agree that the concept of Schelling points is very important for understanding coordination. However, I think this post fails to distinguish between coordination towards good and coordination towards evil. The thing it calls "Schelling goodness" is often a tool for turning people against their own moral intuitions, especially in settings where people aren't able to encourage the behavior they actually want. For example, if you stopped people on the street in a dictatorship and asked what should happen to an opponent of the regime, the safe (and Schelling) answer might be "they should be executed". You might then say that you need to ask people in a setting where they feel safe to give their "true opinions". But power structures often coerce people into internalizing submission to those power structures, so it's hard to even know what their "true opinion" means. And even if you have a reliable way to get someone to think freely about what they prefer, doing so privately doesn't do much to change the Schelling answer—because the Schelling answer is anchored to public knowledge. In other words, even when individuals are able to come to the intuitively right moral answer, the application of power might cause the Schelling answer to remain stuck on something bad (and indeed, I suspect that we can think about this ability as constitutive of power). I discuss many of these dynamics in this blog post. You might hope that these dynamics change when thinking about the cosmic Schelling answer, since it's harder for distant civilizations to coerce each other. But the main reason to take the cosmic Schelling answer seriously IMO is that there are some channels for such civilizations to affect each other, and coercive interactions seem like they're probably fairly easy (e.g. simulating things with high disutility for another civ). On the meta level: I was thinking about making this comment a few days ago, after a conversation with Scott Garrabrant, but was prompted to do so by the recent announcement that SFF is revoking John Wentworth's funding. I don't know who made that call, and want to think more about it before writing publicly about it, but the intuitions in this comment play an important role in how I evaluate decisions like this.