I'm responding to the claim that training scaling laws "have ended", even as the question of "the bubble" might be relevant context. The claim isn't very specific, and useful ways of making it specific seem to make it false, either in itself or in the implication that the observations so far have something to say in support of the claim.
The scaling laws don't depend on how much compute we'll be throwing at training or when, they predict how perplexity depends on the amount of compute. For scaling laws in this sense to become false, we'd need to show that perplexity starts depending on compute in some different way (with more compute). Not having enough compute doesn't disprove that the scaling laws are OK. Even not having enough data doesn't disprove this.
For practical purposes, scaling laws could be said to fail once they can no longer be exploited for making models better. As I outlined, there's going to be significantly more compute soon (this is still the case with "a bubble", which might have the power to get compute as much as 3x lower than the more optimistic 200x-400x projection for models by 2031, compared to the currently deployed models). The text data is plausibly in some trouble even for training with 2026 compute, and likely in a lot of trouble for training with 2028-2030 compute. But this hasn't happened yet, so the claim of scaling laws "having ended", past tense, would still be false in this sense. Instead, there would be a prediction that the scaling laws would in some practical sense end in a few years, before compute stops scaling even at pre-AGI funding levels. But also, the data efficiency I'm using for predicting that text data will be insufficient (even with repetition) is a product of the public pre-LLM-secrecy research that almost always took unlimited data for granted, so it's possible that spending a few years explicitly searching for ways to overcome data scarcity will let AI companies find a way to sidestep this issue, at least until 2030. Thus I wouldn't even predict that text data will run out by 2030 with a high degree of certainty, it's merely my baseline expectation.
It's still premised on the idea that more training/inference/ressources will result in qualitative improvements.
I said nothing about qualitative improvements. Sufficiently good inference hardware makes it cheap to make models a lot bigger, so if there is some visible benefit at all, this will be happening at the pace of the buildouts of better inference hardware. But also conversely, if there's not enough inference hardware, you physically can't serve something as a frontier model (for a large user base) even if that offers qualitative improvements, unless you restrict demand (with very high prices or rate limits).
So your scenario is possible; I had similar expectations a few years ago. But I'm seeing more and more evidence against it, so I'm giving it a lower probability (maybe 20%).
This is not very specific, similarly to the claim about training scaling laws "having ended". Even with "a bubble" (that bursts before 2031), some AI companies (like Google) might survive in an OK shape. These companies will also have their pick of the wreckage of the other AI companies, including both researchers and the almost-ready datacenter sites, which they can use to make their own efforts stronger. The range of scenarios I outlined only needs 2-4 GW of training compute by 2030 for at least one AI company (in addition to 2-4 GW of inference compute), which revenues of $40-80bn should be sufficient to cover (especially as the quality of inference hardware stops being a bottleneck, so that even older hardware will again become useful for serving current frontier models). Google has been spending this kind of money on datacenter capex as a matter of course for many years now.
OpenAI is projecting about $20bn of revenue in their current state, when the 800M+ free users are not being monetized (which is likely to change). These numbers can plausibly grow to at least give $50bn per year to the leading model company by 2030 (even if it's not OpenAI), this seems like a very conservative estimate. It doesn't depend on qualitative improvement in LLMs or promises for more than a trillion dollars in datacenter capex. Also, the capex numbers might even scale down gracefully if $50bn per year from one company by 2030 turns out to be all that's actually available.
Since the end of (very weak) training scaling laws
Precisely because the scaling laws are somewhat weak, there was nothing so far to indicate they are ending (the only sense in which they might be ending is running out of text data, but models trained on 2024 compute should still have more than enough). The scaling laws held for many orders of magnitude, they are going to hold for a bit further. It's plausibly not enough, even with something to serve the role of continual learning (beyond in-context learning on ever larger contexts). But there is still another 100x-400x in compute to go, compared to the best models deployed today. Likely the 100x-400x models will be trained in 2029-2031, at which point the pre-AGI funding for training systems mostly plateaus. This is (a bit more than) a full step of GPT-2 to GPT-3, or GPT-3 to original Mar 2023 GPT-4 (after original Mar 2023 GPT-4 and with the exception of GPT-4.5, OpenAI's naming convention no longer tracks pretraining compute). And we still didn't see such a full step compared to original Mar 2023 GPT-4, only half of a step (10x-25x), out of the total of 3-4 halves-of-a-step (2022-2030 training compute ramp, 2000x-10,000x in total, at higher end if BF16 to NVFP4 transition is included, at lower end if even in 2030 there are no 5 GW training systems and somehow BF16 needs to be used for the largest models).
Since original Mar 2023 GPT-4, models that were allowed to get notably larger and made full use of the other contemporary techniques only appeared in late 2025 (likely Gemini 3 Pro and Opus 4.5). These models are probably sized compute optimally for 2024 levels of pretraining compute (as in 100K H100s, 10x-25x the FLOPs of original Mar 2023 GPT-4), might have been pretrained with that amount of compute or a bit more, plus pretraining scale RLVR. All the other models we've seen so far are either smaller than compute optimal for even 2024 levels of pretrained compute (Gemini 2.5 Pro, Grok 4, especially GPT-5), or didn't get the full benefit of RLVR compared to pretraining (Opus 4.0, GPT-4.5) and so in some ways looked underwhelming compared to the other (smaller) models that were more comprehensively trained.
The buildout of GB200/GB300 NVL72 will be complete at flagship model scale in 2026, and makes it possible to easily serve models sized compute optimally for 2024 levels of compute (MoE models with many trillions of total params). More training compute is currently available and will be available in 2026 than what was there in 2024, but for most of the inference hardware currently available it won't be efficient to serve models sized compute optimally for this compute (at tens of trillions of total params), except with Ironwood TPUs (which are being built in 2026, for Google and Anthropic) and then Nvidia Rubin Ultra NVL576 (which will only get built in sufficient amounts in 2029, maybe late 2028).
So the next step of scaling will probably come in late 2026 to early 2027 from Google and Anthropic (while OpenAI will only be catching up to late 2025 models from Google and Anthropic, though of course in 2026 they'll have better methods than Google and Anthropic had in 2025). And then training compute will still continue increasing somewhat quickly for models until 2029-2031 (with 5 GW training systems, which is at least $50bn per year in training compute, or $100bn per year in total for each AI company if inference is consuming half of the budget). After Rubin Ultra NVL576 (in 2029) and to some extent even Ironwood (in 2026), inference hardware will no longer be a notable constraint on scaling, and after AI companies are working with 10 GW of compute (half for training, half for inference), pretraining compute will no longer be growing much faster than price-performance of hardware, which is much slower than the buildout trend of 2022-2026, and even than the likely ramp-off in 2026-2030. I only expect 2 GW training systems in 2028, rather than the 5 GW that the 2022-2026 trend would ask for in 2028. But by 2030 the combination of continuing buildout and somewhat better hardware should still reach the levels of what would be on-trend for 2028, following 2022-2026.
Even for danger that comes from superhumanly and robustly competent AIs, these AIs might've been to a significant extent created by idiosyncratically flawed AIs of jagged competence. The flaws of these predecessor AIs then shape the danger of their more capable successors, making these flaws a point of intervention worth addressing, even when the AIs with these flaws are not very dangerous directly. Similarly to how humanity is not dangerous directly to a superintelligence, except in how humanity would be able to create another superintelligence if left unchecked.
does not seem like it serves any defensible goal
That shouldn't be a reason not to do a thing (by itself, all else equal).
Seems pragmatically like a form of misalignment, propensity for dangerous behavior, including with consequences that are not immediately apparent. Should be easier than misalignment proper, because it's centrally a capability issue, instrumentally convergent to fix for most purposes. Long tail makes it hard to get training signal in both cases, but at least in principle calibration is self-correcting, where values are not. Maintaining overconfidence is like maintaining a lie, all the data from the real world seeks to thwart this regime.
Humans would have a lot of influence on which dangerous projects early transformative AIs get to execute, and human overconfidence or misalignment won't get fixed with further AI progress. So at some point AIs would get more cautious and prudent than humanity, with humans in charge insisting on more reckless plans than AIs would naturally endorse (this is orthogonal to misalignment on values).
1Persuasion plays games with thinking of its targets, some other modes of explanation offer food for thought that respects autonomy and doesn't attempt to defeat anyone. Perhaps you should be exactly as skeptical of any form of communication, but in some cases you genuinely aren't under attack, which is distinct from when you actually are.
And so it's also worth making sure you are not yourself attacking everyone around you by seeing all communication as indistinguishable from persuasion, all boundaries of autonomy defined exactly by failure of your intellect to pierce them.
Maybe there are modes of engagement that should be avoided, and many ideas/worldviews themselves are not worth engaging with (though neglectedness in your own personal understanding is a reason to seek them out). But as long as you have allocated time to something, even largely as a result of external circumstances, doing a superficial and half-hearted job of it is a waste. It certainly shouldn't be the intent from the outset, as in the quote I was replying to.
If AGI is human-equivalent for the purposes of developing a civilization, a collective of AGIs is at least as capable as humanity, plus it has AI advantages, so it's much more capable than a single AGI instance, or any single human. This leads to ASI being often used synonymously with AGI lately (via individual vs. collective conflation). Such use of "ASI" might free up "AGI" for something closer to its original meaning, which didn't carry the implication of human-equivalence. But this setup leaves the qualitatively-more-capable-than-humanity bucket without a label, that's important for gesturing at AI danger.
I think the other extreme for meaning of "ASI", being qualitatively much stronger than humanity, can be made more specific by having "ASI" refer to the level of capabilities that follows software-only singularity (under the assumption that it does advance capabilities a lot). This way, it's neither literal technological maturity of hitting the limits of physical law, nor merely a collective of jagged-human-level AGI instances wielding their AI advantages. Maybe "RSI" is a more stable label for this, as in Superintelligence Strategy framing where "intelligence recursion" is the central destabilization bogeyman, rather than any given level of capabilities on its own.
you sympathize with them while not taking their worldview seriously
There is no reason at all to take any idea/worldview less than seriously. For the duration of engagement, be it 30 seconds as a topic comes up, or 30 minutes of a conversation, you can study anything in earnest. Better understanding, especially of the framing (which concerns are salient, how literal words translate into the issues they implicitly gesture at), doesn't imply your beliefs or attitudes must shift as well.
if you aren’t willing to change your beliefs, why should they
This is not just an inadvisable or invalid principle, but with the epistemic sense of "belief" it's essentially impossible to act this way. Beliefs explain and reflect reality, anything else is not a belief, so if you are changing your beliefs for any reason at all that is not about explaining and reflecting reality, they cease being beliefs in the epistemic sense, and become mental phenomena of some other nature.
Some observations (not particularly constructive):