If you believe Yann LeCun and David Silver (and others), there's an architecture wall; maybe labs are starting to see this on the horizon. Maybe the DRAM supply constraints are forcing it.

Are these real step changes - big picture wise, or refinements in RL/agentic orchestration/"taste" and advancements due to bigger models and hardware technology/capacity scaling? If they not, does this tactic - and hardware improvements - continue to scale non-linearly like they need to?

It is clear that whatever does change in each model increment has resulted in meaningfully better end user capabilities (as well as regressions in some areas, honestly), but that doesn't prove anything. I'm not sure what I personally believe, but stating with your full chest that a stall is ridiculous ignores a lot of potential evidence to the contrary.

Stupid example: Astra. Its main improvements are: much much better computer use and 3d modeling capabilities; better subagent orchestration; better and more reliable tool use; slightly worse coding.

This looks to me, from a feature perspective, to be an incremental improvement across several functional areas, plus new features which are unquestionably excellent but are probably the result of RL focus, not magic.

Step change? Ehhhh depends on how you squint. But how many more iterations of this do we have? Are we going to squeeze quintillion parameter transformers into GPUs?

Solving millennium problems doesn’t pay the bills.

Their best models might be quite good when directed at extremely difficult and very focused problems, but most business use cases are nothing like that.

Edit:

Not trying to imply these new frontier models aren’t also better at other things, just that there’s really no reason for most people to use them when cheaper alternatives exist that get the job done just as well.

What reason could OpenAI possibly have to lie about their own performance? The fact they were desperate enough for a PR win they stole the research leading to the Millenium problem further hurts their case imo.

At the current rate of 3.4x per year in the US, you get 100x the current capacity in just 3 more years and 9 months, that's not physically impossible yet, but just looking at the impact today makes it hard to imagine the impact 100x would have, and I find it very plausible for top labs to prefer making some sort of truce instead of keeping up this pace.

I never mentioned capability stalling, my point is that if you extrapolate the same rate of growth just a few years into the future, you quickly reach logistical impracticality, I don't think It's hard to deduce that AI compute can't keep doubling every 6 months for long.

The other part is that this is happening because the foundational model business is both extremely successful and competitive, if it wasn't as successful the market demand would've been saturated by now, and if it wasn't as competitive a single monopoly could've paced things more reasonably from the beginning.

Are you sure you don't mean revenue? Given the enormous investments profits seem very unlikely.

Anthropic's profit estimate they released a while ago was a masterclass in deceptive reporting. They didn't include training costs, stock based compensation, or liabilities. Token gross margin is not profit.