> And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.
I suspect this opinion may come across as naive for the myriad software engineers whose job has become pressing the enter key. Your point about timing is based on solid principles, but I don't think your comment contains a satisfactory argument that the timing is incorrect.
If no one is willing to pay increasingly high costs of training frontier models, we'll still have the models that we have now that can be fine tuned for incremental improvements. Still tons of utility in that, but little investor upside.
From a recent METR report, benchmarks point towards an acceleration, LLMs are getting better at a faster rate. There's no sign that we are about to reach the top of the S curve.
> increasingly high costs of training frontier models
Somewhat surprisingly the gains do not seems to come from more compute, we have on the METR report:
> We are confident that the acceleration was not due to an acceleration in the scaling of training compute.
Link to said report: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...
I think this could form part of an argument that acceleration is slowing. It's only because we are not making as much improvement out of hardware gains that we're having to find the improvements in more complex architecture and more tricky to create training data.
The fact that the gains are not coming from the traditional place gains come from in technology might be a sign that a high percentage of the low hanging fruit has now been picked.
On an internal benchmark with internal models, if you look at the public benchmark with public models there is no acceleration. That report also has a big graph labeled "fake data".
FYI Claude Code’s default is Auto Mode these days
In every bubble some say this. And it could be the exact same herd behavior (mentality) that creates a bubble.