I don't think "plateaued" is the right word, but I do feel like there's been something like a logistic curve compression in the difference between smaller and larger models as the field evolves. For inference at least, the scale of practical difference between a single high-VRAM GPU or SFF UMA box, a whole rack, and a whole data center seems to be falling far short of what we might have imagined just a few years ago. The conversations I've heard have largely turned away from breathless anticipation of the next frontier model and toward attempts at hard-nosed evaluation of which tokens are worth the cost.

I'll take the opposite here. If someone put in frontier AI models from like .... last june I guess? in a box and let me run it with "decent" token throughput I would be happy.

I think it's worth acknowledging that the power of LLMs at this point is not really so much in the smarts, but in the coordination and the surrounding harness tech. "Written english" turning into sequences of commands[0]. The whole agentic "stuff" in general. Tools + coordination is the superpower. The reasoning... it doesn't have to be _that_ good for the rest of the stuff to work. On good codebases and infra, at least.

And I say this as someone who really would rather most of this stuff disappear!

[0]: programming is obviously text to commands, but there's a loooooooot of futziness that LLM reasoning has let us remove in some flows

Anything in particular? My experience has been like seeing the addition of retractable cupholders, but maybe different domains.

astra is more parlor tricks than real gains tbh

i swear they trained in on threejs in particular so those idiots on twitter could spam their garbage demos

They've not plataued but they're certainly not as impressive as the hype would have them to be.

The reality is, it doesnt matter if LLMs keep getting more powerful because they still need a human to steer it. Without the human providing inputs to the LLM it just sits there and does nothing.

I think we are in the second knee of the S curve, simply because we are hitting the point where is not enough hardware in the world to throw at this problem. These AI companies have bought everything they can and yet the models keep growing.

People are definitely finding new things to use the models for, and orchestrating increasingly large swarms of agents in useful ways -- every single day, especially the last few months.

But the basic single-NN frontier capability has been pretty stationary since Opus 4.8. Kimi K3 is almost as good as that with open weights, which has the frontier labs terrified.

The only big thing on the horizon is if we can get diffusion models working reliably; that would be a big step forward. Inception's Mercury is AFAICT the leader here. It's stupifyingly fast but has obedience/hallucination problems that the autoregressives solved ~2 years ago. So it's not ready yet but improving.

Also, FFS why is Grok the only model that knows how to do parallel tool calls? Such a useful ability and nobody else trains it in. Or if they do it just doesn't work.

Impressed with the model performance or the chatbot/agent performance?

Really? My employer rolled back to opus 4.8 because 5 was expensive AND crap. Didnt even consider fable because it didn’t add any additional value.

For most software eng and design work opus 4.6-4.8 just works fine. For everyday joe asking ai to plan a trip or home diy work even sonnet works fine.

Any cybersecurity or other areas are niches that cannot support trillion $ valuations. What am I missing? Genuinely curious