Probably, depends on how you measure productivity.

If you measure productivity in terms of number of automated bureaucratic events (e.g. creating files, organizing files, generating lines of code, finding bugs in code, generating emails, responding to email) then yes productivity will continue to increase because LLM's are the killer app for increasing bureaucratic events.

If you measure productivity in terms of changes to the material world (e.g. traditional things like trade goods, buildings, food stuffs, irrigation systems, transportation networks, etc.) then no because LLM's have little meaningful impact on those activities...no AGI is going to harvest lettuce for our wedge salads).

https://openai.com/index/ten-advances-in-mathematics/

Fable 5 is a model you instantly FEEL how smart and superior it is. GPT 5.6 Sol is a HUGE incremental improvement in multiple directions and dimensions.

DeepSeek v4 Flash 0731 is a huge improvement over the preview version, using exactly the same architecture.

Kimi K3 gets open weight models very, very close to the frontier.

No my friend, we are not done yet.

Like imagine how crazy that you can get human-like intelligence in a small device, we should be able to do more than a chat interface.

The "models are getting better with every breath you take" is just an unproven claim that the AI companies want everyone to believe and want the evangelists to use in every argument.

The AI companies have to put out new models regularly, not because of any improvement but because the failure to do so will signal stagnation. That's how it works in the tech industry. It's not like the wine or cheese industry where you just follow your hundreds-of-years-old recipe exactly and people buy the product because of that.

This is the technology - this is what it is. We've all seen it at this point. We know what it can and can't do.

Sure, there's Mistral 7b running locally on a Macbook vs some large frontier model with MoE, context caching and a nice UI - but it's not all that different.

Don't we know what LLMs are at this point?

But it's not like the mystery of AI is fully solved and all opportunity has ended. Now we have to see what can be done with it - that part I think is still mostly unexplored.

People are trying things in the software space, in music, video, legal, and there are quite a few "AI for AI" companies (AI analytics, infra solutions) - so we'll get some innovation there.

"Harnessing" - People will figure out better ways with model routing, multi-modality, caching, combining things we already know.

I'm slowly seeing the philosophy of mind creep in and make contact with software engineering. Somewhere between "AGI" and Chalmers' Hard Problem of Consciousness we'll get some new innovation around the concept of thinking itself and what it means to be an intelligent being.

Autonomous driving is paving the way (hehe) for autonomous humanoid robots. A concept I originally scoffed at, but now consider to be a very likely thing to happen sooner than we might realize. This is going to change so much, it alone is enough reason to say "AI hasn't peaked" (and these will involve interdisciplinary models like vision, navigation, and LLMs).

It's like asking if "programming has peaked" in 1999 and it would kinda be a "Yes". And then a couple years later ColdFusion would hit the shelves in a giant box like a Deluxe Edition RPG - if you need a laugh: https://www.reddit.com/r/webdev/comments/1o26h3l/i_have_deve...

If we're nearing the point where you can spin up 1,000 agents to look for ways improve existing models, then automatically run experiments to validate those ideas, we're more or less at RSI.

As always with AI progress, compute will bottleneck this early on, but a few efficiency improvements could dramatically increase this pace of progress.

I suspect we are at most 24 months from FOOM, but I suspect within about 6-12 months most frontier AI labs will be claiming the majority of their AI research will be AI-driven.