When LLMs have a memory longer than an hour, I will become more concerned.
I'm not sure what an hour memory here means, since context windows are measured in tokens. Even six months ago, LLMs could think for well over an hour before producing an output.
For anyone who is actually very deep in AI *in the real world*, not just heads down in foundation labs - the hyperbole is off the charts on all of this. We are NOWHERE NEAR the exponential yet. These labs can't even get AI to improve their own software reliably, let alone do other things reliably.
This seems odd at multiple levels. First, the people in the foundation labs if anything have more of an idea what is happening than the general population. They are seeing how models are made, what they contribute, and how they go run off the rails. I'm also puzzled by the claim that we're not at the exponential. Two years ago, AI systems could barely do AMC and AIMIE problems, basic high school math contests. Then the AI got to be good enough to do well on Putnam results. Then, early this year, AI got to be good enough to solve multiple open unsolved math problems, including Erdos 1196 https://www.erdosproblems.com/1196 , the Unit Distance Conjecture https://arxiv.org/abs/2605.20695 and many others, and the trends there are just continuing. That's one field, but it is one where the standards are most objective about what is happening. That certainly looks like exponential growth. I'm also puzzled by seeing the labs cannot get the AI to improve themselves is a good standard. From the perspective of a lot of people who are concerned, once that's happening, it is likely too late.
Yann is the most sane of them all because he recognizes we need some massive breakthroughs if we want to achieve real AGI
What you mean here is you agree with Yann. But the point is that lots of people who are as qualified as Yann, disagree. So if, as in your first comment, your primary problem with Coxon is lack of experinece, you should find this situation alarming. Worse, your complaint about Coxon was lack of lab experience, but in your new comment you object to people being too deep in the labs to be objective. This leads to the weird situation where no one is in a position to be relevant to listen to.
. LLM tech has hit its limit, unless someone cracks memory and continuous training.
It is possible that LLM tech has its limits. But people were saying it was hitting its limit 4 years ago, and 3 years ago, and 2 years ago, and a year ago. 4 years ago, people who were saying LLMs were limited would not have predicted they'd be solving unsolved math problems. All those predictions turned out to be wrong. Why should saying it now be more likely? And what if you are wrong here?