The market can stay irrational for a long time but it does eventually have to happen. The 2008 crisis was the result of overleveraging in 1999-2003 and interest rate rises from 2004-2007, so it can take years for it to crumble. 2 years is a long while but it could easily take two more before people realise the emperor has no clothes.

Or maybe they'll just stop releasing frontier models for the proles now.

Not even the first time this is happening. The US & Co. are concerned about being technologically overtaken by Japan / China, but don't make their universities affordable.

I'm guessing they'll stop funding / building new data centres? Not all the ones announced have been built, and not all those built are operational.

They'd keep releasing them if there was money in it.

There is absolutely no reason to expect that you can scale LLMs indefinitely.

Could go the way of the compute market though where there's more money in big contracts than there is the individual consumer. We've already shared our inner sanctum with AI for the past 4 years so they have all the data they need.

My read on this is, that they realize that the full AGI is not coming and they need to focus on computational efficiency to be profitable. We will probably see a lot of work from them focusing on increasing switching costs as the models themselves become commoditized.

Their problem is, that their frontier models get distilled quickly by DeepSeek and co. The distilled models will then go on to provide 90% of the efficiency for 10% of the compute.

Right, they might just focus on big business, or even angle to become a vendor of record for the government. So, individual users might not really be of interest anymore.

Right, I'd argue that China proves you don't need massive data centers for training. And yeah, I think something like Qwen 3.8 is more than enough for tasks most people do. There are a lot of tricks you can do as well with the harness, where there's a lot of attention is shifting now. And it's a lot cheaper and faster to develop better harnesses than train new models. I expect we'll start seeing a shift towards neurosymbolic systems before long where the LLM acts as a stochastic component within a symbolic logic engine.

I'm skeptical they ever expected AGI to come as a result of LLMs, I believe it's just a convenient talking point both for hype, and to distract from more immediate issues, like how corporations use these tools to screw over workers.

They hit a wall and want to prepare everyone for the fact that they won't be able to meet the expectations that they themselves created.

Not now, but I'd expect LLMs to be much more efficient in a couple of years.

They lied their asses off about capabilities and are using “safety concerns” as means to get investors off their asses. Google didn’t get new billions of investments and oh look their model didn’t “escape”.

It’s because they’re hitting model size constraints. There’s only so much memory bandwidth you can get between racks or even rack spaces and memory bandwidth is the constraint for nearly every ml thing.

Expect a reversal once a more memory dense component hits.

They all must have figured out by now that hey are hitting a limit. I honestly don’t think LLMs will lead us to AGI. I’m sure it’s a step on the path to it, but I’m think it’s a lot further than most think.

So, they make this “agreement”, then the slowdown is just being “responsible” so that the investors don’t panic. Meanwhile they all go full tilt behind the scenes to try to find the next breakthrough.

Even more ridiculous then since for quite awhile their students have been keeping our universities fiscally solvent and now we’re discouraging them from participating and cutting support for our universities at the same time.

I can only believe this is willful

That's my view as well, LLMs are likely just one piece of a much bigger puzzle and we're now hitting the limit of what you can do with them in practical terms.

There's no reason to think that the architecture itself can scale indefinitely. It might very well be that LLMs have some hard constraints on the scope of the problems they're capable of solving.

I expect so as well, and my prediction is that we'll have LLMs that are roughly as capable as the current frontier that can be run locally within a year or two. At that point, it's just going to be good enough for vast majority of tasks most people need to do.