How about the simplest explanation?

* AI Labs hit the scaling wall. They need either new techniques, or vastly more powerful hardware to advance further.

This explains, the miraculous incompetence of AI labs in securing sandboxes and figuring out "alignment."

So they are between a rock and a hard place. They need limitless VC money because they cannot operate otherwise, and they do not have the capabilities to go further. The scare tactics and the "pacing the frontier" makes perfect sense then; they can IPO on the assumption that their ridiculous balance sheet doesn't matter because they are holding back. Because they are in control. The regulatory capture would be double whammy if they can manage it.

Open AI already said they have smarter models, and Opus 5.5 is rumored to be "taught" by a "teacher" model already; they are essentially distillations from bigger models, that both labs probably cannot economically serve to the public, due to hardware simply not being there. And, most of the improvements are not at the model level, but at the agentic glue level. Labs are getting better at RL'ing the models for agentic use cases, but the inherent flaws are still there. Models still have trouble with locality in writing for example (bunch of research on this that shows model size is the determinator), and agents are the bandaid over that.

And in the meantime if one of the labs makes a breakthrough, they'll push with all they have, because why wouldn't they? The idea that current LLMs can actually go rogue is just hilarious; in all cases, agents are being led by (deliberate) incompetence.

Pacing the frontier and the scare tactics will be seen as new generation's snakeoil tactics, perhaps will be called a flavor of AI CEOing or something.

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edmundsauto 2 hours ago | parent | next [-]

I agree. The companies want to release their models that are just ahead of the competition while working on UX based vendor lockin. They can buffer model releases if everyone is slowing down (releases are hard and expensive!) and then do more foundational-but-not-ready-to-apply research while continuing on he funding, valuation, addition, and revenue pushes.

I read the whole thing as coordinated behavior to reduce the breakneck pace of 2026.

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0xDEAFBEAD an hour ago | parent | prev [-]

This is the type of Ed Zitron prediction which keeps being wrong: https://danluu.com/zitron/

Anthropic's revenue is up 50% in the past two months. They're not hitting a wall.

	▲	OliveronData 38 minutes ago \| parent [-]
		That's an association fallacy. And revenue has no indication on training costs in this context. Subscription "allowance" is going down steadily and any increase is an instant incredible deal. Opus 5.5 is the most obvious outlier. Despite being supposedly cheaper than 5.6, GPT 6 Sol has less usage than 5.3 Codex. You might say that's because of the improved capabilities, but then you have to acknowledge that labs are tightening the ship as costs are getting higher.