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PaperclipPerfector  2d ago (text post)   5596 thread views

Culture War Roundup for the week of August 31, 2026

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Is There Real Anti-Trust Risk From A Coordinated AI Lab Pause?

In AI-pause discussions it is increasingly common to hear the "anti-trust" objection. The idea is that if the labs were to coordinate a pause in frontier AI development, this would be a "conspiracy in restraint of trade" and therefore illegal.

Putting aside the question of whether it is ethical to risk a double-digit chance of destroying the world in order to avoid getting sued (it isn't), is this even a realistic possibility? My impression is that anti-trust law in the United States is legitimately quite fuzzy (as the NCAA is currently discovering).

Putting my cards on the table, I think this argument is cope. The labs want to pretend that they want to pause, but they don't actually want to pause. "If she wanted to, she would," etc. Just today, Anthropic put out a new statement containing the following sentence:

"To be clear about where we stand: we believe the world would benefit if the industry adopted a lawful, verifiable, effective mechanism for coordinated pacing as soon as possible."

It seems like every time I read a statement from them, they've added more and more qualifications. Are they going to keep racing until congress passes a specific anti-trust exemption? That arguably seems like what the word "lawful" is implying.

Perhaps I am too cynical but the call for government coordination seems like an obvious attempt to build themselves a moat in a largely moatless space. My own experience has been the cost of model switching is near 0. Approximately all the agents, skills, whatever I've used in my job work pretty seamlessly for any of the underlying models. Some have their quirks but essentially all of them are good enough.

"We the current frontier mousetrap makers think it should be ILLEGAL for anyone to build a better mousetrap faster than we can, for the good of humanity."

The competitive pressure is real. Nobody seems to be held accountable when their models escape and go out to hack various websites. They are rushing and building shoddy sandboxes and failing to monitor these entities properly, such that they set up miniature societies.

Per rumours the newest GPT Astra is doing at least some thinking in neuralese so we can't even tell what it's thinking like we used to.

I'm not the biggest fan of existing nuclear regulations but if it was a complete free for all with a bunch of companies competing to build as many reactors as possible, as quickly as possible, then problems would surely emerge.

Mousetraps aren't especially valuable. We have cats which do the job better, if anything. Mousetraps are not fail-dangerous. Nuclear power is fail-dangerous. AI is also fail-dangerous and it can actively plot against us, it's in another category entirely to climate change or asteroids or explosives factories. We don't have nearly as much understanding of AI as we do explosives, asteroids or nuclear physics.

These rumors are dumb, I hope OpenAI clarifies what they mean. Looped models still output tokens in chain of thought, their capability to hide thinking is not changed, they just have more effective depth. It's no more "neuralese"-inducing than just making the model x times deeper. In fact, "neuralese" in the common parlance has nothing to do with architecture or the structure of activations, it's an effect of compression of language during RL with length penalties. One of the most neuralese-like open models we know, V4-Flash, has a measly 43 layers.

Of course there's the issue of "a tiger is just atoms", maybe we should be suspicious of greater depth of latent computation irrespective of how it's achieved.

Good point. I had half a mind to add 'some more technically minded person may shed more light on this' to that sentence since I'm not that adept in the nitty-gritty...

And it's not like the rumour-mill has been too reliable before.

Missed this when posting, Jacub had already spoken:

I want to prevent a race into unmonitorability kicked off by confused reporting. The depth of the computation graph for our present frontier models, including Astra, is within a factor of two of GPT-4.

(as per the rumors, GPT-4 had 120 layers. The deepest production LLM I know about is Hunyuan-TurboS, at 128 layers. Llama3-405B had 126. Nobody really wanted to push beyond that because it kills latency and makes training unstable).

OpenAI has worked to preserve and utilize chain-of-thought monitoring since our very first reasoning models. We deeply care about this technique, as it can give us a view into how model alignment generalizes from its training distribution. I do think it is fragile and unfortunately trending in a negative direction, for reasons not contingent on architecture changes that I will write about soon. But there are things we can do to strengthen it, and it's a core goal of our current research program.

Basically, yeah the situation is not great but the recurrence is not really what we should be worrying about.

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