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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:
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."
Why would the current winners of the race want this? This would be catastrophic for their companies as others caught up. Ironically its the current losers that would cynically want this as it would allow them to catch up. I'm so tired of these takes that hallucinate some cynical interest that falls apart in two seconds of thought because it's impossible to imagine that the type of people who have been publicly posting about and discussing the dangerous ai since before the industry existed could possibly care about the singular thing they've been warning about for over a decade.
Who? Who the fuck are these other better mouse trap makers that they're afraid of? it doesn't make any sense. If signaling that they're willing to give up their lead isn't enough to show you that they are serious about the problem then what possible action could change your mind?
Perhaps because they anticipate or hope that the applicable regulations would impose a lot of bureaucratic hurdles and red tape on new players in the industry.
There are already multi-billion dollar hurdles for new players in the industry to put together enough compute to train a frontier model, the idea that having a legal department is going to be the difference is absurd. This isn't a haircutting industry.
As was pointed out by @confidentcrescent big companies typically start as small companies. And there are billions of dollars of capital potentially available to the right company (or wrong company) which shows promise. That's how the current frontrunners got there.
Not all hurdles can be solved by having a legal department. For example, suppose a license is required to buy more than a modest amount of computing power. And that license requires a background check and investigation of a company's investors and senior officers which takes 6 months or even a year. The delay and prospect of being investigated could easily scare off a lot of potential investors and/or founders.
None of the proposed regulations apply to small companies. They're about frontier training runs which cost many millions to billions of dollars.
If people are doing frontier training runs we indeed want to see them regulated, that's the point. Of course we should not do the regulation badly so that legitimate runs take months or years to get approved but you're just making a general argument against all regulation. These are big boy companies spending big boy money, we can let them speak for themselves.
If being investigated scares you off from being involved in building the kind of thing that has nation state hacking capabilities then I would call that mission accomplished.
I looked at just one proposed law and based on that I disagree with this claim, since the threshold includes affiliates. So for example, if Google acquires a substantial share of your AI startup, you're covered. Given that people create and invest in startups with the hope of being acquired, that's a significant issue. Of course that's looking at just one proposed law.
The thing is, I'm not making an argument for or against regulation. And in fact it might very well make sense to have these sorts of obstacles in place.
In substance, you asked what direct incentive the current leaders in the AI field would have to support these sorts of obstacles. And I answered that question. Whether certain obstacles would make for good public policy is an entirely different question, in my opinion.
It seems like you are addressing a different issue than what I am addressing, and for that reason it seems like it would be counterproductive to have further discussion.
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Yeah, I work in a startup at the moment and having to wait most of a year between demonstrating a proof-of-concept and getting the finalised contract to do the thing you'd already agreed to do is killer. How do you pay your engineers for the six months of no-fees? How do you get investment when you don't technically have a contract? It's a circular nightmare.
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Big companies start off as small companies. Anti-competitive regulation rarely targets the hard-to-kill large companies and instead strangles the small companies to ensure there's nobody around to grow into a big company.
Frontier models also aren't their only competition. Anthropic and OpenAI have both shown a desire to raise prices which is going to make budget and self hosted models more appealing alternatives as they do. Adding large costs to everyone in the industry doesn't just put a big barrier to entry up but also brings budget options closer on price to the big players.
I know what regulatory capture is. I'm asking how the type of regulatory capture being discussed could possibly apply to a product class that takes billions of dollars to bring to market in any scenario. Stop pattern matching and look at the actual fact pattern it's absurd. If a small business was able to bring a frontier model to market alone then we have a whole lot of other things to worry about.
I don't believe these regulations will only apply to the very top multi-billion dollar companies. As far as I've seen nobody has proposed any specific rules yet so there's plenty of space to build an expensive system that everyone in the AI space needs to participate in.
At the very least I expect a regulatory system that applies to frontier AI would need to check models to determine whether they count as frontier AI before they release. Otherwise we'd just be taking the word of companies that their model is totally fine and too dumb to count.
They call for "pacing the frontier" and all the previous proposed bills have had frontier scale compute reporting requirements. Essentially if you aren't using frontier levels of compute and don't believe you're pushing the frontier then you're exempted. We can go over any specific calls for state intervention if you want but as far as I know the labs are not proposing any regulatory framework that applies to smaller models.
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Does it really take billions of dollars? Wasn't Mistral 7B developed for $1 million or so, and Mistral Large for less than $100 million? Didn't DeepSeek or whoever launch a successful model with a low-end stated cost of a few million dollars? Google says that estimates are that they did that with a hardware investment of less than $2 billion, so I guess there's a number of different ways to creatively make the costs seem lower than the true cost of doing business...but if compute costs really are dropping it stands to reason that developing new models will be cheaper in the future, not more expensive...right?
I realize the obvious counter-response is "well but those aren't cutting edge" but as far as I can tell, most people using Anthropic and ChatGPT aren't using their cutting edge models. Thus is seems like edging out smaller providers protects the majority of the primes' customer base (although to be fair I am not sure how much of the majority of the primes' customer base is actually paying for their compute.)
Compute costs are arguably not even falling, as the utility of compute is rising. See memory price hikes per GB, on the same process.
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You can make a tiny model pretty cheap - I've toyed with a 200m one for specialized purposes - but the bigger the model, and the longer the training, the more expensive it gets. There's been a lot of tricks developed so that training costs aren't exponential or super-exponential, and there's more to a model's intelligence than how many parameters it has, but the costs scale quickly.
DeepSeek's costs are also... probably not comparable. They say about 6m for DeepSeek v4, and it's probably not a lie, but it depends on a training architecture built for specialized silicon that you or I can't get, and highly optimized decisions about power costs. Meta 4 Scout at 12m for 109B parameters is probably more representative for anyone not directly supported by a major world power (which, tbf, is not a matter specific to Moonshot or China). Plus the whole MOE thing can make comparison to the real dense models
That said, while smaller models do fine for constrained projects, most have struggled pretty badly with things like tool-calling or multi-step problem solving. Mistral7b's pretty out-of-date as small models go, but Gemma4-12B is still stuck at 'well-read but not-bright intern'. Qwen3.8-Flash-Next can do some amazing stuff, but it's about Opus 4.6-grade if you're being generous. For a lot of purposes, that's fine -- the only one of these items it can't do is the 3d modeling one -- but users tend to notice where the model is brain-damaged as much or more than the broad areas it succeeds.
((I'm not convinced that the small models are safe, but they're probably not capable of hacking the NSA and definitely aren't close to serious self-improvement.))
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Mistral is not a frontier lab and no model they've ever trained would trigger any of the proposed regulatory scrutiny.
No they didn't. The number you're thinking of was the money they spent getting an already existing model to work with chain of thought.
Ok sure, but in the coutnerfactual world where there is no regulation the current frontier labs will have substantially better models.
If they're not cutting edge then the regulations do not apply. They're trying to pace the frontier, not dinky merely hundreds of millions of dollars models.
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Yes. There's a lot of money around. Getting a billion dollars, or 10 billion dollars, is much easier than getting past a regulator controlled by the firms you want to compete with.
So the dastardly plan here is that the frontier labs destroy their own leads on the theory that they'll be able to control the regulatory state so thoroughly as to choke out all other competition and avoid becoming a commodity. Except when the frontier is paused they'll instantly become a commodity with several other players in the same tier already. This just doesn't make any sense. If progress stalls then the margins on current models get competed down to the marginal inference cost with or without other market entrants. Half or more of the nation hates these companies, the idea that a regulatory state is going to be particularly sweet to them is hard to imagine and you're making a general argument against all regulation. I'm sorry but one way or the other we're going to be regulating the production of models with nationstate level hacking ability. The public and state will not stand for any random with a bone to pick having the ability to shut down the power grid.
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