This is just run-of-the-mill rent seeking behavior.

A huge chunk of their revenue comes from US-based enterprise customers. Earlier this year, in an effort to become profitable, they tried raising their rates. Those enterprise customers responded, in part, by ramping up efforts to migrate to open weight models. If those efforts are successful then OpenAI and Anthropic will lose their one remaining plausible path to profitability. The US banning open weight models arguably keeps that path open. I don't personally buy that story, but I do believe hedge funds and institutional investors would. That could be all that's needed for a successful IPO.

As for cybersecurity implications, long term impact on domestic AI industry or the US tech industry more broadly, geopolitics, etc., I don't think OpenAI's or Anthropic's leaders are any more worried about that than your average termite is about the structural integrity of the house it's eating.

▲ xorcist 6 hours ago | parent | next [-]

> OpenAI and Anthropic will lose their one remaining plausible path to profitability

You forgot about Ads. The one true endgame for every tech company.

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bunderbunder 5 hours ago | parent [-]

OpenAI's leaked financials suggest about a $38bn loss last year. They recently claimed their ad business had an annualized run rate of $1bn. Annualized run rate is a bullshit, non-GAAP, obviously fudged number designed to impress people who've never heard of regression to the mean, but still, let's trust it for the sake of argument. And let's assume that "yesterday's weather" is a good enough estimate for how their overall finances are looking in 2026. That implies that all they need for profitability is to quickly grow their ad revenue by, say, 3,500 - 4,000%.

Easy peasy. Shouldn't be too hard at all.

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xorcist 4 hours ago | parent [-]

Ads in an LLM, such as nudging users to pick a certain solution or brand, could easily be orders of magnitude more effective than traditional banner ads. Certainly such numbers are possible if you design the product to fit. We have hardly even seen the beginning of that yet.

	▲	alexfortin a few seconds ago \| parent \| next [-]
		I can't think of a greater incentive to only use open weight models than ads. Disclaimer: I already use open weight models only since ~Jan this year and very happy about it.
	▲	bunderbunder 4 hours ago \| parent \| prev [-]
		[dead]

▲ djierardi 6 hours ago | parent | prev | next [-]

This assumes that everyone doesn't reach "good enough" at some point. The best developed area here -- software -- seems to suggest a "good enough" plateau, if not an absolute plateau.

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pessimizer 3 hours ago | parent [-]

I don't think so - they could get a lot better, but it's going to take a lot of human brainpower to get there. They are barely adequate now, but their weaknesses are sold as intrinsic to the nature of the software in a way that mere civilians are too dumb to understand.

But imagine a world in which you could ask AI to write software to accomplish a particular task, and it understood completely what you meant, made tasteful choices, interviewed you as it went along to clarify things, and generated beautifully commented (maybe even Knuth-style literate) code that wasn't dogshit. Doesn't seem like an impossibility. What is annoying is that we're pretending like we have anything like that now.

Instead, there's this sort of science fiction pitch where we pretend like the LLM is just too damn logical to understand your sloppy human language. It's not logical, it's built on (human-generated) slop and generates slop. It's simply failing. We've come up with better ways to orient it towards big picture thinking and filling in outlines, but we don't know how to train it on big pictures.

Not that this would stop open models/harnesses from following (or even leading) frontier labs and destroying their insane valuations, but I don't think there's any evidence that a plateau has been reached. What we have is not great, and while a wall may have been hit on current approaches, the money, compute, and simply current progress in ideas has us in an environment where everything could change over the course of a crazy day. A single blog entry.

I do think that the frontier labs seem to have reached at least a temporary plateau. But if they figured out how to move past it and this new method didn't require a lot more compute, they'd still be screwed. Their only out is to pass the bag to the public, but Firmus, CharacterAI, CoreWeave...

	▲	autoexec 2 hours ago \| parent [-]
		> generated beautifully commented (maybe even Knuth-style literate) code that wasn't dogshit. Doesn't seem like an impossibility. It seems closer to impossible than possible with the technology we have. Maybe something that isn't an LLM will come around that can do it, but even if that something else was capable of delivering perfect, ordered, maintainable, code without bugs and hallucinations it would have to be practically magical to understand completely what people mean when they talk about what they want because people are incapable of articulating what they want in a way that even other humans can understand. For anything non-trivial, a technology capable of getting that information out of person without countless meetings would have managed to accomplish something far beyond what entire teams of humans are able to do today. > It's not logical, it's built on (human-generated) slop and generates slop. This is why if we haven't hit a plateau yet we very quickly will. All the good human generated slop has already been consumed and is being drowned out by vast amounts of the lossy, error filled, and highly concentrated slop AI produces from the human slop it was trained on. Do you really think an LLM that spews out content based on statistical relationships is going to improve when it's been primarily trained on the slop of previous generations of LLMs? Hallucinations and errors will carry forward and spread like cancer. All the intelligence and creativity LLMs borrowed from human output will homogenize as the LLMs distill and then reiterate, effectively making bullet points out of bullet points, until their mimicry of intelligence becomes unintelligible.

▲ mopsi 7 hours ago | parent | prev [-]

  > The US banning open weight models arguably keeps that path open.

Difficult to see how that could be implemented in practice. Ok, let's imagine only Anthropic and OpenAI hold a US government license to provide AI services in the US. But what would stop US companies from sending the data overseas for processing? Locking that down would require a huge paradigm shift, given how fast and loose they currently are with their (and especially customers') data.

	▲	bunderbunder 6 hours ago \| parent [-]
		Like I said in the very next two sentences, I don't believe it would actually work. But it doesn't need to. All they need to do is keep hedge fund managers distracted for long enough to successfully IPO.