AI Execs Cry 'Communism' as Open-Weight Models Undercut Their Trillion-Dollar Bubble

> In a world where open-weight models dominate, the proprietary frontier models of Anthropic and OpenAI will find it virtually impossible to charge monopolistic pricing.

Maybe they can't charge monopolistic pricing (there's a few market entrants anyway today), but they can still charge premium pricing.

Also two things can be true at once.

American and western AI companies or other companies can rightly be part of the chain of general concern from western societies about anti-democratic authoritarian regimes (Iran + friends, China, Russia, North Korea, Cuba, &c.) getting ahold of technology while simultaneously also being worried about losing their market position and seeking regulatory rules to entrench themselves against actors who do not share the same values and seek to undermine western businesses with trade practices that are intolerable to our societies.

There was an interesting discussion on pricing in the recently leaked DeepSeek investor meeting, where CEO Liang Wenfeng went on at length about his personal philosophy on pricing which in the end, for them, comes down to pricing models so as to be able to recoup the cost of the hardware they are running on in 10 months.

He doesn't want to drop prices below that since at their already very cheap prices doing so doesn't increase demand. More interesting are his reasons for not wanting to price higher, which are hard to summarize from memory, but are based around this being a sustainable business model. Pricing higher will only encourage competition.

Note that DeepSeek are not trying to compete with the likes of OpenAI and Anthropic, at least at this time, acknowledging that they just don't have access to the compute to do so (although they have plently of money). This pricing is not about competing with the west - just his own pricing philosophy.

Maybe DeepSeek is a special case, being a hedge fund, and to large degree developing for their own needs (a bit like Meta, perhaps), but nonetheless they are out there as part of the competitive landscape.

No doubt Anthropic and OpenAI have a radically different strategy on pricing - seemingly more just what the market can bear, but with a company like DeepSeek they are not battling "AI communism", just a de facto competitor with a very different philosophy.

Other companies like Moonshot and Alibaba seem to want to charge as much as p […]

Hardly anybody is talking about the biggest political aspect of open weight LLMs (actually, any LLMs):

Because they instantly and reliably(tm) give you the information you want in the format you want it in, they are going to replace the web search as the dominant mode through which information is disseminated - if, indeed, this hasn't already happened. As such, LLMs are effectively a form of publishing for their training data.

Which means a vicious fight for control over what goes into LLMs, and strong incentives to widely disseminate useful LLMs that encode your structural biases, instead of some adversary's. We already talk about Chinese models that won't talk about Tiananmen Square, but that's eye-rollingly sophomoric compared to what the stakes are now. Imagine a model that subtly discourages entrepreneurship, because its creator doesn't want upstart competitors. All questions of media bias are multiplied incalculably when LLMs are folded into all of society.

I think we need to take LLMs seriously as sovereign projects that are as critical to the democratic experiment as a free press. Funding needs to be nationalized; training needs to be conducted in the open, on open datasets; fine tuning needs to follow democratic principles. Only then will they be fit for purpose for their inevitable structural destiny: arbiters of human opinion.

I don't think the open source models are anywhere near ("we’re talking around four months of lead time") Anthropic's Fable for long-horizon context planning. Sol isn't either. The benchmarks are quite misleading. The short-term question is how many customers need these deep problem solving capabilities? The long-term question is what will the frontier labs build (deepening and broadening of capabilities and products) if they are capitalized as they desire?