Gacha and MOBA games, for example, probably wouldn't make less money because someone rewrote the client program in Rust. And players would cancel those clones spontaneously in the name of anti-cheat.

Not sure if this particular project is also sponsored by taxpayers' money or not, though.

I feel that everyone in this small AI bubble (compared to the "normies") is busy trying to take advantage of AI to surpass everyone else, while fearing being surpassed by everyone else.

Most people assume LLMs are just a tool for making money, an arms race over wealth and social rank. But those like Dario seem to be worried that LLMs could potentially be used as automatic rifles by some dedicated psychopaths. Whether it could be that dangerous, or just an exaggeration, or we should have everyone armed, require licenses to own one, or outright ban them, is debatable. But I can understand his point.

All I can say at the moment is that this is more subtle than the superficial conspiracy/black-and-white narrative.

On token pricing, I think it's very much bottlenecked by hardware (the aggregate of compute) rather than the number of competing models. Assuming that the ceiling of the token price is determined by the economic value a unit of compute can provide, then the less efficient ones would be priced out of the compute allocation. It's not consumers bidding up a limited number of different models, but more like tokens of different models bidding up the limited computing resource. Less-intelligent tokens (which are generated by weaker models) are crowded out by smarter tokens from the limited compute. My prediction is that we'll see a meaningful downward pressure on token prices only when the new batches of next-generation hardware get mass-deployed.

The point is that electricity becomes a commodity, rather than, say, nuclear reactors or gas turbines also need to be commoditized.

Contrary to most people in the software-minded circle, I am not very excited about running local LLMs. Most of what I have seen involves selling wrappers that call APIs on top of an LLM AI, similar to how traditional SaaS has generated revenue from new technologies. So, this would certainly make those people who are working on that sensitive about the pricing. LLM pricing is treated as a continuous cost in the COGS. They don't really use AI to create much value (consumer surplus) other than replacing the existing boring products, like just building the good old CRUD apps, but faster.

What I find REAL interesting about the potential of LLM AI instead is to create new technology out of it, or revolutionize something old to be order-of-magnitude more efficient. In this regard, the expenses on those tokens are more akin to an upfront CapEx.

Cheaper tokens would surely be nice. But if what we are talking about is, like, solving self-driving, curing cancer, or making air-conditioners 100x efficient, the narrow focus on running a cheaper model in my home so I can write my SaaS apps for my get-rich-quick business looks really unpromising and a waste of time for human civilization in a near-singularity horizon.

The frontier LLM labs run on a huge fixed cost and very low marginal cost. They need the economies of scale to make sense of the business (an incentive to expand their user base as large as possible). Imagine that you want to buy a few B300s to run GLM 5.2 and rent the service out to other people. How could this business be viable and sustainable in the first place? You need as many customers as possible. If you charge everyone $1000, you find fewer customers who can afford it. It rots the ROA if the servers are not utilized 100% (you would better buy less compute instead).

Also, the marginal cost for onboarding a new customer is low. And it's getting even lower when you have more customers. You wouldn't leave money on the table (especially for your competitors) if you want to maximize your profit.

By this logic, all frontier AI labs are incentivized to lower the price to maximize their customer base, profit, and ROA.

Though the income of the individuals and businesses that rely on the expertise of the knowledge would be damaged. Is that what you meant?

Edit: At this stage, the revenue made by the AI labs is almost entirely spent on the formation of fixed capital and opex. The demand is mobilizing physical resources with money. Atoms are relocated and reconfigured into compute racks. But eventually, the created productivity will perhaps make supply-elastic goods extremely cheap and abundant, while the supply-inelastic goods will be worth even more relative to the elastic ones.

After all, money is simply a token for the transmission of physical resources. It doesn't create stuff out of thin air. When new stuff is created, it just makes money cheaper, so that the banks can respond with more money to counteract it. More stuff -> deflation -> more money creation allowed to undo the deflation.

But the "exchange rates" between different goods and services will diverge. That's also why I don't think a direct money transfer like UBI would fix the problem, when it doesn't change the divergence of relative economic values of different goods. Let's say, extremely cheap software and entertainment, but unaffordable healthcare and housing. More money for everyone doesn't make limited resources available. So, what I am leaning into is some sort of Georgist policy. That could hopefully mitigate the price divergence, assuming that AI cannot make every commodity equally abundant.

Why don't we capture Meta and Google as they allegedly take advantage of more publicly available information for profit? Let alone the truly valuable knowledge, like mathematics, has nothing to do with the majority of garbage posts that an average person would "contribute" on social media.

If we really want to tax or nationalize some economic activity, then, in my opinion, the target should be what it takes from society, not what it produces for society. By this logic, we should tax all labs, including those lagging ones, that utilize the public knowledge.

However, if everyone can access the public knowledge without rendering it less useful or reducing its available quantity, there should be no reason to tax it.

It's always funny to see people tempted to call open-blobs/open-weights, which are literally shareware like WinRAR or Adobe PDF Viewer, open source, and then need to invent a new term for what is actually open source.

Similarly, some bosses might believe that they can hire 100 cheap, unmotivated SWEs to replace Linus Torvalds or Fabrice Bellard and achieve something slightly worse. But in certain areas, it doesn't work like that.

It's funny that Google, Meta, TikTok, OnlyFans, PornHub, and many other lucrative businesses never open-source their core business software, and people just don't bother about it with that moral standard, simply because we don't need to pay for the service (paid by ads, actually). To me, that is the hypocrisy.