typ

Given the current trajectory of all things, I think that the drawbacks will outweigh the benefits for a project like AOSP due to the changing economics. The reduction of SBOM (and therefore the attack surface) will be far more beneficial than the saving of work from hundreds of imported dependencies. The trend of vertical integration will likely happen in software sooner or later.

Or they will just stop releasing certain genres.

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.

Architectural/algorithmic tweaks do advance the efficiency frontier nicely. But raw intelligence mostly comes from data (not just its sheer quantity, but also how it's curated & cleansed) and the scaling law. The know-how about data curation doesn't seem to get published much, even among the open-weight labs, though.

I think it should apply to all industries. But for many (or most?) countries where power generation is privatized in the first place, this shouldn't be a problem that we need to fix. The trickier question might be about the grid, which in most cases is publicly funded.

I am not against AI data centers. But it's a good thing that the electricity infrastructure expenditures for AI data centers' consumption are paid by the companies making money from AI training and inference, rather than being paid by all taxpayers collectively.

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

Especially when all major labs are building their custom inference chips.

Saw a theory somewhere that, instead of raising the minimum wage, a policy that enables and incentivizes job hopping is what actually works for increasing the median wage level. The inverse implication of the theory is also interesting: any policy that makes job hopping harder than staying would suppress the wage level.

> give people anything except your words.

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.

Me neither. But open weights are not open source in the first place. It wouldn't be surprising that the raw intelligence of an LLM model has little to do with the architecture (which is the part most published). The secret sauce, like how/what data gets curated and cleaned, has rarely been talked about. So, even if the labs upload their weights, competitors are very difficult, if not impossible, to replicate the technology independently.

Electricity becoming a commodity doesn't necessarily make nuclear reactors become commodities. And as we can see, the publication of open-weight models doesn't really diffuse the know-how of training SoAT models as we expected. Even the major cloud providers are still unable to build competitive ones from scratch on their own. That's very different than the traditional FOSS ecosystem, where everyone can copy, learn, and evolve on one's own (like, Bitcoin -> altcoins).

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.

There are some benchmarks I personally trust. But the most reliable indicator other than public benchmarks is the quality of products people are working on, and the model they use for the work. Not a benchmark scoreboard or a one~few shot demo, but the product they have been building for weeks. The reality is, even the diehard advocates who build and sell tooling for open weights, still use proprietary frontier models for their jobs.

Tokens will surely become commodities, but models don't need to.

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.

Abstraction doesn't necessarily lead to a smaller binary. Much of the bloat in modern software is indeed due to (bad) abstractions.

Unlike the belief that frontier AI is expensive due to a high margin, and going to be expensive if there is no competition. My understanding is that, under certain circumstances (which is most likely true), the price will be driven down just because of profit seeking.

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.