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> 2. The capital equation is still favoring more spend. Anthropic's revenue is growing rapidly and they are profitable on inference. Huge upfront spend for realized gains later on is standard for R&D. As long as serving the models is profitable and generates more value than the energy/compute consumed

But they are essentially fighting for smaller and smaller part of the pie. The better open models are the less you need frontier models, so the frontier AI companies are essentially fighting for market that will shrink the better AI is.

Sure there is inertia and the usual corporate enterprise nonsense of "we only buy stuff from other big companies regardless the cost" that keeps the licenses going but we're quickly getting into point where you don't need any of the frontier models to do majority of the tasks

> The better open models are the less you need frontier models

True in theory, but in practice hardware availability and cost will keep open source impractical for years to come.

If your moat is "Our vendors aren't expanding fast enough to supply our competitors!", you don't have a moat.

It's already very practical lmao

It's really not. The hardware to support anything even remotely practical starts in the tens of thousands of dollars. Quantized 40b param models are impressive, but are basically toys.

there are plenty of providers of open source models at lower prices than the frontier companies. you're just not well informed.

It will be impractical to self host, but not to get one that's served from other providers (which won't have to recuperate loses from training).

I don't fully get this line of argument. I am a massive advocate for open source... everything basically, but especially in ML. And open models are making incredible strides. But I'm not sure that models getting good enough for most tasks, making the absolute frontier models (Astra/Fable class) unnecessary for most people, puts the frontier labs in a fundamentally disadvantaged position.

I mainly think this because frontier labs already try to compete at this tier. For example, 6.1 Sol has identical pricing to Kimi K3.1, but with a lower price per task due to Kimi's verbosity. It seems to score roughly the same on most benchmarks, with some (like Artificial Analysis) putting 6.1 Sol significantly higher. If you need to step down, 6.1 Terra is also cheaper than K3.1 or K3 and performs well relative to their benchmarks. Opus 5.5 and much of Anthropic's recent push seems to confirm this as a major area of focus as well.

I still use a lot of open models and harnesses, but I don't know if cost competitiveness (at least not with the current crop of open models) will ultimately drive a huge chunk of average users away.

Things could change, but fundamentally, large labs aren't precluded from training more cost effective models if there is significant demand for them, which is what we're seeing now.

Let's just ignore the many practical advantages of open and self-hosted models...

Did I ignore them? I said that I love open models. If it came across like I'm putting down open models, then I'm genuinely sorry--I love open source ML, have been a involved in it for a long time, and think it's incredibly important. It's also just fun to work with open models, beyond the benefits to privacy and flexibility.

I'm just talking about the idea that frontier labs will lose market share to providers that sell open model inference at sub-Fable prices. And I'm not convinced that this specifically is a threat to frontier labs, because they also train and sell models that fit that demand.

You did not really address the argument.

6.1 Sol is not a “frontier model” and does not command the pricing of one. The reason it exists is because of competition from non-frontier models that are “good enough”.

In fact, you kind of made the same argument. As “good enough” models commoditized, the use cases requiring frontier models and frontier model pricing will shrink.

And, even if performance is not as good, many will prefer open models.

I think I did address the argument? This being the key quote:

"The better open models are the less you need frontier models, so the frontier AI companies are essentially fighting for market that will shrink the better AI is."

The implication in this is that frontier labs' market is exclusively people who need the absolute frontier, i.e. Astra or Fable. And the point I made was that frontier labs are clearly prioritizing the broader market of people who want more cost effective models, and that if OpenAI/Anthropic offers models that are price competitive with the top open models (before factoring in subscription discounts and corporate lock-in), then I don't see how that leads to the average user walking away from the labs in favor of self-hosting or relying OpenRouter/Together/etc.

I also went out of my way to clarify that I personally prefer and use open models wherever possible. I'm not saying that there isn't a real group of people who prefer them. I'm specifically saying that frontier labs also sell "good enough" models and that there's nothing preventing them from training models to fit the demands of users. It's not as if training a frontier model boxes them in such that they can't release an Opus 5.5 or a 6.1 Terra.

> if OpenAI/Anthropic offers models that are price competitive with the top open models (before factoring in subscription discounts and corporate lock-in), then I don't see how that leads to the average user walking away from the labs in favor of self-hosting or relying OpenRouter/Together/etc.

The premise is this: frontier models seem to advance, and OSS models also seem to advance in terms of cost efficiency etc. It's like a race with say a 6 month (WLOG) gap between frontier and OSS models.

For frontier models to win perpetually against OSS, they will need to have a perpetual 6-month lead, or develop an edge OSS can't replicate for some architectural reasons (say MacOS vs Linux). If say a frontier lab trips because of a bad quarter or some approach temporarily maxxing out, they may not be able to maintain an edge vs OSS any more. It is a structural argument, not an empirical description of the advantages today.

> Intelligence per dollar spent/token output seems to be increasing rapidly.

I would argue this is more due to market economics than actual inherent cost. Opus 4 was when the open models started catching up. The R1 bootstrapping chain of thought paper via RL paper was released a couple of months before Opus came out and the open models started to significantly catch up after that. The duopoly was effectively broken and they lost their price setting power.

Don't you mean the opposite, that intelligence per dollar is increasing?

yeah I reversed it when revising my phrasing

What is the unit of measure for “intelligence per dollar”, and how do you measure it? Without a concrete definition, that first assertion is not even wrong.

I would also like to point out that “profitable on inference” is similarly meaningless, because it’s based on a figure that arbitrarily discards whatever bits of their operating costs they need to to get to the claim that they’re profitable on inference. Potentially including ones that can’t be ignored because they’re every bit as much a part of COGS as the actual inference is.

>What is the unit of measure for “intelligence per dollar”, and how do you measure it?

It's going to depend on the task (and this is prone to cheating), but you can get a decent idea by looking at task benchmarks and the cost of completing them.

I was inclined to agree but Anthropic's latest S-1 statement only includes a paper loss due to their value going up. They're objectively making money now, the only question is can they keep it up and can they grow enough to be able to pay the bills they've signed up for in future?

But benchmark performance is not the same thing as intelligence.

Which is a really important distinction. One measures specific points on the jagged boundary. The other is a word that blithely paints it all with a single brush, and sweeps the jagged boundary problem under the rug in the process.

It really is important to keep track of that. Whenever we talk about “intelligence” as if it were a well-defined (let alone quantifiable) thing, we fall into the kind of wooly thinking that lets anyone say pretty much anything. We abandon the realm of grounded statements in favor of pure vibes.

Eh, I get what you mean. AI especially seems to have performance that is much more difficult to reason about than human intelligence (where most aspects of mental capability are surprisingly strongly correlated). But in this context, if you broadly focus on intelligence as the ability to complete problem-solving tasks, models have been improving pretty uniformly at the same time as getting cheaper.