"AI use cases" is a moving target! If you try to define "use cases" by "what AIs today can be used to do", you miss things AIs would do in the future. The more capable and price-performant AIs get, the more there are places where AI has a role.
This is a big part of what the frontier buys you.
The other big part is that going from a local AI to a frontier model often makes the difference between "this use case works 60% of the time, good tech demo" and "this use case works 99% of the time, good business decision". The difference between "clunky and needs human in the loop" and "just works" is worth paying the API price.
This is the problem.
While the frontier labs will always have a market for AI that can do things the other models can't, if the sliver of work that can be done only by the frontier models is narrow enough, then what they spend to train those frontier models may not let them capture all that much extra revenue.
I absolutely think there will be a market for frontier models pushing further and further.
But it's not yet a given whether or not being ahead on capabilities is going to buy them all that much more higher earnings relative to lower tier providers.
If it stays the case that going from local AI to a frontier model makes the difference between 60% and 99%, then they probably will, but there's nothing to suggest local or open model, or second-tier closed models won't keep moving ahead as well, and eat away at the use cases where only a frontier model will do.
Do you think that we are anywhere near that point?
We've already seeing things - like what's happening with AI reverse engineering now. I'm a reverse engineer - and I know that the reason why RE was used as "last resort option" was that expert reverse engineers are rare, expensive, and work slow.
But now that a lot of RE workloads can be tackled by AI instead of a human? The demand for reverse engineering is at a never before seen high. AI made reverse engineering much more viable - and thus, a lot more reverse engineering is happening now.
There are myriads of fields like this - things that go overlooked not because no one wants them, but because the economics of human expertise make them nonviable in most cases. As AI gets better, it can begin to enter those fields.
Except you, of course, need to push the frontier to do it.
No, I don't. But for any given task there is a ceiling above which for that task there is no value in additional intelligence. E.g. it takes very little intelligence (none, in fact) to sort a list. More intelligence does not result in a more sorted list.
And so for a huge proportion of what we do there is a ceiling of value that can be extracted by selling AI tools to do any given task.
Combine that with there being at any given time a ceiling of available capital to be apportioned to buy intelligence to carry out the remaining tasks, and the floor of tasks that non-frontier models can do well enough rising, and there will at any given time be a finite amount of capital that will be accessible to frontier models.
It is by no means given that that slice of capital will grow rapidly enough for frontier providers to be able to create an attractive financial return. That depends on frontier labs being able to continue to deliver sufficiently more value than cheaper models at any given time that enough people won't simply wait.
I'm extremely bullish on AI in terms of its ability to transform society over time, but that does not automatically translate into assuming that the current frontier labs are a safe investment bet.
If "sort a list" becomes a trivial task, everything that was bottlenecked on "sort a list" is unbottlenecked, and other parts of the process become bottlenecks instead. Thus, the demand for non-sorting tasks grows. The saturation of low end feeds the value into the high end.
Sometimes it is. But that is also irrelevant. Replace "sort a list" with any given goal and the argument remains the same.
> The saturation of low end feeds the value into the high end.
But that process is not immediate. There's a reason I was careful to include "at any given time" in my comment.
For the purpose of whether or not a frontier lab is a good investment, it doesn't necessarily help them if there is an ever rising demand for intelligence if their market is eaten from below just as fast by cheaper models from competitors catching up.
It could be they manage to out-pace their competitors sufficiently that they will produce returns high enough to justify their valuations.
But irrespective of whether the demand for more and more intelligence continues to rise perpetually if 2nd tier labs hollows out the bottom of their market fast enough, it could equally be that most of the market ends up being low-margin, highly-competitive inference that won't justify their valuations.
Given the size of their valuations, even if they turn out to be tremendously profitable businesses, it's still not a given they'll give a good return on investment for investors getting in now.
I'm not saying they won't. I'm saying we can't easily judge that based on just assuming growth at the high end will continue without understanding what their moat is likely to look like.
Your assumption here is that the other parts of the process are intelligence. In the real world they usually are not e.g. raw materials, politics, zoning, manual labor, clinical trials, traffic, energy ... And usually those things are such huge bottlenecks that Amdahl's Law renders the speed of delivery of the intelligence part irrelavant.
I could be wrong, of course.
Much of humanity doesn’t think at all btw - that’s why they’ll never pay for these products and Google search had to be free.
Furthermore two things could be true a) market for frontier is large b) Anthropic still continues to generate net loss’s
And that, in turn, makes AI even more valuable if it can produce thinking on demand.
Researchers specifically were never highly paid. And positions were competitive - there were way more people willing and able to do those positions then positions themselves. Same goes for art btw.
That's like saying if 99% of birds don't have huge colorful tails that makes peacocks very valuable. In each case it may or may not be true but you are just correlating. (i.e. It's possible that intelligence just isn't as valuable as 'intelligent' people think it is)
What’s your goal here
Interesting. Are you an Anthropic investor?
There is no business model for competing companies to build forklifts that can lift an infinite amount.
That is not to say attempting to do so would not find new use cases for such powerful forklifts. The problem is the cost of building ever increasingly powerful forklifts while the TAM for that power shrinks.
You don't even have to bring in the enormous difference between forklift rusting theta vs gpu theta.
Models, however, are completely interchangeable on demand. That’s why companies are building these realtime internal marketplaces to route tokens to different models. Anthropic may win the occasional “bleeding edge” use case but the money is in the day to day use cases that get routed to the cheap-but-works options. There’s zero cost to having all the forklift sizes at your disposal and only using the size needed when it’s truly needed.
Anthropic is trying to sell the biggest baddest forklift in a world where companies can swap out cheaper ones easily and on demand. Hence why Anthropic doesn’t appear to have a viable business model in its current form.
And crane, despite being bigger, more powerful and more awesome (they lift skyscrapers and do things nothing else can do), the Forklift is actually more useful to more people.
I think the argument carries solid weight. Making bigger and better things doesn't necessarily lead to bigger and better businesses.