First, how many people will pay to be at the top right of the curve - to be at the frontier? At one extreme there are already use cases that already work just fine with a small, old, perhaps open source model that runs for ‘free’ on-prem or on your phone; at the other extreme there will be some that get better results from the latest, most expensive frontier model, consuming lots of tokens for lots of money; and then there will be many that are somewhere in between. So, how many use cases get better results from going how far up the cost curve, and how many have an ROI for that, and how much of the use is handled by models that are smaller, cheaper, good ‘enough’ and much more commoditised? The Panglossian view is that ROI might go up with more expensive frontier models because they have better results, but where does that really apply?

Second, does the frontier keep moving significantly? This is obviously the most basic science question in AI: how long does the frontier keep getting better, how long does that keep needing more and more compute, and does that continue to happen at a rate that keeps it ahead of downward pricing pressure from efficiency and capacity gains? Does the expensive head of the curve continue to be a thing?

Third, will there still be fierce competition between frontier models? Does the field shrink to fewer and fewer frontier models, perhaps with network effects emerging? Do frontier models diverge, with different models having much clearer leads in different fields? That could be another path to sustainable pricing power. Or do we continue with a mid-single-digit number of companies that are all making frontier models that all have generally equivalent capabilities? At the moment, everyone is using mostly the same science and mostly the same training data, and getting mostly the same results, and we don’t yet know of a network effect or any other winner-takes-all effect that would let one company pull ahead, stay ahead, and do things that others could not, in some sustainable way. Does that change?

Fourth, how much of the value from those high-end use cases is captured by the frontier model itself? How much needs to be wrapped in tooling, process, proprietary data, go-to-market, networks, support, and everything else associated with a traditional software company, even if you do need the big expensive frontier model underneath? Can that model do the whole thing, or is the model, no matter how good, still a piece of infrastructure that you use to make the actual product? At the extreme, can the model itself invent and make all of those things, and would that let them charge by seat, by outcome or just take the profit? Or do even (or especially) the most sophisticated and high-value use-cases need to sit inside hundreds of new companies that can pick and choose which models to use?

None of these are binaries: they're all a question of degree, and they'll probably vary quite a lot by use case. But at one extreme, there are two or three giant minds that run half of everything and have massive pricing power, and at the other extreme LLMs look like databases - there’ll be millions of them, some very big and some very small, and the value is in what you build on top - after all, every SaaS company is a ’database wrapper’. There’s a future in which Anthropic (or a company we haven’t heard of yet) wins the whole thing and can set its own terms, and a future in which dozens of routers run real-time auctions to allocate your tasks across hundreds of low-margin model-farms and a benchmark company takes a fee on every single one.

I don’t think anyone can actually know the answer yet. I’ve said “we don’t know” a lot, and that’s very deliberate. Part of the concept of the ‘S Curve’ is that there’s a stage early in the emergence of a new technology where it’s clear that this is going to be huge but nothing else is clear at all - the mid 1990s for the internet, say, and 2008 or 2009 for mobile. There are places where you can take a view - for example, I’ve argued at length that I think chatbots are a poor interface that will struggle to capture value up the stack - but we should presume there are big questions that we can’t see yet, let alone answer, and anyone picking one of the ten possible outcomes we can see and saying “it will be that one!” is just guessing.

Meanwhile, there is structural uncertainty at the early stages of every big new technology, but the uncertainty now is different, because we don’t have a good theoretical understanding of why these models work so well and so we don’t know how much better they can get. In 1995, we didn’t know how the internet would evolve but we knew that there were less than 100m PCs on earth (and they were expensive) and that telcos couldn’t give everyone FTTH next year; in 2010 we didn’t know what the next iPhone would be but we knew it wouldn’t have retinal projection. We knew the physical limits in ways we don’t really know with LLMs. Next month a new approach could cut inference compute needs by 90%, or double demand, or both.

All of this has people hunting for patterns to recognise (indeed, it’s been observed that all conversations about AI end in a hunt for metaphors). It has become common to draw comparisons with fiber, which had a massive overbuild in the Dotcom bubble that looks a bit like the infrastructure build-out today. The narrow problem with that is that fiber construction was far ahead of demand, where AI compute construction is far behind demand, though, as above, we don’t know what that supply/demand balance will look like in the future. But the more relevant objection, I think, is that fiber construction was mostly fixed cost (digging holes) rather than marginal cost (more equipment), whereas growth in compute needs means you need to buy more compute.

That makes mobile data a more fruitful comparison here. Mobile networks have marginal cost for capacity, and like AI they had an enormous surge in usage 15 years ago, that overwhelmed capacity and had carriers scrambling to add capacity and rebalance their pricing. Meanwhile, selling bits looks superficially similar to selling tokens: it's an opaque measure of marginal cost that doesn't map in any transparent or intuitive way to use cases or value, and needs to be replaced with bundles of some kind. But most importantly, in the last 20 years cellular data traffic has risen by several orders of magnitude, and this has become an enormous industry, with annual revenue of a trillion dollars and capex of $200 billion, but the stocks have gone nowhere, and all the value was captured by other people further up the stack. This, of course, is one of the core questions for AI: is this going to be low-margin commodity infrastructure with all the value captured by other people further up the stack?