Predictions for the Future of AI — Scout Corps
Predictions for the Future of AI
/June 3, 2026<br>by Chris Shaffer
On token economics<br>It's clear today that inference is broadly priced below cost. What will hopefully become clear as we see Anthropic and OpenAI's IPOs is "how far below cost?"<br>The optimistic case, based on what Anthropic has released thus far, is that you're paying $0.6-0.7 for every $1 of compute.
The pessimistic case figures that hyperscaler capex, private credit, circular transactions, and the tax code are each acting as another layer of subsidy. They conclude that you may be paying less than $0.1 for every $1. They also don’t see demand plateauing. While I don't disagree on any particular point, I find the argument that demand will increase exponentially beyond the point where customers can't afford the product to be a bit circular itself.
I can only speculate on how the inevitable re-pricing will affect the industry in the medium term.<br>If the optimists are right, then Moore's law and cheap solar panels might get us over the bump with customers barely noticing.
If the pessimists are even half right, you're going to see demand destruction. But the impact will be heavily use case dependent.
Even assuming a $4k/month Claude bill, I don't think you're going to see "junior coders being hired to write simple snippets of code because LLMs have gotten so expensive that it makes financial sense to treat humans like LLMs". Only the worst companies will pull a high performer into a room and discuss how they ran up $4,100 in token usage, when the average performer next to them got away with $3,900. However, good companies will still care about the token economics of your deployed AI-dependent workflows in production [ related how-to post ]. To put this in 2010 terms, you wouldn't balk at a senior engineer asking for 12GB RAM rather than 8; you would balk at their SQL query eating 12GB of RAM 10 times a day on production, when an hour of effort would have yielded a refactor that'd get it down to 8.<br>Outside the tech sector, I think many AI use cases will vanish in the medium term* regardless of what prices do. Normal people are not going to pay $200 or even $20 a month for an agent to make their restaurant reservations; that use case might work at $2, but price matters more here.<br>On use cases and the long-term<br>If there's one thing I could impart to my mostly tech-heavy and AI-positive peer group, it's how much people outside of it hate the technology. Polling tells us that AI is about as popular on college campuses as a System of a Down concert would be in a retirement community. The media is focused on the "it's gonna take my job" aspect, but in my experience it's more mundane: plumbers are annoyed that their email app wants to reply with hallucinated dates and times they [actually can't] make an appointment, nurses are annoyed that the blouse they ordered didn't look anything like how it looked on the AI-generated model, and normal people just call "feature discovery moments" or "coach marks" what they are: "popup ads".<br>I say "medium term" here, because I think machine learning - LLMs or the next thing - are not going away. The question is how hard you have to squint to make the thing that eventually addresses the use case in 2045 look like the thing no one wants today.<br>You may have been tempted to look at Webvan and Flooz and say, “no one wants to buy groceries on the internet” or “people don’t trust online money”. The predictions about those companies were correct even as the words aged poorly.<br>By “No one wants their AI avatar to go on a date with anyone else's AI avatar” … What we mean isn’t “There will never be an artificial neural net in a successful dating app” … it’s “no one is paying for the goofy trash that’s on offer.“<br>A knave might babble about timing, but for those of us in the industry, it's not timing, it's execution. If you're not making the product or experience better or cheaper, the "technology" doesn't matter. “Pets.com was just too early” is an amateurish take.<br>Squaring the circle<br>Ultimately, if it turns out there’s no way to make the numbers add up for derided tools like Google’s AI summary or Copilot (Windows not GitHub), that loss of demand will ease price pressures on the more sensible use cases.<br>While I would not completely discount the possibility that a bursting bubble leaves so many orphaned data centers that the cost of inference actually declines toward the cost of electricity, I think data center construction takes long enough that most of those hypothetical excess data centers wouldn’t come online.<br>On the consumption side - it’s clear that there’s lots of room to do more with less:<br>Models could be more efficient while sacrificing little ability
APIs route queries intelligently to cheaper models
Consumers today waste tokens because they have little incentive not to. See...