What If... AI Compute Costs Soar?

Many groups are investing heavily in Claude Code, OpenAI Codex, and similar tools for using generative AI for software development, especially for actual code creation. I have played with Claude Code and OpenCode (for use with local Ollama models), and it is genuinely impressive what they can accomplish. The argument for leaning heavily on genAI is: it is cost-effective. One pundit wrote:

And the economics are deliberately provocative: roughly ten dollars an hour of compute versus a developer’s cost of around $150 an hour.

However, that statement makes two assumptions:

• Developers cost $150/hour, which is only true in select locations and for select people — the cited source of that value says that is an estimate for “a professional senior software developer in the United States”

• That the cost of AI compute will remain steady, increasing roughly in line with the increase in the cost of software developers

The economics may not work out so well if either of those assumptions are unfounded. The first one certainly is, as many software developers over much of the world cost a lot less than $150/hour. And my bet is that the second assumption is also flawed, and that the cost of AI computer will climb drastically in the next few years.

“If Something Cannot Go On Forever, It Will Stop”

Uber, and to some extent Lyft, built their businesses up by spending investor cash on subsidizing rides:

Uber passengers were paying only 41% of the actual cost of their trips; Uber was using these massive subsidies to undercut the fares and provide more capacity than the competitors who had to cover 100% of their costs out of passenger fares.

(from “Can Uber Ever Deliver? Part One – Understanding Uber’s Bleak Operating Economics”)

While extremely aggressive, this is a well-known play in the VC-backed startup playbook. The subsidies help in (at least) two ways:

1. They get your customer base “hooked” on using your product or service, by making it so cheap that they do not think twice about using it

2. They help build a moat, by preventing competition from entering the market (unless they too use the same subsidy approach), until such time as you are so large that competition is much less of a risk

There are signs that major AI vendors are doing the same thing, and frankly it would be surprising to me if they were not doing that. Ed Zitron wrote:

Based on an analysis of many users’ actual token burn on Claude Code, I believe Anthropic is burning anywhere from $3 to $20 to make $1, and that the product that users are using (and the media is raving about) is not one that Anthropic can actually support long-term.

Partly, that is based on the Anthropic financial reports. Partly, that is based on a crowdsourced look at how many tokens you get to use with one of the Claude monthly subscriptions, such as Claude Max. Effectively, you get a “bulk rate” discount with those subscriptions, so Anthropic gets less per-token revenue from subscription usage than they do from full-price API tokens.

If that analysis is correct, then the “ten dollars an hour of compute” really needs to be more like $30-200/hour, just for Anthropic to break even. It might have to go higher if Anthropic wishes to turn a profit.

(Not So) Tiny Bubbles

That, of course, assumes that Anthropic survives long enough to raise the prices.

Lots has been written about the prospect that the US stock market is in an “AI bubble”. Much of the recent run-up of the market has been tied to the “Magnificent Seven” firms: Alphabet, Amazon, Apple, Meta, Microsoft, NVIDIA and Tesla. And a lot of that is on the back of a financial ouroboros with AI Company A investing in AI Company B, which turns around and agrees to buy a bunch of AI Company A’s stuff.

Whether due to AI-related issues (e.g., insufficient demand for all the data centers being built) or other impacts (e.g., war), there is a distinct chance that the bubble pops. If we wind up with a 2008-style financial crisis, what happens to OpenAI, Anthropic, and other similarly-placed firms?

It is certainly possible that they could survive, especially if they jack up their revenue by raising prices a lot. It is also possible that they will wind up being acquired by firms that already have hosting prowess (e.g., Alphabet, Amazon, Meta, Microsoft), with a side-effect of reducing competition in the generative AI market. And it is not out of the question that some AI model creators will simply crash and burn… which also reduces competition, by eliminating competitors the hard way.

Oligopolies Gonna Oligopple

(note: “oligopple” is not a real word, though it should be)

When there are few competitors in a market, the resulting oligopoly often results in higher prices. To an extent, that is the point of trying to establish an oligopoly in the first place.

It’s not like there are a lot of hosted frontier model providers. Right now, it feels like a race for market share and to build the moat to preclude other competitors from entering the market. But, eventually, the oligopoly will start raising rates, just because they can. If competition shrinks due to the AI bubble popping, the smaller oligopoly may get to the point of raising rates faster.

A lot of what Cory Doctorow’s “enshittification” stems from is oligopoly: too few competitors in the market and few reasonable substitutes for what they offer. The resulting market power lets those few competitors do all sorts of things that are bad for their customers, like jacking up rates, because those customers have nowhere else to turn.

So, what happens if that Claude Max subscription climbs to $300/month? What if it climbs to $2,000/month? What if the cost of additional tokens rises commensurately? What if the other surviving oligopoly members raise their prices along the same lines? And what if it is no longer Claude Max but Microsoft Copilot Max Powered By The AI Formerly Known As Claude, due to a shakeout as part of an AI bubble pop?

(yeah, that name is long, but branding is hard)

Even at today’s prices, the economics of using frontier models for code generation only make sense when software developers are expensive. In a lot of the world, that is not the case today. With a serious bump in the price of AI compute, the economics will narrow the use cases even further.

What I’m Doing

I have serious ethical issues with hosted frontier models, so while I have a Claude Pro subscription (presently $17/month), my attention is more on local models. Qwen 3.5 has some promise, and it runs comfortably on a 64GB Mac. It is not nearly as capable as, say, Claude Opus 4.6, but some of that can be addressed by advance planning. I am experimenting with OpenCode commands to provide better prompts for Qwen 3.5, for things that I want done routinely. If I run into OpenCode limitations, I can use Koog and set up my own lightweight bespoke coding agent.

I am not trying to have something like Qwen 3.5 completely replace Claude Opus 4.6. I am trying to minimize my use of Claude, though, so I want to find ways that local models can do some of the routine (“grunt”) work. I will reserve Claude for those handful of places where it might be faster than me. As a result, my techniques will handle a massive spike in AI compute cost — I would drop Claude and rely on Natural Intelligence™ (🧠) for its role, while still getting benefit from the local model ecosystem.