Ed Zitron loses his mind annotating an AI doomer macro memo

So I guess we all agree, except that some people think "just statistics" is derogatory phrasing!

If that's the truth, then originally they were subsidizing their models by the same factors.

This is not a great argument no matter how you cut it. And even then I would need to see evidence that this is true.

> all the investors are stupid and they still invest in openai despite unprofitability

Much to the opposite, those people are very smart. OpenAI can be extremely unprofitable and they can still profit massively through an exit event.

> employees of openai and anthropic who have claimed that the unit costs are not high are also lying

Possibly? Especially if they are in the position to profit in the case of an exit event, they would have every incentive to paint a rosier picture about the company.

> all other providers are in on the lie

I have no idea who you are talking about.

> the chinese models like Deepseek is also in on the lie by posting research that is not plausible

As I previously stated, I have no idea if Deepseek is profitable. By the looks of things, neither do you. Mentioning Deepseek's research is a non-sequitur.

> the fact that you can run models in your laptop today that beat previous years models is also not enough

This has no bearing on the cost of inference.

Here goes:

The Epoch data everyone keeps citing measures the price per token charged to API customers. That's the sticker price. It tells you nothing about whether the business is viable, because the existential risk for AI companies isn't the marginal cost of running a query. It's the upfront capital expenditure on chips and datacenters, committed years before you know what demand looks like.

Anthropic CEO Dario Amodei spelled this out in his Dwarkesh interview. Here's the short version: 1. Data centers take 1-2 years to build out. 2. Each gigawatt costs roughly $10-15B per year. 3. The industry is currently at ~10-15 GW, scaling roughly 3x annually. 4. By 2028, ~100 GW. By 2029, ~300 GW. 5. We're talking multiple trillions per year in committed infrastructure spend across the industry.

Now NVIDIA's Q4 earnings [1], which printed today: 1. $68.1B in quarterly revenue, $62.3B from data center alone. 2. Full-year: $215.9B, up 65% YoY. Guiding $78B next quarter. 3. Someone is writing those checks. Those checks are not refundable.

Dario, who believes we're 1-3 years from a "country of geniuses in a data center," described his own demand prediction as a "hellish" problem.

His exact framing: If this revenue comes in at $800B instead of $1T, "there's no force on earth, there's no hedge on earth" that could stop him from going bankrupt if he'd bought compute at the higher projection.

He's at ~$10B annualized revenue today, and he won't commit to buying at the scale his own thesis demands, because being off by a single year is fatal.

This is the actual argument (I'm not saying this is Ed's argument, but this is the argument against these companies). Not "inference tokens are expensive."

The argument is structural: these companies must pre-commit billions in non-recoverable CAPEX based on demand projections that are, by the CEO's own admission, a coin flip.

The gross margins on serving tokens might be great. But the training spend for next-gen models grows exponentially, and it has to be funded before that model earns a dollar.

The Epoch chart measures what customers pay per token. It doesn't measure the $215.9B NVIDIA invoice those customers collectively funded this year, or that these chip purchases are one-way bets against future demand that may or may not materialize.

Inference costs going down 20x is wonderful for consumers. It tells you almost nothing about whether the companies making those chips, or the companies buying them, will survive the demand prediction gauntlet.

And if we're being honest, the Epoch data showing 9x to 900x price drops per year should make you more nervous, not less, because it means the asset you bought last year is depreciating at a rate that makes used cars look like gold bars.

[0] https://www.youtube.com/watch?v=n1E9IZfvGMA&t=2298s [1] https://nvidianews.nvidia.com/news/nvidia-announces-financia...