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Comment by senordevnyc | Hacker News Reader

I must be dense, why does this imply AI can't be profitable?

Tokens are, roughly speaking, how you pay for AI. So you can approximate revenue by multiplying tokens per year by the revenue for a token.

(6.29 10^16 tokens a year) * ($10 per 10^6 tokens)

= $6.29 10^11

= $629,000,000,000 per year in revenue

Per the article

> "It's my view that there's no way you're going to get a return on that, because $8 trillion of capex means you need roughly $800 billion of profit just to pay for the interest," he said.

$629 billion is less than $800 billion. And we are talking raw revenue (not profit). So we are already in the red.

But it gets worse, that $10 per million tokens costs is for GPT-5.1, which is one of the most expensive models. And the costs don't account for input tokens, which are usually a tenth of the costs of output tokens. And using bulk API instead of the regular one halves costs again.

Realistic revenue projections for a data center are closer to sub $1 per million tokens, $70-150 billion per year. And this is revenue only.

To make profits at current prices, the chips need to increase in performance by some factor, and power costs need to fall by another factor. The combination of these factors need to be, at minimum, like 5x, but realistically need to be 50x.

im a little confused about why you are using revenue for a single datacenter against interest payments for 100 datacenters

I just misread the article, as it seems to bounce around between $nX capex for nY gigawatt hours in every paragraph.

But it looks like the investments are $80MMM for 1GW. Which, if true, would have the potential to be profitable, depending on depreciation and electricity costs.

The math here is mixing categories. The token calculation for a single 1-GW datacenter is fine, but then it gets compared to the entire industry’s projected $8T capex, which makes the conclusion meaningless. It’s like taking the annual revenue of one factory and using it to argue that an entire global build-out can’t be profitable. On top of that, the revenue estimate uses retail GPT-5.1 pricing, which is the absolute highest-priced model on the market, not what a hyperscaler actually charges for bulk workloads. IBM’s number refers to many datacenters built over many years, each with different models, utilization patterns, and economics. So this particular comparison doesn’t show that AI can’t be profitable—it’s just comparing one plant’s token output to everyone’s debt at once. The real challenges (throughput per watt, falling token prices, capital efficiency) are valid, but this napkin math isn’t proving what it claims to prove.

> but then it gets compared to the entire industry’s projected $8T capex, which makes the conclusion meaningless.

Aren't they comparing annual revenue to the annual interest you might have to pay on $8T? Which the original article estimates at $800B. That seems consistent.

But it's only for one datacenter...

Broad estimates I'm seeing on the cost of a 1GW AI datacenter are $30-60B. So by your own revenue projection, you could see why people are thinking it looks like a pretty good investment.

Note that if we're including GPU prices in the top-line capex, the margin on that $70-150B is very healthy. From above, at 0.4J/T, I'm getting 9MT/kWh, or about $0.01/MT in electricity cost at $0.1/kWh. So if you can sell those MT for $1-5, you're printing money.

> So if you can sell those MT for $1-5, you're printing money.

The IF is doing a lot of heavy lifting there.

I understood the OP in the context of "human history has not produced sufficiently many tokens to be sent into the machines to make the return of investment possible mathematically".

Maybe the "token production" accelerates, and the need for so much compute realizes, who knows.

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