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# AI inference is obviously profitable

**[sean goedecke](https://daily.dev/sources/seangoedecke)** · 6 min read · 16 upvotes · 4 comments

## Summary

A back-of-the-envelope cost analysis argues that AI inference is genuinely profitable, contrary to popular claims that it requires VC subsidies to survive. Using A100 GPU power consumption, amortized hardware costs, and industrial electricity prices, the author estimates inference costs at roughly $1 per million output tokens — well below the $4.50+ that providers like OpenAI charge, implying 70–80% gross margins. DeepSeek's open-weights models and competitive API pricing further corroborate these margins. The key distinction: inference itself is profitable, but AI labs like OpenAI and Anthropic use those margins to subsidize expensive model training. Pure inference providers without training costs could remain profitable even if the current AI investment bubble deflates.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://seangoedecke.com/ai-inference-is-obviously-profitable>

## Community discussion

Top comments from developers on daily.dev.

**@jensroland** · 25 upvotes

> That article is just one false assumption after the next:
>
> - Nothing in a datacenter draws power apart from the GPUs
> - Staff costs don't matter, neither operational nor development. We all know top lab AI researchers are cheap hires, right? It's not like top talent could [cost 2.7 BILLION dollars](https://bloomcapitalreview.com/2026/06/25/the-limits-of-the-2-7-billion-acqui-hire-noam-shazeer-leaves-google-for-openai/) to acqui-hire.
> - The listed GPU is the $20k A100, and the author claims an A100 bought today will still be in service in five years. Noone is buying A100s in 2026 expecting to...

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Tags: [#llm](https://daily.dev/tags/llm), [#openai](https://daily.dev/tags/openai), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#deepseek](https://daily.dev/tags/deepseek)

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