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title: Why compute might get 10x+ more expensive in coming years
description: An analysis argues that AI compute prices could rise 10x or more in coming years as frontier labs' revenue grows far faster than their compute supply....
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# Why compute might get 10x+ more expensive in coming years

**[Tech Lead Digest](https://daily.dev/sources/techleaddigest)** · 9 min read · 1 upvotes · 0 comments

## Summary

An analysis argues that AI compute prices could rise 10x or more in coming years as frontier labs' revenue grows far faster than their compute supply. Anthropic's revenue reportedly grew 10x year-over-year while lab compute only grows about 3x annually, driven by 1.4x Moore's Law gains, 1.2x new fab construction, and 1.8x AI's share of leading-edge wafer allocation (the latter expected to saturate by 2027). Since margins can't realistically exceed the mid-90s percent and labs are reluctant to shift spend toward inference, the piece concludes compute prices themselves must rise. It cites Google reportedly paying $900 million a month for 110K GPUs from SpaceX, roughly 2x spot price, and spot prices already up 40% since February. A back-of-envelope calculation suggests an H100 running a human-level software engineer at current wages should rent for over $250k a year, about 15x today's spot price, with implications for who can compete for scarce compute and how AI applications get priced.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive>

## Questions this post answers

### Why might GPU compute prices increase significantly in the next few years according to AI lab revenue trends?

Anthropic's revenue reportedly grew about 10x year over year while lab compute capacity only grows roughly 3x per year, a gap that can only be closed by rising lab margins, spending more compute share on inference, or compute itself getting more expensive. Analysis suggests margins can't realistically hit the mid-90s percent needed to fully explain the gap, so compute prices are the more likely lever, with spot prices already up over 40% since February.

_Anyone budgeting for AI infrastructure costs can follow compute pricing dynamics discussions like this on daily.dev._

### How much is Google reportedly paying to rent GPUs from SpaceX for AI compute?

Google is reportedly paying about $900 million a month for 110,000 GPUs, a blend of GB200s and GB300s, which works out to roughly 2x the spot price per hour for those chips. This premium reflects the need for secure, reliable, large-scale compute tranches that AI labs require beyond what spot instances can offer, on top of a spot price already up 40% since February.

_Teams evaluating GPU procurement costs can track real-world compute pricing benchmarks like this via daily.dev._

### What would an H100 need to rent for if AI reaches human-level software engineering capability?

An H100 running a true human-level software engineer, priced at current market rates for software engineers, should theoretically rent for over $250,000 a year, about 15 times today's spot price. This estimate assumes the marginal value of automated labor stays high, similar to how economists argue high-skilled immigration doesn't depress wages long-term due to specialization and innovation effects.

_Developers weighing AI coding tool economics can dig into compute pricing arguments like this on daily.dev._

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---

Tags: [#cloud](https://daily.dev/tags/cloud), [#llm](https://daily.dev/tags/llm), [#gpu](https://daily.dev/tags/gpu), [#anthropic](https://daily.dev/tags/anthropic)

[View this post on daily.dev](https://daily.dev/posts/why-compute-might-get-10x-more-expensive-in-coming-years-xjrwqdbod)

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