DeepSeek Made it Even Harder for US AI Companies to Ever Reach Profitability

By Garrison @ 2025-02-19T21:02 (+30)

This is a linkpost to https://garrisonlovely.substack.com/p/deepseek-made-it-even-harder-for

This is the full text of a post from "The Obsolete Newsletter," a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race to Build Machine Superintelligence. Consider subscribing to stay up to date with my work.

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Anthropic CEO Dario Amodei may have divulged a big secret with worrying implications for AI firms like his own and OpenAI.

Last month, Chinese startup DeepSeek released R1, an AI model rivalling OpenAI’s flagship o1 model on key benchmarks but at a fraction of the cost — about 27-times cheaper. This sent shockwaves through the market. AI chip designer Nvidia saw a record $600 billion wiped from its value on Monday, January 27th, driving nearly $1 trillion in losses concentrated in American AI infrastructure stocks. The narrative quickly emerged: a small Chinese company had matched billion-dollar American models for mere millions, suggesting powerful AI might be far cheaper to develop than previously believed.

In an essay responding to the market panic, Amodei aimed to defend US export controls on advanced AI chips to China. But in doing so, he revealed something striking: DeepSeek's efficiency gains were exactly what we should expect from historical algorithmic progress — suggesting American AI companies have been enjoying healthy profit margins, at least until DeepSeek arrived to massively undercut them.

This assessment resonates with researchers at leading US AI companies. One told me DeepSeek's results "are within the improvement range that we'd expect from standard algorithmic improvement over time." Another was even more dismissive: "I don't think anyone cares very much, it doesn't seem very surprising… Obviously they're talented but nothing about it is unexpected." Notably, none of these reactions came from customer-facing employees.

By offering a comparable model at significantly lower prices, DeepSeek is likely to trigger an AI price war, just as it did in the Chinese market last summer. Lower prices should boost demand, foreshadowed by DeepSeek’s meteoric rise to the top of the iPhone app store.

Microsoft CEO Satya Nadella recognized this dynamic, tweeting: "Jevons paradox strikes again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of." The paradox he references: when technologies get more efficient, we often end up using more of them, not less. This commoditization would benefit companies using AI to enhance their existing services, like Microsoft, Meta, and Google.

However, for pure-AI companies like OpenAI, the implications are troubling. If US companies have already achieved similar efficiencies, then compute costs for these models won’t substantially decrease even if prices fall. This tightens unit economics for AI developers, lengthening their already uncertain path to profitability. OpenAI’s swift release of a “mini” version of its forthcoming o3 model, priced roughly double DeepSeek’s offering, suggests they recognize this threat.

And if Nadella is right about AI's coming status as a commodity, that's very bad news for OpenAI — commodity producers don't typically get valued at dozens of times their revenue.

DeepSeek demonstrates that the “secret sauce” for cutting-edge AI won’t remain secret for long. Months after OpenAI announced its o1 "reasoning" model, competitors have largely replicated its approach and performance. Whether through building off open-sourced innovations or “distillations” of closed models, “fast-followers” can match leaders' capabilities faster and cheaper.

While AI developers face margin pressure, the outlook for AI infrastructure providers like Nvidia is unclear.

Lower AI prices will likely drive up aggregate demand for computing power, but also reduce the profit per chip, which are currently astronomically high. The market initially bet on the downside. But early evidence suggests the demand effect may be winning out. Industry research group Semianalysis reported that prices to rent Nvidia's flagship H100 chip actually "exploded" after DeepSeek released its V3 model in December, with no slowdown after R1's introduction. "More intelligence for cheaper means more demand," they write.

Major tech companies seem to agree and are still planning massive AI and datacenter spending increases this year.

Public and private markets diverged wildly in their response to DeepSeek. The same week that Nvidia lost nearly 20% of its market cap, SoftBank reportedly sought to invest up to $25 billion in OpenAI at a valuation approaching $300 billion — nearly double where the company was valued just months earlier. The day of the DeepSeek market panic, Nvidia closed at a lower share price than it had in early October, when OpenAI announced its last funding round at a $157 billion valuation.

These contradictory outlooks can't both be right. A world of commodity AI services is fundamentally incompatible with the soaring private market valuations of AI companies that have yet to turn a profit.

(The market seems to have mostly corrected — Nvidia's stock is now only down one percent for the month.)

Ultimately, adoption of AI will continue and likely accelerate, as price drops coincide with significant increases to usefulness of the underlying technology. Pure-AI companies are in a long race to turn a profit before their products become commoditized. And DeepSeek just moved the finish line further away.

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