In 1957, the Soviet Union launched the world’s first artificial satellite into orbit, sparking fears in the United States that, unless it took radical action to accelerate innovation, its Cold War adversary would leave it in the technological dust. Now, the Chinese startup DeepSeek has built an artificial intelligence model that it claims can outperform industry-leading US competitors, at a fraction of the cost, leading some commentators to proclaim that another ‘Sputnik moment’ has arrived.

But the focus on the US-China geopolitical rivalry misses the point. Rather than viewing DeepSeek as a stand-in for China, and established industry leaders (such as OpenAI, Meta and Anthropic) as representatives of the US, we should see this as a case of an ingenious startup emerging to challenge oligopolistic incumbents—a dynamic that is typically welcomed in open markets.

DeepSeek has proved that software ingenuity can compensate, at least partly, for hardware deficiencies. Its achievement raises an uncomfortable question: why haven’t leading US industry leaders achieved similar breakthroughs? Nobel laureate economist Daron Acemoglu points the finger at groupthink, which he says prevented Silicon Valley incumbents from adequately considering alternative approaches. He might have a point, but it is only half the story.

DeepSeek’s success didn’t happen overnight. In May 2024, the firm launched its V2 model, which boasted an exceptional cost-to-performance ratio and sparked a fierce price war among Chinese AI providers. Moreover, over the last year or so, Chinese firms—both giants (including Alibaba, Tencent and ByteDance) and startups (such as Moonshot AI, Zhipu AI, Baichuan AI, MiniMax and 01.AI)—have all developed cutting-edge AI models with remarkable cost efficiency.

Even within the US, researchers have long explored ways to improve the efficiency—and thus lower the costs—of AI training. For example, in 2022, former Meta researcher Tim Dettmers, now at the Allen Institute for Artificial Intelligence, and his co-authors published research on optimising AI models to run on less computing power. DeepSeek cited their research in the technical paper it released along with its V3 model.

Put simply, it would have been impossible for any AI firm—especially an industry leader—not to realise that lower-cost models were feasible. But US AI developers showed much less interest than their Chinese counterparts in pursuing this line of innovation. This was not a matter only of insularity or hubris; it appears to be a deliberate business choice.

AI development has so far been defined by the scaling law, which predicts that more computing power leads to more powerful models. This has fuelled demand for high-performance semiconductor chips, with more than 80 percent of the funds raised by many AI companies going toward computing resources.

That is why the biggest winner has been the advanced chipmaker Nvidia, which claimed 90 percent of the market for AI graphics processing units by the end of last year. Thanks to this virtual monopoly in the hardware layer, Nvidia could control the foundations of generative AI. The cloud-computing sector, which provides the on-demand computing power AI models require, is similarly concentrated, with Amazon, Google and Microsoft dominating the market.

But these upstream players aren’t just passive suppliers. They have strategically positioned themselves across the AI value chain by acquiring, investing in, or forming alliances with leading AI model developers. Nvidia has invested in OpenAI, Mistral, Perplexity and others. Google not only develops its own AI models, but also holds a stake in Anthropic, OpenAI’s main competitor. And Microsoft, an early OpenAI investor, recently backed Inflection AI in the US and expanded overseas, with investments in France’s Mistral and the United Arab Emirates’ G42.

Taking this approach has ensured that the entire AI industry depends on a few giant firms and entrenched a dynamic whereby rising demand for computing power across the sector increases these firms’ profits. As dominant players, they had less incentive to improve cost efficiency downstream, which could cut into their upstream profits.

Chinese AI firms have been operating within an entirely different reality, as US-led trade restrictions have prevented them from purchasing the most advanced chips. The goal of US export controls has always been to cripple China’s AI sector. But, as DeepSeek has shown, they have had the opposite effect, spurring precisely the innovations that will enable Chinese firms to challenge American AI oligopolies. Already, DeepSeek’s rise triggered a stock-market selloff of AI-related US companies, not least Nvidia.

This is surely unwelcome news for US President Donald Trump’s administration. Trump has made no secret of his determination to contain China, including by fulfilling his promise to impose a 10 percent across-the-board import tariff on Chinese goods. And he has heavily courted Silicon Valley bosses—once aligned with the Democratic Party—who have eagerly embraced the prospect of lax regulation.

But that does not mean that DeepSeek’s rise is bad news for the US or the AI industry more broadly. Over the past five years, calls to rein in the US’s tech giants have been growing louder. Despite the best efforts of former President Joe Biden’s administration, however, the US Congress has failed to introduce any meaningful legislation on this front. Ironically, thanks to US policies designed to constrain China’s AI ambitions, the US AI sector seems set to get some of the market competition that it so badly needs.

Geopolitics might have contributed to DeepSeek’s rise. But the firm’s disruption of the AI industry is about market—not great-power—competition.