After the release of DeepSeek-R1 on 20 January triggered a massive drop in chipmaker Nvidia’s share price and sharp declines in various other tech companies’ valuations, some declared this a “Sputnik moment” in the Sino-American race for supremacy in artificial intelligence. While America’s AI industry arguably needed shaking up, the episode raises some difficult questions. The US tech industry’s investments in AI have been massive, with Goldman Sachs estimating that “mega tech firms, corporations, and utilities are set to spend around $1 trillion on capital expenditures in the coming years to support AI.” Yet for a long time, many observers, including me, have questioned the direction of AI investment and development in the United States.
With all the leading companies following essentially the same playbook (though Meta has differentiated itself slightly with a partly open-source model), the industry seems to have put all its eggs in the same basket. Without exception, US tech companies are obsessed with scale. Citing yet-to-be-proven “scaling laws,” they assume that feeding ever more data and computing power into their models is the key to unlocking ever-greater capabilities. Some even assert that “scale is all you need.” Before 20 January, US companies were unwilling to consider alternatives to foundation models pretrained on massive data sets to predict the next word in a sequence. Given their priorities, they focused almost exclusively on diffusion models and chatbots aimed at performing human (or human-like) tasks. And though DeepSeek’s approach is broadly the same, it appears to have relied more heavily on reinforcement learning, mixture-of-experts methods (using many smaller, more efficient models), distillation, and refined chain-of-thought reasoning. This strategy reportedly allowed it to produce a competitive model at a fraction of the cost. Although there is some dispute about whether DeepSeek has told us the whole story, this episode has exposed “groupthink” within the US AI industry. Its blindness to alternative, cheaper, more promising approaches, combined with hype, is precisely what Simon Johnson and I predicted in Power and Progress, which we wrote just before the generative-AI era began. The question now is whether the US industry has other, even more dangerous blind spots. For example, are the leading US tech companies missing an opportunity to take their models in a more “pro-human direction”? I suspect that the answer is yes, but only time will tell. Then there is the question of whether China is leapfrogging the US. If so, does this mean that authoritarian, top-down structures (what James A. Robinson and I have called “extractive institutions”) can match or even outperform bottom-up arrangements in driving innovation? My bias is to think that top-down control hampers innovation, as Robinson and I argued in Why Nations Fail. While DeepSeek’s success appears to challenge this claim, it is far from conclusive proof that innovation under extractive institutions can be as powerful or as durable as under inclusive institutions. After all, DeepSeek is building on years of advances in the US (and some in Europe). All its basic methods were pioneered in the US. Mixture-of-experts models and reinforcement learning were developed in academic research institutions decades ago; and it was US Big Tech firms that introduced transformer models, chain-of-thought reasoning, and distillation. What DeepSeek has done is demonstrate success in engineering: combining the same methods more effectively than US companies did. It remains to be seen whether Chinese firms and research institutions can take the next step of coming up with game-changing techniques, products, and approaches of their own. Moreover, DeepSeek seems to be unlike most other Chinese AI firms, which generally produce technologies for the government or with government funding. If the company (which was spun out of a hedge fund) was operating under the radar, will its creativity and dynamism continue now that it is under the spotlight? Whatever happens, one company’s achievement cannot be taken as conclusive evidence that China can beat more open societies at innovation. Another question concerns geopolitics. Does the DeepSeek saga mean that US export controls and other measures to hold back Chinese AI research failed? The answer here is also unclear. While DeepSeek trained its latest models (V3 and R1) on older, less powerful chips, it may still need the most powerful chips to achieve further advances and to scale up. Nonetheless, it is clear that America’s zero-sum approach was unworkable and ill advised. Such a strategy makes sense only if you believe that we are heading toward artificial general intelligence (models that can match humans on any cognitive task), and that whoever gets to AGI first will have a huge geopolitical advantage. By clinging to these assumptions – neither of which is necessarily warranted – we have prevented fruitful collaboration with China in many areas. For example, if one country produces models that increase human productivity or help us regulate energy better, such innovation would be beneficial to both countries, especially if it is widely used.
Like its American cousins, DeepSeek does aspire to develop AGI, and creating a model that is significantly cheaper to train could be a game changer. But bringing down development costs with known methods will not miraculously get us to AGI in the next few years. Whether near-term AGI is achievable remains an open question (and whether it is desirable is even more debatable). Even if we do not yet know all the details about how DeepSeek developed its models or what its apparent achievement means for the future of the AI industry, one thing seems clear: a Chinese upstart has punctured the tech industry’s obsession with scale and may have even shaken it out of its complacency.
