CAMBRIDGE – Thomas Edison, the autodidactic telegraph operator turned entrepreneur, is often considered the greatest inventor of all time, while Nikola Tesla, who worked for an Edison company in Paris before emigrating to the United States, is barely remembered, except through Elon Musk’s electric-vehicle company. Yet it was Tesla’s breakthrough with alternating current (AC), not Edison’s direct current (DC) technology, that made mass electrification affordable. The prohibitive costs of DC would have kept Edison’s urban electrification a plaything of the rich, like many of his other inventions.
Could the Chinese investor Liang Wenfeng’s DeepSeek AI models represent a similar breakthrough in AI, or are they scams like cold fusion and room-temperature superconductivity? And if they are confirmed, should the US treat them as a mortal threat, or as a gift to the world?
Like many transformative technologies, AI had evolved over many decades before OpenAI’s release of ChatGPT in late 2022 triggered the current mania. Better algorithms, complementary devices such as mobile phones, and cheaper, more powerful cloud computing had made the technology’s use widespread but barely noticed. Trial and error had shown where AI could or could not outperform human effort and judgment.
The magical glibness of ChatGPT and other large language models (LLMs) created the illusion that generative AI was a brand-new breakthrough. ChatGPT had a million users within five days of its release, and 300 million weekly users two years later. High-tech behemoths like Microsoft, Meta, and Alphabet placed multibillion-dollar bets on AI products and data centers, quickly forgetting their earlier enthusiasm for virtual and augmented reality.
In 2024, Nvidia, which had invested $2 billion in its Blackwell AI chip, became the world’s most valuable company, with its market capitalization having risen ninefold in two years. Its chief executive, Jensen Huang, predicted that $1 trillion would be invested in data centers using such chips in the next few years. All of this made Apple’s cautious, wait-and-see approach to AI seem quaintly old-fashioned.
Never mind that the new AI did not provide value to end users remotely commensurate with the monumental investment (not to mention its insatiable demand for electricity). Investments continued to grow under the assumption that hyper-scaled data centers would reduce AI costs, and increased use would make the models smarter.
But underneath their shiny new hoods, LLMs, like many of the decades-old AI models, still use pattern recognition and statistical predictions to produce their output, which means that their reliability rests on the future being like the past. This is an important limitation. Humans can imaginatively interpret historical evidence to anticipate what might happen differently in the future; they can also improve their predictions through imaginative discourse with each other. AI algorithms cannot.
But this flaw is not fatal. Since processes that obey the laws of nature are naturally stable, the future is like the past in many ways. Given unambiguous feedback, AI models can be made more reliable through training, and even if the underlying process is unstable – or the feedback ambiguous – statistical predictions can be more cost-effective than human judgment. Wildly off-the-mark ads served up by Google’s or Meta’s algorithms are still superior to advertising blindly. Dictating texts to a mobile phone can produce howlers, but it is still quicker and more convenient than pecking away on a small screen.
By 2022, resourceful innovators had discovered innumerable cases where statistically based AI was good enough or better than alternatives that relied on human judgment. As computer hardware and software improved, cost-effective use cases were bound to expand. But it was delusional to think that LLMs were a great leap forward simply because they could converse like humans. In my personal experience, LLM applications have been worse than useless for doing research, producing summaries, or generating graphics.
Nonetheless, reports of DeepSeek’s prowess have sent shock waves through financial markets. DeepSeek claims to have achieved OpenAI- and Google-quality AI performance using only low-end Nvidia chips and at a fraction of the training and operating costs. If true, demand for high-end AI chips will be lower than anticipated. That is why the DeepSeek news erased about $600 billion from Nvidia’s market capitalization in a single day, as well as hammering the stocks of other semiconductor companies and companies that have invested in data centers or sell electricity to those centers.
To be sure, DeepSeek’s claims may turn out to be inaccurate. Many of Tesla’s claims about his inventions after his AC breakthrough were wildly exaggerated, even fraudulent, and the Soviet propaganda machine routinely fabricated scientific and technological breakthroughs alongside real advances. But frugal, out-of-the-box innovations can be transformative. Just look at Musk’s low-cost reusable rockets. India’s successful Mars mission cost a mere $73 million, less than the budget of the Hollywood sci-fi movie Gravity.
If vindicated, DeepSeek’s technology could be to LLMs what Tesla’s AC inventions were to electrification. While it cannot overcome the unavoidable limitations of backward-looking statistical models, it could make their price performance good enough for wider use. Those developing LLM models will no longer have to depend on subsidies provided by large operators with an interest in locking them in. Less resource-hungry models could reduce demand for data centers or help direct their capacity toward more economically justifiable uses.
What about geopolitics? Last spring, a report by the Bipartisan Senate AI Working Group called for $32 billion in annual “emergency” spending on non-defense AI, supposedly to compete better with China. Venture capitalist Marc Andreessen described the arrival of DeepSeek as “AI’s Sputnik moment.” US President Donald Trump thinks that the Chinese AI model is a “wake-up call for US industries,” which should be “laser-focused on competing to win.” He has announced plans to impose new tariffs on semiconductor imports from China, and his predecessor had imposed export controls on high-end AI chips.
In my book The Venturesome Economy, I argued that seeing transformational advances abroad as a threat to domestic well-being is misguided. Blindly pursuing technological or scientific leadership is a foolish enterprise. What matters most is the willingness and ability of businesses and consumers to develop and use products and technologies stemming from cutting-edge research, wherever it might come from. This is true of DeepSeek’s open-source AI models as well.
Of course, we need to control hostile regimes’ menacing military uses of cutting-edge Western technologies. But this is a different and difficult challenge. If addressing it through export controls were possible, we would have stopped worrying about North Korean or Iranian nuclear weapons a long time ago.
Amar Bhidé, Professor of Health Policy at Columbia University’s Mailman School of Public Health, is the author, mostly recently, of Uncertainty and Enterprise: Venturing Beyond the Known (Oxford University Press, 2024).
Copyright: Project Syndicate, 2025.
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