Investors sold off US tech stocks on Monday after Chinese tech start-up DeepSeek unveiled a ChatGPT-like open source Large Language Model, which it claims has greater efficiency than its Western counterparts.
DeepSeek’s model showed results on a similar level to OpenAI’s ChatGPT at 5-10% of the cost.
Nvidia fell 16.86% on Monday, marking the worst losses for a single stock on record in terms of market cap.
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DeepSeek’s model could rewrite the investment case for artificial intelligence, according to Kenneth Lamont, principal at Morningstar.
“Until now, the conventional wisdom has been clear: the best AI models rely on massive datasets and immense computational power, rewarding scale and favouring hardware giants like Nvidia and Europe’s ASML.
“But DeepSeek’s latest innovations are turning that assumption on its head. The start-up’s new models demonstrate how efficiency gains in AI development can reduce reliance on brute-force computing power. This breakthrough slashes computational demands, enabling lower fees — and putting pressure on industry titans like Microsoft and Google to justify their premium pricing.”
He added that, with many investors heavily exposed to AI’s biggest players, disruption in the sector could ripple through portfolios.
Despite the market shock, Neuberger Berman head of thematic – Asia Yan Taw Boon says the trend is not something new.
“China has always been able to cut the corners and re-create a technology [which somewhat already exists] at a much lower cost given the efficiency ‘DNA’. DeepSeek also said that their key bottleneck is in advanced AI/GPU chips. China is still 4-5 years behind the western world in leading edge semiconductors.
“Without leading edge chips, DeepSeek admits that they can’t scale up their model for a larger user base.”
What next for AI?
Mark Hawtin, head of the Liontrust global equities team, believes the news does not tarnish the investment case for AI.
“We have been crystal clear that the opportunity for AI is immense and nothing in the DeepSeek news changes that. In fact, it can only help the speed of adoption as the infrastructure becomes cheaper and cheaper and potentially a commodity.
“It’s worth flagging Jevons Paradox. When Jevons studied coal consumption in relation to steam engine efficiency in the mid-19th century, he noticed that as steam engines became more efficient, coal consumption increased rather than decreased. As AI gets more efficient and accessible, we could likely see AI skyrocket and turn into a commodity.”
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He adds that AI infrastructure players may well become victims of the “right thesis, wrong valuation” mantra.
“However, there will be a broadening out of the opportunity set. Users of AI to drive productivity and stronger moats will prevail.
“This is early in the cycle and there will be some amazing investment opportunities. Any turmoil around the theme in the short term should be seen as a chance to position portfolios to AI use case winners.”