Did China’s DeepSeek just burst the enterprise AI bubble?
Verdict · Shutterstock

Chinese AI startup DeepSeek has unveiled AI models with comparable features and functionality of some of its most popular US rivals at a fraction of the development cost.

The claim has undermined the prevailing idea that AI development requires exponential levels of funding, compute power and energy, calling vast US AI investments into question.

Markets reacted sharply today (27 January) as tech stocks tumbled at speculation that DeepSeek may have just burst the AI bubble. DeepSeek’s claim to have achieved parity with its US rivals at a fraction of the cost was described by veteran technology investor Marc Andreessen as AI’s Sputnik moment.

Market reaction follows the release of DeepSeek’s new model series DeepSeek R1 on 20 January which has seen its mobile app since surge to the top of Apple’s iPhone download charts.

DeepSeek was founded in 2023 by China’s own Sam Altman character, Liang Wenfeng, who also founded the startup's financial backer Chinese hedge fund High-Flyer. Wenfeng is said to have stockpiled Nvidia A100 chips now under Biden era US export restrictions.

DeepSeek claims to beat any competition among open-source models and “rival the most advance closed-source models globally.” The company claims its DeepSeek R1 has a "performance on par with OpenAI-o1." Most critically, the startup claims to have achieved this at a cost of $5.6m, a fraction of the vast sums spent by its US rivals, as well as using far fewer AI chips. For comparison, OpenAI is said to have spent around $7bn in 2024 on training its AI models.

GlobalData principal analyst Gavin Sneddon noted that there may questions around the accuracy of DeepSeek’s published costings and exactly how open source it is, but if the claims are within an order of magnitude, significant implications still arise.

“As more information emerges about the real development costs incurred by Deepseek, it can be expected that both the government and Big Tech will begin to look more closely at how to measure the return on investment of AI infrastructure going forward,” said Sneddon.

The assumption has been that the restrictions on chip, technology and research which western countries, particularly the US have imposed on China would slow down AI development in the country.

“However, while there are conflicting narratives around this, the possibility must now at least be considered that the impact of these measures has been more similar to that of an athlete 'training at altitude' and has, in fact, enhanced the creativity and efficiency of Chinese AI teams who have been forced to deliver against considerable resource constraints,” said Sneddon.