Google DeepMind CEO Demis Hassabis reportedly told employees Wednesday (Feb. 12) that he is not worried about DeepSeek.
Asked during an all-hands meeting about DeepSeek, which claimed last month that it built an artificial intelligence model at a fraction of the cost of other models, Hassabis said Google has what it takes to stay ahead, CNBC reported Friday (Feb. 14).
Hassabis said some of DeepSeek’s claims are “exaggerated,” adding that the company probably reported only a fraction of the total cost of developing its systems, used more hardware than it said and relied on Western AI models, according to the report.
At the same time, Hassabis said DeepSeek’s accomplishments are impressive and something to be taken seriously, per the report.
Hassabis said of Google: “We actually have more efficient, more performant models than DeepSeek. So, we’re very calm and confident in our strategy, and we have all the ingredients to maintain our leadership into this year.”
DeepSeek’s launch of a new AI model sent tech stocks plunging Jan. 27 as the company claimed that it used fewer Nvidia chips than OpenAI and Meta while achieving comparable performance.
The company’s AI model also supplanted OpenAI’s ChatGPT as the most popular free app on Apple’s App Store.
It was reported a day later that Microsoft and OpenAI were investigating whether a group tied to DeepSeek had improperly accessed OpenAI’s data.
Anthropic CEO Dario Amodei wrote in an essay posted Jan. 29 that claims about DeepSeek’s potential threat to U.S. AI leadership are “greatly overstated” and that the drop in Nvidia’s stock driven by the release of DeepSeek’s models was “baffling.”
Amodei wrote that the DeepSeek model is close to the performance of U.S. models that are between seven and 10 months older; is “an expected point on an ongoing cost reduction curve”; and was trained by a company whose total spend is similar to U.S. AI labs.
Meta CEO Mark Zuckerberg said Jan. 29 that he lauded the innovations of DeepSeek’s lower-cost AI model but that it was too soon to tell whether that means it’s time to reduce AI infrastructure spending.
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