From TechCrunch, January 27:
Chinese AI firm DeepSeek has emerged as a potential challenger to U.S. AI leaders, demonstrating breakthrough models that claim to offer performance comparable to leading chatbots at a fraction of the cost. The company’s mobile app, released in early January, has also topped iPhone charts across major markets including the U.S., UK, and China.
Founded in 2023 by Liang Wenfeng, former chief of AI-driven quant hedge fund High-Flyer, DeepSeek makes its models open-source and incorporates a reasoning feature that articulates its thinking before providing responses.
Wall Street’s reaction has been mixed. While Jefferies warns that DeepSeek’s efficient approach “punctures some of the capex euphoria” following recent spending commitments from Meta and Microsoft — each exceeding $60 billion this year — Citi questions whether such results were achieved without advanced GPUs. Goldman Sachs sees broader implications, suggesting the development could reshape competition between established tech giants and startups by lowering barriers to entry.
Here’s how Wall Street analysts are reacting to DeepSeek, in their own words (emphasis mine):
Jefferies
DeepSeek’s power implications for AI training punctures some of the capex euphoria which followed major commitments from Stargate and Meta last week. With DeepSeek delivering performance comparable to GPT-4o for a fraction of the computing power, there are potential negative implications for the builders, as pressure on AI players to justify ever increasing capex plans could ultimately lead to a lower trajectory for data center revenue and profit growth.
If smaller models can work well, it is potentially positive for smartphone. We are bearish on AI smartphone as AI has gained no traction with consumers. More hardware upgrade (adv pkg+fast DRAM) is needed to run bigger models on the phone, which will raise costs. AAPL’s model is in fact based on MoE, but 3bn data parameters are still too small to make the services useful to consumers. Hence DeepSeek’s success offers some hope but there is no impact on AI smartphone’s near-term outlook.
China is the only market that pursues LLM efficiency owing to chip constraint. Trump/Musk likely recognize the risk of further restrictions is to force China to innovate faster. Therefore, we think it likely Trump will relax the AI Diffusion policy.
Citi
While DeepSeek’s achievement could be groundbreaking, we question the notion that its feats were done without the use of advanced GPUs to fine tune it and/or build the underlying LLMs the final model is based on through the Distillation technique. While the dominance of the US companies on the most advanced AI models could be potentially challenged, that said, we estimate that in an inevitably more restrictive environment, US’ access to more advanced chips is an advantage. Thus, we don’t expect leading AI companies would move away from more advanced GPUs which provide more attractive $/TFLOPs at scale. We see the recent AI capex announcements like Stargate as a nod to the need for advanced chips.
Bernstein
In short, we believe that 1) DeepSeek DID NOT “build OpenAI for $5M”; 2) the models look fantastic but we don’t think they are miracles; and 3) the resulting Twitterverse panic over the weekend seems overblown.
Our own initial reaction does not include panic (far from it). If we acknowledge that DeepSeek may have reduced costs of achieving equivalent model performance by, say, 10x, we also note that current model cost trajectories are increasing by about that much every year anyway (the infamous “scaling laws…”) which can’t continue forever. In that context, we NEED innovations like this (MoE, distillation, mixed precision etc) if AI is to continue progressing. And for those looking for AI adoption, as semi analysts we are firm believers in the Jevons paradox (i.e. that efficiency gains generate a net increase in demand), and believe any new compute capacity unlocked is far more likely to get absorbed due to usage and demand increase vs impacting long term spending outlook at this point, as we do not believe compute needs are anywhere close to reaching their limit in AI. It also seems like a stretch to think the innovations being deployed by DeepSeek are completely unknown by the vast number of top tier AI researchers at the world’s other numerous AI labs (frankly we don’t know what the large closed labs have been using to develop and deploy their own models, but we just can’t believe that they have not considered or even perhaps used similar strategies themselves)....
....MUCH MORE (MS; GS; JPM et al.)
The chip stocks are getting hit with Nvidia down 11%, Broadcom down 13.5%; ARM down 9.5%; and TSM down 9.3%. The toolmakers led by ASML down 9%.
They have all bounced off their pre-market lows.
