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AI Brief: DeepSeek Is A Catalyst For Innovation

This report does not constitute a rating action.

A new large language model (LLM) developed by Chinese startup DeepSeek promises democratization of AI access, competition to incumbents, and potential shifts in investment . Innovative engineering techniques enabled the developers to use relatively cheap hardware, improve compute and energy efficiency, and offer performance comparable to existing LLMs, suggesting that AI remains prone to disruption.

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What's Happening

DeepSeek grabbed global attention on January 27 when it unveiled its open-source R1 LLM, a rival to OpenAI’s o1 model in terms of performance, cost, efficiency, and energy consumption. A resultant stock selloff, notably in U.S. tech, sent global equity markets down about 1.4%, or $1.7 trillion--based on the January 25 to January 28 change in ECAUWRLD, which tracks the global market capitalization of listed companies.

Why It Matters

DeepSeek’s model could prove a material advancement in generative AI (genAI). Its performance is comparable to the most advanced LLMs, but uses a fraction of their compute power and is supported by lower-cost and simpler graphics processing units (GPUs). S&P Global Ratings considers that DeepSeeks' launch will have widespread implications, including with regard to:

  • Competition. It is increasingly apparent that no nation will have a monopoly on AI leadership and that there is room for diversification across genAI. That will put pressure on AI services' prices, particularly as new models are quickly emerging (see chart). Nations will continue to maneuver for AI supremacy, including through export restrictions, so we expect geopolitical dynamics to remain fluid.
  • Democratization of innovation. R1's open-source status provides new access to advanced-reasoning LLMs that could reduce big-tech dependency and costs. This might particularly benefit small- and medium-sized enterprises, startups, academia, developers, educators, the public sector, and nations with limited financial resources. And it could foster collaborative AI development. From a credit standpoint, it could prove a positive factor for those that benefit from implementing AI solutions that differentiate.
  • Engineering advancement. DeepSeek's engineering differs from established counterparts in three significant ways. First, its model was trained using less-advanced GPUs that proved cheaper and more computationally efficient (a choice somewhat dictated by U.S. export restrictions). Second, its model delivers operating efficiencies by partly adapting computing capacity to users’ requests--activating parameters only when needed (a concept known as Mixture-of-Experts, or MoE) rather than always activating all of its parameters. Third, facilitated by its MoE architecture, it deploys intermediate logical deductions to enhance efficiency, a concept known as Chain-of Thought reasoning.
  • Market valuation and volatility. The U.S. tech stock selloff was a reassessment of incumbent, large AI companies' valuations. It also proved the power of AI technological advancements to disrupt markets (and the potential for malicious actors to amplify negative effects in the cyber space) and cause volatility that threatens financial stability and amplifies systemic risks.

What Comes Next

We expect some corporations, investors, and nations will re-assess genAI investments and expensive partnerships with established big tech companies , potentially shifting budgets to more affordable and open-source options instead of higher-cost and proprietary models.

DeepSeek’s example of economy over computing power, and its open-source nature, offers researchers technology to develop sustainable AI models. These could be optimized for edge computing (on devices like phones and laptops) and reduce reliance on energy-intensive cloud technology.

Policymakers may need to harmonize regulation of open-source AI models to combat the risk of miss-use and bolster data security and privacy. DeepSeek's story also highlights the importance of data sovereignty (control and ownership) and could prompt regulators outside China to implement laws requiring data to be processed and stored in the jurisdiction where it is collected.

Related Research

Primary Contacts:Miriam Fernandez, CFA, Madrid 34917887232;
Miriam.Fernandez@spglobal.com
Martin J Whitworth, London 44-2071766745;
martin.whitworth@spglobal.com
Xu Han, New York 1-212-438-1491;
xu.han@spglobal.com
Secondary Contact:Sudeep K Kesh, New York 1-212-438-7982;
sudeep.kesh@spglobal.com
Research Contributor:Sudheesh R Meesala, CRISIL Global Analytical Center, an S&P Global Ratings affiliate, Hyderabad ;

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