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What does the China-based startup’s breakthrough mean for the artificial intelligence industry?
J.P. Morgan Wealth Management
Tech-focused U.S. equities pulled back from all-time highs this week as tech and artificial intelligence (AI) names dragged performance.
Nasdaq 100 (-1.6%) was lower, but large caps (S&P 500 +0.4%), small caps (Solactive 2000 +0.3%) and European equities (+1.2%) gained.
In macro news, data showed the U.S. economy grew at 2.3% (annualized) in Q4 2024. In all, real GDP growth in 2024 came in at 2.8%, which is a full percentage point above economist estimates of 1.7% at the start of the year.
The biggest story in markets this week revolved around the shocking assessment of the DeepSeek release on the AI trade. Sectors levered to AI and its buildout (tech -3.8%, utilities -1.4%, industrials -1.2%) lagged. But what if DeepSeek’s breakthrough is a reason to be even more positive on AI? We provide our take below.
A brief history of the AI trade. Between November 2022 and January 2023, 100 million people started using OpenAI’s ChatGPT. It took Instagram two and a half years to hit the same milestone. Ever since, investors have been captivated by different phases of the AI trade.
The chart illustrates the price returns of different phases of AI-related sectors over time, specifically focusing on Broad AI (hyperscalers), Data Center and Electrical, Power, and AI Productivity Beneficiaries from January 2023 to January 2025.
This description provides a sequential overview of the changes in price returns across different AI-related sectors over the specified period.
Source: Bloomberg Finance L.P.; GS Investment Research. Data as of January 30, 2025. Note: phase 1 = GS TMT AI Basket, phase 2 = AI Data Centers & Electrical Equipment, phase 3 = GS Power Up America, phase 4 = LT AI Beneficiaries. Outlooks and past performance are no guarantee of future results. It is not possible to invest directly in an index
Since the launch of ChatGPT, investors have rewarded companies in phases one through three because hyperscalers have ramped up spending on data center infrastructure and power in the pursuit of AI generated revenue.
Enter DeepSeek, a China-based AI startup founded two years ago. Last Monday (on the eve of President Donald Trump’s inauguration), Deep Seek launched its full R1 model, an open reasoning large language model (LLM). The R1 model matches the performance of o1 (OpenAI’s frontier reasoning LLM) across math, coding and reasoning tasks. Yes, there is still speculation among experts in the industry and financial markets about the validity of DeepSeek’s claims. Regardless, the market believes that their breakthrough challenges the prevailing assumptions that have driven the AI trade.
As a result, AI companies suffered their largest one-day drawdown since the release of ChatGPT on Monday.
We believe DeepSeek’s model represents a step function advancement in artificial intelligence technology. This is likely a positive for the broad market and the economy even if it marginally shifts value from semiconductors, data center infrastructure and power towards hyperscalers and revenue and productivity beneficiaries over the medium term. That is because more efficient models could further commoditize AI and exert downward pressure on capital spending and energy requirements.
At the same time, less expensive models should drive adoption and demand from households and corporations. A key question remains: Will increased adoption offset reduced capital spending from cost and energy efficiencies?
Here’s how we would assess the state of the AI trade:
This table shows the different AI trade themes, sub themes and example companies for each.
The sub themes in this theme and its corresponding example companies are:
The sub themes in this theme and its corresponding example companies are:
For both Data Center Compute and Data Center Infrastructure themes, the Hyperscalers are also mentioned:
The sub themes in this theme and its corresponding example companies are:
The sub themes in Revenue Beneficiaries and its corresponding example companies are:
The sub themes for the and/or theme and its corresponding example companies are:
The sub themes for the Productivity Beneficiaries theme and its corresponding example companies are:
Source: J.P. Morgan. As of January 27, 2025. Note: All third-party companies referenced are shown for illustrative purposes only, and are not intended as a recommendation or endorsement by J.P. Morgan in this context.
Phase 1 – Hyperscalers: Let’s assume the hyperscalers slash capital spending plans due to cost and energy efficiencies. In a bear case scenario, capital spending intensity (Capex/revenue) by hyperscalers could decrease back to 2015 to 2019 levels. This could lead to a decrease of nearly 24% in capex spend by these companies. This would clearly be a negative for the revenue of data center compute and infrastructure companies, but it could be a positive for hyperscaler stocks. Indeed, less capital spending frees up cash flow to return to shareholders, especially if you assume that AI innovations continues apace. However, this is not our base case.
The chart shows the Hyperscaler capital expenditures (capex) and capital intensity ratio for 2027, comparing the base case to the DeepSeek-adjusted bear case for Microsoft (MSFT), Meta (META), Google (GOOGL), Amazon (AMZN), and the total. For Microsoft, the 2027 base case capex is 69.4 with a capital intensity of 0.19, while the bear case post-DeepSeek is 54.8 with an adjusted capital intensity of 0.15. For Meta, the base case capex is 58.8 with a capital intensity of 0.25, and the bear case is 40.0 with an adjusted capital intensity of 0.17. For Google, the base case capex is 58.5 with a capital intensity of 0.14, and the bear case is 54.1 with an adjusted capital intensity of 0.13. For Amazon, the base case capex is 54.6 with a capital intensity of 0.13, and the bear case is 34.6 with an adjusted capital intensity of 0.08. For the total, the base case capex is 241.2 with a capital intensity of 0.16, and the bear case is 183.5 with an adjusted capital intensity of 0.13. Bear case: −24% change in Capex.
Source: Bloomberg Finance L.P. Data as of January 30, 2025. Note: Capital Intensity reflects capex as a percentage of revenues. Bear case capital intensity reverts to 2015-2019 averages.
This week, Microsoft and Meta reported earnings and confirmed continued spending intentions, primarily focused on building data centers for inference rather than training, aligning with the view that most AI investments are needed for inference. That said, as spending shifts towards Application Specific Integrated Circuits (ASICs), which combine hyperscalers’ intellectual property with a design platform, the traditional reliance on GPUs is expected to decrease.
Phase 2 – Data center compute and infrastructure: Decreased spending from hyperscalers could mean lower revenues for data center players. Analysts estimate data center revenues for chipmakers to grow 27% by 2028. We estimate that roughly 37.5 billion of that revenue is at risk amid shifting capex plans. That could translate to a decrease of almost seven percentage points for chipmaker data center revenues by 2028. However, as cost comes down, use cases could increase (elasticity), continuing to boost power demand. In fact, our investment bank noted that x86 virtualization (a technology developed in the mid-2000s that enabled multiple operating systems to run on a single physical machine) increased efficiency but also stimulated demand for semiconductor chips and memory. The transition to cloud computing likewise improved efficiency and drove demand for semiconductors.
Phase 3 – Power and transmission: In 2023, electricity demand in the United States was approximately 4,200 Terawatt Hour (Twh), with data centers accounting for about 200 Twh. Earlier this year, the Department of Energy (DoE) released updated projections for data center electricity demand. The high end of the estimate assumed that AI workloads would drive a 26% compound annual growth rate (CAGR) over the next few years. At that rate, data center electricity demand would double roughly every 2.5 years. Our downside case is even more restrained than the DoE’s. If DeepSeek efficiencies lead to little incremental growth, we believe that data center electricity demand would return to the growth rate of 13% experienced from 2014 to 2023.
Our view is slightly more upbeat. We believe that data center electricity demand will grow at a 20% CAGR over that time horizon. Further, AI is not the only factor driving power demand. Indeed, we believe that electric vehicles and hybrids, industrialization and automation, and non-AI data centers will also contribute to a 2.5% CAGR in overall electricity demand growth over the next several years versus the paltry 0.7% CAGR witnessed over the last decade. This wide range in possible outcomes should lead to more volatility in this theme that tactical traders could use to their advantage. Even if the highest estimates of power demand seem less likely, we still believe that power is an investable trend.
The chart titled "There is a wide range of datacenters power estimates" illustrates the total U.S. data center electricity consumption in terawatt-hours (TWh) from 2014 to 2027. The chart includes several projections for future consumption growth rates. From 2014 to 2023, the data center electricity consumption grew at a compound annual growth rate (CAGR) of 13%.
For the period from 2024 to 2027, the Department of Energy's bull case projects a 26% CAGR, while their bear case projects a 15% CAGR. JPM WM's base case for 2023 to 2027 estimates a 20% CAGR, and their bear case for the same period estimates a 13% CAGR. The chart visually represents these projections with different colored lines and markers: a solid blue line for historical data, an orange dot for the Department of Energy bull case, an orange circle for the Department of Energy bear case, a blue diamond for the JPM WM base case, and a light blue diamond for the JPM WM bear case.
Source: Lawrence Berkley National Laboratory, U.S. Department of Energy 2024 ,'United States Data Center Energy Usage Report'. Data as of December 2024. Outlooks and past performance are no guarantee of future results.
Phase 4 – Cross-sector beneficiaries: Ultimately, the DeepSeek breakthrough may be most positive for the many potential productivity and revenue beneficiaries of AI technology that inhabit most sectors in the stock market. We see this reflected in the price action. Since Monday, the productivity beneficiaries have outperformed stage one to three companies by 7% on average. The software sector was a notable outperformer and we would expect that to continue.
We have embraced the AI trade, both in portfolios and within J.P. Morgan. We think the theme could drive outperformance over the next several years, but we would also advocate accessing the theme selectively through active management in both the public and private space while maintaining exposures that help ensure portfolio resilience.
For questions on how to implement these strategies effectively, reach out to your J.P. Morgan advisor.
All market and economic data as of 01/31/25 are sourced from Bloomberg Finance L.P. and FactSet unless otherwise stated.
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