Market update

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.

How will DeepSeek impact the AI trade?

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.

• Phase 1 – Hyperscalers:  The companies that provide cloud computing technology at scale.
• Phase 2 – Data center compute and infrastructure: The companies that produce inputs like hardware (chips) and electrical equipment that enable data centers.
• Phase 3 – Power and transmission: The companies that supply and transmit the electricity that data centers need.
• Phase 4 – Cross-sector beneficiaries: The companies that incorporate AI to add productivity and reap revenue benefits.

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.

• The first assumption challenged is that hyperscalers must spend billions on advanced AI chips. DeepSeek claims that their model used Nvidia’s H800 chips, which have more constrained memory bandwith to comply with U.S. chip export controls than the more advanced H100s. Further, they claim that training took 2,788,000 graphic processing unit (GPU) hours at a cost of $2 per GPU hour. The total cost, therefore, is only $5.576 million for the final training run. The final training run for the latest Llama model from Meta was 10 times the cost. Investors immediately questioned Nvidia’s expected revenue and margins. Indeed, other chipmakers have posted blogs that instruct developers on how to run the DeepSeek model using their own chips. Nvidia lost $590 billion in market cap on Monday and is down -13% this week.
• The second challenged assumption is that AI’s future demands ever-increasing power and energy. DeepSeek’s breakthrough is in part reliant on their design which uses a “Mixture of Experts” (MoE) system (for more details on the technical aspects of the breakthrough, see Michael Cembalest’s latest Eye on the Market). In total, the model has a capacity of 671 billion parameters, but only 37 billion (less than 6%) are active at any given time. This reduces energy requirements along with overall costs. If AI models maintain efficacy without a commensurate increase in compute infrastructure and power, that may mean that data center driven power demand has been overestimated. Hence, many of the companies (e.g. Constellation, Vistra, Eaton and Digital Realty) that had soared based on bullish electricity demand estimates fell sharply on Monday.
• The third assumption questioned is the U.S.’s perceived lead in AI technology. DeepSeek demonstrated that heavy optimization can produce remarkable results on weaker hardware and with lower memory bandwidth. Meanwhile, U.S. labs have been investing heavily in leading edge technology and building an arsenal of the most advanced chips to gain an edge. The playing field is more level than previously assumed. However, DeepSeek used a U.S. model as its technical foundation (Llama), U.S. chips to run the model (Nvidia) and partnered with a U.S. institution to release their findings (MIT).

What does this mean for the AI trade and its adoption lifecycle?

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:

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.

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.

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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The information presented is not intended to be making value judgments on the preferred outcome of any government decision or political election.

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.

Investing in fixed income products is subject to certain risks, including interest rate, credit, inflation, call, prepayment, and reinvestment risk.

The price of equity securities may rise or fall due to the changes in the broad market or changes in a company's financial condition, sometimes rapidly or unpredictably. Equity securities are subject to "stock market risk" meaning that stock prices in general may decline over short or extended periods of time.

Investments in commodities may have greater volatility than investments in traditional securities, particularly if the instruments involve leverage. The value of commodity-linked derivative instruments may be affected by changes in overall market movements, commodity index volatility, changes in interest rates, or factors affecting a particular industry or commodity, such as drought, floods, weather, livestock disease, embargoes, tariffs and international economic, political and regulatory developments. Use of leveraged commodity-linked derivatives creates an opportunity for increased return but, at the same time, creates the possibility for greater loss.

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Index definitions:

The Solactive United States 2000 Index intends to track the performance of the largest 1001 to 3000 companies from the United States stock market. Constituents are selected based on company market capitalization and weighted by free float market capitalization.

The Russell 3000 Index is a capitalization-weighted stock market index that seeks to be a benchmark of the entire U.S. stock market. It measures the performance of the largest 3,000 U.S. companies representing approximately 96% of the investable U.S. equity market.

The S&P 500 Equal Weight Index is the equal-weight version of the widely-used S&P 500. The index includes the same constituents as the capitalization weighted S&P 500, but each company in the S&P 500 EWI is allocated a fixed weight of the index total at each quarterly rebalance.

The Bloomberg U.S. Aggregate Bond Index is a broad-based flagship benchmark that measures the investment grade, US dollar-denominated, fixed-rate taxable bond market. The index includes Treasuries, government-related and corporate securities, MBS (agency fixed-rate pass-throughs), ABS and CMBS (agency and non-agency).

The Magnificent Seven stocks are a group of influential companies in the U.S. stock market: Alphabet, Amazon, Apple, Meta Platforms, Microsoft, NVIDIA, and Tesla.

The Magnificent 7 Index is an equal-dollar weighted equity benchmark consisting of a fixed basket of 7 widely-traded companies (Microsoft, Apple, Nvidia, Alphabet, Amazon, Meta, Tesla) classified in the United States and representing the Communications, Consumer Discretionary and Technology sectors as defined by Bloomberg Industry Classification System (BICS).

The S&P Midcap 400 Index is a capitalization-weighted index which measures the performance of the mid-range sector of the U.S. stock market.

The S&P 500 index is widely regarded as the best single gauge of large-cap U.S. equities and serves as the foundation for a wide range of investment products. The index includes 500 leading companies and captures approximately 80% coverage of available market capitalization.

Bonds are subject to interest rate risk, credit, call, liquidity and default risk of the issuer. Bond prices generally fall when interest rates rise.

Standard and Poor’s 500 Index is a capitalization-weighted index of 500 stocks. The index is designed to measure performance of the broad domestic economy through changes in the aggregate market value of 500 stocks representing all major industries. The index was developed with a base level of 10 for the 1941–43 base period.

The Bloomberg Eco Surprise Index shows the degree to which economic analysts under- or over-estimate the trends in the business cycle. The surprise element is defined as the percentage difference between analyst forecasts and the published value of economic data releases.

The MSCI World Index is a free float-adjusted market capitalization index that is designed to measure global developed market equity performance.

The NASDAQ 100 Index is a basket of the 100 largest, most actively traded U.S companies listed on the NASDAQ stock exchange. The index includes companies from various industries except for the financial industry, like commercial and investment banks. These non-financial sectors include retail, biotechnology, industrial, technology, health care, and others.

The Russell 2000 Index measures small company stock market performance. The index does not include fees or expenses.

We believe the information contained in this material to be reliable but do not warrant its accuracy or completeness. Opinions, estimates, and investment strategies and views expressed in this document constitute our judgment based on current market conditions and are subject to change without notice.

The views, opinions, estimates and strategies expressed herein constitutes the author's judgment based on current market conditions and are subject to change without notice, and may differ from those expressed by other areas of J.P. Morgan. This information in no way constitutes J.P. Morgan Research and should not be treated as such. You should carefully consider your needs and objectives before making any decisions. For additional guidance on how this information should be applied to your situation, you should consult your advisor.

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RISK CONSIDERATIONS

• Past performance is not indicative of future results. You may not invest directly in an index.
• The price of equity securities may rise or fall due to the changes in the broad market or changes in a company's financial condition, sometimes rapidly or unpredictably. Equity securities are subject to 'stock market risk' meaning that stock prices in general may decline over short or extended periods of time.
• Investing in fixed income products is subject to certain risks, including interest rate, credit, inflation, call, prepayment and reinvestment risk. Any fixed income security sold or redeemed prior to maturity may be subject to substantial gain or loss.
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• Investments in emerging markets may not be suitable for all investors. Emerging markets involve a greater degree of risk and increased volatility. Changes in currency exchange rates and differences in accounting and taxation policies outside the u.s. can raise or lower returns. Some overseas markets may not be as politically and economically stable as the united states and other nations. Investments in emerging markets can be more volatile.
• Investments in commodities may have greater volatility than investments in traditional securities, particularly if the instruments involve leverage. The value of commodity-linked derivative instruments may be affected by changes in overall market movements, commodity index volatility, changes in interest rates, or factors affecting a particular industry or commodity, such as drought, floods, weather, livestock disease, embargoes, tariffs and international economic, political and regulatory developments. Use of leveraged commodity-linked derivatives creates an opportunity for increased return but, at the same time, creates the possibility for greater loss.
• Real estate investments trusts may be subject to a high degree of market risk because of concentration in a specific industry, sector or geographical sector. Real estate investments may be subject to risks including, but not limited to, declines in the value of real estate, risks related to general and economic conditions, changes in the value of the underlying property owned by the trust and defaults by borrower.
• Investment in alternative investment strategies is speculative, often involves a greater degree of risk than traditional investments including limited liquidity and limited transparency, among other factors and should only be considered by sophisticated investors with the financial capability to accept the loss of all or part of the assets devoted to such strategies.
• Structured products involve derivatives and risks that may not be suitable for all investors. The most common risks include, but are not limited to, risk of adverse or unanticipated market developments, issuer credit quality risk, risk of lack of uniform standard pricing, risk of adverse events involving any underlying reference obligations, risk of high volatility, risk of illiquidity/little to no secondary market, and conflicts of interest. Before investing in a structured product, investors should review the accompanying offering document, prospectus or prospectus supplement to understand the actual terms and key risks associated with the each individual structured product. Any payments on a structured product are subject to the credit risk of the issuer and/or guarantor. Investors may lose their entire investment, i.e., incur an unlimited loss. The risks listed above are not complete. For a more comprehensive list of the risks involved with this particular product, please speak to your J.P. Morgan team.
• As a reminder, hedge funds (or funds of hedge funds) often engage in leveraging and other speculative investment practices that may increase the risk of investment loss. These investments can be highly illiquid, and are not required to provide periodic pricing or valuation information to investors, and may involve complex tax structures and delays in distributing important tax information. These investments are not subject to the same regulatory requirements as mutual funds; and often charge high fees. Further, any number of conflicts of interest may exist in the context of the management and/or operation of any such fund. For complete information, please refer to the applicable offering memorandum.
• For informational purposes only -- J.P. Morgan Securities LLC does not endorse, advise on, transmit, sell or transact in any type of virtual currency. Please note: J.P. Morgan Securities LLC does not intermediate, mine, transmit, custody, store, sell, exchange, control, administer, or issue any type of virtual currency, which includes any type of digital unit used as a medium of exchange or a form of digitally stored value.
• The prices and rates of return are indicative, as they may vary over time based on market conditions.
• Additional risk considerations exist for all strategies.
• The information provided herein is not intended as a recommendation of or an offer or solicitation to purchase or sell any investment product or service.
• Opinions expressed herein may differ from the opinions expressed by other areas of J.P. Morgan. This material should not be regarded as investment research or a J.P. Morgan investment research report.