Ride US Economic Tailwinds

US equities hit multiple all-time highs in 2024, outperforming their developed market peers by more than 20%.¹ We expect resilient economic growth, the Federal Reserve’s (Fed) easing cycle, the Republican election sweep, and AI-related Tech leadership to continue to strengthen the economy and corporate profitability in 2025.

In 2025, we expect the US to maintain its economic and earnings fundamentals advantage over the rest of the developed markets — supported by momentum in artificial intelligence (AI) development and the new Trump administration’s pro-growth, deregulated, US-first agenda.

While fiscal policy takes time to enact, less restrictive monetary policy, sturdy labor markets, and the disinflation trend have the potential to extend the soft-landing runway in the US before the effects of new pro-growth measures trickle down to the economy.

We acknowledge the downside risk to global growth driven by potential trade conflicts, and higher inflation due to increased tariffs could complicate the Fed policy decisions. But corporate and individual tax cuts, along with less regulation, should support business investment and consumer spending.

US-led AI development also supports US economic exceptionalism. With enhanced capabilities and reduced costs, AI could help early adopters like software companies and online platforms sustain productivity gains and develop future growth engines. In particular, AI is transforming the drug discovery pathway. And, the positive downstream impact of AI’s capex boom has created more relative value opportunities beyond hyperscalers and semiconductors.

Against this backdrop of stronger economic growth and corporate profitability — and AI-related Tech leadership — investors should consider:

• Cyclical Exposures With Domestic-focused Businesses, including US small caps and regional banks that may be less impacted by potential trade conflicts and capture more tailwinds from increasing domestic investment and consumption.
• The Broad AI Value Chain Beyond the Tech Sector, including AI enablers, early adopters, and beneficiaries.

Broadening US Growth: Small Caps and Regional Banks

Over the past decade, the S&P 500® Index has beaten developed ex-US and emerging markets over every rolling 10-year period since 2015.² This dominance has been supported by faster EPS growth, greater profit margin expansion, and increased productivity and efficiency.³

And with the US leading the next technological revolution centered around AI, it’s a good bet those advantages could continue in the coming years. Indeed, based on consensus EPS estimates, expectations are for US large- and small-cap earnings growth to outpace the rest of the world through 2026 (Figure 1).

A constructive macroeconomic backdrop supports US companies’ ability to meet those growth expectations — and for growth to broaden beyond Tech.

• US 2024 GDP Growth is expected to be well ahead of that of developed country peers.
• The Labor Market is not as tight as pre-pandemic, but real wage growth remains healthy. 
• Consumer Savings Rate’s Substantial Upward Revision underscores persistent strength in consumer spending.

All this, too, supports the US economy growing more than its developed market peers in 2025.

In terms of monetary policy, while investors have dialed back the number of expected Fed rate cuts for 2025, the rate path forward remains lower. The Fed believes inflation is “on a sustainable path back to 2%” — and its current policy is still restrictive, even with 25 basis point rate cut in November.⁴

Expected lower rates may ease the financing burden on businesses and consumers, supporting consumer spending and business investment. This is especially positive for small-cap companies and regional banks, as their financing costs are highly sensitive to short-term interest rates and their revenue is driven more by domestic demand than it is for their large-cap peers.

While the magnitude and scope of tariff increases during the second Trump administration are still unknown, targeted tariffs on certain countries and products could result in higher US growth relative to regions that have trade surpluses with the US, like Europe and China.

US small caps’ and regional banks’ limited international exposure — from both a revenue and supply chain perspective — may help limit the negative impact from global trade uncertainty and capture the tailwinds from stronger domestic growth.

For regional banks, President-Elect Trump’s deregulation agenda may ease federal agencies’ supervision, relax capital requirements, and increase M&A activity in the industry. Together with macroeconomic tailwinds supporting loan growth and net interest margins, less regulation may lead to improved profitability, increased shareholder returns, and higher valuations for regional banks.

Markets have been pricing in a positive US outlook since the summer, with the US beating developed ex-US, US small caps outperforming large caps, and regional banks gaining 35%.⁵ The S&P 500’s price appreciation — 24.5% year to date and nearly 70% since the market bottom in September 2022 — has stretched forward price-to-earnings (P/E) valuations to near their 20-year high in 2021.

Nevertheless, valuations of US small caps and regional banks appear much more attractive relative to the broad market despite their recent strong performance and improved growth outlook, presenting attractively priced growth opportunities (Figure 2).

US Tech Exceptionalism: The Broad AI Value Chain

US Tech exceptionalism has been the major driver of US outperformance over the rest of the developed world since the beginning of the current bull market in October 2022. Tech contributed 66% of US outperformance, 12% attributable to the US overweight in Tech and 54% to US Tech outperformance over their peers in other regions.⁶

This exceptional performance period coincided with US-led technological breakthroughs in AI. ChatGPT’s GPT-3 and other large language models (LLM) now allow businesses and individuals to directly tap into AI capabilities at much lower costs than previous generations of AI models. In the global race of building powerful AI models, the US ranked at the top based on the number of notable AI models by geographic area.⁷

Downstream Impacts of AI Infrastructure Spending

Strong demand for AI capabilities has driven an AI capex boom, creating a growing symbiotic relationship between the Tech sector and other traditional sectors. Global spending on AI, including applications, infrastructure, and related services, is forecast to more than double by 2028 to top $632 billion.⁸

While skyrocketing demand for NVDIA GPUs has driven its stock price up more than 700% since GPT-3,⁹  firms that belong to the broader AI supply chain — like data center REITs, energy suppliers, computer hardware, and power equipment companies — also have seen a boost to demand for their products and services.

The US is expected to be the fastest growing market for data centers, with demand expected to more than triple by 2030, fueled by the expansion of cloud computing and increasing adoption of AI technology.¹⁰ Since vacancy rates in primary US data center markets are at historic lows, the average asking rental rates in those markets are at a 10-year high and expected to increase by double-digits for the next two years.¹¹

This boom in data centers has led to growing demand for electricity and power equipment, such as generators, transformers, and switchgears. Power consumption by US data centers is expected to increase at a compound annual growth rate of about 23% (Figure 3). Supply constraints amid surging demand has more than doubled lead times for electrical equipment like transformers and switchgears.

Taking note of these trends, we think there are more relative value opportunities related to the AI infrastructure theme beyond hyperscalers and semiconductors.

Economic Benefits of AI in Tech and Beyond

The exponential speed of AI technological development depends on easy access to super computing power and massive amounts of data to train AI models. Research shows that it takes less time for AI to reach levels of proficiency close to matching human capabilities than it did in the 2010s. And the capabilities being tested are in more challenging domains such as competition-level mathematics and multitask language understanding.¹²

What’s more, the inference cost, which is the cost of calling a LLM to generate a response, of GPT-4 has come down 12x since its March 2024 launch due to lower AI infrastructure costs and improvements to algorithms.¹³

With enhanced AI capability and reduced costs, economic benefits of AI applications have emerged among early adopters. Software companies have seen productivity improvements and promising signs of return on investment (ROI) around their internal generative AI deployment in areas like data processing, customer support, and developer Copilot.¹⁴ More importantly, these companies can easily translate these internal AI deployments into their products, potentially creating new revenue drivers.

The enthusiasm for AI applications extends beyond the Tech sector, with 41% of S&P 500 companies mentioning AI during their Q3 earnings call, compared to just 12% in 2022.¹⁵ While building AI foundational models is very capital intensive, creating domain-specific LLMs that fine tune foundation models with specialized or proprietary data to deliver outputs for particular use cases usually costs much less and takes less time.

This likely will accelerate AI adoption in the coming years, bringing economic value and productivity gains across a wider range of businesses.

Biotech and Enhanced AI Capabilities

The biotech industry is using AI to expedite the drug discovery process, elevate therapeutic efficacy, and identify promising drug candidates. The recent leap in AI capabilities along with gene editing technology and massive computing have brought the drug discovery process to the next level.

For example, AlphaFold, a breakthrough AI system developed by DeepMind, can accurately predict protein structure, unlocking the mechanisms of disease and deciphering biological interactions in the human body. It also allows researchers to model protein structures for disease targets rather than relying on expensive and time-consuming experimental methods.

Moreover, AI’s capacity to digest vast datasets and powerful predictive analytics can help analyze massive amount of clinical data at speed and scale, meaning promising drug candidates can be identified at lower costs. Despite this paradigm shift driven by AI and CRISPR technologies, the biotech industry has been out of favor over the past two years, underperforming the S&P 500 and Russell 2000 indices by 46% and 24%, respectively,¹⁶ as rising interest rates weighed on industry valuations.

Biotech’s enterprise value-to-sales ratio is currently in the bottom tercile since 2005 and bottom quintile relative to the broad small-cap market.¹⁷ Rate expectations may continue to drive industry performance in the near term, but they also provide attractively valued investment opportunities for capturing the great economic benefits of AI in life sciences.