AI Driving Market Leadership and Economic Momentum
Since the March 30th lows, the recent market rally of over 15% has centered around AI.1 As we have outlined over the past several years in our commentary, AI is serving as the primary driver of earnings growth and equity performance across companies in the AI food chain. In the first quarter, S&P 500 EPS grew by 26.75% on 10.70% sales growth2, demonstrating strong operating leverage across the market. What stands out, however, is the concentration of growth in sectors most closely tied to AI. Communication services and technology, being strongly correlated to developments in AI, have seen the strongest earnings growth in Q1, delivering 54.80% and 42.14% earnings growth, respectively.3
At the same time, the impact is broadening into more cyclical areas of the market. Materials saw 36.26% earnings growth, financials 23.97%, industrials 13.25%, and utilities 10.15%,4 reinforcing that AI is not confined to a single segment but is instead driving a multi-sector expansion across the economy. At the company level, the largest earnings expansion in Q1 was seen in AI centric names with Micron Technology and Teradyne, seeing a staggering 682.05% and 241.33% growth in earnings high due to benefits from AI.5 AI is increasingly acting as the backbone of this earnings cycle, supporting both top-line growth and margin expansion while driving leadership in equity markets.
Compute Strength Remains Robust but Bottlenecks Are Shifting
Semiconductors make up a significant portion of the current AI ecosystem, posting 40% revenue growth and 127% EPS growth, yet the industry has experienced a significant shift.6 Computers have dominated the narrative, but memory is rapidly becoming the defining constraint of AI infrastructure. By 2026, AI workloads are expected to consume roughly 70% of global high‑end DRAM production. As a result, memory prices are projected to rise by 70% in 2026, reinforcing a supply‑constrained environment driven by structural demand rather than cyclical recovery.7 Pricing dynamics highlight how tight the market has become. High‑capacity DRAM module costs have risen sharply in just a few quarters, and memory now represents a materially larger share of total system cost for advanced AI accelerators.
While rising memory costs challenge hardware manufacturers, they create clear beneficiaries elsewhere in the semiconductor ecosystem. Memory producers gain directly from higher average selling prices and improved contract visibility. At the same time, increasing memory density and system complexity raises the cost of design errors in advanced chips, strengthening demand for companies that provide design, simulation, and pre‑production testing tools.
As memory becomes a larger share of system value, the penalty for architectural mistakes grows, elevating the importance of verification and modeling solutions that help customers reduce risk. The underlying force behind this shift is the widening imbalance between compute performance and memory bandwidth. AI systems are increasingly constrained not by processing power but by how quickly data can move through the system. This dynamic has accelerated demand for advanced memory technologies such as high‑bandwidth memory (HBM), where adoption is scaling rapidly as next‑generation AI models grow in size and complexity.
AI Buildout Expands Critical Power and Utilities Demand
As AI adoption accelerates, the limiting factors are increasingly tied to power availability, grid capacity, and the ability to physically build and operate data centers at scale. AI demand is actively flowing through to orders, backlog, and forward-looking growth across industrial and power-related sectors, signaling durability rather than a short-term spike. Within industrials, companies are reporting average backlog growth of 33.75%, alongside orders growth of 24.77% and organic growth of 11.41%, reinforcing that demand is strong and growing.8
The most notable strength, however, is concentrated in power-related companies, as they are delivering 35% sales growth and 300% EPS growth, alongside backlog growth of 72.8% and orders growth of 52.5%. Utilities are also participating, with sales growth of 9.65% and EPS growth of 6%. This performance signals the growing urgency to expand grid capacity and energy supply to support AI-driven data center demand.9
At the company level, this trend becomes even more pronounced. Quanta Services reported 38% backlog growth and 25% orders growth, while GE Vernova delivered 32% backlog growth and an impressive 80% increase in orders. Eaton highlighted 48% backlog growth in its Electrical segment, with orders accelerating 42%, explicitly tied to data center momentum.10 Caterpillar further reinforced the strength of the cycle, reporting 79% backlog growth and noting that orders are at their strongest levels since 2012, with record demand in the first quarter.11 These figures confirm that the AI buildout is driving real, measurable demand across the physical economy.
What this suggests is that AI is driving a structural transformation of the energy ecosystem, requiring sustained investment in generation, transmission, and distribution. As a result, the constraint in the system is no longer just about processing power, but increasingly about how that power is delivered and scaled.
Automation and Robotics Scale the Physical Buildout
As AI adoption accelerates, it is increasingly moving beyond software and into the physical world. Automation and robotics are becoming central to this transition, particularly as infrastructure buildout expands and companies seek to enhance productivity. The sector is already delivering 20.9% sales growth and 56% EPS growth, supported by rising demand for automation in manufacturing, logistics, and data center construction.12 As the AI ecosystem expands, the desire to operate infrastructure efficiently is becoming more pronounced as automation and robotics play a key role in corporate productivity.
This dynamic is already visible at scale, with Amazon now operating over one million robots. Amazon expects automation to help it avoid hiring ~400,000 employees over the next decade, translating into over $2 billion in annual savings and supporting 3%–5% annual productivity gains, or roughly 3 million incremental units each year.13 As a result of these efficiency boosts, Robotics has become a direct downstream beneficiary of the AI value chain, drawing strength from every layer of the stack. Training increasingly sophisticated physical‑world models fuels hyperscale compute demand, while real‑time autonomy pushes high‑performance edge semiconductors into the machines themselves. This is driving increased demand for semiconductors, sensors, actuators, and power systems, further reinforcing the interconnected nature of the AI ecosystem as AI enhances productivity, improving efficiency, and enabling new forms of economic output.
A Durable, System-Wide Capex Cycle
Taken together, the data supports the view that AI is evolving into a system-wide economic driver rather than a narrow technological trend. The combination of strong earnings growth, accelerating orders, and expanding backlogs across multiple sectors points to a cycle that is both broad and durable. Importantly, the underlying adoption curve remains in its early stages. Enterprise integration of AI is still developing, suggesting that demand for infrastructure, compute, and energy will continue to build over time. At the same time, AI’s ability to drive productivity and reduce costs reinforces its role as a long-term growth catalyst for the economy. The result is a self-reinforcing cycle in which increased adoption drives further investment, and that investment enables continued adoption, extending the runway for growth across sectors.
[1] Bloomberg: As of May 5, 2026
[2] Bloomberg: As of May 4, 2026
[3] Bloomberg: As of May 4, 2026
[4] Bloomberg: As of May 4, 2026
[5] Bloomberg: As of May 4, 2026
[6] Bloomberg, as of May 5, 2026
[7] Barclays Analyst Note, As of April 24, 2026
[8] Bloomberg, as of May 5, 2026
[9] Bloomberg as of May 4, 2026
[10] ETN News Release: As of May 5, 2026
[11] CAT Earnings Call: As of April 30, 2026
[12] Wells Fargo Analyst Note, As of November 7, 2026
[13] Bloomberg: As of December 23, 2025
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