Artificial intelligence is no longer a standalone theme layered onto markets – it’s becoming an operating condition that reshapes demand across sectors. But as AI adoption accelerates into 2026 and worldwide spending reaches a projected $2.5 trillion, not all exposure will be equal, and investor outcomes will increasingly depend on where capital ultimately accrues. The primary winners are often not the most visible AI brands, but the companies closest to infrastructure, efficiency, and enablement – those that power, distribute, or monetize AI activity at scale.
Where AI Spend is Being Captured
In 2026, the most direct AI beneficiaries are those positioned at emerging bottlenecks created by the rapid deployment of AI. As models shift from episodic training to persistent use, spending is increasingly concentrated in inference, memory, networking, and utilization — segments where constrained supply and rising intensity drive outsized value capture.
Take memory-intensive chip companies for example. Once an overlooked segment of the semiconductor market, these firms have become essential as AI shifts from occasional use to constant operation. As models process requests continually and store expanding context in real-time, memory demand is rising sharply across both data centers and devices like smartphones that increasingly run AI features on-device.
That shift is already reshaping investment. Spending on DRAM (Dynamic Random-Access Memory) – the ultra-fast-working memory chips needed to train and run AI models – is up 24% year-over-year. Some suppliers have acknowledged they are effectively sold out through 2026, reflecting the urgency of the new supercycle.
Beyond chips, AI usage drives higher demand for data center-adjacent digital infrastructure, including interconnection and networking services. As workloads run longer, move more data, and require lower latency across distributed environments, the market for advanced electrical connectors is projected to grow 56% year-over-year in 2026, following a staggering 190% increase in 2025. This growth reflects the continued buildout of roughly 14 to 16 gigawatts of data center capacity this year, a growth rate of 15% year over year.
Optical interconnects, in particular, are reaching an inflection point. While copper has extended its range with newer designs and is expected to dominate in the short-term, cutting-edge systems running at 1.6 terabits per second and beyond are shifting decisively to optical as deployments accelerate through 2028. Meta’s recent $6 billion agreement with Corning to build new fiber optic cable facilities in the U.S. underscores how important this technology is for the next phase of scaling.
Enablers: The Physical Inputs Behind the Output
AI’s ability to scale is increasingly governed by physical and logistical constraints, rather than by software capability alone. While hyperscalers – global AI infrastructure builders such as Google and Meta – continue to expand capacity aggressively, the central challenge they face is not power availability. It is the ability to connect power to compute reliably and at scale.
The magnitude of the demand shock makes this clear. Power demand from the U.S. electric grid is projected to rise roughly 25% by 2030, driven in large part by AI workloads and data center construction. While meeting that growth would require adding generation and infrastructure at a pace well above historical norms, supply is already struggling to keep up. Gas turbines, for example, are effectively sold out for the next five years, highlighting how quickly AI-driven demand has tightened equipment markets.
This surge in demand is colliding with an electric grid that was not designed for sustained, concentrated load growth. Transmission networks, interconnection queues, and local grid capacity are increasingly under strain, even in regions with ample power generation. These constraints are reshaping energy policy and investment priorities, as rising electricity prices and reliability concerns have forced states to reconsider how new capacity is added and connected.
One practical response has been a renewed embrace of traditional energy sources, like natural gas. The U.S. remains the world’s largest natural gas producer, and with abundant supply and competitive pricing, gas offers a resilient bridge as electrification ambitions collide with grid limitations. Natural gas provides firm, dispatchable power at a time when renewables alone cannot meet baseload requirements, particularly for data centers that require constant, uninterrupted energy.
Going forward, investors should look to energy companies with the scale and resources for larger projects that connect power to the grid – those capable of navigating slow permitting, managing equipment backlogs, and delivering scarce inputs that bring US energy reserves online.
AI-Adjacent Defensives: Efficiency in Large, Complex Systems
AI’s most immediate economic gains often accrue in large, operationally intensive sectors where efficiency improvements matter more than top-line disruption.
Healthcare is one of the clearest examples. The U.S. healthcare AI market is expected to grow to roughly $43-44 billion by 2030, up from an estimated $8-10 billion in 2025, as providers move beyond early use cases like ambient speech – systems that capture and process spoken language – and coding support toward broader adoption in revenue cycle management, patient communications, and imaging.
Currently, roughly 20% to 25% of healthcare providers are using AI tools in revenue cycle management – particularly in claims adjudication, coding automation, and claims denial management – areas where the healthcare provider industry absorbs approximately $200 billion in annual administrative spend. As manual processes, error rates, and cycle times decline, AI adoption is translating into structurally lower costs and expanding operating margins over time.
On the clinical side, pharmaceutical companies are using AI to improve the speed of clinical trials and expedite biopharmaceutical development. This acceleration underpins some of the most ambitious expectations for AI’s impact on healthcare. Demis Hassabis, CEO of Google DeepMind, has suggested that AI could help cure all diseases within the next decade, underscoring the scale of opportunity if development timelines continue to compress.
In a similar way, industrial companies with connected equipment are similarly using performance data for predictive maintenance and smarter design. Chemical firms are leveraging AI to iterate faster on formulations and tailor solutions to precise customer needs, moving away from the traditional “develop and hope” model.
The opportunity is substantial, but it’s also grounded. AI is improving efficiency in established industries rather than reinventing them. For investors, the focus should be on companies with the operational scale, data, and execution capability to translate AI adoption into measurable margin improvement – outcomes that may be less visible, but are often more durable.
