AI's next act: From digital intelligence to real-world economic impact

Markets are navigating a noisy backdrop, from shifting interest-rate expectations to ongoing geopolitical conflict, and that uncertainty can obscure the longer-run forces reshaping growth. One of the clearest of those forces is artificial intelligence (AI). While near-term headlines may drive volatility, AI and the investment required to deploy it looks increasingly like a multiyear transition that could lift productivity and broaden economic opportunity over time.

From content creation to real world action

AI is evolving beyond its first wave of adoption. Generative AI initially drove enthusiasm by improving knowledge work through content creation, coding, translation, and summarization. The next phase—agentic AI—represents a more meaningful shift, as systems begin to autonomously reason, plan, and execute multistep tasks. Platforms are enabling AI agents to operate across tools and datasets to function as personal assistants, research analysts, or legal and compliance aides.

Over time, this intelligence extends further into the physical world through physical AI, where software is embedded into machines such as robots, vehicles, factories, and medical devices. Each step meaningfully expands AI’s economic reach, shifting it from a digital productivity enhancer into a force that reshapes how goods and services are produced across the economy.

Hyperscaler¹ capital spending becoming a macro growth engine

The scale of AI related capital spending has reached a point where it is contributing meaningfully to overall economic growth. Hyperscaler companies are investing aggressively in data centers, advanced semiconductors, networking equipment, and power infrastructure. Consensus estimates for hyperscaler capital expenditures over the next several years have continued to move higher, with projections for the late 2020s now substantially above levels envisioned even a year ago. This makes the AI build-out one of the largest and fastest capital investment cycles on record. Importantly, the economic impact extends well beyond technology companies, supporting activity in industrials, utilities, energy infrastructure, construction, and global supply chains.

Looking further out, some technologists are even exploring space based data centers—paired with orbital solar power and wireless power transmission to Earth—as a potential way to address the immense power demands of AI, though these concepts remain early and highly speculative.

AI adoption cycle looks familiar

History suggests that transformational technologies tend to follow a common pattern: markets recognize the opportunity early, investment accelerates, and measurable productivity gains arrive with a lag. AI appears to be on a similar path. Research that compares AI with prior general-purpose technologies suggests adoption may take on the order of about 15 years to move from early penetration to broad usage across the economy, with the most meaningful productivity gains likely showing up later in the cycle closer to widespread adoption rather than at the outset.

Estimates of the economy-wide lift are necessarily uncertain, but a modest boost over the next decade (roughly +0.2% to +0.3% to productivity growth) could build toward larger gains (roughly +0.5% to +0.9%) as adoption deepens.²  In other words, today’s elevated spending reflects the front-loaded build-out of both digital and physical infrastructure, while efficiency gains emerge more gradually as organizations redesign workflows, upskill workers, and adapt business processes. This dynamic helps explain why near-term questions about return on investment persist, even as the longer-term productivity potential remains compelling. (For more information, see Artificial intelligence: An X-factor in a new investment regime white paper)

AI driven productivity in practice

Agentic AI is already beginning to automate complex workflows that previously required significant human coordination. In professional services, AI agents can act as personal assistants, drafting materials, coordinating tasks, and managing schedules. In research and investment settings, agentic systems can function as always-on research analysts, synthesizing data, monitoring disclosures, and generating insights in real time. In legal and compliance functions, AI can continuously review documentation, flag inconsistencies, and track regulatory changes.

Beyond digital tasks, AI-enabled systems in manufacturing and logistics are optimizing production lines, reducing downtime, and improving inventory management, while in health care, AI-powered diagnostics, surgical robotics, and administrative automation are improving outcomes and lowering costs. Over time, the adoption and productivity impact is likely to vary by sector, with early penetration skewing toward service industries such as information and professional services before becoming more broadly embedded across the economy.

Broadening opportunities far beyond mega-cap tech

While early market leadership was concentrated among a small group of large technology firms, the opportunity set is rapidly expanding. This is increasingly evident in private markets and expected public listings. Many AI companies are capturing investor attention as potential IPO candidates, reflecting optimism around AI enabled platforms, infrastructure, and computing. The anticipated public market debut of these firms underscores how AI is spreading across industries—from aerospace and advanced manufacturing to foundational models and specialized hardware. As AI adoption deepens, benefits are increasingly flowing to power generation, grid modernization, semiconductors, industrial automation, and global suppliers, reinforcing the importance of selectivity rather than blanket exposure.

What could slow the adoption curve

AI’s upside is meaningful, but it is not frictionless. Questions around reliability and misuse (including deepfakes and model hallucinations), evolving regulatory guardrails, high compute and energy requirements, and environmental impacts could all shape the pace and breadth of adoption, reinforcing why the rollout may be uneven across industries and why productivity gains are likely to arrive with a lag.

From investment theme to an economic force

As AI transitions from digital intelligence to autonomous systems and physical deployment, it is becoming embedded in the real economy. Capital spending remains strong, productivity gains are expected to compound over time, and the set of beneficiaries continues to widen. For advisors, this reinforces the case for staying invested through periods of volatility, focusing on companies that enable and benefit from AI across the value chain, and viewing near-term uncertainty as a natural feature of long-duration technology adoption cycles while staying mindful of the practical constraints (cost, regulation, and governance) that can influence the speed of adoption.