A brighter productivity outlook for the AI era

Stephanie Aliaga

Global Market Strategist, Global Market Insights Strategy

Madison Faller, CFA

Global Investment Strategist, Global Private Bank

Barbara Rudolph

Senior Investment Writer, Insights Editorial

Dr. David Kelly, CFA

Chief Global Strategist, Head of Global Market Insights Strategy

Published November 10, 2026

In brief

We believe artificial intelligence (AI) will drive a meaningful, durable uplift to productivity over our 10- to 15-year forecast horizon, adding to our growth and return expectations.
We have incrementally incorporated AI’s potential into our Long-Term Capital Market Assumptions since 2023, but the dramatic acceleration in capabilities and investment now requires more sophisticated analysis. This year, we have rebuilt our methodology to capture both the efficiency gains from scaling AI and the capital deepening required to deploy it.
AI adoption remains early and uneven. Only 6% of U.S. work hours are AI-assisted today, even as two-thirds of tasks may be theoretically exposed. We expect adoption to reach 40% of today’s tasks by 2040 as AI shifts to more agentic, and even robotic, workflows and spreads from knowledge-intensive industries to routine work across sectors.
Our base case estimates that AI will boost U.S. total factor productivity (TFP) growth by roughly 40 basis points (bps) above the pre-AI norm, to 1.2% annually, and lift U.S. capital stock growth by about 30bps, to 2.3% per year. Together, these gains raise our U.S. labor productivity forecast by 50bps over the same period, to 2.2%. In our forecast, improved productivity is the main force behind U.S. GDP growth returning to a 2%-plus pace over the coming decade.
The pace of AI diffusion and the resulting productivity boost are likely to be gradual, with the most significant gains emerging in the 2030s and beyond. This is an important premise of our macroeconomic and asset class assumptions. Steady uptake should limit rapid, economy-wide job displacement while giving supply chains time to adapt.
The U.S. looks well positioned to lead AI adoption. It registers the largest AI-driven TFP increase in our forecast universe, reflecting the economy’s robust technology ecosystem and supportive institutions. Europe, Asia and emerging markets stand to see material benefits while facing varying constraints based on industry mix, regulation, infrastructure and cultural and workplace norms.
We don’t think markets fully appreciate the economy-wide potential of AI, but selectivity and thoughtful diversification are key to investing in this space. We believe portfolios can best capture AI’s opportunity by balancing exposure across the AI value chain and funding cycle, spanning public and private markets.

AI’s promise is becoming real. As the technology races forward, trillions of dollars are flowing into semiconductors, data centers and power suppliers – the new AI ecosystem. But will AI make the economy more productive and, if so, by how much, how quickly and for whom?

No force has a greater impact on our Long-Term Capital Market Assumptions (LTCMAs) than productivity growth, the pace at which an economy turns its labor and capital into more output. Productivity underpins economic growth, living standards, corporate earnings, inflation and interest rates. As the economist Paul Krugman once said, “Productivity isn’t everything, but in the long run, it is almost everything.”

We believe AI will raise productivity meaningfully and durably over our 10- to 15-year horizon, reshaping the nature of work and patterns of investment. While we have incrementally incorporated AI’s potential into our LTCMAs since 2023, the dramatic acceleration in capabilities and investment now requires more sophisticated analysis. To capture that shift, we have rebuilt our AI methodology and embedded it within our macroeconomic building blocks. Anchored around a Cobb-Douglas production function, it attributes an economy’s output to its labor and capital, and how efficiently those are used (see Methodology Handbook).

In that framework, AI lifts labor productivity, measured as output per hour worked, through two channels. First, it makes existing work more efficient, enabling the same people and machines to produce more (total factor productivity, or TFP). Second, it equips workers with improved tools, systems and infrastructure as investment scales (capital deepening).^[1]Here, we present our new methodology and the insight it delivers.

AI capability has outrun adoption

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Back in 2023, AI was essentially a fluent text generator. Today, frontier models can reason through complex multistep problems, write code and call upon external tools and information sources. The newest models can autonomously execute tasks that would take skilled software engineers hours to complete.^[2] Yet business adoption is still early and uneven. Research suggests only 6% of U.S. work hours are currently AI-assisted,^[3] even as over two-thirds of U.S. work tasks may be theoretically exposed (Exhibit 1).

To date, we’ve seen only faint signals of AI’s impact on productivity.^[4] While the tech sector has been an early leader,^[5] economy-wide U.S. productivity growth has averaged 2.4% per year since 2023 – much improved from 1.5% in 2005–19, but below the internet era’s 3% (1995–2005).

Source: Tyna Eloundou, Sam Manning, Pamela Mishkin et al., “GPTs are GPTs: Labor Market Impact Potential of LLMs,” Science, June 2024; J.P. Morgan Asset Management. *Exposure is defined as AI reducing the time to complete a task by at least 50% at equivalent quality. **LLMs (large language models) are AI systems trained on text and code, used directly as a chatbot. AI software refers to the specialized tools built on top of these systems, such as documentation search, code execution and image generation. The frontier combines tasks reachable by AI software (roughly 66%, following Eloundou et al.), with an additional layer of robotics deemed commercially viable by 2040, including autonomous trucking, warehouse and factory automation, construction and agriculture, bringing the total to 72%. Tasks requiring a general-purpose humanoid robot are excluded. Occupations reflect major U.S. groups, weighted by each occupation’s share of total U.S. wages.

Building our LTCMA base case

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What might lie ahead? Forecasting productivity is difficult even in ordinary times, and profound technological change makes the challenge even greater. Our updated methodology should offer a solid framework from which to reason and refine as new evidence develops.

For total factor productivity, we focus on the specific tasks AI can perform, and how efficiently. We then map those gains across the economy, an approach grounded in Hulten’s theorem as applied by Daron Acemoglu and others.^[6]We estimate capital deepening by projecting how AI-related investment affects the economy’s productive capital stock, net of depreciation and “crowding out” (displacing other productive investments).

By 2040, we believe, AI adoption will have shifted to more embedded agentic^,[7] and even robotic, workflows, spreading beyond knowledge-intensive early users to routine work across sectors.

But diffusion will be gradual. We expect a slow start, with adoption accelerating in the early 2030s and then tapering as remaining use cases become harder or less economically attractive. Scaling AI will require integrating with legacy systems and redesigning operating models. Friction may be most acute where oversight and liability management require human involvement, particularly where AI missteps could prove costly.

This gradual pace is an important premise of our macroeconomic and asset class assumptions. Steady uptake should limit rapid, economy-wide job displacement and keep broad inflation contained, even as the build-out strains supply chains in the near term.

In our base case, AI boosts U.S. TFP growth above its pre-AI trend by roughly 40bps, to 1.2% annually,^[8] and it lifts U.S. capital stock growth by about 30bps, to 2.3% per year. Together, this raises our total U.S. labor productivity forecast by 50bps vs. our pre-AI baseline, to 2.2%. Small gains compound: A sustained 50bps boost over 15 years would make the U.S. economy 7%–8% larger than it otherwise would have been. Given this is our fourth year incorporating AI into our assumptions, this year’s incremental productivity lift vs. last year is 20bps.

Relative to other forecasts, our view appears cautiously optimistic. The 40bps AI-driven TFP uplift exceeds the Congressional Budget Office’s more conservative 10bps estimate while landing in the lower half of the OECD’s 27bps to 85bps scenario range.^[9]

Against that backdrop, our analysis first builds a baseline forecast for the U.S., then extends it globally, accounting for regional variation.

1.2

annual boost in U.S. TFP growth

2.3

annual uplift in U.S. capital stock growth

TFP: How does task automation become economy-wide productivity?

Our TFP approach draws on four inputs:

1. AI exposure

The share of labor tasks AI can perform in theory. We estimate just over 70% of today’s tasks are exposed to AI by drawing on Eloundou et al.’s analysis^[10]which weights each task by importance within a job and each job’s share of economy-wide wages.

2. Effective penetration

The share of tasks where AI can be used and is ultimately used. We expect this will rise from about 5% today to roughly 40% by 2040.

3. Gain per task

The productivity lift on the tasks that AI performs. We assume a gain of just under 30%, derived from the median across several task-level studies.^[11]

4. Labor share of the economy

Used to scale task-level gains into a contribution to overall output. We assume an average 52% of U.S. GDP, down from 55% in the past decade and over 60% in the late 20th century.

TFP gains will vary widely across tasks, sectors and economies.^[12]The largest near-term benefits should emerge in knowledge work-heavy industries and in the economies most exposed to them, as we discuss below.

Capital deepening: How much AI investment will prove productive?

Beyond TFP, our outlook hinges on how much the surge in AI spending ultimately increases productivity.

Since 2023, AI demand and enthusiasm have driven U.S. hyperscalers to cumulatively invest close to USD 1.6 trillion in capex, spanning data centers, power, semiconductors and networking. Globally, the total exceeds USD 2 trillion over the same period.^[13]

Our outlook for capital deepening builds on four factors: the path of AI spending, its composition, the rate of depreciation and the extent to which it crowds out other productive capex. Broadly, the first two variables are additive to growth, while the latter two detract.

1. Spending path

We see demand justifying elevated investment, with annual U.S. AI capex^[14] rising from over USD 800 billion in 2026 to nearly USD 1.2 trillion by 2030, then settling at around 70% of that peak.^[15] A steeper pullback is possible, as in the dot-com era, if spending fails to generate sufficient revenue or financing dries up. But we believe AI’s diffusion will be more profound than the internet. Adoption is already tracking three times faster,^[16] ROI is building, and the biggest AI spenders have healthier corporate fundamentals. The dot-com bust was also compounded by a prolonged equity bear market, which we do not project.

2. Spending composition

We assume AI spending will expand beyond today’s hyperscaler-led, equipment-heavy build toward the data centers, power and software that make compute (AI’s raw processing power) deployable at scale.^[17]

3. Depreciation

Across equipment, structures and software, we assume a blended four-year life for AI capital investments. AI hardware such as semiconductors, for instance, turns over especially quickly, so as the installed base grows, more capex serves as replacement rather than net new capacity.

4. Crowding out

So far, the AI build-out doesn’t appear to be materially crowding out other private capital investments. But this should change as spending becomes less self-funded by hyperscaler cash and more reliant on debt and external sources. We assume that about half of AI capex displaces other productive investments, applying a 50% haircut to AI’s net contribution to capital growth.

On balance, we expect AI to deliver a measured and sustained boost to capital growth beyond the 2025–26 boom.

A rising tide lifts economies unevenly

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Developed markets appear better positioned to deploy AI at scale, given their large service sectors, strong digital infrastructure and supportive institutions. Where workforces are shrinking – from Japan and China to the U.S., Germany and Italy – firms will have a greater incentive to use AI to help offset demographic challenges.

But economic gains will vary between AI enablers, which supply key inputs for the build-out, and adopters, which successfully integrate AI (Exhibit 2). The two can be mutually reinforcing: Better tools and infrastructure can speed up adoption, and durable monetization can incentivize further investment.

Source: Eloundou et al. (2024), International Monetary Fund, J.P. Morgan Asset Management. The IMF AI Preparedness Index scores countries’ AI adoption readiness across digital infrastructure, human capital, innovation and regulation (0 to 1, rescaled to 0 to 100). Task exposure is the estimated share of work tasks AI could perform based on each country’s occupation mix, building on the approach of Eloundou et al. with an added robotics layer.

The U.S. leads as both an enabler and an adopter, registering the largest AI-driven TFP boost in our forecast universe. Its concentration of frontier model developers, hyperscalers and software platforms is unmatched. Corporate America also has the financial strength and appetite to sustain the AI build-out, supported by a flexible, innovation-oriented business environment.

Meanwhile, Europe’s early AI gains skew toward a few key enablers in areas where it has outsize global influence, notably semiconductor manufacturing equipment and power grid and infrastructure. Broader gains should follow as AI diffuses across Europe’s services-heavy economies (several of which are more AI-exposed than the U.S.). However, its fragmentated single market, legal and regulatory burdens, labor constraints and energy supply vulnerabilities may slow AI’s rollout.

China is building a parallel AI ecosystem, with homegrown models, domestic chip production and a push into robotics. China’s cost competitiveness may prove an advantage, while deployment of AI (which is often state-mandated) across industrial and consumer sectors could drive economy-level productivity gains. Chinese models are also gaining traction globally, recently overtaking U.S. models in token usage on OpenRouter, a widely used developer gateway. Still, weaker monetization by Chinese firms and export controls on advanced U.S. chips should restrain investment and development.

Taiwan and South Korea should continue to benefit from their central role in global tech supply chains, notably in advanced semiconductor manufacturing. Both economies rank among the largest upward revisions to our GDP growth assumptions. That competitive position is a strength but also a risk, as economic nationalism and geopolitical uncertainty drive the U.S. and China toward onshore alternatives. Finally, Japan arguably has the strongest incentive to adopt AI, given its aging workforce, but long-tenure norms may hinder diffusion.

Multiple paths ahead

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Our base case is just one plausible path among many alternate scenarios (Exhibits 3A and 3B).

AI could disappoint if regulation or social backlash restrains adoption, or if capex resets more sharply due to obsolescence, margin pressure, tighter financing or power constraints. Even with broad diffusion, AI may still underdeliver if oversight adds more work, time savings shift to leisure rather than new tasks, or skill gaps and worker preferences become binding constraints. Such a downside scenario could see AI reach only a quarter of tasks by 2040 (vs. 40% in our base case) and annual TFP growth settle below 1%.

We expect stronger TFP growth and sustained capital investment, but a range of outcomes could unfold

Source: J.P. Morgan Asset Management; LHS: Federal Reserve Bank of San Francisco (Fernald TFP series) for historical data, RHS: company reports. Total factor productivity is for the private nonfarm business sector, 10-year trailing average of annual growth rates. The capex outlook anchors to reported and projected capex from the top five U.S. hyperscalers, with additional assumptions for neocloud, enterprise and government AI infrastructure.

On the other hand, a faster and more durable AI rollout could push adoption even further, toward its theoretical potential. In this upside scenario, higher utilization, clearer monetization and longer hardware life sustain reinvestment and broaden the build-out beyond today’s hyperscalers. Even larger gains could arise if AI accelerates the pace of innovation itself, compressing the timeline from discovery to commercialization for new or improved medicines, materials and power sources.

From productivity to portfolios

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Despite the profound impact AI has already had on markets, we don’t think its potential is fully appreciated.

Investors will seek to identify where value accrues, whether through profit growth, risk premia or pricing power. We believe portfolios can best capture that potential by balancing exposure across the AI value chain and funding cycle, spanning public and private markets. That calls for strategic discipline and tactical flexibility.

Returns have so far favored the AI enablers. But the set of beneficiaries already extends beyond hyperscalers and semiconductors into infrastructure and power. As adoption spreads, more sectors should participate. This expectation underpins our view that equities will deliver stronger, broader earnings growth and sustain structurally higher margins and valuations than they would have in a world without AI.

Value should shift toward adopters deploying AI, in areas such as predictive analytics, speed to market, product fit and supply chain management. Financial services, health care, retail and logistics firms are among such early adopters and tend to rank higher in measures of AI exposure and labor intensity (Exhibit 4).

The U.S. remains at the core of global allocations, offering exposure to a vibrant culture of innovation, tech-heavy indices and deep capital markets. It ranks among the highest in profit margins in our forecast universe. In Europe, value stands to remain concentrated in “pick-and-shovel” enablers until adoption broadens. And while Asian companies should remain essential to tech supply chains, the strength of the current capex wave may have pulled forward some revenue and margin gains, making stock selection crucial after recent investor optimism.

We anticipate that the capex build-out will contribute to our outlook for higher real rates, elevated term premia and steeper yield curves, even if AI produces disinflationary effects over time. Credit issuance is rising as hyperscalers become a more pronounced presence in debt markets, lifting index quality despite biasing spreads wider. Investors may find relative value in issuers that can fund investment without stretching balance sheets, though complex financing structures and off-balance sheet obligations warrant scrutiny. Because the AI ecosystem is so interconnected, cross-asset correlations may shift in ways that make traditional diversification less reliable.^[18]

Source: Bureau of Economic Analysis, Bureau of Labor Statistics, Eloundou et al. (2024). Sector exposure and labor cost scales are wage-bill-weighted across industries, and bubble sizes reflect each sector’s share of the total wage bill. Task exposure follows Eloundou et al.’s theoretical measure. AI exposure is scored by occupation, then aggregated to industries using BLS Occupational Employment and Wage Statistics data weighted by wages, and mapped from NAICS industries to GICS sectors. Data as of July 2026.Source: Bureau of Economic Analysis, Bureau of Labor Statistics, Eloundou et al. (2024). Sector exposure and labor cost scales are wage-bill-weighted across industries, and bubble sizes reflect each sector’s share of the total wage bill. Task exposure follows Eloundou et al.’s theoretical measure. AI exposure is scored by occupation, then aggregated to industries using BLS Occupational Employment and Wage Statistics data weighted by wages, and mapped from NAICS industries to GICS sectors. Data as of July 2026..

Alternative assets can reach parts of the AI value chain that public markets cannot, potentially improving portfolio diversification. Commodities supply key inputs. Infrastructure and real estate add critical capacity across power, cooling, transmission and storage, and offer inflation resilience via contracted cash flows. But risk profiles vary widely, from regulated grid power to data centers and on-site generation, and warrant specialized underwriting.

Private equity managers can enhance value creation and improve operating efficiency by deploying AI across portfolio companies. Meanwhile, venture capital backs many of the AI-native companies now growing to unprecedented scale in private markets, albeit with highly skewed outcomes. Private credit in turn finances parts of the AI build-out, though deal terms will need to evolve in tandem, given direct lending’s meaningful exposure to software and technology. And as disruption accelerates, hedge funds may capitalize on dislocations. Above all, manager selection will be critical, as outcomes may vary considerably by exposure, vintage and strategy.

As we model the world in 2040, we believe the AI ecosystem will evolve in ways that prove transformative. This should drive efficiency gains, support margin resilience and unlock new growth opportunities that extend well beyond the tech sector. But the path of adoption and diffusion won’t be smooth, bringing higher volatility and wider dispersion that necessitate a disciplined, diversified approach. Investors should build AI exposure intentionally and prepare to shift course as a new era of innovation and value creation unfolds.

TFP is economic growth not attributable to labor or capital growth in isolation. Capital deepening is the increase in capital available per worker.
Thomas Kwa, Ben West, Joe Becker et al., “Measuring AI Ability to Complete Long Software Tasks,” METR, May 2026.
Alexander Bick and Adam Blandin, “GenAI Adoption Tracker,” Harvard University’s Project on Workforce, May 2026.
Early signals of AI-driven productivity gains have been ambiguous, consistent with past technological shifts where productivity often lags during early adoption (the “productivity J-curve”). As such, the recent firming in U.S. productivity may better reflect supply-side shocks than AI. See Stephanie Aliaga, “Is AI already driving productivity growth?” J.P. Morgan Asset Management, March 11, 2026.
More discernible signs of AI’s productivity impact are emerging in the information sector, where output per hour has grown roughly 9% annualized since late 2022. That is about three times the pace of nonfinancial corporates, reflecting the information sector’s early role in building and deploying AI.
Daron Acemoglu, “The Simple Macroeconomics of AI,” MIT, April 2024; Philippe Aghion and Simon Bunel, “AI and Growth: Where Do We Stand?” Federal Reserve Bank of San Francisco, June 2024; Francesco Filippucci, Peter Gal and Matthias Schief, “Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence,” OECD, November 2024. See the Methodology Handbook for more detail.
Agentic AI refers to AI models that can operate fairly autonomously, with minimal human involvement.
We assume a non-AI trend TFP baseline of 0.8%, anchored to our 2023 LTCMA projection prior to any explicit AI adjustments.
“The Budget and Economic Outlook: 2026 to 2036,” Congressional Budget Office, February 2026; Francesco Filippucci, Peter Gal, Katharina Laengle et al., “Macroeconomic productivity gains from Artificial Intelligence in G7 Economies,” OECD, June 30, 2025. The OECD reports AI gains in labor productivity terms; we convert to the TFP equivalent by dividing by 1.5, the study’s capital deepening factor.
Tyna Eloundou, Sam Manning, Pamela Mishkin et al., “GPTs Are GPTs: Labor Market Impact Potential of LLMs,” Science, June 2024.
Our research draws on nine controlled studies of AI’s impact on task productivity, spanning coding, writing, customer service, marketing and ad creation. While not exhaustive, our estimate aligns with Acemoglu (2024) and the OECD (2024), which estimate task-level productivity gains of 29%–30%.
Our task-based approach does not capture every channel through which AI could raise TFP, such as input-output spillovers (where gains for producers flow through supply chains) and the creation of entirely new categories of work. We assume such channels would be positive drivers.
Figures reflect annual hyperscaler capex from 2023 to 2026 (expected). Source: Bloomberg Finance L.P.
We anchor our U.S. AI capex assumption to the current and projected spend by the five largest hyperscalers (Alphabet, Meta, Amazon, Microsoft and Oracle). We restrict our analysis to U.S.-located investment, which we then scale up to reflect capex by neoclouds, enterprises and governments.
Projecting AI spending is inherently difficult, and the outlook has shifted considerably in just the past year. This trajectory reflects the best estimate from the LTCMA research and investment teams, and we will revise as conditions evolve.
Bick and Blandin, “GenAI Adoption Tracker,” May 2026.
We allocate AI capex across three categories and assume the mix shifts over time. We estimate that equipment (GPUs, servers, memory and networking) falls from just over 40% of AI capex in 2026 to 30% by 2040. At the same time, structures (data centers and power infrastructure) rise from 30% to 40% and intellectual property products (software and R&D) increase from about 25% to 30%.
“AI-conscious asset allocation: Portfolio construction in a capital investment boom,” Strategic Investment Advisory Group, J.P. Morgan Asset Management, September 2026.

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