The AI Economy

AI already changes work. The live question is how the gains and losses distribute, and whether societies can shape that distribution or only react after the fact.

Tap to enlarge

The scale of exposure

The IMF's January 2024 staff note estimated that nearly 40% of global employment is exposed to AI, rising to 60% in advanced economies and falling to 26% in low-income countries. The IMF distinguishes between augmentation (AI makes you more productive) and automation (AI replaces the task entirely). Roughly half of exposed jobs fall into each category.

Unlike previous automation waves that targeted routine manual labor, AI exposure is concentrated in cognitive, higher-skilled work. This is a structural difference that changes the politics of technological transition. The affected populations are urban, educated, and politically active, which may accelerate the policy response but also the backlash.

Goldman Sachs projected that up to 300 million full-time jobs across the United States and Europe may be affected by generative AI, while suggesting the technology could add roughly $7 trillion to global GDP over a decade. The World Economic Forum's 2025 Future of Jobs Report, surveying over 1,000 employers across 55 economies, projected 170 million new jobs created by 2030 against 92 million displaced, a net gain of 78 million roles.

The net positive is less comforting once it is disaggregated. The fastest-growing roles are in AI development, cybersecurity, and sustainability. The fastest-declining roles are clerical and administrative. If you are a 23-year-old entering a back-office career in 2026, the aggregate net positive offers limited comfort.

What the payroll data shows

Stanford's Digital Economy Lab published a sharper picture. Economists Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen used high-frequency payroll data from ADP covering millions of American workers and found that since generative AI went mainstream in late 2022, early-career workers aged 22 to 25 in the most AI-exposed occupations experienced a 13% relative decline in employment. After controlling for firm-level hiring patterns, the figure rose to 16%. In software development specifically, employment for the youngest workers fell by nearly 20% by July 2025 compared to its late-2022 peak.

This pattern held even after excluding the tech sector, remote jobs, and computer-related occupations. It was not explained by pandemic-era overhiring corrections. A February 2026 update showed the gap had widened further. ADP's own research team confirmed the trend independently.

The impact is occurring through headcount (hiring), not wages. Companies are hiring fewer entry-level workers rather than paying them less. Employment for workers age 30 and older in the same AI-exposed roles remained stable or grew. And in occupations with low AI exposure (home health aides, for example), employment for young workers continued to grow.

Corporate decisions

The corporate sector has been open about its direction. Anthropic's CEO Dario Amodei said that nearly half of entry-level white-collar jobs in tech, finance, law, and consulting could be replaced or eliminated. Ford's CEO offered similar estimates for white-collar roles. Salesforce eliminated 4,000 customer support positions citing agentic AI efficiency. Duolingo announced it would stop using human contractors for tasks AI could handle.

These are decisions already made by some of the largest employers on the planet. When the people writing the checks say the workforce is shrinking, treat that as a different class of evidence than a think-tank projection.

The productivity paradox

A March 2026 Goldman Sachs note found no meaningful relationship between AI adoption and economy-wide productivity at the aggregate level. But firms that measured AI impact on specific tasks reported a median productivity gain of around 30%.

This gap recalls the Solow Paradox of the late 1980s, when Robert Solow observed that computers were everywhere except in the productivity statistics. It took over a decade for IT investments to show up in aggregate productivity data, because adoption, organizational redesign, and complementary investments take time. Goldman Sachs economists argue this is a "J-curve" effect: heavy upfront investment with delayed payoff, consistent with patterns seen with electricity and the early internet.

Daron Acemoglu of MIT has argued that generative AI may produce a modest GDP increase of only 1.1 to 1.6% over the next decade, estimating that only about 4.6% of tasks can be meaningfully impacted in the near term. Goldman Sachs has responded that Acemoglu's assumptions are based on current capabilities, which are advancing at a pace that makes static projections risky.

Acemoglu is right that demo capabilities do not translate to economy-wide adoption on a straight line. Goldman is right that today's limits are a bad 10-year assumption. Goldman still projects measurable macro impact beginning around 2027. Until then the firm-level 30% task gains and the flat aggregate series can both be true.

The concentration problem

The AI supply chain is already highly concentrated. Nvidia designs most of the chips required for training. TSMC in Taiwan fabricates over 90% of the world's most advanced semiconductors. Amazon, Google, and Microsoft dominate the cloud infrastructure needed to train and run models. These same companies are among the leading developers of frontier AI systems.

A paper by Tejas Narechania and Ganesh Sitaraman documented market power at every layer of the AI stack, from hardware to cloud to models to applications. This structure creates a feedback loop. The companies with the most data and compute build the best models. The best models attract the most users. The most users generate the most data. The cycle repeats.

In principle, open-weight models and new entrants can break this loop. In practice, the capital requirements for frontier model training (approaching $1 billion per run) mean that meaningful competition may require either substantial venture tolerance for losses or government funding. Whether AI becomes a broadly shared productivity tool or a mechanism for extracting rents depends on whether this concentration deepens or loosens.

If the performance gap between frontier models narrows, as Raghuram Rajan argued in Project Syndicate, competition may keep prices low and spread benefits widely. If a few platforms achieve lock-in, the opposite could follow.

Who gets hurt, and how

An April 2025 IMF working paper found that unlike previous automation waves, which hit middle-skilled workers hardest, AI displacement risks extend to higher-wage earners. But those same workers' tasks tend to be highly complementary with AI, meaning they can use the technology to become more productive rather than be replaced. The net effect may be a modest narrowing of wage inequality paired with a substantial widening of wealth inequality, since capital owners capture a disproportionate share of AI-generated returns.

There is also a gender dimension. OECD analysis shows that in high-income countries, jobs most vulnerable to AI task automation make up 9.6% of female employment, nearly three times the proportion for male jobs at 3.2%. In the United States specifically, 79% of employed women work in occupations at high risk of automation compared to 58% of men. Whether the AI transition deepens or narrows existing gender gaps depends heavily on whether reskilling programs reach the populations that need them.

Policy responses and their limits

Universal basic income has moved from thought experiment to active testing. The Stanford Basic Income Lab counts over 160 UBI pilots across four decades.

Sam Altman's OpenResearch pilot, providing $1,000 per month for three years, found only a 2% reduction in work (about 15 minutes less per day). The Stockton, California pilot found that recipients actually increased full-time employment relative to non-recipients. Finland's experiment increased trust in government. Canada's 1970s Mincome experiment measured an 8.5% reduction in hospitalizations.

These results challenge the intuition that cash transfers destroy work incentives. But the fiscal math remains difficult. U.S. federal revenue stood at approximately $4.9 trillion in 2024 against a GDP of about $29 trillion. Even a modest UBI program targeting displaced workers at subsistence levels could cost what existing large federal programs cost today. Funding through automation taxes is theoretically possible but politically constrained, and the international mobility of capital makes unilateral automation taxes difficult to implement effectively.

Reskilling is the other policy pillar. 63% of employers cite skills gaps as their primary barrier to transformation. Six in ten workers may require training before 2027, while only half currently have adequate access. Workers with AI skills earn approximately 25% more on average. Reskilling works for the people who get it. The open question is scale and speed.

The distribution question

Speed of displacement and GDP impact get most of the airtime.

Whether the gains flow broadly or concentrate is the one that decides the politics. Electricity and the internet eventually created broad prosperity. "Eventually" ran from one generation to three, and the transition taxed specific communities. AI hits cognitive work, which is where most of the economic value in advanced economies currently sits. The affected people are entry-level professionals in every major city, not a few regional manufacturing towns.

If AI removes the tasks that junior lawyers, junior analysts, and junior developers used to learn on, the pipeline of experienced professionals thins. That looks invisible in quarterly data and can remake a profession over a decade.

Adoption still lags invention. Automated telephone exchanges were technically possible in the 1920s, yet the last human telephone operator in the United States was not replaced until the 1980s. Organizations are slow, regulation is slower, and people renegotiate their relationship with new tools over decades, not quarters. AI needs no new physical plant, only software updates, so it can move faster than electricity did. It can also hit the same organizational friction. Expect both, in different sectors, at different speeds.

The labor-market effect is already measurable in specific occupations, and aggregate statistics hide the concentration. Competition policy, educational investment, fiscal design, and social insurance decide how wide the damage runs. Those are legislative choices. The junior hiring gap is already in the ADP file.