This ICLE issue brief by Eric Fruits and Kristian Stout synthesizes the empirical evidence on AI's effects on productivity, labor markets, and competition through early 2026. The core finding is a striking gap between micro-level and macro-level results: controlled experiments consistently show 15% to 50%+ productivity gains across writing, customer support, software development, accounting, law, and translation, with disproportionately large benefits for lower-skilled workers producing a "skill compression" effect. Yet aggregate labor-market indicators through 2024–2025 show limited disruption — no economywide job loss or wage decline — with adjustment occurring through task reallocation rather than mass displacement. Where effects do appear, they concentrate in entry-level segments of highly exposed occupations, while senior employment remains stable.
The authors attribute the disconnect between micro gains and macro flatness to the "productivity J-curve" — the phenomenon where general-purpose technologies require extensive organizational redesign, training, and complementary investment before gains surface in aggregate statistics — as well as measurement limitations in national accounts that understate quality improvements and time savings. Macroeconomic forecasts range widely, from Acemoglu's conservative estimate of less than 0.66% TFP growth over a decade to Goldman Sachs projections of 7% global GDP gains, with the divergence driven by differing assumptions about task coverage, adoption speed, and complementary investment rather than conflicting data.
On competition and policy, the brief argues that AI lowers minimum viable scale in downstream markets, facilitating entry by startups and small firms even as upstream model development remains concentrated. Evidence shows increased business formation by first-time founders with smaller teams post-ChatGPT. The authors conclude that the literature offers limited support for regulatory approaches premised on widespread displacement or durable monopoly power, instead advocating targeted enforcement of existing law, reduced regulatory fragmentation, and investment in skills and infrastructure.
Microsoft Research's New Future of Work Report 2025 examines how generative AI is reshaping workplaces at a pace exceeding previous technological shifts. Rather than simply automating tasks, AI is now actively participating in workflows — changing how people create, decide, collaborate, and learn. The report draws on large-scale data analyses, field studies, and theoretical frameworks to assess adoption patterns, productivity impacts, and labor market effects across industries and geographies.
A key finding is that AI's benefits are unevenly distributed. Adoption varies significantly by gender, income level, region, and organizational culture. High-income countries lead usage, but growth is fastest in low- and middle-income regions, where gaps in multilingual model support risk reinforcing existing inequalities. Entry-level workers face particular vulnerability, with empirical evidence showing a 16% employment decline for workers aged 22–25 in highly AI-exposed roles relative to less-exposed ones, raising concerns about how future expertise will be built if learning-intensive jobs are automated away.
The report emphasizes that human expertise matters more, not less, in an AI-powered world. Workers are shifting from doing tasks to guiding, critiquing, and refining AI outputs — requiring new skills like prompt crafting, quality oversight, and calibrated trust. Current AI systems, designed primarily for individuals rather than teams, often underperform in group settings, though emerging research into team-oriented AI and collective intelligence shows significant promise. The authors stress that the future of work is not predetermined but will be shaped by intentional choices in system design, organizational adoption, and workforce development.
