The global push toward artificial intelligence (AI) adoption reflected in many government plans and the February 2026 India AI Impact Summit highlight links between AI and economic growth. AI-driven growth acceleration is plausible, but it is not guaranteed—it hinges on AI resulting in sustained productivity gains that have yet to materialize at an aggregate level and the distribution of these gains. Will they be broadly distributed across firms and employees or concentrated on a few dominant companies and their shareholders? The tension between growth acceleration and distributional fairness asks whether AI can raise aggregate productivity fast enough to lift living standards while distributinggains broadly.
The tension is sharper for AI because frontier capacity is scaling faster than organizational change, and because key inputs are concentrated. Competition regulators have warned that foundation model systems can reinforce control over critical inputs like compute, cloud services, data, and distribution models, which could limit contestability and pass-through of productivity gains.
It is useful to separate diffusion into the extensive margin (how many firms and workers adopt AI) and the intensive margin (how deeply AI changes workflows and production). Broad-based growth requires both, consistent with OECD cross-country firm evidence that diffusion differs substantially across sectors and firm sizes and that “enabling” technologies and capabilities (for example, broader digitalization, data systems, and skills) are prerequisites for “advanced” adoption. Broad productivity growth also depends upon innovation in firms and industries using the technology.
Microeconomic evidence to date shows that targeted deployments can deliver sizable productivity gains in certain settings, but diffusion is uneven and aggregate effects remain uncertain. In one field study, access to a generative AI tool increased customer support productivity by an average of 15%, with much larger gains for novices and smaller gains for top performers. In contrast, a trial using AI tools available as of early 2025 found that completion time increased by 19% among experienced open-source developers, showing how “AI help” can also create cognitive overhead for complex, context-heavy tasks. Distributional outcomes therefore are not determined by “AI” as a single force, but instead depend on whether technology displaces labor tasks, creates new tasks, or reshapes complements.
The dialogue will examine competing and sometimes overlapping definitions of “success in AI”: frontier capability, broad diffusion, and distributional outcomes. We will discuss which policy tools shift incentives across these objectives, including skills and management capacity, procurement and public-sector adoption, competition policy, and measurement. We will explore whether policies designed to accelerate broad and equitable diffusioncomplement frontier progress or create trade-offs between these two objectives.
This dialogue will discuss to what extent “success in AI” is primarily defined through frontier capability, broad diffusion, or distributional outcomes and which policy levers (skills and retraining, management capability, procurements, competition, and measurement) move incentives toward broad diffusion without suppressing beneficial use.
Popular narratives in AI diffusion:
These popular narratives are broad outlooks at AI diffusion and sometimes overlap in mechanisms. They may all be true in different sectors or time horizons but serve to surface differences between causal pathways and policy priorities.
- Uneven adoption: AI can accelerate economic growth materially only if diffusion reaches beyond early adopters and is paired with organizational redesign, skills, sector-sensitive measures, and competitive input markets; otherwise, AI is more likely to raise within-firm productivity in pockets while widening gaps between firms, regions, and capital versus labor.
- Broad-based augmentation: Generative AI primarily augments workers, raising quality and speed for common tasks, compressing performance distributions, and lifting wages at the middle and bottom as capabilities spread. Field evidencein customer support and professional writing is consistent with large gains for less-experienced or lower-ability participants.
- Bottlenecked diffusion: AI-driven productivity gains do not spread evenly across firms and benefit organizations that already have the right supporting conditions in place, such as strong digital skills, high-quality data, effective management capacity, and reliable cloud access. At the same time, key parts of the AI supply chain, including foundation models and cloud infrastructure, are relatively concentrated, and switching costs make it hard for firms to change providers or negotiate better terms. That means the main barrier to lasting adoption is whether firms have the organizational capacity and complementary investments needed to use AI models well, not just access to them. OECD work using cross-country firm evidence finds that AI use is more prevalent in certain sectors and among larger firms, and that these complementary capabilities play a critical role in shaping productivity gains. This pattern is consistent with uneven diffusion across the economy. Competition authorities’ scrutiny of partnerships and control over critical inputs also support concerns that these gains may not be widely shared.
- High-friction transition: In the medium term, AI adoption is also likely to be costly and uneven. Adoption can create substantial coordination problems inside firms, require new error-checking and oversight work, increase legal/compliance burdens, and lead to inefficient deployment. As a result, economy-wide productivity gains may remain modest for some time even as tensions over who benefits and who bears the costs intensify. Those tensions may be especially visible in entry-level labor markets. Evidence showing that AI tools can slow experienced developers’ project completion time suggests that, for some complex high-skill tasks, adoption may initially reduce efficiency rather than improve it.
AI productivity measurement limits
AI productivity discussion can conflate three distinct metrics: (1) model capability (benchmarks, scale, capabilities), (2) micro productivity (task- or worker-level outcomes in specific settings), and (3) macro productivity (sector- or economy-wide statistics). Even when AI model capabilities improve quickly, micro and macro productivity can lag because implementation is slow, complements are missing, and measurement is imperfect. One challenge for high-level economic assessment is that AI’s visible technical progress is not yet tightly coupled to measured productivity statistics.
Recent benchmark revisions underscore how uncertain near-term macroeconomic inference can be. With the January 2026 release, the U.S. Bureau of Labor Statistics’ (BLS) benchmarked level for March 2025 was revised down by 898,00 jobs (seasonally adjusted), which changes the labor-input path used in previous productivity calculations. This pattern matches a “productivity J-curve” view of general-purpose technologies (GPTs). Measured productivity can be lower during a buildout period when firms are investing in intangibles, and then can improve as those complements mature and diffuse.
Even if AI is generating surplus, some portion may not appear quickly (or at all) in conventional productivity measures due to how AI software and services are represented in GDP frameworks. More optimistic projections from financial institutions estimate much larger gains (for example, Goldman Sachs Research has projected a sizable long-run GDP level effect and higher productivity growth under faster task automation and adoption).
