A look at why the oft-discussed predictions that AI will deliver double-digit GDP growth in advanced economies are extremely unlikely over the next 10-15 years
Context & Ripple Effects
The growth case has swung between long-run potential and weak near-term evidence: Goldman Sachs' 2023 estimate put generative AI's global GDP lift at 7% over a decade, while economists at Goldman Sachs and JPMorgan said the boom added basically zero to US growth in 2025. Related coverage characterizes adoption as a general-purpose-technology J-curve, in which investment precedes measurable returns.
The essay enters that gap by challenging forecasts that translate rapid model progress directly into economy-wide output growth. Public discussion around Anthropic's scenario model likewise focused on how sharply outcomes diverge only after 2027, making assumptions about deployment, task automation, and the price of work central to the debate.
First-order effects
- The analysis raises the burden of proof for forecasters presenting double-digit advanced-economy growth as a near-term consequence of AI adoption rather than a high-end scenario.
- Anthropic's scenario framing gives policymakers and employers a way to separate assumptions about jobs, wages, and output from a single headline GDP forecast.
Second-order effects
- Big Tech's data-center buildout faces greater pressure to demonstrate returns if enterprise adoption follows the slower J-curve described in prior coverage, while power constraints already threaten planned capacity expansion.
- Consultancies and IT-services providers must plan for an uneven transition: automation can reduce formulaic work before it produces economy-wide productivity gains.
Third-order effects
- If AI diffusion continues to require complementary investment and organizational change, the economic contest shifts from benchmark capability to which firms can deploy it across enough workflows to lift measured productivity.
- The AI cycle may increasingly be judged by the gap between infrastructure spending and realized output, rather than by aggregate GDP projections alone.
The trend: AI economic forecasting is moving from headline growth extrapolations toward scenario-based estimates that account for adoption lags, task-level displacement, and infrastructure constraints.