Survey: 35% of US adults say they think AI's impact on the US will be negative over the next 20 years, while 56% of AI experts say AI's impact will be positive
The public and experts are far apart in their enthusiasm and predictions for AI. But they share similar views in wanting …
Context & Ripple Effects
The gap follows earlier evidence that Americans expected AI to reshape work while resisting its use for final hiring decisions, as captured in Pew's earlier worker-and-hiring findings. It makes the divide between technical optimism and public acceptance a central issue, not merely a difference in forecasts.
Related coverage also finds especially pessimistic expectations for AI's effect on news, including concern about journalist jobs in Pew's news-impact survey. Together, these results suggest that attitudes turn on where people expect AI's costs to land.
First-order effects
- The survey establishes a clear perception gap: AI experts are substantially more positive about the country's long-term outcome than the public is.
- For AI developers and institutions deploying AI, public legitimacy becomes a distinct constraint alongside technical progress, particularly where systems affect work or consequential decisions.
Second-order effects
- Employers, publishers and other AI adopters face stronger pressure to show how human oversight and benefits accrue to affected users, rather than treating adoption as self-justifying.
- The mismatch can widen the gap between products built around expert assessments of capability and the safeguards or explanations that customers and the public expect.
Third-order effects
- If this pattern persists, AI competition will increasingly include trust-building, accountable deployment and governance—not just model performance or cost.
- Sector-specific concerns may become more important than abstract views of AI, producing different adoption and policy debates across work, media and other high-impact uses.
The trend: AI industrialization is creating a widening public-acceptance challenge as expert confidence in the technology outpaces confidence in its social distribution and governance.