The rapid expansion of artificial intelligence is increasingly being accompanied by a shift in public sentiment, with concerns over employment and social disruption becoming more prominent. Recent data show that AI anxiety is rising even as technology companies continue to invest heavily in infrastructure and advanced models, creating an increasingly important gap between the economic promise of AI and how the public perceives its risks.

The development comes as leading AI executives, including Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman, have intensified warnings about the risks associated with increasingly capable systems. Amodei recently called for a slower pace of frontier AI development, while Altman endorsed greater safety measures and coordination.

Public Concern Has Risen Sharply

Data from the Pew Research Center provide a clear indication of the changing public mood. In a June 2026 survey, 52% of U.S. adults said they were more concerned than excited about the increasing use of AI in daily life, compared with 37% in 2021. Only 9% said they were more excited than concerned, while 37% expressed an equal mix of optimism and concern. Among adults aged 18 to 29, concern reached 55%, compared with 31% in 2021.

Employment represents an even stronger source of anxiety. The Pew survey found that 71% of Americans believe AI will lead to fewer jobs in the United States over the next 20 years, up from 64% in 2024. Among younger adults, the figure increased to 73%, indicating that concerns are not limited to workers approaching retirement but are becoming increasingly relevant to the next generation entering the labor market.

Amodei and Altman Change the AI Risk Debate

The warnings from technology executives have helped move AI risk from a largely technical discussion into a broader economic and policy debate. Amodei has argued that frontier systems should be developed at a more controlled pace, proposing independent safety evaluations and greater international coordination. Altman has supported the principle of additional safeguards, illustrating that concerns about AI risks are now being expressed from inside the industry’s largest companies.

However, the relationship between executive warnings and actual economic outcomes remains uncertain. Earlier predictions of an immediate white-collar employment shock have not yet translated into an economy-wide collapse in office employment, and both Amodei and Altman have subsequently moderated some of their earlier employment forecasts. This suggests that AI disruption may be substantial without necessarily following the most extreme timelines previously discussed.

Investment Implications Extend Beyond Technology

For investors, the changing perception of AI could become an increasingly important factor in technology valuations and capital allocation. The debate is no longer limited to semiconductor demand or software adoption; it increasingly encompasses cybersecurity, regulation, labor productivity, infrastructure spending, and corporate governance. Recent market reactions illustrate this broader transition, with AI-related stocks facing pressure while cybersecurity companies benefited from expectations of greater spending on protection against AI-enabled threats.

For Israeli investors and institutional asset allocators, the issue is particularly relevant because global technology exposure is widespread across pension portfolios, ETFs, and international equity mandates. The long-term opportunity remains significant if AI improves productivity and creates new markets, but valuation risk, regulatory intervention, labor-market disruption, and the enormous capital requirements of the AI buildout could produce greater dispersion between companies and sectors.

Outlook: The next phase of the AI investment cycle is likely to depend increasingly on whether technological progress can be matched by measurable productivity gains, credible safety frameworks, and sustainable economic returns. Public confidence will remain a critical variable, particularly if concerns over employment continue to rise. At the same time, a more disciplined approach to AI development could reduce some systemic risks without eliminating the technology’s potential economic benefits. For investors, monitoring the gap between AI expectations and measurable business outcomes may become as important as tracking model capabilities themselves, while geopolitical competition, regulatory fragmentation, and elevated technology valuations remain important downside risks.