The Age Of Artificial Intelligence: Americans' AI Use Increases While Views On It Sour, Quinnipiac University Poll On AI Finds; 7 In 10 Think AI Will Cut Jobs With Gen Z The Most Pessimistic

70% of Americans say AI advancements are likely to decrease job opportunities and 55% say AI will do more harm than good in day-to-day life (64% say more harm in education). Trust is low: 76% trust AI only some of the time or hardly ever, even as use rises (51% use AI for research, up from 37% in April 2025). Large majorities call for more oversight—74% say government not doing enough to regulate and 76% say businesses aren't transparent—and 81% prefer a human+AI combination for medical scans. Expect increased political/regulatory pressure on AI deployment, potential community resistance to AI data centers, and cautious adoption in healthcare, defense, and labor markets.

Analysis

Public unease about AI creates a distinct two-tier market: vendors that enable explainability, human-in-the-loop workflows, verification, and compliance will see durable demand even if headline adoption slows. That bifurcation favors recurring‑revenue software and services over one-off automation projects and raises the effective discount rate for capex‑heavy, autonomous‑only plays.

Local resistance to AI infrastructure (power/water/noise) plus elevated transparency demands will lengthen permitting cycles and raise soft costs for hyperscalers and colo REITs; expect multi-quarter shifts in data center siting and a push toward modular, lower‑footprint designs that re‑price supply chains (transformer capacity, closed‑loop cooling, on‑site renewables). Political and military sensitivity creates event risk windows tied to elections and high‑profile incidents that can trigger rapid policy action — these are catalysts on a months horizon, not a decade away.

The most actionable asymmetric exposures are in cybersecurity, clinical decision‑support/human‑augmented healthcare, and regulated infrastructure. Conversely, ad‑monetization models and pure autonomy vendors face headline and regulatory revenue compression that is likely to outpace any offset from increased R&D spend. The contrarian point: public concern does not equate to enterprise capex pullback across all use cases; firms that can demonstrate measurable ROI plus governance controls should re‑rate, so protective but directional positions (long leaders with hedges) are preferred over naked momentum bets.

Key Decisions for Investors

• Long CRWD (CrowdStrike) 3–9 months: buy shares or buy-the-dip with a 20–35% upside target driven by incremental spend on AI‑aware endpoint and provenance tools; use a 12–15% stop to control downside if risk‑off equity flow accelerates.
• Long MDT (Medtronic) 6–18 months: accumulate exposure to medical device / human‑in‑loop diagnostics suppliers who benefit from regulated, supervised AI adoption; target ~20% total return; hedge with 6–12 month protective puts sized to limit drawdown to ~10%.
• Short META via 3–6 month put spread: size modestly (2–3% portfolio) to exploit regulatory and disclosure risk to political ad revenue; risk capped by bought put strike — target asymmetric payoff ~3:1 if disclosure/ban momentum increases ahead of legislation.
• Long NEE (NextEra) or DUK (Duke Energy) 12–24 months: overweight utilities exposed to grid resilience and local capacity upgrades driven by data center/community negotiations; expect 15–25% upside under stable rate cases, but watch interest‑rate sensitivity and regulatory decisions.
• Long NVDA LEAP + protective collar: keep core long exposure to GPU demand for safe‑compute and enterprise AI (12+ months) but buy short‑dated puts or construct collars to limit drawdown from sudden regulatory or moratorium shocks; this balances the consensus growth case with tangible headline risk.