The Data Center Backlash
Seven in Ten Americans Don’t Want AI Data Centers Near Them, Gallup Finds. A new Gallup poll shows broad, bipartisan opposition to data center construction in local communities. Nearly half of respondents are strongly opposed, signaling that AI infrastructure is becoming a potent local political issue.
Organizer Astra Taylor on the Growing Movement Against Big Tech’s Data Center Buildout. Taylor argues the rapid expansion of AI data centers is creating a “chokepoint” where communities can push back on land, water, and energy use. Maine lawmakers passed (then saw vetoed) the first statewide moratorium, and Utah residents are fighting plans for what would be the world’s largest facility.
Organized Opposition Now Tracks 268 Local Protest Groups Across 37 States. A newly launched site, Data Center Opposition, maps grassroots resistance nationwide. A Sightline Climate analysis estimates 30 to 50 percent of data center capacity expected online in 2026 may not arrive on schedule due to power constraints, permitting delays, and community pushback.
Sanders and Ocasio-Cortez Push Federal Moratorium on AI Data Center Construction. The proposed Artificial Intelligence Data Center Moratorium Act would suspend new builds until national protections are in place. The bill has divided Democrats, with proponents arguing that economic gains should flow to workers and communities rather than just Big Tech shareholders.
Anti-Data Center Activists Are Taking on Big Tech and Winning. More than 70 data center projects were rejected or restricted in the first four months of 2026 alone, exceeding the total for all of 2025. A conservative group has alleged foreign funding behind the movement, but the opposition appears genuinely grassroots.
Why Utah Residents Are Protesting a Massive AI Data Center Backed by Kevin O’Leary. Hundreds of protesters turned out before Box Elder County commissioners approved the project. Voters have now applied to add a referendum to overturn the decision.
Policy & Regulation
NIST Publishes Stakeholder Input on Security Risks of AI Agents. This new report summarizes responses on novel threats from agentic AI, mitigation practices, assessment gaps, and potential government roles including standards and information-sharing. It marks a shift from speculative discussion of AI agent safety toward concrete governance.
White House Infighting Stalls AI Executive Action. Internal disagreements are delaying federal AI policy decisions, including where frontier model testing should sit and what rules should govern AI cybersecurity and government contracting. The bottleneck comes at a moment when evaluation of advanced models is becoming more urgent.
Judge Presses for Details on Anthropic’s $1.5 Billion Copyright Settlement with Authors. A federal judge is scrutinizing the proposed deal, with objections from some authors and related suits still pending. The outcome could set a template for how generative AI training disputes get resolved across the industry.
Brazil’s 2026 Elections Are Its First Real Stress Test for AI Regulation. This analysis examines how AI-generated political personas and synthetic content are testing Brazil’s enforcement capacity. It offers a useful counterpoint to the U.S. and EU focus that dominates most AI regulation coverage.
Anthropic Outlines Two Scenarios for U.S.-China AI Competition by 2028. The policy paper warns that failure to enforce export controls and counter model distillation could allow authoritarian norms to dominate global AI development. It argues democratic leadership is needed to set safety standards before transformative AI reshapes geopolitical power.
EU Reaches Political Agreement to Simplify AI Act Rules and Ban Nudification Apps. The European Commission also opened a consultation on draft guidelines for AI transparency obligations. With the AI Act’s transparency requirements approaching their August 2026 deadline, these updates are shaping compliance decisions now.
State AI Bills Are Moving Fast: 78 Chatbot Bills Alive in 27 States. The Transparency Coalition’s weekly tracker covers Colorado sending four AI bills to the governor, Georgia signing an AI chatbot safety law, and California advancing most of its AI bills out of suspense. Six weeks into the 2026 legislative season, the pace is notable.
Economics & Employment
From Cisco to Block, More Companies Are Citing AI When Announcing Layoffs. The AP examines how AI is increasingly invoked in corporate restructuring announcements, while noting it is often intertwined with broader cost-cutting. The piece avoids simplistic framing but documents real shifts in how companies talk about automation and headcount.
The “Wired Belt” Prophet Says AI Could Displace 9.3 Million U.S. Jobs Within Five Years. Bhaskar Chakravorti projects $757 billion in annual income at risk, concentrated in knowledge-economy metros like San Jose. He argues tech and finance firms are redirecting resources to AI at the expense of white-collar workers, potentially destabilizing the innovation hubs that built them.
UnitedHealth Is Tracking How Much Employees Use AI Tools. Bloomberg reports the insurer monitors daily use of ChatGPT and Copilot as part of an internal transformation push. The implication goes beyond automation: it raises questions about workplace surveillance and the normalization of AI-use metrics as productivity signals.
Stanford HAI Launches Lab to Study How AI Actually Changes Work. The new AI and Organizations Lab will investigate how AI reshapes jobs, team dynamics, and organizational structures. It prioritizes human-centered research on employment shifts and equity rather than benchmark performance.
Apollo Management: Separating Myth from Reality on AI and Labor. This report finds AI will reshape labor markets unevenly, with task substitution in areas like customer service leading to sector contraction while complementarity elsewhere boosts productivity. Large-scale unemployment is not yet evident, but gains may not be evenly distributed.
Ethics & Safety
The Deepfakes We Missed: Detectors Were Built for a Threat That Didn’t Arrive. This paper argues deepfake research over-focused on public-figure misinformation while under-addressing the harms that actually grew: non-consensual intimate imagery, voice-clone scams, and emotional-manipulation fraud. It challenges the threat model underlying much of synthetic-media policy.
Palo Alto Networks: Frontier AI Models Are Now “Remarkably Good” at Finding Software Vulnerabilities. After testing Anthropic’s Claude Mythos model, the company concluded the latest models can turn vulnerabilities into exploit paths in near-real-time. Their May patch advisories are the first where the majority of findings came from AI models scanning their own code.
46 States Have Now Enacted Laws Targeting AI-Generated Synthetic Media. This updated tracker maps the rapidly expanding U.S. legal landscape on deepfakes. The federal TAKE IT DOWN Act’s platform compliance requirements take effect May 19, 2026, and 30 states have enacted election-related deepfake restrictions ahead of the midterms.
HBR: Traditional AI Governance Programs Aren’t Built for Agentic Systems. This piece argues that ethics-focused AI governance is insufficient for emerging agentic AI and societal-scale risks. It urges organizations to rethink their approach before ethical, reputational, and legal problems compound.
Research
International AI Safety Report 2026. Led by Yoshua Bengio and dozens of international experts, this major report synthesizes evidence on general-purpose AI capabilities, emerging risks including misalignment, and needed safety measures. It serves as a key reference for policymakers working on coordinated global governance.
AI Job-Exposure Scores Should Be Based on Evidence, Not Model Guesses. This position paper argues that widely cited AI job-exposure measures rely too heavily on zero-shot LLM judgments rather than reproducible, externally grounded evidence. The authors propose an evidence-based framework that could improve labor policy and retraining decisions.
Fair Outputs, Biased Internals: LLMs Can Look Fair While Hiding Demographic Bias. New research shows that open-weight models can exhibit behavioral fairness in high-stakes decisions like mortgage underwriting while retaining and amplifying demographic biases in their internal representations. The finding complicates current approaches to AI auditing.
Humanwashing: Why “Human-in-the-Loop” Often Obscures Algorithmic Accountability. This paper examines how the human-in-the-loop concept is used indiscriminately to create a false sense of safety in AI decision systems. It argues the metaphor frequently serves to deflect scrutiny rather than ensure meaningful oversight.
Last Updated: 2026-05-18 07:43 (California Time)