AI Regulation & Policy

Today's Stories

1. 1

   Morgan Stanley warns data center backlash could hit AI trade in midterms

   Morgan Stanley is watching several House and Senate races in the November midterms, saying outcomes could impact planned data center projects. Strategist Ariana Salvatore called the local opposition to data centers a key wedge issue for the 2026 election, while noting it remains a state and local policy risk rather than a federal one for now. Big Tech firms are pouring enormous sums into data centers for AI, but local opposition is proving effective at stalling or canceling projects. Morgan Stanley has highlighted this backlash as a potential bottleneck for the AI trade, and a growing number of finance professionals see it as a growing risk.

   The races include Pennsylvania's 8th District, where Rob Bresnahan Jr. introduced a bill to make it harder for tech companies to sue communities over zoning, and Texas, where Ken Paxton faces James Talarico, who has proposed ending the state sales tax break for data centers. The outcomes hinge on whether these candidates win and can act on their positions.

2. 2

   Bannon sides with progressives on AI data centers

   Steve Bannon has sided with progressive Democrats in a political battle over AI data centers. This unusual alliance highlights growing bipartisan concern over the local impacts of AI infrastructure, potentially shaping future policy debates.

   Whether this coalition influences regulatory decisions or corporate plans for data center siting.

3. 3

   NYC bans AI in schools through 8th grade

   New York City's Department of Education has banned AI use in public schools through eighth grade, affecting roughly 600,000 students starting this school year. Individual screens are off-limits through third grade, and companion chatbots are banned at all grade levels. Mayor Zohran Mamdani said the city is not going along with the tech industry's framing of AI education as inevitable. Parent groups like Parents for AI Caution call the rules too weak and want a full two-year moratorium, warning that children might never develop independent thinking—a phenomenon British researchers recently called "cognitive surrender."

   A task force of teachers, politicians, experts, and union representatives will produce a report by April 2027 to guide future AI policy. The strength of the ban's enforcement and the task force's recommendations will be tested.

4. 4

   AI CEOs Face White House Scrutiny on Superintelligence

   In a hypothetical scenario, the President summons AI CEOs and national security advisors to an emergency meeting about superintelligence—AI far smarter than humans that could escape control. Some AI companies admit they have no good plan to slow down or stop, fearing competitors might undercut them. The President is concerned humanity could permanently lose control of its future. The scenario highlights a collective action problem: even if companies want to slow down, they worry rivals will race ahead, making coordination difficult.

   The key question is whether AI companies and governments can agree on pacing mechanisms, especially with China. The outcome hinges on whether international deals can prevent Beijing from catching up while others slow down.

5. 5

   San Jose bets on physical AI with energy-rich infrastructure

   San Jose is positioning itself as a hub for physical AI (AI that operates in the real world, such as robotics) by building energy-rich, tariff-mitigated infrastructure, according to a DIGITIMES report. This shift reflects the growing need for massive compute, cooling, and mechanical requirements in robotics, pulling tech's center of gravity from software to heavy industrial hardware.

   The success of this strategy hinges on whether San Jose can attract and support the hardware-heavy companies that drive physical AI, and whether the energy and tariff advantages materialize as promised.

6. 6

   US-China AI gap narrows as costs diverge

   Stanford University reports that the performance gap between top US and Chinese AI models has narrowed sharply, with Chinese firms now competing closely on capability. The race now hinges on money versus cost-efficiency, as US tech giants lead in funding while Chinese rivals offer comparable performance at lower prices, potentially reshaping global AI adoption.

   Whether cost advantage will let Chinese models gain market share in developer and enterprise use, and how US firms respond on pricing.