AI EcosystemsSemafor

California city bans data center construction as opposition grows nationwide

Read original
regulatory-impactai-policyai-infrastructure-backlash

US opposition to nearby data centers jumped from 42% to 71% in nine months, with young people—those most affected by AI-driven labor slowdowns—leading the backlash

Key takeaways

  • Monterey Park, CA became first US city to permanently ban data center construction with 86% voter support
  • Public opposition to nearby data centers nearly doubled from 42% to 71% in just nine months
  • Growing anti-AI sentiment is strongest among young people experiencing AI-driven labor market impacts

Why this matters for operators: Minimal - regulatory/policy issue, not GTM/productivity tooling

I cover AI×GTM intelligence like this every Wednesday.

Get STEEPWORKS Weekly

More picks

AI DevelopmentLenny's Podcast

Humans will keep inventing new reasons why we must stay in the loop with agents

  • Human resistance to full AI autonomy is not purely technical—it's psychological and organizational; companies will rationalize keeping humans in decision loops even when agents are capable
  • The 'human-in-the-loop' requirement may become a self-perpetuating narrative rather than a genuine necessity, driven by organizational risk aversion and change resistance
  • Product leaders at scale (Notion) are observing this pattern, suggesting it's a widespread phenomenon across enterprise AI adoption, not isolated to specific use cases
ai-agent-adoptionhuman-in-the-loopai-governance
GTM Ops**RevOps Impact (Jeff Ignacio)

Comp plans for consumption pricing

  • Consumption pricing fundamentally breaks traditional SaaS comp models—requires rethinking sales incentive structures around usage vs. contract value
  • Four distinct contract structures exist (pay-as-you-go, uncommitted, committed, hybrid), each requiring different compensation mechanics and sales behaviors
  • Enterprise consumption-based deals create tension: customers want flexibility, sales teams need predictability for quota attainment—comp design must bridge this gap
revenue-platform-consolidationconsumption-pricing-modelssales-comp-design
AI×GTMGTM OS: The Future GTM Operator

3 revenue motions your AI is only half wired into

  • Model parity has arrived: OpenAI/Claude now trade evenly on core tasks, making 'better AI' a non-differentiator—the edge shifts to integration depth into existing revenue motions
  • Waste is quantified: teams paying $17K-$37K/month for AI seats that never touch pipeline generation; real cost is opportunity cost of unused capacity, not subscription fees
  • Lean teams have a structural advantage: cannot out-buy larger competitors on model access, but can out-embed them by wiring AI 1 revenue motion deep (pipeline → content → deals) with proprietary deal context competitors haven't seen
ai-sdr-adoptionrevenue-platform-consolidationback-to-basics-gtm

This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.