Why I picked this
As we go all in on AI, the meta of whether its good, how its positives wlil be distributed, and just how far society is from optimizing for it is so real
ai-policyregulatory-impactmarket-consolidationai-sdr-backlash
“Asked about backlash to AI, Superhuman Mail CEO Rahul Vohra seemed unfamiliar with the premise of the question. After hearing about poor polling around AI, he responded: 'We don't really see that.'”
Key takeaways
- AI sentiment has collapsed across all demographics: only 18% of Gen Z feels hopeful, 70%+ of Americans think it's moving too fast, and negative views doubled from 34% to 50% in three years
- AI executives are dangerously disconnected from public sentiment - Superhuman CEO was literally unaware of negative polling, seeing AI adoption as 'inevitable as the internet'
- Backlash is creating real business consequences: record data center cancellations in Q1 2026 due to community resistance, threatening the compute infrastructure AI companies depend on
Why this matters for operators: Critical for GTM teams positioning AI features - public sentiment is a real adoption barrier that executives are blind to
I cover AI×GTM intelligence like this every Wednesday.
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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.