Intelligence

What I'm watching in AI×GTM

I review 50+ articles a day through the STEEPWORKS intelligence pipeline. The ones worth your time end up here — with my notes on why they matter and what patterns I'm seeing.

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Deep-dive operator posts

Agentic Patterns

I Built a Memory Engine in Claude Code. Then I Started Working in Codex.

Jul 26 · 11 min read

Ai Strategy

The Judgment Engine

Jul 17 · 6 min read

Agentic Patterns

I Built a Whole Pipeline to Stop Slop. It Was Pretending to Work.

Jul 17 · 12 min read

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Daily GTM Feed

Latest curated 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
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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
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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
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GTM OpsDemand Gen Report

The Conversion Reversal Most Demand Gen Teams Haven’t Priced In

  • AI-referred traffic conversion reversed 80 percentage points in 12 months (38% worse in March 2025 → 42% better in March 2026), measured across 1 trillion+ retail visits
  • B2B AI referral sign-up conversion is 11x higher than organic search (1.66% vs 0.15%), with credible 4-10x multipliers across multiple independent studies (Semrush 4.4x, Seer 9x, Ahrefs 23x for signups)
  • Mechanism: AI chatbots compress buyer discovery/evaluation into single 25-minute session before click-through; 95% of winning vendors already on Day One shortlist, making AI traffic functionally high-intent demand rather than awareness
ai-sdr-adoptionsignal-infrastructureintent-data
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