Why I picked this
the vendor fiction thing is true...to a point
ai-sdr-backlashai-sdr-roiconversation-intelligencereal-time-coachingrevenue-platform-consolidationback-to-basics-gtmhuman-first-sales
“If an automated motion outperforms your BDRs, that means your BDRs were bad. The real gain is 2-3x per rep, not headcount cuts.”
Key takeaways
- BDR replacement is vendor fiction—AI augments research/prep, not replaces headcount. CEOs optimizing for headcount cuts are solving the wrong problem.
- Skill decay is measurable and immediate: 40%→80%→40% in 24 hours proves survey-based certification is obsolete. Real-time conversation data is the only valid proof.
- Next-best-action models are over-engineered. Top-three options with peer success rates + rep agency + system learning creates better outcomes than deterministic recommendations.
- Infrastructure-first architecture prevents catastrophic failure modes. Building AI on bought infrastructure beats custom solutions that break at scale (4,000 reps stuck on one data feed failure).
- Revenue-per-rep normalized across role types (net-new, farmer, CSM) is the true metric—not activity counts or pipeline velocity.
Why this matters for operators: Enterprise GTM leaders evaluating agentic sales tools; CMOs under pressure to automate BDR roles; RevOps teams designing AI-assisted workflows; Sales leaders implementing conversation intelligence
I cover AI×GTM intelligence like this every Wednesday.
Get STEEPWORKS WeeklyMore 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.