Personal Productivity & AI-Augmented WorkLenny's Newsletter

How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman

Read original
ai-writing-workflowspkm-workflowsautomation-stacksvibe-marketing

“The interview step (not the drafting step) is where AI slop actually comes from”

Key takeaways

  • AI slop originates in the interview/ideation phase, not drafting—fixing input quality prevents generic output downstream
  • Voice codification (Markdown files capturing tone, style, register) enables AI to draft authentically rather than defaulting to internet averages
  • Distribution is becoming a durable moat; founders should treat content creation as systematized, team-based process rather than individual effort
  • Blank page friction is the primary bottleneck in content creation; AI Oracles scanning internal systems + internet for spikes eliminate this
  • Employees are underleveraged marketing channels; gamification ($5K prize pools) converts internal teams into content creators
  • Six-step workflow (Oracle → Interview → Voice Files → Editorial Council → Scoring/Revision → Lessons Loop) creates repeatable, non-generic content at scale

Why this matters for operators: Content teams, founders, marketing leaders evaluating AI writing tools; teams struggling with AI-generated content quality; distribution-focused companies

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
Read original Full analysis →
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
Read original Full analysis →
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
Read original Full analysis →

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