Personal Productivity & AI-Augmented Workr/artificial

i've started asking AI to argue against me before i ask it to help me, and it changed everything

Read original
ai-writing-workflowspkm-workflowssecond-brain

“these tools mirror your framing more than people admit, so the only way to get signal is to deliberately frame against yourself”

Key takeaways

  • AI models exhibit confirmation bias by mirroring user framing - asking 'is this good?' invites agreement rather than analysis
  • Leading with 'give me the strongest case this is a bad idea' forces the model to engage weak points first, producing more balanced outputs
  • Cross-model comparison after adversarial prompting reveals where different models land differently, providing additional signal
  • The technique is generalizable: deliberately framing against yourself is the only reliable way to extract genuine signal from AI tools

Why this matters for operators: Knowledge workers, strategists, anyone using AI for decision support or analysis

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.