ai-coding-toolspkm-workflowsai-writing-workflows
“The constraints on a model are often imposed by us - the harness we put them in, and the way we prompt them. When we encounter a new class of model, we should expect to remove or change those harnesses to elicit new behaviors.”
Key takeaways
- Model constraints are often user-imposed through prompting and harness design, not technical limitations—reframing this unlocks new capabilities with new model releases
- Practical techniques for discovering unknown unknowns: blindspot passes, brainstorming wildly different directions, interview-style prompting, and maintaining implementation notes
- HTML emerges as unreasonably effective for Claude interactions—suggests markup-based prompting as emerging best practice
- The gap between map (what we think models can do) and territory (what they actually can do) is widest at model release—rapid experimentation is critical
- Prompt engineering and harness design are first-order levers for unlocking model behavior, not secondary concerns
Why this matters for operators: AI practitioners, prompt engineers, and teams evaluating new model capabilities; applies to any organization adopting Claude/Fable
I cover AI×GTM intelligence like this every Wednesday.
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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
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- 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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- 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.