AI DevelopmentSimon Willison
A Fireside Chat with Cat and Thariq from the Claude Code team
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“Adding examples to a system prompt is no longer best practice for models like Fable 5 or Opus 4.8. The Claude Code system prompt recently reduced in size by 80%.”
Key takeaways
- Anthropic's Claude Tag achieves 65% PR landing rate for product engineering—demonstrating real-world coding agent productivity at scale within the vendor itself
- Prompt engineering best practices have fundamentally shifted: examples and negative constraints now reduce model quality; Anthropic reduced Claude Code system prompt by 80%, signaling a move toward minimal, trust-based prompting
- Internal dogfooding ('ant fooding') is core to Anthropic's feature validation strategy—features only ship after demonstrating user retention with internal cohorts, creating a high bar for production readiness
- Coding agents create risk of capability ceiling ('Deep Blue effect'); Anthropic's mitigation strategy is to 'be more ambitious' with work scope rather than constrain agent autonomy
- Auto mode is positioned as enabling technology for collaborative AI workflows; Anthropic's public Slack integration demonstrates culture-of-working-in-public as competitive advantage
Why this matters for operators: AI engineering teams, prompt optimization practitioners, coding agent implementers, enterprise AI adoption
I cover AI×GTM intelligence like this every Wednesday.
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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
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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
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.