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What I'm watching in AI×GTM

I review 50+ articles a day through the STEEPWORKS intelligence pipeline. The ones worth your time end up here — with my notes on why they matter and what patterns I'm seeing.

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Deep-dive operator posts

Ai Strategy

The Judgment Engine

6 min read

Agentic Patterns

I Built a Whole Pipeline to Stop Slop. It Was Pretending to Work.

12 min read

Ai Strategy

The Self-Healing Context Layer: 3 Mechanisms

11 min read

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Daily GTM Feed

Latest curated picks

Enterprise AIKieran’s Substack - The AI Marketing Generalist

The Great AI Sprawl

  • AI sprawl is the inverse of intended outcomes: 78% of employees adopt unapproved tools, 95% of orgs see no measurable ROI, and 54% of C-suite say it's 'tearing company apart'—the mandate for 'AI native' created chaos instead of productivity
  • Negative correlation between AI tool proliferation and actual outcomes: teams using 5+ tools report lower self-rated productivity than 1-2 tool teams; Gartner forecasts 40% of agentic AI projects will be cancelled by 2027 due to escalating costs and unclear value
  • Uber's 'Agentic Pods' model (pairing AI engineers with domain experts on tight workflows) delivers measurable wins (2 weeks→50 min for QA, 15 hrs→30 min for capital allocation), but shipping is only 40% of the job—sustainability and governance are the missing piece
ai-policyback-to-basics-gtmautomation-stacks
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AI DevelopmentGTM OS: The Future GTM Operator

Your output still waits on you

  • The AI operator evolution: from prompt optimization to loop architecture. The real work shifts from writing better instructions to writing better evaluation rubrics (the 'check' step).
  • Rubric-driven QA at scale: Charlie Hills built a 110-edition rubric that allows an agent to score drafts (51→95) before human review, effectively replacing a QA hire with structured evaluation logic.
  • Cost metering is now table stakes: Fable 5's move to pay-per-use (July 12, 2026) forces operators to route strategy work to premium models and execution to cheaper ones—a new constraint reshaping workflows.
ai-coding-toolsautomation-stackspkm-workflows
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Enterprise AIAI News & Artificial Intelligence | TechCrunch

Inside Ode with Anthropic, the startup betting AI services are the future of enterprise

  • Ode represents a new enterprise AI services model: forward-deployed engineers embedded in client firms rather than traditional consulting engagements
  • Significant institutional backing (Anthropic, Blackstone, H&F, Goldman Sachs) signals confidence in AI-powered services replacing traditional consulting labor models
  • Core thesis challenges conventional consulting economics: small AI-native teams positioned to deliver work previously requiring large consultant armies
ai-services-modelforward-deployed-engineersenterprise-ai-adoption
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AI DevelopmentThe Pragmatic Engineer

Context engineering with Dex Horthy

  • Context engineering is becoming critical competency for LLM-era engineers—frameworks like LangChain/CrewAI are being abandoned by practitioners in favor of custom pipelines built on first principles
  • Human code review is non-negotiable: unreviewed AI-generated code creates technical debt that compounds exponentially (4-month failure window, 3-week recovery timeline)
  • The 12-Factor Agents framework emerged from studying ~100 real AI engineers shipping $100K+ contracts—represents practitioner consensus, not vendor marketing
ai-coding-toolsautomation-stacksai-policy
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