Long-form writing

Essays on AI-First Theory, RACE Programming, and the evolution of software engineering practice. Subscribe via RSS to follow new pieces as they're published.

• September 19, 2026 · framework · ai-native · comparison · 5 min

  What Google's New SDLC Paper Leaves to the Buyer

  Google says the AI harness belongs to the team. That leaves engineering leaders to fund, staff and operate a new asset.
• September 17, 2026 · framework · ai-native · comparison · 7 min

  Anthropic wrote the manual. The team is still yours

  Anthropic published The AI-Native SDLC Playbook in August 2026. The model vendor now says what RACE Programming has said since spring.
• September 1, 2026 · framework · scaling · roles · 6 min

  How RACE Will Scale, First Sketches

  Scaling RACE today means adding Pit Crews. Early thinking says otherwise: blend human and silicon crews, and tune the mix. Notes, not doctrine.
• August 7, 2026 · transformation · adoption · leadership · 6 min

  Building an AI Transformation Roadmap for Engineering

  If the three layers of transformation cannot be sequenced, what does a roadmap even look like? Horizons, not phases, and what each one has to prove.
• August 7, 2026 · sdlc · ai-native · adoption · 6 min

  How to Modernize Your SDLC for AI-Assisted Development

  Most SDLC modernization buys tools and keeps the process, which speeds up the step that is already cheap. Here is the order that actually works.
• August 7, 2026 · leadership · adoption · ai-native · 6 min

  What CTOs Should Know Before Going AI-Native

  Six things that decide whether an AI-native adoption produces a real economic result or an expensive tools rollout, from someone running it.
• August 6, 2026 · agile · scrum · ai-native · 5 min

  Is Agile Still Relevant When AI Writes the Code?

  Agile was right because it matched the problem. AI changed the problem. What that means for the values, the ceremonies, and what replaces them.
• August 5, 2026 · requirements · customer development · framework · 5 min

  The Team Principal, Bottleneck or Not

  AI made the Team Principal the new bottleneck. Used correctly, RACE Programming and the Pit Wall are how the role stops being one.
• July 22, 2026 · framework · roles · enterprise-ai · 5 min

  Uber's Agentic Pods and the Open Loop

  Uber's Agentic Pods proved the Team Principal and Pit Wall interaction. Closing the loop takes the Pit Crew, and then it ships enterprise systems too.
• July 15, 2026 · framework · roles · language · 4 min

  Adopting the Forward Deployed Engineer

  RACE Programming renamed its ACE Software Engineer to Forward Deployed Engineer. The industry term now fits the role, and shared language is the point.
• July 14, 2026 · framework · expertise · roles · 4 min

  What Happens to the Specialists?

  The strongest objection to RACE Programming is about depth, not speed. Deep expertise does not vanish. The line between generalist and specialist moves.
• July 2, 2026 · framework · ai-native · cynefin · management · 7 min

  Software delivery is leaving the complex domain

  A reliable AI teammate moves software from Cynefin's complex domain toward the complicated one, reopening the management model and pointing at a factory.
• June 25, 2026 · framework · ai-native · origin · 5 min

  Why RACE Programming exists

  For three years we delivered with AI agents. Then the tools crossed a line, and methods built to coordinate people started managing the wrong thing.
• June 23, 2026 · framework · ai-native · comparison · 7 min

  What AWS AI-DLC gets right, and where it stops short

  In July 2025 AWS published the AI-Driven Development Life Cycle, reaching the same shape as RACE Programming. The divergence is who verifies the AI.
• April 23, 2026 · framework · prescription · agile · 6 min

  Prescriptive, not permissive

  Most frameworks permit variation. RACE Programming prescribes. When AI shifts engineering economics by an order of magnitude, permissive is not enough.
• April 22, 2026 · theory · transformation · 8 min

  The three layers of AI-native transformation

  Organizational, process, and technological change cannot be sequenced. Most AI adoption programs fail trying to, and the failure mode is predictable.