AI is collapsing the old handoff model between product, design, and engineering.

Product managers and designers no longer need to stop at static wireframes, Figma flows, or backlog items. With tools like Bolt, Lovable, v0, Replit, and other AI app builders, they can now create Realistic Interactive Prototypes, or RIPs, that look and feel much closer to working software. These RIPs expose product gaps, usability issues, workflow problems, and requirement misunderstandings long before a full engineering team is committed to building the wrong thing.

Engineers are facing an equally large shift. Tools like Cursor, Claude Code, GitHub Copilot, Codex, and Cline are moving software development from manual code production toward agent-directed software construction. The engineer’s job is no longer centered on typing every line of code. It’s centered on guiding agents, inspecting their work, refactoring aggressively, protecting architectural clarity, and making sure the resulting system can be maintained by humans after the AI session ends.

That shift creates a major leadership challenge.

AI can increase software output dramatically. It can also accelerate technical debt dramatically. Faster code generation creates more code to review, more integration risk, more architectural drift, more hidden complexity, and more maintenance burden for the engineering team.

This is why MVP thinking is becoming too slow for many discovery efforts. Product teams can now use RIPs to validate workflows, assumptions, and requirements much faster, then reserve serious engineering investment for ideas that have already been pressure-tested through realistic interaction.

It’s also why traditional delivery metrics like velocity and DORA are no longer enough to judge engineering health. A team can appear faster while the product quietly becomes harder to change. Deployment frequency may rise. Lead time may fall. Maintainability, architecture, test quality, and human understandability can still degrade.

In this talk, I’ll explain why AI forces CTOs, VPs of Engineering, and Engineering Directors to rethink product discovery, engineering roles, team metrics, and technical debt prevention.

I’ll introduce the concept of the engineer as a Software Composer: a technical professional who orchestrates AI-generated code into a coherent, maintainable, human-centered system. Software Composers don’t merely accept AI output. They shape it, constrain it, challenge it, refactor it, and integrate it into a durable architecture.

I’ll also show the 9 kinds of technical debt I’ve encountered across 21 years of leading teams that delivered mission-critical software to Department of War clients. More importantly, I’ll share the secret I discovered for addressing them: technical debt is conquered by changing the daily engineering behaviors that create or prevent it at the source.

The session will cover practical strategies for using AI without allowing it to turn your codebase into tomorrow’s legacy system. I’ll explain how to embed continuous refactoring, architectural clarity, AI-generated Daily Reports, engineering standards, product alignment, and maintainability discipline into the normal rhythm of product development.

The future belongs to teams that use AI to discover better products, compose better systems, and preserve the craftsmanship required to keep software understandable, adaptable, and durable. 

Target audience:
CTOs, VPs of Engineering, Engineering Directors, senior architects, product engineering managers, technical program managers, and product leaders working with AI-accelerated product teams.

Best fit for teams using or evaluating tools like Cursor, Claude Code, GitHub Copilot, Codex, Bolt, Lovable, v0, Replit, and other AI development tools.

Preferred session duration:
50 minutes: 40 minutes of talk, 10 minutes of Q&A.