AI is changing the economics of software creation. Tools like Cursor, Claude Code, GitHub Copilot, Codex, Bolt, Lovable, v0, Replit, and other AI development platforms make it dramatically easier to generate working code, create realistic user experiences, and move from idea to prototype.

This creates a new leadership problem: generating code is getting easier. Building software that remains understandable, maintainable, architecturally coherent, and safe to evolve is getting harder.

In this session, I’ll explain why AI-accelerated development changes the role of CTOs, VPs of Engineering, Engineering Directors, senior architects, and product engineering teams. Product managers and designers can now use AI tools to create Realistic Interactive Prototypes, or RIPs, that make traditional MVP thinking feel slow for many discovery efforts. Engineers are also using agentic coding tools to delegate raw code generation to AI. The engineering role is shifting from manually writing every line of code to supervising, shaping, refactoring, and composing AI-generated software into systems humans can understand and maintain.

That shift creates the need for a new engineering discipline: the Software Composer.

A Software Composer doesn’t accept AI-generated code just because it works. Software Composers shape systems around clear architecture, intuitive domain models, readable abstractions, durable boundaries, and strong maintainability standards. This becomes more important as AI increases code volume and accelerates architectural drift. Without strong engineering discipline, teams can quickly create larger, more fragile codebases that look productive in the short term but become expensive to modify, test, debug, and extend.

The session will cover the shift from MVPs to RIPs, the rise of agentic coding, the maintainability risks of AI-generated code, and why architecture determines whether software can survive growth. I’ll also explain how AI-generated Daily Reports can improve team awareness, reduce status meetings, surface blockers earlier, and maintain alignment without adding process overhead.

The core message is simple: AI can help teams move much faster, but speed alone doesn’t create durable software. The winning teams will combine AI-enabled product discovery, agentic coding, strong architecture, domain clarity, continuous refactoring, and human-centered maintainability discipline.

Target audience
CTOs, VPs of Engineering, Engineering Directors, senior software architects, principal engineers, product engineering managers, and technical program managers responsible for improving software delivery outcomes in AI-accelerated product teams.

Preferred session duration:
50 minutes including Q&A.