Introducing the 4 AI Native Dev Patterns – with Patrick Debois

Introducing the 4 AI Native Dev Patterns

AI is shifting the software development landscape in fundamental ways. In this episode of AI Native Dev, Simon Maple and Patrick Debois lay the foundation for four key patterns that are emerging in this space. These patterns aren’t rigid best practices, but observed shifts in how developers interact with code, tools, and each other in an AI-powered world.

This post introduces the four patterns at a high level. In the coming weeks, we’ll publish deep dives into each pattern—one blog post per pattern—exploring how they show up in tools, teams, and workflows.

1. From Producer to Manager

Developers are no longer the sole producers of code. As AI takes on more of the writing, developers shift into the role of reviewers and decision-makers. Patrick describes this change as a move from direct implementation to oversight—understanding, validating, and managing what AI creates.

This shift demands a deep sense of context: what is being changed, why it matters, and whether it aligns with business needs. Developers are stepping into a managerial mindset, guiding intelligent systems rather than manually coding every line.

2. From Implementation to Intent

The focus is moving from how code is written to why it exists. Instead of expressing functionality through syntax, developers are increasingly defining intent through natural language, structured prompts, and specifications. Patrick calls this shift "spec centricity" — describing what a component should do rather than how it does it.

This transition changes how we think, communicate, and collaborate. Developers become more like architects or product owners, aligning systems with business goals and user needs. The IDE of the future may not be about writing code, but about articulating intent clearly.

3. From Delivery to Discovery

With AI making code generation and deployment faster and cheaper, the new bottleneck is choosing what to build. This opens the door to experimentation: generating multiple variants of features, testing which works best, and learning from real-world feedback.

Discovery replaces delivery as the primary value driver. Product teams are now empowered to explore alternatives, validate ideas earlier, and evolve products continuously. The shift isn't just about building right—it's about building the right thing.

4. From Content to Knowledge

AI can generate an overwhelming amount of content—code, documentation, logs, and more. But real value comes from turning that content into knowledge that persists across teams, tools, and time.

Patrick emphasizes the importance of data quality and context. Clean, well-structured knowledge helps both humans and AI make better decisions. Whether it's onboarding new developers, debugging incidents, or learning from past decisions, managing knowledge becomes a strategic advantage.

Supporting Forces: Tools, Community, and What’s Next

These patterns don’t emerge in isolation. They are shaped by:

• Tools: The AI Native Dev Landscape tracks the explosion of new tools enabling these shifts.
• Community: Feedback, discussion, and shared experiences are critical. Patrick and Simon encourage open contribution to the evolving patterns.
• The Future: As the space matures, new categories, roles, and responsibilities will emerge. Staying adaptable is key.

Conclusion

These four patterns aren’t final. They’re a first attempt to describe the paradigm shift AI is driving in software development. As Patrick explains, this is a community effort. Listen, reflect, contribute—and help shape what AI native development becomes.

Look out for blog posts on each pattern here at ainativedev.io!

Explore the AI Native Dev Landscape at: landscape.ainativedev.io