Patrick presents the four patterns of AI-native development at ServerlessConf, framing the rapid evolution of coding tools – from simple tab completions to multi-agent systems that autonomously work across entire codebases. He traces the progression from single-line copilot suggestions through chat-based generation, multi-file edits, terminal and browser awareness, and eventually to tools like Devin that run continuous feedback loops. With over 300 tools now tracked on the AI Native Dev landscape, the space is evolving far beyond what most developers realize.
The talk introduces four key patterns reshaping the developer role. The first, “Producer to Manager,” highlights how the time saved generating code is now spent reviewing it, and how tools are adapting with summarized diffs, step-by-step review flows, diagram-based visualizations, and even auto-commit features. Patrick draws a parallel to the DevOps journey – from infrastructure-as-code through CI/CD, monitoring, resilience engineering, observability, and chaos engineering – suggesting that AI-assisted development will follow a similar arc of increasing automation followed by deeper investment in understanding and oversight.
The second pattern, “Implementation to Intent,” describes the shift from writing code to specifying what should be built. Developers are increasingly maintaining markdown specification files, reusable prompt libraries, and structured task breakdowns. Tools are evolving toward collaborative product requirements documents where specifications and code stay bidirectionally linked. The third pattern, “Delivery to Discovery,” argues that as generation becomes cheaper, developers can explore multiple options, prototype rapidly, and even let end users vibe-code their own interface variations – pushing AB testing to an entirely new level.
The fourth pattern, “Content to Knowledge,” covers how organizations can turn scattered documentation, incident responses, Slack conversations, and codebases into structured, reusable knowledge. Patrick highlights tools that capture important insights mid-conversation and turn codebases into onboarding lessons. He closes with a broader organizational perspective, examining how Conway’s Law applies to AI teams, whether we are shifting from headcount to compute budgets, and what it means when agents start appearing on org charts, applying for jobs, and receiving performance reviews – provoking the audience to think beyond the tangible tooling changes to the deeper structural shifts ahead.
Watch on YouTube — available on the jedi4ever channel
This summary was generated using AI based on the auto-generated transcript.