Patrick presents the four patterns of AI-native development at the Future of Software event hosted by Eficode. He opens by tracing the rapid evolution of coding tools – from basic tab completions and chat-based copy-paste workflows to multi-file predictions, terminal and browser integration, reasoning models, and continuous autonomous loops like Devin. The key insight is that the technology stack around LLMs has expanded dramatically, and the gap between what most developers experience (copilot-level assistance) and what is possible today (teams of agents working across codebases) is significant.
The first pattern, “Producer to Manager,” explores how developers are shifting from writing code to reviewing AI-generated output. Patrick highlights the rising cognitive load of reviewing code you did not write, and showcases emerging approaches to ease this burden: condensed change summaries, step-by-step review workflows, diagram-based visualizations, and the concept of moldable development environments that adapt their interface to the problem at hand. He draws a direct parallel to the DevOps automation journey, noting that as systems become more automated, the effort shifts toward understanding failures, building resilience, and training for situations you no longer encounter routinely.
The second pattern, “Implementation to Intent,” describes the move from hands-on coding to specifying what should be built. Developers are creating reusable specification files, adopting intent-based coding workflows, and tools are evolving toward collaborative product requirements documents with bidirectional links between specs and code. The third pattern, “Delivery to Discovery,” argues that cheaper generation enables exploring multiple options – generating five designs instead of one, running rapid prototypes, and even allowing end users to discover what they need through generated interfaces. Patrick suggests this frees developers to focus on building the right thing rather than just building the thing right.
The fourth pattern, “Content to Knowledge,” covers how context fed into coding tools – documentation, guidelines, production feedback, incident responses – can be transformed into lasting organizational knowledge. Patrick highlights tools like Devin that proactively identify important information during conversations and offer to save it as knowledge, as well as systems that turn codebases into structured onboarding lessons. He closes by introducing the AI Native Dev community and its curated landscape of over 300 tools, inviting the audience to contribute feedback on the patterns and help refine the emerging vocabulary of AI-native development.
Watch on YouTube — available on the jedi4ever channel
This summary was generated using AI based on the auto-generated transcript.