In this presentation, Patrick Debois lays out a framework for understanding how generative AI is transforming software development beyond faster typing. He opens by mapping the technology explosion — from basic LLM completions through RAG, function calling (now MCP), autonomous agents, and emerging teams of agents — and draws a parallel with cloud native: just as moving to the cloud meant more than migrating VMs, AI native development means fundamentally new practices, not just AI sprinkled on existing workflows. Every new technology reshapes tasks, making some obsolete, enhancing others, and creating entirely new ones. Patrick distills these shifts into four patterns.
The first pattern, from producer to manager, addresses the cognitive load that increases as developers shift from writing code to reviewing AI-generated code. Patrick demonstrates several emerging approaches to this challenge: condensed review views that strip away noise, step-by-step review flows that break changes into digestible pieces, and visual diff representations like diagrams that make it easier to spot errors at a glance. He introduces the concept of moldable development environments — IDEs that adapt their interface to the specific review context. On the operational side, he covers auto-commit strategies (where the AI commits by default and the developer reverts if needed), file-level permission controls for agents, cost monitoring for long-running agent sessions, and checkpoint-based regeneration to avoid re-reviewing entire chains of thought.
The second pattern, from implementation to intent, explores the shift toward specification-driven development. Patrick traces the evolution from simple spec.md files appended to prompts, through AI-assisted task breakdown and planning, to fully specification-centric workflows where the code becomes secondary to the requirements document. Some tools now support bidirectional synchronization — changing the code updates the spec, and vice versa. He notes that while this approach enables tackling larger projects through iterative regeneration, it also surfaces old challenges: conflicting requirements between stakeholders, political dynamics in specification authorship, and the risk of recreating waterfall-era rigidity.
The third pattern, from delivery to discovery, repositions the developer as a product explorer. Patrick reframes vibe coding as exploratory coding — a deliberate practice for rapid prototyping and idea validation, not careless development. He shares his personal approach of vibe coding for two days to build understanding, then starting fresh with a well-informed specification file. The pattern extends to generating multiple design alternatives simultaneously and enabling real-time collaboration on prototypes. Patrick proposes a provocative extension: letting customers vibe code the interfaces they want on top of your product, turning user feedback into working prototypes rather than written requirements.
The fourth pattern, from content creation to knowledge, addresses how teams capture and preserve what they learn. Patrick discusses bringing production context — call volumes, incident history, error patterns — into the development environment to inform code reviews and architectural decisions. He highlights tools that generate incident response documentation from contextual awareness, convert codebases into structured onboarding lessons, and maintain persistent “feature memory” to prevent teams from unknowingly revisiting abandoned approaches. The talk emphasizes inline knowledge management, where the AI proactively identifies important insights during coding sessions and suggests saving them as durable knowledge for both human and agent consumption. Patrick closes by framing the four patterns as an expansion of the developer role into operations, QA, product ownership, and data engineering — the same cross-functional breadth that good senior developers have always practiced, now accelerated and made accessible through AI tooling.
Watch on YouTube — available on the jedi4ever channel
This summary was generated using AI based on the auto-generated transcript.