Introduction AI is fundamentally reshaping software development roles and activities. While the change is obvious, understanding the actual shifts taking place remains challenging. In this article, we introduce a first pass at four AI Native Dev patterns that are currently emerging. We’re asking for community feedback to evolve these patterns in the open. The aim is for patterns to help you grasp what’s on the horizon and how to position yourself effectively in this changing landscape. community feedback Pattern Overview Pattern #1 - From producer to manager: More and more code is being produced by AI, requiring us to spend increased time reviewing and deciding whether to accept or reject suggestions. As we evolve toward autonomous agents, we’re essentially becoming managers of agent development teams. The same dynamic applies in production environments: when systems fail, we must make the final call and ensure everything runs smoothly. Github research on the impact of Generative AI on Developer experience Github research on the impact of Generative AI on Developer experience Pattern #2 - From implementation to intent: We no longer need to write the implementation and how to do a task. Instead, we are able to focus on what we are building, shifting our focus to supplying our intent. Tools have evolved to provide chat and instruction UI to gather the intent in small chunks, and we are now moving to writing richer requirements that along with full context allows the AI to do much larger sets of work. If there are errors in the output, we simply update the specifications. how what The importance of a Product Requirement Documents(PRD) in Cursor The importance of a Product Requirement Documents(PRD) in Cursor Pattern #3 - From delivery to discovery: How do we know what to build? What do we try first? The cost of experimenting has gotten so much smaller now that we can ask AI to prototype ideas and offer a variety of options. This allows us to discover new ideas or compare alternatives: would this design work better? What solution delivers the best performance? We can then run experiments through our existing CI/CD pipelines to provide a feedback loop that helps us make informed choices. Fast iteration on working prototypes using Lovable Fast iteration on working prototypes using Lovable Pattern #4 - From content to knowledge: I'm sure you have watched a seasoned member of your team leave and you had that painful realization that they are taking a lot of hard earned implicit knowledge with them. We try to compensate by writing documentation, but maintaining it feels like a chore, and the content often becomes stale and drifts from the reality of the codebase which is the true source of truth. AI now gives us a compelling reason to share our knowledge. This benefits both our colleagues and the AI systems themselves. In fact, knowledge might be your company's only unique value proposition as building and delivering products increasingly becomes a commodity. The Agent Devin helps you capture knowledge when it answers you The Agent Devin helps you capture knowledge when it answers you Impact in the Socio Technical Ecosystem Developers are not the only ones helping to deliver value to the customer: product owners, designers, agile coaches, engineering leads, testers and QA, data engineers, tech writers, DevOps & SRE and security all play a vital role. And all roles in this social technical ecosystem are influenced by AI. From a higher level, developers break into other roles and vice versa: From producer to manager: We can learn from all the DevOps observability and automation to help in this shift.From implementation to intent: a domain for the architects and QA people to write down and guard the specifications.From delivery to discovery: the product owner focuses on understanding the business, the customers and the domain.From content to knowledge: similar to data scientists finding a signal in the noise, cleaning and extracting information from a multitude of data. From producer to manager: We can learn from all the DevOps observability and automation to help in this shift. From producer to manager From implementation to intent: a domain for the architects and QA people to write down and guard the specifications. From implementation to intent From delivery to discovery: the product owner focuses on understanding the business, the customers and the domain. From delivery to discovery From content to knowledge: similar to data scientists finding a signal in the noise, cleaning and extracting information from a multitude of data. From content to knowledge More senior developers care about the whole system and AI helps us to break these silos. Shades of Conway’s law by Thierry De Pauw Shades of Conway’s law by Thierry De Pauw Thoughts? Now, we’d love your input! Do you agree with these patterns? Have ideas for clearer names? Have you experienced scenarios that don’t quite fit these patterns? This is a community-driven effort, so your thoughts genuinely matter! We’ve set up a Google doc for you to comment on and provide feedback. Or you can send us an email with your thoughts, discuss it on our discord or comment on the socials. We’ve set up a Google doc for you to comment on and provide feedback Follow along as we break down each pattern — subscribe now.

Four Patterns That Show How AI Is Rewriting the Developer’s Job Description

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