AI coding assistants entered development teams quietly, but their impact grows by the day. What started as autocomplete now shapes architecture decisions, documentation, and testing. And when productivity gains are visible, so are new risks: security blind spots, uneven quality, and the slow erosion of shared standards. Teams move faster, but not always in the same direction.
The challenge has become integration rather than adoption. And new questions have risen: how do you blend automation into established practices without losing oversight? When is human review still essential, and what should the rules of collaboration between developer and machine look like? As AI tools learn from proprietary code, where do responsibility and accountability sit?
Let’s talk about how to redefine those workflows, balancing creativity with control, and protecting code quality in a hybrid human-AI environment. A closed conversation on where AI accelerates progress, where it introduces new debt, and how development culture must evolve to stay credible.
