How AI Is Disrupting Software Development in 2026 | Episode 36

Everyone says AI is making software development easier.That’s only partially true.While AI accelerates the act of writing code, it also amplifies existing problems. Poor inputs produce poor outputs faster. Weak specifications lead to larger-scale inefficiencies. And teams that prioritize speed without clarity often end up with more rework, not less.This episode challenges the idea that AI is purely a productivity multiplier. Instead, it explores how AI is exposing deeper issues in how software is planned, validated, and maintained.AI is unlocking new capabilities, but it’s also introducing new complexity that teams must learn to manage.Top Takeaways:Garry Tan's public statements about his coding activity and the backlash he received for the perceived quality of his codeRelevance of lines of code as a metric for productivity, especially in the context of AI and automationShifting focus from code quantity to business outcomes and customer acquisition How coding practices have evolved with more capable browsers and computers, reducing the need for extreme optimizationImportance of aligning engineering efforts with business goals and outcomesTrend towards open-source models and the challenges of maintaining proprietary advantagesCost of AI models and the economic implications of using different modelsStrategies for managing technical debt through automation and the balance between shipping quickly and maintaining qualityConnect with us:Duncan MapesJason EhmkeDevGrid.ioDevGrid on LinkedInDevGrid on X