Presentation: From Friction to Flow: How Great DevEx Makes Everything Awesome

Forsgren's DevEx framework arrives at a critical inflection point in our industry. We've been seduced by AI code generation promising 10x velocity, yet I'm watching teams deploy models that flood their data pipelines with untested logic, overwhelming validation layers that weren't architected for that throughput. The real constraint isn't code generation—it's observability, testing infrastructure, and deployment orchestration. In my experience, teams adding Copilot without upgrading their CI/CD, data quality frameworks, and monitoring end up with faster problems, not faster solutions. The architectural implication is brutal: you need platform engineering discipline *before* unleashing AI productivity gains. This means investing in dbt testing, schema registries, and deployment gates that scale with generation speed. For data teams specifically, this translates to hardening your data contracts and lineage tracking before your ML engineers start churning out feature engineering code. The honest takeaway is that architecture decides whether AI velocity becomes competitive advantage or technical debt.