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Kevin Hill has spent his career inside systems that don't forgive mistakes. From running Windows Error Reporting at Microsoft, where a single conflicting data point triggered an email from the president of Windows to two engineers at once, to leading engineering at Salesforce, where global enterprise infrastructure runs 24/7. He has built a specific point of view on what scale actually demands from leaders, and it's not what most engineering orgs are currently optimizing for.
In this episode with Egil, Kevin gets into why the real bottleneck in AI-assisted development isn't code generation, it's review and production safety. He also shares how he's structuring teams, building AI adoption from the peer level up, and why the identity crisis hitting engineers and middle managers right now is the conversation most leaders are avoiding.
Topics discussed:
Spec-driven development as a forcing function for consistent AI output quality
Why middle management identity is the most underdiscussed pressure point in AI transitions
The three-tier adoption model and why the middle 60-70% is where leaders should focus
AI champions at team level as a structured peer-to-peer learning mechanism
Why reviewing and safely shipping AI-generated code is now the core engineering bottleneck
Shifting team composition from skills-based to product and value-based structures
Ship more roadmap or cut headcount: the question every enterprise engineering leader is actually wrestling with
How the value of "right skills" is shifting away from language expertise toward architecture and engineering judgment
