Four gaps are opening between AI capability and AI reliability — and three practices close them before it's too late.

It is 2028. You cut 40% of your engineers and let AI write the code. For a while it was glorious — velocity tripled, features shipped in days instead of weeks, the board couldn't stop smiling, and the quarterly numbers looked better than ever. You shipped more in six months than the old team shipped in two years. Costs down, output up, every metric green.

But now your software is unmaintainable. You just survived your third security disaster this year, and every senior you try to hire costs double — because every company made the same cuts at the same time. Your juniors can't evaluate what the AI produces. Your specifications say "build X" and the AI guesses the rest. And your best remaining developers — the ones you kept — have stopped caring. They press buttons, accept outputs, and ship code they feel nothing toward. They used to be 10x engineers. Now they can't code by hand, can't defend their architectural choices, and can't sell what they've built because they didn't build it. You wish you had invested in 2026.

This webinar shows you the four gaps opening between AI capability and AI reliability — and the three practices that close them before 2028 arrives. Research-backed (Stanford, Uplevel, Alibaba), real-world failures (Samsung, Klarna, Boeing 737 MAX), and immediately actionable.

Target Audience

• Developers — sensing quality and skill erosion

• Tech leads — owning code quality in AI-augmented teams

• CTOs / engineering managers — deciding AI adoption, headcount, and hiring

• HR / talent leaders — rethinking junior hiring and succession

• No specific technology or framework required — the content is stack-agnostic.