In 2009, DevOps promised to tear down the wall between developers and operations. Sixteen years later, DORA's own research says only 19% of teams ever actually got there, and by 2025 the industry had to quietly retire its old scorecard because it had become measurement theater instead of a real signal.

Then AI showed up, and instead of closing the gap, it made the fragmentation worse. Teams are now losing close to a full workday per person, per week, wrangling AI tools that don't talk to the rest of the pipeline, and half of all enterprise AI agents are running in complete isolation from everything else.

This talk traces DevOps from its two-person origin story in an empty conference room to its current, mostly-ceremonial state, then makes the case for AI-Native Engineering (AINE) as the shift that actually finishes the job: rebuilding developer workflows, extending SDLC understanding past the dev team, and giving AI a system to amplify instead of a mess to accelerate.

Key Takeaway: DevOps didn't fail, it stalled halfway through, and most orgs mistook the stall for the finish line. AI isn't going to fix that on its own; it just makes whatever's broken move faster. The real opportunity is AI-Native Engineering finishing the job DevOps started: real SDLC-wide understanding, not another tool bolted onto an already-fragmented pipeline.

Why This Talk Matters to the Community: Every team in the room has an AI tool rollout happening right now, and most of them are being measured by the wrong things (velocity, adoption numbers, lines of code) instead of whether the tool made the whole system healthier or just faster at being broken. This talk gives practitioners a way to diagnose that difference using real data (DORA, GitLab, MuleSoft) instead of vendor claims, and a concrete framework for what "doing AI right" in the SDLC actually looks like beyond another Copilot license.