AI has upended software engineering, and the hype couldn't be louder. Token spend is highly visible on the CFO's radar, and CEOs read about 2x, 5x, and even 10x velocity gains all over social media.

Meanwhile, engineering leaders struggle to provide tangible proof of these leaps. They face mounting pressure from executive leadership to replicate those hyped results while justifying spiraling token costs.

Learn what's real and what's hype using results from a study of over 83K devs across 250 organizations, and see the growing productivity gap that has started to open in 2026.

Review a curated set of metrics for tracking your AI productivity gains as well as the factors that drag it down.

Finally, walk away with a practical blueprint for combining deterministic automation with AI flows to neutralize bottlenecks, secure delivery guardrails, and unlock genuine engineering velocity.

I'll be presenting recent research I personally conducted around AI and productivity in large engineering organizations. As the CTO of a market leader in the engineering productivity space, I've been solving in this space for over 6 years.