Life beyond tokenmaxxing: AI efficiency for the long term

Your CFO isn't interested in AI adoption anymore. Now they're asking what your investment in AI has produced. The metrics that got you through the last budget cycle - including adoption rates, token consumption, and seats activated - ring hollow in executive conversations now. The problem isn't the strategy; it's the evidence. Your team's generating more code than ever before, but everything downstream runs at the same speed it always has - or slower. The system is lopsided, and the only way to make the imbalance visible is to measure it. Join Andrew Zigler and Ben Lloyd Pearson for a 45-minute session on the operational measurement model engineering leaders are using to answer board-level ROI questions about AI. You'll see why effort metrics around token consumption and lines of code don't tell the full story, how AI shifted bottlenecks across the SDLC, and how APEX, LinearB's measurement framework for the AI era, gives you the system-level evidence executives have started demanding. All registrants get first access to the companion guide, "Measuring efficiency in the AI-driven SDLC: how to control the executive conversation."