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I am hunkering down in my air-conditioned office while Utah swelters in record-breaking heat. Before Phoenix-based Phil and all the folks who were at D2L's Fusion this week jump in to tell me to hold their beer and opine about real heat, let me tell you what I read this week.

AI adoption intensity matters in predicting post-adoption hiring trends.

Over the past year, higher education has been consumed by debates about AI's impact on hiring, especially entry-level hiring. Unfortunately, much of the evidence has been surprisingly weak. Many studies estimate which jobs are exposed to AI rather than examining what firms actually do. Exposure is a useful thought experiment, but it is speculative, freezes AI capabilities at a particular moment in time, and tells us little about how organizations respond.

New research from the Ramp Economics Lab avoids that imprecise approach and has some encouraging news. The central finding is surprisingly simple: AI spending alone changes very little. What matters is the intensity with which AI is adopted.

Instead of estimating exposure, the authors use observed AI spending from more than 21,000 firms linked to employment records. The paper's most important contribution is identifying a threshold effect. Low-intensity AI adoption had no measurable impact on hiring. High-intensity adoption increased overall employment by about 10% and entry-level hiring by roughly 12%.

The authors argue that high-intensity firms aren't simply buying more ChatGPT licenses. Their higher spending is correlated with more sophisticated AI tools—coding agents, APIs, and workflow integration—and, perhaps more importantly, with organizations capable of redesigning work around AI. There is a lesson here for higher education. Buying AI licenses is adoption. Redesigning advising, assessment, curriculum, and administrative workflows around AI is intensity.