That’s because it never measured what really counts to see ROI from AI

Summary

In this edition…CNN sues Perplexity… IBM and RedHat form $5 billion bug patching project…Snowflake signs a $6 billion deal with AWS…and the White House gives U.S. intelligence agencies $9 billion to build their own AI chip cluster. At Amazon, the Financial Times reported, some employees spun up AI agents to complete wholly meaningless or unnecessary tasks to keep up their token usage stats, which were now being used by managers to assess employee performance. Microsoft has cancelled Claude Code subscriptions for employees in several key product divisions, according to reporting from The Verge. Uber said it had burned through its entire 2026 “token budget” in the first four months of the year, in part due to high usage of Claude Code.

But that still leaves the broader question of why this disconnect exists between AI spend and ROI? Unlike with a technology designed to make a particular process better, which can often have immediate positive productivity impacts, it often takes considerable time for people to figure out how best to deploy a general purpose technology. During this “figuring it out” period, productivity can fall rather than increase. This is because companies need to spend time and money experimenting with how to use the new technology, often without seeing a positive bottom line impact. Only later, once people figure out the optimal ways to redesign business processes around the new tech, does productivity experience a sudden acceleration. Azhar predicts that the same thing will happen with AI, but that most firms are sort of stuck in stage one or stage two of this evolution. Harder still—and something which Azhar doesn’t talk about—is rethinking entire business lines, i.e.