The claim of an AI-driven productivity boom is suspect when compared to the 1990s. Key indicators are moving in the wrong direction: prices for tech commodities like software and chips are rising instead of falling, and real income growth is weak, not accelerating.

Despite strong productivity numbers alongside flat job growth, economists believe it is too early for AI to be the primary driver. The gains are more likely attributable to businesses becoming more dynamic and achieving better labor-market matches following the pandemic disruptions, rather than a widespread technological revolution.

Current spikes in labor productivity are not evidence of AI's impact. They are more likely a statistical artifact caused by a compositional bias towards capital-intensive sectors and companies forcing remaining employees to do more work in a weak labor market. The true AI productivity effect is not yet visible in aggregate data.

Contrary to popular narratives, recent U.S. productivity growth isn't yet driven by AI adoption. Adjusted for capacity utilization, San Francisco Fed data shows productivity is flat or negative. The observed gains come from employees and machines working harder, not smarter through new technology, delaying the anticipated AI dividend.

Initial data from industries with high AI exposure shows productivity gains are driven by increased output, not reduced labor hours. This counters the common narrative that AI's primary effect will be immediate, widespread job displacement, suggesting a period of augmentation precedes automation.

The recent uptick in labor productivity is not from AI making workers inherently more efficient (as measured by Total Factor Productivity). Instead, it's a utilization story: firms are running their existing capital hotter to meet intense AI-related demand for things like chips, which mechanically boosts output per hour.

The narrative of AI causing mass layoffs is premature. Instead, its immediate benefit is indirect: companies are using the prospect of AI to justify leaner operations and slower hiring. This 'apprehension to overhire' boosts profitability before widespread AI adoption delivers direct efficiency gains.

Despite massive investment, the supply-side benefits of AI are not yet widespread. Productivity gains and labor market changes are currently confined to the high-tech sector. Economists predict a broader diffusion of these benefits to the rest of the economy will only begin after the current 3-4 year "build out" phase, likely around 2029 or later.

Glenn Hutchins explains that broad economic efficiency gains from AI are not yet visible because companies are in the initial, costly investment phase. Meanwhile, they are just now reaping benefits from a decade of cloud investment, aided by a new generation of digital-native CEOs.