General-purpose technologies like AI initially suppress measured productivity as firms make unmeasured investments in new workflows and skills. Economist Erik Brynjolfsson argues recent data suggests we are past the trough of this "J-curve" and entering the "harvest phase" where productivity gains accelerate.

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The U.S. economy is entering an 'efficiency era' where AI-driven productivity allows GDP to grow without a proportional increase in jobs. This structural decoupling makes traditional economic health assessments obsolete and fuels recession fears.

The surprisingly smooth, exponential trend in AI capabilities is viewed as more than just a technical machine learning phenomenon. It reflects broader economic dynamics, such as competition between firms, resource allocation, and investment cycles. This economic underpinning suggests the trend may be more robust and systematic than if it were based on isolated technical breakthroughs alone.

Contrary to the feeling of rapid technological change, economic data shows productivity growth has been extremely low for 50 years. AI is not just another incremental improvement; it's a potential shock to a long-stagnant system, which is crucial context for its impact.

Despite AI's narrative as a labor-replacement technology, NVIDIA's booming chip sales are occurring alongside strong job growth. This suggests that, for now, AI is acting as a productivity tool that is creating economic expansion and new roles faster than it is causing net job destruction.

Stanford economist Erik Brynjolfsson argues that a major downward revision of 2025 job numbers, while GDP figures remained strong, mathematically implies a massive productivity surge. This suggests AI's economic impact is finally visible in macroeconomic data, moving beyond anecdote and theory.

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.

The anticipated AI productivity boom may already be happening but is invisible in statistics. Current metrics excel at measuring substitution (replacing a worker) but fail to capture quality improvements when AI acts as a complement, making professionals like doctors or bankers better at their jobs. This unmeasured quality boost is a major blind spot.

Like past technological leaps, AI's economic impact will be sequenced. Expect immediate real income gains as new products emerge. The broader disinflationary effects from productivity improvements will only materialize later, after businesses fully re-engineer their operations.

Just as electricity's impact was muted until factory floors were redesigned, AI's productivity gains will be modest if we only use it to replace old tools (e.g., as a better Google). Significant economic impact will only occur when companies fundamentally restructure their operations and workflows to leverage AI's unique capabilities.

History shows a significant delay between tech investment and productivity gains—10 years for PCs, 5-6 for the internet. The current AI CapEx boom faces a similar risk. An 'AI wobble' may occur when impatient investors begin questioning the long-delayed returns.