Echoing economist Robert Solow's 1987 observation about computers, thousands of CEOs now admit AI has no measurable productivity impact. This suggests history is repeating, where major technological shifts have a long, multi-year lag before their economic benefits are truly realized and measured.

Despite widespread adoption, Patrick Collison notes that AI has not yet produced measurable gains in macroeconomic productivity. He points to recent studies and the lack of corresponding GDP growth outside the U.S. as evidence that the diffusion of these technologies through the economy is slow and complex.

A small cohort of power users are achieving massive productivity gains with AI, while most companies are stuck at the most basic stages. This creates a widening competitive gap where firms that master simple access and training will dramatically outperform those mired in bureaucratic inertia.

Even if AI progress stopped today, it would take 10-20 years for the economy to fully absorb and implement current capabilities. This growing gap between what's technologically possible and what's adopted in the market creates a massive, long-term opportunity for innovators.

A National Bureau of Economic Research survey of 750 financial executives reveals a "productivity paradox." They report significant performance improvements from AI, but these gains are not yet reflected in hard revenue numbers, showing a lag between perceived value and financial impact.

Reid Hoffman isn't surprised by the lack of AI-driven productivity gains in macro data. He sees "magical" speed and efficiency in startups using AI. This suggests the productivity boom is coming; it's just happening in smaller, agile companies first before large enterprises adapt.

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.

AI's "capability overhang" is massive. Models are already powerful enough for huge productivity gains, but enterprises will take 3-5 years to adopt them widely. The bottleneck is the immense difficulty of integrating AI into complex workflows that span dozens of legacy systems.

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.