Companies claim AI is revolutionary for productivity, yet economic studies, including one by OpenAI itself, show no correlation between spending on AI and increased revenue per employee. The hype about transformative efficiency is not reflected in actual economic output.

Conservative GDP growth forecasts for AI often fail because they analyze its capabilities at a single point in time. The most critical factor is AI's exponential improvement trajectory, which makes analyses based on year-old capabilities quickly obsolete and misleadingly pessimistic.

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.

The ability of Western governments to manage their enormous public debt levels is now implicitly dependent on the hope that AI will generate a massive, sustained productivity boom. If AI fails to deliver this unprecedented growth, a widespread fiscal crisis becomes a serious risk.

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.

The market widely assumes AI will deliver a sustained productivity boost of over 2%. However, as companies start to reckon with the steep costs of AI implementation, a pullback is likely if the benefits don't materialize quickly. This could lead to productivity growth slumping closer to 1%, disappointing optimistic forecasts.

A survey of Silicon Valley executives revealed they consider a high-growth "productivity boon" from AI as the most probable outcome. This directly contradicts Moody's own forecast, which ranked this scenario as the least likely, highlighting a significant perception gap between AI builders and economic analysts.

The consensus on AI's economic impact is fractured. Economist Daron Acemoglu forecasts a negligible 0.07% annual GDP increase over 10 years, treating AI as a rounding error. In stark contrast, other models predict double-digit growth driven by recursive self-improvement, highlighting profound disagreement among experts.

A significant disconnect exists between AI's market valuation, which prices in massive future GDP growth, and its current real-world economic impact. An NBER study shows 80% of US firms report no productivity gains from AI, highlighting that market hype is far ahead of actual economic integration and value creation.