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.

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.

Economist Tyler Cowen argues AI's productivity boost will be limited because half the US economy—government, nonprofits, higher education, parts of healthcare—is structurally inefficient and slow to adopt new tech. Gains in dynamic sectors are diluted by the sheer weight of these perpetually sluggish parts of the economy.

The US economy is currently experiencing near-zero job growth despite typical 2% productivity gains. A significant increase in productivity driven by AI, without a corresponding surge in economic output, could paradoxically lead to outright job losses. This creates a scenario where positive productivity news could have negative employment consequences.

While AI investment has exploded, US productivity has barely risen. Valuations are priced as if a societal transformation is complete, yet 95% of GenAI pilots fail to positively impact company P&Ls. This gap between market expectation and real-world economic benefit creates systemic risk.

As AI gets exponentially smarter, it will solve major problems in power, chip efficiency, and labor, driving down costs across the economy. This extreme efficiency creates a powerful deflationary force, which is a greater long-term macroeconomic risk than the current AI investment bubble popping.

The true threshold for AI becoming a disruptive, "non-normal" technology is when it can perform the new jobs that emerge from increased productivity. This breaks the historical cycle of human job reallocation, representing a fundamental economic shift distinct from past technological waves.

Even if AI drives productivity, it may not fuel broad economic growth. The benefits are expected to be narrowly distributed, boosting stock values for the wealthy rather than wages for the average worker. This wealth effect has diminishing returns and won't offset weaker spending from the middle class.

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.