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.
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While there's a popular narrative about a US manufacturing resurgence, the massive capital spending on AI contradicts it. By consuming a huge portion of available capital and accounting for half of GDP growth, the AI boom drives up the cost of capital for all non-AI sectors, making it harder for manufacturing and other startups to get funded.
According to analyst Samuel Hammond, AI's first wave will create a "software singularity" that feels more disinflationary than hyper-growth. While knowledge work is automated, real-world bottlenecks like infrastructure and regulation will limit GDP growth, with gains captured as consumer surplus.
While gross spending on AI appears to be a major growth driver, its net contribution to the US economy is significantly smaller. A large portion of AI-related hardware and software is imported, meaning the immediate GDP impact is diluted. AI's more substantial economic benefit is expected to manifest through longer-term productivity gains.
Contrary to expectations, analysis shows that sectors with low profit per employee, such as healthcare and consumer staples, stand to gain the most from AI. High-tech firms already have very high profit per employee, so the relative impact of AI-driven efficiency is smaller.
The argument is that "economic diffusion lag" is an excuse for AI's current limitations. If AI models were truly as capable as human employees, they would integrate into companies instantly—far faster than human hiring. The slow rollout proves they still lack core, necessary skills for broad economic value.
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.
The slow adoption of AI isn't due to a natural 'diffusion lag' but is evidence that models still lack core competencies for broad economic value. If AI were as capable as skilled humans, it would integrate into businesses almost instantly.
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.
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.
