Andreessen argues the bottleneck for AI's societal impact isn't technology but entrenched economic structures. Professional licensing, unions (dock workers), and government monopolies (K-12 education) are powerful forces of inertia that will dramatically slow AI adoption, tempering both utopian and doomsday predictions.

The discourse often presents a binary: AI plateaus below human level or undergoes a runaway singularity. A plausible but overlooked alternative is a "superhuman plateau," where AI is vastly superior to humans but still constrained by physical limits, transforming society without becoming omnipotent.

While discourse often focuses on exponential growth, the AI Safety Report presents 'progress stalls' as a serious scenario, analogous to passenger aircraft speed, which plateaued after 1960. This highlights that continued rapid advancement is not guaranteed due to potential technical or resource bottlenecks.

Even with superhuman AI, Dario Amodei argues the economic revolution won't be instant. The real-world bottleneck is "economic diffusion": the messy, human process of enterprise adoption, including legal reviews, security compliance, and change management, which creates a fast but not infinite adoption curve.

The most potent counterargument to explosive AI-driven growth is that intelligence itself may have diminishing returns. Past a certain point, even a vastly smarter AI might only solve problems marginally better, not perform "magic." This means the economic benefits could plateau even as intelligence continues to increase.

The AI buildout won't be stopped by technological limits or lack of demand. The true barrier will be economics: when the marginal capital provider determines that the diminishing returns from massive investments no longer justify the cost.

The AI industry's exponential growth in consuming compute, electricity, and talent is unsustainable. By 2032, it will have absorbed most available slack from other industries. Further progress will require potentially un-fundable trillion-dollar training runs, creating a critical period for AGI development.

While ethical debates about AI's risks continue, the actual slowdown in AI's societal integration is being driven by practical constraints like the limited supply of compute, data centers, and grid power. This physical reality is a more powerful force for gradual adoption than any organized pause.

The rapid expansion promised by AI firms faces real-world bottlenecks. These include shortages of key commodities like copper, insufficient power grid capacity requiring years to build new plants, and a lack of skilled construction labor, making promised timelines highly unrealistic.