Developers claiming 10x speedups from AI often aren't 10x faster on their core tasks. Instead, they're tackling new side projects that were previously impossible, creating a perception of "infinite" speedup. However, these new tasks are often less economically valuable, inflating the true productivity gain on business-critical work.

AI infrastructure leaders justify massive investments by citing a limitless appetite for intelligence, dismissing concerns about efficiency. This belief ignores that infinite demand doesn't guarantee profit; it can easily lead to margin collapse and commoditization, much like the internet's effect on media.

AI lowers the barrier to entry, flooding the market with "whiteboard founded" companies tackling low-hanging fruit. This creates a highly competitive, consensus-driven environment that is the opposite of a "good quest." The real challenge is finding meaningful problems.

As companies use AI to do more with fewer people, productivity gains boost profits but don't create jobs at the same rate. This "ghost GDP" concentrates wealth among a few and risks a long-term decline in broad-based consumer spending, as the generated value isn't dispersed to human workers.

A critique from a SaaS entrepreneur outside the AI hype bubble suggests that current tools often just accelerate the creation of corporate fluff, like generating a 50-slide deck for a five-minute meeting. This raises questions about whether AI is creating true productivity gains or just more unnecessary work.

If AI makes intelligence cheap and universally available, its economic value may collapse. This theory suggests that selling raw AI models could become a low-margin, utility-like business. Profitability will depend on building moats through specialized applications or regulatory capture, not on selling base intelligence.

AI accelerates capitalism's natural tendency to compress margins to zero. By automating tasks and replicating solutions cheaply, AI makes it difficult to sustain profits, benefiting only those who own scarce, non-digitizable assets like data, trust, or real estate.

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.

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.