Critics of AI-driven economic collapse argue these scenarios wrongly assume a static economy. Historically, massive productivity gains from technology have lowered costs, expanded markets, and created entirely new industries and forms of consumption, rather than just eliminating jobs.

Drawing on Frédéric Bastiat's "seen and unseen" principle, AI doomerism is a classic economic fallacy. It focuses on tangible job displacement ("the seen") while completely missing the new industries, roles, and creative potential that technology inevitably unlocks ("the unseen"), a pattern repeated throughout history.

Contrary to the consensus view of explosive AI-driven growth, AI could be a headwind for near-term GDP. While past technologies changed the structure of jobs, AI has the potential to eliminate entire categories of economic activity, which could reduce overall economic output, not just displace labor.

There's an 'eye-watering' gap between how AI experts and the public view AI's benefits. For example, 74% of experts believe AI will boost productivity, compared to only 17% of the public. This massive divergence in perception highlights a major communication and trust challenge for the industry.

For current AI valuations to be realized, AI must deliver unprecedented efficiency, likely causing mass job displacement. This would disrupt the consumer economy that supports these companies, creating a fundamental contradiction where the condition for success undermines the system itself.

Tech leaders cite Jevon's Paradox, suggesting AI efficiency will create more jobs. However, this historical model may not hold, as the speed of AI disruption outpaces society's ability to adapt, and demand for knowledge work isn't infinitely elastic.

Current anxiety about AI-driven job losses stems from a few high-profile announcements. These early examples are being extrapolated into doomsday scenarios, even though comprehensive data on the net effect is not yet available, feeding our collective imagination and fear.

AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.

The tech industry mistakenly assumes AI's rapid success in coding will replicate across all knowledge work. Coding is an ideal use case: text-based, easily verifiable, and used by technical experts. Other fields lack this perfect setup, meaning widespread AI agent adoption will be much slower.