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.

Silicon Valley insiders building AI may overestimate its impact due to self-interest (looming IPOs) and a narrow perspective. Their expertise in AI doesn't translate to economics or labor markets, and their track record of understanding the world outside their bubble is poor, making their job apocalypse predictions unreliable.

The narrative of an AI-driven job apocalypse is not a data-driven forecast but a fear-based marketing strategy. Tech leaders and companies, or 'hyperscalers,' create this anxiety to divert capital flows towards them and justify massive capital expenditures, effectively monetizing public fear.

Pessimism about AI-driven job losses overlooks historical precedent. The transition from an agricultural to an industrial economy caused massive job displacement but ultimately created far more new jobs. Similarly, AI will likely generate new, currently unimaginable roles and industries.

Contrary to common belief, new research suggests the Industrial Revolution's new technologies spread too slowly to cause immediate, widespread job loss. Wages held steady despite rapid population growth, a historically positive outcome. This provides a data-backed counter-narrative to fears of rapid, AI-driven unemployment, suggesting a more gradual transition is likely.

Tech leaders catastrophize about AI causing a job apocalypse to make their technology seem seminal and revolutionary. This narrative is a thinly veiled attempt to justify massive valuations and encourage enterprises to invest heavily in their platforms before tangible ROI is proven.

The builders of AI may have a skewed perspective on its real-world impact. They often extrapolate from their tech-centric experiences and fail to grasp how technology diffuses in the broader economy. Their predictions about societal consequences, such as mass job displacement, should therefore be viewed with healthy skepticism.

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.

Throughout history, new technologies have been met with "doom and gloom" predictions that rarely materialize. The fear that email would create a "paperless society" and bankrupt paper companies is a prime example of getting it wrong. This historical perspective suggests today's most dire predictions about AI are also likely incorrect.