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.

The debate around AI's impact presents an asymmetric risk. Underestimating AI's capabilities could lead to obsolescence for individuals and companies. Conversely, overestimating its short-term impact results in some wasted preparation, a far less severe and more recoverable outcome.

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.

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.

People deeply involved in AI perceive its current capabilities as world-changing, while the general public, using free or basic tools, remains largely unaware of the imminent, profound disruption to knowledge work.

The AI boom is being driven by a small group of executives who all exist in the same professional and social echo chamber. This proximity increases the risk of industry-wide groupthink, leading to a potentially historic and collective misallocation of capital based on shared assumptions.

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.

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.