Forecasting what will be scarce post-AGI is like a 1400s Mongolian economist predicting modern spending. They would have assumed wealth would flow to known human services like singers, completely missing the invention of new categories of goods (like cars or iPhones) that would capture demand.

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.

Conservative GDP growth forecasts for AI often fail because they analyze its capabilities at a single point in time. The most critical factor is AI's exponential improvement trajectory, which makes analyses based on year-old capabilities quickly obsolete and misleadingly pessimistic.

Standard "AI exposure" metrics list automatable tasks but miss a key factor: how tasks relate. If tasks are highly complementary (like steps in cooking), weakness in one part renders the whole output useless. Economists can list tasks but lack data on these crucial interdependencies, limiting the accuracy of job displacement models.

AI's predictive power is based on identifying patterns in historical data. While effective when the future resembles the past, this makes it inherently unable to account for new inventions, crises, or paradigm shifts not represented in its training text. It predicts from old maps, not what will come next in a new world.

The confident belief that AI's impact on jobs will "just work out" is dangerously naive. A more responsible approach, advocated by groups like Windfall Trust, is to use scenario planning. Just as governments plan for pandemics or cyber attacks despite their uncertainty, we must plan for worst-case economic outcomes from AI.

The key to predicting AI's economic impact is not focusing on the abundance it creates, but identifying what will remain scarce. As automation made goods cheap, the economy shifted to scarce services. The next economic transformation will similarly be driven by whatever human skills or experiences AI cannot replicate.

Whether AI productivity gains create or destroy jobs depends on how much more consumers buy when prices fall. If demand is "inelastic," firms will fire workers. If it's "elastic," they might hire more. Economists lack sufficient data on this elasticity across sectors, making predictions highly uncertain.

The panic-inducing Citrini paper, which caused a market sell-off, assumes a static economy where AI only destroys jobs. It completely ignores historical precedents where new efficiencies unlock unforeseen demand and create entirely new industries, a concept similar to the Jevons paradox.