The future under AGI is likely to be so radically different—either a post-scarcity utopia or a catastrophic collapse—that optimizing personal wealth accumulation today is a wasted effort. The focus should be on short-term stability to maximize learning and adaptability for a world where current financial capital may be meaningless.

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.

The primary driver of economic change isn't that automated goods become cheaper (a price effect). Rather, the dominant force is the 'income effect.' As AI increases real incomes, people fundamentally change their spending habits to desire more high-elasticity, human-intensive services like education, entertainment, and in-person dining.

For the first time in history, AI could create a world where our ability to produce goods and services outstrips our capacity to consume them. This poses a fundamental challenge to traditional economic models built on scarcity and resource allocation.

As AI commoditizes execution and intellectual labor, the only remaining scarce human skill will be judgment: the wisdom to know what to build, why, and for whom. This shifts economic value from effort and hard work to discernment and taste.

Fears of mass unemployment from AI overlook a key economic principle: human desire is not fixed. As technology makes existing goods and services cheaper, humans invent new things to want. The Industrial Revolution didn't end work; it just created new kinds of jobs to satisfy new desires.

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.

Citing historical failures like David Ricardo's on automation, individual AGI forecasts are deemed useless. A better approach is to model potential scenarios (e.g., labor share collapses) and then identify the crucial, currently missing data (like consumer demand elasticities) needed to determine which scenario is likely.

Countering AI doomerism, Ben Horowitz argues that human desire is infinite. Once AI makes basic goods abundant, people will develop new 'needs'—from complex services to luxury experiences like chef-prepared meals—which will in turn generate entirely new industries and jobs unimaginable today.