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The classic economic theory that humans will always find work through comparative advantage may fail. David Duvenaud argues that for critical roles (e.g., surgeon, politician), the 'transaction cost' of human unreliability—sickness, error, inconsistency—will make it irresponsible to employ a human over a hyper-reliable AI, regardless of niche skills.
When AI makes intelligence and labor functionally free, the economic value of human work diminishes. The last bastion of human value will be the willingness to incur risk—deploying capital, making decisions with incomplete information, and shouldering the consequences.
A crucial function for humans in an AI-driven economy is to serve as a target for lawsuits. Because you can't easily sue a data center, regulated professions will require a 'human in the loop' to take legal responsibility. This creates a valuable economic role for humans: being a legally accountable entity.
The optimistic scenario for human labor in an AI-driven economy is one of complementarity. If there are crucial tasks that only humans can perform (e.g., final approval, strategic oversight), they become a valuable bottleneck. The immense productivity of the machines they oversee would then drive their wages up significantly.
AGI won't eliminate all jobs because many roles contain a "Human Premium"—value tied to human involvement that AI cannot replicate. This includes inherent demands for relationship, embodied presence, trust, legal accountability, translation of complex needs, and encouragement for behavior change, ensuring durable roles for people.
AI's primary impact is not wholesale human replacement but rather collapsing the middle of the value pyramid by automating routine knowledge work. The value of human workers will shift to higher-level judgment and strategic oversight, where AI can structure options and simulate outcomes, but humans retain final say due to liability concerns.
Applying the economic principle of comparative advantage, even if AI achieves absolute superiority in all tasks, humans should specialize where their advantage is greatest relative to AI. This will likely be high-level "thinking," as human attention remains the scarcest resource in the collaboration.
Even if an AGI is better at everything, the economic principle of comparative advantage holds. As long as AGI is constrained by time or resources, it will specialize in its highest-value tasks (e.g., solving cosmic mysteries), leaving other work for humans.
While the caring economy is often cited as a future source of human jobs, AI's ability to be infinitely patient gives it an "unfair advantage" in roles like medicine and teaching. AI doctors already receive higher ratings for bedside manner, challenging the assumption that these roles are uniquely human.
AI requires a "Human Sandwich" workflow, with a human framing the task and evaluating the output. Since AI generates competence based on past data, it floods the market with "good enough" work. This paradoxically increases the demand for high-level human experts who can provide the differentiation and value that AI cannot.
As AI systems become infinitely scalable and more capable, humans will become the weakest link in any cognitive team. The high risk of human error and incorrect conclusions means that, from a purely economic perspective, human cognitive input will eventually detract from, rather than add to, value creation.