The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence (digitaleconomy.stanford.edu)

The Turing Trap argues that pursuing human-like artificial intelligence (HLAI)—machines that imitate humans well enough to pass Turing-style tests—carries both huge promise and acute peril. On the upside, HLAI can drive massive productivity gains, new capabilities (from better diagnostics to more flexible robotics), and deeper scientific insight into cognition. But the essay warns that when HLAI is deployed primarily to automate tasks rather than augment people, it transforms humans from complements into substitutes, eroding workers’ bargaining power and concentrating economic and political control in the hands of technology owners—the core of the “Turing Trap.” Technically, the piece distinguishes automation (machines replacing human labor) from augmentation (machines amplifying human ability) and clarifies that intelligence is multidimensional (so “AGI” is often a misleading label). It links empirical trends—rising productivity coupled with declining labor share and increasing wealth concentration—to incentives that favor automation. Key implications for AI/ML practitioners and policymakers: prioritize designs and business models that preserve complementarity (tools that boost human value), rethink incentive structures and redistribution mechanisms, and steer research and deployment toward augmentation to avoid a locked-in equilibrium where gains accrue to a few custodians of HLAI.