Automation/Robot Taxes

Korinek and Lockwood argue that robot tax proposals conflate two economically distinct policies. Taxes on robot-provided services to end consumers (e.g., robotic delivery, AI customer support) are consumption taxes and economically sound. Taxes on owning or operating robotic equipment are capital taxes that discourage precisely the investment needed for productivity growth — comparable, they argue, to taxing steel during the industrial revolution. This distinction challenges proposals that treat robot taxes as a single category, and suggests the policy question is not whether to tax automation but where in the production chain to apply the levy.

Falk and Tsoukalas show that competition traps firms into an automation arms race even when every firm can see that mass layoffs will destroy the consumer demand they all depend on. They evaluate six policy responses and find that upskilling, universal basic income, capital income taxes, worker equity participation, and Coasian bargaining all fail to fully correct the distortion. Only a Pigouvian automation tax — set equal to the uninternalized demand loss per task — implements the efficient outcome.

Sanders released a report finding that "AI, automation and robotics could replace nearly 100 million jobs in America over the next decade". He goes on to argue that "instead of providing billions in tax breaks to companies that are throwing workers out on the street and replacing them with new technologies, we should enact a robot tax on large corporations and use the revenue to improve the lives of workers who have been harmed".

Will Rinehart argues that robot tax advocates, such as Bernie Sanders, "sidestep the thorny issue of writing a bill because it is incredibly difficult to formally define robots." He notes that existing taxes — capital gains, corporate income, and property taxes — already capture returns from automation, making a robot tax potentially redundant. Rinehart also challenges the premise that AI increases inequality, citing research suggesting AI tools act as "skill equalizers" that raise the performance of lower-skilled workers. He concludes that even if AI displacement concerns prove valid, "this is an argument for better transition assistance, not just a tax on one arbitrarily defined category of capital goods."

Dimitropoulou provides a comprehensive framework for how robot tax policy should be approached, examining how to define robots for tax purposes, whether autonomous AI systems could be treated as separate taxpayers, and what tax designs are appropriate given principles of equity and efficiency under both domestic and international tax settings.

Acemoglu, Autor, and Johnson argue that the current US tax code places a heavier burden on firms that hire workers than those that invest in automation. They call for a more symmetric tax structure that equalizes marginal taxes on hiring and training versus equipment and software investment, though they do not specify a mechanism. Their broader paper argues that this tax asymmetry is one of several market failures driving systematic underinvestment in pro-worker AI.