Employer Tax Breaks

Revana Sharfuddin identifies six key restrictions in the U.S. Internal Revenue Code that effectively penalize human capital investment relative to automation. She proposes a suite of reforms to "restore neutrality," including eliminating the "new trade or business" bar (which prevents deducting training for new roles), abolishing the $5,250 annual cap on tax-free educational assistance, and removing the 5% owner limitation that blocks small business owners from accessing tax-advantaged training. Her central recommendation is to extend full and immediate expensing to all job-related training (mirroring the 100% bonus depreciation currently available for AI systems) thereby removing the fiscal incentive for firms to replace workers rather than retrain them.

To counteract long-term declines in employer-led training, The Aspen Institute has proposed a "Worker Training Tax Credit" that would reimburse businesses for 20% of their new training expenditures. Modeled on the R&D Tax Credit, it aims to incentivize human capital investment just as the tax code currently rewards physical capital and R&D. The credit would apply only to training for non-highly compensated workers (earning less than ~$120,000).

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.