Fiscal Modernization: Captured Value and Tax Base Sustainability in the Age of Automation
As Artificial Intelligence and automated systems increasingly decouple production from human labor, the existing fiscal code—which relies heavily on payroll taxes—faces a structural sustainability crisis. A consistent labor-to-capital shift has been the central theme of economic growth for generations. Technological shifts have altered the dynamics of this relationship as automation consistently enables firms to optimize operational efficiency. Much of organized labor has called for greater democracy and oversight of automation as technology becomes increasingly capable of replacing human effort in value production. High-profile partnerships, such as the agreement between the AFL-CIO and Microsoft, suggest a move toward collaborating on implementation strategies for generative automation—a technology threatening the sway of human-capital-dependent participants within the information and services sector.
Automation poses a significant challenge to revenue streams reliant on the taxation of competitive labor markets. The transition from unpredictable human labor to automated capital investments with modest maintenance costs potentially impacts the labor market and fiscal solvency profoundly. While proponents argue technology can lead to a state of high living standards by minimizing manual input, the equitable distribution of these benefits has historically depended on policies that grant agency to the workforce.
The Payroll Tax Vulnerability
To address the growing structural power of automated business models, it is crucial to challenge the impetus of technological advancement through an expanded social safety net. This can be achieved by redistributing automation savings through taxation on capital, particularly as value shifts toward intangible assets with flexible definitions, jurisdictions, and valuations.
The consideration of automation and intangible asset accumulation is vital to preserve a democratic social welfare state. In the U.S., 64.2% of tax revenue is derived from income and payroll tax (Kovacev 2020, 185). This heavy reliance creates a significant fiscal vulnerability; the decline in payroll tax revenue directly impacts the ability of city governments to fund public transit, schools, and emergency services. The current tax structure encourages investment through deductions and credits, but this incentive backfires if capital is used to automate human-capital-dependent revenue streams. Although automation can theoretically enhance productive output, it exposes the economy to symptoms of overproduction as labor-income-driven demand declines, further destabilizing the municipal fiscal base.
Erosion of the Fiscal Base
Technology that increases the mobility of capital has been credited with increasing inequality through globalization. The mobility of capital allows investment to profit from one jurisdiction while crediting value to another with more favorable tax conditions. This level of technical tax planning is “not available to wage earners, who live, earn, and pay tax in a single jurisdiction” (Marian 2022, 534). Modern corporatization is more recent than the development of progressive income taxation, and current tax governance has not kept up with digitized assets.
For example, when a programmer improves a user interface, they are taxed on their income. However, the negative externalities of automation and digital scaling often allow the resulting corporate profit to reach shareholders without paying social rents. This is achieved through dispersed subsidiaries and instruments such as CPECs (convertible preferred equity certificates), which can engineer investments to be tax-deductible as debt payments. Companies license intellectual properties to themselves at prices optimized to remain favorable in collecting tax jurisdictions (Ibid, 542). In this case, taxation is over-reliant on value capture from labor rather than capital, accelerating wealth conglomeration and gutting the redistributive functioning of public institutions.
Proposed Fiscal Mechanisms: Data and Automation
Intangible assets include branding and aggregated data. Omri Marian proposes that individual data is not valuable until it is aggregated, creating a “network effect” that is scalable to a global monopoly. Marian proposes a tax on high volumes of data transfer to address the over-reliance on payroll taxation in data-centric industries. Taxing volume is easily traceable and allows governments to recapture value from automated systems that currently benefit from tax codes favoring minimal labor input.
Legislative feasibility for such a mechanism is evidenced by the Digital Service Taxes (DSTs) currently being explored in Europe and state-level precedents such as Maryland’s digital advertising tax. Marian argues this would not significantly distort economic behavior, as the sector remains highly profitable. Passing costs to users would allow transactions to be taxed as a traditional use tax, while passing them to data buyers could reinvigorate competition in the advertising space.
Robert Kovacev explores direct “robot taxes,” noting that the 2017 Tax Cuts and Jobs Act encourages businesses to invest in automation over human hiring (2020, 186). Prominent figures like Bill Gates and Elon Musk have endorsed robot taxes to fund retraining for displaced human capital. However, Kovacev argues the Pigouvian nature of the tax isn’t necessarily a net benefit if it simply disincentivizes innovation without providing broader reform. Unlike data taxes, non-localized tradable goods are exposed to tax competition, potentially incentivizing firms to move operations away from their current jurisdictional tax burden entirely.
Conclusion: A Shared Prosperity Mechanism
My effective proposal combines data and robot taxes to decrease dependence on labor taxation and stabilize the fiscal base. Data taxes capture value from firms with massive market-share without distorting incentives. Captured in the jurisdiction of use, this prevents firms from leveraging mobility to evade taxes unless they forgo major markets. Even if it does alter firm behavior, these changes could increase competition in an increasingly conglomerated sector, thereby increasing sectoral employment and marketizing the tax base outside the social safety net.
To address manual automation, conditioning existing tax deductions for innovative investment on contributions to upskilling and job placement services could ensure incentives are economically and socially aligned. A greater burden for social rent could be charged to localized industries that necessitate a fixed jurisdictional presence, such as last-mile fulfillment. By leveraging a data tax with realigned investment incentives that empower existing educational institutions to collaborate with employers, the social safety net can be expanded. Ultimately, these reforms function as a shared prosperity mechanism, ensuring that rapid innovation is encouraged and does not bankrupt the public institutions enabling infrastructure and educated workforces, that facilitate innovation in the first place.
Works Cited
“AFL-CIO and Microsoft Announce New Tech-Labor Partnership on AI and the Future of the Workforce.” 2023. AFL-CIO. December 11, 2023. https://aflcio.org/press/releases/afl-cio-and-microsoft-announce-new-tech-labor-partnership-ai-and-future-workforce.
Green, Robert. 2024. “The Tax Cuts And Jobs Act Mainly Expires In 2025.” Forbes, April 29, 2024. https://www.forbes.com/sites/greatspeculations/2024/04/29/the-tax-cuts-and-jobs-act-mainly-expires-in-2025/?sh=546d66467224.
Kovacev, Robert. 2020. “A Taxing Dilemma: Robot Taxes and the Challenges of Effective Taxation of AI, Automation and Robotics in the Fourth Industrial Revolution.” SSRN Scholarly Paper. Rochester, NY. https://papers.ssrn.com/abstract=3570244.
Marian, Omri. 2022. “Taxing Data.” Brigham Young University Law Review 47: 511–76.
Porter, Eduardo. 2019. “Don’t Fight the Robots. Tax Them.” The New York Times, February 23, 2019, sec. Sunday Review. https://www.nytimes.com/2019/02/23/sunday-review/tax-artificial-intelligence.html.
Schwartz, Robert B., and Rachel Lipson. 2023. America’s Hidden Economic Engines: How Community Colleges Can Drive Shared Prosperity. Work and Learning Series. Cambridge, Massachusetts: Harvard Education Press.
Ward, Brandon. n.d. “Employment in the Age of Artificial Intelligence: A Call for a Statutory Solution – Colorado Technology Law Journal.” Accessed May 6, 2024. https://ctlj.colorado.edu/?p=717.
