AI gains could weaken payroll tax base, Takamol study warns

A Takamol Holding research paper says artificial intelligence could boost output while eroding payroll contributions that fund worker protections, and it urges tax systems to shift toward value creation. The study ties that risk to broader labor-market changes, from informal work and platform jobs to the need for portable protections and better labor data.

Why it matters: - Artificial intelligence may raise productivity faster than current tax systems can absorb, leaving social protection funded by a shrinking payroll base. - The paper argues the policy risk is not just lost jobs. It is a mismatch between how value is created and how worker protection is financed. - The study warns the burden of delay would fall unevenly on younger and older workers, informal workers and women.

What happened: - Takamol Holding published a research paper titled "Policy Priorities for the Global Labor Market Transition to the Capability Economy." - The paper was produced by a research team at Takamol Holding and draws on work presented across three editions of the Global Labor Market Conference. - The research says tax systems should move away from payroll and toward value creation. - The full paper is available in a company publication.

The details: - The paper frames the "capability economy" as a system built around verified human capability, augmented by AI and mediated through data-rich labor market systems. - Its central claim is that labor institutions still assume most workers have a stable job with one identifiable employer. - The study says that assumption underpins social insurance, payroll taxes, worker protections and employment statistics. - The paper argues those institutions fit poorly as work becomes fragmented, platform-mediated, cross-border and AI-augmented. - The paper gives an illustration in which one AI-supported worker produces what eight workers produced before. - In that example, output stays constant, seven salaries shift into profit, and payroll contributions fall by roughly 88%. - The paper says that example is a balance-sheet illustration, not a forecast, and assumes constant output and an unchanged contribution rate. - Available data move in the same direction, the paper says. - The International Labour Organization reported labor’s share of global income fell from 52.9% in 2019 to 52.3% in 2022 and has since remained flat. - The ILO estimates more than two billion workers, or over 60% of the global workforce, are in informal employment. - The paper says reforming one program at a time will not be enough. - On AI transition scale, the paper cites World Economic Forum projections of 170 million new roles by 2030 and 92 million displaced. - The net gain of 78 million jobs still transforms 22% of current jobs and leaves 59% of workers needing training, the paper says. - The study cites research showing substantial productivity gains in customer service and knowledge-work tasks, with effects varying by worker and task. - Human capital represents roughly two-thirds of global wealth and about 70% in high-income economies, according to World Bank wealth accounts cited in the paper. - The analysis says countries that finance education do not necessarily retain the workers they train.

Between the lines: - The study is making a bigger argument than a simple AI-tax warning: labor policy, education policy, tax policy and data systems are increasingly interdependent. - The paper suggests current labor statistics can miss volatility and underemployment when a worker stitches together several short engagements. - Digital labor platforms capture vacancies, wages, skills and worker movement in near real time, while public statistics still rely on periodic surveys built around one main job. - That gap matters because policy decisions still depend on measurements designed for a stable, single-employer labor market. - The study presents six linked priorities, but says fiscal reform is only one piece of the shift. - Those priorities include treating skills as primary economic signals, viewing workforce capacity as strategic infrastructure, governing AI as a workforce multiplier, designing protection that follows the worker and building computational labor intelligence systems. - The paper cites national examples to show pieces of the model already exist, but says no country yet combines all five protection elements it identifies. - Singapore’s SkillsFuture is presented as skills infrastructure, with cross-border interoperability flagged as the main gap. - Germany’s Kurzarbeit and dual vocational training are cited on workforce planning. - The EU AI Act and Saudi Arabia’s SAMAI initiative are cited on AI governance. - Singapore’s Platform Workers Act, France’s Compte Personnel de Formation, India’s Code on Social Security and Saudi Arabia’s Wage Protection System are cited as partial models for portable protection. - Saudi Arabia’s Qiwa platform is cited as a labor-data system using a registry of 11.6 million contracts across 1.6 million establishments, based on figures Takamol supplied from Qiwa administrative data.

What's next: - The paper says the policy task now is to rebuild the link between protection, revenue and measurement around the worker rather than the job. - It calls for mandatory platform contributions, the OECD/G20 global minimum tax framework, earmarked transition funds and a broader value-creation levy still at the design stage. - The study says platform contributions, the global minimum tax framework and transition funds are already in implementation or under debate. - The paper points to cross-border skills records, labor-impact assessments and transparent AI governance as part of the next phase of reform. - The research was shaped across three editions of the Global Labor Market Conference, drawing on nearly 50 hours of recorded content, more than 520 speakers and participants from more than 100 countries. - Takamol Holding says the policy challenge is now to modernize protection systems before AI-driven productivity gains further widen the gap between output and payroll funding.

The bottom line: - AI can expand output while shrinking the wage base that finances worker protections, and the study argues governments need to tax value creation more directly before that gap widens further.