Study warns AI productivity gains could erode the payroll base funding worker protection

A research calls for shifting tax systems away from payroll and toward value creation

ROME, ITALY, October 6, 2026 /EINPresswire.com/ — Artificial intelligence may raise economic output while shrinking the payroll contributions that finance social protection, according to a research paper published by Takamol Holding, which calls for shifting tax systems away from payroll and toward value creation.

The paper argues that productivity gains and fiscal stability have come apart. To show the scale of the exposure, the paper offers an illustration in which one AI-supported worker produces what eight produced before. Output stays constant, seven salaries shift into profit, and payroll contributions fall by roughly 88 percent. It states explicitly that this is not a forecast but a balance-sheet example, assuming constant output and an unchanged contribution rate.

Available data show movement in the same direction. The International Labour Organization reports that labor’s share of global income fell from 52.9 percent in 2019 to 52.3 percent in 2022 and has since remained flat.

The paper is titled “Policy Priorities for the Global Labor Market Transition to the Capability Economy.” It was produced collaboratively by a research team at Takamol Holding and draws on work presented across three editions of the Global Labor Market Conference.

The study defines the capability economy as an economic model in which value creation is organized around verified human capability, augmented by AI and mediated through data-rich labor market systems, and in which what a worker can demonstrably do carries more weight than position or tenure.

Its central claim is that labor institutions assume most people hold a stable job with an identifiable employer. That relationship underpins social insurance, payroll taxes, worker protections and employment statistics. As work becomes fragmented, platform-mediated, cross-border and AI-augmented, the paper finds, those institutions lose their fit.

The mismatch is already large. The ILO estimates more than two billion workers, over 60 percent of the global workforce, are in informal employment. The study argues that reforming one program at a time will not be sufficient.

On the scale of the AI transition, the paper cites World Economic Forum projections of 170 million new roles by 2030 alongside 92 million displaced. The net gain of 78 million still transforms 22 percent of current jobs and leaves 59 percent of workers requiring training. Research on customer service and knowledge-work tasks finds substantial productivity gains, though effects vary by worker and task.

The study recommends four fiscal instruments. Three are already in implementation or under debate: mandatory platform contributions, the OECD/G20 global minimum tax framework, and earmarked transition funds supported by international coordination. A broader value-creation levy remains at the design stage. The paper notes McKinsey estimates that generative AI could create $2.6 trillion to $4.4 trillion in annual economic value.

Fiscal reform is one of six priorities the paper presents as interdependent. The others are establishing skills as primary economic signals through shared taxonomies and portable, verifiable skills records; treating workforce capacity as strategic infrastructure on par with fiscal and industrial policy; governing AI as a workforce multiplier through labor impact assessments and transparency requirements; designing protection that attaches to the worker rather than the job; and building computational labor intelligence systems linking employment, skills, protection, education and tax data.

The research identifies measurement as a persistent obstacle. Digital labor platforms record vacancies, skills, wages and worker movements in close to real time, while public labor statistics still rely on periodic surveys and categories built around one principal job. A person combining several short engagements can appear simply as employed, obscuring volatility, underemployment and protection gaps.

The paper cites national examples for each priority. Singapore’s SkillsFuture is presented as national-scale skills infrastructure, with cross-border interoperability the main gap. Germany’s Kurzarbeit scheme and dual vocational training illustrate 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 each implement components of portable protection, though the study finds no country yet combines all five elements it identifies. Saudi Arabia’s Qiwa platform is cited on labor data systems, drawing on a registry of 11.6 million contracts across 1.6 million establishments, according to figures Takamol supplied from Qiwa administrative data.

The priorities were identified across three editions of the Global Labor Market Conference, drawing on almost 50 hours of recorded content, more than 520 speakers and participants from more than 100 countries.

Human capital now represents roughly two-thirds of wealth worldwide and about 70 percent in high-income economies, according to World Bank wealth accounts cited in the paper. The analysis notes that countries financing education do not necessarily retain the workers they train.

The study concludes that the standard model held together because one stable job linked protection, revenue and measurement, with each reinforcing the others, and that the policy task is to rebuild that alignment around the worker. It warns that delay will fall unevenly on younger and older workers, informal workers and women.

The full research paper is available via takamol: https://takamolholding.com/en/publications/policy-priorities-for-the-global-labor-market-transition-to-the-capability-economy

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