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By Dr. Jiaying Zhao, Dr. Lorne Whitehead, Sen. Yuen Pau Woo, and Sen. Kim Pate

April 8, 2026

Artificial intelligence (AI) is rapidly transforming our lives in ways that have inspired both awe and fear. On the one hand, it is generating hundreds of billions of dollars each year across the global economy. On the other, it is expected to drastically increase unemployment, at least in the short term.

In recent months, large corporations such as Amazon, Block, and Meta have announced thousands of job cuts driven by AI-enabled efficiency gains. A recent analysis suggests that 47% of the Canadian payroll face a high risk of being disrupted by AI in the near future. This disruption is likely to be large and fast enough to reshape the social contract.

That does not necessarily make the future dire, but it does mean adaptation is necessary. As a disruptive force, AI is both a problem and a potential solution. How we respond will determine whether the future is bright or bleak.

There is little doubt that AI-driven productivity gains will be substantial, just as has been true of major innovation since the discovery of tools and fire. The challenge, as always, is to distribute those gains so that everyone benefits fairly. A key worry is that AI’s “shareholders” even in the broadest sense, represent a very narrow segment of humanity. Markets tend to direct value toward those who already own assets. How can we ensure that the wealth created by AI appropriately benefits the broader public?

More specifically, how can the “AI dividend” help provide an economic floor for the millions whose lives will be disrupted by rapid job displacement? These may be the defining political and economic questions of our time.

One solution, championed by leaders including “Godfather of AI” Geoffrey Hinton, entrepreneur and former presidential candidate Andrew Yang, and AI executives and investors, is to provide a basic income (BI): A guaranteed livable income sufficient for basic living distributed as cash transfers to people with low income (see an example of BI in Canada from the Parliamentary Budget Officer). Here, we outline five pathways to build this economic floor with AI while strengthening human capital and innovation.

Tax the machine, fund the people

This is the most direct approach (though also politically challenging): Create an AI Dividend Fund. It would consist of a tiny charge on every application programming interface (API) call, chatbot interaction, AI agent, or a robot performing a task that a human would previously have done.

Such a levy would be fair, since AI relies on training data, public infrastructure, and publicly funded research that made these AI systems possible. A modest “compute levy” or a licence fee could be applied to commercial AI deployments, scaled to usage volume. When an AI replaces a human worker, it would pay a levy proportional to the taxes and benefits that worker would have generated.

In short: The more AI replaces human labour, the more it contributes to supporting the people it displaces. Regulators could even increase the levies during rapid job displacement periods to give people breathing room to retrain their skills and re-orient their lives.

Reduce the tax burden on human labour

Currently, employment is heavily taxed through income and payroll taxes, making hiring humans relatively expensive compared with deploying software. To encourage work and hiring, we can shift some taxation from human labour to autonomous AI agents. This is not regressive, as ordinary citizens would still have free access to AI services for personal use.

Reducing payroll taxes increases take‑home pay without raising employer costs. Workers would gain purchasing power, while small businesses gain flexibility. This shift does not reduce government revenue; it simply changes the collection point, from workers to the technology that is replacing them. It ensures automation happens where it truly adds value, not merely where the tax code favours machines. This realignment allows AI to do what it is best at, while enabling humans to focus on creative, interpersonal, and judgment‑based work.

Bundle BI with AI education and services

Cash is powerful, but cash plus capability is transformative. A politically attractive option is to bundle BI with free or subsidized access to high‑quality AI education, training, and tools, such as AI tutors, legal assistants, health navigators, financial coaches, and career advisors.

For example, Prime Minister of Singapore Lawrence Wong recently announced that citizens will receive six months of free access to premium AI tools if they enroll in AI training courses. In China, children as young as six are being offered AI classes in school. As AI reshapes the workforce, people need time and resources to adapt. BI paired with AI education can strengthen the economic foundation.

Slash the cost of administering welfare

A major weakness of today’s welfare systems is the high cost of administering poverty, rather than alleviating it. Means‑testing, eligibility verification, appeals, fraud investigations, and mandated social services consume enormous resources, and can be humiliating for recipients. In many countries, administrative costs consume 20-30 cents of every program dollar.

AI can drastically reduce these costs by automating tax filing, identity verification, eligibility checks, and case management for welfare, health care, and justice systems. Also, BI is inherently cheaper to administer than targeted programs. With AI support, overhead could approach zero. These savings alone could fund a meaningful share of BI without raising new taxes.

Enforce tax compliance with AI tools

The global “tax gap” — the difference between taxes owed and taxes collected — exceeds an estimated $3 trillion annually. AI‑enabled audit systems can detect tax evasion, model aggressive avoidance schemes, and cross‑reference financial data. The U.S. alone loses more than $600 billion per year to tax non‑compliance. Recovering even a fraction of this could help fund a program of basic income.

In the coming decade, millions of jobs will be restructured, displaced, or reinvented. We have a choice: allow gains from AI to concentrate in the hands of a few or share it more widely. We can experience a harsh technological shock, or build a fairer, more prosperous and stable society. An AI‑funded basic income reflects the principle that as machines become more capable, people should become freer to flourish.

Let us adapt this trillion‑dollar engine of innovation into a rising tide that truly lifts all boats.

Dr. Jiaying Zhao is a Professor in the Department of Psychology and the Institute for Resources, Environment and Sustainability at the University of British Columbia. Dr. Zhao uses psychological principles to design behavioural solutions to financial and environmental sustainability challenges.

Dr. Lorne Whitehead is a Professor in the Department of Physics & Astronomy at the University of British Columbia. His research focuses on the application of novel geometrical approaches to applied physics challenges, with a focus on the interactions of electromagnetic fields with microstructures.

Sen. Yuen Pau Woo is an independent senator representing British Columbia. Sen. Woo has worked on public policy issues related to Canada’s relations with Asian countries for more than 30 years. He is joint chair of the Standing Joint Committee for the Scrutiny of Regulations.

The Honourable Kim Pate is an independent Senator representing Ontario. Senator Pate is a nationally renowned advocate who has spent the last 45+ years working in and around the legal and penal systems of Canada.