On March 17, 2026, the Hoover Institution at Stanford University hosted a conference titled “Productivity Gains and Labour Pains: What Will AI Do to Jobs?” The discussion was led by Condoleezza Rice, a former US Secretary of State, and Rishi Sunak, Britain’s former Conservative Prime Minister. They, along with James Manyika, who oversees Google’s AI Lab, and Gina Raimondo, a US former Secretary of Commerce who oversaw the CHIPS Act, discussed the expected rapid economic and job displacement risks from AI.
What about similar high-level discussions about the implications of humanoid robots on jobs? Humanoids Summits have become annual global fixtures, with the latest in 2025 inviting government policy makers into discussions with manufacturers and their customers. The World Robot Conference held in Beijing in 2025 showcased robotics manufacturers while offering sessions on labour markets. Similar discussions were held at the December World Economic Forum (WEF) in Davos, where a panel led by BlackRock’s Larry Fink and Tesla’s Elon Musk debated the implications of mass-scale humanoid robot deployment on the global workforce.
AI and Automation’s Disruptive Fallout
What exactly are we talking about? A recent McKinsey Global Institute report estimated that humanoid robots and AI would displace up to 800 million workers by 2030, causing 14% of the global workforce to switch career occupations. A 2026 WEF report on the future of jobs estimated that AI and automation (including the deployment of humanoid robots) will impact 1.1 billion jobs in the next decade.
Whether the number is 800 million or 1.1 billion, we are coming into a decade of disruptive change with implications for societies and governments globally. Can this disruptive change be navigated to minimize shocks to businesses, families and governments?
Peter Diamandis, of XPrize and Singularity University, describes what many fear will result from widespread AI and humanoid robot adoption:
- Young people who invest and run up debts from post-secondary education find it impossible to find jobs in their field of study.
- Managers and workers in factories and warehouses whose jobs are eliminated.
- Dramatic falls in family income make owning a home or experiencing a quality of life similar to previous generations, impossible.
- A growing precariat (precariously employed people) who can only find gig economy jobs, with many of these, such as rideshare and delivery services, succumbing to AI and automation.
- Governments that face significant drops in revenue formerly coming from taxes on income.
- Growing mass unemployment leading to the bankruptcy of government programs previously meant to assist workers.
Prescriptions Offered by the Experts
At the Stanford conference, Sunak offered several prescriptive policies. He advocated for payroll tax cuts, AI literacy and labour reskilling courses, including expanded apprenticeships, multiple career retraining strategies, and a universal new paradigm of lifelong learning. Sunak suggests that all of these would avoid a passive drift to job loss.
Rice described AI and automation as a systems, and not a technology problem, requiring the need for governments, business, workers, and academia to work together to shape training, adoption, and policy responses, invoking similar prescriptive solutions to those mentioned by Sunak.
Manyika prescribed a formula involving constant reskilling and large‑scale, repeatable systems and nationwide programs for training hundreds of thousands for new and changing roles, moving people from declining fields into growing ones backed by incentives and support.
Raimondo suggested an FDR-style grand bargain between the public and private sectors, with employers helping to define the skills needed in the AI and humanoid robot-driven economy and educators changing their “train once, work for life” model to one that lets people learn to quickly pivot as roles and responsibilities in the job market change. In the interim, Raimondo called for a safety net to cover the transition period between the onset of new jobs and the loss of old jobs.
Diamandis sees these programs as unsustainable over time and suggests that what worked during the COVID-19 pandemic could be adapted to turn into a universal basic income (UBI), which could be transitory or become permanent. UBI payments by governments to every adult citizen, however, require steady revenue streams from taxes and other general revenue sources. An alternative strategy would be to offer the UBI to only those who are struggling through the transition.
Diamandis describes the job categories in peril: logistics managers, paralegals, customer service workers, junior analysts, and those in low-paying jobs who live paycheque to paycheque. He sees this as a program that could sustain between 50 and 80 million Americans who would receive US$ 3,000 monthly.
To pay for the UBI, Diamandis proposes a consolidation of existing, underfunded means-testing programs, seldom-used tax credits and the burden of their administrative overheads, saving as much as $600 billion.
Diamandis sees a UBI program for the US, costing between $1 and $1.5 trillion and considers it a bargain when compared to the $5 trillion in payouts from the federal government during the first 18 months of the COVID-19 pandemic.
Shorter Work Weeks
Could spreading work requirements out by shortening the work week while still providing full pay, work? This would keep more people working and give governments the revenue from income taxes necessary to support social safety net programs.
Has any country adopted this strategy? Iceland has implemented a large-scale, four-day work week trial with full pay. Similar pilot programs have been tried in the United Kingdom, Belgium, Spain and some of the Scandinavian countries.
Meanwhile, several large and small businesses have been voluntarily implementing AI and automation-driven work hours without pay cuts, including OpenAI, the creator of ChatGPT. Resourceful companies are even using a 100% pay for 80% time as a strategy to attract top talent.
AI and Automation Displacement Fund
Companies that remove human jobs because of AI and humanoid robot adoption should see a significant dividend with increased income before taxes. That’s because algorithms once paid for only have running and maintenance costs. Humanoid robots represent a one-time purchase cost varying between US$ 20,000 and 250,000. Add to this the annual servicing, maintenance, and energy costs. Amazon states that their investment in 10,000 robots, the model they are deploying costs $250,000 at present (likely far less as they buy more and new models come out), will pay for itself currently in 6 months.
How can governments make up for the revenue lost because of displaced workers who no longer pay income taxes? The answer could come in the form of a displacement fund charge issued to companies that adopt AI and humanoid robots. The displacement fund charge could be levied based on a percentage of the salary of every worker whose job has vanished from the company’s payroll. The money collected could then be redistributed to pay for some of the programs suggested by Sunak, Rice, Manyika and Raimondo or others considered to be experts in assessing AI and humanoid robot impacts.
Living and Finding Purpose in a World of AI and Robots
Today, so many of us define ourselves by our work. In a world where most of the work will be done by AI, humanoid and other robots, how will humanity find purpose?
I thought it would be interesting to ask Perplexity.AI this question, and this is a paraphrase of its answer:
People might embrace optional activities to redefine purpose, aligning with innate human drives, such as art, music, philosophy, and exploration, and turning leisure into meaningful engagement rather than idleness.
Evidence from UBI pilots shows recipients invest in education, family, health, and entrepreneurship rather than withdrawing from society. Emerging roles in caregiving, ethical AI oversight, and community building, hard for machines to fully replicate, could provide structure and value.
Universal high income, funded by profits made from the adoption of AI and automation, would make work optional and free people to pursue passions without survival pressures. This post-scarcity model echoes visions from futurists such as Peter Diamandis and Elon Musk, where they talk about abundance enabling personal growth over labour.
AI notes a number of caveats, including human loss of identity tied to jobs, wealth concentration, and social disconnection, demanding proactive cultural shifts toward civic participation, lifelong learning, and mental health support. Societies succeeding here could foster “lives of wonder” through attentiveness, risk-taking, and communal bonds.
