The Word AI Leaders Won't Say: Why AI Demands Universal Basic Income

Displacing office workers is only the opening salvo. As embodied robotics enters the physical economy, detaching human survival from wage labor is no longer a fringe thought experiment—it is civilizational infrastructure.

September 2026 · 11 min read

Listen to any keynote, congressional testimony, or podcast interview with frontier AI executives today, and you will hear a familiar lexicon. You will hear about “unprecedented productivity,” “scientific acceleration,” and an imminent future of “radical abundance.”

What you almost never hear—what is conspicuously, almost pathologically avoided—is the three-letter acronym that represents the only mathematical resolution to their own technology: UBI (Universal Basic Income).

To celebrate the automation of human cognition while refusing to champion the economic floor required to survive it is not optimism; it is negligence. It implies that mass labor displacement will somehow magically sort itself out, or that individuals discarded by algorithmic efficiency can simply retrain into a market where capital no longer requires human hands or human minds.

We think it is time to look at the facts directly.

Phase One: The Cognitive Displacement

For decades, economists assured the public that automation followed a predictable hierarchy: machines would handle dull, repetitive physical labor, leaving creative, strategic, and intellectual work safely in the hands of humans.

Generative AI and modern agentic loops completely inverted that expectation. Over the past four years, the first wave of displacement struck knowledge workers:

• Software development: Automated boilerplate generation, refactoring, documentation, and routine debugging.
• Language and content: Translation, technical copywriting, legal discovery, contract review, and customer support.
• Data analysis: Financial synthesis, report compilation, and market intelligence.

For a brief moment, a comforting new rebuttal emerged: “Well, AI can write text and push code, but it can’t rewire a house, weld a chassis, or move a pallet in a warehouse. Physical labor and the skilled trades will remain the eternal sanctuary of human employment.”

That comforting narrative was flawed from the beginning. Knowledge work was simply Phase One.

Phase Two: Physical Automation Is Already Here

The belief that physical tasks are immune to automation ignores the rapid convergence of modern foundation models and commercial robotics. We are no longer talking about brittle, hardcoded factory arms bolted to concrete floors. We are witnessing the arrival of general-purpose embodied AI.

Consider the empirical, verified developments operating in industrial production today:

• Automotive Assembly: In mid-2026, Figure AI concluded an extensive 11-month commercial pilot at BMW Manufacturing’s Spartanburg plant. Its bipedal humanoid robots (Figure 02) manipulated and inserted over 90,000 sheet-metal components across the production of more than 30,000 BMW X3 vehicles. BMW has already transitioned to testing Figure 03 for complex sequencing logistics—sorting unorganized parts directly into trolleys for just-in-time delivery to the line.
• Logistics and Fulfillment: GXO Logistics, one of the world’s largest contract logistics providers, deployed Agility Robotics’ bipedal robot Digit under multi-year Robots-as-a-Service (RaaS) agreements. In live facilities like Spanx warehouses, Digit repeatedly moves totes and containers between autonomous mobile platforms and conveyor systems. Amazon, an investor in Agility, has run recurring pilots integrating Digit into fulfillment operations, while industrial giants like Schaeffler and Toyota Motor Manufacturing Canada are active deployment partners.
• The Software-to-Hardware Breakthrough: The bottleneck in robotics was historically perception and motor generalization. With the development of Vision-Language-Action (VLA) foundation models—such as Google DeepMind’s RT series and Physical Intelligence’s π0—neural networks translate multimodal visual inputs directly into high-degree-of-freedom motor commands. Robots can now learn novel manipulation tasks via teleoperation and video training, without teams of engineers hand-coding inverse kinematics for every edge case.
• Hardware Deflation: As supply chains scale and actuators are mass-produced, hardware bills of materials (BOM) are tumbling. With capable bipedal platforms entering the market at capital costs below $20,000, the amortized hourly cost of robotic labor drops well below minimum wage, operating without shifts, healthcare overhead, or fatigue.

When cognitive capability and physical manipulation are automated in parallel, the classical economic escape hatch—where workers displaced from agriculture moved to factories, and workers displaced from factories moved to offices—shuts down. There is no “third sector” to absorb hundreds of millions of workers if machines outcompete human labor in both computation and coordination.

The Scott Santens Framework: Machines for Work, Income for People

Writer and researcher Scott Santens has articulated the mechanics of this transition more rigorously than almost anyone over the past decade. If we want to think clearly about UBI, his body of work provides the essential blueprint.

In his foundational essay, Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines, Santens untangles a central confusion: our cultural obsession with preserving “jobs” as ends in themselves. As Santens observes, technology exists to eliminate unnecessary human toil, not to invent modern busywork to justify a paycheck. Punishing people for technological efficiency by denying them basic survival is a systemic design flaw, not a moral virtue.

Santens also formulated The Big Red Button Argument for Unconditional Basic Income. In it, he frames UBI not as an idealistic luxury or an expanded welfare program, but as an indispensable civilizational fail-safe. If autonomous tools compress labor markets faster than institutions can react, an unconditional cash floor is the single shock-absorber capable of preventing widespread economic paralysis and civil unrest.

Furthermore, as Santens outlines in his Guide to Basic Income (UBI FAQ), basic income is fundamentally a Productivity Dividend. Large language models and embodied robots were not created in a vacuum; they were trained on the collective corpus of human civilization—our public writing, research, software, cultural insights, and shared infrastructure. The immense wealth generated by automated intelligence is a shared inheritance, and every citizen is an equity stakeholder.

Looking Back at Andrew Yang’s 2020 Campaign from 2026

It is impossible to discuss basic income in the context of automation without acknowledging Andrew Yang’s 2020 presidential campaign.

Six years ago, when Yang introduced the “Freedom Dividend”—an unconditional $1,000 per month for every adult citizen—traditional political pundits treated it as an eccentric curiosity. Critics claimed his warnings about automated displacement were decades away, dismissing the proposal as unaffordable or unnecessary, and suggesting that displaced industrial workers could simply “learn to code.”

From the vantage point of 2026, Yang’s diagnosis has aged with startling accuracy. The “learn to code” trope did not merely age poorly; it was completely inverted by AI. The very professions heralded as the ultimate safety net were among the first to be compressed. Yang was not premature; our political imagination was simply late.

The Macroeconomic Paradox: Rethinking Money and Value

Beyond immediate job loss, there is a fundamental macroeconomic paradox that free-market orthodoxy cannot answer:

In a consumer-driven economy, wages are not just a cost of production; they are the sole source of aggregate demand. When you dismantle wages without replacing consumer purchasing power, you trigger a deflationary spiral where immense productive capacity exists alongside total consumer collapse.

This forces us to rethink what money actually is.

For centuries, we have treated money as a moral ledger—a scorekeeper that ties a person’s right to food, housing, and healthcare to their ability to sell hours of their life on an open labor market. If your labor has no clearing price on the market, the default assumption of that system is that you have no claim on societal resources.

In an automated world, that assumption is lethal. Money must be recognized for what it actually is in a sovereign fiat economy: a distribution mechanism and an access token for goods and services. When abundance is generated primarily by machine capital rather than human sweat, money must circulate as an unconditional baseline, allowing humans to access the fruits of machine productivity.

We must detach human value from economic work. A person’s worth is not defined by their marginal contribution to corporate EBITDA. It exists in parenting, caretaking, community building, philosophy, art, scientific inquiry, and the simple dignity of conscious existence.

Why Silence From AI Leaders Is Unacceptable

Why do tech founders and venture capitalists who champion artificial general intelligence refuse to lobby for UBI?

Because doing so requires answering difficult questions about taxation, wealth concentration, and the restructuring of capital ownership. It means acknowledging that private software monopolies cannot extract the totality of human knowledge without returning an unconditional dividend to the society that created it.

Talking about vague “abundance” twenty years in the future costs nothing. Advocating for structural redistribution and an unconditional income floor right now requires political courage.

At Merciful, we believe technology should serve humanity, not the other way around. Real mercy is not an empathetic conversational agent or polite customer service; real mercy is designing an economic framework where human beings are never treated as disposable scrap when machines learn to do their jobs.

Universal Basic Income is not an idealistic handout. It is the necessary foundation of any civilized society navigating the intelligence revolution. It is time we start saying it out loud.

— The Merciful Team