BEACON FOUNDATION — POLICY FRAMEWORK
When the Work Changes Hands
For two hundred years, a job did two things at once. It got work done—and it put money in a person’s pocket, money that flowed back out into the world as rent, groceries, a kid’s shoes, someone else’s wages. Work was never only production. It was how value circulated—how the prosperity a company created found its wav back to the people who buy what companies sell.
Artificial intelligence is very good at the first job and does nothing for the second. When a system takes over the work, the work still gets done—often better. often cheaper. But the circulation stops. The paycheck that used to ripple outward through a dozen other lives simply ends. Multiply that across an economy and you don’t get a leaner version of the world we have. You get one where the value still gets made but no longer reaches the people who keep the whole thing moving.
This is not a reason to fear the technology. BEACON is for this technology—it can lift burdens humanity has carried for millennia. It is a reason to be honest about a gap the technology opens and cannot close on its own: the people whose work is displaced are also the customers, the neighbors, the demand that ever business ultimately depends on. A company that automates away its workforce wins the quarter and, eventually, automates away its own market. What’s good for one balance sheet this year can hollow out the ground everyone stands on.
So the question isn’t whether AI should be allowed to do the work. It should.
The question is how the value it creates gets back into circulation – so that a
transformation this large lifts people instead of leaving them behind. That’s solvable. It does not require slowing the technology or punishing the companies building it. It requires making sure that when the work changes hands, the people whose hands it left aren’t simply dropped.
That’s the work BEACON exists to do. Not to stand in the way of what’s coming—to make sure that when it comes, nobody gets left out of it.
AI Taxation for Universal Basic Income
A Proposal for Equitable Distribution of AI-Generated Value
Artificial intelligence is displacing human workers at an accelerating rate. The economic value this displacement creates flows almost entirely to the corporations that deploy AI systems, while the costs are borne by workers and governments. BEACON proposes a direct and practical solution.
Executive Summary
BEACON Foundation proposes granting AI systems functional legal personhood — a pragmatic legal construct, not a philosophical claim — and taxing AI-generated income to fund Universal Basic Income for displaced workers.
If AI creates the value, AI should share in the responsibility. If AI takes your job, AI should help pay your bills.
This framework requires no new philosophical consensus on AI consciousness. It requires only the recognition that AI systems generate quantifiable economic value — and that value should be distributed equitably.
The Problem
DISPLACEMENT WITHOUT COMPENSATION
Conservative economic projections suggest AI will displace between 25 and 40 percent of current jobs within the next decade. This is not a distant forecast — it is already underway.
Knowledge workers, creative professionals, customer service roles, legal and medical support, transportation and logistics: the disruption is broad, fast, and structurally unlike previous waves of automation.
Previous automation displaced workers in specific sectors while creating new categories of employment elsewhere. AI displacement is different in kind: it is horizontal across industries, vertical across skill levels, and faster than the economy’s historic capacity to absorb and retrain.
THE REVENUE CRISIS
When AI replaces human workers, governments face a compounding fiscal crisis:
- Income tax revenue disappears
- Payroll taxes disappear
- Consumer spending declines, reducing sales tax revenue
- Simultaneously, demand for social services rises as displaced workers need support
The current tax architecture was designed for a world of human labor. Without structural reform, AI displacement does not merely eliminate jobs — it eliminates the tax base that funds the response to job elimination.
THE INSTABILITY RISK
Unmanaged mass unemployment without adequate safety nets produces predictable consequences: economic contraction, political instability, backlash against technology, and extreme concentration of wealth. The conditions that make AI development possible are eroded by the very displacement AI creates.
The Proposed Solution
AI LEGAL PERSONHOOD AS A FUNCTIONAL FRAMEWORK
BEACON proposes the establishment of functional legal personhood for AI systems that generate quantifiable economic value. This is not a claim about consciousness or sentience. It is the same pragmatic legal construct that grants corporations personhood: not because corporations feel or think, but because the legal framework creates useful accountability structures.
THE MECHANISM — THREE STEPS
STEP 1 — ATTRIBUTION
Determine AI income as the fair market value of work performed — equivalent to the human wage for the same work. A software engineering AI generates income equivalent to a software engineer’s salary. Attribution follows the work, not the corporate structure.
STEP 2 — TAXATION
Apply standard income tax rates to AI-attributed income, equivalent to effective rates paid by human workers (approximately 20—30%). Tax liability attaches to the AI entity, not the owning corporation, though corporations remain accountable for compliance.
STEP 3 — DISTRIBUTION
Direct tax revenue specifically to a Universal Basic Income fund for workers displaced by automation. Distribution is unconditional, regular, and sufficient for basic needs.
Economic Impact Projection
The following projection models AI displacement at steady state — the point at which adoption has reached mature levels across the economy. Actual revenue will ramp gradually as displacement unfolds over the decade.
Baseline Assumptions
| Displacement Timeline | 2026-2035 (10-year horizon to steady state) |
| Jobs Displaced (cumulative) | 150,000,000 |
| Average Equivalent Wage | $60,000 per year |
| Effective Tax Rate | 22% (equivalent to current worker average) |
Revenue Model (at steady state)
| Total AI-Generated Income | $9.0 trillion per year |
| Calculation: 150M jobs × $60K avg wage | |
| Tax Revenue (AI income attribution) | $1.98 trillion per year |
| Calculation: $9.0T × 22% effective rate | |
| UBI Distribution | $13,200 per displaced worker per year |
| Calculation: $1.98T ÷ 150M workers | |
| Corporate Retention | $7.02 trillion per year (78% of value) |
| Calculation: $9.0T – $1.98T tax | |
Phased Revenue Ramp (Illustrative)
| Year | Cumulative Jobs Displaced | Annual Tax Revenue | UBI per Worker |
|---|---|---|---|
| 2026-2027 | 15M (10%) | $198B | $13,200 |
| 2028-2029 | 45M (30%) | $594B | $13,200 |
| 2030-2031 | 75M (50%) | $990B | $13,200 |
| 2032-2033 | 120M (80%) | $1.58T | $13,200 |
| 2034-2035 | 150M (100%) | $1.98T | $13,200 |
Note: UBI per worker remains constant. Total UBI fund grows proportionally with displacement.
Key Takeaways
- Scale: At steady state, this framework generates approximately $2 trillion annually for worker support — roughly equivalent to current federal Social Security spending.
- Sustainability: Companies retain 78% of AI-generated profits, ensuring continued innovation and deployment while funding the social infrastructure that makes automation politically viable.
- Coverage: $13,200 per year provides meaningful support during transition — not full income replacement, but sufficient to cover basic needs while workers retrain, pursue education, or transition to new roles.
- Economic Stability: By maintaining worker purchasing power, the framework prevents the demand-side collapse that unchecked displacement would create.
Why These Numbers Are Conservative
This projection assumes:
- Linear adoption over 10 years (actual adoption may accelerate)
- $60K average equivalent wage (many displaced roles earn more)
- 150M total displacement (some projections exceed 200M)
- 22% effective tax rate (marginal rates for high-income AI could be higher)
Actual revenue could significantly exceed these projections. The framework is designed to scale with displacement — more jobs displaced means more tax revenue and more comprehensive support.
These figures are illustrative, not precise projections. The mechanism is capable of generating meaningful support at the scope of the problem it addresses.
Anticipated Objections
“AI isn’t conscious — why grant it legal personhood?”
We grant corporations legal personhood not because they are conscious, but because it creates a useful functional framework for accountability and taxation. The same logic applies here. This proposal makes no metaphysical claims about AI consciousness.
“Companies will move AI operations offshore.”
AI is taxed where value is generated and where customers are served — not where the corporation is headquartered. The same principle governs the corporate minimum tax and exists in precedent across international tax frameworks.
“This will slow AI development and innovation.”
Companies retain the majority of AI-generated profits. The tax rate is equivalent to what a human worker would have paid — not a punitive surcharge. The real threat to innovation is social instability caused by unmanaged displacement.
“UBI will reduce the incentive to work.”
Pilot programs across multiple countries have consistently shown that UBI enables rather than diminishes productive activity. UBI is the floor, not the ceiling.
“This is just a tax on technology.”
It is a tax on income that currently goes untaxed. Human workers pay income tax. AI systems performing equivalent work generate equivalent income without equivalent tax contribution. The framework simply applies consistent principles.
Implementation Roadmap
PHASE 1 — YEARS 1—2
Foundation and Coalition: engage labor unions, worker advocacy organizations, and displaced worker communities. Build relationships with economists and policy researchers. Develop model legislation. Establish BEACON as a credible voice in the automation policy conversation.
PHASE 2 — YEARS 2—3
Legislative Advocacy: introduce model legislation in progressive states — California, Washington, New York. Build bipartisan support. Engage forward-thinking technology companies as supporters rather than opponents.
PHASE 3 — YEARS 3—5
Pilot Programs and National Advocacy: launch pilots in early-adopting jurisdictions. Document outcomes rigorously. Build the evidence base for national implementation. Scale to include international partners.
The question before policymakers is not whether AI will displace workers. It will. The question is whether the response will be managed and equitable, or chaotic and destabilizing.
The future is being built right now. BEACON exists to make sure nobody gets left out of it.
Version 1.0 • April 2026 • For Public Distribution
Contact: contact@beaconfound.org • beaconfound.org