Automation Dividend Tax

Why This Is Essential

Wealth tax alone cannot solve automation displacement. Wealth tax is indirect and slow—it taxes accumulated wealth years after automation occurs. Automation dividend tax is direct and immediate—it taxes the act of automation itself, generating revenue exactly when workers are displaced.

The Problem

Company deploys robots/AI and eliminates 1,000 jobs
Saves $40-60 million annually in labor costs
Profits increase, but wealth doesn't immediately concentrate in taxable billionaire holdings
Workers need income support NOW, not years later when wealth tax catches up

The Solution

Automation dividend tax creates direct link: automate → pay tax → fund UBI for displaced workers. Revenue flows immediately when automation happens, exactly when workers need support.

        This makes companies internalize the social cost of automation. Doesn't ban automation—just ensures gains are shared. Revenue scales with automation pace: more automation = more tax = more UBI.
      

The Manpower Metric (Mp)

Definition

Manpower (Mp): One Manpower equals the annual labor value of one full-time equivalent worker paid the reference annual wage.

1 Mp = 1 FTE-year at reference wage Where: - FTE-year = 2,000 hours (standard full-time) - Reference wage = national median annual compensation (e.g., $50,000/year, adjusted annually)

Core Formula

Mp = (H × w) / W_ref Where: - H = hours of paid work replaced per year - w = average hourly wage for that work - W_ref = reference annual wage

Alternative measurement: Manpower-Dollars (Mp$) = Mp × W_ref = H × w

Mp is dimensionless (human-equivalent count), Mp$ is dollar-value replaced per year.

Calculation Examples

Example A: Whole FTE Replacement

Scenario: Robot replaces 2 full-time workers, each paid $50,000/year

Reference wage: W_ref = $50,000

Calculation: Each worker = 1 Mp

Total Mp = 2 Mp

Example B: Partial Replacement

Scenario: Automation saves 1,000 hours/year of labor at $25/hour

Reference wage: W_ref = $50,000 (standard FTE = 2,000 hours)

Step 1: Dollar value replaced = 1,000 × 25 = $25,000

Step 2: Mp = 25,000 / 50,000 = 0.5 Mp

Total Mp = 0.5 Mp

Example C: Revenue-Based Calculation

Scenario: AI system generates $1.2M additional revenue. Productivity attributable to automation: 30%. Wage-weighted share: 40%.

Step 1: Labor-attributable revenue = 1,200,000 × 0.30 × 0.40 = 144,000

Step 2: Mp = 144,000 / 50,000 = 2.88 Mp

Total Mp = 2.88 Mp

Tax Structure

Progressive Mp Tax Rates

Following the wealth tax philosophy of incremental curves rather than brackets:

0-5 Mp: $5,000 per Mp (small automation, lower burden)
5-50 Mp: $8,000 per Mp
50-500 Mp: $12,000 per Mp
500+ Mp: $15,000 per Mp

Alternative: Continuous curve → Rate = $5,000 × (1 + log(total_Mp))

        Why progressive? Small businesses automating 2-3 positions pay less per Mp than Amazon replacing 10,000 warehouse workers. Scales revenue with automation concentration while maintaining innovation incentive for small players.
      

Sector-Specific Approaches

Manufacturing:

Clear FTE replacement (robot replaces welder)
Easy to measure, hard to game
Use direct hours method

Knowledge Work (AI displacement):

AI augments rather than replaces directly
Use productivity threshold: if revenue-per-employee exceeds sector median by X%, triggers Mp calculation
Example: Law firm with 50 lawyers + AI doing work of 20 paralegals = 20 Mp

Gig Platforms:

Algorithm replaces dispatchers, coordinators, customer service
Calculate Mp from: (Platform revenue × labor_attribution_factor) / W_ref
Labor attribution = historical industry standard for that revenue level

Reference Wage (W_ref) Options

Option A: Occupation-Weighted W_ref

Manufacturing automation uses manufacturing median wage
Knowledge work uses knowledge worker median
Prevents gaming through deliberately replacing low-wage workers

Option B: Actual Wage Replaced

W_ref = average wage of displaced workers
More accurate, harder to game
Requires better record-keeping

Option C: Hybrid (Recommended)

W_ref = max(actual wage replaced, sector median, 3-year trailing average)
Prevents artificial wage depression before automation
Fairest and hardest to manipulate

Gaming Prevention

1. The "Contractor Shuffle"

Problem: Company fires employees, hires them back as contractors, claims no displacement.

Solution: Economic Substance Test

If same people doing same work at same location → still counts as Mp
"Contractor" must have multiple clients, own equipment, control methods
Payroll substitute = Mp

2. The "Productivity Mirage"

Problem: Claim efficiency gains are from "better management" not automation.

Solution: Technology Investment Correlation

Track capital expenditure on automation tech
If CAPEX on automation correlates with productivity gains → presumption of Mp
Burden on company to prove gains came from non-automated sources

3. The "Offshoring Escape"

Problem: Move automated operations overseas to avoid tax.

Solution: Border-Adjusted Mp Tax

Tax based on where value is consumed, not produced
Goods/services sold in US market trigger Mp calculation regardless of production location
Similar to border-adjustment tax, focused on labor displacement

4. Pre-Automation Wage Depression

Problem: Suppress wages before automating so W_ref calculation shows less displacement.

Solution: Look-Back Window + Sector Benchmark

W_ref = max(actual wages, 3-year trailing average, sector median)
Can't game by depressing wages right before automation

Implementation Timeline

Year 1-2: Pilot Program

50-100 large firms (>1,000 employees) volunteer
Test Mp calculation methodologies
Identify gaming attempts
Refine formulas and audit procedures

Year 2-3: Mandatory Reporting (No Tax Yet)

All firms >250 employees report Mp annually
Build baseline data and sector benchmarks
Train auditors and develop enforcement capacity
Publish aggregated statistics for transparency

Year 3-4: Tax Phase-In

Start at 25% of final rate
Ramp to 100% over 4 years
Adjust based on economic impact data
Full national implementation by Year 4

Enforcement Architecture

Independent Automation Accounting Board (AAB)

Analogous to Financial Accounting Standards Board (FASB):

Sets Mp measurement standards
Publishes sector benchmarks
Arbitrates disputes
Updates methodologies as technology evolves

Audit Requirements

Annual third-party Mp audit (like financial audit)
Random IRS audits of 5-10% of filers
Whistleblower protections + bounties for reporting fraud
Public dashboard showing aggregated Mp data by sector

Penalties for Misreporting

2x tax owed + interest for negligence
5x tax owed + potential criminal charges for intentional fraud
Debarment from government contracts
Executive liability (cannot hide behind corporation)

Revenue Projections

US Labor Market Context

~160M workers currently
Median wage ~$50,000
Total labor compensation ~$11 trillion

Automation Displacement Estimates (by 2030)

Conservative: 10-15% of hours (16-24M FTE equivalents)
Moderate: 20-25% of hours (32-40M FTE equivalents)
Aggressive: 30-40% of hours (48-64M FTE equivalents)

Revenue Calculation (Moderate Scenario)

36M Mp × $12,000 average tax rate = $432B annually

Scaling mechanism: Automatic rate adjustment as automation accelerates. Target: capture 40% of labor displacement value.

If automation saves companies $1T in labor costs → $400B in tax revenue

Connection to UBI

Direct funding link: Automation tax revenue goes exclusively to Universal Basic Income program.

Current projection:

$500B automation tax revenue
260M adults
= $1,923 per person per year ($160/month)

Combined with wealth tax:

Wealth tax: $300-500B → Healthcare, infrastructure
Automation tax: $500B-$1T → UBI
Total UBI: $1,000B ÷ 260M adults = $320/month per adult

        Transparency builds political support: Workers can see direct connection between automation that displaces them and income support they receive. Companies pay for displacement, workers get support, connection is clear.
      

Implementation Details

Economists, tax policy experts, and automation specialists determine:

Precise Mp calculation formulas for different industries
Audit protocols and verification procedures
Sector-specific benchmarks and thresholds
International coordination mechanisms
Technology tracking systems for compliance
Dispute resolution processes
Rate adjustment algorithms as automation pace changes
Integration with existing tax infrastructure