Infrastructure-Based Taxation for the Post-Employment Economy

Jason Huxley Version 3.0 February 2026

Abstract

As automation achieves parity with human productive capacity, employment-based tax systems face structural collapse. This paper proposes the Sovereign Energy and Bandwidth Excise (SEBE), a novel fiscal framework that taxes the physical infrastructure of automated production: energy consumption (kWh) and data throughput (Mbps). SEBE generates £34-46 billion at launch in 2030 (2026 prices, larger than Inheritance Tax + Stamp Duty + CCL + Tobacco combined), growing automatically with automation to £93 billion by 2040 and £159 billion by 2045. This paper presents SEBE as a general-purpose revenue mechanism that addresses the fiscal crisis of technological unemployment; an accompanying working paper details one illustrative distribution model (two-stage UBI to Universal Living Income).

Keywords: Automation, taxation, Universal Basic Income, fiscal policy, energy economics, digital economy, post-employment

1. Introduction

1.1 The Automation Paradox

Advanced economies face a fundamental contradiction:

Result: Fiscal crisis precisely when society is most productive.

1.2 The Employment Tax Dependency

UK fiscal model depends critically on employment taxation:

Tax Source Revenue (£B) % of Total Employment-Linked
Income Tax 329 26% Yes
National Insurance 205 16% Yes
Corporation Tax 100 8% Partially
VAT 160 13% Indirectly
Total Employment-Linked 534+ 42%+ Direct dependency

As employment decreases, this revenue base erodes.

1.3 Research Question

Can infrastructure-based taxation replace employment taxation in a post-labour economy, and what transition mechanism enables a credible path from current fiscal arrangements to universal income provision?

This paper proposes and analyses one such mechanism: the Sovereign Energy and Bandwidth Excise, with a two-stage distribution model that addresses the sequencing problem.

2. Theoretical Framework

2.1 Value Creation in Automated Production

Classical economics: Value = Labour + Capital Automated economics: Value = Energy + Capital + Information

Implications:

2.2 Physical Infrastructure as Tax Base

Advantages of infrastructure taxation:

  1. Measurable: Physical infrastructure (power lines, fibre optics) provides objective data
  2. Unavoidable: Production requires energy and data (can’t offshore completely)
  3. Progressive: Large-scale operations use more, pay proportionally more
  4. Future-proof: Captures automation dividend as technology advances

2.3 Pigouvian Benefits

SEBE functions as double dividend tax:

2.4 The Sequencing Problem

Previous universal income proposals face a credibility gap: full universal income (matching median living standards) requires revenues far exceeding any single tax instrument. This paper addresses the sequencing problem through a two-stage model where Stage 1 ramps with a single new revenue source (starting modestly and growing with automation), creating an economic feedback loop that funds the transition to Stage 2.

3. The SEBE Mechanism

3.1 Component 1: Sovereign Energy Excise (SEE)

Tax base: Commercial electricity consumption above threshold

Coverage: Facilities with IT load >500kW

Measurement infrastructure:

Three-point metering (Hardware Root of Trust):

Liability calculation:

SEE = PoL - (PoS_final - PoS_initial) x Efficiency_Allowance

Prevents evasion:

3.2 Component 2: Digital Customs Duty (DCD)

Tax base: Commercial data crossing the UK digital border (both directions)

Implementation: Internet Exchange Point (IXP) level enforcement

Who pays DCD:

Who does not pay DCD:

Technical enforcement:

DCD rate rationale: DCD is set so that offshoring compute is always more expensive than operating domestically (where the operator pays SEE). This incentivises building UK data centres. The rate is derived from the SEE-equivalent cost per unit of border-crossing data (see SEBE Revenue Model Section 4 for full derivation).

3.3 Rate Structures

Energy (derived from DESNZ consumption data, see SEBE Revenue Model):

Bracket Rate (£/kWh) Taxable TWh Annual Revenue
500kW-5MW 0.08 ~25 £2.0B
5MW-50MW 0.20 ~25 £5.0B
>50MW 0.45 ~20 £9.0B
Non-compute commercial weighted ~60 £8-12B
Total SEE   ~130 £24-28B

Note: Total UK commercial/industrial electricity is ~150 TWh, but only ~70 TWh is consumed in facilities above the 500kW threshold. The remainder falls below the exemption. Non-compute commercial energy (manufacturing, logistics, automated warehouses) contributes a further ~60 TWh at lower weighted rates. Revenue grows with automation (see growth projections below).

Bandwidth (derived from data centre economics, see SEBE Revenue Model):

Type Rate Annual Revenue
Domestic traffic Exempt £0 (pays SEE instead)
Cross-border (< 10 PB/yr) £200/TB  
Cross-border (10-100 PB/yr) £400/TB  
Cross-border (> 100 PB/yr) £800/TB  
Total DCD   £7-10B

Combined SEBE at launch (2030): £34-46 billion/year (2026 prices, CPI-indexed)

Growth trajectory (automation-driven):

Year SEE DCD Total SEBE
2030 (launch) £30B £8B £38B
2033 £40B £8B £48B
2035 £50B £7B £57B
2040 £83B £10B £93B
2045 £140B £19B £159B

SEBE revenue is self-scaling: as automation replaces human labour, the tax on automation infrastructure grows automatically. All rates are CPI-indexed; all projections are in 2026 real prices.

4. Revenue Analysis

4.1 Scale Comparison

SEBE at launch (£34-46B, 2026 prices) is larger than:

SEBE becomes a major revenue source within 10-15 years, tracking automation growth. By 2040, SEBE reaches £93B (mid-scenario); by 2045, £159B (approaching National Insurance scale).

4.2 Tax Incidence

Who actually pays?

Short-term: Corporations (direct liability)

Medium-term: Mixed incidence

Long-term: Efficiency gains

4.3 Economic Efficiency

Distortions:

Efficiencies:

Net assessment: More efficient than current system if rates calibrated correctly

5. Revenue Application

5.1 The Income Tax Replacement Argument

SEBE’s primary function is replacing the employment-linked tax revenue that automation erodes. This is not a welfare proposal; it is a fiscal sustainability mechanism.

Year SEBE Revenue (2026 prices) As % of Income Tax (£329B)
2030 (launch) £34-46B 10-14%
2035 £57-80B 17-24%
2040 £93-135B 28-41%
2045 £159B+ 48%+

SEBE does not need to replace income tax immediately. It needs to grow faster than income tax shrinks. At projected growth rates (10-15% per annum, tracking compute capacity), the crossover occurs in the 2040s.

5.2 Illustrative Distribution Model

One possible use of SEBE revenue is universal income provision. An accompanying working paper (SEBE Distribution Model) details a two-stage model:

This is illustrative. SEBE revenue could equally fund NI reductions, deficit reduction, NHS expansion, or a combination. The mechanism is the contribution; the distribution is a political choice.

5.3 Economic Feedback Loop

If SEBE revenue is redistributed (as UBI or public services), a self-reinforcing cycle emerges: redistribution increases consumer spending, which generates additional conventional tax revenue (income tax, VAT, business rates), which funds further redistribution. The magnitude of this feedback loop is an empirical question requiring formal macroeconomic modelling.

6. Macroeconomic Effects

6.1 Aggregate Demand Maintenance

Problem: Automation leads to unemployment, demand collapse, recession

SEBE + UBI solution:

Prevents: Technological deflation spiral

6.2 Inflation Management

Concern: Universal income payments cause inflation

Stage 1 response:

Stage 2 response:

Key insight: The two-stage approach allows inflation dynamics to be observed and managed incrementally, rather than requiring a single large fiscal expansion.

6.2.1 Automation Reduces Production Costs

A critical and under-discussed dynamic: the same automation that SEBE taxes simultaneously reduces the real cost of producing goods and services. Automated manufacturing, logistics, energy management, and service delivery all drive unit costs down over time. This has three consequences for SEBE:

  1. The inflationary concern is weaker than it appears. SEBE adds a cost to energy consumption, but automation reduces the total energy (and labour, and materials) required per unit of output. The net effect on consumer prices is ambiguous and may well be deflationary.

  2. UBS becomes cheaper to deliver over time. Free public transport costs less to operate when vehicles are autonomous. Free energy costs less to generate when renewable infrastructure is automated. Free broadband costs less when network operations are AI-managed. The fiscal burden of UBS declines in real terms even as the service improves.

  3. The tax is self-limiting in a virtuous sense. SEBE taxes the infrastructure of a process that makes everything cheaper. It does not tax the productivity gains themselves (a FLOPS tax would). Companies that invest in energy efficiency pay less SEE for the same output, preserving the incentive to innovate.

This dynamic strengthens the long-term fiscal case for SEBE: the revenue grows (more automation = more energy consumption at the macro level), while the costs of public provision shrink (automation reduces unit costs). The fiscal space widens from both sides.

6.3 Labour Market Effects

Concern: Universal income destroys work incentive

Stage 1 response:

Stage 2 response:

6.4 Wage Restructuring and the Future of Work

Under full ULI, wage dynamics invert. Three categories of future employment emerge:

1. Essential but unpleasant work (care, sanitation, construction): wages rise. With ULI providing a comfortable floor (£31,500 combined), employers must offer genuine premiums to attract voluntary workers to physically or emotionally demanding roles. The human care worker’s role evolves from physical drudge work (increasingly handled by robotic aids) to emotional labour and companionship. Demand is permanent and near-universal (humans crave human contact, particularly the aged and infirm), but the willing labour supply shrinks because ULI removes the coerced majority. Result: high wages for a smaller, genuinely motivated workforce.

2. Specialist roles (surgery, diagnostics, engineering): largely automated. AI diagnostic models already exceed human performance in pattern recognition (radiology, pathology). Robotic surgery eliminates fatigue, distraction, and bias. Once automated systems demonstrably outperform humans, there is a moral obligation to transition: the alternative is knowingly subjecting patients to inferior human performance. The residual human role is supervisory: review findings, authorise procedures, abort if necessary. Fewer specialists, in an oversight capacity, not operational.

3. Human interface work (hospitality, teaching, therapy, creative): the dominant category of future voluntary employment. The product is the human relationship itself. “Served by a human” becomes the service equivalent of “handmade” for goods: a premium marker of authenticity. This creates an artisanal/vocational economy with real wages, voluntarily entered.

Fiscal implications: Total business salary overhead shrinks (fewer employees, automation handles production and specialist functions), even though some individual wages rise. Lower costs plus same or higher output equals wider profit margins. More taxable profit generates additional conventional tax revenue (corporation tax, business rates), creating a second fiscal feedback loop distinct from the consumer spending multiplier.

Inequality under ULI is intentional. The floor (£31,500) is high enough that nobody suffers. Above the floor, market dynamics allocate rewards: premiums for scarce essential work, artisanal pricing for human-interface services, voluntary exchange between people whose basic needs are met. This is closer to a genuine free market than the current system, where most accept employment terms under implicit threat of destitution.

6.5 CPI Behaviour Under Automation

Traditional CPI may become a poor measure of cost-of-living in a substantially automated economy. Several dynamics interact:

Deflationary pressure from automation: Manufacturing, logistics, energy generation, and service delivery all see falling unit costs as automation scales. Computing costs already fall ~30% annually. Renewable energy costs have dropped ~90% in a decade. If these trends generalise across the economy, CPI growth slows substantially or turns negative for goods and standard services.

Inflationary pressure from scarce human services: As argued in Section 6.4, essential human work (care, hospitality, artisanal services) commands wage premiums under ULI. These services become relatively more expensive. The CPI basket shifts: automated goods approach zero cost while human-delivered services inflate.

Net effect: Uncertain, but the composition of CPI changes fundamentally. A basket dominated by manufactured goods shows deflation. A basket weighted toward human services shows inflation. The overall index depends on weighting, which itself becomes a political question.

Implications for SEBE fiscal dynamics:

  1. SEBE revenue growth is driven by base expansion (more TWh consumed, more TB crossing borders), not by CPI adjustment of rates. Even in a deflationary environment, revenue grows because automation grows.
  2. ULI costs grow with CPI. If CPI is low or negative, ULI costs grow slowly or not at all in nominal terms, while purchasing power increases.
  3. The gap between revenue growth (compute-driven) and cost growth (CPI-driven) widens in the exchequer’s favour under deflation.

This is a structural advantage of SEBE over employment-linked taxes: SEBE tracks the physical expansion of automation infrastructure (energy, bandwidth), not the monetary value of output. It is inflation-agnostic at the revenue level and inflation-beneficial at the cost level.

ULI definition: For this reason, ULI is defined as the median full-time take-home pay at the point of implementation (currently £31,627, ASHE 2025), CPI-adjusted annually thereafter. It is not a permanently fixed nominal figure. See SEBE Distribution Model Section 4.4 for derivation and rationale.

7. Technical Feasibility

7.1 Metering Infrastructure

Current UK capability:

Cost: £2-5 billion (metering infrastructure deployment) Timeline: 3-5 years

Precedent: UK successfully deployed smart meters nationwide (2010-2025)

7.2 Digital Border Implementation

Current UK capability:

Required additions:

Cost: £500M-1 billion Timeline: 2-3 years

7.3 Administrative Overhead

SEBE collection:

Administrative cost: <1% of revenue (£2-5B)

Compare to: Income tax collection (HMRC budget £4.5B for much more complex system)

8. International Implications

8.1 Competitive Dynamics

UK implements SEBE unilaterally:

Risks:

Mitigations:

8.2 Digital Sovereignty

SEBE enables:

Strategic benefit: Compute sovereignty (like energy independence)

8.3 Export Potential

Other nations face same crisis:

UK SEBE model:

UK positions as: Policy innovator in post-employment economics

9. Comparison to Alternative Approaches

9.1 Robot Tax / FLOPS Tax (Direct Compute Tax)

Several proposals tax automation directly: per robot, per AI model, or per unit of computation (FLOPS). All share fundamental problems that SEBE avoids.

9.1.1 The “Robot Tax” variant

Proposal: Tax robots or AI systems per unit deployed.

Problems:

9.1.2 The “FLOPS Tax” variant

Proposal: Tax floating-point operations per second (FLOPS) as a proxy for compute work. This is the most technically sophisticated variant and deserves detailed rebuttal.

Problem 1: FLOPS is not a well-defined unit.

A floating-point operation varies by precision: FP64 (double), FP32 (single), FP16 (half), BF16 (brain float), INT8, INT4. Modern AI inference runs predominantly at INT8 or INT4. A GPU rated at 1,000 TFLOPS in FP16 produces a different number for FP32 and is not meaningfully comparable to a TPU running INT8 matrix multiplications. There is no standard “FLOP” that maps to a unit of economic work.

Problem 2: No physical measurement point.

Energy has meters. Bandwidth has packet counters. FLOPS has nothing. The only way to measure FLOPS is through hardware performance counters (software-readable, trivially spoofable) or self-declaration (obvious gaming incentive). There is no equivalent of a power meter that can be installed at a facility boundary and trusted.

Problem 3: Evasion is architectural.

To reduce FLOPS liability:

Each of these is a legitimate engineering choice. Taxing FLOPS creates perverse incentives to adopt them purely for tax avoidance, not efficiency.

Problem 4: Punishes efficiency, rewards waste.

A newer GPU that completes a task in 100 TFLOPS pays less FLOPS tax than an older GPU that takes 500 TFLOPS for the same task. This incentivises keeping old, power-hungry hardware running (fewer FLOPS per watt) rather than upgrading to efficient silicon. The environmental effect is the opposite of what any green tax should achieve.

Problem 5: International enforcement is impossible.

How do you tax FLOPS consumed offshore? You cannot meter a GPU in Virginia from London. SEBE solves this with DCD (border tariff on cross-border data), which is measurable at Internet Exchange Points. FLOPS consumed offshore are invisible to the taxing authority.

Why energy is the correct proxy:

Every computation, regardless of architecture, precision, instruction set, or physical location, consumes energy. A TPU doing INT8 inference uses watts. A neuromorphic chip uses watts. A quantum processor uses watts. Energy is:

SEBE taxes the universal physical input to all computation. A FLOPS tax taxes one particular arithmetic operation on one class of hardware.

9.2 Negative Income Tax

Proposal: Integrate welfare into tax system

Problems:

SEBE advantage: Independent of employment, simple universal payment

9.3 Land Value Tax

Proposal: Tax unimproved land value

Strengths: Economic efficiency, hard to evade

Problems:

Complementary: LVT + SEBE together cover both land and automation

9.4 Financial Transaction Tax

Proposal: Tax stock trades, currency transactions

Strengths: Large potential base

Problems:

SEBE advantage: Taxes real production (energy/compute) not financial transactions

9.5 Job Guarantee

Proposal: Government guarantees employment for all

Problems:

SEBE advantage: Adapts fiscal system to automation rather than resisting it. UBI/ULI is unconditional, not coerced.

10. Limitations and Further Research

10.1 Acknowledged Gaps

This paper provides:

This paper does not provide:

Required next steps:

10.2 Key Uncertainties

Energy component:

Bandwidth component:

Distribution model:

10.3 Research Agenda

Phase 1: Data Collection (6 months)

Phase 2: Modelling (12 months)

Phase 3: Pilot (24 months)

Timeline: 3-4 years to full economic model and pilot validation

11. Policy Implications

11.1 Short-Term (2030-2033)

If SEBE implemented:

11.2 Medium-Term (2033-2038)

As automation accelerates:

11.3 Long-Term (2038+)

Post-employment transition:

See SEBE Distribution Model for one detailed illustrative model of how SEBE revenue could fund a two-stage universal income transition.

12. Implementation Considerations

Required legislation:

Constitutional issues:

12.2 Administrative Capacity

New institutional requirements:

Staffing: 5,000-10,000 civil servants (compare to HMRC’s 65,000)

IT systems: Central database, automated invoicing, fraud detection

12.3 International Coordination

Bilateral agreements:

Without coordination:

Recommendation: UK proposes SEBE as international standard (G20, OECD)

13. Risk Analysis

13.1 Implementation Risks

Technical:

Mitigation: Hardware Root of Trust, regular audits, graduated thresholds

Economic:

Mitigation: Offshore penalty (2x rate), phased implementation, competitive analysis

Political:

Mitigation: Broad coalition building, clear public benefits (UBI), constitutional protection

13.2 Macroeconomic Risks

Inflation (Stage 2):

Recommended: Two-stage approach allows gradual phase-in with inflation monitoring at each step

Competitiveness:

Financial markets:

Recommended: Coordinate with Bank of England, gradual rollout, international credibility building

14. Conclusion

The Sovereign Energy and Bandwidth Excise represents a viable fiscal framework for post-employment economics. By taxing the physical infrastructure of automated production (energy and data), SEBE:

Key advantages over alternatives:

Critical uncertainties:

Recommended research priorities:

  1. Detailed econometric modelling (CGE analysis)
  2. Industry consultation (compliance cost assessment)
  3. Pilot programme (voluntary participation, data validation)
  4. Feedback loop simulation (Stage 1 stimulus multiplier)
  5. International feasibility study (EU/OECD coordination)

SEBE provides the technical and economic foundation for transitioning to a post-employment economy. Revenue starts modestly and scales organically with automation, providing increasing fiscal capacity for redistribution without requiring politically impossible fiscal expansions at launch. Further research is essential to refine parameters and validate assumptions, but the core mechanism is sound and implementable. An accompanying working paper (SEBE Distribution Model) presents one detailed illustrative model for how SEBE revenue could fund universal income provision.

References

Source Data Date
ONS ASHE 2025 Median gross annual earnings (full-time): £39,039 April 2025 (provisional)
ONS Mid-Year Population Estimates UK population: 68.3 million Mid-2023
Ofgem Price Cap Typical household energy: £1,758/year Q1 2026
HMRC Income tax and NI rates 2025/26 2025/26 tax year
ORR Rail Finance Rail fares income: £11.5B, govt funding: £11.9B 2024/25

Full data sources, staleness dates, and detailed cost workings are available in the accompanying cost model document.

Additional references (to be expanded):

Author Bio

Jason Huxley is an infrastructure and automation engineer with extensive experience in large-scale enterprise systems and network architecture. Background includes defence sector experience (Royal Corps of Signals) and current work in quantitative research infrastructure.

Contact: [To be added]

Appendices

Appendix A: Technical Specifications

Hardware Root of Trust Metering:

Digital Border Infrastructure:

Appendix B: Revenue Sensitivity Analysis

Variables:

Launch range (2030): £34B (pessimistic) to £46B (optimistic) Central estimate: £38B (2026 prices, CPI-indexed rates) 2040 range: £70B (low growth) to £135B (high growth)

Full derivation in SEBE Revenue Model.

Appendix C: Cost Model Summary

Full working available in SEBE Cost Model, including:

Appendix D: International Case Studies

Norway Energy Taxation:

Luxembourg Free Public Transport (2020):

Singapore Digital Services Tax:

Estonia Digital Infrastructure:

This is a working paper. Comments and critiques welcome.

Suggested citation: Huxley, J. (2026). Infrastructure-Based Taxation for the Post-Employment Economy: The Sovereign Energy and Bandwidth Excise Framework. Working Paper, v3.0.

© 2026 Jason Huxley Licensed under CC-BY 4.0 You may use, adapt, and distribute this work provided you credit the original author.

© 2026 Jason Huxley. Licensed under CC-BY 4.0.