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Introduction: The AI Boom Has a Human Cost
Artificial intelligence is rapidly becoming one of the most powerful engines of economic growth in modern history. Companies are using AI to automate customer service, software development, analysis, logistics, marketing, manufacturing and countless other tasks that once depended heavily on human labor. Productivity is rising, businesses are discovering new revenue opportunities, and investors are pouring enormous amounts of capital into AI infrastructure.
But behind the impressive productivity numbers is a difficult question that governments can no longer avoid: Who pays when machines replace workers?
That question is becoming increasingly important as AI moves from experimental technology into the core operations of businesses. If companies can produce more with fewer employees, the financial benefits may increasingly flow toward shareholders and technology owners while workers face redundancy, wage pressure or the need to acquire entirely new skills.
J.B. Mohapatra, former chairman of
The idea is controversial, but it deserves serious consideration.
The Central Argument: Tax the Gains, Not the Technology
The proposal is not necessarily about putting a simple tax on every AI model, chatbot or automated process. The broader idea is to capture some of the economic gains generated by AI-driven productivity and use that revenue to strengthen the social safety net.
Mohapatra described AI as a mechanism capable of transferring substantial wealth from the labor side of the economy toward the capital side. In practical terms, a company that previously needed 1,000 employees might eventually accomplish comparable work with 700, 500 or potentially even fewer people because AI systems can automate portions of the workflow.
The company may become more productive and profitable.
The worker may become unemployed.
That imbalance is at the heart of the debate.
A Different Kind of AI Dividend
One possible approach would be to establish a dedicated AI transition fund. Companies benefiting significantly from AI-driven productivity could contribute to the fund through taxation or another carefully designed mechanism.
The money could then finance programs designed specifically for workers affected by automation.
Those programs could include technical education, vocational training, subsidized certifications, income support during retraining, relocation assistance and incentives for businesses willing to hire displaced workers.
Instead of treating taxation purely as a way to collect government revenue, the system would effectively turn part of the AI productivity dividend into a workforce transition mechanism.
Why Retraining Matters More Than Compensation Alone
Simply paying workers after they lose their jobs may provide short-term relief, but it does not necessarily solve the long-term problem.
The more important objective should be helping people move from declining occupations into growing ones.
A manufacturing worker displaced by robotics, for example, could potentially be retrained for equipment maintenance, industrial cybersecurity, robotics supervision or advanced manufacturing.
A customer-service employee affected by AI agents could potentially transition into AI operations, quality assurance, escalation management, compliance or customer-experience roles that still require human judgment.
The challenge is making these transitions happen quickly enough.
Companies Could Become Part of the Solution
Mohapatra also suggested that companies could receive tax incentives when they retrain their existing employees.
That idea could be particularly important because businesses often understand their future labor requirements better than governments do.
If an enterprise knows that AI will reduce demand for certain administrative positions while increasing demand for AI supervisors, cybersecurity specialists and data professionals, it may be economically smarter to retrain employees than to dismiss them and recruit entirely new workers.
Tax incentives could make that decision easier.
India’s Contradictory AI Tax Position
India faces an especially interesting policy dilemma.
On one side, the country wants to attract massive investments in data centers and AI infrastructure. Tax incentives can make India more competitive as global technology companies search for locations capable of supporting enormous computing requirements.
The article points to
That policy is designed to encourage investment.
But it also raises an obvious question.
If governments provide substantial incentives to accelerate AI infrastructure while AI simultaneously threatens to disrupt employment, where does the money come from to support workers during that transition?
Growth Versus Redistribution
India cannot afford to discourage AI investment simply because automation creates economic disruption.
AI infrastructure requires enormous capital expenditure. Data centers require electricity, land, cooling systems, network connectivity and specialized technical workers. Restricting investment too aggressively could push projects toward competing countries.
The challenge is therefore not choosing between AI growth and worker protection.
The real challenge is designing policies that allow both to happen.
A successful system would encourage companies to build AI infrastructure while ensuring that a reasonable portion of the resulting economic gains contributes to broader economic resilience.
Could India Tax AI Consumption?
Mohapatra suggested that India could consider a modest levy on AI consumption.
This would represent a fundamentally different approach from taxing corporate profits or imposing broad digital services taxes.
A consumption-based mechanism could potentially target certain categories of AI usage without attempting to determine precisely how much profit an AI system generates.
However, implementation would be difficult.
Should a company pay more because it uses an AI model heavily? Should taxes depend on computing power, number of API calls, revenue generated, automation levels or the number of workers replaced?
Each approach creates its own problems.
Lessons From
India has experimented with a concept that provides an interesting historical comparison.
The country previously imposed a 5% R&D cess on imported technology, with proceeds supporting technology development through the Technology Development Board.
The principle was relatively simple: economic activity involving imported technology could contribute toward developing domestic technological capabilities.
An AI transition levy could potentially follow a similar philosophy.
Instead of using the money primarily for technology development, however, governments could direct it toward human capital development.
The Danger of Taxing Revenue Instead of Profit
One of the strongest arguments against poorly designed AI taxation is that some digital taxes focus on revenue rather than profitability.
Mohapatra highlighted the problem with unilateral digital services taxes: a company with very low margins—or even a loss—can still face the tax because the levy is based on revenue.
That creates an important policy distinction.
A company generating billions in revenue and billions in profit is not economically identical to a company generating billions in revenue while operating at a substantial loss.
Any AI-related tax system that ignores this difference could punish businesses during periods when they are struggling financially.
The Global Tax Problem
AI companies operate globally, which makes taxation even more complicated.
A technology company can develop an AI model in one country, train it using infrastructure in another, serve customers worldwide and book profits through a multinational corporate structure.
Traditional tax systems were not designed for this level of digital mobility.
Permanent-establishment rules remain important, but digital activity does not always fit neatly into traditional concepts of physical presence.
This is why international tax cooperation will matter increasingly as AI becomes more economically significant.
The
The
Pillar Two establishes a 15% global minimum effective corporate tax rate for large multinational enterprises participating in the framework. According to the article, 140 countries agreed to the framework, while dozens have already legislated it.
The basic philosophy is to reduce incentives for companies to shift profits toward extremely low-tax jurisdictions.
That principle could become increasingly relevant to AI because many of the world’s most valuable AI companies operate across multiple jurisdictions.
Why Pillar One Is More Difficult
Pillar One was intended to redistribute some profits of the world’s largest multinational enterprises toward market jurisdictions where customers are located.
But negotiations have stalled after the United States withdrew from the deliberations.
This demonstrates how difficult global taxation becomes when enormous economic interests are involved.
AI makes that challenge even larger because the technology allows companies to deliver highly scalable digital services across borders without establishing large physical operations in every market.
The Numbers Behind Global Tax Reform
The article cites OECD 2024 results indicating that the global tax framework increased effective tax rates by approximately 1.7 percentage points and generated an estimated $90 billion to $134 billion in additional tax revenue.
Those numbers illustrate why governments are interested in international tax reform.
Even relatively small changes to effective tax rates can produce enormous sums when applied across the global corporate economy.
The potential revenue from AI-related economic activity could eventually become significant as AI systems become embedded across almost every major industry.
But Tax Alone Cannot Fix Automation
This is perhaps the most important point in the entire debate.
Tax is not the solution.
It is only one component of a much larger policy response.
If governments collect additional AI-related revenue but fail to create effective education systems, affordable retraining, modern labor-market infrastructure and incentives for emerging industries, the tax money may simply become another government revenue stream.
The objective should be much more ambitious.
The objective should be building an economy capable of continuously moving workers from shrinking industries into growing ones.
Deep Analysis: How an AI Transition Fund Could Work
A Practical Economic Model
A hypothetical AI transition system could operate through several layers.
First, governments would identify industries experiencing measurable AI-driven productivity increases.
Second, policymakers would establish thresholds to determine which businesses are large enough to participate.
Third, contributions could potentially be linked to additional profits, automation intensity or another measurable economic indicator rather than simply taxing every AI transaction.
Fourth, the revenue would enter a ring-fenced workforce transition fund.
Finally, workers could access training, income support and employment services through that fund.
Monitoring AI-Driven Workforce Changes
Governments would need better labor-market data.
A basic analytical workflow could begin with collecting employment and productivity statistics:
python labor_analysis.py \n--industries manufacturing,finance,retail,software \n--metrics employment,wages,productivity \n--period 2018-2026
The purpose would not be to automatically determine who should be taxed.
Instead, such analysis could identify sectors where productivity is increasing significantly while employment is declining.
Measuring Automation Exposure
Organizations could also build occupation-level risk models.
python automation_risk.py \n--input occupations.csv \n--skills skills_database.csv \n--output automation_exposure.json
Such a system could compare occupational tasks against AI capabilities and identify where retraining investments are likely to produce the greatest return.
Funding Skills Instead of Preserving Jobs
Governments should avoid designing policy around the assumption that every existing job must be protected indefinitely.
Technology has always changed occupations.
The better objective is protecting
That means funding transferable skills rather than permanently subsidizing obsolete tasks.
Creating AI Transition Accounts
One possible policy would be individual AI transition accounts.
Workers could accumulate publicly funded training credits throughout their careers.
If automation threatens their occupation, those credits could be used for approved education programs.
This would make retraining proactive rather than something people discover only after losing their jobs.
Incentivizing Employers
Businesses could receive tax deductions when they retrain workers instead of replacing them.
A company investing in an
That would transform workforce development from a government-only responsibility into a shared responsibility.
Supporting Smaller Businesses
AI taxation should not unintentionally favor giant corporations.
Large technology companies can often absorb new taxes more easily than small businesses.
A badly structured levy could therefore slow AI adoption among smaller companies while leaving the largest enterprises relatively unaffected.
Thresholds and exemptions may therefore be necessary.
The Energy Connection
There is also another dimension to the AI taxation debate: infrastructure.
AI data centers consume enormous quantities of electricity and require specialized infrastructure.
As AI expansion accelerates, governments may increasingly face pressure to finance power generation, transmission networks and grid upgrades.
A carefully designed AI economic policy could therefore consider not only employment but also infrastructure costs.
Avoiding a Punitive AI Tax
There is a major difference between a transition tax and a punitive technology tax.
The first attempts to redistribute part of AI-generated productivity gains.
The second simply makes AI more expensive.
The distinction matters.
If taxation makes productive AI applications prohibitively expensive, companies may reduce investment, move infrastructure elsewhere or delay innovations that could ultimately create new employment.
The Risk of Automation Without Redistribution
The greatest danger is not AI itself.
The danger is an economy in which productivity rises rapidly while the benefits become increasingly concentrated.
If corporate profits, intellectual property and AI infrastructure ownership become concentrated among a relatively small group of companies and investors, wealth inequality could widen even while national economic output increases.
That would create a politically unstable combination: technological abundance alongside economic insecurity.
The New Meaning of Productivity
For decades, productivity growth has generally been viewed as positive.
AI complicates that assumption.
If productivity rises because machines allow fewer people to produce more goods and services, GDP may increase while employment in particular occupations decreases.
This does not automatically mean society becomes poorer.
It means the distribution of the gains becomes more important.
AI Could Also Create Entirely New Jobs
History provides reasons for optimism.
Technological revolutions frequently destroy particular occupations while creating others.
The problem is that the transition is rarely painless.
The worker who loses an existing job may not automatically qualify for the new job created by the technology.
The skills, geography, education and timing may all be different.
That is precisely where a transition fund could have value.
The Speed of AI Changes the Equation
Previous industrial transformations often unfolded over years or decades.
Generative AI and autonomous agents can spread much faster.
A software company can deploy an AI system to thousands of employees almost immediately.
A financial institution can automate entire workflows after integrating a new model.
A call center can introduce AI agents across a massive customer base without constructing a new factory.
The faster the transition, the faster policy mechanisms need to respond.
What Undercode Say:
- AI Taxation Is Becoming an Economic Question
The AI debate is no longer limited to technology companies and developers.
It is becoming a question about the structure of capitalism itself.
02. Productivity Alone Is Not Enough
A country can become dramatically more productive while ordinary workers fail to share equally in that growth.
03. The AI Dividend Must Reach Society
If AI generates extraordinary productivity gains, governments should consider mechanisms that allow society to capture part of those gains.
04. Taxation Should Be Carefully Designed
A simplistic AI tax could damage innovation.
A targeted transition mechanism could instead strengthen economic resilience.
05. Governments Need Better Data
Policymakers cannot manage AI displacement effectively if they do not know which occupations are actually being transformed.
06. Retraining Should Begin Earlier
Waiting until mass layoffs occur would be a policy failure.
Training should begin when industries first show signs of structural change.
07. Businesses Should Participate
Companies benefiting from automation should have incentives to help their existing employees transition.
08. AI Infrastructure Deserves Attention
Data centers, chips, electricity and networking infrastructure are becoming strategic economic assets.
09. India Has a Difficult Balancing Act
India wants to attract AI investment while also preparing its workforce for automation.
10. Tax Holidays Can Have Long-Term Consequences
Generous incentives can accelerate investment, but governments must consider what happens when those incentives reduce available fiscal resources.
11. AI Consumption Taxes Could Be Complicated
Measuring AI consumption fairly across companies would be technically and economically difficult.
12. Profit-Based Approaches May Be Fairer
Taxes linked to actual economic gains could avoid some of the problems associated with taxing gross revenue.
13. Global Coordination Is Essential
AI companies operate across borders, making unilateral taxation increasingly difficult.
14. Digital Presence Will Matter More
Traditional permanent-establishment rules may become harder to apply as AI services become increasingly borderless.
15. OECD Reform Is Important
Global minimum taxation demonstrates that countries can cooperate on difficult corporate-tax questions.
16. But International Consensus Is Fragile
The difficulties surrounding Pillar One show how quickly geopolitical disagreements can derail ambitious reforms.
17. AI Could Increase Inequality
Ownership of models, chips, cloud infrastructure and intellectual property could become increasingly valuable.
18. Workers Need Mobility
The best safety net is not necessarily permanent unemployment compensation.
It is the ability to move into the next opportunity.
19. Skills Are Becoming Economic Infrastructure
Education and retraining should be treated as strategic infrastructure in an AI economy.
20. Governments Should Fund Transition, Not Obsolescence
Public money should help people acquire future-proof capabilities rather than preserve permanently declining occupations.
21. Smaller Businesses Need Protection
AI taxation must not unintentionally create an advantage for the largest corporations.
22. AI Adoption Can Still Be Positive
Automation can remove dangerous, repetitive and inefficient work.
The goal should not be stopping automation.
- The Goal Should Be Sharing Its Benefits
Economic policy should ensure that technological progress creates broader prosperity.
24. AI Agents Raise the Stakes
The emergence of increasingly autonomous AI agents could accelerate workplace automation far beyond traditional software automation.
25. Cybersecurity Will Become More Important
As companies automate more processes, protecting AI systems and automated workflows will become a major employment category.
26. Human Oversight Will Not Disappear Overnight
High-risk decisions will continue to require accountability, judgment and regulatory oversight.
27. New Jobs Will Appear
AI will create demand for AI security, model governance, auditing, infrastructure, evaluation, compliance and human-AI operations.
28. The Transition Will Not Be Equal
Highly educated workers may adapt faster than workers whose occupations depend heavily on routine tasks.
29. Geography Matters
Workers cannot always relocate to technology hubs simply because new jobs appear there.
30. Remote Work Could Help
AI-enabled remote work may allow some displaced workers to access opportunities without moving.
31. Governments Should Experiment
There is no perfect AI tax model today.
Pilot programs could reveal which mechanisms actually work.
32. Transparency Will Be Critical
Companies should increasingly report how automation affects employment and productivity.
33. AI Tax Revenue Must Be Ring-Fenced
If governments promise workers a transition fund, the money should not quietly disappear into unrelated budgets.
34. Corruption Would Destroy Trust
Any large AI transition fund would require strong auditing and transparent spending mechanisms.
35. Workers Should Have a Voice
Employees and unions should participate in designing retraining systems rather than being treated purely as recipients.
36. Education Systems Must Change
Traditional degrees may not be sufficient for a labor market changing every few years.
37. Short-Cycle Training Could Become More Valuable
Certifications and modular education could allow workers to acquire new skills faster.
38. AI Could Ultimately Raise Living Standards
If productivity gains are distributed effectively, AI could reduce working hours and increase prosperity rather than simply eliminate jobs.
39. The Political Question Is Distribution
The technology determines what is possible.
Economic policy determines who benefits.
- Tax Is Only One Piece of the Puzzle
Mohapatra’s strongest warning may also be the most important: taxation can support the solution, but taxation alone cannot solve AI-driven displacement.
✅ AI Can Shift Economic Value Toward Capital
The central economic argument is credible: automation can increase productivity while reducing demand for certain categories of labor, potentially increasing returns to capital and technology ownership.
However, the outcome is not predetermined. AI can also create new jobs, increase demand for complementary human skills and generate entirely new industries.
✅ A 15% Global Minimum Tax Is the Core of OECD Pillar Two
The
Its implementation varies by jurisdiction, so the existence of the agreement should not be confused with identical domestic tax rules everywhere.
⚠️ AI Taxation Is Still an Emerging Policy Debate
There is no universally accepted definition of an “AI tax.”
Possible approaches include taxes on AI consumption, excess profits, automation-related gains, corporate profits or computing activity. Each model has significant economic and administrative challenges.
⚠️ The Impact on Employment Is Difficult to Predict
AI will almost certainly automate some tasks, but predicting the number of jobs that will ultimately disappear is much harder.
Historically, technological change has simultaneously eliminated occupations, transformed existing jobs and created new categories of work.
❌ Taxation Alone Cannot Solve AI Displacement
Even a highly successful AI tax would not automatically provide workers with the skills needed for future employment.
Education reform, retraining, job-matching systems, regional development and private-sector investment would still be necessary.
Prediction
(+1) AI Transition Funds Will Become More Attractive to Governments
As AI adoption accelerates, governments are likely to experiment with mechanisms that capture a portion of AI-driven economic gains and redirect them toward workforce development.
The most politically sustainable systems will probably avoid directly taxing the existence of AI. Instead, they may target excess profits, large-scale automation gains, corporate income or specific high-value AI activities.
Countries that successfully combine AI investment incentives with strong workforce-transition programs could gain an important competitive advantage.
(+1) Retraining Will Become a Major AI Industry
The AI economy will not only require models and data centers.
It will create an enormous market for workforce transformation.
Companies will need platforms that identify vulnerable roles, map existing employee skills against emerging occupations and recommend personalized training paths.
(+1) Human-AI Collaboration Could Become the Real Winner
The future may not be defined by humans versus machines.
It may be defined by companies that know how to combine both.
Workers who can supervise, verify, secure, manage and strategically deploy AI systems could become substantially more valuable.
(-1) Poorly Designed AI Taxes Could Slow Innovation
If governments impose taxes based on simplistic measurements such as AI usage or revenue, they could unintentionally penalize companies that are still investing heavily without generating significant profits.
That could reduce adoption, discourage infrastructure investment and make certain jurisdictions less attractive to global technology companies.
The Bigger Picture: Turning AI Disruption Into an Opportunity
The Choice Ahead
The world is entering an economic transition in which intelligence itself is becoming increasingly automated.
That does not necessarily mean mass unemployment.
It does mean that the relationship between productivity, employment and wealth distribution is changing.
Governments therefore face a choice. They can wait for disruption to happen and attempt to repair the damage afterward, or they can build transition mechanisms before the effects become overwhelming.
An AI tax may eventually become one of those mechanisms.
But the smarter vision is broader than taxation.
Building an Economy That Can Adapt
The real objective should be an economy where workers are not trapped inside occupations simply because those occupations existed for decades.
If AI makes certain tasks obsolete, people should have accessible pathways toward new opportunities.
If businesses become dramatically more productive, society should share in the gains.
And if governments provide enormous incentives for AI infrastructure, those incentives should be balanced against the long-term responsibility of preparing citizens for the labor market that follows.
The AI Revolution Does Not Have to Leave Workers Behind
The most important question is not whether AI should be taxed.
It is whether governments, businesses and workers can build an economic system capable of distributing technological progress more broadly.
AI could produce extraordinary wealth.
It could also deepen inequality if ownership and economic power become excessively concentrated.
The difference will not be determined by the technology alone.
It will be determined by policy.
Taxation can help finance the transition. Education can prepare workers for it. Companies can accelerate it. And governments can create the framework that connects all three.
The AI revolution is already underway.
The next challenge is making sure that the people whose work helped build today’s economy are not forgotten while tomorrow’s economy is being automated.
Tighten the repeated analysis sections
Make the policy proposal more concrete
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