WE CAN DREAM, CAN’T WE? HOW TAXING AI COMPANIES COULD SAVE VULNERABLE POPULATIONS. AI scenarios run by The caring world.

Don’t tax billionaires, tax AI. That is the premise we want to explore. What happens if you tax AI companies, can you address some of the world’s most critical health and poverty issues? 

How would you approach the taxation? A 1% tax on valuation or revenue? That is where we started with our friendly AI, who appeared to be excited at the possibility of saving millions and millions of lives.

Ok, let’s begin.

The cumulative market value of pure-play and hardware-dominant artificial intelligence companies is estimated at roughly $12 trillion to $14 trillion, driven primarily by NVIDIA (~$5 trillion), Alphabet (~$4.5 trillion), and other major tech infrastructure providers, plus hundreds of private unicorns. [1, 2, 3, 4, 5]

Calculating a precise "cumulative value" for all AI companies depends on whether you count dedicated AI firms or include the massive hardware, cloud, and software giants driving the AI economy. The ecosystem breaks down into distinct valuation layers: [1]

Mega-Cap Hardware & Hyperscalers (The Foundation)

These publicly traded giants represent the vast majority of current dollar value attributed to the AI trade because they own the silicon, data centers, and cloud fabric: [1, 2]

• NVIDIA: ~$5.0 trillion market cap

• Alphabet (Google): ~$4.5 trillion market cap (heavily weighted by Gemini and cloud infrastructure)

• Microsoft: ~$3.6 trillion market cap (inclusive of Azure AI and OpenAI partnership stake)

• Meta Platforms: ~$1.5 trillion market cap (driven by Llama models and AI-optimized ad engines) [1, 2, 3, 4, 5]

Private Frontier Labs & Specialized Unicorns

Private pure-play AI labs and infrastructure providers add hundreds of billions more: [1]

• SpaceX / xAI: Combined valuation tier approaching ~$1.5 trillion

• OpenAI & Anthropic: Valued in the tens to hundreds of billions collectively in private secondary and primary funding rounds.

• Specialized Hardware & Cloud Startups: Firms like CoreWeave and Cerebras add roughly $100 billion combined. [1]

Broader Industry Metrics

If looking past market capitalization to annual economic footprint:

Global AI Market Size: Evaluated at roughly $514.5 billion in direct annual market value/revenues.

Annual AI Spending/Capex: Projected to surpass $2.5 trillion globally as data center and infrastructure buildouts peak.

If you take that number and put a 1% tax in it, what is the number?

Then compare that to what is spent on global health, break that out for US spending and global spending to help vulnerable populations.

If you take the estimated cumulative value of all AI companies ($12 trillion to $14 trillion) and apply a 1% tax, the resulting number is $120 billion to $140 billion.

Comparing this hypothetical tax revenue to the realities of global health spending highlights a stark contrast between global healthcare infrastructure and foreign aid budgets for vulnerable populations.

Comparing the 1% Tax to Global Health Spending

According to data tracked by the World Health Organization (WHO), overall global spending on health (combining all government, private, and out-of-pocket healthcare expenses worldwide) sits at roughly $9.8 trillion annually. [1]

However, the vast majority of that $9.8 trillion is spent internally by wealthy nations on their own citizens. To help vulnerable populations in developing nations, the world relies on a subset of money known as Development Assistance for Health (DAH), which hovers around $30 billion to $45 billion annually. [1, 2]

The Impact: The 1% AI tax alone would be 3 to 4 times larger than the entire world’s annual foreign aid budget for global health security, disease eradication, and maternal care.

US Spending vs. Global Spending for Vulnerable Populations

When looking strictly at foreign aid and global health funding earmarked specifically to protect vulnerable populations (combining efforts against infectious diseases, maternal mortality, and childhood malnutrition), the numbers break down as follows:

Funding Source Annual Amount What it Covers / Where it Goes

🇺🇸 US Global Health Budget $11.3 billion Funded via regular appropriations. Includes $5.2 billion for HIV/AIDS (PEPFAR), $1.29 billion for Maternal & Child Health, and $1.06 billion for Malaria.

🇺🇸 US Supplemental Humanitarian Aid $2.0 billion Direct emergency commitments managing food insecurity, conflict zones, and medical crises for at-risk populations globally.

🌍 Combined Global Aid (Non-US) $18 billion to $30 billion Contributions from the other 31 OECD donor countries, philanthropic groups (like the Gates Foundation), and multilateral pools.Funding Source

Key Takeaway

Historically, the United States has been the single largest donor to global health, traditionally making up nearly 45% of all global health aid. However, following policy shifts including the U.S. official withdrawal from the World Health Organization and an "America First" foreign aid strategy, direct regular U.S. global health appropriations dropped to $11.3 billion, down from a peak of $12.9 billion. [1, 2, 3, 4]

If the $120 billion to $140 billion from a 1% AI tax were directed into global health aid, it would instantly multiply the total global budget for vulnerable populations by roughly 300%, completely offsetting recent superpower funding deficits. [1]

With the surplus from the tax to fund healthcare for vulnerable populations, what amount of money could be spent in AI companies’ home countries for social services and healthcare for the vulnerable?

If you subtract the total global budget for protecting vulnerable populations from the hypothetical $120 billion to $140 billion revenue pool, you are left with a massive domestic surplus.

Since Development Assistance for Health (DAH) totals roughly $30 billion to $45 billion globally, funding it entirely would still leave a surplus of $75 billion to $110 billion.

Because the vast majority of top-tier AI companies are headquartered in the United States, the bulk of this domestic funding windfall would be allocated there. A smaller portion would flow to other major AI hubs, such as the United Kingdom, France, and Canada.

The impact of injecting this remaining surplus into domestic healthcare and social services for vulnerable populations in AI-heavy home nations would be transformative.

The United States Allocation (~$65 Billion to $95 Billion)

Given that the U.S. hosts roughly 80% to 90% of the world’s AI market cap (led by Silicon Valley and Seattle giants), it would claim the largest slice of the domestic surplus.

Contextualizing the Scale: The U.S. Department of Health and Human Services (HHS) requested a discretionary budget of $111.1 billion. An extra $65B to $95B would nearly double the country's entire discretionary funding for public safety nets. [1]

Potential Spending Power:

• Medicaid Expansion: It could completely fund healthcare for several million low-income Americans, or permanently bridge the coverage gap in states that have refused Medicaid expansion.

  Community Health & Infrastructure: This money could double federal funding for Maternal and Child Health programs and fund thousands of local, community-based health centers in underserved rural and urban areas.

  Addressing AI Displacement: As lawmakers debate taxing AI to counter job automation, this money could establish nationwide technical apprenticeship and tuition-free training programs for vulnerable workers displaced by technology. [1, 2, 3, 4]

Other AI Hub Countries (~$10 Billion to $15 Billion)

The remaining billions would return to the home countries of secondary AI hubs, primarily supporting publicly funded healthcare systems facing intense structural strain.

United Kingdom: The UK is home to major research hubs like Google DeepMind. Its portion could infuse several billion pounds directly into the National Health Service (NHS), specifically targeted at reducing vulnerable patient wait times, expanding mental health infrastructure, and improving social care for the elderly.

France & the European Union: Home to rising open-source contenders like Mistral AI, France could use its share to subsidize universal childcare, boost rural medical access, or fund local automated workforce transition programs.

Canada: Anchored by AI hubs in Toronto and Montreal, Canada could inject its windfall into provincial healthcare systems to cover vulnerable populations' out-of-pocket pharmaceutical costs or expand long-term senior care.

The United States Allocation (~$65 Billion to $95 Billion)

Given that the U.S. hosts roughly 80% to 90% of the world’s AI market cap (led by Silicon Valley and Seattle giants), it would claim the largest slice of the domestic surplus.

Contextualizing the Scale: The U.S. Department of Health and Human Services (HHS) requested a discretionary budget of $111.1 billion. An extra $65B to $95B would nearly double the country's entire discretionary funding for public safety nets. [1]

Potential Spending Power:

Medicaid Expansion: It could completely fund healthcare for several million low-income Americans, or permanently bridge the coverage gap in states that have refused Medicaid expansion.

Community Health & Infrastructure: This money could double federal funding for Maternal and Child Health programs and fund thousands of local, community-based health centers in underserved rural and urban areas.

Addressing AI Displacement: As lawmakers debate taxing AI to counter job automation, this money could establish nationwide technical apprenticeship and tuition-free training programs for vulnerable workers displaced by technology. [1, 2, 3, 4]

Other AI Hub Countries (~$10 Billion to $15 Billion)

The remaining billions would return to the home countries of secondary AI hubs, primarily supporting publicly funded healthcare systems facing intense structural strain.

United Kingdom: The UK is home to major research hubs like Google DeepMind. Its portion could infuse several billion pounds directly into the National Health Service (NHS), specifically targeted at reducing vulnerable patient wait times, expanding mental health infrastructure, and improving social care for the elderly.

France & the European Union: Home to rising open-source contenders like Mistral AI, France could use its share to subsidize universal childcare, boost rural medical access, or fund local automated workforce transition programs.

Canada: Anchored by AI hubs in Toronto and Montreal, Canada could inject its windfall into provincial healthcare systems to cover vulnerable populations' out-of-pocket pharmaceutical costs or expand long-term senior care.

This exercise demonstrates that a microscopic fractional tax on AI valuation could theoretically solve the world’s foreign health aid deficits while simultaneously delivering historic funding boosts to domestic safety nets at home.

Let’s look at annual AI revenues versus market cap.

Shifting the exercise from market capitalization (what the market thinks companies are worth) to annual revenues (the actual cash coming in from AI products, chips, and cloud services) dramatically changes the financial math. [1, 2]

The global artificial intelligence market is generating approximately $600 billion in direct annual revenue. [1]

Revenue vs. Market Capitalization

The core difference lies in the price-to-sales ratio. Market cap prices in decades of future growth, whereas annual revenue represents real economic activity happening right now. [1, 2]

Financial Engine Total Industry Pool/ 1% Annual Tax Yield/ What it Funds

(Asset Wealth) $12.0T – $14.0 Trillion $120B – $140 Billion Funds 100% of global foreign health aid plus a $75B+ domestic surplus.

Annual Revenues

(Cash Inflow) $600 Billion $6 Billion Funds roughly 13% to 20% of the global health aid deficit.

The Global Impact of a $6 Billion Revenue Tax

A 1% tax on annual AI revenues would yield $6 billion. While this is significantly less than a wealth/valuation tax, $6 billion in annual cash injection is still highly effective if deployed surgically to protect vulnerable populations.

Instead of wiping out the entire $30 billion to $45 billion Development Assistance for Health (DAH) pool, a $6 billion fund would have to be hyper-targeted. It would likely be distributed among the world's most critical multilateral health funds:

The Global Fund: If given entirely to The Global Fund to Fight AIDS, Tuberculosis and Malaria, it could expand their entire multi-year budget by nearly 15%, saving an estimated 1 to 2 million additional lives through distributed nets, diagnostics, and therapeutics. [1]

Gavi, the Vaccine Alliance: This amount could completely fund the child immunization pipelines for dozens of low-income nations, protecting millions of children from preventable diseases.

Maternal Health: It could fully bridge the World Health Organization's funding shortfalls for emergency obstetric care in Sub-Saharan Africa and South Asia.

The Economic Trade-Offs: Revenue vs. Wealth Taxes

Economists and policymakers debate these two approaches due to how they affect the economy:

The Revenue Tax Dilemma: Taxing annual revenue behaves like a sales tax. It increases the cost of AI software and chips for consumers. It would slow down corporate AI adoption and reduce the pace of technological deployment. [1, 2]

The Market Cap Dilemma: Taxing market capitalization means taxing unrealized asset value. It is incredibly difficult to enforce because stock prices fluctuate wildly daily. It can also force founders and investors to sell off shares just to pay the tax, depressing stock markets.

The Trend Line

The gap between revenue and market cap is narrowing rapidly. Driven by enterprise software adoption, agentic AI systems, and continuous hardware upgrades, global AI revenue is growing at a compound annual rate of 29.3%. It is projected to top $3.6 trillion by 2033. [1, 2, 3, 4, 5]

By the next decade, a 1% tax on AI revenues would yield $36 billion annually—enough to cover the world's entire vulnerable population health budget by itself without ever touching the companies' market caps.

One last analysis. what would the market cap tax need to be to equal the supplemental amount necessary to fully pay for global health for vulnerable populations.

To completely fund the entire world’s foreign health aid budget for vulnerable populations, the tax on cumulative AI market value would only need to be 0.27% to 0.38% (roughly one-third of 1%).

The Final Math

According to recent tracking from the Institute for Health Metrics and Evaluation (IHME), total global Development Assistance for Health (DAH) has declined to approximately $38 billion to $42.5 billion. [1, 2]

If we apply a micro-tax to the $12 trillion to $14 trillion pool of AI valuation, the rate needed to cover this entire budget breaks down like this:

• At $14 Trillion Market Cap: A tax of just 0.27% generates $38 billion.

• At $12 Trillion Market Cap: A tax of just 0.35% generates $42 billion.

What This Reveals About the Global Economy

This exercise highlights a staggering imbalance in the modern global economy: the massive divergence between digital wealth creation and human survival needs.

The Power of Fractional Wealth: The daily fluctuations of just two or three mega-cap tech stocks (like Nvidia or Microsoft) frequently erase or create more value in a single afternoon than the entire world spends keeping millions of people alive via global health programs in an entire year.

A Sovereign Shift: Historically, global health has relied on the discretionary budgets of superpowers. With recent policy pivots—including the 67% drop in U.S. health aid driven by the "America First" strategy—the global safety net has shrunk significantly. A tiny, permanent tech-infrastructure levy would decouple vulnerable human lives from volatile geopolitical political shifts entirely. [1, 2, 3, 4, 5]

Some final thoughts.

This is radical thinking but we need radical ideas to confront our future with AI. We will need policy thinkers with vision and legislators with courage. 

We, as citizens, can not sit and watch the world destroy itself.