The AI Bubble: How Debt and Geopolitics Inflate the Tech Boom

Artificial Intelligence represents the most profound technological revolution since electricity. Every country, every institution, every business, and every investor is trying to place their bets on AI. Naturally, global investments have exploded. According to multiple industry estimates, the world will invest over $1.5 trillion in AI-related spending in 2025 alone.

This number sounds massive, even frightening. But in the context of a global GDP exceeding $100 trillion, $1.5 trillion is only about 1.5%-not an irrational amount if the technology truly boosts global productivity by 5–10% over the next decade. From this perspective, investing in AI is not only logical but essential.

So the issue is not AI itself. The issue is how AI is being funded, how companies are manipulating valuations, how geopolitical loopholes inflate demand, and how the entire AI infrastructure is being financed by a dangerous combination of debt, shell companies, circular transactions, and depreciating hardware.

Like every historic bubble-from the speculative mania of 1929 to the dot-com frenzy of 2000 to the subprime crisis of 2008-the early signs are always the same:

• Money chasing growth at any cost
  
• Creative accounting hiding real risks
  
• Artificial demand masked as real revenue
  
• Investors convinced the future will only go up
  
• Companies borrowing more than they can repay
  
• Narrative dominating fundamentals
  

The AI industry today exhibits all of these symptoms simultaneously.

In this article, we’ll explore how the AI bubble is forming, who is feeding it, how a complex network of engineered transactions is inflating valuations, and why this structure is far more fragile than most investors realise.

This is not fear-mongering. This is not doomsday speculation.
>This is a dispassionate look at the mechanics of financial engineering that are currently holding the AI ecosystem together.

Let’s break this down in a detailed, structured 10-part analysis.

1. Circular Financing

The first pillar of the AI bubble is a phenomenon known as circular financing-a financial loop where money flows in circles but produces the illusion of genuine economic growth.

The centerpiece of this loop is the relationship between Nvidia, OpenAI, and the enormous GPU ecosystem.

OpenAI’s Problem

OpenAI is currently the world’s most visible AI company, but it is also one of the world’s largest loss-making companies. Public estimates suggest OpenAI loses nearly $15 million per day.

Why?
Because the cost of training frontier AI models is astronomically high. The largest component of this cost is hardware-mostly Nvidia GPUs, the backbone of today’s AI infrastructure.

Nvidia’s Solution

To keep fueling AI growth, Nvidia committed to investing as much as $100 billion into OpenAI.

Then the loop begins:

1. Nvidia invests in OpenAI.
2. OpenAI uses that money to purchase Nvidia GPUs.
3. Nvidia reports higher revenues.
4. Nvidia’s stock surges.
5. OpenAI’s valuation increases as it is now “well funded.”
6. Nvidia’s market value increases, giving it more leverage to invest again.

This cycle creates the illusion that AI demand is organically skyrocketing, when much of it is being artificially created through self-funded customer purchases.

In traditional finance, this would be called revenue washing-when a company funds a customer who then uses that money to buy its own products.

In AI, it is the new normal.

The Result

Nvidia’s revenue grows.
OpenAI’s valuation grows.
Stock markets celebrate.
Analysts justify absurd valuations.

But behind the scenes, the money that created this growth never left the ecosystem-it simply rotated within it.

This structure forms the bedrock of today’s AI bubble.

2. The OpenAI–AMD Engineering Play: How $40 Billion Materialised Out of Thin Air

The second major bubble driver is the financial engineering triangle between OpenAI, AMD, and the stock market.

Nvidia dominates the AI chip market, but AMD wants a piece of the multi-trillion-dollar opportunity. However, AMD cannot compete with Nvidia’s ability to fund its customers with billions of dollars. AMD’s market cap is about $400 billion-far smaller than Nvidia’s.

So how does AMD ensure OpenAI buys its chips?

Not through cash.
Not through loans.
But through warrants.

According to AMD’s SEC filings, the company gave OpenAI:

• 160 million shares
• At an exercise price of $0.01
• Giving OpenAI a potential stake worth over $40 billion
• In exchange for deploying 6 GW of AMD compute capacity

This is extraordinary. OpenAI can acquire a massive shareholding in AMD without spending actual money. AMD essentially handed OpenAI a lottery ticket, hoping the partnership announcement would inflate AMD’s stock-and it worked.

Outcome of the Engineering

• OpenAI wins: It gets a stake worth tens of billions for almost nothing.
• AMD wins: Its stock price rises on the news.
• AMD sells more chips to OpenAI.
• Investors interpret this as real demand.

But once again-no independent economic activity took place.
OpenAI didn’t invest capital. AMD didn’t receive real cash. No true market demand was validated.

It’s valuation manipulation dressed as strategic partnership.

And it inflates the bubble even more.

3. CoreWeave: The AI Infrastructure Unicorn That Benefits From Every Side

CoreWeave is one of the most dramatic examples of AI bubble mechanics.

Originally a small crypto mining firm, CoreWeave reinvented itself as an AI-focused data center operator. Today, it has a valuation exceeding $50 billion, and its stock has jumped over 150% since its IPO.

Why CoreWeave Matters

CoreWeave is central to the circular ecosystem:

• It builds massive AI data centers.
• It rents GPU computing power.
• Nvidia is one of its largest suppliers.
• OpenAI is one of its largest customers.

So far, this is normal. What makes CoreWeave unusual is the financial loop:

Loop 1: CoreWeave buys Nvidia GPUs

Massive bulk orders → Nvidia revenues explode.

Loop 2: Nvidia buys cloud services from CoreWeave

Nvidia signs a $6.3 billion agreement to use CoreWeave as a cloud partner.

On announcement:
CoreWeave stock surges.
Nvidia stock rises.

Loop 3: Nvidia owns 7% of CoreWeave

So Nvidia benefits from CoreWeave’s stock surge, caused partly by Nvidia’s own spending.

Once again:

• Nvidia funds the ecosystem.
• The ecosystem buys Nvidia chips.
• Nvidia books more revenue.
• Nvidia’s market cap rises.

It is the most profitable self-reinforcing cycle in modern corporate history-and one of the biggest contributors to the AI bubble.

4. Geopolitical Loopholes: How Sanctions Created Fake Demand and Higher Prices

Geopolitics is adding a dangerous new layer to AI bubble formation.

US Restrictions on China

The US has restricted the export of high-end AI chips (H100, A100, etc.) to China. Initially, the government allowed the export of “downgraded” versions, but in late 2024 and 2025, even those were banned.

This should have logically reduced Nvidia’s Chinese revenues.
But the opposite happened.

The Nebius Loophole

Nebius, a Dutch company, buys Nvidia chips legally because Netherlands has no export restrictions. Nebius then offers cloud computing services powered by Nvidia chips. Chinese firms are allowed to rent these services even if they can’t buy the chips.

This creates a bizarre scenario:

• Nvidia sells chips to Nebius
• Nebius rents computing power to China
• China gets access to banned chips anyway
• Everyone technically complies with the law

And because Chinese firms must rent instead of buy, Nebius (and indirectly Nvidia) charge a massive premium.

Hidden Twist: Nvidia is also a Nebius investor

So Nvidia:

• Supplies chips
• Benefits from Nebius’s inflated rental prices
• Maintains Chinese demand indirectly
• Preserves revenue growth
• Strengthens shareholder confidence

But the demand is artificial, driven not by business needs but by geopolitical loopholes.

Once again, this inflates the AI bubble from yet another angle.

5. SPVs: Shell Companies Used to Hide Hundreds of Billions in AI Debt

Perhaps the most dangerous element of the AI bubble is the widespread use of Special Purpose Vehicles (SPVs).

An SPV is a legally separate entity created solely to run a specific project or hold specific assets. SPVs allow companies to hide debt and shift financial risk away from their main balance sheet.

Case Study: Elon Musk’s xAI and the $20 Billion Supercomputer “Colossus”

To build an AI supercomputer like Colossus-which requires massive quantities of Nvidia GPUs-xAI would need $20 billion in funding.

If xAI took this debt directly:

• The company would appear financially risky
• Growth prospects would look weak
• Investors might pull back
• Valuation could collapse

So instead:

xAI creates an SPV

The SPV raises:

• $12.5 billion in debt
• $7.5 billion in external equity

The SPV then:

• Buys Nvidia GPUs
• Owns the physical hardware
• Rents the GPUs to xAI

xAI gets:

• Full access to the hardware
• Zero debt on its books
• A “clean” balance sheet

Meanwhile, debt investors mistakenly think they are financing a safe, high-growth asset.

The Twist: Nvidia is also a major investor in this SPV

Meaning Nvidia indirectly funded:

• The purchase of its own chips
• Through a shell company
• Using hidden leverage
• While reporting higher GPU sales
• Which inflate its stock price
• Which then gives Nvidia more ability to fund the next cycle

This is financial engineering at a scale that rivals the pre-2008 mortgage packaging system.

And it’s not just xAI.
Meta, Microsoft, and several other AI players are doing the same.

6. The Real SPV Risk

SPVs don’t reduce risk. They relocate it.

But what makes AI SPVs especially dangerous is the nature of the underlying asset: GPUs.

GPU Lifespan Is Short-Extremely Short

Most GPUs lose economic value within:

• 2-3 years due to rapid AI model evolution
• Even faster when the next generation launches
• Some estimates suggest their rental value drops 75% within 12–18 months

But SPVs are borrowing money with repayment expectations that assume:

• Long useful life
• Stable rental rates
• Consistent demand
• Growth that never slows

This is statistically impossible.

What Happens If AI Demand Slows?

The SPV:

• Cannot repay lenders
• Cannot recover cost by selling used GPUs
• Cannot attract new rental customers
• Gets stuck with rapidly depreciating hardware

The risk hits:

• Lenders
• Pension funds
• Private equity
• Insurance companies

This architecture closely resembles:

• Subprime mortgages → packaged into synthetic CDOs
• GPU hardware → packaged into SPV financing structures

The bubble is not just in tech valuations.
It’s inside the financial plumbing that nobody sees.

7. The Dot-Com and 2008 Parallels: The Same Movie, Different Actors

Every bubble feels new, but all bubbles rhyme.

Parallels with the 2008 Subprime Crisis

2008 was built on the idea that:

• Housing prices will never fall
• Bad loans can be spread out
• Risk can be hidden
• Borrowers will always repay
• Demand will always increase

Today’s AI bubble is built on:

• GPU demand will never fall
• AI companies will eventually become profitable
• SPV leverage can be safely hidden
• GPU rental rates will stay high
• AI computing needs will only grow

But GPUs are worse than houses because:

• Houses appreciate long-term
• GPUs guaranteed depreciate
• Houses have 50–100 year life
• GPUs have 2–3 year effective life

This makes the underlying collateral far more fragile.

Parallels with the Dot-Com Crash of 2000

Before the dot-com crash:

• Every company added “.com” to its name
• Investors funded hype, not revenue
• Growth was assumed to be infinite
• Any product remotely online was considered futuristic

Today:

• Every company adds “AI-enabled”
• Toothbrushes, fridges, and washing machines claim to be AI products
• App builders advertise “instant AI integration” even if unnecessary
• Investors fund AI startups without revenue or business models
• Companies exaggerate their AI capability for stock price gains

But just like the dot-com bust:

The crash doesn’t kill the underlying technology.
It kills the hype, excess, and over-leveraged players.
The internet survived and thrived.
AI will too.

But valuations will not.

8. Why Apple and Google Are Avoiding the Bubble (And Why They’ll Survive the Crash)

While the rest of the tech world is drowning in debt-driven AI infrastructure spending, Apple and Google are taking fundamentally different, far more stable approaches.

Apple’s Strategy: No AI Data Center Arms Race

Apple’s philosophy is extremely clear:

• Let others burn billions building cloud infrastructure
• Apple will integrate only the best AI outcomes
• Apple will rely on local compute
• AI models will run on-device, not in the cloud
• Zero dependence on massive GPU clusters

This drastically reduces Apple’s capital expenditure.
Apple has:

• No SPVs for AI
• No hidden debt
• No multi-billion-dollar GPU commitments
• No exposure to inflated infrastructure costs

Instead, Apple partnered with Google to integrate Gemini into Siri as a custom version.

The company’s actual AI advantage-the silent revolution-is its chip architecture. Apple’s M-series and A-series processors allow powerful AI execution on-device, with near-zero marginal cost.

This protects Apple from everything fueling the bubble.

Google’s Strategy: AI Powered by Its Own Hardware (TPU)

Google built its own processors-TPUs (Tensor Processing Units)-specifically designed for AI workloads. This gives Google:

• Enormous independence from Nvidia
• Better long-term cost efficiency
• Control over its own AI infrastructure
• A reliable upgrade path
• Lower financial risk

Google also has the world’s strongest distribution funnel:

• YouTube
• Search
• Android
• Gmail
• Maps
• Chrome

Google is already monetizing AI today-not projecting monetization five years from now.

This is why Google and Apple remain relatively safe even if the AI bubble bursts.

9. When Will the AI Bubble Burst?

No one can time a bubble perfectly.
But we can analyse historical patterns, macro cycles, and underlying structural weaknesses.

Why the bubble hasn’t burst yet

• The US wants to stay ahead of China
• Companies don’t want to lose the AI race
• Shareholders love rising stock prices
• CEOs can’t afford to be left behind
• Investors want to ride momentum
• The government doesn’t want a pre-election crash

This is why every major CEO knows a bubble is forming-but continues spending.

Just like Citigroup CEO Chuck Prince said in 2007, one year before the global crisis:

“As long as the music is playing, you’ve got to keep dancing.”

Today the AI industry is dancing harder than ever.

A Reasonable Timeline Estimate

Stock markets tend to move in 8-year cycles.

• Last cycle peak was expected around 2023–24
• But the pandemic broke the cycle
• A new cycle started in 2020
• Next peak → around 2028
  

This places the highest probability of a bubble burst somewhere between:

2025 → 2028

But remember:
Bubbles burst when no one expects them to.

10. Will This Affect Indian Markets? How Indian Investors Should Prepare

Yes - India will be affected.
No - India cannot decouple from the US yet.
Yes - Indian markets will fall if the AI bubble bursts.

Why India Will Be Hit

• Foreign institutional investors will pull out
• Risk-off sentiment will dominate
• Global tech slowdown will affect Indian IT
• Commodity prices may fall
• Capital flows will shrink

India is a fast-growing market, but it is not yet insulated from global shocks.

How Indian Investors Should Protect Themselves

1. Build an Emergency Fund

This is non-negotiable.
It protects you from forced selling during a downturn.

2. Create an “Opportunity Fund”

Allocate 30–50% of future monthly investments to:

• Debt mutual funds
• Fixed deposits
• Government bonds
• Treasury bills

This gives:

• Steady returns (7–8%)
• Liquidity during a correction
• Capital to deploy when valuations fall

3. Learn Basic Hedging

Indian investors must learn:

• How PUT options protect portfolios
• How covered calls reduce risk
• How to hedge index exposure

For US investing:

• Use inverse ETFs (they go up when markets go down)

Hedging is no longer optional.

4. Avoid Overexposure to High-P/E Tech

When bubbles burst, the highest valuation sectors fall hardest.

5. Focus on Cash-Generating Businesses

Prioritize companies with:

• Strong cash flows
• Low debt
• High ROCE
• Reasonable valuations

These fall less and recover faster.

Conclusion

AI’s future is bright, powerful, and inevitable.
But the financial structure built around AI today is fragile, dangerous, and unsustainable.

• Circular financing inflates revenues
• SPVs hide enormous debt
• Geopolitics artificially boosts demand
• GPU infrastructure depreciates too fast
• Valuations outrun fundamentals
• Companies spend money they don’t have
• Investors believe infinite growth is possible

The bubble will burst.
The only unknown is the exact timing.

When it does burst:

• Billionaires will escape unharmed
• Institutional investors will survive
• Retail investors will suffer most

The only true protection is:

• Financial literacy
• Proper asset allocation
• Hedging strategies
• Emergency buffers
• Avoiding hype
• Investing in fundamentals, not narratives

AI is the future.
The bubble is not.