In Part 1 of this series, we explained how a single strong jobs report wiped roughly $1 trillion (about ₹83 lakh crore) off the US market in one day, and how AI stocks sat at the centre of it.
This piece goes into that AI trade itself. And we will be honest with you: this is the most fascinating, and the most dangerous, story in markets right now.
We are not here to scream “bubble.” We are not here to tell you AI is a scam, because it clearly is not. We are here to show you both sides, so you can decide. Grab a coffee. This one is worth it.
The Number That Should Make You Stop Scrolling
In just the first three months of 2026, four companies — Microsoft, Amazon, Google, and Meta — committed roughly $725 billion (about ₹60 lakh crore) to building AI infrastructure.
Let that land with one comparison: India’s entire Union Budget for the year is around ₹50 lakh crore. So four American companies are spending more on AI in one year than the Indian government spends running the whole country.
$725B
Committed in Q1 2026 alone
₹60L Cr
More than India’s Union Budget
45–57%
Of revenue now spent on capex
$1T+
Forecast AI spend in 2027
$7.6T
Cumulative 2026–2031 (Goldman)
Here is the stat that genuinely stunned us: these companies now spend 45% to 57% of their revenue on capital expenditure, building things. That is a ratio you normally only see in electricity utilities and railways. Tech companies were never supposed to spend like this.
Four Companies vs. an Entire Country
Annual AI infrastructure commitment compared to India’s full-year Union Budget
India’s entire Union Budget (FY)~₹50 lakh crore
4 US tech giants — AI spend, one year~₹60 lakh crore ($725B)
Illustrative comparison. AI commitment figure from Q1 2026 company disclosures, annualised. India Union Budget approximate. This is the largest corporate building spree in human history.
So understand this clearly: this is not a normal investment cycle. The only question that matters is, will it work?
History Rhymes: The Cisco Story
Let us go back to 1999–2000. The internet was the new big thing. Everyone knew it would change the world. They were right, it did. But here is what people forget: being right about the technology did not save investors from losing everything.
At the centre of the dot-com boom sat Cisco Systems. Cisco made the “picks and shovels” of the internet, the routers and networking gear every website needed. If the internet was going to grow, everyone needed Cisco. Sound familiar?
March 2000
#1
Briefly the world’s most valuable company
The crash
−85%
Cisco stock fell from its peak
Profitable?
Yes
Still successful 26 years later
Recovered?
Never
Stock has not retaken its 2000 peak
The technology was real. The demand was real. The company survived. And investors who bought at the top still lost money a quarter-century later. Today, the company at the centre of AI is Nvidia, with the same airtight logic: AI is the future, everyone needs our chips, therefore we are worth a fortune.
History doesn’t repeat. But it often rhymes. A real revolution, a “picks and shovels” company at the centre, sky-high valuations, and total faith that demand will justify any price.
Yesterday’s $2 Trillion Signal: The SpaceX IPO
Something happened literally yesterday (12 June 2026) that tells you everything about the mood. SpaceX went public. Its value shot past $2 trillion, the largest IPO in history. Elon Musk became the world’s first trillionaire.
Here is the part most people missed. SpaceX is famous as a rocket company, but the only consistently profitable part of its business is Starlink. The rockets and the AI division both lose money. So why did it IPO looking like an “AI company”? Because in February 2026 it acquired xAI, and its own filing brags that it owns “the largest AI training data centre clusters on Earth.”
And yet the company has accumulated total losses of $41.3 billion since it was founded. When a loss-making company can become a $2 trillion company by leaning into the AI narrative, that tells you how badly investors want to believe this story. That hunger to believe is exactly what we saw in 1999.
The Bear Case: Why This Might Be a Bubble
Here are the strongest arguments from the people who think this pops. The loudest voice is Michael Burry, the investor who predicted the 2008 crash. We will walk through them as five core concerns, with the numbers behind each.
Bear 1: Customers spend, but cannot find the profit
A major MIT study (Project NANDA) looked at hundreds of company AI projects. The finding: 95% of corporate AI projects delivered zero measurable profit. If the companies buying all this AI are not making money from it, they will eventually stop buying, and the whole spending chain collapses.
The Uber Story
Burned its entire annual AI budget in 4 months
Uber gave engineers AI coding tools billed per use. Usage exploded; they had to cap each employee at $1,500/month to regain control. The COO admitted the link from AI bills to useful customer features “is not there yet.” Even Microsoft reportedly cancelled most direct Claude Code licences after token bills spiked.
The Starbucks Story
AI that couldn’t count, killed after 9 months
Starbucks rolled an AI “Automated Counting” tool across 18,000+ stores in Sept 2025. By May 2026 it was quietly killed, it confused similar products and miscounted. Its own launch video showed the tool missing a syrup bottle in plain sight. Staff went back to counting by hand.
The lesson is not “AI is useless.” It is that the gap between a slick AI demo and AI working reliably in the messy real world is often much bigger than the hype admits. Multiply that across thousands of companies, and you understand the MIT 95% number.
Bear 2: The money goes in a circle
This is the concern that, once you see it, you cannot unsee. Nvidia invests in OpenAI, who buys chips back from Nvidia. Microsoft invests in OpenAI, who spends it on Microsoft’s cloud. AMD gives OpenAI stock, OpenAI buys AMD chips. OpenAI pays Oracle $300 billion for cloud capacity, and Oracle turns around and buys $40 billion of Nvidia chips with it. Almost every dollar runs in a circle back through the same handful of companies.
Follow the Money: The Circular AI Trade
Investors fund AI firms — who spend it right back on the investors’ own products
The pattern to notice:
Nvidia, Microsoft & AMD hand OpenAI money or stock → OpenAI hands it straight back as chip and cloud purchases. Almost every arrow runs through OpenAI — the single point of failure. Oracle is the conveyor belt: it routes OpenAI’s $300B spend onward into ~$40B of Nvidia chips. The money barely leaves the circle.
Deal figures from public announcements. The bulls’ honest counter: this is normal supply-chain securing, locking in scarce chips and capacity ahead of real demand.
Why this is dangerous, the Nortel ghost: in the dot-com era, telecom giants Nortel and Lucent lent money to their own customers so those customers could buy Nortel and Lucent equipment. It created an illusion of booming demand. When the customers could not pay, the “revenue” vanished and Nortel collapsed into one of the biggest bankruptcies in history. That is the exact pattern flashing today.
The $800 Billion Hole (Bain & Company)
Annual revenue AI companies need by 2030 vs. what they are on track to generate
Revenue needed to pay for the build-out~$2.0 trillion / yr
Revenue actually on track~$1.2 trillion / yr
→ ~$800 billion annual shortfall, filled by more outside money or more circular deals
Source: Bain & Company study. The shortfall must be filled each year for the maths to hold.
Bear 3: $120 billion of hidden debt
To build data centres, these companies need enormous debt. But debt makes a company look risky. So what did they do? They moved the debt off their balance sheets, using structures called SPVs (Special Purpose Vehicles). More than $120 billion of AI data-centre debt now sits in separate entities that do not show up cleanly on the main books.
Debt Moved Off the Balance Sheet
Analysts are openly using the word “Enron” about these structures
| Company | Debt Moved Off-Balance-Sheet |
|---|---|
| Oracle | ~$66 billion |
| Meta | ~$30 billion |
| xAI (Elon Musk) | ~$20 billion |
| CoreWeave | ~$2.6 billion |
Why does this rhyme with history? Because this is almost exactly what Enron did before it collapsed in 2001, it hid its debt in off-balance-sheet entities until the whole thing came crashing down. You cannot judge a risk you cannot see. Right now, a lot of the AI debt is sitting where most investors are not looking.
Bear 4: The accounting may be flattering profits
When a company buys equipment, it spreads the cost over the equipment’s “useful life.” Claim a longer life, and your yearly costs look smaller and your profits look bigger, without earning a single extra rupee. Here is the smoking gun, all in the same recent period:
Same Chips. Same Technology. Opposite Accounting.
“Useful life” is a management choice, not a fact
| Company | Change to Server “Useful Life” | Effect |
|---|---|---|
| Microsoft | Extended 4 yrs → 6 yrs | Lowers reported costs, lifts profit |
| Meta | Extended to 5.5 yrs | Cut costs by $2.9B (~4% of pre-tax profit) |
| Amazon | Shortened to 5 yrs | Took a $920M charge, cited fast AI obsolescence |
Even Nvidia’s own CEO, Jensen Huang, admits the chips age fast. About his previous-generation chip he literally said: “When Blackwell starts shipping in volume, you couldn’t give Hoppers away.” Michael Burry estimates this accounting choice is hiding about $176 billion of costs across the industry between 2026 and 2028, meaning profits could be overstated by 20%+ at some companies. He has put real money behind it, betting against Nvidia.
Bear 5: Slowing cloud, a wobbling anchor, and real energy bills
If AI demand were truly limitless, cloud revenue would keep accelerating. The multi-year trend went the other way: Amazon’s AWS slowed from 36% average growth (2018–2022) to 17% (2023–2025). Google slowed from 45% to 29%. Meanwhile the anchor customer is shaky, OpenAI missed its own internal revenue and user-growth targets, and ChatGPT’s share of AI web traffic fell from 86.7% to 64.5% while Google’s Gemini jumped from 5.7% to 21.5%.
The energy bill is real and recurring. Oracle raised $43 billion of debt in a single fiscal year, and lender Blue Owl Capital walked away from a $10 billion Oracle data-centre deal over spending concerns. The International Energy Agency projects that by 2030, the world’s data centres will consume as much electricity as the entire country of Japan does today. In the US, data centres are on track to drive nearly half of all electricity demand growth.
The First Crack? Why Oracle Fell on Record Earnings
On 10 June 2026, Oracle reported arguably its best quarter in 15 years, records across the board. And the stock fell 7–10%. When a company reports records and the market sells it anyway, pay attention.
Revenue
$19.2B
Record, up 21%
Cloud Infra
+93%
Growth in the quarter
Backlog
$638B
Up 363% year-over-year
The stock
−40%
Already down from Sept 2025 peak
What spooked investors: over half that $638B backlog, roughly $300B, is tied to one customer, OpenAI, which is losing money. Oracle also announced raising another $20 billion of debt and guided FY2027 capex to $70 billion. Cloud costs rose 56% while cloud revenue grew only 47%, the cost of growth is rising faster than the growth. And of the giant $638B backlog, only about 12% (~$77B) is expected to become real revenue over the next 12 months; the rest is years away.
In Part 1, good news crashed the market. Here, record news crashed a single stock, because investors finally asked the only question that matters: who actually pays, and can you afford to build it?
The Bull Case: Why This Might Be Real
Now let us be fair, because the people who say this is not a bubble have strong, credible, audited arguments. These are not opinions; they are the companies’ own audited numbers and the exact words executives said on their earnings calls.
Bull 1: The AI itself is now making money, and getting more profitable
AI Margins Are Rising, Not Falling
The single strongest fact in the entire bull case
| Business | Operating Margin Before | Operating Margin Now |
|---|---|---|
| Google Cloud | 17.8% | 32.9% (income tripled to $6.6B) |
| Amazon AWS | 31.4% | 37.7% |
| Microsoft (AI) | CFO: AI margins “were actually better” than the cloud transition | |
Bull 2: The revenue is real, huge, and the backlogs are better quality
This is not 1999, when companies had stories but no sales. This is real revenue, in the bank, growing at triple-digit rates.
$37B
Microsoft AI ARR, up 123%
$20B
Google Cloud revenue, up 63%
+800%
Growth in GenAI-built products
$150B
AWS annual run rate, up 28%
$462B
Google Cloud backlog (doubled)
Google addressed the “fake backlog” fear directly, saying it expects to recognise just over 50% of its backlog as revenue over the next 24 months. Amazon’s $364 billion backlog is crucially diversified, the opposite of Oracle’s risky concentration. And research firm Gartner found AI chips are sold out 18 to 24 months into the future. In a fake bubble, you would see unsold inventory piling up instead.
The twist: the problem has flipped. A year ago the fear was “what if the demand isn’t real?” That fear is now dead. The new problem is the opposite, demand is so far ahead of what they can build that they physically cannot deliver it. The bottleneck is electricity, not demand.
Wait — This Is Why Your Next Laptop Costs More
Here is the part that affects you even if you never buy a single share. AI data centres need a special type of memory chip (HBM and high-grade DRAM). To make it, chip factories are shifting production away from the ordinary memory that goes into your phone, laptop, and PC. The result is a global memory shortage, and prices are exploding.
+50–60%
DRAM memory, in a single quarter
+110%
Consumer RAM prices
+147%
SSD prices, early 2026
+15–20%
PC prices (Dell, Lenovo, HP)
~20%
Memory’s share of a laptop’s cost
So the AI boom in faraway American data centres is literally making your next phone, laptop, and PC more expensive. When you hear “AI capex,” it sounds abstract, but the bill is already reaching into the pockets of ordinary consumers in India and everywhere else.
The Honest Verdict: What’s the Same, What’s Different
Here is the truth most loud voices on both sides will not tell you: a bubble can exist AND the technology can be real, at the same time. The internet was real, and still created a bubble that wiped out trillions. So the question is not “is AI real?” It is “are today’s prices justified by today’s reality?”
Same as Dot-Com
The worrying echoes
- Capex vs sales is now above the year-2000 peak.
- A single “picks and shovels” company (Nvidia, like Cisco) dominates.
- Circular vendor-financing (the Nortel echo).
- Debt hidden off the balance sheet (the Enron echo).
- A flood of money chasing anything labelled “AI” (see: SpaceX).
Bubble-like features are clearly present
Different from Dot-Com
The reassuring contrasts
- The big spenders are profitable, cash-rich giants, not empty startups.
- There is real, contracted, signed demand (the backlogs).
- AI workloads are getting more profitable, not less.
- Chips are sold out 18–24 months out.
- The bottleneck is power, not demand.
A fundamentally healthier problem
In 2000, companies couldn’t find customers. In 2026, they can’t build fast enough to serve the customers they already have. That is healthier, but it does NOT automatically make the prices safe.
The most honest summary: timing the pop is much harder than spotting that one exists. Bubbles can inflate for years before they burst, Cisco doubled after the first people called it a bubble, before it eventually crashed. Nobody can tell you the exact day. What we can do is recognise the warning signs and position sensibly.
What This Means for You, the Indian Investor
“I don’t own Nvidia or OpenAI. Why does this affect me?” Three ways.
Through your mutual funds
If you own any international fund, US-focused fund, or global tech fund-of-fund, you very likely own these AI stocks indirectly. Check your portfolio.
Through FII flows
When US markets fall hard, foreign investors often sell Indian stocks to cover losses back home. A serious AI correction can drag Indian markets down temporarily, even though India’s own economy is fine.
Through Indian IT companies
TCS, Infosys, and Wipro are deeply tied to global tech spending. AI is double-edged, it could automate away some traditional work, or create huge new demand for AI implementation services.
So what should you actually do? Not panic. Not sell everything. Not bet the house on AI either.
- Know what you own. Check if your funds have heavy US-tech / AI exposure.
- Diversify. Don’t let one theme, any theme, dominate your portfolio.
- Think in years, not days. If you believe in AI long-term, short-term volatility is noise.
- Keep your emergency fund. Always, six months of expenses in safe, liquid assets.
The investors who survive market cycles aren’t the ones who predict the future. They’re the ones who don’t get wiped out when their predictions are wrong.
The Bottom Line
The AI revolution is real. The technology is genuinely transformative. But “the technology is real” and “the stock prices are sensible” are two completely different statements, and the gap between them is exactly where investors got destroyed in 2000, even though they were right about the internet.
We are not telling you it crashes tomorrow. We are telling you: understand what you own, respect the history, and don’t get swept up in the mania just because everyone else is. That is what separates investors from gamblers.
Not Sure What AI Exposure Is Hiding in Your Portfolio?
If you own international or global tech funds, you may hold these AI stocks without realising it. Let us look at what you actually own, stress-test your exposure, and build a plan that compounds through cycles instead of getting wiped out by them. Book Free Consultation →Disclaimer: This content is for educational purposes only and is not investment advice. Markets carry risk. Please consult a qualified financial advisor before making investment decisions. All data is sourced from public reports and company earnings calls as of June 2026 and may change. Part 2 of the “Market Explained” series.