$3.6 Million an Hour—and Other Ways to Measure Elon Musk’s Fortune
Millions of houses, thousands of jets, every NFL and NBA team—imagine the things a trillionaire could buy.
3 min
The initial public offering for SpaceX could make Elon Musk the world’s first trillionaire. Just how wealthy is the tech founder?
His fortune now stands at roughly $970 billion, mostly in stock, according to a Wall Street Journal analysis.
Accumulating that amount over his career averages out to $992 a second.
Musk’s wealth includes $538 billion for his pre-IPO stake in SpaceX, $167 billion for his stake in Tesla , and another $150 billion or so for stock options in those companies he could exercise just about any time, the Journal analysis found.
Then there is $5 billion apiece for The Boring Company, which drills tunnels, and Neuralink, the brain-implant firm he founded, and $104 billion in property, aircraft and other investments and assets as estimated by Altrata, a wealth-intelligence firm
He has made about $3.6 million an hour over 31 years
Musk is 54 years old and co-founded the first of his many U.S. tech- and engineering-oriented companies in 1995, 31 years ago. To amass $970 billion in that time meant accumulating roughly:
An American household earning the median U.S. income ($83,730 in 2024) would have to work more than 11 million years to make his wealth.
To be sure, the success of Tesla and SpaceX also has made billions of dollars for investors who bet on Musk and made millionaires of employees who got shares in the businesses.
Ingrid Robeyns, a philosopher and economist, has written that the wealth of the world’s richest has soared so much it is nearly incomprehensible for laypeople to grasp.
She recently estimated that Musk would make about $4.2 million an hour in his career, if he worked 70 hours a week without vacations until he is 75 years old.
Musk, of course, is known for sleeping on factory floors and rarely taking vacations. After buying Twitter, he has said, his work exploded to more than 120 hours a week from about 80 hours before.
Buy 2.4 million U.S. homes or 10,000 private jets
Most of Musk’s wealth is tied up in his companies. He famously said in 2020 that he would “own no house” and sold off several California properties, only to later buy homes in Texas.
Musk can borrow billions against his holdings in SpaceX and Tesla, but much of his wealth is on paper—not cash he can easily spend.
Here are some things a person with $970 billion could do with that amount of money:
$970 billion is bigger than the yearly output of most countries
Musk’s net worth eclipses the annual economic activity of more than 125 countries, including Norway, Thailand, Argentina and South Africa.
His self-made fortune, built on electric vehicles rocket ships and artificial-intelligence ambitions amounts to about 3% of U.S. gross domestic product today. On that basis, he easily surpasses John D. Rockefeller , the richest American who ever lived before Musk.
A century ago, Rockefeller rode the wave of industrialisation by building Standard Oil into a behemoth, wielding influence over railroads and pipelines. The monopoly was ultimately broken up by the federal government.
Rockefeller amassed a fortune of about $1.4 billion by 1937—roughly 1.5% of U.S. GDP at the time. Here is how that compares in terms of today’s economy:
This explanatory article may be periodically updated.
Sources: Altrata (Musk net worth excluding options; Bezos, Ellison and Zuckerberg net worth); Securities and Exchange Commission filings (Tesla and SpaceX stock options); Census via St. Louis Fed (2024 median U.S. annual household income, first-quarter 2026 median U.S. house sale price); Forbes (NFL and NBA team values); Liberty Jet (G700 operating costs); International Monetary Fund (2026 GDP by country); Harvard Business School case study (Rockefeller wealth) undefined Photos: Getty Images (Musk); Associated Press (Musk, Bezos, NBA, NFL); Reuters (Zuckerberg, Ellison); Bloomberg (homes, jet)
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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