Trump and SoftBank Promise to Create 100,000 AI Jobs. It Won’t Be Easy.

SoftBank Group CEO Masayoshi Son stood besides President-elect Donald Trump at Mar-a-Lago on Monday, and announced a commitment to invest $100 billion in the U.S. over the next four years—and create 100,000 jobs. The investments will be concentrated in AI.

“My confidence level to the economy of the United States has tremendously increased with his victory,” Son said at the news conference.

SoftBank is an investment holding company with a range of technology investments all over the world, but especially in the U.S. At the end of September, the total value of its investments was $136 billion, so the new commitment would represent a substantial expansion of SoftBank’s balance sheet.

Raising that sort of money may prove difficult, but the bigger obstacle could be the commitment on jobs—the focus on AI companies, in particular, complicates the goal. AI companies spend a lot on salaries, but these are some of the most expensive employees in the world right now. And they don’t tend to employ a lot of people overall

OpenAI, which has raised $18 billion and has a private market value of $157 billion, has 1,372 employees. To fulfill Trump and Son’s commitment, in other words, the investments would have to create 73 AI companies on the scale of OpenAI.

“A lot of advanced tech these days, including AI, is capital intensive and also highly dependent on high-paid skilled workers,” labor economist Guy Berger of the Burning Glass Institute told Barron’s . “I’m not sure how much head count $100 billion spread out over four years gets you.”

A selection of a dozen AI start-ups with valuations over a billion dollars reveals the uphill climb to the 100,000 jobs goal. These companies have raised a combined $42 billion and have an aggregate private market value of $309 billion, according to FactSet. Anthropic, which has raised almost $12 billion, has only 425 employees. All told, these companies employ less than 10,000 workers, an average of 785 workers per start-up.

Databricks, a data analytics start-up with a valuation of $43 billion, accounts for almost half of those employees. Excluding Databricks, the per start-up employee count falls to 399.

Other barriers in the labor market exist, as well. The overall unemployment rate is 4.2%, but narrow that down to workers with masters and doctoral degrees who are most likely to be AI employees, and the rate drops to 2.0% and 1.0%, respectively. In all, there are only 483,000 workers with advanced degrees looking for work, and most of them aren’t AI engineers.

“The labor market is not super loose right now,” Berger said. “A lot of gross jobs created here might simply involve reallocating people who already have jobs.”

Monday’s press conference recalled a similar one from 2016, when Trump and Son stood in the lobby of Trump Tower and promised $50 billion in U.S. investment and 50,000 jobs. SoftBank didn’t reply to a request for comment about the progress of that 2016 commitment.

A 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