October 16, 2026
For much of the artificial intelligence boom, investors focused on demand for chips, data centers, cloud computing, and the businesses building the infrastructure behind the technology.
That conversation is beginning to change.
A recent Wall Street Journal article highlighted growing concern that higher borrowing costs could pressure the enormous amount of capital still required to build AI infrastructure. The issue is no longer simply whether demand for AI remains strong. Investors are beginning to ask which companies can finance that growth without putting too much pressure on their balance sheets.
Michael Landsberg, Chief Investment Officer at Landsberg Bennett Private Wealth Management, believes this could create an important divide within the AI trade.
“Picks-and-shovels companies making AI hardware are better-positioned than those relying on cheap borrowed money to expand.”
His conclusion was equally important:
“This is not a riskless trade. There’s definitely going to be winners and losers.”
The next phase of AI investing may therefore be less about owning anything connected to the theme and more about identifying which businesses have the financial strength to keep participating as capital becomes more expensive.
Building artificial intelligence infrastructure requires significant investment.
Data centers need chips, networking equipment, power systems, cooling technology, real estate, and increasingly large amounts of electricity. Much of that infrastructure must be financed before the revenue it generates is fully realized.
When borrowing costs were lower, financing aggressive expansion was easier.
That equation changes as interest rates rise.
Businesses that depend heavily on debt may have to pay higher interest expense, offer more attractive terms to lenders, or issue additional equity to keep funding their expansion.
That does not necessarily mean AI spending stops.
It means the economics of spending become more important.
Demand for AI computing capacity remains significant, but Michael’s comments highlight why investors should separate the strength of the overall theme from the strength of individual businesses participating in it.
Some businesses sell the equipment, components, and infrastructure required for the AI buildout. They may benefit directly from continued investment across the industry.
Others must borrow significant amounts of money to construct the infrastructure before they can earn a return from it.
Those are very different financial models.
A company can operate in a rapidly growing industry and still face difficulty if debt costs rise faster than its ability to generate cash.
This is why Michael views AI as an investment theme with both winners and losers rather than a single trade moving in one direction.
One of the more important developments highlighted by the Wall Street Journal was weakness in debt tied to data-center businesses.
That matters because credit markets can provide a different perspective on financial risk.
Equity investors may focus on revenue growth, market share, and future opportunities. Bond investors tend to place greater emphasis on whether a company can service its debt and repay lenders.
When borrowing costs rise or bond prices begin falling, investors may be signaling greater concern about the financial structure supporting that growth.
AI has therefore become more than an equity-market story.
It is increasingly influencing the bond market as well.
Artificial intelligence has also become deeply connected to broader economic growth.
Investment in AI infrastructure has supported spending across technology, construction, power generation, industrial equipment, networking, and other industries.
That means a slowdown in AI investment could affect more than technology shares.
The Wall Street Journal highlighted how businesses with meaningful AI exposure have contributed substantially to broader market gains.
This creates a concentration issue that Michael has discussed repeatedly.
A portfolio may own businesses across several industries but still have significant exposure to the same underlying AI spending cycle.
The labels may be different, but the economic driver can be the same.
Higher Treasury yields also change the investment calculation.
When investors can earn higher yields from government securities, businesses must offer stronger potential returns to compensate for the additional risk of owning equities or corporate debt.
For AI businesses, that places greater emphasis on cash flow and profitability.
Businesses with strong balance sheets and substantial internal cash generation may be able to continue investing without relying heavily on outside financing.
Businesses dependent on borrowing may face a more difficult environment.
The result could be a widening gap between financially strong AI businesses and those whose growth models were built around inexpensive capital.
Michael’s reference to “picks-and-shovels” businesses reflects a familiar investment concept.
During a large-scale infrastructure boom, the businesses supplying the tools required for expansion can sometimes benefit regardless of which end user ultimately wins.
In AI, those tools can include semiconductors, networking systems, electrical equipment, cooling infrastructure, and other components required to build and operate computing capacity.
These businesses still face risks. Demand can slow, valuations can become elevated, and competition can increase.
But their economics can differ substantially from businesses that must borrow heavily to construct large amounts of infrastructure.
That distinction may become more important if interest rates remain elevated.
AI investment has become meaningful enough that a slowdown could have broader economic consequences.
Large infrastructure projects support construction, manufacturing, energy demand, equipment spending, and employment.
If financing becomes too expensive, some projects could be delayed, scaled back, or canceled.
The key question is whether demand for AI computing remains strong enough to justify continued investment despite higher financing costs.
For now, demand remains substantial.
But the combination of elevated rates and increasingly large capital requirements means investors may need to pay closer attention to how each company is financing its growth.
We continue to believe artificial intelligence represents a significant long-term investment theme.
But a strong theme does not make every company attached to it equally attractive.
As borrowing costs rise, we believe balance-sheet strength, available cash flow, debt levels, and the ability to fund growth internally will become increasingly important.
Infrastructure providers supplying essential systems may be positioned differently from businesses that depend on large amounts of borrowed capital to expand.
We also believe investors should understand how much AI exposure is already present across their portfolios. Technology, industrials, utilities, energy, and infrastructure holdings can all depend on the same spending cycle.
The AI opportunity remains significant, but the next phase may reward financial strength as much as technological exposure. The question is no longer simply who participates in AI. It is who can continue funding that participation when money is no longer cheap.
Source: New Cracks in AI Trade Push Wall Street to Worst Week Since June (subscription required)
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