Why Software Stocks Fell: Artificial Intelligence Was the Spark, but Credit Stress Made the Fire Bigger

When people see software stocks falling sharply, the easiest explanation is usually the simplest one: “Artificial intelligence is killing software.”

That explanation is partly true, but it is not complete.

A better explanation is this: software stocks fell because investors suddenly became less confident about their future profits, while the financial system was already nervous because of stress in private credit. Artificial intelligence was the obvious headline. Credit stress, high valuations, and crowded positioning made the fall more violent.

To understand this properly, we need to separate four things:

First, what software companies actually sell.

Second, why artificial intelligence is a real threat to some of those profits.

Third, why private credit stress can spread into the stock market.

Fourth, why a falling stock price does not always mean a broken business.

1. What software companies sell

Many large software companies sell subscriptions. A business pays every month or every year to use software. For example, a sales team might pay for customer relationship software. A human resources department might pay for employee-management software. A finance team might pay for data, reporting, or planning software.

This model is attractive because the money is recurring. Recurring means it comes back again and again. If a customer pays £100 every month, the company does not need to make a new sale every month. It only needs to keep the customer happy enough to stay.

That is why software companies used to receive very high stock market valuations. A valuation is simply the price investors are willing to pay for a business. If investors believe a company can grow for many years with high profit margins, they are willing to pay a high price today.

Here is the simple idea:

Company value depends on what investors think future profits will be worth today.

If investors believe future profits will be very large, the stock price can be high.

If investors suddenly believe future profits may be smaller, the stock price can fall quickly.

This is exactly why software stocks are sensitive to artificial intelligence. The threat is not that every software company disappears tomorrow. The threat is that some customers may need fewer paid software seats, fewer manual workflows, or fewer separate tools.

A “seat” simply means one paid user. If a company pays £50 per month for each employee using a piece of software, then 1,000 users means 1,000 paid seats.

The calculation is easy:

\[1{,}000 \text{ seats} \times £50 \text{ per month} = £50{,}000 \text{ per month}\] \[£50{,}000 \text{ per month} \times 12 \text{ months} = £600{,}000 \text{ per year}\]

Now imagine artificial intelligence helps the same company do the work with 700 seats instead of 1,000 seats.

\[700 \text{ seats} \times £50 \text{ per month} = £35{,}000 \text{ per month}\] \[£35{,}000 \text{ per month} \times 12 \text{ months} = £420{,}000 \text{ per year}\]

The software company has lost:

\[£600{,}000 - £420{,}000 = £180{,}000 \text{ per year}\]

That is why investors are nervous. Even if the software remains useful, the amount customers pay could shrink.

2. Why artificial intelligence caused real fear

The software selloff in early 2026 was not imaginary. Reuters reported that global software and services stocks lost about 830 billion dollars of market value over six trading days from 28 January 2026. The Standard and Poor’s 500 software and services index fell nearly 13 per cent over the same six-session period and was down 26 per cent from its October 2025 peak. Reuters linked the selloff to investor fear that artificial intelligence tools were moving into valuable enterprise work such as legal work, sales, marketing, and data analysis. (1)

Market value means what the stock market says a company is worth.

The formula is:

\[\text{market value} = \text{share price} \times \text{number of shares}\]

Suppose a company has 1 billion shares.

If each share is worth 100 dollars:

\[1 \text{ billion} \times \$100 = \$100 \text{ billion of market value}\]

If the share price falls to 80 dollars:

\[1 \text{ billion} \times \$80 = \$80 \text{ billion of market value}\]

The market value has fallen by:

\[\$100 \text{ billion} - \$80 \text{ billion} = \$20 \text{ billion}\]

So when people say hundreds of billions of dollars were “wiped out”, it does not mean cash physically disappeared from a bank account. It means the market price of the companies fell.

Anthropic launched Claude Opus 4.6 on 5 February 2026. Anthropic said the model improved coding skills, could sustain agentic tasks for longer, and included a 1 million-token context window in beta. In plain English, that means the system could handle much larger amounts of information and work through longer, more complex tasks than earlier versions. (2)

This matters because traditional software is often built around humans clicking buttons. Artificial intelligence agents are different. They can read information, decide what to do next, use tools, and complete multi-step tasks. If that becomes reliable inside companies, then some old software pricing models become weaker.

But this does not mean artificial intelligence will kill all software.

A serious business still needs databases, permissions, audit trails, security controls, accounting records, customer records, and reliable systems. Artificial intelligence may sit on top of those systems. It may reduce some manual work. It may also make software more valuable by making it easier to use.

So the correct conclusion is not “software is dead.”

The correct conclusion is: artificial intelligence has made the future profits of some software companies harder to estimate.

When the future becomes harder to estimate, investors usually demand a lower price.

3. Why high valuations make falls worse

A stock can fall badly even if the company is still good. This is especially true when the stock was expensive before the fall.

Imagine two businesses.

Business A earns £10 million per year and the stock market values it at £100 million.

Business B earns £10 million per year and the stock market values it at £300 million.

Both businesses earn the same profit. But Business B is priced three times higher.

A simple valuation measure is:

\[\text{price-to-earnings ratio} = \frac{\text{market value}}{\text{annual profit}}\]

For Business A:

\[£100 \text{ million} \div £10 \text{ million} = 10\]

So investors are paying 10 times annual profit.

For Business B:

\[£300 \text{ million} \div £10 \text{ million} = 30\]

So investors are paying 30 times annual profit.

Business B may deserve the higher price if it grows faster. But if investors start to doubt the growth, the fall can be much sharper.

This is important for software stocks. Many software companies had been priced as if growth would continue for a long time. When artificial intelligence created uncertainty, the market did not need proof that profits would collapse. It only needed doubt.

A small change in belief can create a large change in price when a stock is expensive.

4. What private credit is, in plain language

Private credit means lending money outside the public bond market.

A public bond is like a loan that can often be traded in a market. Investors can see prices more easily. There are more buyers and sellers.

Private credit is more like a private loan agreement between a borrower and a lender. The lender might be an investment fund, insurance company, pension fund, or asset manager. These loans are often less easy to sell quickly.

The Financial Stability Board estimated the private credit market at between 1.5 trillion and 2 trillion dollars and said its links with banks, insurers, pension funds, and private equity firms are deepening. It also warned that private credit has not yet been fully tested in a severe economic downturn. (3)

The International Monetary Fund also warned that, in a severe downturn, private credit losses could be hard to assess because the market is opaque. “Opaque” means hard to see through. The International Monetary Fund also said that because of links between private credit and other investors, insurance companies and pension funds may be forced to sell more liquid assets. (4)

A liquid asset means something easy to sell.

A share in a large public company is usually liquid because there are many buyers and sellers.

A private loan is often illiquid because there may be no easy market for it.

This creates an important problem.

If an investor owns a bad private loan and needs cash quickly, they may not be able to sell that loan at a fair price. So they sell what they can sell. Often, that means selling public stocks.

This is how stress can move from private credit into the stock market.

Not because the software company caused the credit loss.

Not because the software company suddenly became worthless.

But because the software stock is liquid and can be sold quickly.

5. The private credit warning signs were real

The private credit stress was not just theory.

Fitch reported that the default rate among United States corporate borrowers in its private credit monitor reached 9.2 per cent in 2025, up from 8.1 per cent in 2024. Fitch’s data covered 302 companies with private credit debt and recorded 38 defaults among 28 borrowers. Reuters also reported that many of these loans were floating-rate loans, meaning their interest cost moves with interest rates, which made borrowers more vulnerable when rates stayed high. (5)

A default means a borrower fails to meet its debt obligation. This could mean missing a payment, entering bankruptcy, or restructuring the debt because the original terms can no longer be met.

The default-rate formula is:

\[\text{default rate} = \frac{\text{number of defaulted borrowers}}{\text{total number of borrowers}}\]

Using Fitch’s reported borrower numbers:

\[28 \text{ defaulted borrowers} \div 302 \text{ borrowers} = 0.0927\] \[0.0927 \times 100 = 9.27\%\]

Rounded, that is about 9.2 per cent.

This tells us something important. Private credit was not perfectly calm. There were real signs of stress.

There were also high-profile failures. First Brands, an auto-parts supplier, filed for Chapter 11 bankruptcy protection in September 2025. Reuters reported that the company disclosed liabilities between 10 billion and 50 billion dollars in an initial filing, and later reporting said court documents showed total liabilities of 11.6 billion dollars. (6, 7)

Tricolor, a subprime auto lender and dealership, filed for Chapter 7 bankruptcy in September 2025 after Fifth Third Bank warned of alleged fraudulent activity. United States prosecutors later charged several Tricolor executives over an alleged fraud scheme involving double-pledged collateral. (8, 9)

Double-pledged collateral means the same asset is promised to more than one lender.

Here is a simple example.

Imagine I own one car worth £10,000.

I borrow £7,000 from Bank A and say, “If I do not repay you, you can take the car.”

Then I borrow another £7,000 from Bank B and secretly promise the same car again.

The problem is obvious. There is only one car. Both banks think they are protected. But if I fail, the car cannot fully protect both lenders.

That is why double-pledging is dangerous. It makes the lender believe the loan is safer than it really is.

6. Why credit stress can hit software stocks

Now we can connect the pieces.

Artificial intelligence made investors question the future profits of software companies.

Private credit stress made investors more nervous about hidden losses.

High valuations made software stocks vulnerable.

Crowded ownership made the fall sharper.

The key idea is forced selling.

Forced selling means an investor sells because they need cash or because their risk limits tell them to sell, not necessarily because they think the asset is bad.

Imagine a family needs £5,000 quickly.

They own two things:

An old piano that may be worth £5,000, but it could take months to find a buyer.

A liquid investment account that can be sold today.

If they need cash immediately, they sell the investment account, even if they like the investments.

Financial markets can work the same way.

If a fund owns private credit that is hard to sell, and also owns public software stocks that are easy to sell, it may sell the software stocks first.

That is why a fall in software stocks can be bigger than the change in software fundamentals alone.

But we must be careful. This does not prove private credit caused the software selloff. It shows a credible transmission mechanism. In plain English, it shows a possible route by which stress in one part of the financial system can spread to another.

The evidence supports a multi-cause explanation, not a single-cause explanation.

7. Why “artificial intelligence killed software” is too simple

A simple story is emotionally satisfying. But investing usually requires more than one cause.

The software selloff can be explained by several forces working together.

Artificial intelligence changed the market’s view of future software profits.

High valuations meant there was little room for disappointment.

Private credit stress made investors more cautious.

Some investors may have sold liquid stocks to raise cash or reduce risk.

Once prices started falling, other investors may have sold too.

This is how a normal concern becomes a market rout.

A rout means a sharp, disorderly fall in prices.

The important lesson is that stock prices do not only move because of company fundamentals. Fundamentals mean the actual business facts: revenue, profit, customers, cash flow, debt, and competitive position.

Stock prices also move because of liquidity, fear, leverage, and positioning.

Liquidity means how easy something is to buy or sell.

Leverage means using borrowed money to invest. Leverage increases both gains and losses.

Positioning means how many investors already own the same thing. If everyone owns the same stock and everyone tries to sell at once, the exit door becomes crowded.

8. The middle-school version

Imagine a school cafeteria.

For years, one lunch counter sells the best food. Everyone buys from it. Because it is popular, people believe it will always make money.

Then a new machine appears. The machine can prepare some meals automatically. Students start asking: “Will we still need the old lunch counter as much?”

That is the artificial intelligence part.

At the same time, some students have lent money to other students privately. A few borrowers fail to repay. Suddenly, everyone becomes nervous.

That is the private credit part.

Now some students need cash quickly. They cannot easily sell the private loans, because nobody knows what they are worth. So they sell something else that is easy to sell: their shares in the popular lunch counter.

That is the forced-selling part.

The lunch counter may still be good. But its price falls anyway because many people are selling at the same time.

That is the software stock selloff.

9. What investors should actually ask

The right question is not: “Is software dead?”

The right question is: “Which software companies still have durable value after artificial intelligence changes the workflow?”

Durable value means value that can survive change.

A strong software company may have several protective features.

It may own important customer data.

It may be deeply embedded in a company’s workflow.

It may control sensitive records where security and audit trails matter.

It may serve a regulated industry where mistakes are costly.

It may use artificial intelligence to make its own product better.

A weaker software company may have the opposite problem.

It may only automate simple tasks.

It may charge per human seat even though artificial intelligence reduces the need for humans to do that work.

It may have customers who can switch easily.

It may depend on features that artificial intelligence can copy cheaply.

This is why stock-picking matters more after a major technology shift. The market may sell many companies together at first. Later, it starts separating the strong from the weak.

10. The final lesson

The software selloff was not caused by one thing.

Artificial intelligence was a real spark. It made investors question whether some software companies could keep charging the same prices in the same way.

Private credit stress was a real background risk. Fitch’s data showed private credit defaults had reached a record 9.2 per cent in 2025, and regulators had already warned that stress in private credit could spill into public markets. (5, 4)

High valuations made the fall worse because expensive stocks have less room for disappointment.

Liquidity pressure may have amplified the move because investors often sell what they can sell, not what they want to sell.

So the cleanest explanation is this:

Artificial intelligence changed the story.

Credit stress weakened the floor.

High valuations made the drop larger.

Forced selling may have made the move faster.

That is how modern markets work. Prices do not always fall because a business is broken. Sometimes they fall because the future becomes harder to value, the owners become nervous, and the easiest assets to sell get sold first.

For an investor, that is both a warning and an opportunity.

The warning is that expensive stocks can fall hard when the market changes its mind.

The opportunity is that a forced selloff can sometimes push good businesses below their true long-term value.

The hard part is knowing which is which.