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Fintech Stocks Slide Amid Tariff Uncertainty

Market Volatility Raises Concerns Over Consumer Credit and Loan Repayments. Financial technology companies—including Robinhood and buy now, pay later (BNPL) provider Affirm—have been caught in the crosshairs of President Donald Trump’s sweeping tariff policy, with shares tumbling as investors brace for economic uncertainty.

Fintech Faces Growing Pressure

Since Trump’s April 2 tariff announcement, global markets have been rattled, sparking fears of higher consumer prices, weaker demand, and a potential recession. Fintech firms, which rely on consumer spending and loan repayments, are particularly vulnerable to economic downturns.

  • Affirm (AFRM.O) shares have dropped over 21%, reflecting investor concerns over BNPL customers’ ability to repay loans.
  • Robinhood (HOOD.O) is down more than 17%, as its revenue from debit and credit card transactions could decline with softer consumer spending.
  • SoFi (SOFI.O) has lost nearly 20%, given its exposure to personal loans and banking services.

“A recession typically hits mass-market consumer businesses—including fintechs—harder than other sectors, as lower-income consumers cut back first,” said James Ulan, director of research at PitchBook.

Delinquencies On The Rise?

For credit-extending fintechs like Affirm and SoFi, the key concern is rising delinquency rates.

  • Affirm reported 2.5% of its monthly loans were delinquent by over 30 days as of December 31—slightly up from the previous year.
  • SoFi said 0.55% of its personal loans were delinquent by more than 90 days in the same period.
  • For comparison, banks reported a 2.75% delinquency rate on consumer loans, according to the Federal Reserve.

“With renewed inflation, excess cash flows are squeezed, and the ability to service debt weakens,” said John Hecht, analyst at Jeffries.

A Silver Lining?

Despite the turbulence, some analysts see a potential upside. If tariffs push Treasury yields lower, borrowing costs for fintech lenders could drop, making credit extension less risky.

“This could have unintended positive consequences for fintech stocks,” said Dan Dolev, senior analyst at Mizuho, arguing that markets may be overreacting.

Investors are also watching for potential negotiations on tariffs, which could ease recession fears and help stabilize fintech stocks.

“The real damage so far is mostly psychological,” said Nick Thompson, research analyst at Intro-act. “If we see quick relief, markets could rebound fast.”

Copyright Law Struggles To Keep Up With AI Training

Courts Are Still Applying Old Copyright Rules To AI

AI companies train models on enormous amounts of published material, including books, articles and academic research. Whether using that content without authors’ permission violates copyright law remains unresolved.

Much of the debate centres on fair use, which allows copyrighted material to be used without permission in certain circumstances. Courts consider factors such as the purpose of the use, how much material was involved and its impact on the original market.

Anthropic Case Sets An Important Precedent

A major case involving Anthropic and a group of authors provided one of the clearest rulings so far. Judge William Alsup found that using copyrighted books to train AI models was lawful, comparing the process to people reading and studying literature before creating something new.

Anthropic was nevertheless ordered to pay $1.5 billion in a settlement. The penalty concerned books the company had obtained from illegal online libraries rather than the AI training itself.

For AI companies, that distinction could prove significant because it separates studying copyrighted material from directly copying it.

Competition Could Be The Key Issue

A case involving Thomson Reuters and Ross Intelligence offers a different perspective. A court ruled that Ross could not claim fair use after using Reuters’ copyrighted material to develop a competing AI-powered legal research platform.

The decision suggests courts may be less willing to consider AI training fair use when copyrighted content is used to build a product that directly competes with the original.

For authors, an unresolved question is whether AI-generated content should be considered competition for the works used to train these models.

The Law Has Yet To Catch Up

US copyright law predates generative AI by decades, leaving courts to apply old principles to new technology. Questions also remain over copyright protection for AI-generated works. In Thaler v. Perlmutter, a court ruled that material created entirely by AI cannot receive copyright protection.

Major AI companies remain involved in copyright litigation, and different courts could reach different conclusions. For now, there is no universal rule: the legality of AI training will depend on the circumstances of each case and how courts ultimately interpret copyright and fair use.

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