Good morning & happy Tuesday! Today, all eyes are on Klarna and why this digital bank is being priced as a broken subprime lender (deep dive into Klarna's latest financials, breaking down the most important facts & figures, what they mean & why Klarna might be worth your time and money this year + bonus deep dives into Klarna's AI plays inside), and Robinhood, which is about to allow every investor to invest in the most promising startups (why Robinhood Ventures Fund I is a Trojan horse for the private markets, what to expect next + bonus deep dive into Robinhood's latest financials inside). So let's jump straight into the interesting stuff '' Following the money ' Buy Now, Pay Later (BNPL) pioneer Klarna KLAR 0.00%' recently posted its latest Q4 2025 financials, and it presents one of the more compelling risk/reward asymmetries I have encountered lately. Sure, the stock price chart doesn't look inspiring at all, but consider what that valuation buys: a company that generated $3.5 billion in revenue in FY2025 (up 25% YoY), processed $127 billion in GMV across 26 markets, serves 118 million active consumers, and just delivered its first billion-dollar revenue quarter....

Generative AI is transforming retail pricing decisions by providing an accessible and low-cost alternative to traditional pricing algorithms. Unlike traditional approaches, LLM-based pricing relies on natural language prompts, not custom code and historical data. However, LLM-based pricing introduces challenges around consistency, explainability, and potential biases. Implementation examples demonstrate how to prompt LLMs and use their recommendations to optimize product and service pricing. How can recent advances in generative AI tools be applied to transform pricing decisions' By lowering technical and financial barriers, such tools democratize access to sophisticated pricing capabilities, empowering even small businesses to benefit from artificial intelligence without the need for costly, bespoke solutions. There are fundamental differences between GenAI-driven approaches and traditional algorithmic pricing that should inform its use. Effective pricing recommendations depend heavily on how prompts are crafted (for now, at least). In this article, drawn from my forthcoming book on pricing in the age of AI, I'll be discussing the recent, promising trend of using large language models to support pricing decisions....

Several recent court cases contend that the pricing algorithms used by hotels and multifamily landlords pose antitrust risks. Federal regulators have intervened to argue that these systems can lead to unintended collusion and antitrust violations, even without explicit agreements among the parties. Businesses can reduce the risk of collusion with algorithms that rely on decentralized ' as opposed to centralized ' decision-making and use only public, not private, data. For decades, hotels, airlines, casinos, and other companies have used revenue management systems to help them set prices, maximize revenues, and gain competitive advantage. Now, in a series of legal cases, plaintiffs have argued that some of those systems' pricing algorithms could be used to facilitate illegal price-fixing in violation of federal antitrust law. Typically, collusion over pricing requires explicit coordination among competitors, the kind one might imagine occurring in the stereotypical smoke-filled room. What makes the recent lawsuits worth paying attention to is the idea, expressed by federal regulators, that the use of pricing algorithms can lead to collusion without such overt agreements ' and even if companies didn't intend to collude. If this view of collusion prevails, it could pave the way for even more antitrust lawsuits over algorithmic pricing....

Surveillance pricing has dominated headlines recently. Delta Air Lines' announcement that it will use artificial intelligence to set individualized ticket prices has led to widespread concerns about companies using personal data to charge different prices for identical products. As The New York Times reported, this practice involves companies tracking everything from your hotel bookings to your browsing history to determine what you're willing to pay. The reaction has been swift. Democratic lawmakers have responded with outrage, with Texas Rep. Greg Casar introducing legislation to ban the practice. Meanwhile, President Donald Trump's new chair of the Federal Trade Commission has shut down public comment on the issue, signaling that the regulatory pendulum may swing away from oversight entirely. What's missing in this political back-and-forth is a deeper look at the economics. As a business school professor who researches pricing strategy, I think the debate misses important nuances. Opponents of surveillance pricing overlook some potential benefits that could make markets both more efficient and, counterintuitively, more equitable....