By Dave DeFusco
A Journal of Corporate Finance study by Pablo Hernandez-Lagos, director of the MBA program in the Sy Syms School of Business, has found that AI companies can sometimes boost profits by keeping their technology less transparent, even if greater openness would increase user adoption.
To understand why, it helps to start with how modern AI works. Systems like chatbots or recommendation engines improve by learning from users. Every interaction—every prompt, click or correction—becomes data. The more people use a system, the more data it collects and the better it becomes. Economists call this a “feedback loop”: more users lead to more data, which leads to a better product, which attracts even more users.
At first glance, this suggests that companies should want users to fully understand their technology. After all, if people trust and understand a product, they’re more likely to adopt it. Hernandez-Lagos shows that this intuition can break down, especially for companies that rely heavily on investors.
In his model, firms face a balancing act. On one side are users, who generate data and revenue. On the other are financial investors, who fund the company and shape its valuation. Investors don’t directly see how good the technology really is. Instead, they infer its value from signals, especially how much the company spends developing it. That’s where things get complicated.
“When you open the newspapers and see companies spending $100 billion on AI, it’s puzzling,” said Hernandez-Lagos. “These firms are spending more than entire countries on research and development. That made me question what’s really driving those decisions.”
In theory, spending should reflect genuine progress, but, in practice, it also serves another purpose: signaling. Big spending can convince investors that a company’s technology is valuable, even if its true potential is still uncertain. Hernandez-Lagos calls this effect “manipulation power.”
“Because we don’t know the value of the technology yet, firms can use their spending as a signal,” he said. “If they spend a lot, people might believe the technology is better than it actually is.”
This creates a subtle incentive. If users understand the technology very well, they adopt it with more confidence. That increases data and revenue, but it also forces the company to spend even more to meet expectations and maintain its image with investors. In some cases, those extra costs grow faster than the benefits. The result is counterintuitive: better-informed users can actually reduce profits.
“The key mechanism is that more transparency leads to more adoption,” said Hernandez-Lagos, “but that also amplifies spending to a level that can hurt the bottom line. The extra revenue can be offset, or even outweighed, by the extra costs.”
To make this concrete, he offers the simple analogy of a job interview. Candidates often put in enormous effort to impress employers—far more than they might sustain on the job itself. Companies, he argues, behave in a similar way with investors: they “overperform” through spending to signal quality. That pressure creates what he describes as a kind of arms race not necessarily with competitors, but with expectations.
“Companies would be better off if they could coordinate and just spend what’s needed,” he said. “They don’t, though, because each one wants to look like the leader in the eyes of investors.”
This dynamic also helps explain a broader trend in the AI industry: why many powerful systems remain opaque. Despite widespread use, the inner workings of large AI models are often difficult to fully understand even for experts. This isn’t just a technical challenge, according to Hernandez-Lagos, it’s an economic choice.
“If the technology is very uncertain, firms may avoid making it fully transparent,” he said. “Transparency can tie them to higher spending, because they have to live up to the expectations it creates.”
His model shows that this lack of transparency isn’t mainly about hiding secrets from competitors or avoiding regulation. Instead, it stems from how companies interact with financial markets. The need to impress investors can distort decisions about how much to explain and how much to spend.
Hernandez-Lagos suggests that if companies rely less on investor money and more on revenue, or on public funding, they may feel less pressure to signal through excessive spending.
“Public institutions like universities could help develop and validate the technology,” he said. “That would reduce the need for firms to prove its value through massive expenditures.”
Another approach is for companies to rethink their “capital structure”—the mix of funding from investors versus customers. Finding the right balance, he said, could reduce wasteful spending and encourage greater openness.
“The stakes are high,” said Hernandez-Lagos. “As AI becomes more central to the economy, understanding these incentives matters not just for businesses, but for society. Calls for transparency are growing louder, especially as people worry about the risks of systems they don’t fully understand.”
