When AI starts making databases more legible, the company holding the database becomes exponentially more powerful. That's the core tension Experian CEO of Technology Alex Lintner navigates in an in-depth Decoder interview with Nilay Patel. The credit bureau has built 200 AI agents into its products. It's deploying small language models to detect model drift in lending algorithms. It holds financial behavior data on 247 million Americans. And now AI is giving Experian—and competitors like Equifax and TransUnion—new ways to query, connect, and monetize that information. The conversation doesn't resolve whether this amplifies consumer empowerment or further concentrates power among data brokers.

The conversation starts with a deceptively simple question: What is Experian? And Lintner gives the practiced answer—a global data and technology company that helps consumers and businesses make financial decisions. But Patel keeps pressing on the primitive fact underneath: Experian maintains a massive database about people, their credit histories, their ability to pay. Everything else is layers built on top of that foundational concentration of data.

Then Patel introduces the actual thesis: AI is about to make those databases vastly more legible. Not just queryable in the old sense, but dynamically connectable. More patterns visible. More edge cases detectable. More sophisticated risk models deployable at scale. And that changes the power equation entirely.

This is where Lintner's defensiveness emerges. He pivots away from "database" language entirely, reframing Experian's infrastructure as "cloud-native, AI-enabled platforms" that keep "privacy, consent, and security at the center." But the reframe doesn't quite land because Patel's thesis isn't about Experian's intentions—it's about structural power.

The proof is in the operational layer. Experian has built 200 AI agents into its products. It's using small language models to detect "model drift"—the moment when actual loan losses diverge from predicted loan losses in lender risk models. When that happens, the AI tells the financial institution's oversight team exactly which variables are causing the divergence. The lender then adjusts their model. This is technically "AI for good," Lintner argues. More accurate lending decisions. Fairer outcomes. Better regulatory compliance.

But here's the inflection: this same capability—the ability to detect patterns across millions of datapoints, correlate variables in real time, and surface insights that humans would miss—is what makes Experian more powerful to hold data in the first place. The governance tool is also the power amplifier.

Lintner acknowledges the tension but doesn't resolve it. When Patel asks whether increasing centralization and scale have led to feeling less empowered, Lintner says: "Unfortunately, I would say you're correct with that." Then he offers solutions—easier opt-out mechanisms, a US-based call center, the free Experian Boost tool for building credit.

None of these are wrong. Experian Boost is genuinely free for consumers and banks. The real-time credit update feature is legitimate innovation. The data security infrastructure—with its 25-shard encryption and compartmentalization—is sophisticated.

But they're solutions to the wrong problem. The issue isn't whether Experian uses its power responsibly in a specific instance. It's whether the power asymmetry itself is sustainable as AI makes legibility cheaper and more sophisticated. A consumer can opt out of Experian, Lintner says. But in a financial system where 247 million Americans participate in credit markets, not participating means exclusion, not empowerment.

That contradiction sits at the heart of the entire interview. Lintner positions Experian's data as a public good—it's the infrastructure that enables credit access, which is strongly correlated with economic mobility. He's not entirely wrong. Countries without credit scoring systems have slower economic evolution than the US. Immigrants without credit histories face genuine hardship. Lintner himself spent years with a 90-minute commute because he couldn't access credit to buy a car.

But that argument conflates two different things: the necessity of credit information systems and the necessity of centralized, privately-held databases. You could theoretically have credit scoring without Experian. The question is whether AI amplification makes centralized holding of that data more or less justified.

When Patel presses on whether Experian is becoming Palantir, Lintner's answer is defensively narrow: "We're not Palantir, so we don't do reputation scores. We are very much in financial services, healthcare, automotive, and digital marketing. That's where we play."

But that's not the meaningful distinction anymore. Palantir's power comes from its ability to connect disparate databases and surface patterns that are individually invisible. AI is making that capability increasingly commodified. If Experian builds 200 agents into its products, why couldn't the same agents eventually connect financial data, healthcare claims, automotive history, and digital marketing signals in ways that create a composite individual risk profile? The technical capability and the business incentives are already aligned. Only policy is holding it back.

Lintner insists human oversight through data scientists is the critical control. Nothing goes to production without validation. When AI systems fail, Experian tests and isolates them. This is responsible governance. But it's also a scaling problem that gets worse, not better, as AI deployment accelerates. Experian has 11,000 technologists. Even if 10% of them focus exclusively on AI oversight, that's 1,100 humans trying to validate the behavior of thousands of agents across millions of transactions. The math stops working at some scale.

The security component adds another layer of risk. Lintner says security is "the first dollar" Experian spends, and the company hasn't had a breach in 10 years. But more data, more agents, more connectivity creates more attack surface. As AI systems become better at pattern matching, attackers become better at exploiting them. Experian bought Neuro-ID to detect bots—but that's an arms race, not a solution.

What emerges from the conversation is not a market inflection point but a policy vacuum. Experian has implemented the responsible practices available to it given current regulation. But the technology is moving faster than governance. AI makes databases more legible. Legibility amplifies power. Power without accountability creates political risk. And that risk will eventually trigger regulatory response.

Lintner sees this coming. When asked about consumer disempowerment, he doesn't argue the premise—he concedes it and offers tactical solutions. That's not confidence in the current system. It's acknowledgment that the current system is under pressure. The inflection isn't whether Experian will change. It's whether policy will force it to.

The Experian interview reveals a company caught between technical capability and political risk. AI is making data legibility cheaper and more sophisticated, which amplifies Experian's power to influence lending decisions at scale. The company has implemented thoughtful governance practices—human oversight, staged deployment, security-first architecture—but governance can't scale fast enough to match AI acceleration. The real inflection isn't happening inside Experian. It's happening in policy. Watch for 2026 regulatory moves on AI transparency in credit scoring, data holder liability for model bias, and mandatory algorithmic audits. That's when the tension between legibility and empowerment becomes a regulatory problem rather than a corporate governance challenge.