What If the Price Tag Lied? Rethinking the CPI with AI

Imagine stepping into a grocery store where every price tag is a little off. The milk says $2.59, but at the register it rings up $4.19. The bread you swear cost $3.10  last week now quietly sells for $3.10. After a while, you stop trusting the tags altogether. You grab what you can afford, muttering that the system is rigged. That’s roughly how Americans feel about the Consumer Price Index today.

The CPI was meant to be a yardstick for inflation, a way to measure the changing cost of living. Instead, it has become a source of distrust. People hear that inflation is “only” 3 percent while their rent jumps 10, their groceries climb 8, and their health insurance creeps up year after year. The numbers feel disconnected from lived reality. Which raises a deeper question: if people don’t trust the data, can they really trust the institutions built on it?

The stakes couldn’t be higher. The Federal Reserve sets interest rates using CPI as a compass. Social Security payments are adjusted by it. Even wage negotiations often anchor around it. If that compass is faulty, the whole ship drifts. So what if we rebuilt it? What if, instead of a blunt tool from the 20th century, we used artificial intelligence to create a living, breathing map of how prices move through different segments of American life?

The CPI is, at heart, an average. Government statisticians build a “basket of goods” to represent the typical consumer’s purchases. They then track how the prices of those goods change over time. That sounds reasonable—until you realize just how messy “average” is. Take housing. In many urban centers, rent has doubled over the last decade. But CPI measures housing costs partly through a statistical construct called “owners’ equivalent rent,” which asks homeowners how much they think they could rent their houses for. That number often understates what actual renters pay, softening the blow.

Or consider food. The basket assumes a certain mix of meat, produce, packaged goods, and dining out. But if your diet skews vegan, or you live in a food desert where fresh produce is scarce, your experience diverges dramatically from the official line. In short, CPI does what it was designed to do in 1919: smooth the mess into a single story. But in a world of wildly different lifestyles, geographies, and income brackets, that single story often feels like fiction.

Artificial intelligence could change that. AI is, at its core, a pattern recognizer. Feed it mountains of data and it can spot relationships that traditional models miss. That’s exactly what we need in a world where price data is both abundant and fragmented. Instead of one basket for “the average consumer,” AI could build dynamic baskets for different segments of the population. Think low-income families in rural areas, middle-class households in mid-sized cities, high-income renters in coastal metros. Each group faces different pressures, and AI could continuously recalibrate those baskets based on actual spending data.

Timing is another difference. CPI today is a monthly release, often lagging by weeks. But with access to anonymized transaction data, retail scanners, and online prices, AI could track changes almost in real time. Imagine a dashboard showing how grocery prices are trending this week in Dallas versus Des Moines. That’s not science fiction—it’s already possible. And unlike the static weights CPI uses, AI could account for the context of each household. A 15 percent jump in eggs matters more to a family spending half its income on food than to a household where groceries are a rounding error. AI could weight categories based on their actual share of spending, creating a more human-centered measure of cost of living.

Picture how this might look in practice. Start with anonymized consumer transaction data, already collected by credit card companies, banks, and large retailers. Feed that into an AI model trained to cluster households by spending patterns rather than crude demographic averages. Instead of a single national basket, you get a constellation of baskets that better reflect lived realities. Overlay that with geographic data. Inflation in Miami often looks different than inflation in Minneapolis. With AI, regional CPIs could be updated continuously, feeding into a national picture that respects diversity rather than erasing it.

Transparency would matter as much as technology. One reason people distrust CPI is that it feels opaque. A modern system could publish not just the headline numbers but the breakdowns: here’s what rent did for lower-income renters in Chicago, here’s how groceries moved in rural Montana. When people see their own lives reflected in the data, trust follows.

Some will argue this is technocratic tinkering. But perception is power. If Americans believe official numbers are disconnected from reality, the legitimacy of those numbers erodes. That skepticism seeps into politics, fueling populist anger and distrust of institutions. A smarter CPI would not just help the Fed fine-tune interest rates. It would give policymakers sharper tools to design targeted relief. It would help employers understand wage pressures in specific sectors. And it would give households a more honest gauge of how they’re doing relative to the broader economy. In a sense, it’s about fixing the price tags in the grocery store. When the numbers line up with what people actually feel, confidence in the system rises.

Of course, AI is no magic wand. It inherits the biases of the data it’s trained on. If transaction data underrepresents cash users or unbanked households, their experiences risk being excluded. Privacy, too, is a real concern. The government would need strict guardrails to ensure anonymization and prevent misuse. And there’s the risk of complexity itself. A CPI that updates daily, across dozens of segments, could overwhelm rather than clarify. The challenge would be to design outputs that are granular enough to be useful, but simple enough to be actionable.

The Consumer Price Index has survived a century because it served a purpose: give policymakers a stable, digestible read on inflation. But stability has come at the cost of accuracy. In an age of data abundance, we no longer need to choose between the two. Artificial intelligence offers the chance to rebuild CPI into something more honest, more responsive, and more trusted. Not perfect—nothing is—but better aligned with how people actually live.

The real test, though, is not technical. It’s cultural. Can government institutions admit that the old compass is broken? Can they embrace a tool that reflects the messy, diverse reality of modern American life? until they do, the price tags will keep lying.