# Uber and Lyft Use Artificial Intelligence to Price Rides > Source: > Published: 2026-06-18 15:07:23+00:00 # Different Prices for the Same Ride: How Uber and Lyft Use AI to Get More Money Out of You A Consumer Reports investigation found that customers see dramatically different prices for the same rides ordered at the same time. The study also raises questions about consumer discounts. Uber and Lyft, the two most popular ride-hailing companies in the U.S., routinely charge different customers significantly different prices for the same rides, a monthslong Consumer Reports investigation has found. Across the routes we tested, the median difference between the lowest and highest price groupings was about 50 percent. Both apps also regularly entice customers to book rides by offering supposed discounts on what appeared to be inflated original prices, a practice that experts say not only is deceptive and manipulative but also may violate several states’ consumer-protection laws. We found that nearly 11 percent of all discounts advertised on both platforms fell into this category. We believe these discounts to be fake—what experts and regulators call false reference pricing or fictitious discounts. Uber and Lyft deny that they engage in any fictitious pricing, attributing our findings to real-time marketplace conditions. They also challenged our methodology and conclusions and stated that they do not personalize base fares for individual consumers or engage in behavioral or surveillance pricing. CR is not disputing this; rather, it is questioning whether the price differences observed are based only on market forces. Uber also disputes that the rides our volunteers priced should be considered the same. We define the same ride as a trip from the same starting point to the same ending point priced at almost the same time—generally within a few minutes of one another and, in many cases, within the same minute. Uber says they are not the same trip. “In a real-time marketplace, a trip is defined not only by *where* it starts and ends, but also by *when* it is requested and what marketplace conditions exist at that exact moment,” the company writes. Algorithmic and AI-driven pricing tactics like those used by Uber and Lyft are attracting growing attention and criticism from consumers, lawmakers, and regulators alike. A [CR investigation](https://www.consumerreports.org/money/questionable-business-practices/instacart-ai-pricing-experiment-inflating-grocery-bills-a1142182490/) of the grocery delivery app Instacart found that the company used AI-enabled software to group customers and charge them different prices for the same products at some of the nation’s largest grocery chains. This year, Connecticut and Maryland became the first states in the U.S. to ban certain forms of personalized pricing, and other states are considering similar measures. Uber and Lyft have both exploded in popularity since they launched in 2009 and 2012, respectively. After years of rapid growth, Uber had more than 200 million active users at the end of 2025, while the much smaller Lyft had nearly 30 million. Uber is considered a pioneer of “dynamic” and “surge” pricing, where prices can rapidly fluctuate based on changing supply and demand. Americans have grown accustomed to—[if also frustrated with](https://www.ipsos.com/en-us/whatever-item-people-dont-want-pay-surge-pricing-it)—the idea of paying more for goods and services during periods of high demand or low supply, as with flights, hotel rooms, and concert and sporting-event tickets. But the pricing practices observed in our Uber and Lyft tests are different from dynamic or surge pricing. Because our volunteers booked identical rides at roughly the same time, the dramatic price differences we saw can’t be explained away purely by the economics of supply and demand. In addition to the dramatic range of prices for similar rides and the fake discounting, our tests found that Uber and Lyft take between 43 and 49.5 percent of each fare, a percentage that has been growing in recent years as drivers’ shares have fallen. To calculate the average amount each company takes from each trip, we also conducted a first-of-its-kind test in which volunteers requested rides and were subsequently matched with a driver from a select pool of workers affiliated with the Drivers’ Union in Portland, Ore., where there are minimum pay levels and city-imposed fees. We then compared the receipts from both riders and drivers to see how much people were paying and how much drivers were receiving from fares. CR’s testing was conducted in March and April and consisted of both “virtual” testing, in which volunteers checked the prices of 30 select routes (15 for Uber and 15 for Lyft) across 17 states, and 12 in-person tests in which volunteer riders purchased the same rides at the same time in Portland. The CR tests examined advertised offers and promotions for both Uber and Lyft before rides were ordered and paid for. The analysis did not control for certain marketplace variables, such as driver supply, differences in estimated arrival times, routing differences, traffic changes, rider location precision, or network latency, because those factors were outside the scope of the rider-facing data we collected. (These factors are also generally not accessible to outside researchers like CR.) The analyses were designed to evaluate observed rider-facing pricing patterns and platform economics across comparable routes and closely aligned booking windows, rather than to model all internal marketplace variables that may influence platform pricing in real time. In addition, our volunteer sample was not representative of the U.S. population. We do not believe these limitations substantively impact our findings. ## Different Prices for the Same Rides Camille opens her Uber app on a Wednesday evening and looks for an UberX ride between two towns near Florida's Gulf Coast. The price is listed at $94.96. It has no promotion or discount. Chuck opens his Uber app at the same time and looks at the same Florida route as Camille. His undiscounted fare is $65.95. Same route. Same day. Same time. Different prices. This wasn't an isolated case. In New York City, a 30-minute, 8-mile Uber ride from Chinatown in Manhattan to Long Island City in Queens would have cost three customers less than $40. Seven other riders saw prices between $40 and $47. Seventeen got prices between $47.94 and $47.96. And two other customers saw prices of nearly $49 and almost $50. Another example: For an 18-mile Lyft trip across the Kansas City metro area, about half of the 55 volunteer riders would have paid roughly $40. But seven were quoted less than $31 and six others were quoted around $50, $55, and $65. The highest fare, in other words, was more than double the lowest. Across three virtual and two in-person CR tests, 174 volunteers priced more than 40 routes on Uber and Lyft across the U.S. All were checked at nearly the same moment, and on every single route, the same trip came back with a constellation of different prices. Some had promotions and discounts. Many did not. Each square at right represents one rider at the price they would have paid. **The median difference between the highest and lowest price groups on the 30 virtual routes was 50 percent.** Experts who reviewed our findings said that, while they expected to see evidence of dynamic pricing, they didn’t expect to see such large price differences between the highest and lowest fares. “The magnitude of the high/low price differentials is astonishing,” says Len Sherman, an executive-in-residence at Columbia Business School in New York City who has written several papers on the economics of the rideshare industry. There’s a simple reason companies like to offer different prices to different customers for the same thing: It enables them to increase sales and profits. For years, both Uber and Lyft followed a generations-old taxi industry practice of setting rider fares based on fixed per-mile and per-minute rates, with additional fees, surcharges, surge premiums, and the occasional discount mixed in. But starting around 2016, both apps began shifting to “up-front pricing,” where they show would-be riders algorithmically generated fares for yet-to-be