Is Your Uber App is Lying to You? Exposing Fake Ride Share Discounts: Ep. 129
About this episode
Ride-share discounts may be misleading, and the show walks through Consumer Reports’ investigation into “fictitious pricing” and “fake discounts.” Hosts connect that to how Uber and Lyft use algorithmic, upfront, and surge pricing—sometimes varying fares for the same trip at the same time. They also discuss controlled testing, targeted promotions, and the “black box” behind app pricing. The conversation closes with how regulators are responding, including “surveillance pricing” bans.
surge pricing
"And, you know, they upended the generation's old taxi model, you know, the per minute, per mile, you know, rate card, the meter that you would see in taxis. They completely changed that. And then, you know, years after they started, they unveiled upfront pricing. So the idea that you would open your app, you would look for a trip, a route, and you would see a price. And that price was, you know, a good faith estimate of how much it would cost you once you actually completed the trip. [351.2s] But it's algorithmically driven. And it's both on the rider side and the drivers also get their own sort of algorithmic price for that fare, right, that trip that they're going to perform. Same thing, independent contractors, right?"
Surge pricing means the app charges more when lots of people want rides at the same time. It’s meant to encourage more drivers to go online so you can still get a car.
Surge pricing is a dynamic pricing system where ride-share fares increase when demand is high and available drivers are scarce. Uber popularized it as a way to balance supply and demand in real time.
rate card
"And, you know, they upended the generation's old taxi model, you know, the per minute, per mile, you know, rate card, the meter that you would see in taxis. They completely changed that."
A rate card is the taxi’s official price list—often based on how long you ride and how far you go. It’s the older way of pricing compared with what the apps do.
A rate card is the published pricing schedule used by taxis, typically based on time and distance. The host contrasts this with ride-share’s app-based, algorithm-driven pricing.
upfront pricing
"And then, you know, years after they started, they unveiled upfront pricing. So the idea that you would open your app, you would look for a trip, a route, and you would see a price. And that price was, you know, a good faith estimate of how much it would cost you once you actually completed the trip. [351.2s] But it's algorithmically driven."
Upfront pricing is when the app tells you what the ride will cost before you get in. Even though it’s shown ahead of time, the number is still calculated by the app and can change.
Upfront pricing is the ride-share feature where the app shows an estimated fare before you confirm the trip. The estimate is still algorithm-driven, so the final cost can differ based on real-world conditions during the ride.
price gap
"And when we saw the price gaps, very similar, we actually saw a median price difference there, even larger than Instacurt, of 42.4% across all trips, 30 routes across the country when we looked into it."
They’re talking about how the same ride can cost noticeably different amounts. Even if you’re going to the same place at the same time, the price you see can be higher or lower than someone else’s.
“Price gaps” here means the difference in cost for the same ride (same route, same time) when booked through different accounts or channels. The hosts use it to show that ride-share pricing can vary substantially even when the underlying trip should be identical.
median price difference
"And when we saw the price gaps, very similar, we actually saw a median price difference there, even larger than Instacurt, of 42.4% across all trips, 30 routes across the country when we looked into it."
The “median” is the middle result when you line up all the price differences from smallest to largest. It helps show a typical difference without being thrown off by a few weirdly high or low cases.
A “median price difference” is the middle value of price differences across many trips, so it’s less distorted by extreme outliers than an average. In this segment, it’s used to summarize the typical gap between what different users pay for the same ride.
predetermined routes
"we brought in hundreds of volunteers, we had them price out trips, again, same time, same route, 30 predetermined routes across the country, 17 different states."
They picked specific trips ahead of time and used the same ones for comparisons. That way, they can tell whether price changes are really due to the booking/pricing system rather than a different destination.
“Predetermined routes” means the researchers selected specific trip paths in advance and then repeatedly priced the same route under controlled conditions. This matters because it reduces variables when comparing ride-share pricing.
in-person testing
"We also did in person testing too, because it's one thing to look virtually and, you know, look at the prices on your phone or your laptop. It's a different thing to actually take the ride and to see that trip play out."
Instead of only checking prices on a phone, they actually got in the car and took the ride. That helps confirm whether the app’s shown price matches what you really pay.
“In-person testing” is a research approach where they actually take the ride to verify what happens in the real world, not just what the app shows on a screen. The hosts contrast this with virtual price-checking because the final outcome can differ from the displayed estimate.
Portland, Oregon
"So we actually went to Portland, Oregon, and we actually matched volunteer riders with volunteer drivers in the same place."
They did part of their real-world testing in Portland, Oregon. The point was to see if the pricing differences still happen when people actually take the rides.
Portland, Oregon is the specific city where the hosts conducted an in-person ride-share test. They used it to validate that the pricing discrepancies they saw virtually also show up when the rides actually happen.
matching experience
"So we actually went to Portland, Oregon, and we actually matched volunteer riders with volunteer drivers in the same place. We all took sort of corresponding trips together, sort of a unique matching experience."
They set it up so volunteers were in the same place and took similar trips at the same time. That makes it easier to compare whether different people see different prices for essentially the same ride.
“Matching experience” describes their controlled setup where volunteer riders were paired with volunteer drivers in the same location and then took corresponding trips together. The goal is to keep the trip conditions as similar as possible while comparing what different users are charged.
airport trips
"And so, you know, there can be significant price gaps, especially for long trips, especially for airport trips, which a lot of people use Uber and Lyft 4."
They’re calling out rides to and from airports. Those trips can be priced differently than normal rides, and they claim the price differences are bigger there.
“Airport trips” refers to ride-share journeys to or from airports, which often have different demand patterns and pricing dynamics than typical commuting. The hosts say the biggest pricing discrepancies show up especially on these trips.
Uber
"And so, you know, there can be significant price gaps, especially for long trips, especially for airport trips, which a lot of people use Uber and Lyft 4."
Uber is a rideshare app that matches you with a driver and charges you for the trip. They’re discussing how the price you see can vary a lot for the same ride.
Uber is a ride-hailing platform that prices trips dynamically based on demand and other factors. In this segment, it’s mentioned in the context of airport trips where the hosts claim the app can show large pricing differences.
Lyft
"And so, you know, there can be significant price gaps, especially for long trips, especially for airport trips, which a lot of people use Uber and Lyft 4."
Lyft is a rideshare app like Uber. They’re saying the pricing differences they found can show up on trips people commonly take using Lyft too.
Lyft is another major ride-hailing platform, also using dynamic pricing. The hosts mention it alongside Uber when discussing where they observed especially large pricing discrepancies.
fictitious pricing
"Yeah. And maybe I can explain that, that last point a little bit more, but [702.5s] so the idea of fictitious pricing is sort of the academic term or fake discounts, right, is what [707.5s] we described it as."
Fictitious pricing means the app may show a fake “before” price to make the “after” price look cheaper than it really is. The goal is to make you think you’re getting a discount when the comparison isn’t trustworthy.
Fictitious pricing is a practice where the “original” price shown to customers is misleading or not a real price that was previously charged. In this episode, it’s used to describe how ride-share apps can present a higher struck-through fare to make a lower price look like a discount.
historical comparison messaging
"That's not really a discount. That is what they call historical comparison messaging. [752.7s] And there's a, there's a legal gray area there, you know, is that a bona fide discount, you know, according to regulators."
Historical comparison messaging is when the app says “prices are lower” by comparing today’s fare to an earlier price. It may not mean you’re getting a true discount versus what you would have paid immediately.
Historical comparison messaging is when a company justifies a “discount” by comparing the current price to a past price, rather than a price that was actually available to you right before purchase. The episode notes Uber’s claim that this is what’s happening in the example shown in their testing.
legal gray area
"And there's a, there's a legal gray area there, you know, is that a bona fide discount, you know, according to regulators. [757.8s] And, and that really can present, you know, some, there you go."
A legal gray area means the law isn’t totally clear, so it’s not obvious if the practice is allowed. In this case, it’s whether the “discount” wording is genuinely compliant or misleading.
A legal gray area is a situation where the rules aren’t clear-cut, so it’s uncertain whether a practice fully complies with regulations. Here, it’s about whether the app’s “discount” presentation counts as a bona fide discount under regulator standards.
bona fide discount
"And there's a, there's a legal gray area there, you know, is that a bona fide discount, you know, according to regulators. [757.8s] And, and that really can present, you know, some, there you go."
A bona fide discount is a real, legitimate price cut—not just marketing. It means the “original” price used to show the discount is trustworthy and the lower price is genuinely a reduction.
A bona fide discount is a legitimate discount that meets regulatory expectations—meaning the “before” price used for comparison is real and the reduction is genuine. The episode frames this as the key question regulators would consider when judging whether the app’s pricing claims are truthful.
fake discounts
"And then of those discounted prices, 12% of them had this, what we would call fictitious pricing or fake discounts where, you know, the original price was higher and then it was brought down to something lower, but that lower price was what everyone else in this group of shoppers had already seen, right?"
A fake discount is when the app pretends you’re saving money, but the “deal” price is really just the usual price. The higher crossed-out number is there to trick you into thinking it’s a bargain.
Fake discounts are promotions where the app’s “discounted” price is not actually lower than the real market price for that ride. The “strike-through” higher price is used to create the appearance of savings.
strike through
"you see this original price of 82.08, you know, the strike through and then the lower price of 65.95."
A strike-through is the crossed-out number you see on a screen. It’s meant to make you think the price used to be higher, so the new price looks like a discount.
A strike-through is the crossed-out “original price” shown on a screen to imply the price has been reduced. In this context, it’s used to visually suggest a discount even when the “original” number isn’t a true reference.
black box
"We can't see into the black box. So we don't know, you know, whether GPS signals or network latency or all these other sort of factors are playing a part in some of the prices that we see."
A “black box” means you can see the inputs and outputs, but you can’t see how the system makes its decisions. In this case, it’s the app’s hidden pricing logic.
A “black box” is a system whose internal logic isn’t visible to the user or even to outside analysts. Here it refers to the ride-share app’s behind-the-scenes pricing system, which the hosts say they can’t inspect directly.
GPS signals
"We can't see into the black box. So we don't know, you know, whether GPS signals or network latency or all these other sort of factors are playing a part in some of the prices that we see."
GPS signals are the location “pings” from satellites that your phone uses to know where you are. Ride-share apps can use that location info to calculate things like the route and price.
GPS signals are the radio transmissions from satellites that a phone or navigation system uses to figure out where you are. In ride-share pricing, the app may use location data derived from GPS to estimate routes, fares, and pickup/drop-off details.
network latency
"So we don't know, you know, whether GPS signals or network latency or all these other sort of factors are playing a part in some of the prices that we see."
Network latency is basically lag—how long it takes for data to travel over the internet. If the app is calculating prices in real time, that lag can sometimes affect what you’re shown.
Network latency is the delay between when your phone sends data to the ride-share service and when the service responds. If the app’s pricing or routing calculations depend on live data, latency can contribute to small differences in what different riders see.
net price
"But, you know, again, when you're looking at it from a consumer point of view, what really matters to people is the end price, right? The net price and the promotions and"
Net price is the final total you end up paying. They’re saying what matters is the final number after discounts and promos.
Net price is the final amount you pay after any promotions, discounts, or fees are applied. The hosts emphasize that consumers care about the end price, not just the “before discount” number shown in the app.
algorithms are sort of invading our everyday financial lives
"matters to people, especially when we're dealing with affordability and how algorithms are sort of invading our everyday financial lives."
The idea is that computer systems are using your data to decide what you’re shown and how much you pay. The worry is that it’s not always clear or fair how those decisions are made.
This refers to algorithmic pricing and targeting, where software models use user and trip data to influence what you see and pay. In this context, the concern is that pricing and discounts may be driven by data patterns rather than transparent, uniform rules.
promotions and discounts
"Uber and Lyft told us is that they don't personalize base prices. And that's a fine point that they're making, right? So that's the core or the underlying price of a particular ride, but that they do personalize promotions and discounts."
Promotions and discounts are deals that lower the price of a ride. Even if the starting price is the same for everyone, these deals can make the final price you pay feel totally different.
In ride-hailing pricing, promotions and discounts are temporary offers (like coupons or reduced fares) that can change what you actually pay for a trip. The key point here is that they can effectively become the “real” price even if the base fare is unchanged.
base price
"Uber and Lyft told us is that they don't personalize base prices. And that's a fine point that they're making, right? So that's the core or the underlying price of a particular ride, but that they do personalize promotions and discounts."
Base price is the starting cost of a ride before coupons or special deals are applied. The discussion is about whether that starting number is the same for everyone, versus whether discounts are tailored to you.
Base price is the starting fare for a ride before any targeted promotions, discounts, or other adjustments. In the segment, the hosts contrast base price (claimed to be non-personalized) with personalized discounts (which can change the final cost).
effective price
"many multiples increase over the last few years, that the net or the effective price of a particular service is being fundamentally altered."
Effective price is the final price you actually pay after any discounts or deals. The point is that the deals can matter more than the original listed fare.
Effective price is what you end up paying after promotions, discounts, and any other pricing adjustments are applied. The segment claims that these promotions/discounts are increasingly changing the effective price so much that they function like the new “real” fare.
differential pricing
"So, you know, that's an important point. We're not alleging any sort of illegality here. That said, you know, the differential pricing is pretty clear from what we saw."
Differential pricing just means the price changes depending on the situation. For ride apps, it can mean riders see one price while drivers get a smaller cut for the same ride.
Differential pricing means charging different prices for the same basic service based on factors like time, demand, or customer/market segment. In ride-sharing, it often shows up as the app quoting riders a higher fare than what drivers receive for the same trip.
algorithmic pricing
"And that corresponds with their shift to upfront pricing, so this algorithmic pricing that they use in the marketplace."
Algorithmic pricing means the app uses a computer model to set the fare. That model can raise prices when demand is high, even if driver pay doesn’t rise the same way.
Algorithmic pricing is pricing generated by software that adjusts fares dynamically based on inputs like demand, supply, traffic, and location. In ride-sharing, it can create a gap between what riders pay and what drivers earn per mile or per fare.
GridWise
"according to GridWise, which is a good sort of price tracking group that releases an annual report looking at this data"
GridWise is a group the host cites for tracking ride-share pricing over time. The episode uses their report to argue that rider fares have been rising faster than inflation.
GridWise is referenced as a price-tracking organization that publishes an annual report used to support the claim that rider prices rose faster than inflation. The segment uses it as an external data source for fare trends.
per mile
"whereas the pay, you know, per mile or per fare pay for drivers has not kept up with the increase in customer fare"
“Per mile” is pay based on how far the car drives. The point here is that drivers may be getting paid about the same per mile while riders pay more overall.
“Per mile” refers to how driver compensation is calculated based on distance traveled. When the discussion says driver pay per mile hasn’t kept up with customer fares, it implies the driver rate is not rising as fast as rider pricing.
highway tolls
"But the ride also involved like a lot of like highway tolls and stuff like that. So those presumably go to nobody other than the toll company, right?"
Tolls are fees you pay to use certain roads. In ride-share pricing, they’re usually added to the fare but don’t represent Uber/Lyft profit.
Highway tolls are road-use fees collected when you travel on certain paid routes. In ride-share accounting, tolls are often treated as pass-through costs that affect the total fare but not the platform’s core margin.
government fees
"Uber and Lyft, both, they really focus on this calculation of how much they take in, how much the riders pay or drivers paid, and then how much goes to government fees, tolls, airports or charges, things like that."
These are extra charges that the government adds to a ride. The app collects them, but the money doesn’t stay with Uber or Lyft.
In ride-share pricing, “government fees” are charges that the platform collects on behalf of public agencies. They’re typically separate from the platform’s own take rate and can include things like taxes or regulatory surcharges.
commercial insurance
"Uber and Lyft will say in defense is that, you know, at the end of the day, our net take hasn't changed. Commercial auto insurance has changed. It's an increasing percentage of how much we're paying in order to keep the platform going."
This is insurance for cars being used to earn money, like ride-share driving. If it gets more expensive, it can change how much of the ride price goes to costs instead of profit.
Commercial auto insurance is insurance coverage for vehicles used for business purposes, not personal driving. The hosts are discussing how changes in this cost can affect how much of each ride fare goes to expenses versus the platform’s profit.
captive finance companies
"they own captive insurance companies that are subsidiaries that basically, you know, price out the risk of, you know, liability and other claims."
A captive insurance company is an insurance business owned by the same company that needs the coverage. Instead of buying insurance from a third party, the parent handles the risk through its own subsidiary.
Captive insurance companies are insurance subsidiaries that a parent company sets up to insure its own risks. The idea is that the parent can “price out” risk internally rather than buying coverage from an outside insurer.
self insured
"when we research, you know, a lot of their risk is also self-insured. They own captive insurance companies..."
Self-insured means the company is paying for certain losses itself, rather than relying entirely on an insurance policy. That can change how costs show up in the numbers people compare.
Being self-insured means a company covers some or all of its own losses instead of purchasing traditional insurance for that risk. In the segment, this is used to argue that Uber’s and Lyft’s risk costs may not be fully reflected the same way as external insurance premiums.
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