Yves here. The individualized price gouging that Delta plans to implement seems to be coming soon. But as Hubert Horan explains, this case of “AI is coming to eat your lunch” does not look as easy to pull off as Delta might think.
Even so, Congresscritters are already saddling up to head Delta off at the pass. The proposed bill below is in addition to objections raised by three Senators that Hubert mentions below.
You shouldn’t be charged more or paid less because of your ZIP code or search history.
I just introduced a bill to ban “surveillance pricing” — when corporations use your data to hike up prices or cut your wages.
It’s invasive. It’s exploitative. And it needs to end. pic.twitter.com/4vOn6AFPF2
— Congressman Greg Casar (@RepCasar) July 27, 2025
The Economist just reported on another ruse that some US airlines tried implementing. It falls short of the individual pricing threat, but still got travelers’ dander up:
In recent years airlines have grown ever more sophisticated in their pricing techniques. American carriers’ latest method of singling out business passengers, though, is strikingly simple—and has sparked outrage in the travel blogosphere.
In May Thrifty Traveler, a website about travel bargains, reported that America’s three big legacy airlines—American, Delta and United—had started charging higher per-person fares for single-passenger bookings than for identical itineraries with two people. Kyle Potter, the author, grumbled that the practice amounted to carriers “weaponis[ing] their fares” against solo travellers who “can’t clone themselves”. Brian Kelly of The Points Guy, another travel site, called it “greed getting out of control” and said that airlines were “asking for government intervention”. Although no airline has yet commented on the subject, Delta and United reportedly scrapped the practice amid the criticism.
To investigate further, The Economist turned to Serpapi, an automated interface to Google Flights, a fare database. For all direct domestic journeys on America’s three big legacy carriers, we downloaded one-way main-economy fares as of July 20th for one and two passengers to travel on Monday July 28th, choosing whichever flight had an airline’s cheapest single-passenger price for that route that day. We also pulled return fares—one the following Friday and the other on Saturday—for both one and two passengers. In total, we amassed 19,000 prices across 3,200 routes.
Delta has indeed abandoned the technique: its two-passenger price is always at least twice the fare for one. But American and United have persisted. Solo flyers who travel with them within the work week can end up paying more than everyone else, including solo travellers whose journey involves a weekend stay and those who travel with others, regardless of whether their trip stretches into a weekend.
In other words, airlines are exposed to consumer backlash, and even, as regulated entities, political pressure.
So individualized pricing were to get traction, some airlines could tout their abstention. I could see Middle Eastern carriers, like Qatar Air and Emirates, which cater to a high end clientele, advertising that they don’t nickel and dime customers this way.
By Hubert Horan, who has 40 years of experience in the management and regulation of transportation companies (primarily airlines) and has been publishing analysis of Uber since 2016
Delta’s President Glen Hauenstein told investors that it sees AI-driven personalized (or surveillance) pricing as “a full re-engineering of how we price, and how we will be pricing in the future”. He said “we’re really excited about partnering with Fetchrr” (Delta’s Israel-based AI consultant) whose tools were helping Delta “get inside the mind of our consumer” so that eventually, “we will have a price that’s available on that flight, on that time, to you, the individual.” He said that AI tools were now setting prices on 3% of Delta flights but he expected 20% of flights would be AI priced by the end of 2025.[1]
The definition of personalized/surveillance pricing is well understood and represents a radical departure from other forms of algorithmic pricing. [2] Algorithmic pricing simply means that many of the rules (algorithms) governing when prices should be adjusted in response to new information about consumer demand or competitor actions have been automated. Airlines pioneered pricing automation in the 1980s, which incorporated second degree price discrimination, so different fares can be offered on flights with higher or lower demand, and different fares can be offered with different conditions (advance purchase requirements, different checked baggage or seat assignment surcharges).
But under second degree price discrimination every consumer sees the exact same price offering at any given time. Every consumer has the identical opportunity to pay less if they buy in advance and can only sit in the very back of the plane or pay more at the last minute and sit closer to the front. Delta is reengineering its pricing around using first degree price discrimination. Personalized/surveillance data is used to estimate the maximum price each individual would be willing to pay. Individuals would no longer see the same prices that all others are seeing.
Any claims that first degree price discrimination would improve overall efficiency or benefit consumers are false. The original use of second degree price discrimination in the 80s did improve efficiency and consumer welfare. Price sensitive customers who were willing to buy tickets on lower demand in advance got much lower fares than previously available, and more time-sensitive customers willing to pay more got greater access to higher demand flights at the last minute. Airlines achieved much higher capacity utilization (load factors), earned much more revenue on flights that had previously been half empty, and avoided the huge expense of adding aircraft that were only needed at peak periods.
But the efficiency gains from price discrimination were exhausted long ago. Increased price discrimination would only be a wealth transfer from consumers to airline shareholders. To use simplistic Economics 101 terminology, when everyone sees a standard market price, a consumer surplus is created because some consumers would have been willing to pay more. First degree price discrimination using personalized/surveillance pricing attempts to eliminate as much of that consumer surplus as possible by charging each customer the most they would be willing to pay. Passengers pay more without getting anything of value in return.
Lots of companies have tried to introduce personalized/surveillance pricing but corporate announcements including Delta’s usually generate consumer backlash. Three Democratic Senators complained that this could support predatory pricing and demanded that Delta disclose what information about individual consumers it would be using to set prices. [3] Airline passengers understand that airlines don’t have their best interests in mind, can quickly see through attempts to claim first degree price discrimination is the same thing as traditional second-degree discrimination and readily dismiss ludicrous attempts to claim personalized/surveillance pricing will actually lead to lower prices. [4]
The shift from second degree to AI-driven first-degree price discrimination would be one of the biggest aviation changes in decades. So I went to the Fetcherr website and found surprisingly that it said that its airline AI offerings don’t actually do any of the things Hauenstein was claiming. [5] They don’t collect data about individual customers or analyze the price elasticities or other characteristics of individual customers or fine-tuned customer segments. Fletchrr emphasizes that its AI/data tools are designed to be easily integrated into existing pricing practices and there is absolutely no suggestion they were designed to help an airline to completely reengineer its pricing function.
Something is seriously wrong here, but what? Are Delta and/or Fletchrr being less than honest about what they are doing?
Major investments in AI tools are only appropriate in cases where a company needs to process massively greater amounts of external data than before and wants to divine correlations that human analysts using PC based software could never find. Large Language Models identify text patterns that can help automate preparation of article summaries and documents. Activity specific models find correlations between millions of historical inputs and outputs that can support the automation of basic tasks like coding or accounting.
Two major AI applications are especially relevant here. The primary “get inside the mind of the consumer” case is advertising, where large companies like Google and Facebook used their access to massive amounts of data about individual users to program real-time ads more effectively than traditional human marketing analysts. The primary “use Big Data to totally re-engineer previously human+PC software analytical functions” came in the 1990s when hedge funds used hitherto unprecedented amounts of computing power to find factors correlated with asset price changes that drove more higher average returns and incorporate them into high-speed computerized trading strategies. [6]
One possible explanation for these apparent contradictions is that Fletchrr doesn’t understand airline pricing very well. Its founders come from the hedge fund world where AI tools are critical because there are millions of traders, billions of transactions, individual markets are highly volatile, can be influenced by an unpredictable range of external factors, and major traders are constantly changing their strategies. Fletchrr never explains why this experience would apply to airlines where supply and demand are highly stable in the short/medium term, the number of airline competitors is limited and while their pricing approaches can evolve over time they haven’t changed dramatically or unexpectedly in decades.
Why did Fletchrr see airlines as a prime target for its AI sales efforts? Because they thought airlines were “outdated” “undisrupted” and had seen few recent technological advances.
In fact, airlines were perhaps the non-military industry quickest to drive innovation in operations research, IT technology, and modern pricing/distribution tools, and of course saw business models totally transformed in the last quarter of the 20th Century. They may not seem highly dynamic to someone whose experience is limited to the recent development of hedge fund quant models, but very few industries anywhere experience that rate of technological change.
Fletchrr says airlines need AI tools to handle today’s faster rate of change, but the only examples of hard to handle change it offered were nonsensical–the impact of Covid on demand and the challenge of previously unscheduled flights into Doha for the FIFA World Cup. Fletchrr doesn’t provide any concrete examples of major problems it can solve that current pricing systems can’t deal with. The input data for Fletchrr’s AI market model is the exact same input data airline revenue management systems have been using for decades.
There might be value in a tool that can quickly process greater volumes of input data; every corporate function could be improved at the margin, and perhaps these gains would justify the IT investment. But these would be marginal improvements, would not represent the complete reengineering of pricing that Delta claimed, or anything that could produce the major revenue/profit impacts that would justify major announcements to investors.
Another possible explanation is that Delta executives never thought through the requirements, potential gains and implementation risks of re-engineering its existing systems into personalized/surveillancepricing. Perhaps Delta never bothered to figure out that Fletchrr’s software was only offering marginally greater automation of traditional pricing tasks and never considered that Fletchrr’s software wasn’t designed to drive the dramatic changes it thought its investors would value. Delta hasn’t made any attempt to define the shortcomings in its existing pricing systems that it is hoping to address or explain how a future reengineered system would differ from today’s.
Maybe Delta executives had drunk the Kool-aid of the AI hype machine, assumed anything labelled “AI” would have magical, powerful impacts anywhere and never stopped to consider whether the conditions that allowed AI tools to create value in other industries applied here. Perhaps Delta executives more cynically thought that splashy announcements of big AI projects would juice the stock price, would reinforce Delta’s image of having more progressive management than United or Delta, and assumed that investors would never hold management accountable if there wasn’t a big revenue/profit boost.
Even if it is I possible that Fletchrr’s hedge fund trained AI experts overestimated the applicability of its tools to airlines and that Delta management accepted too much AI hype it seems rather improbable that both these two sophisticated companies would announce a major effort where the goals were badly misaligned.
One more plausible explanation is that Fletchrr fully understands that Delta is determined to achieve first degree price discrimination, and both parties wanted to obscure this. It could be that Fletchrr knew its AI-driven Market Model was well suited for personalized/surveillance pricing but deliberately excluded any mention of this from its promotional material to help shield airline clients from external criticism in cases like this. Fletchrr’s website gives Delta a way to plausibly deny outside critics (“that’s not what these AI tools do!”) without having to formally disavow their pursuit of personalized/surveillance pricing. It could be that Fletchrr’s models were never designed to process huge volumes of personal information but Delta believe they can be adopted to support first degree price discrimination.
It remains possible that Delta’s effort to use AI-driven personalized/surveillance pricing will fail to materially boost profitability. The articles reporting Hauenstein’s claim mentioned a range of potential obstacles to personalized/surveillance pricing including the ready availability of data on market (non-personalized) prices that would allow flyers to see if Delta was trying to get them to pay above-market fares.
While it is widely understood how Google and Facebook can use terabytes of personal data to tailor ad displays, no one has publicly explained how personal data would allow an airline to calculate price elasticities for each customer and reliably predict that this individual shopping for this specific flight would be willing to pay more than it was asking other customers to pay.
It is not even clear that the price elasticities of individual customers can be measured, or that a system could identify how an individual’s elasticity varied from trip to trip (e.g. critical last minute sales meeting, attending a conference that may or may not have value, taking the kids to visit grandma).
Many observers assume that first degree price discrimination would require forcing most passengers to not only grant Delta access to much more personal information than they have now, but to force them use Delta-controlled sales channels. A recent American attempt to force corporate agents serving higher yielding passengers to use a captive channel actually reduced revenue by over a billion dollars and was withdrawn. [7] Delta’s best customers might similarly resist any attempt to force them to use a channel that prevented them from seeing what true market rates were.
Uber provides a case example of a company where shifting to first degree price discrimination did produce a very large profit boost. Uber previously offered the same fare to any customer and offered the same payment to any driver (based on factors such as distance and time of day) with a system that estimated the highest fare/lowest payments they would accept.[8]
But if Delta was attracted to personalized/surveillance pricing by the big profit boost Uber achieved it may be badly disappointed because Delta has none of the structural advantages that allow Uber to maximize exploitive discrimination. Uber rides are last minute purchases and riders have no ability to compare prices. There are no independent Google/Kayak/Expedia-type sources of true market taxi pricing information. Delta frequent flyers understand airline pricing and would quickly figure out if changes were unfavorable. Uber users have no real idea how Uber pricing works, and Uber (and Lyft) have achieved quasi-monopoly pricing power after using billions in predatory subsidies to drive independent competition out of the market.
It should be emphasized that the central issue, should Delta have any success pursuing first degree price discrimination is not “AI technology” or “pricing algorithms” but the ability to exploit anti-competitive market power.
Simplistic Economics 101 models say producers can’t achieve the capture of consumer surplus that Delta hopes to achieve using first degree price discrimination because consumers would be protected by market competition and the availability of perfect information about market prices. First degree price competition works for Uber because they tightly control all marketplace information, have eliminated all meaningful competition and any ability of elected officials to enforce consumer and labor law protections. Even Uber investors have no ability to see how pricing and driver compensation changes affect profitability.
Thus any attempt to implement first degree price discrimination requires subverting the proper workings of competitive markets.
Without significant artificial market power Delta would have no ability to force its best customers to use Delta controlled distribution channels, or to limit their ability to see if Delta is only showing them fares higher than other customers can get. Without significant artificial market power Delta would have no ability to blow off customer backlash, negative publicity and complaints from Congress. The ability to impose first degree price discrimination should perhaps be seen as prima facie evidence that a company has artificial anti-competitive market power.
While normal companies that achieved a multi-billion profit recovery would aggressively publicize the brilliance of its management moves, Uber management has gone to great lengths to keep the public from becoming aware that it made a major shift to first order price discrimination and that that shift was the major reason Uber finally became profitable. Uber understands that a greater awareness of passenger/driver exploitation could not only drive serious customer/political backlash but create awareness that its profitability had nothing to do with management brilliance but was driven entirely by the subversion of market competition.
Delta might rationally believe that it already has the artificial market power to pursue personalized/surveillance pricing while ignoring the objections of its best customers but Uber management might warn them to take these risks more seriously. American’s CEO has already called Delta’s proposed pricing shift a “bait and switch” plan. [9]
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[1] Hauenstein’s original comments about AI pricing were made at a Delta “Investor Day” last November, but there was no press coverage until he made follow up comments during a recent quarterly earnings call. Gary Leff, Delta Is Turning Ticket Pricing Over To AI—By Year-End, 20% Of Fares Will Exactly Match The Most You’re Willing To Spend, View From The Wing, 10 July 2025; Irina Ivanova, Delta moves toward eliminating set prices in favor of AI that determines how much you personally will pay for a ticket, Fortune 16 July 2025; Matt Novak, Delta Set to Expand AI-Powered Dynamic Ticket Pricing by the End of 2025, Gizmodo, 17 July 2025;
[2] For a review of over 300 journal articles discussing both traditional algorithmic and personalized pricing see Seele, P., Dierksmeier, C., Hofstetter, R. et al. Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing. J Bus Ethics 170, 697–719 (2021).
[3] Michael Kan, Wendy’s Clarifies Digital Surge-Pricing Strategy After Blowback, PCMag, February 28, 2024; Cory Doctorow, Surveillance pricing lets corporations decide what your dollar is worth, Pluralistic 24 Jun 2025; David Shepardson, Delta plans to use AI in ticket pricing draws fire from US lawmakers, Reuters, July 23, 2025.
[4] Gary Leff, Airlines Now Quietly Let AI Set Ticket Prices—Surprisingly, That’s Great News For Your Wallet, View From The Wing, 15 July 2025
[5] https://www.fetcherr.io/technology which includes a white paper about its Large Market Model a 45 minute talk about Fetcher’s AI tools apply to airlines by AI Chief Uri Yerushalmi, and a You Tube video interview with Virgin Atlantic VP of Pricing and Revenue Management Chris Wilkinson about his use of Fletchrr’s tools. Fletchrr says Azul, WestJet, Virgin Atlantic, and VivaAerobus are also clients of its AI airline pricing tools.
[6] For the story of Jim Simons, Robert Mercer, Renaissance Technologies and the development of algorithmic trading by hedge funds see Gregory Zuckerman, The Man Who Solved The Market: How Jin Simons Launched the Quant Revolution, New York, Penguin Books, 2019
[7] Benjamin Zhang, American Airlines CEO Admits It Messed up Ticket-Sales Strategy Change, Business Insider, May 29, 2024, Justin Dawes, American Airlines Recovering After Failed Direct Bookings Strategy, November 12, 2024
[8] Here are a couple recent articles about external studies of Uber’s radical new algorithmic approaches: Simon Goodley, Rough ride: how Uber quietly took more of your fare with its algorithm change, The Guardian 19 June 2025; Simon Goodley, Second study finds Uber used opaque algorithm to dramatically boost profits, The Guardian 25 June 2025. For a fuller explanation of Uber’s financial turnaround see Hubert Horan: Can Uber Ever Deliver? Part Thirty-Five: What Drove Uber’s Recent $8 Billion P&L Improvement?, Naked Capitalism, 25 Feb 2025
[9] Christine Boynton, American Airlines CEO Blasts ‘Other’ AI Talk: ‘This Is Not About Tricking’, Aviation Week, 24 July 2025
Based on what I saw with another industry, what happens if those airline AIs are instructed to raise the prices on ordinary fare paying passengers and then using that extra revenue to lower the price on business class and first class passengers. I could very easily see this happening.
I travel a lot on Delta, and I believe I have been targeted in this way in recent months. The increase in business class fare offerings has been amazing and I believe that once you’ve shown a willingness to pay $2k for a cross-country seat or $10k for an overseas flight, for a business trip where your dates are not flexible [and you’re being reimbursed], that’s what they’re going to show you for all travel from now on.
But I don’t believe they can fill the cabin that way. Many users are sucked into the loyalty program or (like me) limited to certain airlines based on a smaller home airport, but if they’re going to price gouge, and other airlines refrain, I’m going elsewhere.
Hopefully the Consumer Financial Protection Bureau can save me . . . oh, wait. But if you really think about it, they are mostly targeting the PMC here, so maybe there will be some legislative pushback after all.
I don’t know if it’s the result of AI, but something changed with Delta’s domestic pricing recently. I fly the same route regularly, and within the past few weeks the price of the particular ticket I usually buy increased substantially in comparison to other options. The result is (1) for the first time in quite a while I checked Expedia to see what my other options are, and (2) I will buy the cheapest ticket instead of the one I usually buy.
I also noticed on my last few flights that the relative number of people boarding in the last (cheapest ticket) group has increased substantially, and the flights are not as full. Maybe that benefits Delta somehow, but I don’t really understand how.
Unclear whether what you saw is related to the recently announced software program, which is still in early testing. But it could have been the result of a previous cruder non-AI algorithm Delta was using.
As I mentioned it is well understood that frequent travelers fly for a wide variety of reasons over time with widely divergent price elasticities and no one has ever explained how any “personalized” system could afjust prices for each different situation
My recommendation to anyone who thinks they have been a victim of first degree price discrimination is to quickly look up fares on an internet travel agency (Kayak, Expedia, etc) or signin to Delta with the ID of a spouse/friend who doesn’t have a similar usage of full-fare/premium tickets. Take screen shots. If its clear you’ve been offered a higher-than-market fare, complain to Delta with a “why are you trying to gouge me?” email
The airlines are presumably not the only ones who can play this high tech arbitrage game, and they could wind up hoisted on their own petards. What is to stop some enterprising young nerdbox from developing a bot with the profile that would cause it to be offered the lowest price, and then buy up all the tickets for a given flight and sell them on the secondary market? If it can be done with tickets to concerts and sporting events, why not plane tickets too?
When you buy a plane ticket you have to enter your passport or some form of ID. The ticket is not transferable. At least this is my experience. Nice thought though! Turning the tides on them and all
Huh? Not in the US or when I buy international tix to Southeast Asia. I provide the passport # only at check in time.
Expat2uruguay, I haven’t traveled much since 9-11, so my memory of buying tickets for travel and procedures at the airport when one is checking in is quite rusty. I did a cursory search on transferability of tickets and found that most airlines don’t allow it, and the few that do generally charge fees to do a name change. Sometimes the fee itself can be so expensive that it makes more sense to purchase a new ticket for the person who will actually be flying. Regardless, the person showing up for the flight has to produce an acceptable-to-TSA I.D. which matches the name on the ticket. That alone would preclude the idea that lyman alpha blob proposed. Hope this helps.
I booked a flight to the UK for October and have to apply for an Electronic Travel Authorization, which I did not have to do when I flew there last year.
https://www.gov.uk/eta
They want photos of my passport and of my face.
While maybe the AI will make it worse but fare manipulation has been know about for years if not decades.
What day of the week you look or buy, did you look recently, and location etc all of which means you’re presented with higher prices.
I know I’ve seen this happen to me before. And definitely not for biz/first class.
> It is not even clear that the price elasticities of individual customers can be measured, or that a system could identify how an individual’s elasticity varied from trip to trip (e.g. critical last minute sales meeting, attending a conference that may or may not have value, taking the kids to visit grandma).
Patently false. My former mentor (Louviere) made his name originally by doing PRECISELY this for Qantas in the early 1980s using (what were at that point pretty primitive) discrete choice experiments (DCEs) and often used the very bad “conjoint analysis” techniques which he later freely disavowed calling most of his early 1980s research “crap and not worth reading”.
But DCEs have been used to map demand functions for years, and these days with the rise of the web and online panels, map them under all those different types of contexts mentioned. The most successful applications are not ones you’ll find referenced….I’ve mentioned before the one that flew under even our radar (macquarie bank’s use of them which I discovered by accident). It’s such cases that should really worry people.
My point that individual elasticities cannot be measured was in the context of an airline booking system that has to deal with millions and millions of booking/fare inquiries and needs to decide instantaneously what to show to each specific individual.
My dubiousness reflects some familiarity with attempts in the past (perhaps including the QF example you cited) that were totally useless plus some other issues. What I’d seen in the past wasn’t based on individuals but on market segment categories, e.g. investment bankers, salespeople, government employees, people who attended a given trade fair every year) with the aggregate elasticity estimate applied to anyone placed in that category. Also useless in real world airline pricing.
But if you have any studies that purport to estimate individual elasticities in a large scale operational setting that you think might be somewhat legitimate, please let me know
Thanks for engaging. I’ll clarify as much as I can. As you might have guessed, a load of Louviere’s work with Qantas was not published as it was commercially sensitive. The stuff that WAS published (like the “conjoint” stuff using rating scales) was garbage (as subsequently said by the man himself). Thus if you want a good well known empirical example there is one which won the “Nobel” in Economics: Dan McFadden’s construction of the demand curve for the (not even yet built) BART in SF Bay Area, based on a synthesis of revealed preference data (what choices people ACTUALLY made in real life) PLUS stated preference data (what they’d do when given hypothetical but realistic scenarios).
McFadden’s results were proven correct once BART was built. It is the ultimate example of how we can map out the demand function pretty easily, if you put in the work. Online panels make it trivially easy. My worry here was the siloisation of academia: nobody looking into other fields to see if something had been solved there.
DCEs are sometimes criticised because the respondents aren’t sufficiently familiar with the options on offer. Most passengers on flights know exactly what everything entails and I designed surveys to give real time results on people’s trade-offs. The “what to show an individual” is trivial and I have zero doubt that big airlines have been using DCE data to do this for YEARS. We’ve gone WAY beyond the market segment objection you made. I can model individuals. I don’t need to use ANY inter-individual data or inferences. I describe this in my book. We were doing this over 10 years ago with no AI.
I think they will because people really want to travel and have proven themselves to put up with any kind of humiliation from airlines and ”security checks”.
Somewhat related, airlines make their money on their loyalty programs and credit cards. The high transaction fees make this work. Using points for tickets has become more challenging as airlines mostly use dynamic pricing for award tickets. Some now require login to display award pricing, so surveillance pricing is likely active for some.
Data: US Airlines Operate As Loss Leaders For Their Loyalty Programs OMAAT
The US carriers are all terrible now. They should take lessons from the big Asian or the ME3 airlines.
I’m so old I remember fares printed in ink in schedules. And economy was a pleasant experience. Thank you Jimmy Carter for neoliberal crapification!
Generally I agree but Ryanair was different. I don’t actually think it deserves the bad rap it gets. Not ACROSS THE BOARD anyway. I used it quite a bit 2009-2015 when I was living down under but attending Euro conferences (with trips to family tagged on). On the “non-bucket-holiday routes” I never had problems. You got what you paid for and expected.
It was only the routes (often UK to certain popular holiday destinations) frequented by drunken idiots from my own home country of UK that made the flight awful.
Ryanair has, IIRC remained profitable or at least not get hit anything like as bad as other carriers even in bad years like 2020. Of course I do wonder if an increasing amount of their profit comes from the abominable scratch cards etc they push on you…..
It has been six years since I last flew commercial.
Don’t miss it at all!
The last airfare I paid was for vacation with son’s family, Granpa needed to be with Grandkids.
Otherwise, too much business travel makes me rather drive 30 hours
Thanks for this post.
Thank Hubert!
Whether prices are set by humans or AI, consumers will only purchase tickets if they consider the price to be reasonable.
Regarding Uber and Lyft some anecdotal experience. I have found checking prices between them that Lyft is usually cheaper and when speaking to the drivers they get a higher percentage of the fare. In a recent situation my daughter used Uber and at the end of her ride the driver asked her what the fare was (as the driver couldn’t see it) and offered a $10 discount for a direct payment which my daughter did and the driver canceled the ride which resulted in no charge through the app. In major city airports there are taxi stands that often are significantly cheaper than using uber and readily available. I expect these work arounds will expand in time. In addition Waymo etc… is much cheaper and will also start cutting into Uber and Lyft’s profits. (No pesky driver to pay). As far as surveillance pricing for airlines and grocery stores I expect a great deal of on the ground resistance.
Just as a reference point on AI… this is NOT intelligence or even ARTIFICIAL intelligence.
As I and others keep pointing out this is not “intelligence” as people general think. This is just MASSIVE computing power. Companies have been doing this type of pricing, marketing and targeting for years!
However, connecting a customer and his personal data (customer provided at the POS and ACQUIRED from other 3rd party sources) and computing a pricing target at the POS has only been a dream until recently.
This is not “intelligence” this is massive computing power of data in a real-time fashion against a developed statistical model.
Yes, the outputs may make senses for maximizing profits, but the pricing points do not come from any computer intelligence as we would recognize.
That said, it doesn’t mean we’re screwed, but the elite have always been dreaming of that, how else are they to pay for their lifestyle.
Almost certainly this is the explanation. Horan, bless his heart, assumes that decisions about AI are made on the basis of hard-headed technical analyses. What we have been seeing in fact is that companies are adopting AI strictly out of a fear of missing out or being last to the party regarding the technology they don’t understand. This process is accelerated by the two or three companies that are pushing AI for all its worth.
Fetchrr is clearly looking for ways to sell its snake oil to large businesses before the whole AI bubble collapses.
Well, one advantage of government travel was GSA had contracted rates for just about anywhere you would need to go. Sometimes you could finesse this — flying from Oxnard CA to DC the contract was with United, but an option was a one-way car rental to LAX and American had the LAX-DCA contract.
Way back when I was stationed in Crystal City in VA (how the Navy got kicked out of Main Navy on Constitution Ave and ended up in leased space in Arlington is a question for another day) I was a member of a locker room facility we used for jogging at lunch. There were some guys from US Air who were usually there (at the time they had some sort of offices at National) and they would always be discussing among themselves how airline X had changed its pricing on a certain route and how US Air should react. I guess from the days before software took it all over.
Back then Piedmont had a monopoly on DC to Norfolk or Newport News flights, and it was always a question if it was better to drive it than fly.
Google flights has served me well as it tracks price fluctuations anonymously. Price wars are quite evident there. I buy separate one-way tickets to fly round-trip US coast-to-coast every few months domestically. My “status” usually gets me front-economy seats on AA with some leg room and one free checked bag. Good enough even with AA’s ongoing deterioration to not bother with competition. Thankfully I no longer must fly to SA and Oz occasionally for work, and I can plan domestic trips several months in advance.
it’s long been rumoured (long before “a/i” was a thing) that surfing with a mac usually results in higher prices than with a windows or linux machine. with my main browser, i used to spoof my user agent string. never proved a thing. cuz the user agent isn’t the only data used? who knows?