To You, the Individual: Delta, AI, and the Price of Being Known

The boarding gate closes on Delta Air Lines Flight 2895 from Boston to New York LaGuardia. I settled into seat 1A, boarding pass in hand that I paid for $239. My neighbor, a finance manager on a business trip, had sprinted miles to the gate and boarded with Zone 8 passengers, paying $609 pre-departure. For the same flight, the same day, and the same pre-departure drink, he was being charged a 150% premium for reasons neither of us could explain. That gap—invisible, unjustified, produced by some calculation neither of us could see—became impossible to ignore after July 2025, when news spread that Delta was testing artificial intelligence (or AI) to refine its ticket pricing. Delta President Glen Hauenstein had announced at an investor event that the airline was pursuing a “full reengineering” of “how we price”—a system that would eventually offer a fare on “that flight, on that time, to you, the individual” (qtd. in Estes).

A Delta A350 aircraft, not related to this newsletter.

The story moved quickly beyond aviation trade press. Senator Ruben Gallego sent Delta an angry letter accusing the airline of “using AI to find your pain point, meaning they will squeeze you for every penny” (qtd. In Estes). Consumers expressed outrage online. On Reddit’s Delta forum, one user asked whether the announcement was “a subtle admission that fares had been influenced by illegal coordination” (Status_Fox_1474). Another was more blunt: “They are going to create a new algorithm that massively boosts profits and call it ‘AI’ so customers think they are getting a deal” (Dino_Spaceman). The comments collected hundreds of upvotes within hours. What had seemed like a niche question about revenue management had become, for ordinary passengers, a confirmation of something they had long suspected.

The controversy centers on whether Delta Air Lines should be allowed to use AI and customer data to personalize ticket prices. Delta’s own answer was emphatic. Peter Carter, Delta’ EVP, issued a public statement declaring that “there is no fare product Delta has ever used, is testing or plans to use that targets customers with individualized prices based on personal data” (Carter). The company frames AI as a decision-support tool—a way to help human analysts process aggregated market information faster. What is telling is the language Delta chose: words like “innovation” and “operational enhancements”, are trying to rebrand the act of studying you into the act of serving you. Industry experts offered some support for this framing. Pricing researcher and Assistant Professor at the Leeds School of Business at CU Boulder, Övünç Yılmaz noted that, “airlines have been practicing dynamic pricing for decades, adjusting fares in real time based on demand and available seats” (qtd. in Hill). AI, from this view, is simply the latest iteration of a long-standing and legal practice. Delta’s partner, Fetcherr, was “helping the airline aggregate purchasing data, forecast demand for specific routes, and factor in thousands of variables simultaneously—none of them personal” (Carter).

Critics, however, argued that this characterization misrepresented the nature of the shift. Where Delta called it innovation, critics called it something closer to predation (Estes). Adam Clark Estes, a senior technology correspondent at Vox, characterized the trend as surveillance pricing—a meaningfully different practice from conventional dynamic pricing. Where dynamic pricing responds to broad market signals, surveillance pricing uses behavioral data to estimate what each individual consumer is willing to pay. Estes writes that Delta aims“ to sell the same product to two different people for two different prices (Estes)”. He also described it as “a more sophisticated and algorithmically driven and selective pricing gouging” (Estes). That shift moves the airline from responding to demand to anticipating the individual, and the individual has no way of knowing it is happening.

Lawmakers echoed this concern. US Transportation Secretary Sean Duffy put the government’s position plainly: if any airlines used AI to individually price seats based on personal data, his department would investigate (Shepardson). The scrutiny that followed centered on one compelling phrase: the “personal pain point”—a term Senator Gallego used to name what he believed the algorithm was actually built to do. A personal pain point is the maximum price a specific individual will pay before deciding not to buy. The algorithm’s job is to find that ceiling for each passenger and price as close to it as possible. As NYU Law School Professor Oren Bar-Gill warns, “we are approaching a world in which each consumer will be charged a personalised price for a personalised product or service” (qtd. in Oxford). Delta was not the first company to move in this direction. Former Federal Trade Commission Chair Lina Khan has pointed out that “Uber has found that those with a low battery tend to accept the surge price regardless, because they need a ride home that minute” (qtd. in Oxford). Hence, the pain point logic was already embedded in the apps Americans use every day. But Delta’s announcement made it visible. When Gallego finally received Delta’s response, he was direct: “Delta is telling their investors one thing, and then turning around and telling the public another” (qtd. in Shepardson). Hauenstein had promised investors a system that knew what each passenger would pay. Carter had promised senators a system that knew nothing personal about anyone. Both promises cannot be true.

The price difference between my seatmate and me had no explanation, and that absence of transparency was the whole problem. Delta denied using personal data. Regulators said they would investigate. Experts cautioned against drawing premature conclusions. And yet, as Kelsey Vlamis, senior business reporter for Business Insider reported, travelers were still “spooked” —complaining about pricing gouging before any investigation had confirmed wrongdoing (Vlamis). Tim Sanders, vice president of research insights at G2, put the dynamic plainly: “Trust in AI arrives by a mule. It leaves on a Maserati” (qtd. in Vlamis). For me, the mule had already left. I found myself fear the most that systems know more about me than I know myself.

That structure of feeling has a name. Shoshana Zuboff, in The Age of Surveillance Capitalism, argues that modern platform companies have built a new kind of economy. One in which human experiences is treated as “free raw material for translation into behavioral data” (Zuboff 8). The difference between a company that collects your data to serve you better and one that collects it to extract more from you is precisely what Zuboff is drawing. In surveillance capitalism, companies extract information in order to generate what Zuboff calls “behavioral surplus”—data beyond what is needed for any service function, repurposed to produce “prediction products” that anticipate and shape future behavior (Zuboff 8). The knowledge flows in one direction. Companies, Zuboff writes, “know everything about us, whereas their operations are designed to be unknowable to us” (Zuboff 11). Economists call this information asymmetry—“a circumstance in which the parties involved in a situation include one party who has more information or more accurate information than the other party or parties” (Kte’pi). Hence, as Delta uses AI to collect its consumer’s information, the consumer is rendered predictable, while the system that does this remains opaque.

Hence, when Hauenstein told investors that the goal was to arrive at a price available “to you, the individual” (qtd. in Estes), he was describing, in the language of corporate strategy, precisely what Zuboff identifies as the endpoint of behavioral surplus: the individual, fully profiled, becomes the target. The pricing system stops watching the market and starts watching you. As Elise Philips, policy counsel at Public Knowledge, puts it, “algorithmic pricing exploits a vast asymmetry of information — given that there is often little transparency in how these algorithms function, it's fundamentally unfair to consumers” (qtd. in Estes). Whether or not Delta is currently doing this with personal data, the aspiration itself is the controversy. Consumers who felt anxious about the announcement were registering a direction of travel. As Brent McDonald, a lawyer based in Salt Lake City noted, the worry is that companies with sufficient market power could use AI “to maximize the amount of money they’ll get from each customer” (qtd. in Vlamis). In hub cities like Salt Lake City, where Delta controls most direct routes and passengers have few alternatives, that maximization has nowhere to stop.

But what does AI actually do when left to optimize? Who benefits — the company or the customer? Four economics professors Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello ran simulations to find out. Writing in the American Economics Review, they found that AI pricing algorithms "learn these strategies purely by trial and error" and are "not designed or instructed to collude". Yet still drift toward monopoly-level prices (Calvano et al. 3268). The algorithms coordinate on prices well above the competitive baseline, simply because optimization has no ceiling and no competing instruction to stop. But it does have one objective, which is to find the ceiling of what consumers will pay. Because that is what optimization means.

This is what makes Delta’s defense so difficult to evaluate. The company said its AI assists analysts and responds to market data. That may be true. But Calvano et al. suggest that good intentions embedded in a system’s design do not govern what the system learns. Tell an algorithm to maximize, and it maximizes. And maximizing means pushing until something pushes back. Consumers rarely push back, because they cannot see what is happening. Business travelers rarely push back either, because someone else is paying the bill.

Every price contains a theory of who you are. Dynamic pricing responds to the market: when demand rises, everyone pays more. Personalized pricing adjusts to people: it asks what you will bear. That distinction matters because it changes the relationship between the airlines and the passenger. One is a shared condition. The other is a private calculation made about you everytime you open a search tab. As you are the person the system has studied. The price you see is the conclusion about your ceiling.

Jonathan Adams, Min Fang, Zheng Liu, and Yajie Wang tracked how fast this shift is actually happening. Writing in the Journal of Monetary Economics, they measured AI pricing adoption across the entire U.S. economy using firm-level job posting data, and found that the “share of AI pricing jobs increased more than tenfold between 2010 and 2024”—spreading across industries, including transportation (Adams et al. 1). More tellingly, “firms that adopted AI pricing saw faster growth in markups (or profits) on average” (Adams et al. 17). Markups are the gap between something costs to produce and what consumers pay for it. When markups grow faster than everything else, the gains are flowing somewhere specific. They are flowing away from the passenger in 1B.

Airlines did not invent this logic. Hua Hsu’s review of Liz Pelly’s Mood Machine in The New Yorker describes Spotify doing something structurally similar. Pelly, a journalist and adjunct professor at NYU Tisch whose book critically examines Spotify’s business model, tracks how the platform records what you listen, when, for how long, in what mood. Then it uses that portrait to keep you on the platform as long as possible. It does this by feeding you more of your favorite songs, then similar genre songs, thereby narrowing your listening into a loop that feels like preference but is actually prediction. Pelly frames the problem as one of autonomy: are you choosing what to listen to, or has the algorithm already decided for you (qtd. In Hsu)? That question has no clean answer, which is exactly the point. The passenger booking a Delta flight and the listener opening Spotify are in the same position. Both hand over behavioral data in exchange for a service. Both assume the transaction is straightforward. And in both cases, something else is happening underneath. The difference is that Spotify measures its gains in your attention. With Delta its gains in your dollars.

Dollars are harder to ignore than attention. That may be why Delta’s announcement landed differently than most corporate stories. Americans have quietly accepted a great deal—cookies, targeted advertising, personalized recommendations that know what you want before you do. Each concession felt small enough to swallow. But airline pricing sits in a different category of American life. Flying is already expensive and opaque. NBC News Reporter Emily Lorsch points out that “airlines’ fares rose anywhere from 14.8% to 56.7%” (Lorsch) this year due to the Iran War and rise in oil prices. When AI entered that space, it did not introduce a new anxiety so much as confirm an old one: that the system has always been watched, and now it is watching more precisely. Tim Sanders, a technology analyst, put it plainly: “when you say you have artificial intelligence in a consumer product, it raises red flags with consumers” (Vlamis). People who had spent years handing their data to platforms in exchange for convenience suddenly saw the same logic apply to something they could not opt out of. You can delete your cookies, you can also close Spotify. But you cannot avoid buying a plane ticket by switching to a competitor that does not use algorithms, because there is no such competitor. The outrage over Delta was loud partly because the exit was gone.

When Flight 2895 landed at LaGuardia, my seatmate and I gathered our bags and went our separate ways. He had a meeting. I had a train to catch. The price difference between us dissolved into the ordinary business of the day. But the question it raised did not. Somewhere between Boston and New York, at thirty thousand feet, a system had looked at both of us and decided we were worth different prices. It did not ask permission or explain itself. It just calculated, quietly, while we made small talk and drank the same complimentary orange juice. That is the world Delta was building toward. The only question left is whether we will build something back. But before that, every price will be a theory about who you are, written by someone you will never meet, in a language you are not allowed to read.

Work Cited

1. Adams, Jonathan J, et al. “The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance.” Journal of Monetary Economics, vol. 157, no. 103875, January 2026. https://doi.org/10.1016/j.jmoneco.2025.103875.

2. Bloomenthal, Andrew. "Asymmetric Information in Economics Explained." Investopedia, 31 Jul. 2025, www.investopedia.com/terms/a/asymmetricinformation.asp.

3. Calvano, Emilio, et al. “Artificial Intelligence, Algorithmic Pricing, and Collusion.” The American Economic Review, vol. 110, no. 10, October 2020, pp. 3267–97, https://doi.org/10.1257/aer.20190623.

4. Carter, Peter. “Delta Responds to Misinformation Around AI Pricing.” Delta News Hub, 7 Aug, 2025. https://news.delta.com/delta-responds-misinformation-around-ai-pricing.

5. Dino_Spaceman. Comment on "Delta Is Turning Ticket Pricing Over to AI." Reddit, r/delta, 2025, https://www.reddit.com/r/delta/comments/1lx3vg5/delta_is_turning_ticket_pricing_over_ to_ai/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&u tm_content=share_button.

6. Estes, Adam Clark. “Delta is using AI to give you a personalized airfare. It could be the future of pricing.” Vox, 24 Jul 2025. https://www.vox.com/technology/420940/delta-american-airlines-flight-discount-amazon.

7. Garg, Nitika. "AI Is Using Your Data to Set Personalised Prices Online. It Could Seriously Backfire." The Conversation, 19 Oct. 2025. https://theconversation.com/ai-is-using-your-data-to-set-personalised-prices-online-it-cou ld-seriously-backfire-266995?utm_medium=article_clipboard_share&utm_source=theco nversation.com.

8. Hill, Katy Marquardt. “Your next airline ticket could be priced by AI.” CU Boulder Today, 20 Aug 2025. https://www.colorado.edu/today/2025/08/20/your-next-airline-ticket-could-be-priced-ai.

9. Hsu, Hua. “The Spotify Syndrome: What Is the World’s Largest Music-streaming Platform Really Costing Us?” The New Yorker, 30 Dec. 2024.

10. Kte'pi, Bill. “Information Asymmetry”. Encyclopedia of Crisis Management, edited by Penuel, Matt Statler, Hagen, 1st ed., Sage Publications, 2013. Infobase, https://access.infobase.com/article/11062737-information-asymmetry?aid=237298.

11. Lorsch, Emily. “Flying in America Is About to Get More Expensive and Less Fun.” NBC News, 16 Mar. 2026. www.nbcnews.com/business/travel/travel-airlines-iran-tsa-jet-fuel-rcna263342.

12. Oxford, Dwayne. "'Surveillance Pricing': Why You Might Be Paying More Than Your Neighbour." Al Jazeera, 2025. https://aje.io/c6wjat.

13. Shepardon, David. “Delta Air Assures US Lawmakers It Will Not Personalize Fares Using AI.” Reuters, 1 Aug 2025. https://www.reuters.com/business/delta-air-assures-us-lawmakers-it-will-not-personalizefares-using-ai-2025-08-01/.

14. Shepardson, David. “US Criticizes Use of AI to Personalize Airline Ticket Prices, would investigate.” Reuters, 5 Aug 2025. https://www.reuters.com/world/us/us-criticizes-use-ai-personalize-airline-ticket-prices-w ould-investigate-2025-08-05/.

15. Status_Fox_1474. Comment on "Delta Is Turning Ticket Pricing Over to AI." Reddit, r/delta, 2025, https://www.reddit.com/r/delta/comments/1lx3vg5/delta_is_turning_ticket_pricing_over_ to_ai/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&u tm_content=share_button.

16. Vlamis, Kelsey. “Backlash to Delta’s AI Pricing Shows the Tightrope Companies Walk When It Comes to AI.” Business Insider, 2 Aug 2025. https://www.businessinsider.com/delta-ai-pricing-backlash-shows-tightrope-companies-w alk-ai-adoption-2025-8?utm_source=copy-link&utm_medium=referral&utm_content=to pbar.