OpenAI advertising, Hinge, Chargebacks

OpenAI is developing AI chatbots to personalize advertising based on individual chat history, but the effectiveness may be limited because users can counter-program ads by asking other chatbots for alternative opinions, potentially reducing conversion rates for high-value purchases.

Selling Pintos Tomasz Tunguz 1 selling-pintos-note-fn-1 , GP at Theory Ventures, was on The Information talking about https://www.youtube.com/live/veG9nEN kTQ?si=84aU0MCoXfMWvKCD&t=1180 how AI chatbots will be particularly good at personalizing advertising for each individual based on chat history and individual context. Google Ads are great at giving users options when the intent is clear in a search. Meta, with Facebook and Instagram, has remarkable targeting of interests and creative formatting. OpenAI is banking on being able to steer users through the consideration stages of a purchase nimbly without turning users off. LLM ads are imagined as a single trusted salesperson voice. That world won't materialize, which will mean disappointing results for OpenAI. Google Ads shows you words that you wrote 2 selling-pintos-note-fn-2 to sell you whatever is in the blue link. Meta shows you pictures or videos that look like things you might like. OpenAI can show you words that ChatGPT writes just for you and in the format you are most receptive to. Ads in ChatGPT can be counter-programmed—users can ask another chatbot why the product being pitched is or isn’t a good fit for them and get an immediate alternative perspective. Ads that Google and Meta serve can’t be counter-programmed. We don’t yet know how important the counter-programming distinction is. I think it will push ads in ChatGPT to focus on lower-value purchases rather than higher ticket items and services. The high-value decisions will warrant a second opinion from another LLM. There is reason to think that steelmanning and strawmanning for purchase decisions is important, particularly for high-value purchases. People can be indecisive for large, infrequent purchases and often seek advice or perspectives from friends. Today, we see many people getting advice from chatbots. The highest chatbot usage for purchase consideration is for bigger purchases. Big purchases are where there are lots of dollars at play but also other voices to consider. Let’s say I’m interested in buying a new car. ChatGPT is steering me towards a Pinto, 3 selling-pintos-note-fn-3 letting me know how great the heating system is for cold winters. I am really partial to warm steering wheels, so I lean in. ChatGPT seems to be making some sense And ChatGPT kindly told me with a little note that it does have an advertising relationship with Ford, so I know the Pinto is being shown in the best light. Since buying a car isn’t something I do regularly, I want to feel like I am being diligent, and so somewhere mid-conversation I decide to get another opinion. Google’s Gemini informs me of my impending folly and links me to some YouTube videos to watch of cars going up in flames. For every signal, strand of logic, or emotional pull that an LLM can execute on a user to induce purchasing or steer behavior, there is a potential counter-signal. The LLM telling me that I should upgrade to leather seats because I will appreciate the easy cleaning with kids and because it has a strong positive impact on resale value is hitting high notes that I like to hear. It’s skipping over the parts of the script about impressing a date or comfort for long trips. Another LLM might very well counter-program the advertisement by highlighting that I’ll be happier waiting another year before buying as early leaks of the next model year features are a better fit for the cargo space I’m looking for. I feel like a very smart car buyer as a result. I don’t know how the car companies feel. It’s like demand was flipped topsy-turvy by an LLM. 4 selling-pintos-note-fn-4 The world of the single trusted salesperson is an illusion. The world will always be filled with multiple voices competing for your cash, time, attention, and energy. Can OpenAI build an unbelievably good salesperson? Sure. In fact, I think I’ve already been sold on that. But if other AI labs build great salespeople as well—OpenAI’s conversion rate will drop. 5 selling-pintos-note-fn-5 I think you'll like him Hinge, the popular dating site owned by Match Group, rolled out a new feature enabling friends to leave testimonials 6 i-think-you-ll-like-him-note-fn-1 on a friend’s profile page. According to Bloomberg https://www.bloomberg.com/news/articles/2026-07-15/hinge-debuts-friend-s-take-feature-letting-inner-circle-leave-testimonials?srnd=homepage-americas : The tool is meant to offer endorsements from well-meaning, preapproved wingmen. Daters can enlist as many as 10 close contacts to share written feedback, photos, voice notes or videos about them. Friends will be invited to answer prompts, such as recalling a moment they still laugh about or imagining what the person is presently doing. Hinge is trying to shrink the gap between what people are expecting before the first date and the real person they encounter, which they view as a top priority to improve. 7 i-think-you-ll-like-him-note-fn-2 The friend endorsement feature has a lot of second-order implications for both Hinge and users. The barrier to getting started with a profile is now higher because new users carry a negative signal if they don’t have any friends hyping them. New users have to consider which friends they’ll invite to provide a note before finishing their profile. Asking for endorsements seems very LinkedIn-like. 8 i-think-you-ll-like-him-note-fn-3 I’m optimistic about the tone—the writing might end up like aspirational mini-bridesmaid/groomsman type speeches with less alcohol involved. There’s a real explosion of signals to read now on profiles as a result of the change. Testimonials create more to read and infer and consider before the date. What does one do if one finds that the endorsing friend is more attractive, funnier, and a better writer than the match in question? Will there be a way to message the friend and ditch the match? The move at Hinge also ties to some of the innovation being worked on at sister company Tinder, which is also part of the Match Group. At Tinder, group hangs are coming https://www.bloomberg.com/news/features/2026-05-20/tinder-launches-live-events-ai-features-and-group-dating-to-attract-gen-z?srnd=phx-technology : Tinder is also working on a new “Groups” concept that allows female users to form a group of friends who can then connect with a cohort of single men to set up a larger “group hang” of six to over a dozen people. The feature, due out in the coming months, aims to take the pressure off meeting one-on-one without sacrificing a night out with friends. Group hangs could be the physical manifestation of the friend endorsements. We could imagine six friends of each side of the match coming together for a group hang—rolling in with a crew on each side like it’s West Side Story https://en.wikipedia.org/wiki/West Side Story . Online, the presence of the endorsements does make having deep numbers behind you a show of confidence and a repudiation of solitude. Other people like this person is a powerful reminder while waiting for a date to show up. Hinge is tacking in a human direction rather than leaning into an AI matchmaker agent, which, as we’ve seen https://www.marginpoints.com/essays/matchmaker-matchmaker , seems to be where Bumble and Known are heading. Hinge and Tinder are betting on more human interactions amongst networks, not just individuals. A benefit of having multiple brands owned by the same company in the market is seeing different takes on similar readings of the zeitgeist. Endorsements are a very smart growth hack. The dating friend who wants their profile filled up talks to friends to ask them for an endorsement. The request brings new people into the Hinge app, to either re-engage or sign up. Hinge isn’t requiring friends to have an account to offer a testimonial, but they are now in the marketing funnel and have become familiar with the product for free. Match Group previously launched a friend feature in its Ship app back in 2019 https://www.theverge.com/2019/1/22/18192750/betches-ship-friends-swipe-dating-app-match-group . The feature let friends swipe into matches for each other. The app was shut down in 2022 https://www.globaldatinginsights.com/news/match-groups-ship-no-longer-available/ . Friends helping friends with dating is a fun concept and testimonials are a better mechanism than the previously tried swiping for a buddy. There’s also bound to be viral upside for Hinge as users take funny screenshots of friend testimonials and share them on social media. There is no way that this feature will be limited to friends. I’m not sure which will come first: the heartwarming yet slightly concerning mom-endorsing-son testimonial or the ex-girlfriend assuring everyone that the height of the gentleman in question is accurate? 9 i-think-you-ll-like-him-note-fn-4 I didn't buy that Credit card chargebacks have been rising. According to Bloomberg https://www.bloomberg.com/news/articles/2026-07-13/credit-card-holders-are-using-friendly-fraud-to-get-back-at-retailers?srnd=homepage-americas : Chargebacks — petitioning your bank to reverse a credit or debit card transaction — have long been a last-resort method of recovering funds for victims of theft or fraud. Now…they’re a first line of defense, and the number of people using them has risen precipitously over the past five years. AI buying agents, which go out and buy things and book flights and hotels for people autonomously, were not mentioned in the article as part of the problem today. We can look ahead though and see how credit card chargebacks are going to become more prevalent as AI buying agents start to wield more credit cards on behalf of consumers. In the past, consumers felt some amount of social uneasiness if they frivolously wielded the chargeback cudgel unfairly against merchants. Not only would the merchant have the money taken away, but the merchant would also face chargeback fees. Consumers have gotten more and more comfortable with quickly turning to chargebacks to protect themselves, but there still is some hesitation for many customers. The social stigma disappears when the AI agent does the bidding. Plausible deniability creeps in. In the past, some work was required to initiate the chargeback. The consumer had to get on the phone to make it happen and talk to another human. Now there are ways to do it by clicking a few buttons. Now 53% of consumers https://www.reddit.com/r/stripe/comments/1rec2ub/53 of customers never contact you before filing a/ don’t contact the merchant before doing a chargeback. With AI buying agents, the chargeback will be completely touchless and guiltless. The AI buying agents can be instructed to “get the best deal, make no mistakes” and come back with a nice jacket in your size to wear to an event. The AI can inform you that it has already initiated a chargeback against the merchant under the premise that the item wasn’t received. You are wondering how that circumstance is possible given that the jacket is right there in your hands. The AI informs you that, shhhhhh—it’s a new merchant we’ll never use again. There’s an obvious opening for data sharing across merchants to spot the bad robots who, while buying, aim to take advantage of every loophole and policy generosity in the name of saving their buyers’ time and money. Some people, if confronted in person with the facts of their AI buying agent’s behavior, would apologize and offer to pay for the item. Some people would instead remark that the jacket wasn’t quite right and there was another option that was a better value that got passed over. Some people would relish the idea of a totally defensible pint-sized negotiator stiffing merchants left and right while maintaining plausible deniability for the cardholder. Before we think that all the mess will simply land at the feet of a cardholder, we should note that AI buying agents now have access to their own funds and credit card accounts. 10 i-didn-t-buy-that-note-fn-1 The chargeback trail will need to be followed—thankfully, the credit card companies and fintech ecosystem can build AI enforcement agents for all the sleuthing that’ll be needed. 11 i-didn-t-buy-that-note-fn-2