Travel Through the Lens of AI with with Booking.com CEO Glenn Fogel

0:00There is no such thing as a moat. There is no such thing as somewhere you're going to be protected against innovation. Today, we have a competitive advantage on areas. Absolutely. But those can go away tomorrow. The only one to win long term is to continue to develop new services, new ways to do things. How can we do your business better? What do you need? Where you need more demand? Where are you hurting? It's so much more complex. If you think you're just going to come in and do this business and knock away these very big players, I'd say you should really understand what business is before you decide to commit your capital.

0:43today I know priors we're talking with Glenn Fogel the CEO of booking holdings a 20-something year veteran of the company who joined when it was just Priceline and worth a few hundred million dollars in the year 2000 and has since helped grow it to a hundred billion dollar plus company with multiple massive assets across travel that are now being used in the AI era we will talk about a variety of topics with Glenn that span everything from the growth and changes that have happened in bookings over time, as well as his own career and perspective on AI. So Glenn, thank you so much for joining us today.

1:16Well, thank you very much for having me. Yeah. So you've had a really interesting career. You know, you went to Wharton, you went to Harvard Law. Along the way, you worked in, I think, the MIS program at Morgan Stanley. I'd love to just hear a lot about your early background and how that led you eventually to Booking.com and eventually Booking Holdings? Yeah, I can do that. So real short, I came out of work with Green Finance. Now I end up back off with MIS. My first job is putting tapes on a drive in a data center of IBM 3084s and the tape drives and putting the tapes on the drive. That's where I started.

2:00Then there's an operator, actually being an operator of a mainframe, very different, nothing that you prepared for that in college for that. And I then become a developer, and I basically learned that this is not a career for me. I should do something else. And all the people I knew, undergrad at Wharton, were going off to invest in a bank and made all this money. And I thought, I should try to do that. But you can't go. I've now gone down one chute, and you can't just jump out of that one to go become an investment banker. So I said, oh, I got a degree in finance board, and Harvard let me in the law school, so I'll do that because that's another route.

2:37I did that, and I end up getting a job on Wall Street as a banker. I did that until 1995, and then the bank was bought by another bank, and they fired all the bankers almost. Not everybody. They fired most of them, including me, but not everybody because they fired everybody. Well, they fired everybody. but you didn't fire everybody there was actually some people who were picked to stay that was not one of them that was pretty bad so a real good lesson though having been fired and knowing what it's like that is something I've kept with me throughout my career about how to do it right and how to do it wrong and understand what goes through the other person when you tell them that I'm sorry but there's no longer a room for you here and it's not the place for you to be I really learned that first hand being on that side of the table And so now I'm unemployed.

3:31And my father had just died not that long before this happened. My father's died, lost the job. My grandmother died and even the dog died, which is kind of sad. And I'm like, what do I want to do with my life now? My young 30s, I'm alone, I'm saying, oh, whatever. And I said, you know, I always wanted to write a book. So I start writing a book. I write a novel. I get it done. And now I'm going to try and get it published. And it's not really self-publishing. You're doing it on your own. You're trying to get some agent to pick it up. And I was introduced to a woman as a blind date program. And I think she was a lawyer.

4:09But I don't know. They said, well, she used to work at Random House as an editor. I said, oh, I'm very interested. And so we have a date and the book never gets published. I do end up marrying her and have two great kids. It's a wonderful life. But while I was trying to get the book, you know, an agent to be interested in it, eventually she said, you know, if this relationship is going to go forward, you should get a job. I said, damn it. I didn't want to go back to banking. And I said, I don't know. And a friend of mine from law school was a senior type person at Morgan Stanley. And I told him, look, Amy says I have to get a job.

4:45Do you have any thoughts? She said, well, we have this trading position here that you can do. I've never traded anything in my life. He said, ah, don't worry, you'll be fine. He's like, okay. So I end up being head trader for a guy named Barton Bay. He's kind of a Wall Street legend and stuff. I do that for a number of years. But I just don't like it. It's not that exciting. It's just not for me. And that's when the internet was really taking off, that first real explosion. You know, the internet boom. That's late 90s. So 1999, I started trying to interview. And I said, well, I got some skills.

5:16But when I was an IT person, I got that. And I know a little bit about corporate development because being a banker, and I started interviewing. The only real company on the East Coast at the time of internet, you know, abilities was Priceline. And they had a job for corporate development. I was perfect. So again, an offer from that. I said, I want to wait. I want to wait until I get my bonus for$19.99, which gets paid at the end of February 2000. So I go and I get my bonus check, and I'm ready to start. And that's when, of course, that's when the NASDAQ peaked, thereby having gotten long internet a week before the NASDAQ peaked and stuff.

6:00And it proved that I shouldn't be a trader, having just been the absolute wrong way. And, you know, Priceline has its difficulties after that. Our stock went from where we were in public. In a week or so, we were worth$30 billion, which back then was real money. And that meant something back then. And by the time I joined, I joined after the, you know, a few months after the IPO, I joined, we're probably down to about$15 billion. That's now, you know, February, March of 2000. In nine months, our market cap is now down to just a couple of hundred million. And our stock is now trading at a dollar a share.

6:40We're going to get to you. This is going to drop below a dollar a share. But stick with it. We do a reverse split to make sure we don't get delisted. So it goes to$6 a share. I stay and I'll be there on my 27th year. And we went from that, that now reverse split$6. And in last summer, we came very close to$6 ,000. Wow. Over that, you know, a little more than a quarter of a century, up 1 ,000 times. And, you know, market cap was peaked around$180 billion. Remember, it was a few hundred million. Yeah, that's amazing. It's been a good ride so far. But, of course, you never stop. Every day is a new adventure.

7:19Every day is a big headline in the FT when I had an interview with it. I said, got to fight for a customer every day. How do you think about the lessons from that internet era in terms of the current AI wave? Because we're seeing this massive shift in market cap. You think like this is a explosion of new things, of all these, of everything, just like in the late 90s. The optimism of technology is going to be wonderful. But then you get a little bit of the backlash coming back then too. And now it's just bigger than it was then. The numbers are much bigger. The issues at hand are much bigger, the pluses and the minuses.

8:04So I do see a lot of parallels. Yeah, because when I look at it right now, some companies clearly have enormous revenue bases. OpenAI and Anthropoc are rumored to have$30 to$50 billion in revenue run rate each. And in parallel, you see companies that are extremely highly valued, 10 billion, et cetera, that don't necessarily have even revenue yet. And if you look at the internet era, I think it was something like 450 companies went public in 99, 450 went public in the first few months of 2000, maybe 500 went public before that. So you had 1500 companies of which what, two dozen are left at most, you know, the other thousand, 480 are gone.

8:45And so do you think the same thing will happen to the AI set of companies? You think something different will happen. And those are IPOs, by the way. Those are the very strongest or perceived to be strongest companies, right? Right. Well, I wouldn't want to even guess at what the ratio of success to failures will be this time around versus that time versus any other time when there was incredible boom. And, you know, if we go over the last 150 years, there have always been these kind of springing further back. Yeah. Speculative booms that create tremendous innovation, people coming in, just both money and people.

9:25I mean, California, you know, the 49ers, I mean, everybody's running off to the hills to gold and it was going to rain. I'm sure there were a lot of companies selling, you know, axes and cameras. Yeah, yeah. Detroit auto boom, same thing. You know, we've seen this before. And how many of them? So I don't know, but I think this is not new. And there'll be a great deal of disappointment. There'll be a lot of people who are going to lose a lot of money, of course. That's just the nature of how we're coming to work. And when there's speculative bubbles, that will bring out a lot. But that doesn't mean that there aren't a lot of companies.

10:00It's actually a real value that aren't going to be. How do you think as a founder running a company or as a CEO, you should make the decision in terms of whether to keep going or whether to sell? It's kind of like, okay, Priceline is worth a couple hundred million dollars. And the decision was made, we'll keep going no matter what. and there may or may not have been options in terms of exits. I have no idea. But in today's era, there's quite a few options in terms of exits. Should people mainly be thinking about exiting right now, do you think? Do you think they should keep going? Yeah, I think that's right.

10:27I don't think we can give a general rule without knowing what the facts of that specific situation are. By the way, there were times early in the day where a private site would have been happy if somebody had made an offer. Yeah, yeah. It really depends a lot on what the situation is, how confident is management and people who have put the money in that there's going to be a future, and how concerned about you. What are you really trying to do? Are you trying to accomplish something? The goal is to actually make something that matters, or are you just here to make money? And nothing wrong with that.

11:03I'm not against that. You've got to understand what is your motivation? What are you trying to achieve? we have on average if you're American male for help because I think that's what I've put that I have a 78 year expected lifespan when you're born of course the longer you live the higher the expected whatever it is and how are you going to spend those years what is important to you what do you want what's the meaning to it and I'll let the people actually involve those situations I would not I would not give them general advice makes sense so I mean back to booking you know one of the categories that you all obviously are really crucial to is travel.

11:42And there's a number of the next gen sort of AI companies who've started experimenting with things like OpenAI

11:52had checkout in ChatGPT. And, you know, one of the main use cases was travel. And then they canceled that feature. And I think at the time, booking went up 8 % on the news. What do you think didn't work there? Or what do you think, how do you think people should think about travel through the lens of AI? So I think we should back up a little bit so we understand what's going on here. So in any type of situation, you'll have people who are not that knowledgeable about an industry or about how things actually happen in the business. And so from the outside, it looks rather easy to, oh, this is easy.

12:31AI will take care of travel and all the travel companies won't be worthwhile, won't be worth anything. And that was why companies like ourselves took a big hit as some of the new models were dropped that had a much better way of doing agentic commerce as it was perceived at the time. And then when people make analysis, we're not going to do that, like when OpenAI said we're not planning to be a merchant of record, or we're not even going to keep in this app type of way of doing commerce. We're not doing that. Then people say, oh, well, I was wrong. I'm not going to worry about it as much. It puts the other side.

13:10The truth is, the way we look at AI is an incredible, beneficial tool and a way for us to be able to do our mission easier, cheaper, and better for our customers. All business, what is the purpose of a business? A business is to do something of value to its customers. We have two kinds of customers. We've got travelers and we've got partners. We are in the middle of that for a marketplace. How can we do it better? AI, particularly AI using large language models and other things like that, can help make it a much more valuable method for people, travelers, to get information they need to do what they want to do and beneficial to our partners.

13:58That's the thing. Now, the idea of Chat TV, you know, Chat TV no longer having one method. I wouldn't read too much into that one way or the other. Yeah, that makes sense. You know, it's interesting because I'm in the middle of Silicon Valley where people are very AGI-pilled, right? People strongly believe that AI will drive all sorts of things, and in some cases it will, and in some cases it'll take longer, and in some cases it won't. It reminds me a little bit of crypto where crypto was going to solve everything and it didn't, but it was very important for certain aspects of the financial system.

14:28And I think stable points and other things are increasingly viable there. On the AI side, what a lot of people are really moving towards is more agentic work. And that could be specific companies like Decagon having agents to do customer support. But it's also the larger platforms like OpenAI, Anthropic, Google, etc., providing increasingly self-driven systems, Codex or Cloud Cowork or some of the things Gemini is doing. And one of the arguments people are making is that the nature of UI is going to change and you're going to have agents doing transactions on your behalf and sourcing things like trips or figuring out your travel itinerary for you or buying or purchasing the actual different aspects of travel?

15:14A, do you think that's a correct vision of the world? And B, do you think, or how do you view that interacting with booking and what you all provide as a service? So again, we want to reduce this to understanding what does the customer want? Many people would travel find it very frustrating. I know that. trying to put together a complicated trip with a family that's saying multiple destinations, different things you want to do. It's complex and it's pain. You start planning it and then you stop because there's too much of pain. And everybody would like something else. Many people would like something else to do for them.

15:52In fact, that's why you'll find very wealthy people have travel concierge people who are actually human beings who really understand the needs of that customer, what they really like, not able to do a lot of the hard work for them. People aren't quite in that wealth zone. They'll have their partner or their spouse trying to do it for them. I'll be perfectly honest. I'm exposing myself here, but I'll say it, okay? My wife and I sometimes argue about, okay, who's going to have to do all the travel planning for this trip? Because it can be frustrating, et cetera. Now, with AI, the beauty is it's going to make it so much easier.

16:27And it's doing it right now. We are doing it right now. And let's use that generic term. Let's call it an agent. So I can't wait until we, booking holdings in our companies, are offering up these personalized agents that are, that know everything about you, everything you want, and able to do so much more for you than any human travel agent could ever do. Because the machine never forgets anything. The machine has an infinite amount of permutation. You know, rapidly look through and choose what is the best thing. and it can go down and then back up. That doesn't work. Why? This doesn't fit that way.

17:03And can come back with, now people always want some agency. So they make the decision themselves or at least confirm they want it. Most people are, for the most part, a complicated thing. They don't want to double check, they'll take the van or whatever, which is different than say a business person says, I got to go from New York to Chicago. And you know, your assistant, your human assistant, I'm doing it for you. That's like an agent doing it for you. It knows what you need and all that. That's great. But when it's complicated, you want that agency. So we at Booking, holding all our companies, we are doing that right now.

17:35In fact, if you go to Penny, which is Priceline's authentic AI system, and I just did it the other night, I put in a very complex need for a travel with the family, where it was my wife and I, we want to go up in front of the bus. I want the young adults who are adults, but I'm paying, so I want them in the back of the bus. they're starting so I got two cabins now one person has to go back to a different city we're going to Europe, we're going to a city we're not actually doing the trip from that city where we're going to land, how are we going to get from one to the other should we have the hotel where we land and then travel the next day to the other city or should we go that night how are we going to do it, what restaurant all the things and I did it on Priceline Penny and it was incredible, I also added in other things I told it that, by the way I got a lot of frequent flyer miles So should I be using my miles or should I be using cash and for which ones?

18:32And it was just beautiful how it went back and forth and asked me questions like, how many miles do you have with each airline? Now I'm giving it. And then we're going through the flight part and how much of the calls. And by the way, as I suspected, I'm using my miles for the upfront part for my wife and I. We're paying cash for the kids on the flight. We're going to go to the hotel in the city that we land in. The next day, we're going to get a show. It's wonderful. It's wonderful. And that's what we want even more so. Here's the real core thing is, when things go wrong and things go wrong in travel, nobody's full many times.

19:08Weather, mechanics, it happens, okay? You want to have that one point of contact that can fix everything because travel is like dominoes. One thing falls over and it all starts falling over. And that's the beauty with AI, being able to figure out, being able to look ahead, what can we do? So my goal is to have a system that actually we are able to predict well enough what the problem will be before it happens. And so just changing things. I have so many examples of this, but I see the future. I guess at a sort of generic level, because, you know, your team on a call that we had prior said that penny adoption, which, again, is this agentic tool that you develop for Priceline, has doubled every month for the past few months.

19:49and it's generated a lift in conversion plus faster search or a path to booking, lower cancellation, higher customer success. So it seems like it's working in really interesting ways. Are there a common set of use cases that you think are most common for Penny? Are there specific things that it doesn't do well that you just need the underlying models to get better for? I'm a little bit curious about. Well, the actual weeds have some areas where we're going to be coming out with some new things on it. but here's something really important. So you ask me the question. You say it's so great, but you tell me it's not really doing much, you know, the numbers.

20:26It doesn't really show up much in the numbers yet because it's still really, really small. It's absolutely not. We've got to talk scale here. You know, last year we did$186 billion with the travel. That's a lot of travel. You know, we did a billion of room nights. So, you know, the actual numbers, so there's a lot. Well, part of it is, we're not pushing it really fully now. this one of the issues that i always want to think about is what's the cost of us and that's not the way you understand what is the cost and that's what's developed of running it how many tokens are we consuming here we are well right or how many times are they coming back and forth we're doing tell me how much was the cost of us getting that trip for that person and what is our ROI going to be then the next thing is well what's the return long-term did it be lifetime value Do they come back?

21:15The loyalty's up for what? How much? And will it change? These are things that we don't know yet that we're going to have to do. We need to work on and develop so we know. And by the way, the whole thing of token economics now, which model should we be using for which purpose and when and stuff? And obviously, you can get tokens a lot cheaper. It's a lot cheaper. Different models can be a lot cheaper. And that's something that we have to look at very closely, too. So it is fascinating that we can do things that I'm so thrilled that we do. For example, things like using customer service right now, customer service where we're using AI, it's great.

21:54It happens much faster. Instead of having to staff your humans, at peak time, somebody's got to wait for somebody to pick up. We've all been in that queue. Wait for somebody to pick up. We hate it. But now with AI, the computer can pick it up. It's not a problem. And solve the problem even better and faster. You never will have in the future. When you finally talk to a human, the human says, I'm sorry. You'll have to be on hold again while I get somebody else who can solve that problem. And then you want to thawel somebody. AI will solve that problem. But here's the question again about that. Sometimes, though, you want to figure out because people want to talk to humans sometimes.

22:36You've got to balance that. Because what you don't want to do is end up, yeah, you can do it all on AI, but actually that's not what the customer wants. It's always what's best for the customer. Yeah, that makes sense. And I think you said on your Q1 call that customer service costs are already down about 10 % for reservation. Let's go with this. Our costs for customer service per contact are down. So that's great. Customer satisfaction is up. That's even better. But we have to make sure that we are able to always recognize some customers want a good being and some customers are, you know, the happiest could be just through AI.

23:23Yeah, that makes sense. I mean, I think you also mentioned that you're investing something like$550 million of cost savings into AI and platform on that same early call. Where are you investing it? Where are you putting the brunt of that, both capital and effort? Yeah. So the amount we're investing is actually higher. We talked about$700 million approximately this year is being invested. But it's in many, many different areas, not just developing more AI. Though AI, there's definitely money going into, call it tech enablement, but there are different projects, different areas. I would not put that all into a, oh, you're investing in tech, not all in AI.

24:03That's not correct. There are lots of areas of investment. We talked to them on the call. That's not what's important. It's the idea that you've got savings. You've got money. You've got cash flow. How much should you be putting in reinvesting in a company? How much should you be looking at perhaps acquisitions? And how much should we be handing back to shareholders? And that's always a balance trying to figure out what's the right ratio. It really is the first thing is do we believe investing in the company is going to give a positive ROI that's sufficient to justify doing that. Or after that, other acquisitions.

24:40And if you can't do either of those, then get the money back to the shareholders because they can then invest it better than you can. And that's what I've always believed in. It makes a lot of sense. And I think you folks did something like a record$4 billion in Q1 in terms of buybacks and other sort of returns to investors. You know, I really am very proud of the fact that over the last, let's say, dozen years or so, we've bought back approximately 40 % of the outstanding share. That's good. And we offer a dividend. We have a nice dividend. So in the fourth quarter, we bought back$3.6 billion worth of stock.

25:17We gave out approximately over$300 million in a dividend. And in addition, we also were paying for the taxes for the equity grants that invested. And you had to pay the taxes by withholding the shares that were part of the best. That's another$300 million or so. So anyway, a lot of money, making sure it's going back to the shareholders if we don't think that we can use it properly ourselves. And it's coming from an investment banking background, maybe back then, or maybe trading, where companies that just build up huge amounts of cash and they're not doing anything with it and they're not giving it back to the deal.