Is AI Creating a Bubble? | Alan Trefler on How AI Will Change Jobs

0:00The world of software today is in a state of enormous confusion. So you're describing a bubble? The expectations people have of limitless growth is not going to be sustainable.

0:12Richard Farleigh: How much would your workforce be impacted by AI? There's no question it's going to massively change jobs that is already in our company. I'm honoured to have Alan Trefler today, who is the CEO of Pegasystems. You started the company yourself, I believe, about 40 years ago. It's now listed on NASDAQ. And it's a multi-billion dollar capped company with offices all over the world. And you've just flown in from Boston. So I'm really thrilled to have you on my podcast and get a chance to catch up with you. Well, thank you, Richard. I actually like to describe myself as founder and still CEO. Though, as you point out, it's been four decades, so I'm feeling pretty secure in that.

0:56So, I mean, where do we start? What does your company do? How did it start? The company started because I had been working as a software architect, trying to plan out software systems for typically large New York banks. I lived in Boston, but went every week to New York. And my boss, who was a consultant, sent us in to really try to figure out how to make both things at the time around money transfers and ATMs and other sorts of things were coming. and we were deeply involved in trying to build those. And I saw some opportunities in the way that we were building software in a whole new way that we could build it differently.

1:34This is the early 1980s, isn't it?

1:37Richard Farleigh: This would have been late 70s. So you were a graduate. You studied computer science or something? Yeah, so it's interesting because I went to university and I was recruited into the computer science department because I won a major international chess tournament as a sophomore. And we were trying to teach computers to play chess. and that was a really interesting problem at the time. So because you played chess, they assumed you'd be a good programmer. Well, I think they assumed that I could explain to them how a chess master looks at a chess position and that at that era, the computers weren't terribly, terribly fast.

2:14So there was a strong belief that you couldn't play a game like chess with brute force, that you needed to be able to really understand the logic and the sort of mental machinations somebody went through to figure out what to do. And I also learned, of course, to be a programmer and part of my whole career, actually.

2:35Richard Farleigh: So in the late 70s, early 80s, you were working in software, and you spotted an opportunity and left the company that you were working for. Yeah, the company I was working for was purchased, and I needed to decide what to do next. And I considered going back to university for an advanced degree. I could have gotten another job. But I had this idea that I had approached my boss with, and he had basically sort of rejected, but that I believed could make a lot of sense based on what I was seeing. Have you stayed in touch with him? Yeah, he came back to me about a decade later, and he said, you know, we really should have done that, Alan.

3:10Richard Farleigh: So what's an example of that, you know, practical, you know, single example of how someone would have used what you were doing? So one of the things that I saw and was able to convince some other people to join me in, was that many of these, in particular banks, but it also is very true for governments and health organizations, et cetera, have lots of things you would consider a workflow. They have a new member of household you want to onboard, or you have a product you want to sell that goes through a set of steps and stages. And as you go through those, you want to do certain things, ask certain questions, drive certain outcomes.

3:47And what we did is we said, you know, we can codify these workflows. We can make these workflows smarter than if somebody just wrote them in computer programming languages. And if we do that, it will do two things. One, it will make the workflows more effective. But the second, it will let business users, people who aren't very technical, understand them and even change them. And that was the original vision of the company. And despite all the massive changes in technology over the four decades, in many ways, that's what we do today just for many larger situations. It's on a different scale. But so what would be one example?

4:29If you've ever had a credit card dispute, you look at your credit card, you see something on the bill that you don't think you charged, the odds are about 80 % that a Pegasystem somewhere in the world will actually fix that for you. So you just started with you and a friend, a colleague? Two other guys. Yeah, it was. Two other guys? Yeah. I started, I went out on my own. The other two weren't sure. But after I was at it and they realized I wasn't going to stop, they sort of joined in. And we went, you know, in terms of bootstrapping something, really creating an idea from scratch, you know, I think we're almost a poster child.

5:08We went nine months before we got our first customer, and it was a very, very difficult period.

5:18Richard Farleigh: So you sort of financed from your savings and from friends and family? I financed from money that had to be paid back. So one of the first things I did is I took out a life insurance policy for half a million dollars because I had borrowed money from my mother, and she was going to need to live on it. I wasn't going to kill myself if it didn't work. But if it went badly and somehow I was unable to earn the money back over a number of years, she had to be paid back. How do you start winning customers? During the days, I was the architect, which meant I would try to figure out what the software should do.

5:55And then the second half of the day, I was the salesman. So I picked four banks and went and called on them and told them what we were trying to do, why it would be better than what they were doing today. and got them interested. And none of them, of course, would give us any money to buy because we didn't have anything that they could actually use. But as they saw it develop, we had a miraculous moment in the ninth month in which we went from having no customers to having the two biggest banks in the U.S.'s clients. And it was a pretty, like, shocking transition from being starving to completely oversold.

6:38Richard Farleigh: From famine to feast. Yeah, well, I did. But with those nine months, was that sort of expected because you were sort of building the software so you weren't really marketing, you know, you can't sell what you haven't got, or did you already have a product? No, we didn't have a product. So really the nine months is developing a product before you can go and sell it. But we didn't wait until we had it to go talk to customers. I think it's important to understand whether the things you're talking to a customer about are going to be appealing to them. And so we would go. We would say, hey, we're building this.

7:08We're thinking of building this. Let me give you a little bit of a demonstration. And it was a very different age in computer land because there was no cloud. We had to go buy a computer. I mean, a big part of the capital we had, we immediately exhausted because we had to get a machine that we could use that would be suitable for building this sort of technology. And the reality in business is typically when you need something, nobody will give it to you. So all of the computer companies who subsequently were really happy to kind of throw things at us, in the early days were only happy to sell things to us.

7:47How did you get your foot in the door with those banks? Well, I had personally done some work for some of them. So I had a bit of a personal reputation, and that helped leverage the meetings. I knew some people there, and they were willing to meet again. It was not the case, however, that they were willing to go and buy something for hundreds of thousands of dollars from a company that had - Three guys and a dog. Yeah. No track record. So they wanted to see things, but it's interesting. It was only many years later that I realized how much courage those initial buyers had to trust that we would deliver on the things that we said we would.

8:32And I would say that as a firm, the proudest thing, I think, that we feel as a company collectively is that those first two clients from 1984 are not just clients to this day, but they're huge customers of ours to this day across massive shifts in technology.

8:56Richard Farleigh: And you won big customers to start with. So it wasn't like some early adopters who were just like a regional bank down the road where they'll give it a go. Well, we ended up picking, and I decided the only way that we would pull this off is if we could focus our energies. and so we picked two massive banks and two regionals. And I wasn't sure which would be easier, but I knew somebody at each one of those. And it turned out that we were kind of hoping we'd get one big bank or maybe two of the regionals and have a chance to finish and show what we were capable of and then go back up to market. and it turned out that both large banks within the span of like 48 hours signed up.

9:44Richard Farleigh: So when you're introducing a new product in software as well, like was there any faking it till you make it or did you like suddenly like you've sort of over promised a little bit to get the deal done and then like truth, we've got to get this software doing what it's supposed to do. You know, we've got to add this bit on. We've got to, you know, I promised that. You know, was there any of that or was you very confident that what you'd already designed and had running was going to operate? We were changing the design every week. We would get the feedback from the customers. And, you know, it's interesting.

10:15Some organizations just can't operate that way. Some customers can realize that if they work with a more dynamic company and if they feel they can trust that company, and if it works, they'll get something that lets them take something of a leapfrog step. and those were the personalities of all the organizations we went after. We were looking for organizations that would be willing to put a stake in the ground because we didn't consciously overpromise, but the nature of these things is you just don't know sometimes. And so you just don't know if it's going to take a month or two months to do, and so you do your sort of best guess, you can't be the conservative and under-promise because you won't get a chance to do it.

11:10But if you over-promise and you miss, it doesn't take more than one or two of those to make the customer say, well, I'm not going to believe the next thing I hear. So there's a very, very delicate balance that you need to go through. And part of the way of addressing that is to ask the question, and I think this can be true for a lot of businesses, a lot of industries, is to say to the customer, look if I'm able to deliver this will you buy it and customers will kind of say yes and then you say well would you be willing to put down a small deposit and that actually helps you tell whether they're just sort of being agreeable yes or whether they're willing to put it put in something not not enough to keep it alive yes but enough to really validate the seriousness that's a

11:58Richard Farleigh: golden advice. So is there a way to prevent the not invented here when the in-house team of a customer will just try to do what you are doing themselves? Sometimes they'll see what you're doing and they'll say, boy, that's cool. I'd like to do that too. And, you know, it's even in some cases, not even a matter of expense. It's sort of a matter of interest. What you have to do to combat that is you have to be able to pick a target that's hard enough that it will scare some people off and have a have a sort of directional angle that creates anxiety that people say hmm maybe I could build the first generation but if these guys have been working on it for two for ten years Will their generations always be ahead?

12:53And will that matter for the business? The other aspect of it is that if we can build it and sell the heart of what we do to 20, 200, 2 ,000 companies, then we can get an economy of scale and a level of insight from what those companies are doing that should keep the product both more cost effective, but also more inspired and more able to keep up with what's going to change in the market.

13:23Richard Farleigh: Well, you are, you know, Pega is now kind of the IBM. You know, you don't get fired for buying. I hope not. You know what I mean? You don't get fired for using Pega. The world of software today is in a state of enormous confusion. How do you see the technology market right now? We are really at a, there's a company called Gartner that rates technology firms. You know, they talk about technology sometimes going through a peak of inflated expectations where people just expect everything from it. And then a trough of disillusionment when sometimes comes true. And then usually with most technologies, you find a place where there's a level of productivity you get, etc.

14:05We're right now, I think, vacillating between wild expectations, mass delusionment, and a lot of, there's a lot of confusion out there that is really, well, at least to us it was evident that it was going to occur, that I think the market is just now beginning to discover.

14:25Richard Farleigh: I think Bill Gates said people overestimate what's going to be achieved in the next 12, 18 months, but they underestimate what's going to be achieved in five years or something like that. And that's common. And then there are some things, of course, where we keep thinking that in five years, how many times have we heard that fusion is five years away? And at some point it will be. Or driverless cars. I mean, we're getting them. We're getting them. Some of these things, they operate on their own schedule. But this whole new push with AI and what's going on in the market is creating enormous confusion.

15:03And as a result, I think there are some things that people should be worrying about that they really are missing. What do you think is missing in the AI market right now? Well, we think, and when we saw the generative AI capabilities come out at the end of 22, we thought, boy, these are really interesting technologies. How would we want to apply them relative to our customers, et cetera? And I think we hit a really good balance. Now, one thing we saw was that this use of the generative AI, as you said, was not always reliable. And we operate in businesses and we operate in general with customers who'd like to be reliable, or some cases by law have to be reliable.

15:54So we said, how can we use the power of the AI to stimulate thought, to help people reimagine things? But then on execution, figure out how to do something that will be reliable. And, you know, a good analogy here is let's say you wanted to open a restaurant. You might hire a great chef. You'd create a test kitchen. And that chef would go and distill down a set of recipes that you wanted to offer. You take those recipes, put them in the recipe box, move them onto a menu, and you've got there a highly creative business system, but operationally has the ability to scale. And when we wanted to model how we were going to use AI primarily, it was to say, look, let's use this AI to redesign the way the business process should work, to challenge us, to think, do we need these steps, do we not need these steps, how do we want to do things?

16:56convert those into workflows, and then at runtime, when somebody actually wants to do something, find the right workflow, execute it, and not turn the world over to the language model at runtime, but use it when you're thinking at design time. Now, what's interesting is this is quite different from the way that a Microsoft or a ServiceNow or Salesforce have gone after this problem. They've been talking about just turning it over to the language model, Let's a language model figure out what your business should do from some English language prompt. And that has a couple of problems. The first of the big two is that it is not going to be predictable.

17:40You're not going to get a consistency of outcome because part of the beauty of the language models is they do introduce variation. Using that variation when it's trying to stimulate you to do good thinking, that's awesome. But you're curating it. You're figuring out what good is and what bad is. When you're actually running, we think it would be nice to be able to operate off of, as much as possible, a standard set of procedures and only wing it when that made sense. The other problem, which is just broken in the last six or eight weeks, that these companies that were spending, first they said billions, then they said tens of billions, then they said hundreds of billions.

18:22And now. We're talking serious money. No, it's actually trillions of dollars being spent to build data centers to run the AI and answer the questions that they wouldn't want to get paid back. And so for the first couple of years, these drug dealers began making it so that it was either free, you could open the AIs for free, or for$20 a month, you could basically get pretty much all you wanted. And it was magical. It got better and better and better. Well, what's happened is they've now decided they want to go public. And to do that, they have to generate revenues. And all of a sudden, dramatic price increases in what Microsoft and these other companies are charging for these AI tokens, which is the unit of measure they use to measure the amount of AI you use.

19:18and people don't really know how many tokens they're going to use until they get the bill. I mean, so it's just incredibly opaque, but it also can become, as we've learned, very, very expensive. Well, the only reason that's a problem is because they convinced the industry to go down these paths of reimagining how they wanted to handle a customer interaction or how they wanted to handle a situation from scratch every single time they did it. they convince them that you don't have a recipe box. That what you do is just a really good chef, and he comes in, and somebody shows up, and he's going to design for you the perfect meal.

20:02You may not be able to have a price list on it. But, you know, look, for some people in some cases, in some situations, that could work fine. But in terms of the basis for industry, I think there are some pretty serious problems if you don't have predictable outcomes and if you don't have predictable cost. So we've gone firmly down that path.

20:23Richard Farleigh: I'll just jump in there for a second because you might know Demis Hassabas. Do you know Demis? I know of. Yeah, I'm friends with him because of chess, and you're a chess player as well. But he's from the UK, and now he's head of AI at Google. I said to him, I said, you know, I like Gemini, his software. But I said, oh, ChubGBT is so friendly. It's so nice to you. You know, it's kind of easy to use it. He goes, that's so bad for humanity. It gets people to overuse it. So are we using AI? Are we not accounting for the cost of it, which is what you're saying? Well, I think there's use that is adding value to people.

21:03And that's great. There's use that's just waste. And rethinking the same thing over and over and over and over again.

21:12Richard Farleigh: Designing a new menu, the same menu every day. Designing the menu, redesigning the menu for every patron as they walk in the door, as opposed to saying, hey, I'm going to have some inventory of things that reflect what we're capable of doing and have learned to do well. That's not AI's fault. That's the people who harness it, not using the right judgment. The user. Well, I would say my examples are really mostly business examples here as well. If you want to develop a relationship with ChatGPT or Gemini, for that matter. I wouldn't fault you for that. But that also doesn't destroy the planet. It's the heavy-duty uses, the ones that happen hundreds of thousands or millions of times in a week, that are the ones that are really driving it, when in fact, if they didn't have to use it, if you could just think about it once, record it, re-execute, that seems to me to be not just a a more reliable way of doing it but it certainly is a much greener way so the world we're in now with ai um do there's a lot of little companies pursuing particular niches or and there's obviously the big guys you know who's going to win well i think different companies will win in different ways you know i'm not sure that starting another software company is what the world needs this incident.

22:39Is that right? Yeah, well, I was speaking at an AI conference two months ago, and flew to San Francisco, and been there for a little bit, and was driving down the 101, which is this big highway that goes from San Francisco to Palo Alto, and it was mind-blowing. There was billboard after billboard of AI companies, you know, XYZ.AI and ABC.AI and whatever, 80 % of which I've never heard before. And I'm in the business. And there are just so many. Part of it is that the emergence of cloud computing has reduced the entry cost. But that will not, in and of itself, make those companies successful. It will just allow a lot more to sort of jump into the pool.

23:34To be successful, you have to have something that's distinguished. Now the other end of the spectrum are the companies who are not taking advantage of ease of entry, but are taking advantage of being able to raise and spend vast amounts of capital to be able to create these enormous and increasingly large models. And, you know, to be candid, a lot of that is just unsustainable. The amount of money being invested, the amount of capital being spent, the amount of energy being consumed, I think we'll get to a tipping point and people will say, well, you know, we've had enough.

24:16Richard Farleigh: You mean with the bigger companies? Yeah. I mean, the bigger companies can, you know, they're... So you're describing a bubble? Financial bubble. I think they're a bubble. it's not a bubble in the sense that it's like pop, going to all go away. But it is a bubble in the sense that the expectations people have of limitless growth is not going to be sustainable because it just never works that way. What about the job market? I mean, in general, if you had a teenage child, what would you advise? And equally, another question is, you know, your workforce, you know, how much, you know, you're a software company, you know, how much would your workforce be impacted, the number of people you have be impacted by AI?

25:04Well, I think we've gone through a set of headlines that suggested that massive cutbacks at a company called Block, which is in the charge business. Dorsey, their CEO, announced a 4 ,000-person cut in tribute to AI. But I think a lot of the cuts you see really are the result of a period of overhiring that occurred in the post-COVID era. Having said that, there's no question that this is going to massively change jobs, and it's already in our company very dramatically changing how people work, their productivity. And I think we're in a situation where lots of companies are trying to figure out what this really means.

25:50I don't think people will do computer programming the way they did three or four years ago ever again, But I do think understanding how computers work is a good thing. And so I would recommend that that would still be something people study, though maybe not with an eye to the subsequent job here as well.

26:13Richard Farleigh: But what's it called when there's a word for it, as you get a higher and higher sort of programming languages, where effectively now you can just talk to, say, Claude Cowork, and it'll design a website for you where there's no program at all. or just English language is the language. Well, they call it vibe coding. Right, right. You get the vibe. Yeah. So does that mean all of us can be programmers? Like I did a website which took two hours, you know, which my son, who's designed a website, it's so depressing. You know, that would have been, you know, that's a job for someone out there. It would take them a week.

26:44Yeah. So the interesting thing is you can do lots of things that would have taken you either long periods of time or caused you to go out and buy consultancy services or buy a product. The things, though, that that, you know, it's a little bit like before you had Microsoft Excel. Only a limited number of people could create, like, a model of a business. And once that became available, it really sort of democratized that sort of thing, which, you know, I think ultimately is good for society. It adds a level of productivity and a level of transparency. I think that there's a difference between things that have a life span and things that are kind of one-offs that you're prepared to throw away and redo.

27:38So if you want to build a website, and when you get bored of it in 90 days, you want to create another one, that's the sort of thing that that may be a great solution for you to do. If you want to create a business system that knows how to, for instance, handle the financial ramifications of how somebody should retire, that's not going to be some little system. That's going to be something with lots of components and elements.

28:10Richard Farleigh: But that may have taken 20 a team. A 20 may now take a team of 10. I think the teams are getting way smaller. Certainly in our company. we are restructuring how the teams are organized. We used to do something called Scrum, which is a common way that people used to develop software. And a Scrum team would typically be a team of six to eight people who would go off and handle things in two-week increments. That was the sort of standard way. Now you're dealing with smaller teams with much faster cycle times, a day or two, literally. And the output is different, so it's not completely parallel, but it's going to be very good.

28:57So this will make it less expensive to build things and make it less expensive to create choices. Having said that, we're optimistic that software like ours, which also can be vibe-coded. We let somebody actually talk to our technology to build a new workflow. So you can just describe what you want and the system will come back and do it. We've been able to adopt those sorts of techniques. We think that workflows have attributes and have capabilities that are going to continue to want to be used to achieve other things. And so that we can be a surviving platform in this world where there's going to be so much change.

29:44And, you know, it's funny because 30 years ago, we came up with a tagline for PEGA, what our motto wants to be and what our driving principle wants to be. And that tagline is Build for Change. And I think that this era actually is a Build for Change era. and that's why I'm happy that we were able to take AI and pull it into the building for change as opposed to sitting out there and saying well it's never going to work because it became pretty clear to us that there was something there that was going to work yes it was a great position to

30:24Richard Farleigh: be and to have the power you know the muscle you know and the client base to be able to you know make those sort of decisions and and make you know to be part of it you know as you said you know you were saying well a lot of little companies just won't make any you know they'll think they'll make progress, but they won't. But you obviously have a customer base and a reputation and the skills to be able to actually really ride this wave. We hope so. You can't take anything for granted. On the employment situation, if you had a teenage child, what would you advise them to study if that was what they were trying to do?

30:53Well, it's kind of funny, but I think what's going to be really important is philosophy and business judgment. I think being able to understand how to make decisions in this world where a lot of the things you would have historically studied are now retrievable as opposed to having to be discernible through a lot of detail. I think those are the important skills. I think that... Philosophy? I would say philosophy. I would say decision making. Economics or business? Business, decision making. Being able to draw on the experience of others to be able to find scenarios comparable to things we haven't run into yet.

31:36Richard Farleigh: So something you use as judgment where there's not too much data which just suddenly make you irrelevant or make you, you know. Well, if it's highly dependent on data and retrieval and fixed research, there are things that are going to just be able to do that. I also think that it's not bad to understand how the computers work, because you won't program them in the way you used to historically. Right. But they will have a greater and greater influence. Right. And there are lots of professions, candidly, where, you know, I recently read that, you know, plumbers in the U.S. have the best job prospects that they ever have.

32:13There's going to be lots of practical things.

32:15Richard Farleigh: So on to chess, because you have a phenomenal chess story. I've sort of been played in a couple of chess Olympiads, played some serious chess as well. And I think you're a U.S. master. I'm a U.S. master. So we've got some parallels there. But you'd leap ahead in the sense that you won the U.S. Open chess. I won the 1975 World Open. I found that when I went into, because I was in finance, you know, running a macro fund and then went into business. But I found, you know, because I was a keen chess player as a teenager, I had to unlearn some chess things because there's no randomness in chess. So, you know, in business, you can do the right move.

32:55Richard Farleigh: In finance, you can do the right move and still lose money. There is there. There are obviously, you know, elements of business that are clearly beyond control. What I always loved about chess is it's a game of perfect information that the the the two players, the spectators, everybody sees the same thing, but they have different opinions. And I always found that really very, very appealing. But that's another point. Like I haven't found chess players to be, you know, not the only skill in business because you have to, there's an unlimited amount of unknown unknowns, things you couldn't possibly have even thought of.

33:32But I think there are ways of thinking about business that really, for me anyway, draw heavily from chess. I think a mistake that some lower chess players make, but I also see people in business make, is they go at a problem as if if they think about it, think about it, they'll find the answer. And I think only by looking at candidates do you get really the insight as to subtle differences and how you might want to make a choice.

33:58Richard Farleigh: Well, I think some advice I got in chess early on was when you think of a good move in your head, you know, when you think of a good move, look for another one. Instead of going, that's it, you know, look for another one. because it's the that it approach. I think that's what dooms a lot of chess players, and I think it dooms a lot of business people. So I might just finish up on your personal story. I know your father was, if you don't mind me asking about it, was in the Holocaust, wasn't he? Yeah, he was. He came over to the U.S. after World War II. So he survived the war, came over, and on my mother's side, they were Ukrainian, actually.

34:36So it's interesting when you have all the conversations about immigration, et cetera. You know, America is a country of immigrants and we're examples of them as well.

34:50Richard Farleigh: So they probably arrived with nothing? Yeah, absolutely nothing on both sides. Well, they're probably quite proud of their little boy. Well, you know, it's interesting. My dad died at a young... But my mother always had strong opinions about the business and how it should work as well. Did she? Yes. Yes, yes, yes. Were any of them good advice? Well, it's actually funny you say that. There are four YouTube videos about four minutes in length that are hilarious and were done as a shock to me by my younger brother, who actually works in the business also, and two of our very clever friends. And they went out and got actors to play me and my brother when we were children.

35:34and it's called Mother Treffler's Good Advice. And if you Google them, I would tell you that they're worth a couple of minutes. They actually have, each of them has a pretty tactical business message. Right. They're all done in the spirit of a children's story. Aesop Fable type thing. Very much.

35:53Richard Farleigh: Well, my friend laughs, you know, he runs a big company, but he laughs at his mother's sort of advice is to buy high, sell low, but make up for it with volume. So, you know, I don't know if that one is worth taking on. Well, Alan, listen, I know you've been, it's really a real privilege for you to come and speak to me for the Boardroom podcast. I know you're going from one meeting to another to another, and you've just flown in from Boston. So I really do appreciate it. And I found it fascinating to chat to you. So thanks very much for the time you've taken. Well, thank you, Richard. I found it enjoyable.

36:30and I'll give you a copy of the chess life and you can look at the games and decide how I did.

36:37Richard Farleigh: You know, I played chess, wrote the moves down the other day, and I took a photo with ChatGPT, said, what do you think of this game? Came back with all the analysis. It told you this was a good move, this wasn't. That's just from a photograph of the moves written down. You know the thing I'll tell you? ChatGPT is not the best AI to use for them. I know. There's other systems. There are others. There are others that are far. Just ask Demas. Well, or DeepMind would do better. And Stockfish is another one. Just as a throwaway, you know, it just takes one minute. I was quite impressed, though, with what it was generally right.

37:14The thing about ChatGPT is whether it's right or wrong, it's always confident.

37:20Richard Farleigh: And when you say, that's not right, oh, yes, you're right. Yes. Exactly the case. Anyway, thanks again, Alan. Thank you, Richard. It was a pleasure.