The future of digital markets
Gabriel Weintraub studies how digital markets evolve.
In that regard, he says platforms like Amazon, Uber, and Airbnb have already disrupted multiple verticals through their use of data and digital technologies. Now, they face both the opportunity and the challenge of leveraging AI to further transform markets, while doing so in a responsible and accountable way. Weintraub is also applying these insights to ease friction and accelerate results in government procurement and regulation. Ultimately, we must fall in love with solving the problem, not with the technology itself, Weintraub tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. I thought it would be good to revisit the original intent of this show. In 2017, when we started, we wanted to create a forum to dive into and discuss the motivations and the research that my colleagues do across the campus in science, technology, engineering, medicine, and other topics. Stanford University and all universities, for the most part have a long history of doing important work that impacts the world, and it's a joy to share with you how this work is motivated by humans who are working hard to create a better future for everybody. In that spirit, I hope you will walk away from every episode with a deeper understanding of the work that's in progress here, and that you'll share it with your friends, family, neighbors, coworkers as well.
[00:00:48] Gabriel Weintraub: We came up, uh, with a way of standardizing products in a scalable way where now you can compare, uh, directly products that are similar or, or equivalent, and that increased price competition quite a bit. So, as I think of the revenue of how, you know, technology can, uh, you know, really help, uh, public market, uh, make more efficient. And, you know, we reduce, in that case, you know, the, the budget by eight percent, like the spend was reduced by eight percent, which amounted to like tens of millions of dollars per year.
[00:01:25] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you're enjoying the podcast, please follow it in whatever app you're listening to right now that'll ensure that you never miss the future of anything. Today, Gabriel Weintraub will tell us about how markets work and how they change when you make them digital, and when you add in AI, it's the future of digital markets. In today's episode, we're gonna continue our brand-new feature, the Future in a Minute, where I ask the guests a few rapid-fire questions and they respond with some rapid-fire answers. Also, before we get started, remember to follow us in whatever app you're listening to right now. That'll ensure that we notify you about all the new episodes.
[00:02:13] What's a market? A market is a place where people buy and sell things. It's basic. There were markets in ancient times. There's markets now. The difference is you'd like your market to be great. You'd like it to be able to have everything you need, and you'd like to be able to pay prices that are reasonable. In the last twenty years, we've moved from physical markets to digital markets, and now AI is in the mix. This makes market creation and evaluation more interesting, more complicated, but potentially more impactful. Gabriel Weintraub is a professor at Stanford University in operations information and technology, and an expert on digital markets and the impacts of AI on them. I'm gonna ask him what is a digital market? What makes it good and how are they evolving over time?
[00:03:00] Gabriel, what drew you to study digital platforms as a major focus of your research at the business school?
[00:03:08] Gabriel Weintraub: Well, hi Russ. Uh, thanks, uh, first for having me. Uh, it's uh, uh, it's a long story that I'll make short, so I was always interested in markets and designing markets. At some point I was designing markets to buy school lunches in Chile, uh, for the government, uh, which was, uh, quite interesting. Uh, and then, uh, you know, sort of without realizing it, uh, markets, uh, went online and digital and like large companies that scaled up. Uh, and it turns out that the questions I was, uh, thinking when deciding school lunches, uh, you know, options and not too different to the questions is digital platforms were asked, asking to run efficient markets. So, it somehow, like, you know, it was like a natural transition in this, uh, digital world.
[00:03:57] Russ Altman: So, it all started with a kid’s school lunch. I love that. So, so for those of us who don't think about markets all the time, what are the features of a good market or a market that like, 'cause you say you design markets, and I find that very intriguing. So, what are the features that you would like to see in a market that is well designed and, and is this debatable or is it generally agreed upon in your field? Like what makes a good market and what makes a bad market?
[00:04:23] Gabriel Weintraub: That's a great question. So, I, I think what may be debatable is what the objective, what are you optimizing for? Uh, so you could optimize revenues, you could optimize economic efficiency, social welfare, consumer surplus. Uh, and, and so I think the first step is decide, you know, what's your objective, uh, and maybe in different circumstances there's going to be different objectives. Uh, once you, um, you know this, uh, fix the objective, I think there are some well understood best practices. You wanna have markets that have liquidity that are not congested, that, uh, if you think about having supply and demand, that is easy for supply, to find demand, it is easy for demand to find supply, that there's trust, that people trust, uh, the market. And, you know, maybe those are like sort of high level best practices and, and then there's like a lot of engineering and a lot of details, uh, in terms of the operations on how you run this markets to achieve, uh, those best practices that ultimately are supporting your objective, whatever that is, it's if it's like revenues or consumer surplus.
[00:05:29] Russ Altman: Got it. Got it. That's very helpful. And so, when you moved, when you moved from the school lunch kind of, and not just school lunch, but all of the kind of old-fashioned markets where there was a lot of face-to-face or there was a non-electronic communication, what are the big challenges that come up when people started building. And, and you know, it's, it's, it's all happened in my life. I'm, I'm probably older than you, but this is almost a hundred percent, uh, uh, to my knowledge of a phenomenon of the last twenty or thirty years. What are the special challenges of online or digital platforms?
[00:06:00] Gabriel Weintraub: Yeah, so I would say, uh, the challenge is also the opportunity, which is that the disability that you have as some market operator is something we've never seen before. So basically, in a digital market, uh, you have access to pretty much everything, right? Like you see all the transactions, you see all the prices, uh, you observe all the clicks. What, what are, what is the interest of, of, of users? Um, so you have all this incredible amount of information. That's the opportunity. And the challenge is, okay, how do you best use that information to run, to, to run, uh, to run your market, right? And, uh, you know how to use it to optimize different aspects of the market. What do you share with users? What type of information, you know, to what extent, uh, you share it. So, I, so I think the vast sort of it is, you know, really like the IT revolution that it's like all over the place in, in these marketplaces.
[00:06:56] Russ Altman: And so, another thing that I know you're thinking about a lot is the, the now emergence in the last four or five years of these AI, uh, LLMs and other AI technologies. I'm guessing that this really changes some of your calculus, but, but, but maybe not. So how, how do you, how has AI impacted these digital markets, the way you design them and the way they run?
[00:07:18] Gabriel Weintraub: It's a, I think that's still an open question, uh, in terms of what's going to be the impact, or we've seen some use cases where, for example, like it's much easier to, uh, summarize information like, say, products. Uh, you have all these reviews and so you can summarize information, you can personalize information, but I, I would guess that there's going to be like much larger changes.
[00:07:40] A lot of people are thinking about like search, uh, you know, uh, if you replace, you know, these traditional ways of search with more like unstructured ways or even agents that know your preferences and go out and find goods and services for you. And I think this is all pretty nascent and people are trying different things. So, I think we're going to see a lot of, um, you know, a lot of action in the next, say like three to five years, trying to find those like successful use cases where we're using like Gen AI, LLMs to, you know, facilitate search. I think that is asking to be a very important aspect, um, in, in, in these marketplaces.
[00:08:15] Russ Altman: So, as I think about our discussion marketplaces, one of the things that we probably should discuss is, you know, for somebody, again, who's not a professional, there's a few marketplaces that many of us interact with all the time, like Amazon, right, is one, is one that's obvious. In your world, um, I'm, I'm guessing that there's a deep, deep, uh, set of markets that you look at. Maybe some of them are not even, uh, kind of aware. They're not in the awareness of the consumer because they're perhaps business to business markets. So, can you tell us a little bit about what, what the other major markets are that are kind of ripe for either redesign or rethinking, or are being revolutionized, whether they like it or not, by these technologies?
[00:08:57] Gabriel Weintraub: Yeah. Yeah, it's a good question. So, I mean, yeah, we all know about like, Amazon, Uber, or Airbnb and probably frankly, like, um, a lot of the research has been, uh, driven or, or, you know, motivated by, I guess companies or like Google, like advertising markets. Uh, I think like the later, uh, generation of marketplaces that we're seeing, it's one much more verticalized. So, it's, you know, it's not something like Amazon where you can like buy everything.
[00:09:22] Russ Altman: Which is everything.
[00:09:23] Gabriel Weintraub: Exactly, uh, exactly like, uh, uh, show and, and, uh, but it's more specific and, and also the market operator, uh, the platform, uh, is playing a more significant role in maybe selecting the supply and, uh, uh, being more, much more active in terms of, uh, what's being sold in, in, in, in the marketplace. And so, for example, you could, like, an example would be brief example that I've seen is, is some marketplace, uh, for nurses. So, it's like much more specific. And then there's a lot of screening done by the marketplace, so there's like a guarantee of like trust and safety and, and in principle, uh, you know, you can, uh, hire someone that's going to be a high-quality nurse. Uh, so you, we are seeing like more of, of those like verticalized, uh, maybe more so personalized experiences, uh, rather than this more like generic, uh, marketplaces.
[00:10:23] Russ Altman: Yes. And, and as you describe these, it strikes me that there's gonna be the, there, I, I would guess that there are sometimes losers. As, as the market gets rejiggered, people who used to take advantage of position in the market or control of information, they might lose information. I'm thinking about, you know, as you know, I'm sure you know very well the real estate industry is in the middle of this big churn. Because, you know, this six percent, uh, kind of, we paid it automatic for the, my whole adult life. You bought a house, it's, there's gonna be six percent off the top for the agents, kind of whether you like it or not. And there's now a suggestion that some of these numbers might change. Does that fall into your work? Like do you look at the way in which kind of power balance switches and that there's kind of some winners and losers as a new market emerges?
[00:11:09] Gabriel Weintraub: Absolutely. So, you know, if you think about what is the role of marketplaces, what the value they provide is, is, is really reducing frictions, like reducing transaction costs. And you know, if you think about something like, let's go to a, a traditional marketplace like Airbnb, right? Like, how would I know that there's like an empty room in the middle of Rome that will be like, helpful for my next trip, right? So, Airbnb is like reducing that search and information friction and, and, and, and that it's basically what's, you know, doing is also reducing intermediaries. Some of the things you were mentioning, like people that maybe get a fee for, uh, you know, finding you a place, finding you a product, find, find, you know, facilitating a transaction.
[00:11:48] And now you have this like, platform that is kind of replacing all those intermediaries and, and facilitating the, the transaction. So maybe old fashioned, uh, maybe older fashioned ways of, you know, for example in travel that's, you know, all these like agencies are the big losers. And things like Booking.com, Airbnb.com, et cetera, are the big winners. And now sort of the platform becomes intermediary, right? And of course, gets a cut, right? Gets a fifteen, twenty percent cut. Uh, so I think, uh, kind of technology is, uh, replacing, uh, maybe these older ways of intermediating, uh, which they also of course get, get a fee. Uh, but, you know, one would think, one would hope that, uh, because of technology, these are more efficient, more like faster ways of, you know, making, uh, demand meet supply.
[00:12:38] Russ Altman: Yes. So, I know that beyond looking at business, uh, markets, you've also looked at, uh, uh, government and public sector, uh, issues. And, uh, especially in your, in your, uh, home nation of Chile. That's, so tell me about, um, the opportunities for, like these markets or at like, digital platforms in this setting of government or kind of not purely economic, uh, uh, business transactions.
[00:13:04] Gabriel Weintraub: Yeah. Yeah. So, we've done a fair amount of work with, uh, procurement agencies in Chile. Uh, you know, so these agencies, uh, buy purchase something like three to five percent of GDP every year. Uh, that's the amount of transactions. So, any efficiency that, you know, you can get goes directly back to like, you know, taxpayers. Uh, and this can be a fair amount of money. And this, you know, without overgeneralizing, typically these markets are not as sophisticated as these tech platforms that we've been talking about. So, there's like a huge opportunity, uh, to introduce, uh, this type of tools like, you know, digital, um, intermediation, now, AI to make, uh, this, uh, markets more efficient. So just to give you like a very concrete example. Uh, we were, uh, you know, working with this type of markets in Chile and, and basically the products were all described in natural language and there was this like huge like Excel spreadsheets and the thing, you know, was so unstructured that you couldn't compare two products that were identical.
[00:14:10] Like you, the system didn't know that this, this is a Diet Coke, and this is another Diet Coke because they were like described slightly differently. So, there was no price competition. And so basically using like NLT and like now more, more recently LLMs, we came up with a way of standardizing products in a scalable, uh, way, uh, where now you can compare, uh, directly products that are similar or, or equivalent and, and, and that increased price competition quite a bit. So, as I think of the revenue way of how, you know, technology, uh, can, uh, you know, really help, uh, public market, uh, make more efficient and, you know, we reduce, in that case, you know, the, the budget by eight percent, like the spend was reduced by eight percent, which amounted to like tens of millions of dollars per year.
[00:15:00] Russ Altman: So, from your experience with Chile and, and other governments, uh, uh, are they, are governments ready to do this? So, I'm imagining that there's a technology kind of comfort that you have to have if you're moving from your old fashioned of getting bids, uh, and, and we all, I'm from New York. Like, we all know that bids can be very complicated in the old days and lots of, uh, other factors, but, um, are these governments ready? And what, what, what do you or others have to do to get them ready so that they can move to this kind of, obviously more efficient way of doing business?
[00:15:31] Gabriel Weintraub: Yeah, so, so I think there's like two challenges. One is that sometimes the technology is, is so obsolete that it needs like a pretty and, and so, and inflexible that you need like a pretty big overhaul just to make basic things work. I think that, but that's, I think, you know, that's an obstacle that I think you can jump over, but I think what's really key is to have, uh, the right leadership. So, in every single case that we've been successful is because the leadership, like the very top leadership was super supportive, like really understood what was the opportunity of introducing technology in, in making these markets more efficient. And so, we had a champion that had power, uh, and, and, and that, that was like a key partner to make this successful. So I, I, I think whether, uh, I probably ask more like which agency is ready and, and, and the agencies that I would, uh, say already are the ones that have leadership that as, as aware and has, uh, uh, you know, they're, they're, they're very conscious about the potential of, of these tools and approaches.
[00:16:37] Russ Altman: So, so, so, you know, this eight percent savings is very impressive in in the Chilean example. Uh, as you look at local, state, and federal governments in the United States, are there similar opportunities where, where is the United States with respect to creating such markets?
[00:16:52] Gabriel Weintraub: Yeah, I think, uh, you know, I haven't worked directly in those markets, so this is maybe more like, uh, a bit more superficial and second-hand knowledge. But my sense is that they're like similar opportunities. That, uh, like if, if you go, there's a, uh, you know, inefficiencies, uh, in, in many pockets of government, local governments, federal government, and, you know, I have colleagues that have, um, you know, done work, uh, designing these markets to make them more efficient and different dimensions of government, and I think my sense, like the experience is similar, like you need the right leadership. It's just like the, the bureaucracy is just so big and you, you, you, you need to, uh, get passed through that bureaucracy that without the right leadership and the right partner is just like very hard. But once you have it, it, there's like amazing things happen.
[00:17:40] Russ Altman: Yeah. The, the picture that you painted that the mayor of the city or the governor of the state, they have to be the champion because they need to be able to clear the way, uh, for, uh, people who otherwise might be obstructive for kind of, you know, irrelevant or random reasons.
[00:17:53] Gabriel Weintraub: Yeah. Or even the director of an agent, in our case has been like the director of like the National Procurement Agency, uh, or the director of like the program of the school lunches that we were discussing at the beginning. Uh, those were like our big, uh, big champions.
[00:18:09] Russ Altman: This is The Future of Everything with Russ Altman. We'll have more with Gabriel Weintraub next. Welcome back to The Future of Everything. I'm Russ Altman and I'm speaking with Gabriel Weintraub from Stanford University. In the last segment, we learned about what a market is, what a digital market is, and what makes a good market. In this section, we're gonna talk about how governments can benefit from some marketing principles and also from generative AI, and we'll talk about what it means to have an AI strategy, either for government or for business, and how you should think about that. Don't forget, at the end of the interview, we're gonna have the new Future in a Minute segment where I'm gonna ask Gabriel some quick questions and he's gonna provide some quick answers.
[00:18:59] But Gabriel, I know you also work with government. We touched upon government a little bit, still in the market arena, but you've worked on governments in a more broad way to think about, uh, AI, generative AI and the challenges. So, especially with in Chile. So, tell me about some of the projects and, and what are the goals there?
[00:19:16] Gabriel Weintraub: Yes, of course. Yeah, so I think this is like a huge opportunity in terms of making governments more efficient, uh, using Gen AI. And, and I think there's like a very good match with what Gen AI can do, is very good at, which is, you know, processing information, summarizing information, getting insights from information. And a lot of the bureaucracy that happens in governments, which is, uh, if you think about regulations is like incredible amount of like, paperwork and, and processing this, uh, all, all these documents. So, so there's like two projects, uh, we've been working on. One is, uh, to take, uh, a lot of, uh, you know, most of the regulations, uh, involved in Chile for all the mining and energy projects, two of the key areas for economic development, Chile.
[00:20:00] And, and these are, uh, you know, thousands of pages of documents. Each one of, like, for each project there's this like obligations that they need to put in place to run the project. And we've used Gen AI to convert all that like unstructured information on actual data. And so really quantify what is the number of obligations, how much they've been growing, what is the type of obligations. So, so this is like the first time we get this kind of snapshot that is quantified, that is precise, that is rigorous of what exactly are we asking from firms and what the associated cost may be. There's this sense, there's this intuition that, uh, in Chile, like all these obligations have gone to the roof and they're like, you know, to the point that they're really, uh, you know, making investment very slow and disincentivizing investment. Uh, and, and this is really like evidence we're providing like, uh, you know, strong evidence, quantitative evidence about this. Uh, so these are one project.
[00:20:59] Russ Altman: So just so I can understand, are you analyzing the text generated by the government or the text generated by the projects that are being proposed or both?
[00:21:09] Gabriel Weintraub: So, at the end of the day, it's the text, uh, that was generated by the government that's a result of the interaction between, uh, the company implementing the project and the government.
[00:21:21] Russ Altman: And so, then you can see and, and then it, it kind of, it kind of cuts through all of the kind of fog, the fog of text. And gives you a quantifiable, here's the projects, you know, deliverables, here's how much it costs, and, uh, and, and that, and that's working. Is that deployed or is it, uh, a demonstration?
[00:21:38] Gabriel Weintraub: Yeah, so we're, we are launching this actually in two weeks from now. We've got a big event. Uh, yeah.
[00:21:45] Russ Altman: Whoa. Okay, so there'll be a trip to Santiago is my guess. I interrupted you, but what's the second project?
[00:21:54] Gabriel Weintraub: And the second project is we're working with local municipalities that give, um, uh, construction permits. For everything that you build, you need a construction permits for local municipalities. And these things could take like months, uh, and, uh, sometimes is like very inefficient, uh, and making sometimes it's so long and makes a project that was profitable to unprofitable. So, we are introducing, uh, Gen AI tools to speed up, uh, the whole process. What is interesting is we're introducing a bunch of tools, uh, for the project applicant, and similar tools for the reviewers. The idea is that they talk to each other, to, to, you know, to, uh, to make the whole process more efficient. And we are going, uh, to launch this, we're launching a product like next week, and then we wanna run a randomized controlled trial to estimate the effect. And hopefully that effect was like positive. Uh, we are starting with two pilots, two municipalities’ pilots, and we wanna launch it to like tens of municipalities. So, uh, yeah, we'll see how that goes.
[00:22:53] Russ Altman: Okay. So again, these are gonna be bellwether projects, that if they're successful, you can imagine, I can imagine getting a lot of attention on a lot of press really globally. Anybody who's done construction doesn't have to theorize twice to know the potential value of anything that would speed up a, a, even a kitchen remodel is like a nightmare.
[00:23:12] Gabriel Weintraub: So, yeah, that's right. So yeah, so I think we're, we're doing this in Chile, but I think like the use scale is, is very general.
[00:23:20] Russ Altman: Yeah. Yeah, yeah. So I, I, I, so in both the examples that you've given, you talked about the markets and the businesses, and you talked about the government, and it, it raises this issue that like they, the, the businesses and the governments, they might be thinking of this as like our AI strategy. Like, okay, now AI exists. How are we gonna use it, uh, to make our world and our business or our government better, more efficient? Um, and I know you've written about this, and you've talked about this AI strategy. I'm, I'm, I'm guessing you get a lot of incoming requests to help with AI strategy. And how do you take a, a business, either big or small or medium sized and help them figure out what the impact of AI or the opportunity is for them?
[00:24:00] Gabriel Weintraub: Yeah, so, so I think a key idea that it, it sounds like obvious that people in the middle of all these tech hype forget is that you should like fall in love with the problem and not with the tech. So, and so I think a typical dynamic is like the board says, oh, we need to do something in ai, but they don't know what, and then like people start just implementing things that turn out to be, uh, useless. And, and, and I, I, I think the problem is that they're starting with the tech, not with the problem. So, so, so I think like a basic pillar of any strategy is that like, it's just like basic strategy, which is start like, what's the problem you're solving? Like how, how are you creating value? How are you capturing value? And then really understand what the technology, what the AI technology is good at, and, and like make a match between the technology and problem. And maybe that for some problems you don't need AI, for some like AI is like a perfect fit. Uh, but really start, uh, from the problem, like the value creation and the value capturing proposition that, uh, business has.
[00:24:56] Russ Altman: Yeah, I was struck 'cause in, in reading some of your articles, preparing for the interview, uh, you basically made the point that it's not really an AI strategy. You should have a business strategy, and you can see where AI helps that strategy. But the strategy is not focused on the AI, it's focused on whatever is like creating friction in your business.
[00:25:14] Gabriel Weintraub: Right. I think AI technology, I think it, maybe it's different because it would really like impact like maybe every aspect of your business, but I think you should start from like what is your business strategy? Yeah.
[00:25:26] Russ Altman: Now I, I, I also get asked this question every now and then, and one of the things I wanted to ask you and is the degree to which there's a barrier because of the workforce, right? So, they, they, they can say, okay, we're gonna do, we're gonna listen to you, Gabriel. We're gonna think about our business strategy, then we'll think about AI. Even if they do it the right way, what, what do you tell 'em about the workforce? Like, are, is it your sense that we have enough AI savvy people out there to help individual businesses or is this still a barrier for entry for the, for the businesses, just getting the talent to kind of use the LLM or install whatever needs to be installed. What are you seeing in these governmental, uh, collaborations or even in the business, uh, collaborations?
[00:26:09] Gabriel Weintraub: Yeah, you know, I think people are getting more savvy with these tools, but I think, uh, you know, depending where you go, what type of company, if it's maybe more like not, not a tech company, that, that this is still a challenge. Uh, so I, so I think sort of training, uh, workers to be able, uh, to, you know, add value using these tools is, is something that is, uh, yeah, that I, I think it's quite, you know, quite important.
[00:26:35] Russ Altman: In another setting, we've given advice that you need to enable your workforce to play with these LLMs and not have it be like, and, and be open about it. Tell your supervisor, I'm trying out LLMs, I'm doing some experiments to see if it can help us do X or Y. 'Cause the only way you figure this out is by, is by doing experimentation. And yes, you can bring in consultants, but really if the people who are doing, solving the problems day to day start to do these little experiments for themselves, they start to see what might be possible.
[00:27:03] Gabriel Weintraub: Yeah, totally. I think like that's the best advice is to, to just learn, learn by doing. And I also think where like, it's a huge opportunity if you, if you think about like small and medium, medium businesses that, and, and maybe like owners that are not very tech savvy, uh, I, I think it's like a huge opportunity because, you know, there's like amazing things that you can do and it's with natural language, right? You don't, you don't need to be a coder or a sophisticated technical person. So, so like train, also like getting like training programs. Uh, going for like those type of business owners, like smaller, medium enterprises. I think that's, uh, could also be very powerful.
[00:27:36] Russ Altman: But it sounds, just as my final question, it sounds like in all of these collaborations you've done in Chile, you have found the workforce to, to, to implement.
[00:27:45] Gabriel Weintraub: Yeah, I mean, we found the, the, the leadership and yeah, we found the workforce and some of it has been a lot of like training, uh, uh, like along the way. Uh, and, and I guess maybe one, um, word of, of caution is, uh, there's also like a bit of a fear, right? Once you're implementing the systems, uh, you know, there's the fear that, well, if the system works, I'll, you know, I'll be replaced as a worker. So that's something, uh, that also we need to grapple with. We need to, uh, you know, really be aware and, you know, hopefully design systems that, uh, work in tandem with humans to add value in, in, you know, whatever setting we're, we're operating.
[00:28:26] Russ Altman: This is fantastic and thanks to Gabriel for, uh, for this discussion and for this lesson on markets, AI and government efficiency. Uh, now it's time to move to our new, uh, feature. We're calling it Future in a Minute, uh, where I'm going to ask Gabriel some, uh, a few questions. I will try to make them short and sweet, and we've asked Gabriel to, uh, try to match that with short and sweet answers. Thank you very much for your willingness to do this.
[00:28:51] Gabriel Weintraub: Of course.
[00:28:51] Russ Altman: Are you ready?
[00:28:52] Gabriel Weintraub: Yeah. Let's, let's,
[00:28:53] Russ Altman: Uh, okay. First question. What is one thing that gives you the most hope for the future?
[00:28:58] Gabriel Weintraub: That a lot of, uh, you know, talented people like, uh, yourself, Russ, are very optimistic and, and engaged in using AI tools to solve like the most pressing problems we have, like in terms of education, health and, and more generally.
[00:29:14] Russ Altman: What is one thing you want people to walk away from this episode remembering?
[00:29:19] Gabriel Weintraub: That in this AI future, uh, I think it's a much more interesting to think about how we're going to design this future as academics, as, uh, entrepreneurs, as, as public officials or as citizens, uh, rather than just forecasting, uh, the future.
[00:29:36] Russ Altman: Aside from money, what is the one thing you need to succeed in your research?
[00:29:41] Gabriel Weintraub: Passion and resilience?
[00:29:43] Russ Altman: Boom.
[00:29:43] Gabriel Weintraub: I guess maybe like in anything else in life. Yeah.
[00:29:46] Russ Altman: If all goes well, what does the future look like?
[00:29:49] Gabriel Weintraub: Well, a future that, uh, this amazing technology solves, uh, you know, uh, some of the most important problems we have, like grave diseases, lower barriers to education. That's something I'm very excited about. And, you know, maybe hopefully access a force, uh, uh, equalizing things in society, you know, ultimately creates, uh, you know, better quality of life, uh, more wellbeing for people.
[00:30:12] Russ Altman: And finally, if you were starting all over again and you needed to get your PhD in a different discipline, what would it be?
[00:30:19] Gabriel Weintraub: So, I, I, I did my PhD in management science and engineering at Stanford, actually. Uh, I work in the interface of economics, so economics would be like a natural pick. But coming from Latin America, you know, like a PhD was always the second best. Uh, I, I wanted to be a soccer player, but I wasn't good enough, so.
[00:30:36] Russ Altman: PhD in soccer.
[00:30:37] Gabriel Weintraub: Exactly. Uh, yeah.
[00:30:39] Russ Altman: Thank you very much. That was the Future in a Minute. Thanks to Gabriel Weintraub. That was the future of digital platforms. Thank you for listening to this episode. Don't forget, we're almost at 300 back episodes of The Future of Everything, which means you can spend lots of time listening to really interesting discussions on The Future of Everything. If you're enjoying the show or if it's helped you in any way, please consider rating and reviewing it. That is great for us, especially if you like it and give us a 5.0, but only if we deserve it. You can connect with me on many social media platforms, including LinkedIn, Threads, Bluesky, and Mastodon where I'm @RBAltman or @RussBAltman. You can connect with the Stanford School of Engineering @StanfordSchoolOfEngineering, or @StanfordENG.
