Trading Tomorrow - Navigating Trends in Capital Markets
Welcome to the fascinating world of 'Trading Tomorrow - Navigating Trends in Capital Markets,' where finance, cutting-edge technology, and foresight intersect. In each episode, we embark on a journey to unravel the latest trends propelling the finance industry into the future. Join us as we dissect how technological advancements and market trends unite, shaping the strategies that businesses, investors, and financial experts rely on.
From the inner workings of AI and ML to the transformative power of blockchain technology, our host, James Jockle of Numerix, will guide you through captivating conversations with visionaries who are not only observing the future but actively shaping it.
Trading Tomorrow - Navigating Trends in Capital Markets
Debunking AI Myths: A Glimpse into Tech’s Future with Don Welch
Have you ever questioned whether AI is more hype than substance? In this episode of Trading Tomorrow, Don Welch, Vice President for Information Technology and Global University CIO at NYU, delivers a pragmatic take on artificial intelligence (AI). Known for his candid skepticism, Don traces AI's history from its early origins, debunking common myths about its capabilities and limitations. The conversation also dives into the growing problem of AI washing, where companies overstate the capabilities of their AI products. This is an engaging conversation, you don’t want to miss.
Welcome to Trading Tomorrow navigating trends in capital markets the podcast where we deep dive into technologies reshaping the world of capital markets. I'm your host, jim Jockle, a veteran of the finance industry with a passion for the complexities of financial technologies and market trends. In each episode, we'll explore the cutting-edge trends, tools and strategies driving today's financial landscapes and paving the way for the future. With the finance industry at a pivotal point, influenced by groundbreaking innovations, it's more crucial than ever to understand how these technological advancements interact with market dynamics interact with market dynamics.
Speaker 1:To most of the world, on November 29th 2022, ai was known as an aspirational technology, something more closely associated with a Will Smith movie than real life. One day later, that changed. When ChatGPT launched. A frenzy began and by January 2023, chatgpt hit 100 million monthly active users, one of the fastest-growing technology audiences we've ever seen. It brought the AI conversation to the forefront as people witnessed the advances of this technology for themselves. While there was and still a lot of excitement, there is also a lot of fear and confusion, but AI technology has been around for themselves.
Speaker 1:While there was and still a lot of excitement, there is also a lot of fear and confusion, but AI technology has been around for decades and, while it recently moved into the mainstream conversation, those working in IT and technology have been looking at its potential power, challenges and impact for quite some time. Don Welch, the Vice President for Information Technology and Global University CIO at NYU, is one of those people. He's a self-proclaimed AI curmudgeon. Don began his career as an Army officer, specializing in a combat-oriented field. Don pivoted towards information technology, earning a PhD in computer science. He has since held influential roles in the IT sector, including CTO, ceo and CIO. Don, thank you so much for being with us.
Speaker 2:Thanks, thanks for having me.
Speaker 1:So Don you have described yourself as an AI curmudgeon. That's a pretty strong characterization there, so maybe you can explain what that means and why you've taken this perspective.
Speaker 2:So I will start off saying you know what is AI. My favorite definition of AI is an unfortunate word choice made in the 1950s. It is, you know, an umbrella over, depending on how you want to count over, a dozen different programming techniques and methodologies that have been around. You know, some of them since the 50s and some of them, you know really were, you know, developed in the 90s and 2000s, but it really only come to fruition with more computing power that we've got right now and recently. You know, my perspective is this is still software. It's software that is very good at solving certain categories of problems and really bad at solving other categories of problems. It is not Skynet, it is not going to kill us all. It is, you know, it is just technology, and we've been dealing with technology for, you know, 50, 60 years now, and you know, I think we should just continue to deal with it that way. So, and that way, I think I'm kind of an outlier.
Speaker 1:Well, you know so, breaking news it's not going to kill us. I think you know you just shut down half the podcast universe in terms of AI. But you know, I mean, there's so much hype surrounding Gen AI at this point and you know what would you say some of the biggest misperceptions are about it.
Speaker 2:Yeah, so you know Gen AI and of course, even that is a broad category. You've got Gen AI that will generate sounds or images, and most of us think of it as the text generators, the large language models. And you know, I think the biggest misconception is, unless there is another you know program, if you will, or model behind it, if you will, or model behind it In Gen AI, the way I like to think about it is it writes. It has a store of, you know, many, many millions, in most cases, of programs to write text in some language, you know, in our case in English, and so it is this really great interface of how we can interact with a computer, but it doesn't necessarily have good models of the world or whatever you're talking about, unless you put something behind there. You know so in some cases they've done that.
Speaker 2:But if you ever try to do math and chat GPT, you know you were dealing with, you know someone in the elementary school.
Speaker 2:If you were lucky and depending on how you worded your questions, the, you couldn't even get addition to come out right. So so I think a lot of people and I've seen this especially especially amongst our business leaders they assume because the AI is speaking very coherently and eloquently, that it actually knows what it's talking about. And I tell people to think about it as your know-it-all uncle at Thanksgiving, who can speak very eloquently on a topic and sound very pervasive and in some cases he might even know what he's talking about. But you really have to be skeptical and you know and understand. You know, is it correct? Obviously, people have heard of hallucinations and so forth. But even in the areas where it's not a complete hallucination, a lot of the LLMs don't have that kind of depth that people assume that it does in terms of you know, like human intelligence, or even you know computer intelligence. So you know. It all comes down to the right tool for the right problem.
Speaker 1:It's not going to kill us. All right. So that's headline one, but you know what are your top three biggest concerns about AI? You know, let's go three to one, let's start at three.
Speaker 2:OK, the top three biggest concerns. So I think it is. You know, it is a great set of programming techniques that are great for solving certain classes of problems. I'll say, with my number three concern is that we are going to over-rely on AI and, as we were talking about earlier, ask it to do things that it's not very good at. If I go to my number two concern, it's the other direction, that we are going to under rely on it. And I will take the example of a self-driving car, and I right now would rather have face a self-driving car on the road than I would a teenager with a cell phone.
Speaker 2:I think we are being overly cautious about the progress we can make in some cases.
Speaker 2:That we're a little too worried and perfect will be the enemy of better when we're with AI. And then, if I go to my number one concern, it is that we are going to regulate it in a not smart way. I think it is very difficult to regulate new technologies. You know that's difficult, regardless of what we do, and you know you hear all the discussions about social media and so forth, but it's hard and any attempt at regulation, I think, is going to be highly prone to not having the impact that's intended. Of course, we see it a lot of times, but in deference to the people who are trying to write those regulations certainly our experts, but it is, you know, it will not be easy and I think we could really end up with a lot of unintended consequences. Forget about the fact that other countries, especially some of the countries that we may be competing with in many different fields, are not going to put restrictions on their development and are going to rush right ahead, and that could have a lot of unintended consequences.
Speaker 1:I was going to ask you to follow up on country-specific regulation because even now it seems even the way the models are being trained is potentially dangerous within certain regimes that are looking to influence will over populations and things of that nature. So, is there any way to combat that?
Speaker 2:It's a really hard problem. One of the more interesting solutions that I heard develop AI systems that will monitor AI systems. That sounds good, but once again, you know that's going to be hard, but I think it is. It does point in the right direction. So you know, if we think about, you know, ai in a really general sense.
Speaker 2:Ai is good about dealing with messy input and providing imprecise answers. So what I mean by that? If you've got a computer that is calculating your bank account, you want precision. If you are asking a computer to write a paragraph for you, happy to glad doesn't make any difference. You Happy to glad doesn't make any difference. You want it good enough, and AI is really good at good enough solutions, especially when dealing with messy data. Being able to do something that a human would do, which is look at an AI system and see if the inputs and outputs and results are things that you want. That's going to be a lot of messy data, so the future may lie in that area, but I think right now it's just. You know it's a dream and it's probably in a lot of researchers' labs or their high-performance computing environments, but I think short of that, it will be really hard for us as humans and especially the messy way we do our government and so forth to come up with the right solutions and balance all those competing perspectives and requirements.
Speaker 1:I'll say this Messy government seems to be a popular theme on this podcast as of late. So you know, one thing that has been a popular concern recently is around the concepts of AI washing. You know, the practice of companies or people exaggerating or falsely claiming their products or services or technologies and what can be done with AI. Obviously it's done to enhance their appeal or marketability. I mean, have you seen any examples of this?
Speaker 2:I think the better question is have I seen any marketing or sales pitches from software that have not included AI washing? And I don't remember any that have not told me about their cool new AI feature, so forth. So, and once again, I think if we go back to, you know why I consider myself a curmudgeon, even though people are doing the you know AI washing. We go back to first principles and due diligence. If you're going to buy a piece of software, before you do it, understand what your requirements are. What problem are you trying to solve? What does it need to do for you? And then evaluate does this actually solve my problem? And if you do that and it's using AI techniques, it's not using AIT techniques, it doesn't really matter, as long as it's, you know, doing a good job of solving your problem.
Speaker 2:Of course, you know the bane of every IT person is the business leader that comes and says I want this solution. Well, what problem are you trying to solve? This will solve my problem, you know. But let me buy X software and you know trying to get them to go back. Okay, let's identify it. And of course, you know business leaders. Look at us. We're. You know we're slow, we're bureaucratic, you know we. You know we slow things down. But you know, obviously we have our reasons to make sure we buy the right software, that we integrate it appropriately, that we train you know all the things that make software implementations successful. We really have to do and it's not something that you know we just pick a solution and turn it on and life is better. And you know, unfortunately most IT people know that lesson quite well.
Speaker 1:You know. One of the things I'm curious about is you know how can enterprises, you know, build and maintain trust if you will with AI, whether it's internally among employees or more externally with their customers? You know how do you maintain trust.
Speaker 2:I think that's kind of at the bedrock of every IT organization and software service that we deliver. If you've been involved with any software deployment, there's a high level of mistrust with anything that's new, even if forget AI. But you're asking somebody to change their business processes. It's not doing it the old way that they were doing the button's in the right instead of the left and this is just too confusing and so forth. So that trust, I think number one coming up with a good quality solution is kind of foundational. Does it really solve the problem that you are trying to solve? And then having a good change management program why are we introducing this? Making sure people understand the why and what the goals of it are. Giving them the right training. You know providing hyper care when you deploy it so that you can help them leverage it and get that success. You know delivering all the capability that is. You know that was asked for originally.
Speaker 2:You know all of those kinds of things I think contribute to that trust overall. So whether you know you're bringing in a new AI system or you're bringing in a new ERP which may have AI or not, you've really got to go through all those steps and pay attention to them, because I you know, I think one of the things that we in IT sometimes do is we look too much at the software and the system itself and say this works as designed, it's working well, and so forth, and ignoring the aspect that this is a tool that has to be used by people. And so you've got to pay attention to the people aspect and, as you point out, build and maintain that trust. And that comes from you know the support, understanding what their requirements are, understanding what their fears are and their hopes for the new software. All of those aspects of relationships, I think are you know the way you build and maintain that trust.
Speaker 1:You know, one of the things we've observed over the past X amount of years is really IT coming a lot closer with you know the end users. You know in terms of not just software decisioning but you know, as part of the procurement process, having you know to upskill in terms of domain expertise as it relates to things that are being deployed. You know, and I think in some ways you know the cloud became a facilitator of that. You know very close collaboration. I mean, how are you seeing the evolution of IT evolve at this point? You know, within that decisioning process?
Speaker 2:A good question for an old guy that's been around and seen things move and you know, if you think of technology as a stack, which most IT people do, at the very bottom you've got, you know, your hardware and then you've got system software and all the way on up to what we call the wetware, the user. Most of IT's work has been moving up the stack over the years. When I first started, for every one computer you had a fairly large team of operators to do the care and feeding and keep that machine going, and they were all focused on the technical aspects of it, not about solving a business problem. And then over the years we're more. If I think back 30 years ago and you might be able to have a system administrator, you know administering. You know a dozen computers and but later you know 50 to one was a good ratio and you know now, with you know, as you mentioned, like with cloud systems, cloud platforms, whatever your administrators are supporting 500 machines or even more. So you've got fewer people that are focused on those purely technical tasks and the difficulties of keeping the technology running, and that has freed IT. Teams have changed and you have a lot more people in the team who are focused on managing the projects, doing the change management, the training, the business relationship managers who are working with the business leaders.
Speaker 2:I have a friend of mine who we were socializing a little back a bit. I have a PhD in computer science and he asked me we were talking about my job and what I do. And he's like, how much time do you spend on, you know, on actual technical things and technology? And I said, well, if you really want to stretch it and count the times that I'm in rooms with people who you know, who deal with the technology, or when other people are talking about technology, you know maybe 15, 20%. You know.
Speaker 2:All the rest of the time it is, you know, dealing with people dealing with business problems. You know understanding those problems and I think that's very indicative of the role of IT overall is that it's not about the technology, it's about providing solutions, and we have said those words for years. But I think there really is a transformation there that the future is for, at least the short-term future, are people. You have to understand that business domain. You have to understand the users, have to be able to relate to them and understand the technology to deliver those solutions. So you know this shouldn't be a surprise to anybody, but certainly I think that trend will continue.
Speaker 1:What's your advice to IT leaders or what do you find is important in terms of collaboration with non-technical teams when integrating AI solutions? I mean, clearly, every vendor in the world is in an AI arms race. You're not moving forward unless you have AI. And then I've seen instances where IT all of a sudden steps in and goes whoa, whoa, whoa, whoa, I'm worried about security. I'm worried about security, I'm worried about this. You know, let's deploy things in small ways and test it, and you know. So what's your advice in introducing? You know, arguably, you know, solid productivity gains if used right. You know how should people be thinking about it from the IT and the non-technical IT person.
Speaker 2:Yeah. So I'll use a possibly completely unrelated analogy, but I was an offensive tackle in high school and college During my career. Not that I was that great, but I never actually touched a football in a game Never, you know. Never, you know, picked one up or was given one or whatever. But you know I played this instrumental part to you know, in high school, our fullback setting the state rushing record one day.
Speaker 2:And you know I think IT. You know we are very much offensive linemen in an organization that is not a technical organization. If you're a finance organization or retail, in my case, education, we're not about technology, but our organizations can't succeed, can't even exist without technology. It's the lifeblood of us now. So if you think of yourself in that role of facilitating the success of your business, you know you're the offensive lineman for the offense. You make that quarterback or that halfback look good. That's your role.
Speaker 2:And, as I've said to many, you know business leaders that I deal with, like my job, is to make you look good. You know so I, you know, help me, help me make you look good, help me make you succeed, and you know so some of the things that I do can be annoying, you know you will. You know we'll see in a football game when a running back is behind a couple of the linemen waiting they could run much faster, but that lineman is doing their job and trying to move the linebacker out of the way or whatever. But it takes time, and so asking people to fill that role of let me do my job and I'll make your job easier. You know, if we address the privacy and security issues now, you know we can avoid, you know, bad adverse impacts on our business.
Speaker 2:If we, you know, if you let me do my job now, this tool will work better longer. You know, we make sure that we have the data integrity and, you know, and all those kinds of things that business leaders don't want to necessarily hear about, but they want, you know, they depend on it to work. So so that's, that's kind of that's my approach, and you know an analogy that you know many people understand and many people don't. But what the heck? You know.
Speaker 1:So, sadly, Don, we've gotten to the final question of the podcast. We call it the trend drop. It's like a desert island question. So you know, if you could only watch or track one trend in AI, what would it be?
Speaker 2:Self-driving cars, autonomous vehicles. So to me it kind of it is really a good indicator for AI and software systems, intelligence systems, going forward. It has a very real, physical presence. I mean, we all know what cars are and how they drive and so forth. I think the potential is great. Basically, once we're all self-driving cars and they're all talking to each other, traffic jams disappear, auto accidents disappear, et cetera, et cetera. A huge upside. But there is a large downside, especially in the transition. So how do we as a society address self-driving cars and do it in a way in which we balance all the competing priorities you know, the security, the privacy, the risk and so forth and you know, hopefully get as close to the maximum amount of benefits that we can get. And so I think that's one thing that people all understand and will watch, but I think it's also indicative of the way we will use technology for all other kinds of things going forward.
Speaker 1:You know it's funny. I know it's the final question, but I want to follow up on that because I think the whole self-driving car issue is fascinating, because it's also going to bring in that whole messy political situation as well. Right, and especially around societal issues equity, affordability, all of that and I think it's not just the technology, which is amazing in the sense of AI, cloud, edge computing, et cetera but you're going to have this whole social component as well, and I think it's one thing to put in automated toll systems and eliminate 60 jobs or 150 jobs in New York State, but it's another thing when you create a society of haves and have nots, and you know so I think there's going to be a lot of mess with this as well. You know, what do you? How do you react to that?
Speaker 2:Yeah. So I think there's always been a lot of mess with any technology change, certainly as we move from an agricultural society to industrial society. There are a lot of people who suffer in that transition, but generally we ended up better off. I think as we've moved from an industrial to more of a knowledge-based same kind of thing, there's a lot of pain. We've got Rust Belt cities and so forth that are there. I don't think that this will be any different.
Speaker 2:As we incorporate more and more technology, people who are doing jobs that are routine are going to find their competition is from technology and, in a good sense, we are going to have very capable people who can do more, because AI is doing more of the simple and the rote things.
Speaker 2:But that's going to be a challenge for a lot of people to be able to step up, provide value to their organization, so that there's organizations that make sense, to compensate them appropriately so that we don't have that much of a divide. I think it's going to be a very large challenge. It's going to have to start with our you know our K-12 education and go all the way through lifelong learning, but we're certainly not prepared to transition, you know, an entire society into the kinds of things that we could. And, as you say, you know, there are politics involved. There are, you know, there's technology, there's education, there are all these social issues. And I think we're going to have to navigate it very, very carefully, as we do any transition, and you know, and who knows, what the next one will be. I probably won't be around for that one. This one will be challenging enough for me, but the I think that's a, you know, it's a very valid concern.
Speaker 1:Well, if the New York Taxi Limousine Commission thought they had a handful of Uber, wait till they take on Elon Musk. Don, I want to thank you so much for your time today and your insights, and I really appreciate you having me on the pod.
Speaker 2:Yeah, thanks so much, James. I really enjoyed it. This was a lot of fun.
Speaker 1:Thanks so much for listening to today's episode and if you're enjoying Trading Tomorrow, navigating trends in capital markets, be sure to like, subscribe and share, and we'll see you next time.