The AI Investment Strategies You Need to Know: Eye-Opening Insights from Jonathan Brill

Hello, we're bringing data and AI to life, a podcast by Informatica. I'm Amy Horowitz, our VP for Solution Sales for Data Integration and Data Governance. And I'm Nick Diamonds, Worldwide VP and Field CTO here at Informatica. If you've ever felt lost in the chaos of data and AI, you've come to the right place. We'll be conversing with industry experts who are here to shed light on the challenges all rocked up within these arenas. We're here to bring clarity to the chaos, myth-busting the confusing parts, and providing insights and guidance for complex data problems by delivering trusted data for analytics and AI. Good afternoon, everybody. My name is Amy Horowitz, and I'm very excited to welcome to this episode of Bringing Data and AI to Life, our podcast from Informatica. Today, I'm so excited to welcome Jonathan Brill to our show. For those of you that know Jonathan, or for those of you that don't know Jonathan, Jonathan has been around really in the AI space, very influential in this space, and really one of the future-facing voices of AI. So Jonathan, I'm very excited to have you here. Why don't you jump in and tell us a little bit about yourself and how you got started in AI? Thanks, Amy. It's great to be here. Kind of by accident, I was working with a professor at the MIT Media Lab 25 years ago. I decided to take a class with this guy named Marvin Minsky, who had been reading his books since I was like 12. He was one of the people who really coined the term artificial intelligence. Since then, I've been hooked. I've run research labs that are engaged in AI and machine vision work, AI and sensor work, for about 25 years. That's amazing. I think this is now covering not just younger generations, but to your point, it's been around for a long time. We can jump right into your Microsoft days. I know you were involved in the Microsoft early exploration of the touch interfaces and machine learning. I remember those like it was yesterday. I don't want to date myself, but I was a heavy user of those technologies as well before AI really became mainstream. The headline that is today, by the way, we hear AI at dinner conversations. We hear it at water coolers. Let's talk about the foundational lessons. What lessons did you learn during those early days? And really, how did they help shape how you think about AI today? I've been doing a lot of work in that space, looking at new user interfaces, how we might use artificial intelligence to simulate things like architectural systems, geeky, boring stuff. But then Microsoft reached out and they asked me to come in and work with them on the design of Microsoft Surface, which was, for those who don't know, a table-sized iPad. And it was exciting work because I don't think the iPad had come out yet. It was really groundbreaking. And the idea was that we created this computer for multiple people, where multiple people could touch and work with content at the same time. The takeaways for me were that there are two things that are really important. One is the economics. The challenge with Microsoft Surface was the only application that really made sense was kind of retail sales. And within that, this thing costs $50,000 and the software costs $300,000 and then it would break. And so you had to figure out where on earth was their high-end retail they could afford that kind of thing, and it would drive markups. And so the two places we ended up in were trucking, customized trucks, where you have large buildings where truck dealers sell trucks, and you're selling often fleets of customized vehicles. So that was a no-brainer. You don't have to build the vehicles. You just whiz it up. The second place was in retail banking, because people really don't understand their money. They don't understand how investments work, and they don't understand how mortgages really work. But when you can kind of play with it, you can kind of get it. And so it was a way for the wealth manager or banker to have an interaction with the consumer. So when we think about artificial intelligence, I think both of these concepts are incredibly important. What are the actual economics, not of the technology, but of how it sits in the larger system that people will use it in? The second thing is, how do you make it usable? How do you make it fun? Because at the end of the day, there's so many things we could do that we won't do. I got to tell you, being in sales, I was in IT most of my career, and when I took the jump to sales, having the option to have a Surface as a device that I carried around changed my life, versus the fat client and the fat device that I had to carry in my bag that was very heavy. The Surface was really cutting edge and was just amazing. So what a great experience that you had there. Let's switch topics here, and let's talk about some Samsung, you know, stuff you did with Samsung. As I was researching your background, Samsung on their AI investment strategy and priorities really gave you a vantage point to where you place your big bets. I think everybody's asking today, where do we place big bets with AI? From your experience, Jonathan, what indications or what indicators do you look for when really trying to decide what long-term strategic value of AI looks like? I think where, when I talk with executives today, their focus is, where can we automate? Where can we decrease headcount, right? They'll do different things, but that's what they're really interested in. What we're seeing in the research, and this is consistent with my experience, is that if you want to get the maximum value out of AI, you need to actually think about how you design your organization. Because at the end of the day, this is going to change the way we work because it changes the way we communicate, the way we coordinate, the way we work with information. And at the end of the day, that's why we have the governance mechanisms in our firms that we do. So when we think about our investment strategy moving forward, we need to think about certainly how we contain costs. And more importantly, like over the next five years, how we deal with the demographic issues, especially in the United States, in Europe, in parts of Asia, because labor is not available. When you look at one of the places where we're going to see a lot of AI is in self-driving trucks. Companies like Convoy, companies like Aurora are building self-driving trucks. You might think, hey, they're going to replace the truck driver. What you're missing is today, there's about a 325,000% shortage of truck drivers going up to 1.2 million in 2030. You could fight against the mom and pop businesses who have truck drivers and their physical capital they've already paid for. Or you can take all of that opportunity where there's no one doing the work. And so I think we need to really be thinking about that, right? Not where are we optimizing labor, but where can we capture new opportunity? And where do we need to change up the way we work, the way we manage decisions, the way we think through organizational coordination, right, our routines, and then our culture. Because at the end of the day, right, what we're seeing is that everybody who has an information type job, if they know how to work with these tools, is becoming dramatically more productive. So how do we take advantage of that? How do we give them decision rights? How do we have them work together as teams with artificial intelligence to be super intelligent? And that's the subject of my next book, which just came out, which is AI in the Octopus Organization. Is it available today? Are readers and listeners can find it? It is available on Amazon as we speak. I love it. I really, the three strategic themes I just heard you talk about, cultural shift, we hear it all the time, reskilling and human in the loop. I think when we talk to people, customers and prospects, their first concern is, oh my gosh, all of my jobs are going away. But I love what you just said about really taking advantage of understanding where you can add better together versus, hey, let's just get rid of everything. So I really, I love that. When I was the futurist in residence at HP, there was a conversation about, well, we have all of these bookkeepers and wherever they are, Chennai, and we don't know what to do with them. And they've been with us for 20 years. And I was like, the last thing we should do is cost reduce them. And here's why, because they know your company, they know how to work in your company. They're good with data and they're good with organizing numbers. They've got basic mathematical skills. These aren't bookkeepers, they're data scientists in disguise. What would happen if we gave them eight or 12 weeks of training? And how much cheaper would that be than trying to build a decision science workforce? We need to think differently, not just about the things people do, but the things they are capable of. And how do we build the emotional capability to make that jump? And how do we build the technical steps, right? This technical building block so that they can get from one place to another. When we do that, it's gonna be so much cheaper in a lot of cases than trying to offload labor and then try and find exactly the same labor that everybody else wants today. Are you seeing that today in the industry? As I research these topics, when I talk to our co-presenters here, one of the things I've been noticing is that these organizations are making drastic cultural shifts, to your point. The bookkeepers need to go, we're gonna replace everything with AI. And now I think they're slowly, and I would love your opinion, they're slowly starting to realize that maybe removing all of those bookkeepers per se is not the right move today. Are you seeing that in a lot of organizations? There's the board level drive, and then there's the operators drive, and then there's the CFOs drive. And this is the tension. The challenge I think that people are missing is that when you just look at the quality improvements over the last year of artificial intelligence, it's on humanities last exam. I like that one because I like the title, but it's a metric by which we test quality improvement. And what we've seen is over seven times increase in the last three quarters. You know, conservatively, you say, hey, that slows down a bit. Five years, we're talking about tools that are 30 times better than they are today per dollar spent, 30 times more. Now, let's think about what that means for your people. We're talking about a world where literally everybody plus AI in very real ways could be smarter than Einstein. The question we need to ask is, how do you manage Einstein? I managed research labs for 25 years. I will tell you that my research scientists, the last thing I would do is tell them what to do. That's the worst way to get productivity and the worst way to get innovation. This is important because we are moving into a world where AI is dramatically accelerating innovation, which means it is decreasing the time window in which you can monetize it. The innovation window is shrinking dramatically from years to oftentimes weeks or days or in pure information businesses, minutes. This means that every single person on our organization can't be a cog anymore. They have to be innovators to get the real value. And when we talk about offloading labor, the reality is what we need to do is keep the labor we have. That is our differentiator against the machines and figure out how to make that labor dramatically more effective with these new tools. And that is a cultural issue. It was not a technological issue. And frankly, it's not a human issue. It's not the limitations of your people. It is the limitations of your organization and your ability to give them love, trust and the space to accelerate. I love that. This is a conversation I hear all the time. I'm so excited to continue our conversation. I think it's great. You hear the conversation. Do you actually see organizations making the shifts to radically devolve decision rights? Because at the end of the day, until you do that, you cannot be radically more innovative. Right. I think to be honest with you, some of our customers, I'm going to use a broad stroke. So bear with me. When I talk to some executives, they are either on chapter 12 of the book of AIs. They've been doing it to your point. They've been around for a long time. They know what they need to do. They might not have the same board pressure that you were talking about. They don't have folks, prospects and customers that are on chapter one that don't even know where to start. So I think to your point about really understanding the cultural shifts, you know, what are you doing? You're not going to offload all your people. They are the differentiator. I hope your book will help drive some of this and really lay a roadmap at what people should be looking at. So I'm excited about that. Frankly, it's the first practical book that does that. So AI and the Octopus Organization, just to plug it again, is, I think, incredibly well-informed about that topic. Some of the people who have blurbed it and read it, you just look at the list and it's like the one. Yeah, I can't wait to get it. I think it will also be, for those of you out there, let me put a plug in. This would be a great gift to give C-level executives because they have a different perspective, I think. So yes, go buy the book. Let's switch gears to, and you've hit on some of this, but I want to dig into this a little bit deeper. So radical change, right? You've been around to drive a ton of radical change in organizations and waves of transformation. We hit on this a little bit more, but I don't know if there's anything else you want to add around AI and fit. So where does AI fit within the larger ecosystem of change and how do you see it accelerating or destabilizing decision-making? So this, and I love your response on this one because I have some thoughts. Yeah. So when we think about the way firms are organized today, they've been organized the same way since the 1850s, since the first organizational chart was printed in 1855 by the New York and Dury Railroad. And they were built on the idea of the great man, the great brain. And when you look at an organizational structure, the staff and line model, it looks a whole lot like a nervous system where the great brain tells everybody else what to do. The problem is that means that if something happens over here, something happens over here, something happens over in the corner, the great brain, A, doesn't know, but B, you can't have the agility you need in a world of accelerating change. There are other ways of thinking, of innovating, of exploring in nature, other metaphors than the great man theory of the 19th century. The octopus is the most amazing animal. It doesn't have one brain. I mean, I know you've got a couple on you, Amy, but the octopus has nine. It has a big brain behind his eyes, but that's only about a third of its neural tissue. The majority are actually in the arms. These are weirdly not called tentacles. And what happens is when you look at an octopus, you can actually see this when you watch a video, the tentacles are out there, all of them doing weird cattywampus stuff, right? And it's because they're actually operating independently. They're thinking, they're literally thinking independently. When one of them spots something, it smells a thing through its sockets, or it feels, it reports that back up to these tentacle brains, one at the head of each shoulder, and they talk to each other through a neural necklace, and they make a decision on what to do. And then they tell the big brain what they've just done. So let's think about that. The decision essentially gets made out at the tentacle, and then it gets coordinated faster than the C-suite can even imagine what the answer is. And you can watch as that happens, that the eyes move, and it starts to see like, what has just done, fascinating thing. But the thing you should be thinking about as a leader is like, how do you manage governance in a situation like that? You're just going to have chaos. Well, the other thing is that it can take and look its eye at a tentacle and take control of that tentacle and tell it exactly what to do. The reason that we have organizations like we do today is historically, you haven't had that visibility, that granularity into your organization to have a real-time call and response. The thing about transcription tools like Otter, I'm on read.ai at the moment, is that they give your organization the ability to look at what's going on across the company. The ability to look at unstructured data, unstructured documents, give it the ability to look across the company, across the organization, across the industry for the first time. And that means that, well, your people are super powered and they're going to have better decision skills. They're going to have better context. They're going to literally be able to simulate with tools that are more powerful than nation states have today. The impact of their decisions, like everybody, your intern, that means that you will too. And so you can look down into the organization, which means that you can give up command without giving up control. And that is the foundational challenge of our time. How do we give up the power we spent like 40 years, 30 years getting good at making decisions to these junior people? And that's the, this isn't a technological problem. It's a sociological problem. And to get back to your question of what's different about this time around this transformation around the reason we see so many transformations fail, I believe is that someone at the top has an idea. They hire some consultancy. They, they run through as fast as they can and do a bunch of interviews. They don't necessarily understand the business. And I'm not talking about like I'm not calling out one. Accenture thinks like these things fail 85% of the time. And I can tell you why is because there's people at the bottom of the organization know the tools they need to do their job. And we're given tools that don't allow them to fulfill that. In the world of artificial intelligence, it's growing, it's scaling, it's innovating so fast for the next five years. Every week, something new will be possible. So how do you innovate against this? You let the people out at the edge of the tentacles explore with these tools, see what's possible, so on and so forth. Your job as a leader is to spot, to look down to spot where there's pockets of useful indigenous innovation that you could scale out and rapidly do that. So instead of this top down pyramid, the interesting thing about this transformation is it's going to happen bottom up. The pyramid is getting inverted. And if you want to take advantage of these tools, that is the trick. Let's talk about governance for a second. You mentioned that just a second ago. Governance is very important to me. I think it's not only from a technology perspective, but we've talked about this cultural shift for this last 30 minutes that we've been on this podcast. Let's talk about the humanity piece of governance. So as an advisor and strategist, you see this day in and day out. How do you balance that? Hey, I got to put some guardrails around what we're doing without hindering what folks are trying to do. And this could include around bias or around explainability or accountability. How do you tell an executive or how do you, what do you envision the guardrails being around this kind of stuff? I think it starts off with the point of view. So my friend, Amy Edmondson, she coined the term psychological safety. It's like, Amy, I love the theory, but how do you actually create a psychologically safe environment, especially in strong command and control situations, maybe high performance, you know, quarterly performance driven organizations where things are slowing down. So here's the deal. It doesn't matter what you feel about it. At the end of the day, people need to believe and therefore you need to actually deliver on this statement. We are in this together and I will do it with you. That is the first thing. If we have a perspective that we are going to offload labor and 20% of our workforce is going to go and we're going to try and like shenanigan them in to doing what we need and then drop them, that's not going to work. If you say, hey, and Ray Dalio, obviously a guy who could afford this, you know, when he transformed his investment organization, he said, hey, we're going to have to reshape this thing. We're going to be outsourcing a lot of labor. And what I can tell you is that we won't all make it to the finish line, but you will be taken care of. We will make sure that you find a new home. We will make sure that you find a new opportunity. And that is my commitment to you. And he did. And so the question going into this is, are you willing to make those commitments to your people? And how do you create a world of trust where they will believe them? Because if you're faking it, they know. Yeah, and I think for those that are faking it, you're going to see a drastic change in their go to market and their ability to compete. And I think it's going to be something that we're going to as a as a culture, not just North America, a global culture, we're going to notice. We're going to be aware and we're going to see it happen. I think you need a shirt that says be an octopus or be the octopus. I love that. Really, you should point like I have a I have a team motto on my team and it's be the reason, like be the reason people and you can fill the blank. We're doing it. Let's do it. OK, I have one selfish question and then I'm going to I'll close this out with our last question. Selfishly, you know, I'm a mom of a 17 year old girl that's about to embark in the college journey. If you were a parent today of a 17 year old, that's now seeing where AI is going, what advice would you give these kids going to school or even going into a different career path, not necessarily college, but their next step in their journey? What can they do to prepare for this? I think we've misunderstood, especially in the United States, the goal of a university. The goal of the university historically has not been to get you a job is to to expand your month. I think they still do a great job at that. We've also tried to turn Stanford into a trade school. It's a humanities program. And so the question to ask is, what are you learning how to do? What are the goals? And I probably shouldn't talk too much about this, but because there's this geeky little corner of philosophy is what I'm obsessed with. It's what I spend my time working on, really, like we have different names for it. But it's this little corner of a philosophy called epistemology, which is the study of how we know what we know, how we know new things. As an inventor, as a person who guides organizations, that is my entire job. And there are four ways you know new things. And these are the four things that your kids need to get good at and need to get good at with AI, right? One, how do you use deductive reasoning? Assuming that the universe of facts is true, else must be true. We teach this in algebra class, except we forget to teach people the actual point. Inductive reasoning, thinking like a scientist. Let's do an experiment that tells us the most likely thing to be true applies to everything in life. And yet in science class, we have people dissecting frogs and they don't know why. The third piece, and we do less of this in the U.S. than we should, is Bayesian reasoning, which is statistical thinking. And if we stack together data, can we figure out probabilistically that we wouldn't be able to do using the other two tools alone? This is what AI is really good at. Not perfect, but this is actually what the tool does. The fourth piece is what's called abductive reasoning. And this is like thinking like an inventor. So we've talked about thinking really like a lawyer, thinking like a scientist, thinking like an economist. Thinking like an inventor is asking two questions. What would happen if my facts turned out to be untrue? How would it change what I do? The second thing is, what would happen if something new came to light? The interesting thing is you don't need to know what the wrong facts are or what might pop out of thin air to figure out how to invent your way around it. And so I think these are the things that are most important for children to learn today. And it's actually all we teach in school. We just forget to give them the frame so they fail to contextualize it, so they fail to become super thinkers. Context. It's all about context, Jonathan. I love it. I think, yeah, thank you for answering that question. I know it's not part of our typical thing, but I know there are a lot of people my age out there that are listening to this that have kids in the same boat, and we're all questioning like, what should we be doing here? So thank you for that. I don't know, but I know that you need to know how to do that. And that has been always the basis of value creation, and that is always going to be the basis of value creation. Even if we have our machines do it for us, the people who help our machines figure that out are the ones that are going to make the money. Great. Let's talk about the future. So let's bring this home to the future and where we see AI going and really designing the future. So looking ahead, Jonathan, what emerging intersections of AI with other disruptive technologies or geopolitical forces do you see will define the next decade of this change? We've talked a lot about this, but let's bring it home for our listeners. This is the biggest question in the world. I love it. So I think that what we don't fully understand in the United States is the scale of technology development in China. By 2030, they're going to likely have a space base on the moon. The U.S. will not. We are moving into a world where technology has escaped the U.S. sphere. That means that there's going to be so much more potential created around the world. We need to look outside of our border at what all of that means, how we take advantage of it, how we compete with it. That's going to be probably the most important technology conversation of the next five years. It's not about quantum computing. It's not about biology. It's not about medicine. All of those things may or may not be true. But what I do know is that technology has escaped the bounds of the U.S. educational industrial complex. The second thing is that we need to be thinking about demographics. When you take a look at Germany and its economic growth right now, I mean, it is subpar. I don't know if it's even really going to be inflation once you look at it in the next couple of years. As populations get older, we don't really have an economic theory for how growth works. And we need to rethink that. That's going to be a lot of what happens. And then the third piece, and this is tied to these two, is what is the role of the U.S.? What is the role of Europe? What is the role of China? What is the role of Japan and India in this new world? And so what we see in the big picture is Trump saying, as he does, effectively, here's the deal. You're an ally. We have a political partnership. We have an economic partnership. And we have a security partnership. They all need to be co-equal. And we need to be at the head of that table if you want to play with us. It seems like a relatively reasonable position, given that we're the largest of those economies. But that is going to reshape the nature of the world as China comes to something like economic parity with the U.S. in the next number of years. And going on into toward 2040, India starts to move into the similar sphere of economic parity. And we need to really rethink what is the nature of our society? What are our priorities? How do we manage security? How do we innovate? And how do we create a coherent society in a world where there is so much potential? We're talking about a 50% increase in the U.S. level middle class, but not in the U.S. Like in Asia, what does it mean to have that come online? And how do we work with that in peace? How do we work with that in prosperity? How do we share technology in ways that everybody wins? Jonathan, what an amazing podcast. Thank you so much for your time today. Please tell us the name of your book again so our listeners can hear and where it is. AI and the Octopus Organization. And we're going to make some shirts for everybody, all the listeners that say the Anonymous. Listen, seriously, thank you. What amazing thought leadership sharing with our listeners out there and our prospects and customers alike. Thank you again. For those of you that are new to our podcast, please remember, please subscribe wherever you get your podcasts, Spotify, Apple, wherever you get it. We would love to have you as listeners. Jonathan, again, thank you. Have a great weekend. And we appreciate you. Thank you so much, Amy. Thank you. Take care. Bringing data and AI into life is brought to you by Informatica. To find out more about Informatica and how our Intelligent Data Management Cloud can help you achieve better business outcomes, head to informatica.com. Stay tuned for more illuminating discussions until we meet next time. Keep harnessing the power of data and AI to bring transformative outcomes to your life and business. Make sure to click subscribe so you don't miss any future episodes and tell your friends about us too. On behalf of the team at Informatica, thank you so much for listening.