← Travis Makes Money
Travis Makes Money
INTERVIEW | Make Money by Making Better Decisions in the Age of AI with Cheryl Strauss Einhorn
00:28:46
Loading summary
Transcript29 lines
- [00:00]
A
Study and play come together on a Windows 11 PC. And for a limited time, college students get the best of both worlds. Get the unreal college deal everything you need to study and play with select Windows 11 PCs. Eligible students get a year of Microsoft 365 Premium and a year of Xbox Game Pass ultimate with a custom color Xbox wireless controller. Learn more@windows.com studentoffer while supplies last ends June 30th terms at aka mscollegepc. You're listening to the Travis Makes Money podcast presented by gohighlevel.com for a free 30 day trial of the best all in one digital marketing software tool on the planet, just go to gohighlevel.com travis what's going on everybody? Welcome back to the Travis Makes Money podcast where it's our mission to help you make more money. Today on the show, I have a new friend, Cheryl Strauss Einhorn. Cheryl is the creator of the Area Method, a decision making system for individuals, companies and nonprofits to solve complex problems. She's the founder of the decision sciences company Decisive, offering leadership training, curriculum, coaching and professional development services and is an adjunct professor at Cornell University. She is the author of the award winning books Problem Solved, about personal and professional decision making and Investing in Financial Research, which is about financial and investment decisions and then Problem Solver about the psychology of decision making and problem solver profiles. For more information, check out Sheryl's Ted Talk and visit area meth.com Cheryl, what's up? Welcome to the show.
- [01:33]
B
Thank you Travis. Good to be here.
- [01:35]
A
So let's go back in time. Cheryl, before we jump into the book and some of the things that you have going on right now, tell me the first time you ever got excited about a dollar that you made. The first time where somebody paid you for something and you were like, wow, I can't believe I just got paid to do this.
- [01:50]
B
Yeah. The first time I ever got excited about money that I made, I think that it was probably one of my first jobs. In high school I got a job doing data entry at an accounting company and it was next door to a Friendly's so I could take the money and I could go buy fribbles. And that was something I used to do with my grandfather. And every time I got paid I would also go and get a fritball and I would think about the nice times with him.
- [02:30]
A
That's awesome. I love that story. What, what were some of the other things that shaped your decision making coming out of high school and, and what you were going to do with your life?
- [02:39]
B
Yeah. So I worked my way through college by working for the New England regional office of the Federal Trade Commission. And they enforce a lot of the consumer protection laws and also some of the antitrust laws. And I ended up running in the afternoons their consumer complaint department, which is all the people who call in because they feel like they've been ripped off about something. And it really showed me what I don't want in a job. And I think sometimes we don't recognize how valuable it is to also know what we don't want in addition to what we want. And I thought that recognizing that I could be making a decision about aspects of this job and, you know, not wanting to be an attorney, even though it's a very respectable profession, I thought was extremely valuable and really helped change my pathway in college.
- [03:36]
A
What did you end up going for then?
- [03:39]
B
Well, what I ended up doing was going to graduate school at Columbia. I went to the journalism school there. But I realized coming out of college at the time, the job market really wasn't great. I wasn't excited about the jobs I was interviewing for. And I realized what I love to do is to research. I love to write, and I love to talk to people, to hear their stories. And so I thought when I got into the graduate school of journalism, no matter what I want to do, being able to figure out how to listen to people, how to ask them questions, that that's a lifelong skill and a muscle I want to develop.
- [04:15]
A
What are some of the benefits that you've gotten from that skill?
- [04:19]
B
Yeah, so I think some of it has to do with active listening. That's a bit of a trade off, though. I've realized, you know, sometimes it takes mistakes for you to realize that there are other lessons in it. So, for example, when you're actively listening because you're interviewing somebody, you're also beginning to articulate in your mind and formulate the wording of a question. And so that means that you're both in the moment and in sort of your own metacognition, the thinking about the thinking. And that means you're not listening. And so I found at times that even when I think I'm listening, I'm not listening as well as I should. So I think being a really good interviewer is a bit of a struggle in your mind for the attention and whether the intention is in the moment or is actually preparing for the future. That's. That's something that I thought valuable.
- [05:16]
A
You're preaching to the choir right now, Cheryl. That was probably the thing that I, I. It was really just what I was not expecting to have to learn when I started the podcast was like, you know, it was the last thing on my mind. I was thinking about the tech, and I was thinking about the microphone. I was thinking about the, the topic and things like that. But then when you start asking questions and then they finish answering for the first time, and then you go like, oh, oh, that's on me. What do I say now? What do I. Like what, how do I transit? What do I. And then, like you said, then you start getting better at asking questions, but then you stop listening because you're thinking about the next question that you're going to ask, and then you're leaving some gold on the table because you're not going to drill down further on this thing because you've already been thinking about this other thing that you were going to ask before. And like you said, it's counterintuitive because you are trying to listen, yet you also need to steer the conversation without just not listening to make sure that you don't leave anything on the table. You know what I mean? So it is, it is a, it is a skill set, for sure.
- [06:11]
B
I think that's absolutely true. And one other thing I would say that's really unhelpful about preparing for the question is that you're no longer trying to walk in the footsteps of the other person. Right? If you're really actively listening, you're not looking to respond necessarily. Right. You're looking to let that person have the moment. So when you are taking yourself out of the listening to be in that metacognition, to formulate the question, you are also actively switching into your own perspective. And that is, I think, also a disservice to the honor of the conversation and to the sharing that the other person's gracing you.
- [06:57]
A
You mentioned you loved writing. Did you always love writing? When did your first book come about? How excited were you about that?
- [07:05]
B
Yeah, so I've always loved writing. I wrote so many short stories when I was growing up, and I was such an avid reader, and I, I always thought in my mind, wouldn't it be great to write sort of a young adult novel, you know, like a Judy Bloom or a Beverly Cleary or, or one of those moments that we all recognize and we can share across time. And then when I began teaching at Columbia Business School, I taught there for over a decade, and also at the Graduate School of Journalism at Columbia, I started to realize when I was teaching about my decision making system that I call the area method jat. You know, students shouldn't have to get it the first time. The way that I'm explaining it when we're in class together is not necessarily the way that they should have to walk out of the classroom and easily be able to then have it internalized. And I started to think about maybe I should write this down. At the time I was also raising three kids, super busy as any, as any busy parent might be, or any busy individual. And my kids were leaving for camp at the end of June, June 26, to arrive back on August 13. And I thought in that particular summer, I'm going to get up every morning. I don't know if I have a book in me, but I'm going to write down what I've been teaching. I literally finished it on August 13th. And we went and picked up the kids from camp, went on our summer family trip. And then I started to look for an agent. And then, and then she quickly sold the book. So I didn't know if I had it in me. But I do think nobody is going to believe in you unless you believe in you. And, and so that was the first book. And then since then you mentioned at the outset when you were introducing me, the first book, Problem Solved, about personal professional decision making, then turned into investing in financial research. The second book, which is how do we Make Financial decisions. And then the third book is what I learned from the first two books about the different ways that people solve problems and the fact that there's five different sort of problem solver profiles and the book Problem Solver. And then those three books turned into the book that's coming out in May, the Human Smarter Decisions in the Age of AI about how do we actually stay human and make artificial intelligence uniquely work for us as we're all using it to help us solve small problems, but also really, really big ones as well.
- [09:55]
A
I'm really glad you brought that up because I have a question for you around. I mean, you're an adjunct professor at Cornell. You're author, you're essentially a thinker for is what is what is what you do. And then, you know, you have the ability to communicate the things you're thinking about in a simple way that people can digest and learn from and apply my. Okay, so. So as a professor and as an author, in terms of the downsides of AI and what that means for the new generation, my, my initial thought, when I saw all the backlash that it was inevitably receiving from especially professors and teachers, people who are just like now they're. And it's more difficult to tell if it's being plagiarized. Before it was like oh yeah, you copied and pasted this Wikipedia article. You know what I mean? Like it was a little bit easier to tell. And then all of a sudden generative AI comes out. Now it's like, oh well how do we crack down on this? How do we make sure. And in my mind first, the first place my mind went was well, if that is a skill that is going to be taken by a software program, then is it a skill that's still worthy of building? Like, you know, because we type all the time now without thinking about it. And you know, kids aren't learning penmanship like they used to have to learn because everybody types and that's how we get messages out there. But then my second thought was that yeah, but writing isn't just about the writing. It's not about the final product. It's about, it's about the thought process that you're forced to go through to get the things in your brain out onto paper. And that exercise is a mental ex. It's literally exercising your brain and in. And there can be actual atrophy if you don't continue to exercise that muscle. And that's more, more. What I'm concerned about is just like a generation of people who just don't. They don't think anymore because they outsource their thinking to something that's just going to write the final product. But I'm curious to hear your take on this, especially having written this book recently.
- [11:52]
B
Yeah. So teaching right now a course at the University of Miami in the business school with the human edge block. So it's fascinating to see that the younger generation in particular knows the risks. They know that if they over rely on the machine that they're not exercising those critical thinking skills. At the same time, what they are doing is that they want to make sure that that final work product is as close to perfect as it can be. Right. So they are turning to Gen AI even after they've done their own thinking to ask it, you know, smooth this out or what do you think? And that I think erases some of the beauty of the thinking. And I've had to talk to them a lot about. I actually want to hear you. Right. I'm working with being. I'm not looking for perfect. If I was looking for perfect, we wouldn't all be in school together. Right. And we wouldn't be human because that's the Human journey, we're never going to get there. Right. So it's always in striving. And so I think that if people could recognize that their own imperfections are part of the beauty and uniqueness of who they are, I also wonder if that would speak to people at sort of an intuitive level to get them to feel more confident that they don't have to rely on AI to polish them away.
- [13:26]
A
Yeah, yeah. To remove the aspects of them that make them who they are.
- [13:32]
B
Right. Absolutely. Absolutely. Because I think what you're saying is absolutely true, that this is a machine that's actually asking for our cognitive load, which is very different from how we've interacted with computers, you know, when we were saving something or creating a file or whatever, or drafting an email. And so what my book is really trying to do is to say, okay, when you are making a decision, there's sort of eight moments that uniquely call for human judgment. And since AI doesn't know anything about you when you're working with it, it's only going to give you solutions that are from other people. It doesn't know the other stakeholders. It doesn't know your context. It certainly doesn't know your motivation. And so if you can identify where your human judgment is most valuable, you can actually lead this powerful tool to be working specifically for Travis or specifically for Cheryl. And so that's what the book is trying to give people access to.
- [14:37]
A
Yeah, My friend Drew Dunn, he's a standup comic and he has a great joke about AI. He said something about, like, he said, if you use AI, AI to basically pass all of your college classes and then your job gets taken by a robot, you kind of had that coming to you. Yeah, it's a. It's a great joke just to say that, like, yeah, I mean, you can't complain about AI taking your job if you literally are depending on AI to do all the things for you. You know, of course, like, why wouldn't. If that's the most qualified thing to do the job, even in your own description and your own action, then why wouldn't it be the thing that gets the job later on? You know, like, there has to this unique element, this human element that you can bring to the table. So what are you mentioned that there were five different things that you go over in the book. Can you talk a little bit about those?
- [15:27]
B
Sure. There's eight. So within complex problem solving, these eight moments each are part of the chain. So if at any one moment, any one of these are weak, you can weaken Your whole decision. So the first one is problem definition and figuring out what problem are you actually solving. That's a uniquely human moment. I find people often assume that they know the problem and they start asking for a solution. But a lot of times that means that you can either be solving something adjacent or incomplete or something that outright can't work in your situation. So the first moment is how to define the problem. The second is your motivation, right? There might be many reasons for solving a problem and AI can't possibly know them, but you do. And that makes all the difference as to which solutions are going to be best suited for your problem. So that's another moment to be asking yourself, well, why do I want to solve this problem? And make sure that you convey that to AI. Notice that in each of these moments, the step is human first, AI second. And then, of course, you also need to react to AI afterwards to see if the output is what you expected or if you need to continue to work with it, to refine it, to make it right for you. The third moment is your context, right, because you can have perfectly good solutions, but if it doesn't work for the environment that you're in, it's not going to work. So what is the context that you're sitting in and what are those important contextual factors? Who are the people? What is the time urgency? What is the environment? For example? The fourth one is research. This is a real AI strength. But you, the human, need to both direct the research and refine it, because otherwise you are faced with too much information. And that can lead to analysis paralysis and slow you down and can also make you really anxious and delay the decision because you think maybe it's in there. I got to read all this stuff. So if you first can identify the pathways that you want AI to investigate for you, you're more likely to get information that is directly responsive to what you're solving. And then the next step is analysis. There are many different ways that information can be interpreted, and a single piece of data can have multiple diagnosticity, right? So actually making sure that you're asking AI to make meaning from the data in the way that you need it to is going to be really important, because this is another real AI strength. The step after that is to really think about bias, because we are all loaded with our own dirty windshield. We see the world through our assumptions and judgments and our cognitive biases, and AI can really move us more quickly in the wrong direction as it helps us to confirm a hypothesis that actually isn't going to work out, so checking and challenging our biases. And then also asking AI, its data can be incomplete, can be biased, can be off target for what we're looking at. And so asking the machine, not only where might I be making unfounded assumptions, but what is it in your knowledge that could be steering me wrong or lack credibility or be outdated, that's very useful. And then the last three, the last few steps are stakeholders. At this point, we all know that at some point, other people factor into the success of our decision. Even once we think we have a preferred pathway. Actually working with AI to say, I want to have these conversations with these three different people. This one always pushes back, this one always says yes, but they actually have good thinking. How do I bring that forward? So working with AI on how do I share the decision that I'm thinking about with the stakeholders, how do I step into their perspective? What might they be concerned about here? And then finally thinking about failure before coming to conviction. And at this point, you have a pretty good idea, this is what I'm going to be doing. But I suggest that people consider failure as a way to identify what's weak about the decision before you execute it. And in identifying that weakness, you can shore it up and prevent the kind of failures you've identified. Now you've given yourself a much stronger way to actually think that you can have confidence and conviction, that the decision can succeed for you. So each of these moments are important. They're uniquely human. And when we can identify them before working with AI, we can actually make this tool work specifically for us.
- [20:14]
A
What, out of all those, out of those eight points that you just made, was there any of them in particular that you find? AI is like, it's, it's, it's the super, it's the superpower in any of those where, like, in this one particular area, it's doing a really good job and you should definitely use it for this thing.
- [20:32]
B
Yeah, research, I mean, it holds its memory more than we can, and it can also spin it a lot faster. So the idea that we all can access something that has this vast knowledge, I think is really exciting in so many ways to accelerate our ability to learn. But the question really becomes, how do you actually make it targeted and focused? Because nobody wants to sit there and read through all of the things that AI could bring us to potentially research.
- [21:11]
A
As someone who, I mean, you've obviously made a career out of deep, deep research, and I find that there sometimes when I pour through a bunch of research, it's difficult for me, as, I don't know, quote, unquote, layman, just like a regular guy. Like, I didn't go to, you know, college. I don't have a master's. I didn't, you know, have. I'm not. I'm not a professor. You know, it's sometimes difficult for me to even pore through that, understand what it's trying to say, and then find which pieces of the research are actually valuable and meaningful to the project overall. As somebody who's been involved in research pre AI and now post AI, what are some of the things that you look for that tell you whether or not this piece of research is actually helpful or useful?
- [21:55]
B
I think that's a good question. And I think with AI, one of the things that is so beautiful is we can sit there and have it help us make our mistakes before we make them. Because we can say, oh, my gosh, you've given me ten different research studies. What actually is most recent? What is most credible? What is the largest scale study? What is most germane to the topic that I am looking at? And you can say to it, I don't want 10 answers. And give me the links for everything that you've shown me. One of the things that's really tricky, that I'm sure you've heard about, is that AI gives you things that are made up. And a lot of times those things that are made up look so good. I remember I was doing research for something and it gave me a study by a researcher who I respect, and it seemed to fit squarely into her circle of competence. And I thought, I'm going to use this. And then as an afterthought, I said, cheryl, sit back down. Check that. And you know what? It was totally fabricated. And it sounded so credible to me,
- [23:11]
A
like the AI fabricated it, like it didn't even exist.
- [23:14]
B
The AI completely fabricated it. This researcher had not written this area. This journal didn't exist. And so what I would say is, we're finding, you know, and we're reading in news articles people are using AI for help understanding a medical diagnosis or with financial information or some other problem that actually has consequences for us. And AI doesn't care about our consequences, but we do. So anything that it gives you recognize that any time saved must be reinvested. You are actually not looking to save time overall. You are looking to reinvest the time to make sure that if you are going to go and do something with. With the information that it's grounded in something that you feel completely comfortable with
- [24:07]
A
that's a great point that it's not used to save time. It's just, you have to reinvest that time there. The first time I realized that there was. I was. I was prepping for a podcast interview and thought I had all my research dialed in. I had a bunch of questions and stuff. And then we released the episode, and I found out that the guest that I had had on had this, like, big thing happen, like, maybe a month before we released the episode. But it just. It didn't come up in any research that I was doing with AI and, like, I didn't get any information. Found out that it was working on models that were like, six months old, and it missed this, like, massive thing that happened in this person's life. And then so all the comments are pouring in, like, you know, talking about, why didn't you talk about this? Or, like, why didn't you drill down on this further? And it was like, I. I didn't know. And again, I have to take personal responsibility for that and be accountable to that. But. But to your point, you can't just trust that it had the answers. You have to then go validate and even ask itself sometimes and, like, are like, are you sure this is true? You know what I mean? Like, cite the recent example that you have for me here so that I don't, you know, miss this. This massive gap here in what we're actually trying to accomplish. Cheryl, I appreciate you taking the time to even write this book. I know you're a wildly busy person and you have a lot of different topics that you could be writing about, but I appreciate you for taking the time to be even concerned with making sure that we don't lose the Human edge in. In the age of AI. So the book is the Human Edge. If you're listening right now, please go pick up a copy book. There's a lot of people out there that talk about AI all the time who, how would I say this without being offensive, Are not as qualified as Cheryl would be to speak about the topic. So find people who are credible and go learn from them, because I know Cheryl put in all the research required to make this a great product. The Human Edge is the name of the book. Cheryl, where can other people go or where can people go to get more stuff from you?
- [25:57]
B
Well, Travis, thank you so much for having me. I've really appreciated your questions. And to find out more about me, I hope you'll Visit me at area method a r e a method.com. please connect with me on LinkedIn as well.
- [26:12]
A
Thank you area method.com Go pick up a copy of the Human Edge as well. Cheryl, thank you so much for taking the time. I do not take that for granted. I know you're extremely busy. Everybody else listening. Remember, money only solves your money problems, but it's a little bit easier to solve the rest of your problems when you got money in the bank. So let's solve that one first here on the Travis Makes Money podcast. Thanks for tuning in everybody. We'll catch you next time. Peace.