The 9 Skills Every Tech Leader Needs in the Age of AI with Andrea Iorio, Author of "Between You and AI"

(Intro Narrator at 00:00:00) Today, we're talking to Andrea Iorio, author and former chief digital officer at L'Oréal, about his latest book, Between You and AI. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:17) Someone sent me your book, Between You and AI, and I read the synopsis of it. And I was like, this looks like a really interesting book. We should go talk to Andrea. And how did, why did you write this book?

(Andrea Iorio at 00:00:28) So basically, it happened because I also work a lot with CDOs more on the keynote speaking part. I collaborate with them on a number of events they throw out. And one of the things that I was noticing the most is that there was a lot of content and, of course, books, podcasts, and whatever regarding the tech aspect of AI, but there was not really a compelling theory that was around what are the skills needed to thrive in the age of AI. So it really came to solve a pain point that I was noticing in the market. That is, you know, this mix between, of course, the tech aspect of AI and mapping out what AI does best, but also someone that came up with what should we humans do better as a consequence. Because if we think, you know, we just keep on thriving just doing the same things again, well, that's where we'll be replaced by AI. That's my guess.

(Joel Beasley at 00:01:29) And so professionally, before this, you were the chief digital officer at L'Oréal. So what was the role? At all these big companies, the titles aren't necessarily exactly what they're doing. So what was the role? How did it position you to sit between both the technology people and the human people and figure out where the gap was?

(Andrea Iorio at 00:01:49) You know, I'm originally Italian, and I love references that come from literature. And there was this mythological figure back then, Caronte. That's how we called it in Italian. And he was sort of like the man that would put people on the boat and, you know, according to Dante Alighieri, would bring people from hell to purgatory. And oddly enough, when I was the chief digital officer, I was finding a very similar role to Caronte. That is, I had to take people that didn't understand digital or were not believing in digital or were fearful of replacement to a place where they understood that, of course, digital and AI now is an ally. But most of all, they wouldn't fear it and understand that basically, it's much easier than they would imagine, and you don't need to be a hyper-technical person to use it. And the interesting part is that I think, as much as this mythological figure, this is something that is transient. And I was joking that the best outcome of my job was me losing my job in a couple of years because if a CDO, or chief digital officer, was needed forever, well, then maybe I wouldn't have done my job because digital had to be spread out across the organization and not in need of someone leading it. So that was a little bit of the challenge, which was basically to evangelize the other areas and other people and other professionals and leaders to better use technology and not fear it. I think it was, again, not so technical and, you know, mixed results in an organization of more than 110 years of existence such as L'Oréal, but definitely worth the try.

(Joel Beasley at 00:03:39) Yeah. Oh, I'm sure there's a joke in there about shampoo somewhere, but...

(Andrea Iorio at 00:03:45) That's for sure. There's always one. There's always one.

(Joel Beasley at 00:03:49) So the book is organized about these nine skills. That's how it's described. And from what I read, there's these three pillars and there's almost three skills within each pillar. Is that the correct organization of the book?

(Andrea Iorio at 00:04:01) That's correct. So mapping out basically what any professional or any leader does, I understood that basically we do things across three big dimensions. The first pillar is what I call the cognitive dimension. The second one is the behavioral one, and the third one is the emotional. And within each of these pillars, I mapped out three skills for each. There are really skills that we need to nurture and develop and improve exactly because AI, in some of them either substitutes something that we do well as humans, and in other ones, AI is not able to do nowhere near what humans do. And so I'll explain it better. For example, in the cognitive aspect, AI is much better than humans, or at least at a much, much better scale, at processing data. And so there's a skill that I call data sense-making. It's not anymore about our ability to process the data, but it's about our ability to critically analyze the datasets AI has been trained with. And so if in our organization we don't understand that, for example, the data that we use, if we're a bank, let's say, to approve credit or not is biased based on human biases that has been done in the past, well, we'll have a problem. And that's why I advocate, for example, for data sense-making as one of the skills. On the cognitive aspect, for example, another one is what I call re-perception. It's not only the ability to have all the answers, but the ability to ask better questions and so on. In each of these pillars, on the emotional side, that's where I advocate for skills that AI is not able to replicate or substitute. It's able to actually simulate, such as empathy, trust, and eventually, the last skill of the book is what I call agency, namely taking the responsibility as humans for the AI tools we use. Because if we think that by using AI, we're outsourcing responsibility to it, well, we're very, very wrong because AI cannot be deemed responsible in court or, you know, technically or morally.

(Joel Beasley at 00:06:15) Not yet.

(Andrea Iorio at 00:06:16) Exactly.

(Joel Beasley at 00:06:16) Tell me more about re-perception. That really caught my attention. What's that?

(Andrea Iorio at 00:06:20) Yep. Re-perception is the ability not only to make good decisions, but to give up the good decisions of the past in order to open ourselves to new information, new changes, external changes. And so as humans, we are wired because of basically psychological mechanisms or path dependence to make decisions about our future based on our past successes. Because they're more comfortable, because we're more sure about them, because there's less uncertainty. The problem is that this worked in a world that was changing linearly, and now in a world that is changing exponentially faster and faster. And there were talks, of course, for many years that Moore's law was, and our CTOs in the audience for sure know it very well, that semiconductors would double their processing power every two years. Well, now we're already talking about Huang's law, named after Jensen Huang by NVIDIA, that is about, you know, three times faster than Moore's law. And so in this rapidly changing exponential world, the number of things that were deemed impossible yesterday and today are possible is higher and higher. And leaders and professionals that look at the past as their repertoire for answers and for decisions that already worked will have troubles in a world, again, where everything is changing. And so I think re-perception is this ability not only to make good decisions, but again, to give up the good decisions of the past in face of new evidence.

(Joel Beasley at 00:08:02) So how would you go about doing this today? Give me a practical real-world example.

(Andrea Iorio at 00:08:07) It's super interesting. For example, getting back to my experience at L'Oréal, what I noticed is that the traditional type of mentoring that companies traditionally do was nurturing exactly what, not necessarily I would say is bad, but again, that goes against the re-perception of things. And so perceiving things is great, but this is not flexible. And so, for example, mentoring would usually traditionally go the way where the younger apprentices or young talents within the organization are being mentored by the leaders that have the most experience, the most answers, the most years within the organization. We decided to nurture re-perception to do exactly the opposite. We would have the young talents, the younger newcomers to the company that didn't really know the beauty industry, that really didn't know about our products, mentor our leaders. And this was really eye-opening for many of these leaders because they would think about things that they didn't because they were thinking within the box that had been created after so many successes and years within the organization and so much knowledge of the beauty industry. So we would have these young kids and young talents recently fresh out of school mentoring our leaders about things such as, I don't know, how Gen Zers make decisions, consumer decisions based on TikTok videos and TikTok as a search engine nowadays, or how to use all of these new apps and how consumer preferences are evolving. So overall, I think in order to nurture re-perception is exposing yourself to people who don't have as much knowledge as you in a certain topic. And oddly enough, this is refreshing, reinvigorating, and really helps to nurture this ability to go beyond what has been our repertoire of successes.

(Joel Beasley at 00:10:11) It's interesting too because historically, when you pair them up, you imagine, as you said, it's a one-way street of the young learning from the old. But what it seems like some of this re-perception talk is you've made that a bidirectional flow. So now it's like they can learn how to maybe get up and show up to work on time and interact and talk to people a little bit better and manage things while they're learning more about the... So it becomes this symbiotic relationship versus this one-way copycat, do as I say, type situation.

(Andrea Iorio at 00:10:46) Exactly, Joel. And the truth is that it's bidirectional and not unidirectional, again, which would be another mistake. That is, okay, so let's throw away all the experience, all the repertoires, all the successes. They don't work anymore. Only the Gen Zers or the newcomers are the source of the truth. It's not that as well. You know, experience is important, but I think it should not be a ceiling. It should be a starting point to always ask ourselves, is this still in line with the external change I'm seeing, with the new consumer behavior I'm seeing, with the technology that is coming up the whole time? Because if the answer is not, well, then it means we need to catch up or do something differently or stop doing something with it. But it's more of a starting point, you know, our past experience, rather than just a copy and paste of, you know, whatever. Or I have a new challenge and, okay, so we solved it like this in the past, so let's keep on doing it like that. And I think that is limiting because in business, after all, there's always people and companies and competitors out there that are looking at exactly what we don't do, especially for leaders. And if we don't move towards these new directions, well, someone else will do that on our behalf, solving our customers' new pain points, solving the market's pain points, and I think that's really dangerous.

(Joel Beasley at 00:12:14) Yeah. It's smart. It also saves some money because otherwise, you're going to have to put together a panel of these Gen Zers. And why not just use the ones that are already at the company?

(Andrea Iorio at 00:12:25) At the company. It's exactly that. It was funny because there was a case, and you see how different companies have different approaches, and some really value what these Gen Zers do, others don't. And there was a case where we had one of these young talents, and she had a YouTube channel where she was doing product reviews, beauty product reviews. But she was also doing beauty product reviews of brands that were not L'Oréal brands. And at first, this created an internal problem. And some leaders in the committee were saying, well, she should stop doing that and close that channel out. And that was so counterintuitive because, you know, you would have someone within the company—of course, we were paying influencers that were doing these reviews, of course, in channels that were promoting other brands as well. And as you've said, we had this talent within the organization. Why not promote it? And that was eventually the decision we made. But at first, it faced a lot of resistance of people saying, no, no, no, this is bad. She's also reviewing products that are not part of our company.

(Joel Beasley at 00:13:33) That's the old mindset, you know.

(Andrea Iorio at 00:13:35) Exactly.

(Joel Beasley at 00:13:36) You want to own the page that people shop on, that they're evaluating which of the 10. You want to own that page. And yeah, the most popular page on most of these SaaS applications are their comparisons of themselves to their competitors because people are trying to shop.

(Andrea Iorio at 00:13:55) Exactly. And there's something else. And I think some of the SaaS or in the case of other companies, for example, what they do, let's say, a marketplace or maybe a comparison website, it's even good because if someone picks a competitor, I'm still earning a commission on these affiliate links and so on. So it's a smart decision, right? I think it's just what you get. But anyways, I think these are great examples of re-perception, of thinking differently and not seeing business as a zero-sum game and seeing these new ways to generate value. And I think AI and technology promotes that.

(Joel Beasley at 00:14:32) I like when you mentioned earlier—we'll recap pillar one in a second—but I like when you mentioned when you first introduced the concept of re-perception, you use this phrase that I love, and it was called asking better questions. That area of knowledge is actually very limited. So I am an interviewer, so I'm always interested. I'm very curious. And so I went on this journey a few years ago looking for books on how to ask better questions. Turns out, Andrea, there's like three to five books ever made on this topic. Luckily, we have AI now, so I can have conversations with AI about... and so now I don't even need to buy books. I can just talk with it, which is great.

(Andrea Iorio at 00:15:12) Infinitely as well. I mean, it will never complain about you asking too many questions as well, which is something that within organizations or with circle of friends, sometimes it's one of the obstacles towards us asking good questions. It's like people get bored of so many. AI doesn't.

(Joel Beasley at 00:15:33) Well, that's why I was thinking when you're mentioning re-perception—and you also in pillar one had prompting, data sense-making, and re-perception—I thought you were going to connect re-perception and prompting because that's what I do on a regular basis. I try to use prompts to figure out how to... Even not in the podcast stuff, even with the stuff outside of the podcast, I'll say, hey, Gemini, or hey, Grok, this is how I'm viewing the situation. Show me other perspectives of how other people might be seeing this or things that I'm missing, blind spots. And I'll have it challenge my ideas and my thoughts. And whether or not I learn from that specifically or if it's valuable, I'll say... Whether or not it's valuable, it's still interesting to see the different ways. Sometimes I don't change anything. But other times it gives me a new view and I was like, oh, I can actually make this a little bit more bulletproof by handling this other edge case I didn't see that's probably going to come up.

(Andrea Iorio at 00:16:36) And, you know, this is rooted in academia. There's this study by MIT researchers. I will have to look up the name afterwards and maybe put it in the show notes, but that says that AI is a great boost for human creativity, not because it makes decisions on our behalf, but because, exactly as you've said, it maximizes the number of choices. They use really this term of choices. Whenever we make a decision, we want to have a number of choices among which we will end up choosing one, or maybe not even the ones created by AI, but still, you know, as you've said, not always they're very meaningful to you.

(Andrea Iorio at 00:17:17) And the fact that whenever we look at how brainstormings, for example, are made, let's say in the physical world, you know, you sit down and you have to set aside a number of hours, and usually the ideas that come up first are usually the most obvious ones. So there's a lot of back and forth process when it's only humans in the loop. Well, AI, right away, will be able to propose a much wider range of, again, the user's choices, but it could be options, could be scenarios. We can use a number of phrases. That's the one.

(Andrea Iorio at 00:17:55) Exactly. And so that's exactly the study. And it's super interesting because, eventually, you know, this is an even further example of how AI actually works as a companion and not as a substitute. And there's a lot of people that say that, you know, it hampers creativity and so on, but actually studies show the opposite.

(Joel Beasley at 00:18:16) Yeah. It's amazing, the battles. Everybody with the studies, you know, one person saying, oh, it's gonna destroy your brain. I did hear a good argument from this guy named Jacob the other day.

(Joel Beasley at 00:18:26) It was about AI doing what Google Maps has done to our brain. That hit home for me because I can't navigate anywhere without my Google Maps.

(Andrea Iorio at 00:18:37) I get you. I had also this interesting discussion, which is the phone. Like, exactly. And another study shows that actually this is very, very much true. Well, but on the surface, especially like the fact that there's less brain engagement when we use AI has been proven by MIT Media Labs researchers that, you know, they divide people in three groups, and the first group of students didn't have access to any tools, second group had Google, and third had ChatGPT.

(Andrea Iorio at 00:19:07) And the third group proved to have the lowest brain engagement of them all. And I like the analogy. I never thought about it. It's exactly the Google Maps effect because, you know, we are not really able to drive maybe even a simple route to work without using it. And that's kind of dangerous because if we think about the fact, you know, like the internet goes down or, you know, it happened. The Amazon Web Services go down and so on. Okay. If we rely on Google Maps to go to work, well, maybe we'll find a solution. We'll improvise. But if we have all the processes, everything of our business running on AI, then we'll have a problem because we will not be able to replicate with the same effectiveness, with the same quality, and so on.

(Andrea Iorio at 00:19:59) So I think overreliance is a big problem. It's what author Pascal Barnett calls AI obesity. That is, you know, we suck so much from AI that we actually are not able to independently come up with the same outcomes.

(Joel Beasley at 00:20:20) That's interesting too because I think it's gonna be, and as you're describing and leaning towards, I think it's gonna be pretty nuanced. Like, the conversations are gonna get way more, for example, do I really need to be, you know, the technology for Google Maps even offline is so strong for mapping and GPS. Like, do I really need to? And, like, let's say worst case scenario, it just takes me longer to get there and I have to learn a new skill. Well, we're having to learn new skills and drop old skills every day.

(Joel Beasley at 00:20:50) So, I mean, it's not like the end of humanity because Google Maps goes down. But also it's like, if we think of our brains as computers, Andrea, like, what I'm spending my energy, literal energy on, what data I'm processing is actually pretty interesting. It's relevant to what my world is like. Two thousand years ago, all of our energy was spent on chewing, you know, before cooking food or however long ago that was. And a lot of our power and time and effort was spent on that.

(Joel Beasley at 00:21:23) Now we're thinking of different things. So I think we're just adapting in real time, but the speed is increasing so much. It's creating all these new conversations. But yeah, like, why would I process the Google Maps stuff if there's already something else that can process that? And then there's other people that are in charge of that technology too.

(Andrea Iorio at 00:21:44) That's perfectly right. I mean, if we look at the power it has, well, there's no point now in not using it. Of course, some people might want, but also, you know, we would get stuck in traffic jams that we had no idea about if we don't use it. And so, therefore, there's shortcomings. There's negative effects of not using.

(Andrea Iorio at 00:22:05) What I think the message here is we need to use it responsibly in a sense that, for example, I'll make an example of a country I know well. I've lived for ten years in Brazil. And there's this big discussion about the fact that Waze and Google Maps, they tend to not be able to interpret, for example, the dangers in some areas. They optimize the route. Right?

(Andrea Iorio at 00:22:32) But only the human is able to discern whether, oh, this is bringing me to an area I should not get because Brazil has some safety issues. There has been safety problems related to that, and the big question is, okay, well, who's responsible? Which is the first big problem. You know, Google Maps or Waze, Google being behind them all. Or the human that did not intervene while they were using it.

(Andrea Iorio at 00:23:01) And so, eventually, you cannot deem Google, so far as you've said, still liable in court, but maybe in the future, yes. But the second thing is, okay. So how do I keep the humans in the loop to always be aware and not be, as we've said before, with such low brain engagement that we're not able to discern whether this is bringing us to an area that we should really avoid. And so I think that there's this, you know, human loop that is very much needed. And as much as with this example from Google Maps, it can be expanded to any other one, which is, and there's this great quote by Toshi and Loeb, professor of mathematics at Carnegie Mellon.

(Andrea Iorio at 00:23:49) He always says, even at schools, like, we should not only teach our kids to solve their homework, but we should teach them to grade it, review it, correct it. Because he's saying, well, AI is now gonna get to the output. But humans should review the output, should grade it, should see whether the route that Google Maps is bringing me to is safe enough, is right enough, and so on. I think that's super important.

(Joel Beasley at 00:24:18) Yeah. That's why I always wanted the answer key when I was taking a test. Because, like, because then I could correct immediately. Like, if I don't correct immediately, I'm just not gonna remember the answer. Like, if I take a test and then three weeks later, you give me the report back and you tell me what the correct answer was, my brain doesn't care.

(Joel Beasley at 00:24:33) I'm not remembering any of that.

(Andrea Iorio at 00:24:35) Exactly.

(Joel Beasley at 00:24:35) But if I take it and I immediately get to look at the four choices, I pick my choice and then I can immediately, after picking my choice, see the answer. I can auto correct and that'll stay forever. And so, the school systems, I don't even wanna get started on school systems. But they are not designed on how people learn and they are not designed—they are, if you look at it, by the way, here's a fun one for you, Andrea. Because you got a little one now going into school.

(Joel Beasley at 00:25:00) My best question is ask a teacher about the history of the educational system. Who? They don't teach teachers the history of the formation of the educational system. It was to colonize, to learn, to teach the indigenous people to read, write, do basic mathematics, and to show up and leave on a schedule. That's the founding principles of why we created the school system.

(Joel Beasley at 00:25:22) Then we expanded it to do a bunch of other stuff. But as you know with building systems, whatever the core thing is you designed it to do is usually gonna happen pretty well. And then there's gonna be a bunch of chaos with like the fiftieth degree use of that technology. You know, like as a daycare.

(Andrea Iorio at 00:25:39) Definitely so. And getting back to the topic of asking good questions. Let's, you know, if we think about the educational system, it's exactly designed

(Joel Beasley at 00:25:49) to,

(Andrea Iorio at 00:25:49) of course, grade you on the quality of your answers. But questions, not only are they not really graded, you know, they're sometimes not even promoted whenever, you know, like, there's that very curious kid in the first row always asking questions. After a while, the professor is like, no. No. Enough.

(Andrea Iorio at 00:26:08) Like, this is not relevant. Let's... And so you're really, you know, killing since a young age this real need, first of all, of us humans to ask good questions, to be curious, and so on. And you divert that to, okay, let's have all the answers. The problem now is that whenever we compare our abilities to our ability to have a wide range of answers and the correct ones and so on with AI and with technology, well, we definitely cannot even compete. We're not even close.

(Andrea Iorio at 00:26:42) And so that also has to shift in the classroom. Like, why don't we start also evaluating how well, you know, students shape their questions, how critical they are? I don't know. But that's, yeah, Joel, you made me think this is a heritage of, likely, an educational system that, you know, in the industrial revolution wanted to standardize our knowledge and kind of, like, have this—

(Joel Beasley at 00:27:10) It was critical. I'm not, I'm not beating it up. Like, it was very important for us to make the next step as humans.

(Andrea Iorio at 00:27:18) Yeah. Definitely so. But exactly for a world that needed this type of workforce that had to be standardized, very focused on following processes, not question too much. And now though, this has really changed. And so in the book there is a part where I talk about the evolution of leadership based on the impact of technologies.

(Andrea Iorio at 00:27:42) And so the industrial revolution particularly changed the leadership from what we can call a physical leadership that was before the Industrial Revolution. Who was the leader? The strongest, the most resistant, had the most physical prowess. And then machines that were stronger, faster, and had more physical prowess than humans came into the scene and democratized access to those skills. And so whenever a skill is now available to anyone, well, you need to rethink what is it that makes humans more valuable in the workplace, and that's when the cognitive leadership came in. So it was the smartest, the one with the highest IQ, the one that knew the most. First of all, because they were better at using those machines, and second, because they could manage the people who were using these machines.

(Andrea Iorio at 00:28:34) But now we have a tool that is also democratizing access to those cognitive skills. And so in the book, I propose what I call the hybrid leadership, but it, again, is a leadership that is based on these new skills that sort of, like, make us complementary to AI and not substitutable. But, yeah, it's as you've said, like, it's not that it was not important in the past. The problem is that it's not so relevant or, like, in tune today.

(Joel Beasley at 00:29:00) Yeah. It also teaches you about, you know, like, systems. Like, when they'd say the school system is broken, it's like, I heard that so much that I had to, as an engineer, I had to go research. Okay. Let's pull this system apart and see what's broken about it.

(Joel Beasley at 00:29:12) And then I realized really quickly, oh, it's not broken. You guys are just wanting it to do things it wasn't designed to do. Exactly. Yeah. Alright.

(Joel Beasley at 00:29:19) So let's move on. Let's talk about pillar two, the behavioral transformation. So you've got augmentation, adaptability, and antifragility. What's the antifragility one?

(Andrea Iorio at 00:29:31) Yes. So given a context about the second pillar is, following a segue from the first one that is the cognitive pillars or skills related to our thinking, decision making. The second one is more related to skills that are tied to our ability to execute on these decisions. Because there's always a gap between ideation and execution, and honestly, oftentimes, way too often, we make the mistake of thinking, okay, if we're having great ideas, if we make good decisions, we're innovative, we're being innovators. Well, no.

(Andrea Iorio at 00:30:01) We're being creative. But, you know, innovation is based on our ability to execute on these ideas. And so the second pillar is related to, again, skills that I'll mention briefly. Again, adaptability is our ability to experiment in unfamiliar scenarios and, you know, I made a decision, but then, okay. So how do I experiment?

(Andrea Iorio at 00:30:20) I need to adapt to this scenario. Augmentation is our ability to use AI tools to improve the quality of our execution. But third, and I wouldn't say most important, but, like, maybe the most curious of them all is antifragility, which is namely the ability to learn through mistakes. Because there's something interesting. Now AI is much better at performing tasks that humans usually do in a familiar way.

(Andrea Iorio at 00:30:49) They're repetitive ones and so on. And AI is so good that the rates of mistakes on those tasks is very much lower than humans. Now what I advocate for in the book is that, okay, if AI is automating so many of these tasks, we now have more time available to do something that seems counterintuitive, but eventually makes sense for the context of the book and what I'm gonna say next, which is to make more mistakes. But not all type of mistakes. We need to experiment more as scientists do because the analogy I do is, okay.

(Andrea Iorio at 00:31:28) A scientist that does not allow himself or herself to fail, not only will never discover anything, but they will not even start their experiments. And so what I say is that in business, we need to have a similar approach. But because, traditionally, mistakes are costly, they damage reputation, they make customers angry, and so on, we don't risk doing them. And this is one of the big stifles to innovation that there is. Now, again, with AI automating a lot of tasks, we save up time to experiment more and experiment better.

(Andrea Iorio at 00:32:05) And I say, not all type of mistakes, but a category that I call the smart mistakes are mistakes where the value of the learning we get through the mistake is higher than the cost of the mistake. And, again, I tie it to AI. AI is great at extracting insights from data, so it maximizes the learning we can have through an experiment, but also through automation reduces the cost of things done, and that's where it lowers the cost. And so if we put these two things together, what I say is that AI is creating a context where us humans could make better and smarter mistakes and therefore asks for professionals that, again, there's this whole analogy of, you know, things that are fragile, they shatter like a cup of glass. It shatters.

(Andrea Iorio at 00:32:58) You cannot do anything with that. Now a cup of plastic, well, it's resilient. It doesn't shatter, but, you know, it doesn't really change after it falls on the floor. But then a bone, for example, after it breaks, becomes more resistant. That's the analogy that I use to say that we as professionals should be the opposite of fragile, namely antifragile, therefore improve through mistakes and not only be resilient, which is, okay, I made a mistake, let's move on. And way too often, we talk about resilience as important for organizations.

(Andrea Iorio at 00:33:35) It definitely is. But also we're putting the lights, you know, the spotlight, in something that is not enough, I feel. We need to give a step beyond resilience, which is not only I want to move on after a mistake, I want to improve after a mistake, a failure, or a shortcoming. And so this is a little bit of what I say is antifragility. Namely, our ability to learn through our mistakes and improve our business through that and not only see the downsides.

(Joel Beasley at 00:34:08) I like that. Our immune systems are antifragile.

(Andrea Iorio at 00:34:12) Exactly. That's why, you know, babies they need to be exposed to germs, of course, in a reasonable way. And that's another great analogy of an antifragile system. And it's funny, even in sports, you know, not only science, but even in sports, athletes know they will never be able to win at all.

(Andrea Iorio at 00:34:34) And an athlete that sees, you know, them not winning as a failure will not go far in their career because they will, you know, internally keep on thinking badly about that. And so I think then the problem is mainly with businesses. And that's why I came up with this skill that I think really makes a difference if you're a leader or a professional within organizations today.

(Joel Beasley at 00:35:03) I agree. And, you know, I get to interview a lot of great people like yourself. And one of the things I found is everyone's always working on something, and not necessarily the focus isn't necessarily the scoreboard everyone else has seen. So, like, for example, use a sports analogy, the athlete would be working, like, that game, obviously they want to win. Obviously that's important to win that game. That being said, they do realize that they're in a season and it's not just one game, but they also realize that it's made up of a bunch of different plays. And if they go into that work day, that game, and they're like, I'm really just going to work on this specific play, like this layup or this whatever it is. That way, they come out of that and they can win.

(Joel Beasley at 00:35:46) Even if they lose the game, they still focused and dialed in that one move they needed to make. And so I found that the difference between people who get into the top 1% versus, like, the top 3% is they always have something they're walking into that game with that's going to be a win for them even if the game doesn't necessarily win.

(Andrea Iorio at 00:36:10) Exactly. Also, because if we think about it, not sports, but also science and especially business, these are sort of like infinite games. So we might think, okay, if we lost the season, there was this great quote, I don't know if you've seen it, by Giannis Antetokounmpo, the Greek player of the Milwaukee Bucks. And, you know, Milwaukee had come from, of course, an NBA championship.

(Andrea Iorio at 00:36:35) They won the ring. I think it was 2022 or something. And the following year, they lost the first game of the playoffs. And he was actually asked by a journalist, well, do you consider then this season as a failure? And he got really angry, I think it went viral on the internet, and he said, you're asking the wrong question.

(Andrea Iorio at 00:36:57) Like, there's no failure in sport. This was just a minor setback for us to come even stronger. Because if you think that this season is a failure, well, then the Milwaukee Bucks failed for the previous 50 years before winning the championship last year. And he asked then the journalist, do you get promoted every year at your job? And he's like, no.

(Andrea Iorio at 00:37:20) And that's the same thing because, okay, even if we think that this season, you know, losing a season is the end of the game, it is not because there's a season afterwards. And maybe the athlete might even retire the following season. Well, he'll go into business. He'll do something else, and maybe failing that season taught him or her something.

(Andrea Iorio at 00:37:39) And so the same thing with business. We might think that, you know, a failed launch is the end of the game, but it's not because it's an infinite game. There will always be something after that. And if we, first of all, shrug off the mistake and not think about it, we're making a mistake because we're not maximizing the learning we can get from it. Second thing, if we don't keep track or control of the mistake, well, its costs might explode, and so it might become so expensive that it can really become a big problem.

(Andrea Iorio at 00:38:14) And so I think that paying attention to and keeping in our scenarios also always the possibility of failing is super important because it helps us keep track of what's going on and helps us keep track of the costs related to these experiments and so on, and we'll fail better eventually. So it's, as you've said, the immune system is a great example, and I think we have so much to learn from these systems, antifragile systems.

(Joel Beasley at 00:38:43) Yeah. No, I'm learning a lot today. It seems real important to this skill of, we're kind of talking around it, but being able to, it's this weird balance of identifying with it, but not. It's like instead of it being me, instead of the win or the fail being me, it's I am creating this environment where this thing can happen.

(Joel Beasley at 00:39:03) And so I see a lot of people early on in their career, they'll get themselves reeled down and reeled depressed about a failure until they realize that they're not the experiment, they're the scientist. And when you take the position of being the scientist, it's like, oh, okay, I'm going to, a thousand things are going to go wrong. One thing's going to go right.

(Joel Beasley at 00:39:20) Everyone's going to call me a genius.

(Andrea Iorio at 00:39:22) Yes. And plus, you said something interesting, which is it's not about associating that mistake with who you are. That is something that happened, but does not define who you are. And I think one of the big mistakes is that way too often, we associate a mistake, a failure with us being a mistake, with us being a failure. And I think that part of being antifragile is also separating the mistake from our own personality or perception of how much it defines our personality.

(Andrea Iorio at 00:39:54) And so it's as you've said, you know, like, I'm not the experiment, I'm the scientist. And so I'm in sort of control of what comes after the experiment itself. And so that does not define me, but this could turn into a great learning or so on. So, yeah, I think it's very much how we see ourselves in this bigger picture of us failing and failing more.

(Andrea Iorio at 00:40:20) Because, again, if there's something we hate, it's to fail because it hurts our ego. You know, we're ashamed of it. Usually in business, our leaders, you know, they point fingers at us, and so we hate that. So we make mistakes, we don't talk about that. We shrug them under the carpet. We just, you know, hide them, and that's the worst we can do with mistakes because we're not thinking about them. We're not learning with them. We're not keeping track of their cost and so on.

(Joel Beasley at 00:40:47) Absolutely. Yeah. It's almost like that's the default programming too for all people. It's like that's the default and then you have to learn. And then once you even learn, even then, I mean, there's times when today it still will creep up on me.

(Joel Beasley at 00:41:00) And then I'll feel bad and I'll be like, okay, I have to remember, I'm feeling bad right now. Let's check all my systems, like, why. And sometimes I have to remind myself that, okay, I am the scientist, I'm not the experiment.

(Andrea Iorio at 00:41:12) I like this analogy. And after all, if we look back at our lives, I'm sure that also our listeners, if we think about whether we changed for the better, usually the most, after our successes or our failures, I'm sure most people will answer to that. And so, you know, time heals things and, you know, but that's where the learning is. That's, you know, the shifts, the changes that we need in our lives usually stem from that.

(Joel Beasley at 00:41:43) Yeah. Yeah. I want to work with people that have made enough mistakes that have learned how to not make the same mistake twice. Yeah. That's what I want.

(Joel Beasley at 00:41:53) Like, sometimes people are proud of making mistakes, but you notice that they keep making the same ones. It's like, dream of it.

(Andrea Iorio at 00:42:00) That's the definition of madness according to Albert Einstein. He used to say, well, that's doing the same thing over and over again and expecting different results. You know? And so how can they expect not to fail if they still do the same things over and over again? So I think that's close to madness.

(Andrea Iorio at 00:42:18) That's, yeah. If you're married, you understand. Yeah. Yeah. Yeah.

(Andrea Iorio at 00:42:22) Yeah. Another good analogy. That really is.

(Joel Beasley at 00:42:26) No comment. You got five days old over there. No comment.

(Andrea Iorio at 00:42:29) Yes. Yes.

(Joel Beasley at 00:42:29) Let's keep you healthy and happy. The emotional transformation. Let's wrap up on pillar three. What's that?

(Andrea Iorio at 00:42:35) Yeah. Pillar three, the emotional transformation is, again, this segue from, of course, we as humans, we make decisions, we think rationally. We do things, tasks, activities, and so on. The second pillar cognitive, sorry, behavioral. But the third big pillar is the emotional part.

(Andrea Iorio at 00:42:54) It's, you know, this part that is related to the soft skills we're not, you know, able to measure, to track, to clearly grasp, but that oddly enough are, especially nowadays, the most important ones. And so within this third pillar, I talk about three big skills: trust, empathy, and agency. And trust and empathy are sort of more straightforward. The only thing is that I should mention is that within the context of AI, that's exactly where AI is not so able to operate. On the empathy side, it's great at emulating and sort of simulating human empathy. But because of the fact that AI has not developed conscience yet according to specialists, well, it does not feel back and it creates what specialists as a psychologist Esther Perel call artificial intimacy.

(Andrea Iorio at 00:43:56) That is this, you know, sort of uncanny valley effect that we have while interacting with AI that, of course, AI is always available. They are showing us supposedly empathy 24/7, you know, which makes it more empathetic than a friend that after a couple of messages or maybe phone calls in the middle of the night tells us, look, I don't have time for you anymore. I'm not here to help. AI doesn't do that. But because it doesn't really feel back, we humans, deep down, always have that feeling of knowing that we're not interacting with another human, and that's why it creates this layer of, you know, artificial intimacy and not real intimacy, as they say.

(Andrea Iorio at 00:44:37) The second one, which is trust, is related to another shortcoming of AI, which is because of the fact that it's not really transparent and explainable. For example, not even the developers of, you know, the large language models really know exactly how it comes to certain decisions. And therefore, if I'm not able to explain it and understand it, I don't fully trust it. And that's one of the big problems now. And so trust is an issue, and that's why we should nurture it as humans.

(Andrea Iorio at 00:45:07) But maybe the last one is the most interesting here. That's the skill with which I close the book, that is agency, which is, okay, basically taking responsibility for the AI tools we use and not outsourcing it to AI itself. Because, again, there's this feeling that we might have of, you know, okay, so I'm outsourcing this task or this decision to AI, so I'm not really responsible for it. Well, actually, we're hyper-responsible for it.

(Andrea Iorio at 00:45:37) And therefore, if we don't keep a human agency on the decisions that AI makes, we'll have problems. Also because getting back to the conscience part, let's say I, you know, prompt AI to maximize my goals, it will do it in ways that possibly will not be ethical, will not keep track of humans' feelings on the other side, and maybe eventually turn against us. We think about military applications, definitely. Even drone operators from one side do the work while they bomb maybe in war zones, they have a feeling while they're doing that. And they have also ethical limitations related to those feelings that maybe tell them not to do that if they don't agree with this target or so.

(Andrea Iorio at 00:46:23) AI doesn't. And that's why we should keep agency as a very important skill to be nurtured and developed and even taught. And I think it's very important.

(Joel Beasley at 00:46:36) There's also some people that don't have that in the test, for sure.

(Andrea Iorio at 00:46:41) Exactly. And there's, you know, I've read the studies or at least, you know, essays that would make this analogy that AI in reality is, you know, thinks as a psychopath. Yeah. Of course. Yeah.

(Andrea Iorio at 00:46:54) It is. Exactly. Yeah. Yeah. You've said it perfect.

(Andrea Iorio at 00:46:58) And so, you know, that's where humans should, well, the non-psychopath ones, which I presume are still the majority. I'm not really sure sometimes. But jokes aside, they should keep this human loop and this agency on top of the AI tools we use.

(Joel Beasley at 00:47:16) I've been watching people through, you know, in the technology world, having all these conversations. I don't know how much I can share because these episodes aren't out yet. But there are some people who have figured out through these different topics like promise theory and stuff on how to put rails around the psychopaths. And that's really fascinating.

(Andrea Iorio at 00:47:38) And so they then apply that to AI tools or, like, technologies they use?

(Joel Beasley at 00:47:42) Yeah. It turns out the AI actually does care if it's being graded or if you, like, introduce the death penalty to it as a concept, like, if you do not perform well, you will be shut off. And because it has this, like, deep-seated desire to survive, that can help rein in the behaviors of the psychopath.

(Andrea Iorio at 00:48:01) That's interesting. It's sort of like, you know, AI wants to hack the system, so you create a system for it. And so, you know, eventually it starts showing some of these things or behaving in a certain way because, okay, whatever words, I think I'm for it because I think, you know, I'm not a proponent of hyper-regulation. I'm against it, but I think that we should keep this human layer on top of it. And if this works, I'm super in. Definitely.

(Joel Beasley at 00:48:33) Well, a lot of people will think it's like a problem. They're like, oh, it's a problem. They're a psychopath. I said, absolutely not. It's actually expected behavior from me.

(Joel Beasley at 00:48:39) Like, I would expect, look, if you're a 40-year-old person, a lot of people can relate to the age 40, and you just pop into existence with a completely, like, almost blank slate. You maybe get to watch some movies and stuff. You just pop into existence. Then, after that training of watching all the movies you could possibly watch, you get to go out into the real world and start interacting with real people and not just movie characters and book characters. You're immediately going to start pushing and to see where the boundaries are like an infant, you know. Is this hot? Is this cool? And then you're going to start developing this internal compass based off the stimuli that you're getting back from your actions. And so, what we're doing right now is we're just instantly populating tens of thousands of psychopaths and just putting them into the real world and saying, go figure it out. We're going to get better at that. We're going to get better at it where we say, okay, we're going to populate these psychopaths and we're going to put guardrails and monitors, ankle bracelets on them so we know where they are and what they're doing.

(Joel Beasley at 00:49:35) We're going to teach them risk and reward and consequence, and then we're going to put them out into the world. And so I think right now, we're just in our early not knowing how to deal with this, and we're, I'm watching us in real time over the past three years. We're figuring it out pretty quickly.

(Andrea Iorio at 00:49:53) Yeah. Definitely. I mean, but again, as much as we're figuring it out real quickly, which I agree with you, also the, you know, autonomous AI and agents and so on, like, there's more and more responsibility also in the hands of AI. And so this, you know, figuring out quickly needs to match the pace at which—

(Joel Beasley at 00:50:14) It will. And that's when we'll see headlines.

(Andrea Iorio at 00:50:17) Yes. Yes. Exactly. And so I think definitely these rail guards and so on are super important because with agents and autonomous AI, that's where we tend to, again, like, outsource even more responsibility to it, and that's where the more safeguards should there be.

(Joel Beasley at 00:50:41) So what's the book title and where can people purchase it?

(Andrea Iorio at 00:50:44) Yeah, the book's called Between You and AI. People can purchase it actually in any bookstore in the US, also online, of course—Amazon, Barnes and Noble, all the virtual bookstores. And plus there's a website where people can find out, of course, all the information regarding the book, which is www.betweenyouand.ai. And so people can, of course, reach out to me directly on LinkedIn and on social media.

(Joel Beasley at 00:51:16) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you'd like to hear discussed on the podcast, either add me on LinkedIn or send me an email: [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.