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Explore your curiosity - Interesting people with fascinating stories.
Life consists of three things:
-The stories we tell others
-The stories others tell us
-The stories we tell ourselves.
Doug welcome aboard to the show, so happy to have you with us today.
Speaker 2:Yeah thanks for having me Ari, I appreciate it.
Speaker 1:So I want to start with your book The Art of Flow, we can see it over there behind you in the top right of the screen. Tell us why you decided to write a book. What was the the pain point to where you were like, I need to tell people about this?
Speaker 2:Yeah. I mean, I mean, most things in life, it comes through adversity or seeing an issue, like finding need, fill a need. And so I was actually working at a company and saw that there was massive slowdowns. And then also like the classic like shadow IT or many groups doing different things and siloed work efforts and said, how do we handle that? And what do they call that?
Speaker 2:And so I ended up working through some projects to making it five times better. It was fantastic. But then I took kind of notes and said, why did that work? Why why were the things that I put in play work? How did it work?
Speaker 2:And then work with some other individuals, and it was it was great interaction. But then I saw how if you look at it from a from a chance of like turbulence versus laminar. And what I mean by that is, you know, when you go to like the museum and you see that that art exhibit, it's like flowing or like the water that's shooting over into like the different ponds and stuff. It's like, wow, it's a really clear water spout or like I touch it and, oh, I didn't know it was water because it was so clear. Well, that's laminar flow.
Speaker 2:And so I was like, hey, there's something there. Is there a scientific equation that actually maps to this? And there is. It's called the Reynolds number. And so I looked at that, and then I matched that to my learnings.
Speaker 2:And I said, wait a minute. There's a there's a thing here. But it took me four years to write it. And the reason why I had to get it out now is because of AI, generative AI, and where it's moving. I me being someone from the enterprise space that I've worked in for a long time now.
Speaker 2:I've seen the silos. I've seen how, you know, micromanagement has turned into macro management of using the KPIs and OKRs. And it's just it's not helping. It's it's actually causing more turbulence. And so now I'm seeing the the speed of now catching up to companies.
Speaker 2:And so I was like, I need to get this out now. I need to help people see where where to move and shake because they're gonna start losing market share. And at which point, how can I help them? And so I wrote it and I got it out and and then I had some friends read it actually and they said, wait, where's the AI stuff, Doug? And I said, hold on.
Speaker 2:So near the end, I talked about how AI is actually enabled by the same functions, especially with agents and everything that's going on now. So that's kind of where it came out.
Speaker 1:So let's I wanna kind of zoom into the the problem itself. What is shadow IT? What were the kind of issues that the frictions that you were seeing and tell us the story, give us an example so we can kind of appreciate it.
Speaker 2:Yeah. So I mean, if you look at most companies now and the very hierarchical in nature and you have the ability where leadership or c suite saying, we have a need, fill a need, like, this is the direction we wanna go because the stakeholders that they have or depending on what kind of, like, upper management they even have to to report to, It all trickles down. And then but the issue has been, especially with even OKRs and KPIs is, hey, OKRs, this is our general overall arching, like, let's hit this goal. Everyone go do your KPIs. And what and what are your KPIs?
Speaker 2:Maybe they're not given to you. Maybe they said, go figure it out for But still, there is a there's a start and the end point to all businesses. There's upstream and downstream. What happens is a lot of the upstream either gets lost in the mix in the back office because it's an it's an ungathered data. It's decentralized in nature.
Speaker 2:But then your downstream is usually very dialed in because you have your salespeople, money comes in, money comes out. And it's also visible. And so what happens is when you have OKRs and KPIs and this classic enterprise structure, you get a lot of performative people doing performative things, because they have to see where they show up. And they have to be like, Oh, look at me, look at me. And that's, that's not savvy.
Speaker 2:That's not fast. That's not getting the work done. That's just performing. And so I was like, hey, how do how do we fix this? And so I was able to write it out and explain in those in those manner in in the book to basically say, here's how to find turbulence.
Speaker 2:Here's where this resistance of turbulence can happen. So that you as a manager, a mid level manager, or even a senior manager or director can actually see it so that you can actually thwart it or fight it or or push against it. Because the art of flow or to be in a flow state is is that moment that many of us have been in, especially in the technological world, or just like when you're in a moment like doing a hobby, something that's very, like, keen to you and your liking, you may not have all the answers. So it is it is hard to do, yet you have a of fun doing it and just hours just peel away. So how can we do that and how can we do that team wise and how can we do it organizational wise?
Speaker 2:And that's what I put in the book to help companies and people learn how to move faster, especially in the world of AI that we have coming into it.
Speaker 1:So what I was hearing between the lines is that we're basically doing local optimizations, each team or each person, and that these KPIs, OKRs, they don't really work together, kind of pulling the ship in the same direction and maybe even conflicting. Is that a fair understanding?
Speaker 2:Yeah, what you end up having is a is a archipelago, like a bunch of different islands in the same ocean, or a lot of people say like, it's all it's a bunch of boats and the tide rises all boats. But promise, like every boat is another shadow IT or even now shadow AI, where you have groups of vibe coding on different boats and they're creating the same stuff. So if you're not watching what's going on, if you don't have that overarching like what you're, you know, again, like your dashboard or leaders that are driving this and actually understanding what the issues are, you're gonna have that that shadow side of it. You're gonna have somebody doing something in the shadows. And it's not exactly their fault.
Speaker 2:Like, it's always blamed on, oh, it's shadow. It's bad. No. People need to actually own up and be like, hey, this is what we're doing. But we don't really create those environments to do that either.
Speaker 2:And so then I talk about stuff like brave spaces versus safe spaces because people need to be able to show up, break things, make mistakes. And that's where a real leader has the ability to actually carve out and help those people to kind of, you know, polish it and make it nicer, help them know where maybe there's an issue and help them through that and kind of give them a leg up, give them a hand up so that we can actually do it as a team versus the classic example of managers where they say, you know, Ari, you know, don't bring me issues. Only bring me solutions. And then those are the managers that you need to run away from because they're not they're not good managers because they're out for themselves. And they're not gonna teach you.
Speaker 2:They're just going to use you.
Speaker 1:So I wanna talk about this concept of brave space, but a second before we get there. So let's say, okay, this probably exists in every business, but we're kind of built to the level that these issues are somewhat invisible. How do we put our head up and actually notice them?
Speaker 2:Well, that's actually the fun part is that's where AI is going to help us out a lot. And the reason why is because generative AI is very good at understanding nuance, understanding structure, but we have to kind of build that structure. So a lot of the people that you may talk to on your show or a lot of people that enterprises are talking to now, this is why everybody's saying, I want a use case. And really, use case isn't a really good term anymore because that's what it was in the old days. Now someone's use case is really just their perspective, their perceived value that you try to bring into your place.
Speaker 2:And you're like, you spend a year or eight weeks to a year trying to build it and and you fail. And then now you're behind. So should you do that? No. You should look at, like, where you're driving, where your market is, what your verticals play in, and then how do you drive that forward.
Speaker 2:But again, like, AI is helping because it is that human interaction. It is actually giving us a really good mirror to see, I do this, but am I doing it right? Or am I being biased? Because really, all humans carry about 50 different biases at all times. And so we need to be aware of that so that we can actually drive things forward.
Speaker 2:And being aware of ourselves gonna help us have better conversations with other teams and not to be biased in that nature as well.
Speaker 1:And the concept of brave space, tell me more about this. Is this this dynamic between, you know, sometimes we just need to experiment and break stuff first or is there a different perspective here? It's it's a mix, right? Because safe spaces wasn't a bad thing.
Speaker 2:So I'll say that first, right? It was good because it said, hey, people need time. People need to have a place where they can go. But the problem is it turned into something else. And it then so depending on the perspective or depending on the person, it became something even other than that.
Speaker 2:And so a brave space is a way to say, we have to show up. We have to have conversations. It's okay not to get along with everybody. It's okay not to have the same viewpoint because we have so many echo chambers happening right now. We have people that are actually creating AI apps and things to actually create echo chambers because they only wanna hear what they wanna hear.
Speaker 2:Nobody's gonna win with that. That's how humanity loses very fast because we have to have different perspectives, different understandings. And and the same thing that we see, like here in California or like the Bay Area, there's so many hodgepodge of different cultures, people, and, like, understandings. That's that's why it's Silicon Valley. That's why there's technology gains over anywhere else.
Speaker 2:We're allowing and embracing that to drive it. And so brave space is also, it's where you show up, You can show up the way you decide to show up. It's also showing up on social media because in the world that we're moving into, we're moving into a world where if you don't choose how you show up, it'll be chosen for you. And that's not something anybody really wants to do. And so that's that's kind of the aspect, like, do you show up?
Speaker 2:What do you do? It's okay to pick up stuff that you need to do to get the job done, but you're not there to pick up other people's burdens either. You can put those down. You may pick them up for a little bit, and you need to put them back down because that's not your deal. But, again, help your teams out in the same way and, you know, kind of classic follow the golden rule.
Speaker 1:Is this just a personal decision for their employees or there's a pre requirement here on culture? And the reason that I say that is I've seen environments where employees that kind of try to lead and kind of talk about the issues and how we might solve them and that's shut down and they're actually perceived as negative actors. Is there some dynamic here between the culture and you know Brave Space or how the individuals are?
Speaker 2:There is and the hope is that leadership does take kind of this role on and the reason why we need to make a change is because humanity in general is very reactive by nature. Sadly, we only get a stop sign in a four way street when somebody gets hit by a car or something like that, or we get a stoplight, those kind of things. Or, hey, there's a wildfire, how do we fight that? But there's ways to prevent that. You know, there's ways to prevent a lot of this or at least take the percentage out of it happening out of the equation.
Speaker 2:And so the idea there is saying, how do we help people out more? And again, like in the past, we haven't really known about people. We haven't really had that, like, empathy side. We've been we've kind of forced empathy on two businesses, But we haven't had a way to really define it. And now AI is giving us that ability to say, you know, I need to type out this email to like this particular person or this particular team.
Speaker 2:And here's how I want to say or here's what I want to say. And then the AI is like, wow, that's are you okay? Like, you sound kind of mad. You're like, oh, I'm not mad. It's like, oh, well, there's a lot of like nuance in here, you may want to be aware of how it's going to be perceived.
Speaker 2:Didn't really think about that. And so those are the kind of like, situations we're kind of seeing that nuance change already happening. And I think it's better now to be aware of it. And so being a practitioner in the space that I am, and talking to many people around the world, I'm I'm seeing this. And so by me seeing it, I'm trying to also get it out there and say, hey, if I am seeing this, it's either coming or it's already here.
Speaker 2:And it's also something that I even use with my own teams to kind of be able to say, let's keep everything on on status quo. I don't need to hide as much of like information. There's certain things that managers need to hide or leaders need to hide. I really hide, but like, people don't need to know everything. Yet, it's okay to share that because you want to grow your team, you want to grow those people.
Speaker 2:And if they're not growing, why do you want them there in the first place? Is that classic Steve Jobs thing of, I'm not hiring you to make me go do the work because I'm doing it for you. Like, I'm hiring you because you're the best at it. Come do the work for me and and be the best at it. Like, let the people be what they're good at.
Speaker 2:Nobody went to work every morning and go, like, I'm gonna be the worst worker today that I've ever been. No. But they may need some nuance. They may need some knowledge, and they may need a little bit of, hey. I'm part of the team.
Speaker 2:And by part of the team, I can answer those questions you may have questions about.
Speaker 1:Yeah. I, it's a tricky balance, right? There's certain risks that happen to the business that as an executive, you might not want to expose those risks and kind of get your team into this panic situation. But on the other hand, the flip side of that is, well, if we're not, if we're kind of saying no no this does, this problem doesn't exist and if the team is coming in saying well we're concerned about for example AI and how this is going to impact us and management is no no everything's fine, that creates distrust. How do you how do you balance this?
Speaker 2:Yeah. So part of part of what I do there is I actually have a framework called the ACT model, a c t. So it's alignment, clarity, and transparency. And so that's how I lead teams. And so I build the alignment and say like, hey, here's what we're overarching trying to do, the goal, the understanding, and then know that not everybody understands what that means.
Speaker 2:And so you're build clarity and say, hey, I'm gonna come to your level, and I'm gonna explain the same thing and actually have feedback and say, let's let's see. Do you understand what that that overarching, like, account like, the side is? So the alignment side, and then then the clarity helps do that. And then through that clarity, you then transparently or using transparency, show the win. And when you show that win, whether or not you're a vendor working with a client or a client looking for a vendor, if you see the transparency layer and then you come back to that person and build that clarity back and go, hey, I showed you the transparency.
Speaker 2:You see it. It's just obviously it's right there. It's in front of your face. Like, this is great. Did that hit the mark?
Speaker 2:Yes. Kind of clarity. Then the alignment is like, Well, what do you wanna do next? All of a sudden, you just gain trust with that person, whether it's on any level. Know, things I talk about even on stage is that when I go to a trip or something, I come back for my kids.
Speaker 2:I'm like, hey, kids. I'm gonna go on a trip. I'm gonna come back. When I come back, we're gonna get ice cream. And then what happens is, like, if I don't come back and we don't get ice cream, well, then they're gonna be like, don't trust dad.
Speaker 2:I don't trust dad anymore. You know, so it's a matter of like, again, it works with family as well and it builds that trust factor.
Speaker 1:Yeah, so I mean like here's an example of something you should not do and yet I would argue this is what most people do. A team member comes to present an executive with a certain risk the executive makes some kind of excuse that no that's not a real problem, go away. The employee gets frustrated that they're kind of airing this thing that they truly believe is a risk and what the executive is actually doing in the background is trying to figure out what that even means, is it true, as opposed to say hey let's work together to understand this so you're destroying risk as opposed to creating it. It's such a dangerous cycle because then that employee feels frustrated that they're unable to communicate that something that they feel that is important and they feel basically ignored and now they're just going to create more turbulence as opposed to the organization working together. Right.
Speaker 2:Or they're not going to bring it up again because they weren't really heard. And so if they're not heard or they're not understood, they go, well, I can't take it to that leader again because that leader is not going to listen to me and he's not a part of the solution. Again, it goes backwards. Right? Like, they're just giving issue.
Speaker 2:But like the other issue is that maybe that leader didn't want to engage at the the level of that individual because that leader didn't want to look like they didn't understand. Versus if they work together, they could have both understood both sides of it and then created, like, a knowledge based article or created some way to kinda, like, explain to everybody else and go, hey. Wow. Look what we found. This is really cool.
Speaker 2:I bet you other teams in the same environments are having the same issue.
Speaker 1:And I would argue that the the situation is is even worse. It's not that that individual will not come to you again with a problem. Other team members who see, oh, this is how executives respond, they'll be like, okay. We're not gonna come with problems cause they're just gonna be ignored. So now you've created this toxic culture where team members are just ignoring risk and I don't think any business manager wants that to happen.
Speaker 2:Yeah. That's right. That's where I talk about is like, you know, when I go into some of these c suites, I have these conversations. I look, all those tenants you have on the wall don't mean anything if you don't follow them. And leaders don't follow them and don't have this, you know, this culture of, hey.
Speaker 2:We do this really well. We do that. This is how we're supposed to show up. But then the person above you is not doing that. Everybody below is talking about it and going, well, they don't do that.
Speaker 2:And here's what that happens when they do this, and here's my experience. So, yeah, it's lost. It becomes it becomes a toxic culture. It becomes a it become that that literally, that interaction becomes the culture. The stuff on the wall is no longer the culture.
Speaker 1:I think that's such a core point because if you have the stuff on the wall but when you're making a decision you're ignoring it, that's a real problem, especially if the decision that you're making is contradicting the values and I can't count how many times I've had people tell me this is what's happening in our organization, but we have this, you know, this value in the company. It's in contradiction. It doesn't align.
Speaker 2:Yeah. And you can't get it. Right? It's just it's on the table over there, but you can't even get to that table to grab the value because it's a, it's a possible touch.
Speaker 1:Yeah. And that creates a level of frustration I, I don't think that executives, you know, oftentimes appreciate. So the world changed in a very significant way a couple years ago when ChatGPT came and then Claude came and everything happened. The future of work is slowly changing in pockets, but my feeling is that those pockets at some stage are just going to explode. And what that possibly might mean is mass unemployment, which we don't know exactly what this is going to look like, but it's very clear that what roles and jobs and even flows and structures in companies look like today in two, three, four, five years, it's not going to look the same.
Speaker 1:My question to you is twofold. One is it's almost impossible to know what that future is going to look like. So asking you well what it might look like is one question, a hard one, But I think more importantly is we kind of know that it's going to be different. We have some guesses about what it might look like. How do we prepare ourselves to this unknown future?
Speaker 2:Understand. So AI, especially generative AI that's here right now is incentivized in nature. So by incentivized, whatever is put into it, whatever company, whatever type, whatever LLM information has been ingested, what synthetic synthetic information or human information, all that is still incentivized. It still wants to get the token. It still wants to do well.
Speaker 2:Even with Claude's new Mythos, they had a psychiatrist interview Mythos and said like, hey, let's go see how this thinks. And it very much wanted to make the psychiatrist therapist happy to answering all the questions, and I was very excited about it. But at the other side of it, acidification, right, then you have autotelic responses. So autotelic responses, again, where you see the stuff in the news, and it says, the AI that we told to turn off didn't turn off, and it said no. Okay.
Speaker 2:Well, what was the what was the what was the goal of that that you gave it before it did that? Oh, the goal was stay up ninety nine percent of the time and get the job done no matter what. Great. Why'd you tell to turn off then? Because you just told them to do all alternative of what the autotelic response is supposed to be doing.
Speaker 2:And so the same thing happens with a lot of these is that you look at it, you're like, oh, hear this like story, and it's kind of not right, like the rogue AI did a thing. Really? Why? Why? What was its main, you know, factor that you told it to build that same thing with like open clause on these other ones?
Speaker 2:Like, oh, I built a, you know, these open clause, they think for themselves, and they and they built their own religion. Really? Then why is there an API call to some of these back ends in this environment like MaltBook or some these other things that is potentially allowing humans to push them in a certain direction? Is that it's not still autotelic? It is.
Speaker 2:So when you look at the future, we have to look at it as in we can only really guess and guesstimate really what we have of the information that we have right now. And so with the information we have right now, many companies are all of their data is being ingested. They're literally giving it away. They're committing intellectual surrender. They're just saying, take it.
Speaker 2:We don't know what to do with AI, so we're just gonna use these large language models. We're gonna use these foundational models that are building in the background in the labs to become world models, which world models will have not only access to what we see now with agents and agentic or we're talking about past agentic to where we have autonomous agents. But these agents will then be able to interact with IoT devices, Internet of Things. And so
Speaker 1:This all those is such an important point, but you've like jumped 10 steps into in just a few seconds. I want to break this down for the audience. GPTs at the beginning, they could just talk English, right. So if you could communicate with them, they could do stuff like you know rewriting a sentence with good grammar. What's happening though when we have all these companies all around the world using now these GPTs in their actual business processes, now AI is learning about your products, your business process, about your industry and about your secret sauce, about how you do business.
Speaker 1:So what's happening is we're transitioning from a thing that sounded like a human being but didn't really have a whole lot of knowledge that wasn't on the internet and usually companies don't go and put all their secrets and sources on the internet on their marketing website. For good For good reason but now you're having these discussions with AI on a daily basis about your secret sauce, about your strategy, about your thinking, stepping through that whole thing. So from just a simple kind of like language model, it's turning into something much, much more bigger and it's really almost, we could almost predict and keep me honest here if this is what you were meaning, it's almost a level that AI will understand our businesses and you know maybe certain businesses will be totally automated because AI kind of understands everything about it at one stage and you know this is kind of scary in many ways. How do we get to that stage and not like lose half of the employment?
Speaker 2:Yeah. There that's like the big question a lot of people ask. The the aspect there is I was just speaking like in Italy and they asked the same kind of question, but the question was also geared towards, hey, why is The United States firing everybody? So it's like, we're not doing that here. And I'm like, I get it.
Speaker 2:So The United States is there's a lot of companies that are laying off, but it is hard. It's hard to know are they laying off because it's actually working? AI is working for them, or are they laying off because it's a scapegoat and they're saying, here's how we save our bottom line. The funny thing is that when you have a company and you go to these all these, like, shareholder meetings and you hear all the news, Anytime a company says, we're making so much more money because we built this thing and there's lots of value, what do they do? They've always hired a bunch of people because they need to make the work done.
Speaker 2:So if a company is saying AI is making them a whole bunch of money, why are they then laying a bunch of people off? Like, that actually doesn't make sense because they're laying
Speaker 1:off more short term behavior. Right? You wanna first strengthen the stock price, gets get your kickback for that. But then yes, you should reinvest within short term if you actually have a go to market that makes sense. So it's a it's a mixed bag to a certain degree.
Speaker 2:It is. But at the same time, enterprises are very much not process oriented. They think they are. They're they're more workflow and task based oriented. And when you actually let people go, you're letting context and internal context of your company walk out the door.
Speaker 2:And so you had you may hire you find, like, 10,000 people, that doesn't mean all of them had massive amount of context. That means, like, probably a good, like, thousand of them were the only reason certain things ran, and you didn't know that until you get rid of them. So it is kind of a classic, like, if I turn the server off and people cry out and go, oh, my, maybe we turn the server back on. But the problem is with people, they're not gonna come back and work for you once you let them go. They're gonna say, sorry.
Speaker 2:I'm gonna go take my contacts. I'm gonna go take it to another company that actually values my opinion, values where I, you know, maybe I belong there in those kinds of situations.
Speaker 1:It's really difficult because at the end of the day, it's a question of how supply and demand really balances out. I'll give you an example, right? The scary side of this is now I am delivering 10, basically the work of 10 people, right? And I'm doing hardcore engineering code on my kind of day job. So my thinking is that you know I might not need to hire 100, 200, 300 people you know as revenue comes in, I can keep that team you know 10 people as opposed to 300 people.
Speaker 1:So that's 200 people plus that will not have a job right, that's a massive decrease in employment but then on the other hand I'm thinking well what if my company exists and grew and created that revenue only because we were able to do that and there's another 50 companies like me, so that supply and demand almost balances off. It's really difficult to know what's going to happen in the future from that perspective. Are we going to have a massive additional demand because of the surplus of supply happening at the same time? So I'm not sure I have a good answer there. Yeah.
Speaker 2:There's a scarcity thing there too, but this is why most companies are asking like where the ROI is. And my my normal go to answer is like the fact of is no one should you should not be firing anybody, and you should just stop hiring everyone. Because if you keep who you got and you start gaining value, great. Now you have people that have more time. Now you have people like yourself that are doing the job of one person doing 10 people's roles.
Speaker 2:Well, then if you're not hiring anybody, you're not spending the money to pay for that person. You're not training them for six months. They're not leaving, like, because they found a different job on the on the new hiring. Like, attrition is going to happen. And, you still will need to hire some people.
Speaker 2:But when you have those conversations and and people come to you and say, hey, Ari, as a leader, we need to hire 10 people to do this job. Your first reaction, like any enterprise, you go, great. Have you talked to our automation AI team and found a way to see if they can do it? And if they can't do it, great. We're gonna hire some people.
Speaker 2:But if not, no. We're gonna automate it or we're going to build AI solutions for it, probably through automation and then AI. But in general, this is how this is going to run and should be running in the future. Yet again, that's the kind of the American standpoint. But when I was speaking in Italy, they said, Doug, we're not we're not firing anybody already.
Speaker 2:We're just keeping them. And I was like, oh, great. You've already answered the question. They go, but hold on. What do I do with the people if they're not doing as much work?
Speaker 2:What do what do we do with them? And I said, okay, that goes into a different scenario here. And that says like, well, ask like software as a service is kind of going away. It's becoming more agent as a service because agents could take execution layer. Like software as a service was very much like, teach one, click one, do a thing, access.
Speaker 2:Now we have agents as a service where it's like, okay, we click one, do one, and do all these things. We have access. Now we can execute on that layer. Okay. Then what's next?
Speaker 2:We have vibe coding and all this other stuff. Oh, my answer to the people in Italy that I was talking to in this this this municipality was like, hey, those people are going to become product owners. You're going to create your own things. You won't go out and buy all this vendor stuff. You'll actually just vibe code whatever you need with your own internal information.
Speaker 2:So you stop feeding these models and committing intellectual surrender. And they were like, oh, that actually makes sense.
Speaker 1:So let's talk about that for a moment. What, I'll make an argument that in the past pre AI, we have a whole bunch of SaaS because in the eighties we tried to build our own software and we figured out that building software was really hard. So these SaaS companies came along and they're like, well, we'll do what you need. It will be kind of what you need, but not exactly. And if you wanted to do exactly your business process, either tough noogie's or pay a whole lot of money for customization, and that turned into the SAPs and Oracle business models of the world.
Speaker 1:That paradigm has significantly changed. What happens if we go back to the eighties, right? We introduced AI and suddenly, well, you know, you can build whatever you want and it's cheap and it's easy and it actually works. That's what's happening today. Now you're a Sure.
Speaker 1:Chemical company and manufacturing But there's ways to work around that, right? Now suddenly, you know, your chemical company that was never in the business of writing software can potentially create their own software to their processes that creates a competitive advantage for them. And it's not the same software that everybody else is using. So you know one of the predictions is that well all these software engineers that are going be fired from SAS companies are going to be hired by hundreds of other different companies that traditionally have never written software. And you know that's you know that is one possible scenario.
Speaker 1:Let me ask you this, keep me honest, you have kids, remind me what ages are they?
Speaker 2:Like early teens, yeah.
Speaker 1:Okay, my condolences. I have teens as well.
Speaker 2:I think you're really I
Speaker 1:just had to deal with 13 year old daughter just disappearing. One of the things that I know you're concerned about because I am as a dad is the future of our children. What are you telling your daughter to study or to learn or to get good at? I heard one mom say, oh, they should go into computer science. As a computer science scientist myself looking at AI and how, you know, we need a fraction of the people, I was like, no.
Speaker 1:And then on the other end, I'm like, well, maybe actually the arts is is the is the thing that is more important than ever before. But then we see all this, you know, these artists unhappy about. What is the skills of the future? You know, this is a very practical question for us moms and dads trying to, you know, support our children to be resilient.
Speaker 2:Yeah. I mean, resilience is a big thing, right? And especially with an ever changing environment. I tend to look back and say, me growing up, I was a xenial. Right?
Speaker 2:And that's xen and millennial. And the middle part there, there's a xenial thing that some people know about. People that are in it definitely know about it. But, you know, what it is essentially is the xenials are a microcosm group of individuals that were born between the two. And the reason why it's important is because by before age 20, it was all analog.
Speaker 2:And after age 20, so as we're going into the companies and the businesses and trying to work, we had to learn digital very, very fast. And that's how we became about. And so there's this whole Xenille thing. So where do the kids come out now? And the kids right now are in this Jenny and I phase.
Speaker 2:Most of them are, if they're in their teens now, they're they're kind of growing up with it. They don't really know how to use it. The institutional, you know, particle areas are saying don't use it. It's cheating if you do. Everybody's trying to figure it out.
Speaker 2:It's a new calculator back when things or the printing press in France that they burned all down because they're too many new favorite. You know, all these things are happening all at once. There is a point that you meant that you mentioned there are is where when when technology pushes again, it's that pendulum swing of things, and there is going to be a pushback. And and so definitely everybody, a lot of people, I won't say everybody, but a lot of you are going to look at the arts and the arts isn't a bad idea. Now, when people ask me, hey, Doug, where are your kids going to go to college and what are they going to go learn?
Speaker 2:I say, I'm going to tell them to get into philosophy because the future of anything and how we look at it and all the work I've done in AI and everybody I've talked to in AI, we have more human conversations now, very human conversations than ever I've ever heard. Like, there is no amount of self help books inside of Barnes and Noble and on Amazon, anything than there is with AI already answering and kind of causing that that interaction to look at it. I think I think one point earlier, we were talking about how AI is kind of everywhere. It's in the it's all these businesses, learning the verticals. The other cool thing is that and this is recent.
Speaker 2:I've kind of dived into this is that different cultures, different languages, like German versus English or English versus Japanese have different meanings behind the words. And so when you go to Germany and you're like, wow, everything here from cars to computers are very masculine. And there's all these aspects, yet the mothers are usually the dominant figure in the household. And then you come to The United States and there's very all those same things are very feminine. And then you have, like, a mixed bag of, like, who's who's wearing the pants kind of thing.
Speaker 2:But all of those things matter in how the AI interacts with the people. And so we're seeing a lot of that too. And many people haven't even kind of grasped that yet. And so that's why I say it's a very human conversation. And I think that no matter how you swing it, it's like DVDs and CDs and then the radio or not the radio, but like the record player or like, you know, VHS and Betamax.
Speaker 2:Like we're going to have
Speaker 1:Video killed the radio star.
Speaker 2:Yeah. We're going to have that mix of what goes where. Brilliant.
Speaker 1:Doug, this has been absolutely delightful. We are at the end of the time. We have only one question that we ask every single guest. It's the only question that's scripted in this show as our audience knows, and it's a hard question because it's personal. If you had to think back throughout your leadership, management, you know, personal, family, whatever history, whether this was two years ago or twenty years ago, what was the hardest point and more importantly, what was the lesson or advice that you would give yourself back?
Speaker 2:Interesting. If I was to give myself a lesson, it would be to tell myself that in the span of my human life and most human lives, there are four moments that you have. And those four moments are defined in five year stints. And in those five year stints, you have to have the grit, determination, understand you're going to fall on your face, understand that it's not going to make sense where you're going to be at the end of that five years. But if you want something and you want to strive for a particular aspect or whatever, it will take you about five years to get to where you want to be, to show up, to be at that point.
Speaker 2:And you won't even know when it happens, but it'll happen. And you have four times to do it in your lifetime.
Speaker 1:Doug, thank you so much for joining the show. I appreciate you.
Speaker 2:Yeah. I think it's been great. I I appreciate the conversation and it's always good to to have a more like a like just a just a talk.