E34: Beyond the Hype: How AI is Transforming Business—and Why Human Touch is Key with Peter Sharples

AI is not perfect, right? It will cast illusions sometimes. It will give wrong results sometimes. And if you don't have that judgment to question what's being produced to you or question how you should use it effectively, that's the big risk for individuals and for organizations right now because the scalability of AI, you do something wrong, it can go very big in a hurry. You can be wrong at scale. If you don't use the right judgment around how you're using it. So I think there's a few interesting learning curves that the next generation of talent needs to get through before AI becomes mainstream. So until then, it's all about human judgment. Unfortunately, these kids have the least amount of judgment across the industry cohort right now.

Peter Sharples is the Vice President of Revenue and Partnerships at Builder.ai, which is a Series D Microsoft-backed unicorn, and it just basically uses AI to make apps very easy and simply for users. Prior to this, Peter was at AWS for 8 years where he was the head of partnerships across the APAC region. Peter has lived and worked in Sydney, Singapore, and Seattle, and today he's going to talk to us about his journey and the change in the industry from cloud to AI and what's coming up in the future. So let's jump right in. So Pete, I'm so excited to have you with me today in this conversation. Tell me what has shaped your journey so far.

Great to be here, mate. It's good to see you again. Look, I think if you look back, it's been a succession of mistakes, to be honest. Like when you go through and look back at your career, like it's the big rocks, it's the things you get wrong that define the direction of where you go. Like, you know, coming out of university, I was a civil engineering graduate. Nothing to do with where I am today. I was helping people's houses stay up, not helping people build code. So, it's a very different shift than many of people in my generation in tech, we fell into the industry. So, I recall fondly, I went for an interview. I was working at Salesforce at the time. I went for an interview as a pre-sales engineer, like a solutions architect doing network design stuff in the federal government, and worst interview I've ever done. But once in my career, I've been told in the interview, you didn't get the job, and that was it. So coming out of that abject failure, disaster, right? Thankfully, one of the guys on the panel, and never give this guy enough credit, his name was Richard Ashley. Remember him fondly. He probably still works for Telstra down in Canberra somewhere. Called me up about a week later and said, hey, you'd be really good as an account executive. And me being young, dumb, and stupid said, what's that? I didn't realize it was a sales role. So I took a run at it 'cause just, hey, he was a senior guy. He saw something in me, so let's go and give this a go. And I loved it. Like, to take like an engineering brain that's very methodical and very process orientated and apply that to working with people that I enjoy doing to solve real-world problems that were meaningful was just like the trifecta. And I just found it by accident. And it's probably bad on us as an industry. We don't teach this stuff early enough. We don't teach these careers in high school or in university. It's up to the individual to To stumble into it or to find it out through a friend, relative, or mentor. So, spent another couple of years in that domain, sort of honed my skill with Telstra. Highly recommend it as an early place to work. If you're coming out of school, university, you want to get into sales, Telstra is a great place to learn the basic skill of getting that foundational sales skill. Then left to join Symantec. One of Mike's colleagues went across and sort of said, hey, what are you doing? Do you want to come across? And of course, it was a high-five moment. Your salary sort of doubles overnight and you go into industry with a software company, you go into all the studios. stupid things that young kids do. You go and lease expensive cars and tap out in a massive mortgage. All those things you look back now and go, you're an idiot, what were you doing? Could have put that money in an investment fund or something. Went and did that. And then, look, 2 years later, I was made redundant. Like, dude, 114% of my number. I qualified for club in the Bahamas 2 weeks before. Meeting with HR, sorry, your job's been eliminated, it's going to Sydney. Man, talk about a shot of humility. Because like back then, you know, early 30s, you're flying high, you know, you're living the life. You know, I was in Canberra there, so the bar of the high life, just meter that accordingly. But, but looking back now, like, it sort of made me a little bit bulletproof from a career perspective because that's, that's the worst that can happen. And I went through it. I went through it to the stage where I had a brand new house and my son Max was 3 months old. So that was a bit of a career-defining moment because what it did is it took away the aperture of risk because that was it. That's the riskiest you can get and come at any time. So, it took that away from me, which then sort of led to a whole bunch of other opportunity opening up. Joined CA Technologies, which at the time, every single one of my mentors said, don't do it. Very legacy company, but I found a way to be successful there. Met some great people and they got me to Singapore. Again, another failure. I applied for a job in London, I got the job, but it was right when the EU was coming in. So, it was way easier to hire some dude from Germany than it was to hire a descendant from Australia. So, I lost out on that opportunity. But my GM went, hey, we're building up a Singapore branch, go for it. And I think you can count the seconds between when I came home and told my wife, hey, there's this Singapore thing. She's like, yes. Especially honeymoon in Singapore. So, she's like, yep, let's go. So, up there, did all the stupid things there as well. Like, I didn't get smarter. Like, first day to work, suit and tie in Singapore. Like, everyone just laughs at you, right? You're the expat, the Anglo white guy turning in suit and tie and like look back and go, what an idiot. So, I was there for a couple of years. Then, an old customer of mine, Cam McNaught, joined AWS, heading the partner organization for APAC at the time. I was looking at another role in AWS and he saw my CV just in their pile and went, what's Pete doing? And I think my interview was pretty much at a bar with a sheet of paper going, here's the problem we have to solve, and me drawing the outcome of how I'd solve it. And then, yeah, going through the loop process at Amazon, but amazing how I used AWS. Like, you look at major career inflection points, like, the learning there, man. Like, anyone who gets the chance to work at AWS, like, the leadership principles and the learning is phenomenal. I had the chance to go and spend 2 years in Seattle, like right at hypergrowth stage. That was a phenomenal experience. Like the talented people in Seattle were just crazy good. And I guess like a lot of Aussies, right? You got a chip on your shoulder. Like you think you're really good. You don't know how good until you go to the US and work it out yourself. So I had that sort of competitive streak and 2 years there, then COVID cut that short, then back home to Oz and then recently the move over to Builderall. So, Yeah, it's been a fascinating 3-continent whirlwind journey based on accumulation of poor judgment mistakes.

Well, that actually sounds incredible. And, you know, you've touched upon a number of things that I concur with so greatly. You know, I think first of all is that you talked about a series of failures and, you know, I think you've been humbled, but, you know, what comes after that is you keep making better decisions because once you've experienced how not to do it, you get probably better at how you should do it. And just even, I'm thinking through, you know, you're talking about going from civil engineering into sales and figuring out what you enjoy a lot more just by being open to an opportunity. That's an amazing story.

So, life's about chances. Like, you can, even like with the move to Builderall, I could have comfortably stayed at AWS. I love the place. It was a great culture, great company. I could have sat through the next evolution of what AWS becomes. But when you look back, when you get to the end of the career journey and you eventually hang up your sales hat and your leadership hat and you start to do other things, you want to look back and just know that you wrung the life out of it, that you did everything that you wanted to do. Because I've said this to a lot of A lot of young people that I work with that, look, if you're in this industry and you're competent and you've got good character and you're willing to learn, you'll be successful just by extension. But you just do those things, you will be successful in this industry. Now, there are things you can do to, to over-exceed in this industry, absolutely. But just doing those things, you'll be successful. The measure then becomes, did you, did you poke, did you turn over every stone? you know, did you ask every question? Did you challenge yourself in those, you know, in those fleeting moments where the opportunity comes by? Did you grab it or did you let it go? You know, that's the defining things at the end where you go, yeah, I grabbed them.

Again, listening to you talk about, you know, Sydney, Singapore, Seattle, you know, the experiences that have come through that, and I just relate at a personal level having started my, my journey in, in Mumbai and then ending up in Chicago then Dubai, then Sydney, the change of context and ecosystem is so drastic at times that the adaptability that it demands from you. So let me ask you a question, because I want to dwell on this for a moment. You talked about AWS being a major inflection point in your life. Looking back, just off the cuff, what are some of the big ahas and learnings that you had at AWS that you look back at those chapters in your life and you've pulled out from there?

Look, I think the first aha moment that I had was when I went through and I read the leadership principles for the first time. Back then, there was, I think it was 14 back then because they'd moved around a little bit. They'd added 2, they combined a couple as well. That was the first aha moment where I just, it was a skeptical aha moment. Like, this can't be true. I can't believe that this is the way an organization actually behaves. As I read through them coming from, not to disparage CA Technologies at the time, but different culture. Their culture was literally stuck to the fridge in the lunchroom, and that was it. You might get a quarterly award if you did something that roughly aligned to what was on the fridge in the lunchroom. But the way that AWS interviewed, spoke, wrote, performance managed, promoted was all around leadership principles and around culture. So, for me, that was a very big thing for the rest of my career is that culture is really, really important. It is super important. Like, coming into Build, still at that growth stage, still in the scale-up stage, we're yet to define our culture very well. It's sort of morphing as we go. But what I found is in somewhere like AWS, having that culture, the organization self-heals because you have these guiding principles and you have humans that are, for the most part, strongly aligned to those guiding principles, and stuff just happens. Whereas when you don't have those, stuff doesn't happen often. You have to go back and manually recorrect and recalibrate. So, that was one of the big ones. The second one was, I guess, you'd sort of call it habitual learning, because at AWS, you were force-fed. Every month, you would have a new training course on a new topic. So, you had that habit every month of completing a new training course on a new topic, and you had to do it. It was compulsory. leave at entry level, we'll have to do it. And that's sort of flown through into my personal life. I always make sure that I've got a book that I'm reading. So, just those habits of just continual learning, and that thirst for learning came from AWS. And that was a big personal aha moment for me. The last one was, I guess, the long-term benefits of putting the customer first. AWS is called customer obsession. People call it different things elsewhere. At Builderall, it's putting the customer at the center of everything we do. The long-term benefits of that in a recurring revenue model are profound. Again, going back to previous companies where it was a sale-type process without a lot of recurring revenue, it just didn't resonate. As long as you got the deal, it was fine. And that was the culture at the time. Coming to AWS, it was the opposite. The deals is important, but the customer experience is critical. If we get the deal at the sacrifice of the customer experience, then we haven't done the right thing. And it takes a lot of organizational maturity, right, to do that, because you're effectively giving up sales revenue where you don't think you meet the customer experience. And that's a really mature organization that can do that.

That's so impressive. And especially, I think, you know, from the the culture on the fridge, which is common, which is typical for most organizations. You know, you've got the odd poster somewhere up, you know, with these platitudes on one hand. And then what you've described as people actually living out that, that is, I can see, can be transformational in your career because environment dictates performance. So, Based on that, my next question is, it would take something pretty special to pull you out of that experience and environment and into Builderall. So tell us about what is it that, you know, you've— you're clearly not doing, you know, one company a year. You've had very measured moves. So tell me about what you're doing at Builderall.

And why? Yeah, it's a bit— it's very predictable. Look at my CV, it's like 8 years, 8 years, 8 years. Something happens at the 8-year mark. I don't know what it is yet, but something happens. But it was a really tough decision. Like, it was really like when it's tough, I mean super, super tough, really, really hard. Um, because I had a lot of great managers. Um, like, um, like Jaime Vargas who runs APAC, amazing leader, great guy. Had an awesome manager in Julia Chen. My skip level, remember, Borno was fantastic. Like, just a really good leadership team, great peers. You guys like Kobe Briscoe, and it was just a really fun team to be around. But I had to make a decision around, like, I don't want to sound old, but going into, like, the back half of my career, what did I want to be? And to stay at AWS, like, my path forward was taking on a larger role within the partner organization. And I felt to do that justice, I would have to do another 2 to 3 years. And I thought if I did that, it was harder to come back into this regional VP, GM-type role that I really wanted, that I really aspired to, right? I'd known Sachin Devdegulsa, our CEO at Buildup, for a number of years, gotten really well with him, had no idea what he's doing on the AI side, right? Invisible to me, couldn't see it. And he said in passing once, hey, you should come work for us. I'm like, haha, very funny. Came back 6 months later and said, no, seriously, you know, we've got our Series D funding, we're going to open up an APAC business, we want you to come work for us. I went, huh, okay, this is interesting. Um, so it was at the right time for me that like I could see the next step at AWS, but I felt that it was a bit of a one-way door to go that way. I probably had to commit to being a career partner person was the way I was thinking about that move. Whereas the move into Builderall, a couple of things drew me to it. One was about the right size. You know, I didn't want to go and be— I didn't think I was ready to go and be a GM of a massive business yet. I want something that I could really control and make my own and have that, have the confidence of the leadership team and the CEO to go and do it in APAC. So, and I thought I had that with Builderall. The technology was brilliant. Like, the first time I saw Natasha just build an application in front of my eyes, just using my voice and what I wanted to build and give me the full frameworks, what it looked like and the price and how it's going to take to get production, I'm like, there's no one on the planet that's not going to want this at some stage. The last thing I had was I'm really convicted in myself that this next decade's a decade of AI. The last decade was cloud. AI is this decade. And I felt that I wanted to be in that domain somewhere. If I could move in as a regional VP of someone who has a great product in that domain at this point in time, whilst maybe a bit 12 months early, But around this point in time, I really felt that was the right move for me and I was excited by it. So, really tough decision to wrap it up, walked away from a company that I loved, but to an opportunity that really inspired me.

Yeah, that is— I can see with great clarity why that transition would occur. Congratulations. You know, you mentioned Natasha. What is Natasha?

So Natasha is our small language model, right? So Natasha has the ability to turn your voice into software features and combinations, right? So let's say you're in recruiting. Well, let's say you wanted to go and you want to build an app with a chatbot where candidates could upload videos of themselves to say, hey, I'm Pete, I'm looking for a job in web whatever domain, um, you could literally say those words to Natasha. She'll ask you questions back and sort of like, what's the sort of demographic that you're looking at, uh, what are the features that you want, um, you know, what languages do you want the application available in. And then she will go through and build you out a wireframe of every single screen, that entire application start to finish, within like 5 minutes, and it's done. And you can go through and you can manually manipulate what you want. You add features in, take features out. For everyone you add or remove, you can see the price you're adding or the price you're taking off. And then at the end, she'll give you a fixed price to develop that software and tell you how long it'll take.

Easy.

So like anyone who's building software or business person has an idea, you just took 3 months off your life in a half-hour conversation with Natasha. That's, it's, it's— and what impressed me the most, Pri, is the rate of change. like how good she is getting, like, and how fast since I've joined, like, the conversation. So I was in a— I was with a client the other day. They wanted to build an app for the sailors coming into shipping ports around the world because they have to know the standard operating procedures for every port, right? And we're going through designing the app with Natasha. Natasha stopped us and said, well, given these guys are sailors and they're at sea, shouldn't you also have an offline version? And like the whole room just gasped that an AI asked that question. So like, it's just the— where we are right now is super interesting. The rate of change is what's more compelling, like how quickly this thing is going.

That's amazing. I need a mind-blown emoji at my side when I'm just listening to you. It's because I'm like, whoa. That is incredible. That is incredible. I could keep— I think I could spend hours just asking about use cases, really, because that's really where the rubber meets the road, right? So, tell me, you know, all this— there's so much talk about generative AI. We've seen what's happened with Canva and Leonardo. And, you know, it's happening. We are at the early stages of a transformative decade, and you are at, you know, world top 3 most innovative companies, Series D funded, you know, you're pretty much at the— right in the front of the ship looking ahead at what's coming. So, is it all, you know, is it hype? Is it real? You know, what do you see?

Yeah, yeah, look, it's— I get that a lot actually. This is all hype. I think it's underhyped, to be honest. And like, there's a couple of things that like give me that sort of mental model. And the first one is, man, follow the money. Like, you look at the— so you can bucket the industry and say, right, there's the big players at the top, right? Who's going to be the one ring to rule them all? And you've got Amazon, Microsoft, Facebook, Apple, Google, Elon Musk and xAI, all gunning it. He got $6 billion of pre-seed funding. $6 billion at pre-seed. Like, he's— the valuation of xAI is $24 billion. Hasn't turned a buck yet. $24 billion. Hasn't made it. Hasn't made a cent. Like, it is just insane sort of money that's flowing into this space, right? So looking forward, like, my last name's not, not Musk or Altman or Jassy or Nadal. I, I don't know what those guys do, but you look where the money is going right now. And one of my early, early mentors said follow the money. Like, for any decision, any commercial decision in life you want to do, follow the money. Look where the money is going, um, and that's your best predictor where the success is going to come from. And the early money is all going into AI, and it's in astounding amounts. The backend, you've got a bit of a choke point right now with NVIDIA and GPUs, but they're going to work that out pretty quickly. I think the competition GPU layer is coming thick and fast. And I think once industry solves for that, that's probably one of the last rev limiters, I guess, on the AI market space right now. But on the flip side, just touch on the rate of change with Natasha. So what's happening under those large LLMs is this is this sea of specialized models that are evolving, right? And like you mentioned, Leonardo is a great example, like in the digital and design space. You know, there's us in the application development space. You've got chatbots coming from a whole bunch of virtual agents coming down. Every possible CRM tool now has their own sales AI features. You've got traditional companies that have their own flavor of AI that they're pitching right now. I don't know, different levels of efficacy. I don't know how they're going to work, but there's this huge expansion below. And I think what's going to happen is my gut feeling, this is just my thoughts coming through the cloud hype, right? Where everyone had their own cloud, like AWS, and there was Azure coming in hot, and then GCP came in hot. We had Alibaba in the Asian markets, but then you had like HP, Cisco, Dell, all the old guys trying to reinvent themselves for that cloud market space. And what you saw is expansion and then contraction, and contraction either through acquisition or through the economics not stacking up and going, you know what, let's find another way to be successful. And I think you're going to see the same in the AI space. Enterprises are either going to go, you know what, I want to invest and build my own. If you're a multinational, building your own LLM is potentially viable, but man, the success rate's not high right now. But even the hyperscalers seem like 5% conversion rates on expensive POCs right now. So it's the economics aren't quite there yet. Or the plan B is for enterprise to go, you know what, there's a sea of these small language models that are specialized that can give me point-in-time return at not huge upfront investment. So a lot of organizations going that way and saying, let me get some point tools that solve my problems. I can start to learn how to adopt um, GenAI in my business, and then maybe at some stage in the future come back with a macro-level LLM over the top so that one ring to rule them all, or use a, you know, off-the-shelf platform like OpenAI or Amazon Q or xAI. So, um, looking at that rate of change and the way the industry is forming up and the money going into it, um, you know, I think it's going to be one of the biggest leverage points of the next decade and The one thing that I see, like, history repeating a little bit, it's funny, as you get older, you start looking back more, not looking forward to it, but looking back. You look at the major inflection points, right? And let's go back to distributed computing, right? Go way back, right? Distributed computing, internet, smartphone, cloud, AI, all have one thing in common, is that the industry push is being driven by consumer pull. And I mean like end customer consumer pull, right? So think about the app economy, right? So without a smartphone wouldn't happen. Without cloud computing, half the apps wouldn't exist because the economics just wouldn't have been there to build them. But the consumer pull of these apps means that enterprises need to suck through cloud computing to build fast enough to compete with the digital natives, right? So you have that flywheel. You're seeing the same thing right now with AI. Like, I'm the VP of an AI company. My 18-year-old daughter kicks my butt when it comes to using AI. Like, she's far more native than I am. So as the next generation comes in, they're going to expect it and they're going to know how to use it really well as well. So I think that inflection point of that flywheel of enterprise push and consumer pull is what's going to make it the next big thing. If you look back to virtualization, CRM, like big technical inflection points for the organization, for the enterprise, so the supply push was there, but you never got that consumer pull to make it a huge industry-changing event that AI will. So that's just my hypothesis. I think with those 2 things in motion, that gives you that momentum for this to be really big.

Yeah. I mean, you've, I think, done a great job of narrating the history of the universe or the technology industry in the last 10, 15 years in a really nice cohesive narrative. Because I'm sitting and listening to you and I'm thinking, you know, just this last week at the dinner table with my kids, we were having a conversation which also had ChatGPT open on voice. With someone's talking to ChatGPT, asking questions on voice. And increasingly, they don't even want to type on Google. They just want to ask the question and get the answer, right? I'm also thinking earlier this week, I was having— I downloaded the ChatGPT mic extension and I was having a voice conversation with a journaling app. It was a cognitive behavioral therapy app. So already, I'm reluctant to type if I can speak because it allows me to interact. The interaction is so much better. So leave alone, you're talking about consumer demand. And I can see if it's smarter, it's better, it's faster, It's, yeah, it's just exactly what you said. You know, it's, it's where the consumer is going to go. It's where the money is going to go.

I was on that before we jump forward. Think about, just take a step back and just think how, like, you look at the case studies, like the things that didn't go as well. Like, you look back to, like, the BlackBerry as an example, right? There's a really good movie on BlackBerry right now. You should go watch it if you haven't. It's really good. But you look back and go, surely Amazon and Apple are going to go, we had this. We had Alexa and we had— I'm going to say the S-word because otherwise she'll answer back. But they had it, right? The problem was the design of it because it was a closed ecosystem. You had to buy an Echo Dot or an Echo Show to use Alexa. I've got one in my house. It's now just an overpriced egg timer that works half the time. The at-bats, like the number of times it got asked questions was limited by the population of people asking questions. Same with the S-word. You have to have an iPhone or an iPad or an iMac to actually go and actually use it. OpenAI changed the design, right? They said, well, we're going to make this easy. It's as easy as putting something into Google. It's as easy as starting up a WhatsApp chat and then open it. So the volume of training that it got for free based on all the questions, all the kids around the world trying to cheat on their homework, they were asking questions. allowed this massive knowledge graph to grow so quickly. So I think we're going to look back and go, you know, wow, look, the bigger guys could have got there 3 years earlier. Just the interface, they got the design interface wrong, the closed ecosystem interface. The open interface grew the model so quickly for OpenAI.

That's amazing. What are some of the biggest impacts that you're seeing with, you know, organizations, uh, right now? You know, what's happening right now from business impact point of view? Early adopters?

Yeah, I think I touched a little bit, right? So we're gonna go one of two ways. You're gonna try and build it yourself, like, which is right now a little bit of a, a little bit of a solution for a problem, to, to be fair. Like, organizations, there's a bit of fear of missing out. And we see a lot of people pushing to get to AI without knowing the problem that they're really, really trying to solve. So the business case becomes pretty fragile once the costs start to ramp very quickly, which they inevitably will. On the other side, it's that small language model strategy, right? How do I start to pick apart the problems I do have and find a model that I can buy relatively off the shelf that's going to come and solve that problem for me? Then build my organizational skill and muscle around those models, and then plot my direction on where I want to be. But I think the really interesting thing for our industry, and you probably see it in yours as well, is around what does it mean for people and the talent that's coming through, right? Because that's going to massively impact organizational culture as we move forward, as AI starts to really flex its muscle. And we see the changing of the guard from a saturation of what you call, like, pre-GPT staff and post-GPT staff, the next generation coming through, you're gonna see a very interesting transitional period, I think.

Yeah, look, I think that that's, you know, I see even with how recruitment works at the moment where a hiring manager will give us a specific brief about what they're looking for. And, you know, LinkedIn, ideally in this future state, you can actually have a voice interface where you actually speak into LinkedIn what exactly that you're looking for. And it's able to bring up the people and as well as send them a compelling message. And if they don't reply to the message, get on, you know, create a voice conversation, cross-referencing 3 friends, building some credibility and getting them to an interview with you within, 5 to 10 working days. We're still a little ways off from that because that's what we've got to kind of work through. But I can see the power of voice automation at one end or voice-driven commands at one end and a system that can deliver an outcome at the other end. That's— I can see that we're heading in that direction.

The big risk, I think, is the judgment in the middle, right? That's the challenge we have, right? Because I had a— not saying names— I had a rather junior candidate send me a cover letter that was obviously generated. Obviously, if she read it, no one writes like that. It was obviously generated. And the fact that it referred to the wrong company in the letter, probably meant it was generated. Hey, but that's a problem, right? The challenge, right, is that these tools are providing so much efficiency, right? And this next generation coming in, they're all about that instantaneous gratification, right? They've grown up with smartphones. They have zero patience. Like, they just want to move. So, if they can go and they can use something really quickly and get an outcome, that's their gratification done. Or if you move on to the next thing now. And the challenge— a great quote at Amazon that Like, one of our best, um, leadership principles I loved at Amazon was, I write a lot, right? Leaders are right a lot. There's a comma, a lot. Um, young kids aren't. They haven't been wrong enough to know when to be right, right? They haven't. So judgment is earned, not learned. And the challenge is you're putting these, these highly efficient, highly scalable tools in the hands of these kids that haven't really learned or earned the judgment to use them at scale, right? Because the problem you can have is, you know, AI is— GenAI is not perfect, right? It will cast, um, you know, illusions sometimes. Um, it will give wrong results sometimes. Um, if you don't have that judgment to question what's being produced to you or question how you should use it effectively, uh, that's the big risk for individuals and, and for organizations right now because The scalability of AI, you do something wrong, it can go very big in a hurry. You can be wrong at scale if you don't use the right judgment around how you're using it. So I think there's a few interesting learning curves that the next generation of talent needs to get through before AI becomes mainstream. So until then, it's all about human judgment. Unfortunately, these kids have the least amount of judgment. across the industry cohort right now.

Yeah, no, you've absolutely nailed it because that is what I perhaps did not say when, as much as I envisage a situation where, you know, in my industry, somebody can tell LinkedIn what they want, LinkedIn will go and do everything for them. And, you know, it's booked into the calendar. We had an instance just last week where a company has had a revolving door through bad hires. And I asked the question, why is this happening? And there were a set of assumptions which I could tell were actually contributing to the ongoing revolving door because you're looking for a certain type of person who's actually not suitable for that job. And the difference between maybe a recruiter with 2 years of experience and another with 20 years of experience is actually questioning the question in the first place, rather than just going off and giving you exactly what you want. Because when you, like you said, when you're wrong, you can be wrong at scale, as against getting it right, your lens accurate in the beginning so that the end is actually where you want to be, not the wrong destination.

So, yeah. Look, in the other element, I see is around learning. Like, because the problem is a lot of the junior guys coming through, like, they don't— they feel they don't have to learn because why do we need to learn? I'll just ask GPT or my favorite, you know, LLM platform that gives me the answer. Like, and something that, like, so they're missing that proactive exploratory learning because everything's reactive and instantaneous. So I love asking— I did a bunch of interviews this week for some junior candidates. And what book are you reading right now? I want to read a book. I know, we had this conversation. I know if you tilt your camera that way, you'll see a whole bunch of books lined up on the shelf, and they're very similar to the books I have on my shelf over there. Like, it's that behavioral— those ones, those ones. There's that behavioral learning that they're not picking up on because everything's instantaneous. I have a world of knowledge at my fingertips. But what it doesn't help you do is get that proactive learning. I'm going to be inquisitive and explore. No, I don't need to do that. That's not cool anymore. I'm just going to ask GPT the question. So that worries me for the next generation coming through and for organizational culture as well, right? If you want to build an innovation culture, you need to build that proactive learning, that experimentation, And that inquisition in terms of how people operate and behave.

Brilliantly put. I, from having a 20-year-old, a 17-year-old, and a 13-year-old who I— they're jet skiers, meaning they can get across a lot of information very, very quickly. But they're not pearl divers, which I think age and experience brings. Like, you're more likely to consider a matter more deeply. Why is this the way? What's a better way of doing it? And I think somewhere in between is you've got to teach the other skill. So, if you're good at skimming a lot of information, you've got to be able to learn how to go deep, really deep, and give contemplation and consideration because one without the other will produce a generation where you're going to make some mistakes at scale.

Correct. Which is, again, you just heard from me, like, making mistakes is fine. But the mistakes I've made have predominantly been personally impacting, not broadly organizational impacting. So I think making those mistakes builds that muscle, it builds fortitude, it builds judgment. And I think the more of that that, you know, the next generation has, then the more effectively they can be using tools like, you know, like GPT and other LLMs.

Peter, I've got another 100 questions for you, but in the interest of your day, I'm going to be— I'm going to say one last question as we wrap up. This has been informative. I'll still keep my mind-blown emoji on my side for the rest of the video. But tell me, how do you see— how is AI going to impact culture and the future of work? What are your closing thoughts on what do you see So, look, I think predominantly 3 areas, right?

And I think we touched on 2 of them. One's the learning culture of an organization. How do you inspire people to experiment and invent and go and push the boundaries and horizons when the instant gratification of AI can serve you up an answer instantaneously? Second one is how do you use your judgment to work out the right answer and when you should apply it? So where you should and you shouldn't. We don't have an account plan template or an opportunity plan template at Builderall. Scale-up problem, just don't have one. So I'll use GPT to build one. It was easy, right? I know the blast radius is limited. It'll get me close enough, won't get me in the hole, but it'll get me on the green. And on the green is good enough for now. Let's get it out and get it moving and we'll iterate it ourselves. The last one is, The customer, right? The customer is not a bot. The customer is actually a human. And I think anytime you have a bot or an output of an AI communicating directly with your customer, I think tread carefully. You know, we see it a bit at Builderall. Like, Natasha is fantastic, but she is brutally honest when it comes to price. She cares not for your budget. She cares not if you had like a bad day yesterday. She doesn't care. She's an AI. She says, this is the price, right? So she's— there are elements there where it's refreshing to get such an objective answer, but AI is not going to be good at subjectivity for a while. Like, and subjectivity is a big leap, like, for something like an AI model. So you've got to be careful in that regard in terms of dealing with your customers. They're still humans. They'll expect a human touch. They'll expect a level of empathy. They'll expect a level of subjective decision-making. And you've got to be really careful because AI is not likely to provide those things in a hurry. So, I think they're the 3 areas that an organization needs to watch out for culturally, that if you go too hard at AI, you're going to be tactful in those areas.

Fantastic. I'm listening to you walk us through this, and I'm thinking of the butterfly effect where small changes can lead to monstrous, dramatic impacts downstream that are not evident at the beginning. So, it's very much, you know, it's got to get that handle with care kind of label on it, you know.

Yeah. Also, learn with passion. Also, learn with passion. It's going to impact us so much. It's going to impact us so much. Learn with passion. If you're out there and you're mid-career, Like, you're the most at risk. Like, the guys that are at later career, by the time this gets mainstream, will be towards the end of our careers. It's the guys in mid-career, the guys that come through in their 20s, maybe AI native. You've got to keep ahead of those guys. Those mid-career guys have judgment, exercise it, but learn the tools quickly.

Pete, thank you so much. I am indebted to you for your time, your insight, and this is going to help so many people who listen to this because it's been a fun conversation, but it's been very insightful. And like me, I'm sure they're going to have a mind-blown emoji if you actually look at their, their faces when they're listening to this. So thank you.

No problems, mate. Good to see you again. Thank you. Appreciate it.