Inside the AI Hiring Pipeline: Interns, Apprentices, and Full-Time Coworkers | Vinay Gidwaney & Mike Sullivan, OneDigital

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Speaker B: that we have been, um, a living experiment, uh, Liam, for the past two and a half years about how do you deploy AI in your organization?

Speaker C: You deploy agents throughout your company and somehow do not reduce your employee count. And that seems very counterintuitive to the average on view of this.

Speaker D: One of the key insights that we had early on was to differentiate between what we think of as co workers, builders and agents.

Speaker C: All right, night for nay, my first time having two people on the podcast at one time. We've got a bit of a, a roundtable of experts, not including myself. So welcome to the podcast.

Speaker B: Thank you very much. Thank you, Liam.

Speaker C: I am excited to jump into today's conversation. I think some good framing to set everything as the thesis that we're talking about here. And from what I understand, your kind of core argument here is that AI transformation is fundamentally about talent and it's about leadership. It's not so much about technology. I was wondering if you can expand on that to begin the conversation.

Speaker D: Go ahead, Mike, you first.

Speaker B: Well, look, I would say that we, um, have been a living experiment, uh, Liam, for the past two and a half years about, um, how do you deploy AI in your organization, um, and how do you deal with the fact that, you know, we're a, we're 6,000 people in an advice giving business, which basically means that we have thrived on there being asymmetry of information.

Speaker C: Right?

Speaker B: We know things our clients don't know. We can give them advice on what to do. And that has allowed us to build a company and a revenue stream. And it's been really good for a long time. All of a sudden the asymmetry of information is going away, right? A.I. is going to, um, be able to do things that were not possible in the past. So we have come to the realization that we have to understand how to harness AI differently than we've ever thought about it before. We need to figure out how to get all of our people moving in a direction differently than what they've done before. We have to sort of make decisions on what work gets done by AI and what Work gets done by humans and we need to move fast. So Vinay and I have been on this journey largely end of 25 and throughout 26, of basically saying we need to reshape what 6,000 people do and the workforce and get everybody rowing in the same direction. And so this AI journey is. We're two and a half years deep and it's really been an amazing journey. But I think it's today and more recently that we have come to the realization that we can do things for our clients we never thought possible. And so we're very much on the amplification of our people and then amplify our firm and go do things that are amazing for our clients. Um, but I would say it has been a journey to get to this point. But I don't know how you think about that differently.

Speaker D: Yeah, no, I think that's absolutely correct, Mike. Um, what I would add, Liam, is there was a key insight that came to us early on in our AI journey that I think has really shaped everything on how we think about things. It was that AI has to be thought of as talent, not technology. And what I think a lot of people sometimes think about is they're either in the camp that, look, it's about chips, it's about data centers, it's about large language models. They live in that very technology orientated frame of reference around AI. We're clearly not doing that. And I think part of the reason we're not doing that is that there's trillions of dollars, quite literally trillions of dollars being spent on companies and technology there to do amazing things. We're not going to reinvent or change any of that. It's what we do with it that matters most to us. And in the camp of what we do with it, most people start to think about AI as a technology adoption issue. Right. And, uh, the problem with that framing is that it's not like any other technology. It is by definition a general purpose technology. So if you approach your AI implementation like you're approaching your CRM adoption, it's going to fail. Um, because it's not like adopting a CRM. And the closest analogy that we could find, the closest way that we could start to think about this in its most expansive opportunity, was to actually just start to think of it as talent. So we have human talent and we have AI talent. And if we start from that basic premise, then I think adoption takes a completely different form.

Speaker C: I want to know how you arrived at that conclusion. So when charged with first came out a Couple of years ago, everyone's playing around with it, like, maybe we'll give this to our employees, we'll use this. And to be on the right path, you have to take a lot of wrong paths. So I'm wondering and getting to this, framing this mindset that the AI is talent, as AI, as a coworker, what are the wrong decisions you made and you decided that is not a right way to look at it?

Speaker D: That's a good question. Actually, it made me think of a little story, um, on when I started thinking about AI that way. Um, so less about what we were not doing or we were doing. Incorrect. It was at a conference at Stanford and I was watching somebody speak. I don't recall who it was, but I do recall is in the breakouts I had a friend there and that I've known for many years. And I went up to him, I

Speaker B: said, listen, I have a great idea for a business.

Speaker D: Um, because you know, I wasn't doing a startup, but he was thinking about doing a startup. I'm like, you should go do this. I said, you should build the workday of AI agents because people are going to need to manage all these agents in the same way that they manage people. And this was like two, three years ago, right? So this was before any of this conversation right now. And then the guy said, I think that's really interesting, let's go talk to some VCs. And of course we're in a conference at Stanford. So like the whole courtyard is full of a bunch of VCs that I could literally just go test this idea on. And I've done a lot of VC pitches, so nothing was nervous in my mind about like, oh my God, I'm going to burn a bridge by talking to this big shot vc. So I just went up to a bunch of them and saying we should build a workday for AI agents. And I think out of maybe the seven or eight conversations that I had, I think all but one of them was, that's the dumbest thing, don't do that. Like, go figure out how to get large language models working properly. You're talking about things that are way beyond that. And that's like, don't even go spend any time on that. And then one guy was like, that's interesting, email me about it. And I emailed him and of course he never responded. So that was the sign for me that like, okay, uh, maybe I'm onto something here because, uh, I think that we're talking about AI in the wrong way. And we need to talk about it in a different way. Now, of course, the idea that you build a workday for your AI agents, there's two or three, I'm sure unicorn companies that are already working on that. But this was a few years ago that it was relatively novel. So I think we took that path really early on. Um, we had a bunch of experiments that we did internally. And I think one of the things that failed for us internally was we had initially a focus on automation. Like we looked at everything that people are doing and we said, okay, people are filling out RFPs manually every time. And we're increasingly entering a situation where the client or the prospect will put an RFP and we've got to fill out this, you know, 500 line spreadsheet. And why are we doing that manually? Obviously we should have AI just do it. Should read the answer, go into its corpus. Uh, sorry, read the question, go into its corporate, fill out the answer and we'd be done. And what we realized is that it's really hard to replace a human in that. It's really hard to get those answers really, really good. And again, this was back, you know, two years ago, the models back then. And what we realized was that there was a lot greater value in augmenting human thinking and helping people with cognitive tasks than it was to help people with automation. So instead of do this for me, it was help me think this through better. And we found a lot more value in the latter than in the former. I think a lot of people stay in the former and they just stay there. They don't realize the true potential from what we've seen is helping people think very differently.

Speaker B: I think that I would add to that, Liam, that it was probably two and a half years ago, um, Vinay sent me Ethan Mollick's book Cointelligence. And I read that book and he's positing that we're looking at anywhere from 20 to 80% productivity gains for your teams. And when you sort of begin to think about that. So we immediately. And I think one of the things early on that I would recommend any company do is pair up a technology guy with a non technology guy who are leaders in the company and come at it with a, uh, dual lens of it can't be about one or the other, it has to be about both. And you have to be able to credibly drive, uh, forward inside an organization. But we read this book, we had 10 or 15, uh, top performers inside the company. Read the book, we pulled together a 10x group. Like we said, if we can empower the 10x crowd, folks that are, generally speaking, 10 times more effective than the average employee, let's see where we can go with that. So we pulled together this group and started. And even with models back multiple years ago, we began to see like, there are huge possibilities to amplify talent. And I think we almost, uh, backed into this whole get the most talented people first and see what you can do to free up more of their time to go be more effective with clients and prospects. So that was a catalyst for everything that followed. But I almost think probably one of the most serendipitous things that happened early on was you got a co founder of the company and a chief product officer to literally be attached at the hip in this process. And I've learned so much. Um, and it was really Vinay's job to make sure what we were setting up was being set up the right way. And honestly, it was my job to be the hammer in the company. Basically saying, we are doing this because we have to and beating that drum. I think earlier on in the process, and I think today we probably have 80, 90% adoption of AI inside the company. And we've gotten to the point now where it's like we're going to change what we do. Not just amplify our people, we're going to amplify the firm. And it's exciting, um, but it's been a lot of, uh, getting buy in along the way and probably more than anything, getting through the process of basically saying, don't be afraid for your job. We're doing everything possible to make sure you keep your job. But the quid pro quo is you got to get moving with us. And that's kind of where we're at.

Speaker C: Yeah. The fundamental concept that you've arrived at is that, ah, we're not replacing people through automation. You found that that doesn't work. We're teaching people how to think on a larger scale to be more effective. Obviously this is something that you've arrived at now, but when you were first implementing AI, was that the thing that you were pushing with and what pushback did you see?

Speaker D: Yeah, um, we actually started to frame it in a pretty simple term and it came m from the interesting thing about AI. I'll share a stat real quick is it's a very personal technology. You cannot think about AI adoption and AI transformation without having adopted it and transformed yourself. No leader can do that. Right. And, and we found a stat with our we'll get into more about like how our AI coworkers work. But we found something where if a manager in the company, we're 6,000 people, so we have a lot of people managers. If a manager in the company uses AI coworkers actively, and I'll explain what active means in a second, um, their team's usage doubles. It was night and day, didn't matter internal, external, team, big or small, it always came down to leadership adoption. If the leader had transformed themselves in the work that they were doing, it immediately resulted in transformation amongst the employees. And that was sort of a hunch. And now the data, after a lot of data collection is showing that that's definitely the case. And the thing that we sort of remind people about is that the transformation that you go through personally is going to be the transformation your team goes through. And the transformation that I went through personally when I started using AI way back when and ChatGPT and so on is that initially I would start to use it as a transaction vending machine. I sort of thought like, okay, I'm going to go into it, I'm going to get what I need done and then I'm going to pop out and continue with my day. But when you started to realize that you could collaborate with AI, that you could go back and forth with it, and I'm sort of a curious person that got lazy over time and got a life and then had family and like responsibilities. But I used to be the kid that would just go do a bunch of research on something because I was interested in it. But now if I'm like ever interested in something pre AI, I would think, oh, I wonder how that works. And then just kind of go on with my dance or let that fark away, not actually go look that up because who would have the time now when I want to know how something works, I'll just ask Chad or ask Claude or whatever, right? And then you start to realize that you can collaborate with AI. And what I tell people that it's, it's not the answer that AI gives you, it's the thinking that causes within you. It's that collaboration that's the magic. Because anybody can get the same answer. We know that now. Everybody has access to the same quality of answers. Now this did loaded statement because of different models and closed door models and stuff like that. But generally everybody has access to the same power of AI. So what's the value that you bring to the table? It's your lived experience, it's what you can do with the AI that Makes the difference. And so getting people to realize that they were an essential part of the equation, that this wasn't about them being replaced, but it was about how they collaborate with AI that truly makes them valuable. That was the key thing that we just had to keep on telling people. And it only came out of your personal experience with it.

Speaker B: Yeah, I had that moment, Liam. Um, I can't remember exactly what it was, but I'm so deep in it now that my wife and daughter actually call me Claude from time to time. My go. So I'm not. I'm not wildly excited about that nickname. But, um. But I will tell you, like, I had this moment where Vinay. I would always come to Vinay and say, um, I'm hung up on something. Help me get to the next level. And so I've. I just gotten to the point over the past two years where, you know, I'm. I've been the guy in the company that's sort of like, I want to understand our value proposition. I want to understand how we talk to clients when all of a sudden, you're a spatial thinker and you have unlimited ability to reach beyond anywhere you used to go. It's tough shutting it down. Like, we could do this. We could reshape this. So being continually presented with possibilities that you've never thought of before is absolutely intoxicating. Then you've got to decide, how do you prioritize this stuff? But once you activate your team, you know, I run an M and A team. We've got 35 people on that team. You just. It cascades through the entire organization. But to Vinay's point, we had. We took a step back probably two months ago and said, which senior leaders in the company have not had that moment yet? Like, holy smokes. This is. This changes everything. I started calling and basically set up calls and said, okay, let me. Let me show you what I would do. I would share my screen. I would talk about their business, and I would present to them new ways of thinking about their business. And they're like, oh, they were. You know, they were kind of using the vending machine. I'll ask a question. I'll get something back. I'll move on. And this is so much better than Google. It's like, no, that's not what we're talking about. So you start this process of literally scheduling calls with senior leaders. And that's where I was sort of ingesting. I was the hammer. I'm like, you. You not having that moment yet is no longer an option for us as a company. So you start going through the senior leadership team. So I would say, uh, across the board, everybody is there. It's cascading from the C suite down. Makes all the difference in the world. Um, why I think we're where we're at as a company right now.

Speaker C: You. Yeah. I'm wondering if you as a team have noticed an acceleration in this. So for me, the way I like to describe these moments that you're describing is an epiphany. Right. It's sometime you're using it and you do something. You're like. That would have taken me $10,000 and three different experts to do that last year. And I've just managed that by myself. It's an epiphany and I think it's accelerated for me. Right. Like me using chat key two years ago, I was okay with it. Uh, I ran this newsletter for three years now. Now within the past six months to a year, what I can do with agents, what I build, I've noticed such an acceleration and how, how many moments of amazement I have. And I'm wondering if you've seen the same on your end.

Speaker B: Yeah, look, I will, I will just. You know, Vinay chuckles about this. I mean he knows exactly what I'm going to say. But you know, I had this um, this just under, like under the skin concern about jobs. Like where is employment going. Right. We're in a knowledge based business that's kind of come knocking to knowledge based companies first. And so Vinay got me all situated. Um, I was well underway with my uh, sort of core use of Claude, um, primarily. But then he set me up with a replet, ah, account. Right. Replit is uh, you know, it builds software. So I was most concerned about what is the conventional wisdom from. And, and I ended up with like 14 major data points, uh, from the World economic forum, from McKinsey, from 14 different firms on the impact of employment, unemployment, um, from AI. Right. In knowledge based businesses. So I really spent somewhere between 8 o' clock one evening till 6 o' clock in the morning building this disruption calculator.

Speaker D: Right.

Speaker B: Started in Claude, spent half the time there, went to replit, started building this disruption calculator and over a cup of coffee in the morning I had that moment where what materialized was this four quadrant, you know, risk analysis view that, that had one digital blinking, blinking red in the upper right quadrant, which is not the quadrant you wanted to be in. And it basically said if you don't change what you do, you should expect to have to eliminate 1800 jobs based upon what the conventional. So 6000 people basically getting cut by a third if we don't evolve what we do. And I literally sent it to Vinay the next day and said, dude, this is not happening to us. Right? We see faces, not headcount. It's not about margin, it's about if it, if, if it does this now, it's going to do it to our firm later. We need to get moving. And that for me, Liam, that was that moment where one a history major can actually build software, which is mind boggling in and of itself. But it was that moment where it's like if I activate my mind, I can impact the path we're on and I can impact the way other people are thinking about it. That was probably my most profound moment that I waited to connect with Vinay in the morning going, we need to move.

Speaker D: I sometimes joke with Mike that uh, AI for him, AI for a visionary founder like him is like crack cocaine. It's like every day he's in there, It's definitely crack. Now

Speaker C: I, uh, I'm curious about the thrilling here, right? So you get to this realization of what's possible, but then you guys actually integrate agents, you deploy agents throughout your company and somehow do not reduce your employee count. And that seems very counterintuitive to the average on viewer of this. So explain that through line for me.

Speaker D: Yeah, no, it's a, it's a great question. So let me explain a little bit about how we translate that statement of AI is talent into like an agent. Like what does that actually mean?

Speaker B: Right.

Speaker D: And, and let me also clarify that. Um, one of the key insights that we had early on was to differentiate between what we think of as co workers, builders and agents. So right now I think of AI, generative AI in a company in those three categories. Is there a fourth and fifth?

Speaker B: I don't know, we'll see.

Speaker D: But there are definitely coworkers which we classify as AI that is helping you think. Which is different than a builder, which is AI, which is producing an asset of some kind of a piece of software, a document, something like that, a presentation. And then there are agents which are carrying on carrying out a task on your behalf, whether it's doing it autonomously or semi autonomously. Agents are sort of doing things in the background. Maybe a human is in the loop, maybe they're not. We actually started with coworkers. We're very far along with coworkers. I'll explain why in a second. We're pretty Deep into builders and we're really just starting on agents. And I'll say that most companies do that in reverse order. They start with the agents and that's where most of them have been and they continue to be and in my opinion, where the ROI calculation hasn't been as clear because the infrastructure that you need to do agentic AI and the amount of infrastructure that you need to plug those agents into and have reliable data to connect to and reliable actuators to actually make change and where you put the humans in the loop as a very, very difficult problem to solve at scale. And it's only valuable to solve it when you have it at scale. There's no point in trying to say, well, we have 20 people in this 6,000 person company that we can automate. Like, it's just 20 people. Like why? Why spend the time doing that? It only makes sense when you're doing it amongst hundreds of people. If you're doing anything amongst hundreds of people, you're dealing with very complex processes and there's a lot of work to go there. So a lot of companies get mired into that agentic AI transformation and they're just stuck in the weeds. We started on the coworker side and we said, okay, how do we treat AI as talent and how do we give people a better way to think, give them more insights, a brainstorming partner, that collaboration that I was talking about, how do we give them AI like that? And we basically said, well, let's actually just treat the AI like we're hiring it. So we actually, my AI coworker team is really, I think of as a staffing team. They're an internal staffing team. We have recruiters, we have onboarders, we have a normal HR function behind it and we treat each of the AI that we ingest and bring into the organization like we are hiring it. So it starts, everything starts with a job description. So we write a job description for the AI. It looks exactly like the job description that you would post on a job board. The only difference is, is that you don't need to look for candidates. You can just take that job description, feed it into a model and say, meet the needs of this job description. And so we actually have a vanilla coworker that we call ace. He's sort of general purpose, knows everything about onedigital, but isn't really skilled in any particular area. We clone ace, we feed that job description in. ACE becomes the intern for a particular area. And we start with a three stage hiring process. The Intern is where we start to throw prompts at it to see what kind of performance it has. We've got criteria that it must pass in order to uh, graduate beyond an internal. So just like any other intern that you would hire from uh, you know, my wife went to Harvard, so I'll say this, but like it's like a Harvard Business School grad who kind of thinks they know everything but doesn't really know anything yet. And you need to give them a little bit of experience. And so that's how the intern starts. And then we graduate them into an apprenticeship where we actually start to build out the resume for the coworker. So we give it best practices, we give it guardrails, we, we even give it a little bit of a personality. And the most important thing we do at that point is we apply a human supervisor. So the intern is ready to contribute to the organization in a meaningful way. They need to be supervised by a human being. And that human supervisor is oftentimes the subject matter expert. They're the person who you wish you could clone, who you wish you could make part of every client relationship or on every project, project of some kind. The co worker is the amplification of that person's skill set. That supervisor is like a full time role. They need to manage that coworker in perpetuity because that requires a lot of effort. But the ROI is certainly there. When they meet all of the criteria, then they get hired into full time availability. So we have dozens of co workers that have gone through that hiring process. We have many more in the hiring pipeline. We, we used to fire a lot of interns. We don't do that as much anymore, uh, because, you know, the models have gotten better. We take this analogy even further. In some cases we've made a coworker, we've put them on a pip, a performance improvement plan because we found a degradation in their quality or they're being asked to do things that they don't quite know how to do. So we'll pull them into a pip, put them back into the workshop, tinker some more, put them back into apprenticeship, and then promote them back into full time when they ready. Um, so it is a very HR centric approach because it helps us start to think about AI in this mindset where it's not about the use cases, it's about the fact that I need a bunch of talent that's able to do something and I'm going to train that talent to be able to do it. And then they're Going to work with other talent and amazing things are going to come out of it. And if you just take that generalized approach, we've seen some really great success out of it.

Speaker B: Liam. One of the things, just like a real world example. So a big part of our business is we're a large employee benefit consult. So we help in the States, we help people, we help employers build their benefit packages that employees um, are ultimately eligible for. Um, we started, we're very creative with our names. So the um, the coworker is named Ben and we took probably one of the most talented uh, client managers that we have across the entire country and she came out of that job and became Ben's full time manager because we wanted effectively to start with what's in her head and get that in Ben's head today, what, 18 months later, um, 1600 benefit consultants interact with Ben every single day. Ben is the sum total of all the knowledge, all the best practices and a continual weekly daily update by Shelley of everything that Ben needs to know. So we're, we're moving towards uniformity in terms of solution set. We don't worry about, hey, I hope Mike remembers what to tell this client. If you're not talking to Ben before you're talking to your client, you're not doing your job in today's world. And so there's now hundreds of thousands of conversations that have gone on with Ben so effectively from a talent standpoint. And Ben is like a super coworker that is almost an unlimited repository of information and best practices on being an employee benefit consultant that is amplifying everything our people do. And I think more and more we're creating the capacity so that if Mike was a benefit consultant, I spend much less time doing work and preparing for client interactions and much more time in front of clients, engaging at a level I never had before. So we view it like our uh, talent is being totally enabled by Ben. And then we have simply rolled out those and will continue to roll those coworkers out. But pound for pound they are these emerging assets inside our organization that um, are talent personified. Um, and to Vinay's earlier thing, I don't know somehow how we backed into some of these decisions, but leading with coworkers and having literally thousands of people around the country go, wow, this is really helping me as opposed to the agentic side where it's like what work is this automating again? There was a, uh, you know, I can't remember how we ended up that way but, but the talent first approach um, and co workers has been a real. It got everyone acclimated and everyone got better. And it was a great place to start for us.

Speaker C: It's funny you mentioned that, uh, exact example of, uh, preparing for calls. You're spending less time preparing for calls and more time actually engaging. Um, I have a cloud cowork set up, so when you booked in for the podcast, I have an agentic function that recognizes the calendar event goes away, uses perplexity, prepares a research brief, and then pings me that it's ready. So in preparing for this conversation that we're having today, that was all done the moment that you booked a call for me. Um, and it means I can spend more time on actually enjoying and engaging in this conversation rather than doing the eight hours of research that it might have taken me three years ago. The thing that I'm really curious about with this, so I understand the call concept. I want to know from a practical sense what's actually being deployed. Are you using proprietary models? Are you working with Claude? Are you working with ChatGPT? Are some of these POD solutions? Are you using Paradox and Olivia, what does that actually look like in a practical sense?

Speaker D: Yeah, so it's a great question. So we want to try to separate. And this terminology, we didn't have words for a year ago or six months ago. The industry is now starting to emerge with these terms, so I'll use them. But we were sort of playing with these concepts before they were concepts. So we do differentiate between, like the LLM layer and the intelligence layer and the harness layer. So first thing is, is that we're LLM agnostic, and that's just the smartest thing to do anyways, because you need to make sure that you can use whatever model makes sense for the task at hand. Uh, you also need to maximize token usage and the cost of tokens. The last thing you want to do is send everything to Opus 4.8 and just pay a boatload for things that you don't need to. You need to send things to Haiku and Opus, but sometimes you need to send things to Gemini and Chat or whatever other models that are available to you. So first of all, LLM agnostic is the first thing. Second thing is the intelligence layer for us is really. And again, we were talking about an intelligence layer before the concept of skills and cloud skills and all that kind of externalization of the training, so to speak, that goes into an LLM became much more commonplace. But we think about the fact that Ben, for example, that Mike referred to or Another example is we have another coworker, her name is Dex. Dex is non denominational, so his or her, they're them. So them. So Dex is a, uh, it knows deeply about prospecting, right? So Dex can go and do research about a uh, prospect, can look up all their information on LinkedIn, on the web and so on. Very similar to how you use perplexity to prepare for this call. So what Dex can do is a set of training. There are playbooks, there are skills, there are, there are lots of basically system prompts or MD files that define that intelligence layer. Now the good thing that's going on in the world of tech right now is that people are starting to define the knowledge structures, right? With open knowledge format and other things that are coming out that help people understand how to encapsulate the context and knowledge that you give into this intelligence layer. Most of ours is rag based but we're starting to experiment with different formats to supplement how you can pass chunks or information into every prompt. But there's a really sort of thick intelligence layer there that really defines where these centers of intelligence exist and how we train them and most importantly externalize them. Because we really believe in this idea of owning your own intelligence. And the last thing that you want to do is to have your intelligence layer tied to one LLM. That doesn't make any sense, but we also don't necessarily want to have it tied to one harness. Also doesn't make any sense. And so you want different ways for your intelligence to show up. So one of the harnesses that we use, it's a great partner of ours called Cassidy. Cassidy AI is a chatbot harness. It wraps any LLM. It gives you access to all the knowledge capabilities. It's very easy to set up what they call an agent, we call a coworker. You can set up workflows, you can set up all the commonalities and the knowledge bases and everything like that. So we encapsulate that intelligence layer for Ben and Dex and others in Cassidy. But the other thing we do though is we have other harnesses, we have applications that we've built, we have other ways that these co workers can express themselves. It's not just about chatting with them. In some cases they're creating artifacts, in other cases they're the thinking engine behind an analytics tool that we've built that helps a consultant visualize how we can drive down healthcare costs. And Ben does all the thinking. So Ben shows up all over the place in our stack because it's that most Common transferable intelligence layer.

Speaker C: I don't have experience in a, uh, company of your size and stature and something I'm seeing a lot in the news right now that we're covering in the newsletter is that for the first time, a lot of companies are actually cutting their usage of AI because intelligence is getting more expensive. There was this story that came out two weeks ago, that company that gave their employees unlimited access to Claude, and I think they ran up 500 million. Or was it 250 million dollar bill?

Speaker B: Is it good?

Speaker C: A ridiculous amount.

Speaker B: That was a bad day.

Speaker C: I'm wondering how your team thinks about this. Right. So obviously deploying AI and deploying agents in your company right now makes sense because the graph has been going this way, right, in terms of the productivity and the gains from this. But now we're moving towards this model of metered intelligence. And, and there is a prospect that this gets more expensive. Uh, how does that factor into your calculations when you're deploying AI?

Speaker D: Yeah, it's a few things and I think the whole metering. So there's two kind of things that happened in the last couple of months, I would say, that has made every company take a step back and really think hard about this. One is the metering issue.

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Speaker D: Whether it's metering, because we're recognizing that this stuff is expensive and you have to really think hard before you sort of have a runaway token budget. Um, it's also about access, right? The whole debacle with Fable a couple weeks ago with the White House and it being a security threat, what everybody realizes is that we're spending a lot of effort integrating these AI systems into our companies and we don't really control them. Somebody can meter them and turn them down and say you don't have access to as much intelligence right now. Right? Just like, uh, you can have a power cut, right? What if we just had a blackout? Okay, now you don't have access to AI for a second. How are you going to run your business? And that's not an uptime issue. Maybe that's a little bit of an uptime Issue like a blackout. But what we're starting to see here is that the metering issue is, is not a defined approach. There are no rules, right? And then it's the access issue. Like, believe me, when as soon as Fable came out, I was doing a lot of things in Fable that were not possible with opus 4.8. And then when Fable was gone, I was like, okay, we can't do that now. And so uh, what if you based your entire company on that? I'm pretty sure some startup created themselves after Fable and then basically had to shut down the week later because they weren't possible anymore anymore. Like imagine that at the scale of an organization like ours. So very quickly this is not just a monetary issue, it's not an economic issue, it is a risk issue, it's an ownership issue. It's where are you going to have the true intelligence of your company live? And predominantly we've had the structures to do that pre AI. Where did the intelligence live? In your humans, in your people. How did you make sure that you held on to that? You paid them well. You incentivize them, right? You gave them a reason to want to come to work and show up and use their intelligence to maximize the value to your clients or whatever you were doing. We don't really understand how to do that in the world of AI, uh, because we don't fully control everything that happens there. And the levers are very different. They're not the same as just paying people. So I think that like this conversation of, you know, metering and access and things like that is opening up this bigger issue that we need to start to think about as companies, as responsible corporate citizens, is where is the intelligence in my company going to live? How do I gain better understanding and control of it? How do I balance it between human intelligence and artificial intelligence? That's another big issue. And very importantly, how does this not become an albatross on my neck and it becomes an enabler, right? And for a lot of people, they're going to head down that first direction because they're not realizing what they're doing.

Speaker B: The thing I would add to that as sort of the non technology, non token ah, person on this call. I, um, think that if you looked at uh, we just brought in new investors last year and we showed a five and an eight year projection of what the company was going to look like and you, uh, know, I can think of a visual that said we're going to go from being a $1.6 billion top line revenue company with 6,000 people to a $3 billion company with 10 or 12,000 people. Right. That is not going to happen.

Speaker C: Right.

Speaker B: So I think you're going to find that, uh, part of the issue in the economy is job growth, because of what AI is going to do is going to be fundamentally different. Fundamentally different than what we were thinking last September.

Speaker C: Right.

Speaker B: We're not going to staff the same way going forward. Coworkers are going to change the way work gets done. And you know, Vinay, in a very sort of, um, intellectually profound way for our company, basically said there needs to be a way that we can measure and effectively score the growth of our intelligence. It'll be the combined intelligence of humans and digital talent. And it's something we did need to monitor, and we need to sort of correlate that to token spend. But effectively, we're of the belief that you can measure your workforce intelligence, which will be a blended workforce. And as long as you keep an eye on how that is growing, it needs to grow commensurate, I believe, with. With the way in which you have to spend for that compute power. It's early in the game, but, uh, you know, I don't know how you think about that differently, Vinay, but I think we're very much, um, of the mindset that for our business, as long as the spend is something that we can understand, manage, and have some predictability in, I think those are dollars well spent in terms of the impact they're going to have on the throughput to the client. Um, but it is going to be, uh, a little bit of a tricky

Speaker D: thing that we're going to have to

Speaker B: continue to figure out.

Speaker C: I think it's the Peter Drucker quote, the famous one, what gets measured gets managed. Right. Um, and I think with this, so many people are finding it hard exactly what to measure. Top line revenue makes sense. How do I measure productivity? How do I measure employee satisfaction? What are the things do I not know that I'm not measuring that do matter? I'm curious, with all the deployments you've done, is there anything that, like the average onlooker wouldn't really suspect as a signal of success that you are measuring that is having an impact?

Speaker D: That's a great question. Um, m. Maybe I'll answer in. In the way in which we approached the issue of people feeling threatened by AI because I think that how people approach AI and how effectively they use it is a reflection of how you choose to measure it. So if you go in and say, look, we're going to measure AI effectiveness based upon the number of tokens you're using, then that's going to be the incentive, right? And then you have this token maxing trend, right? Where people are just, you know, burning through tons of tokens for the fun of it, and they're measured based upon that. And so therefore they're just using tokens for whatever, right? So obviously, that's not productive. M if you measure by time saved, right? If you said, look, these are the number of hours that we want to save with AI, um, again, you're going to get a certain type of behavior out of that. People will cut corners, right? Maybe they'll just stop doing certain things. When we attached some goals and principles really behind our AI adoption, um, we struggled with this for a little while, but eventually we settled on two basic ideas. And whenever I talked about AI internally, I just mentioned these two things all the time. One is our AI initiatives are designed to give you the gift of time back. If you survey our people, which we do, and you ask them what's the number one issue affecting them in their happiness at work, they'll talk about burnout. They'll talk about just not having enough time in the day to spend with their clients because they got into this business because they wanted to help people, Whether it's a company, a small business trying to figure out how to get benefits for their employees, or it's an individual just trying to retire in the vision that they have. They got into this business because they wanted to spend time with people, and they're not doing that enough. They're not doing that enough to satisfy themselves, or they're finding that they're, uh, doing that during the day and at night and on the weekends, they're doing all the other work that they have. So the gift of time back was not one about shaving hours off of some process. It was a psychological feeling of, I have now agency over my time so I can now pursue why I do this work and do it more and more. Second reason give you better insights. Because while I'm trying to figure this out, I'm also in a very, very dynamic field. Whether it's health insurance, property insurance, wealth management, you never want to show up in front of your client not knowing what they're going to ask, right? You want to know the answers. That's why they're paying you. As Mike talked about, we're in the information asymmetry business. We're supposed to know more than our clients. The world is changing really quickly. I need to know more and I don't have enough time in the day to even figure that out. So my AI can help me gain better insights into what my client needs, into connecting it to what's happening in the world and giving me ideas to focus on. Focusing on those two, uh, concepts of the gift of time back and better insights was the way that we dealt with this is going to take my job away. No, this is about giving you time back in the day and making you smarter. But it's also the way that we measure because what we look for are, uh, what are people doing now with their time that they have back, right? What are they doing with that other time? Are they spending more time with their clients? Are they delivering better insights to their clients? Are they showing them how they can reduce healthcare costs even more? Because we use data more effectively. Like those are the things that actually matter as opposed to like what the AI is doing specifically. It's almost the inverse is what you want to measure.

Speaker B: Look, I think that we have taken a very pro kind of human. When your job is to help people live their best lives, you think about every aspect of the human piece of it. And we have gone to our folks and very directly said, look, we expect you to bring the right attitude, the right energy and the right intelligence to the job every single day. And we're going to do everything possible to amplify you in front of clients. Um, but we need you to adopt this technology and we need you, for all intents and purposes, get better at ah, what you were doing because you have capacity beyond anything that any of us would have imagined several years ago. So again, the quid pro quo is meet us in the middle, adopt, explore, engage and dream about possibilities in terms of what we can do for our clients. Help us evolve as a company. And we're gonna, we're gonna do everything possible. Like, short of an AI native, it can do everything better than one digital with no employees showing up, knocking on the side door. Our view, but we do view it, Liam, um, like we cannot be sitting around saying two years from now, we do what we do today more efficiently or more efficiently than we do today. That is not the game. What Vinay and I are sort of, and literally hundreds of other people at onedigital today is that we are going to redefine what the client experience does, amplify your people and leverage AI in new and interesting ways. And we are going to blow our clients minds with what we're going to be able to do for that in the future. It's not about doing what we do today more efficiently. It's about doing things we never thought we were going to be able to do for clients. Because rather than looking backwards and explaining what happened, we are daily getting in a position of signaling them. Things that insight that's been created that day, that, uh, hour that we're coming to you because AI is driving this signaling, and our business intelligence is getting so much better. So the possibilities are really remarkable. But it's not about standing still and doing things more efficiently.

Speaker C: From my perspective, what you're both touching on here is aligning people more with purpose, essentially. Right? What you're not trying to do is to make people more efficient. You're not trying to make people into machines. But what you are trying to do with your employees is connect them more closely to the client experience, connect them more closely to the purpose that they're trying to deliver. And ultimately, I can only assume people that feel purpose in the work, people that feel some sort of dedication, some sort of pull, some sort of personal connection to the things that they're delivering, they're going to want to do more, they're going to be more motivated, they're going to face less burnout because they're not doing a job that machines are meant to do. They're doing something that is innately human. And with that, you have employees that are actually enabled to find that purpose and connect more closely with it. Am I kind of on the right track here?

Speaker D: Yeah, absolutely. Um, and what I. It's very interesting because what we ultimately think about, and going back to the comment that Mike made earlier around workforce intelligence, is what we're trying to understand is if you look at any particular, and I don't want to say necessarily a department like client service or sales, but more of like work centers, where work happens in your organization. Because usually it happens across departments, across sort of divisions in the company. People collaborate. But where work happens, if you draw a circle around where a particular type of work happens and you start to analyze what are the irreducible skills, what are the things that humans are using uniquely qualified to do that you can't break down to a process, you can't reduce them down into a series of steps. You could sort of think about it, but it's a little mushy.

Speaker C: Right.

Speaker D: Versus the reducible skills, the things that you can say, this is the five things that you do. There's some variation, but it's pretty much these five things. When you start to break down the work into those Two buckets. You start to realize what AI should be doing and what humans should be doing. And what you'll realize also is that when you give people more time to do the things that humans should be doing, they're happier, right, because they're not doing the things that a machine should be doing. Now the very interesting thing though, I found is that we often, like you hear this all the time, is like, oh, humans are better at building trust and building empathy. Machines can't do that, so on and so on. I read an interesting quote, uh, from the CEO of a company called Lemonade. Lemonade, uh, sells home insurance.

Speaker B: Okay.

Speaker D: So they have millions of people who buy insurance right from their phone. It's a really unique model, how they do it. And they in the CEO was commenting very recently that they moved a lot of their call center, uh, claims analysis or claims call center to AI. So when you submit a claim, you're talking to AI first, and you would think, okay, this is where empathy matters, right? You just probably had the worst day of your life because you're calling, because you had a flood in your house or whatever, you're pretty distraught. They found that the AI agents delivered higher level of empathy than the humans that they employed. So the concept that, oh, only AI, you know, only humans can do empathy and AI can't. I think there's enough people spilling their guts in a therapy session with ChatGPT than there are with real people. So my only reason I say that is that the target of what is appropriate for humans and what is appropriate for AI, that's moving. That's a moving blob, right? I think of it like a ying and a yang within that work center that's constantly changing. I think it's going to be very important for leaders to get a pulse on that to figure out what is it that people should be doing, what is it that AI should be doing, and constantly trying to evolve that based upon the opportunities that are in front of them. Um, nobody's having that conversation yet because they think of this AI thing as just a hammer to go in and knock some people down and knock some processes down. But instead, it's a very fluid partnership between humans and AI that you need to figure out how to set up.

Speaker B: The thing I would add to that, Liam, is like, we're also not naive enough to say, you know, if you're, if you're a first chair consultant and you have multiple people on your team and part of the. One of the jobs on the team was I put together spreadsheets so that someone, uh, can go out with financial illustration. So my job is to all the research and I put together the financial illustration to give to the first chair consultant to go talk to the client. Well, there is an issue with that person's job and where that work is going to get done in the future. So part of the issue here is we have not been an organization that has fundamentally understood how to reskill and redeploy people into new jobs. One of the things we are really going to have to ramp on is how do you take someone who is very detail oriented, very task oriented, who does this job and has done it for the last five years and basically say that work is going to be done by AI you are going to get reskilled into this job over here. There is a lot of the, you

Speaker D: know, like, uh, I, I think Mike is touching on. One of the most important things that leaders need to sort of open their eyes to is that the value of somebody's brain and their time and dedication to your mission is incalculable. Right. I used to work in a, I, I did neuroscience research for a couple years, right? And we would open up a mouse's brain and look, in the first time I did that, I was like, holy crap, that's really small brain that's able to do a lot. And everything going on there seems like it's pure black magic, right? And it's like, uh, we don't fully understand what's going on there and what the brain is capable of doing, okay. What it's capable of coming up with and synthesizing is unlimited. It's infinite. So that's the true valuable part of your company. The question is, how do I get the human brains in my company doing what human brains are best equipped to do. And by the way, if you felt like, hey, this person was doing a really menial job today and you know, copying and pasting and preparing spreadsheets and I'm going to have AI do that. So therefore I don't need that person. Why would you get rid of a brain that you already have that's committed to your organization that knows a little bit about what you do as a company that you can easily, potentially easily train into doing higher level things, that human being is way more valuable than to you than that job cut that you were going to get, right? And that's that thinking about amplifying humans, not minimizing humans. It's that mindset that we want people to have.

Speaker B: And look, the other thing I would Add Liam is like, again, I'm trying to. Vinay and I talk all the time about, you know, this concept of do you see faces or you do head head or do you see headcount? Right. I can tell you, you go talk to the HR department in any company, they see faces. You go talk to finance and finance might have a different lens that they're trying. So that's why I think it's really important that leadership and firm has to say, look, the most important thing is that we survive as a company and that AI doesn't replace us in terms of what's going on in the marketplace. So you know, we have made this commitment of like we are going to evolve and we are going to do amazing things for our clients. Um, but there is a lot of sorting out that needs to happen and our view is we're going to take a people centric, invest in our people and understand how we can do more with clients. But there is friction in that process and there is sort of real world what goes on every single day. Um, but I think the C suite has to set the tone for what kind of company, you know, we, we refer to it of late as the humanity test. Right. It's like, I don't, you know, everyone who sort of beats the drum that like AI is just going to eliminate whatever number of jobs in the uh, knowledge based arena. Look, I, you know, there are people way smarter than me that have applied a lens that I don't fully understand to this equation. But at some point, if employment is dramatic, I don't know who's buying the product after employment has been so reduced that folks don't have what they need to actually buy the products that are being built. So there's a circular nature of this that I ultimately think we are beating the pro employment deployment of AI, like employment with logical deployment of AI that keeps humans in the game. Um, but you know, sometime when I have my bad days, it's like, do I even fully understand what's going on here? But I can tell you we are trying hard as an organization to say we're all in on our people and we're all in on our clients and we are going to move, move and we're not going to stand still. Um, so it's, you know, these are exciting times, but there's a lot to sort out.

Speaker C: Yeah, I mean I think something I'm taking from this conversation and to give you context part of our business as we do enterprise training and like LMS development, stuff like that. And we always find that the problem isn't access to information, that's people being comfortable with it. And from this conversation, I can see that you, more so than other companies, are just aggressive with the handholding. Take someone's hand, grab it, and I will take you through this from start to finish. And then the second part of that is the practicality. Like, I think a lot of organizations deploy strategy around this. They have a lot of verbiage, and they speak a lot about it. But then they can never get someone to the aha, uh, moment. That epiphany that we spoke about at the start of the conversation has to be, here's what we're doing, here's why we're doing it, and here's how it's real for you. Here's how you get to that, Hamilton. Here's how you get to the epiphany. And when you combine those things, I think that changes everything.

Speaker D: Yeah, yeah. And I think that that personal epiphany, um, it cannot be overstated on how important it is. And that's why adoption is so hard in these companies, because you're not, again, you're not trying to roll out a new CRM. You're trying to teach people log in. This is how you reset your password, this is how you create an account, this is how you update your deal, all that kind of stuff. And people are just droning on and they're like, I already do it in different ways. So nobody really uses a CRM and just explained what most CRM implementations look like. And this is very much about your own personal potential. Right. And so when AI, and I think we take this for granted, those of us who sort of cross to the other side, is that for a lot of people, AI feels like a dark cloud over them. Right? It's this looming threat to their livelihood. It's a looming threat to their family. You know, if you have kids coming out of college right now, it's a threat to them. You're worried about them, and your company's telling you to use AI, and you're like, this is a cloud, and how do I survive? Right. And I think when you hold people's hand through this and they realize the expansive nature of AI, it becomes this enabler. And if you give them confidence in their own human qualities that add to that equation, it's like, holy crap, this is what I can do now with AI. It's just exploded the opportunities that I have as an individual to make an impact. And again, when they cross over, it's just like it's a different world. And in our company we have, uh, maybe a few thousand who've crossed over. My goal is to get to like 99% of people have crossed over and that when they join the company, they're already there. Um, that's the kind of organization we're trying to build.

Speaker C: When I hear this, here's what I think. I think the people that are most effective with AI is people that are original, people that can practice their metacognition. Um, people that are rational thinkers, people

Speaker B: that are problem solvers.

Speaker C: And my question to you is this. Does everyone have the same capacity to think in that way and deploy AI in this way in terms of them being enabled? Or are some people better or worse based on their innate traits and characteristics?

Speaker B: Yeah. So, um, someone who was affectionately referred to as an AI vampire, um, the other day, again by my loving family.

Speaker C: Well, the AI vampire.

Speaker B: Yeah. It's just like, I can't. I can't turn it off. So I, I do think that like, when we. I remember distinctly when we started our 10x program years ago, um, I. There was a conversation I had with a really, really successful sales advisor who called me up and said, I don't, like, how do you figure out what questions to ask AI And I'm like. I was like, I don't understand. Like, what do you mean, how. It's like just, you know what I mean? I couldn't process. And we were totally on different sides of the, of the ledger there. Um, but again, Liam, I do. Like, there was part of me that basically says that we sort of lived in a world where, you know, if you, if you look at so much of. In so many jobs that the more structured you were in your thinking and the more process oriented you were, there were all kinds of jobs where that could be applied. Now, you, uh, know I've always said we have people in our company. I have, you know, partners at, in the C suite where the line I always use is. You know, there are those people that think like an Excel spreadsheet. I'm like a Word document. I think spatially I put puzzle pieces together on the strategy board. I think like, this is an upper unbelievable opportunity for a guy like me to expand in terms of capability. And that's someone who, you know, we started One Digital in 2000 and I was already well into a career. So, um, I ain't no spring chicken when it comes full of stuff, but it's amazing in terms of what it's done from an activation and yet there are folks that I sit with where it just doesn't click. And I think we're going to have to figure out, um, and you use the term and it probably is right, um, the culture that you're starting with as an organization, like, you know, we're a bunch of huggers, we're a bunch of like, um, hands on people where see the faces, know the names, make the impact. Like that culture is a big part of sort of the DNA of the firm and it's playing out in our AI deployment strategy. I think where you are starting from a cultural standpoint as a firm probably has a huge impact on ultimately where this goes and how it goes. Um, but we, we stepped back months ago and basically said we have to take our learnings and get them out into the world. So other people are like, this is doable. There is a, there is sort of a how to and if we can get more people heading in the direction of pro employment, pro jobs, amplification of people. Because all, all you kind of hear now is like layoffs and you know what Square's doing and what Meta is doing, it's like, is that the tip of the iceberg of what's coming? And we're like, let's beat the other drum, which is there's an alternative to that solution. And I'm hoping that we're right. I think we're well on our way as a company, but we need tens of thousands of more employers, if not hundreds of thousands of employers basically saying the starting point is pro people, pro amplification. Let's get moving and let's innovate, not let's extract. We'll see how it plays out.

Speaker C: Look, I think it's the most needed narrative. Um, my job has been to consume AI media for the past three years and there's just such a cognitive dissonance between tech CEOs and just everyday reality. Like they'll hop on a podcast and talk for an hour and a half about the bottom line and how their efficiency is gaining. And then every like 99 of people watching that are like, well like what's going to happen to me? And never gets addressed. And uh, like the, the answer is always some vague thing around like the Industrial Revolution. It's always the, it's like the, it's the given answer for every single one of them. It's going to happen around the industrial revenue. Like what you've given us nothing. You've not actually delivered any candidate to this conversation. So it's so needed. This Level of honesty and an opposite perspective on it. So thread that through for me. We have workforce intelligence releasing on August 25th. Take me from when this was a seedling of an idea to this finished product that it is now.

Speaker D: Yeah, I think, um, way faster than you would imagine. Yeah, um, you know, I think it was, it came out of these conversations that Mike, um, and I were having about the transformation that was happening to our own workforce. And it also came from a deep desire that, you know, look, OneDigital serves 100,000 employers in the country. A lot of them are small businesses. Small businesses are people just trying to make it every day, right? Trying to grow their business, trying to serve their customers, do what they are meant to do. And AI is going to come and just have a wrecking ball to a lot of businesses. And we knew that we had to go through that transformation ourselves in order to be in a position where we could share that information with our clients to help them through this transition. And what became immediately clear was they had to know one a playbook, right? So we took a lot of our own understandings, our own playbooks, and just put them in a book, said, look, this is actually how we hire a coworker. This is actually the job description that we use. This is actually the process, process that we go through and just put them in the book so that somebody has a playbook they can get going with right away. But we also wanted there to be a call to action for HR leaders to run this transformation. You know, Mike talked about, like, you know, what finance sees is numbers on a sheet. What HR sees are faces. And when we think about this tremendous transformation that we need to go through as a society, I try to think about like, who are the ones to lead us through this on the ground, right? Do you really want the tech bros leading us through this? Or, uh, do you want the folks in your HR department who care about people because that's their job. Right? They got into HR because they care about making sure people succeed every day, day that are enjoying their work, that are maximizing their potential as human beings. I want that person in my company to tell how we should be using AI. So this book is really about that call to action to get people to do that and give HR the language for them to be successful.

Speaker B: Yeah, look, ah, uh, I think that there is an opportunity for every firm to reimagine what they do. Like we are literally reimagining what we can do for our clients. And you know, Bene and I did our first Presentation two weeks ago to others in the leadership team of like what? This ability to create agents that read narratives that will allow us to signal in advance to clients things we never thought you could deliver to clients. Clients, real time. It's all about leveraging AI in new ways, um, at a consulting level. Um, so, you know, our view is, and, and we really do believe, like, there is a linear aspect to amplify your people and you will amplify your firm. Extract or hollow out headcount. It is just a matter of time before AI gets to everything you do.

Speaker C: Right?

Speaker B: So, you know, we're, Vinay and I are sort of like. And the rest of the leadership team, Adam, you know, my partner Adam, who co founded the company, he is literally like, he sees faces every single day. And we're a PE backed firm, right? So we go to investors and sit down and basically say, this is, this is how we feel. And like, if everyone's sitting there going, you know, wow, we could really expand margin by doing this. You have to sort of, what does this look like? 12, 24, 36 months out, when every single company out there is extracting margin.

Speaker D: And well, and worse yet, they've extracted margin and they've handed over the keys to their organization, the intelligence layers over to these AI companies that are metering it back to them. Like, that's a double whammy. You got rid of all the smart people in your company and now all the smart intelligence is being rented back to you where the, where your landlord can just ratchet up the rates whenever they want. Like, that is not a good situation for any company.

Speaker B: But what we ultimately said, Liam, is like, I think invariably there's an ocean of people who aren't sure what to do, who are waiting for some greater level of clarity on how this is all gonna play out, hoping for the best. Meanwhile, um, you have millions of quote unquote workers that are sort of like Morales banging against all time lows and people are nervous about the future. We collectively as a species need to, um, push back and push back strongly on an alternative lens to what this all plays out. I have this, um, and again, without getting into politics, I have this sort of view of Elon Musk in Washington on a stage with a chainsaw cutting jobs like USAID and the Education Department and whatever. And on the other side now being a trillionaire and like, there's a middle ground between those two things that we can figure out. Amazing, uh, levels of innovation to support everything that we need to do to keep people in the game. But sitting on the sidelines hoping for clarity is not like, everyone's got to get a glove and get in the game and understand what their firm's going to do differently. And if we can be one small drum beat that starts beating the drum that basically says, see the faces and move. Just that thing. See the faces and move your company in a. In a direction that is going to do more for the clients you serve, we'll end up in a better spot.

Speaker C: Yeah. I feel as if you may have answered the last question I was going to ask both of you. Anyways. Um, the through line that I'm drawing here, as you said, private equity backed. I think the most recent round was 7 billion stone point in CPP. Correct me if I'm wrong, you then released this book that's actively helping people operate and run their businesses better. The cynic. The cynicist inside me. And maybe this is because I'm from the uk. It's like, why would you help other businesses perform better? I don't get that. But then the last question that I always ask people on this podcast is, why do you do what you do? And I guess keeping all the framing in mind and what you just said there, for each of you personally, why do you do what you do?

Speaker B: Well, I would say that Vinay and I have had this discussion where it's like, I have three young adults. It's children who have to find their way in this world. And I'm not worried about what AI is going to do to my wife and myself, but I see the faces. So I am literally like, you know, Vinay and I were out recently talking to a, uh, political operative, basically saying, can you help us build a movement? Like, could we actually build a movement that is like, see the faces? And honestly, Liam, I don't want to live in a world where AI is more important than my neighbor.

Speaker D: Neighbor.

Speaker B: And if I can't even get my head around why everyone doesn't feel that way, like, when I. When I have discussions and someone says, but can you. You know what this is going to do to ebitda? I'm like, you know what it's going to do to your neighbor? Like, how. What's more important to you? Like, you know what I mean? How much is enough? And I'm so. I'm sort of like, that's why it is. Because I see not just the faces of people on one digital, but I see my kids. I'm about to be a grandfather for the first time. If you don't See the faces, then you're. You're sitting in the wrong seat, doing the wrong thing. And that, that's. That's my take on it.

Speaker D: I think, um, my answer would be very similar. I have, I have four younger kids, um, in everything from a high schooler down to a one year old. So I get to see firsthand the evolution of a young person's life with AI at the. You know, as an AI native, not just a digital native, but AI native. You know, I think when I look back at my career, there's been a central through line on the role that humans have with technology. The way the interface between technology people has always been a big interest of mine, and it's everything that I've always done, whether it's been in software, in neuroscience, in medicine, and so on. We are at the precipice of a potentially unhealthy relationship between people and technology for the first time ever in our species. Maybe the nuclear age was the last time, but that was at a country, state level. This is at an individual level. And the relationship that we have with technology individually is about to change dramatically. And the reason that we do this work every day is because we care about the faces, we care about the people that are on the receiving end of this change. You know, and I'm a technologist. I spent a lot of time understanding and building the technology, but I don't care about the technology. I care about the people.

Speaker C: Both great answers. I firstly want to thank you both for your time. I really appreciate it. Um, this has been really insightful for me, and it's been awesome to have you both on second. If people want to find out more about who you are, what you do, or the book that it's about to release as well, give me some more information there.

Speaker D: Sounds great.

Speaker B: Well, look, we appreciate it, Liam. Um, it's early in the game for us in terms of beating the drum, but, um, hopefully, um, there's lots of people listening on your podcast that see the faces and want to learn more about what we're up to.

Speaker D: And, uh, yeah, the book is called Workforce Intelligence. Feel free to find it on your favorite, uh, uh, website and then also@onedigital.com awesome.

Speaker C: All right, thank you both.

Speaker B: Thanks, Emilio.

Speaker D: Thank you, Liam.

Speaker A: Uh, what if doctors could detect Alzheimer's before symptoms appear or. Or treat cancer with unprecedented precision? On the new season of tomorrow's Cure, the chart topping and 2025Ambie Award finalist podcast from Mayo Clinic, we explore how breakthroughs like these are already reshaping the future of healthcare. I'm Lindsay Sievert, the new host of Tomorrow's Cure, and this season I'm joined by leading physicians, researchers and Mayo Clinic experts exploring everything from AI powered diagnostics to precision cancer treatments. Follow Tomorrow's Cure wherever you get your podcasts.