[00:00:03] [SPEAKER_02] Welcome to the HR Data Labs Podcast, now part of the Work Defined Podcast Network. Join us as we explore the vital role of compensation, strategy, data, and people analytics in navigating today's complex business world. With the resources of Work Defined, we're now bringing you deeper insights and actionable ideas from top experts. Now, here is your host, David Turetsky.
[00:00:26] [SPEAKER_02] David Turetsky Hello and welcome to the HR Data Labs Podcast. I am your host, David Turetsky. And we have a couple, actually, we have three special guests for you today. I'm going to let them introduce themselves. Today's episode is sponsored by Work Carousel. Work Carousel, where you can bring your authentic self to work. Join at workcarousel.com slash join. Well, Vinay, Mike, welcome. We've been talking about having an episode for a while. Very excited to have you here. Vinay, who's your special guest?
[00:00:54] [SPEAKER_00] Vinay, my special guest. My special guest is my youngest, Samay. He's a little unwell, staying home from daycare today, and the nanny's on our way, so she'll be here in a minute.
[00:01:05] [SPEAKER_02] That's totally okay. While we wait for the nanny to get here, why don't you and Mike give us a little bit of background on who you are and where you work?
[00:01:14] [SPEAKER_00] Go ahead, Mike.
[00:01:15] [SPEAKER_01] Mike Sullivan, co-founder and now chief AI activation officer at OneDigital. So we are a large or pretty good size insurance and financial services organization. We started in 2000, so we've been chopping wood for about 27 years now. And we are immersed in, you know, everything to do with the workplace and helping employers understand how to take care of their people.
[00:01:39] [SPEAKER_01] And so we spent a lot of time, employee benefits, everything to do with paying, taking care of, and providing total rewards for employees. And now really trying to understand how a blended workforce of us and AI is going to get managed going forward. So Vinay and I have been immersed in that, but appreciate the opportunity to chat today.
[00:02:00] [SPEAKER_02] Yeah, it's exciting, to be honest with you, that what you just said there of how do we introduce the AI workers into the world of work has been a constant theme for us for, it's going on four years now. We actually started talking about AI, ethics of AI and HR about four or five years ago. So, but lately, you can't get away from it. Yeah. Vinay, any extra you want to add?
[00:02:24] [SPEAKER_00] Yeah, I just, it's a pleasure to be here, David. My name is Vinay Gidwani. I'm the chief product officer at OneDigital. So I run all of our AI initiatives with a great team here. And as we'll talk about, I think we have a unique view on AI and its role in the workplace and excited to get into it.
[00:02:42] [SPEAKER_02] But before we get into our topic, we always have to ask, what's one fun thing that no one knows about each of you?
[00:02:49] [SPEAKER_00] Ah, Mike, you first.
[00:02:51] [SPEAKER_01] So, well, actually, everything got us started on this writing a book and synthesizing our thoughts about managing people and AI together. During an evening where a guy who has never spent a moment coding a thing used software that Vinay introduced me to to build a software program to understand what the potential disruption could be to our company.
[00:03:17] [SPEAKER_01] If, if what everybody was beating the drum on employment was going to happen, what would it, how would that impact our company? And so 10 hours later, I had built a disruption tool just by talking to software, which kind of was a revelatory thing in and of itself and really said, look, if I can do this, then there's going to be a lot of change coming. So we better get moving.
[00:03:38] [SPEAKER_00] Proud of you, Mike. You did it.
[00:03:40] [SPEAKER_01] Yeah, absolutely.
[00:03:43] [SPEAKER_02] Yeah. As a fellow geek, Vinay and I are obviously fellow geeks. I've been a product management and head of product management for a bunch of companies. So I'm proud of you, too. I actually spent this entire weekend building a product. So Vinay, how about you?
[00:03:55] [SPEAKER_00] Well, I do definitely code on the side here and there. But before this guy came along a year and a half ago, I was a part time EMT. I decided to pursue in my 40s doing night shifts in Revere, Massachusetts, which is an interesting place to be an EMT. And then this guy came along. So there are my nights are filled with something else now.
[00:04:17] [SPEAKER_02] Oh, that's wonderful. Well, thank you very much for doing that. But I used to live in Franklin, but I didn't have to get a call from you. Or you didn't have to get a call from me, I guess. Yeah, that's about right. Thank goodness. All right, cool. Well, we're going to transition to our topic. And the topic is one that's near and dear to HR Data Labs listeners as well as to myself.
[00:04:38] [SPEAKER_02] In fact, one of the first books I wrote started with a CFO calling a HR analyst into their office talking about how headcount was incorrect. And why did they look at the wrong number? And so our topic for today is headcount is the wrong number. What HR should measure once AI joins the team? We're going to get into that right after this.
[00:05:24] [SPEAKER_02] So when we start talking about this topic, obviously it gets me very excited. But when we think about it in the context of the world of HR and HR analytics, you know, we start thinking about lots of different things. Tell us what your perspective is on workforce intelligence and how did this become a thing for you guys?
[00:05:48] [SPEAKER_01] Well, I would say that Vinay and I were participating in a discussion inside of our company where two different industry verticals, one in the PEO space, one in the HR consulting space, were having a debate about who quote unquote owned human capital management in our company. And we had been immersed in introducing AI coworkers that more and more of the work inside of our four walls was getting done by teammates that were made up of artificial intelligence.
[00:06:16] [SPEAKER_01] And we were, we had this discussion, aren't we arguing about the wrong thing? We need a different expression for, you know, this layer that lived above human capital management. You got to pay people, you got to take care of career path, all those kinds of things. But how are we going to manage and measure a blended workforce of people and AI coworkers, which was a journey we had already started. And it got us into this entirely new thought process that said,
[00:06:43] [SPEAKER_01] every single company in the world in one way, shape or form, is going to have to figure out how to manage human talent and AI talent side by side, manage it, measure it. So it, it got us down this path of creating this concept of workforce intelligence, putting a lot of academic rigor behind what is actually happening inside of our four walls. And how do we get help thinking through it, but making a decision that we needed to get these concepts into the world.
[00:07:09] [SPEAKER_01] And we needed to provoke a different kind of discussion that said, this can be done in a way that amplifies people and allows organizations to grow both AI talent and human talent. And so we have been on this journey for the past couple of years, put our thoughts into words to get them out into the world. But a lot of work that still needs to be done in terms of how do we figure this out?
[00:07:32] [SPEAKER_02] Mike, as you're talking, one of the things that comes to my mind is that this isn't really a new discussion. I mean, it's a new discussion because we're talking about introducing artificial intelligence or robots or whatever you want to call them, workers into the equation. But we've been having discussions about whether or not we're going to hire people as W-2s or 1099s, whether we're going to do, you know, use gig workers from Fiverr or some of those other platforms.
[00:08:00] [SPEAKER_02] Is this now just another choice that we have about how we get work done? Or is it something deeper? You talked about, you know, career paths and whatnot. We don't need career paths necessarily for gig workers either, right?
[00:08:12] [SPEAKER_00] I just to pipe in on that, I think that it is different in that the nature of work has to change significantly, right? I think if you look at the, you know, what you talked about, the gig economy or in the past, whether it was BPOs or outsourcing, you know, offshoring people, you know, it was always a sort of one-to-one replacement, right? So a human being had a particular task or job to do, and now I can get that done elsewhere.
[00:08:39] [SPEAKER_00] In reality, what we're doing here is we're changing the nature of work in itself. We're having to rewrite job descriptions and change what people actually do with their time. Because a lot of what they can do or have been doing, obviously all the administrative work, but even more than that nowadays, AI can and should do. So it's really a question of what do we do with all that extra time? You know, we call this... Extra time. Yeah, it's actually, it's kind of funny because, you know,
[00:09:07] [SPEAKER_00] we have a pretty significant level of adoption within our company for AI. So we have, in some cases, over 90% of people in a business unit using an AI co-worker every single day. And yet, if you go and ask them, do you have more time back in your day? Do you feel less busy? And to be like, no, I feel more busy. I feel more productive, but I feel more pressure to do more things. Exactly. And what's happening is that we're essentially seeing what we sort of think of as a capacity dividend.
[00:09:37] [SPEAKER_00] Right? Something has come as a result of that. We know this through our analytics that individuals are getting back in their time, back in their day. But they're not applying that time to something that adds value to their lives or the organization or our clients. And that direction around how to apply the capacity dividend, that's the extra step that has to be done.
[00:09:58] [SPEAKER_02] But, Vinay, I think some of that comes from the learning curve of adoption. It also comes from the slop that we get sometimes that we have to actually do extra QA on. So while there is extra capacity, because your AI co-worker is doing something, some of that means it's forcing you to become a quality assurance agent to make sure, or the quality assurance person, to make sure that whatever it's produced is good enough.
[00:10:27] [SPEAKER_02] Because we've seen lots of very visible cases where lawyers have gotten eviscerated in court for a paralegal creating an argument or creating some evidence or case law that was made up by the AI. And that has a real cost.
[00:10:44] [SPEAKER_00] Yeah, and it's not just hallucination, by the way. It's just the volume, right? I sometimes think to myself as I send an email back to some people that, okay, I'm going to send a one-line email because people know that AI didn't write it. If I send a paragraph, I'm not going to read it.
[00:11:03] [SPEAKER_02] Well, yeah. Well, I have one of those AI things, one of the agents that looks through my email and says, hey, you know, here's a great response. And then I look at it and I go, well, that's not my language. I don't talk like that. So I have to then usually edit it for me. And again, it's a time saver, but not really.
[00:11:21] [SPEAKER_01] Yeah, look, I think there's well-documented issues like the J-curve where things go down before they go up. But I do believe in what I think Vinay and I are concerned about is that work is going to change. AI is getting better and better.
[00:11:35] [SPEAKER_01] Particularly, if you look at the coworkers, our model of instead of having people engage with, let's say, Claude directly on a bunch of individual journeys, we are building best practices and institutional knowledge around centers of excellence and centers of intelligence. So the work product is getting better and better and better and more refined and more expansive.
[00:12:00] [SPEAKER_01] So we see that happening at the same time our people are beginning to have more hours back in the day.
[00:12:08] [SPEAKER_01] But, you know, what is challenging us and what I think is going to challenge every single organization is the fact that it is easier to think about it as an efficiency play as it is to then say, what can we do for clients that we are not doing today that is uniquely human that if we can amplify our people, they can go blow our clients' minds by what we deliver. That's a harder task.
[00:12:33] [SPEAKER_01] It takes more strategic thinking about the art of the possible versus, you know what, maybe we just don't need as many people as we once needed. And I think, you know, the placeholder that we've put out there is that if AI gets to your people first, it probably gets to your firm and what you do next. So we think we need to go on this exploration of adding more and more things that we can do for our clients that we've never historically done for them.
[00:12:59] [SPEAKER_01] And that has to be part of the puzzle or we're going to be challenged at some point in the near future.
[00:13:04] [SPEAKER_02] Absolutely. And no question, this is a revolution as much as it is an evolution of work. And I think both of you had said that in, you know, during as we started our discussion. One of the things that I wanted to kind of ask you and kind of throw back at both of you is where do we see the efficiency versus the cost? Because this is not free.
[00:13:26] [SPEAKER_02] And I personally at HR Data Labs, we spend thousands a month on all the different platforms or at least a few different platforms that are AI based. And there's got to be a tradeoff here. Mike, you were talking about this as well. There's the human element, which is still important. And I can't replace people, but I can amplify people. But it's still costing me a lot of money to leverage these tools. And that has to come into the equation somehow.
[00:13:57] [SPEAKER_00] Yeah. In fact, we, every coworker in our company has a salary. Every AI coworker. That's great. So we understand how much it costs to, for any of these coworkers to operate every single day, every single week. Now, the interesting thing, of course, is that we can spend however many thousands of dollars a month on one coworker, but they can help a lot of people at the same time. So they scale very differently than humans do. But I agree that, you know, the cost of tokens has to be well understood. And, you know, we use open weight models.
[00:14:26] [SPEAKER_00] We use non-frontier models. We use some frontier models because it's that mix and match. And again, if you apply a HR lens to this, it's really like, okay, I need somebody who's a Harvard Business School graduate. So I'm going to pay more for that quality model, right? Versus somebody I need who's doing more administrative tasks and I'm going to pay for their education, so to speak. And so I think if we apply that lens, we'll understand that not everybody needs to be a high-paying executive in your company. Everybody needs to be using a frontier.
[00:14:57] [SPEAKER_02] And what's fascinating is the frontier models are actually evolving fast, very fast. Of course, yes. So whether it's Claude and it's some of their frontier models, their frontier models, like I think Haiku, is Haiku OpenAI or Anthropic? Either way, it becomes a yesteryear.
[00:15:14] [SPEAKER_00] Yeah, I mean, if you look, and this is not like an HBS graduate whose first job is $150,000 a year, but the graduate next year's first job is $75,000, a deflationary impact of compensation for education is very rapid in AI. And that's actually honestly a good thing because it's making it much more ubiquitous and available.
[00:15:39] [SPEAKER_02] But it's good, but it's also very confusing. And back to Mike's point of, and actually it was both of your points, of the tokenization of work versus the benefit you get from it. I think you said that, you know, do you need a Harvard business graduate, business school graduate on everything? And the answer is no. If you're just trying to get two Excel spreadsheets to kind of combine and you don't want to do VLOOKUPs on it, there are some non-frontier models, I forget the name of them right now,
[00:16:08] [SPEAKER_02] that are actually really good and can do a lot for you. GPT OSS is one that we use for a lot of that, and it's fine. It works. And actually, it's run on my boxes. I have two NVIDIA DGX Sparks sitting on my desk. And so relatively free, ha, but they cost $10,000 to buy them. So yeah.
[00:16:28] [SPEAKER_01] We are, David. So I've learned a lot from Vinay, even though it pains me to say that out loud. But the reality is that we expect our token spend to go down significantly next year, even though the adoption is going to continue to go up. And the fact of the matter is we're talking more and more to our clients about the concept of making available and utilizing all different types of models, putting a harness around that, connect to the harness,
[00:16:57] [SPEAKER_01] have the harness connect. That navigation of using what level of model for what type of work is going to become an art as much as it is a science today. It will change the cost of tokens, I think, for all of us. I think there's a heading towards some level of commoditization of tokens in the future. But for the vast majority of the work we do, which is knowledge-based synthesizing and analysis of information,
[00:17:27] [SPEAKER_01] we don't need frontier models to do that work. And we're going to change and plug and play so that spend gets managed differently. But what we do going forward is a more important task that we need to figure out. And we think many, many employers today need to figure out. That's the challenge.
[00:17:46] [SPEAKER_00] I think one of the things that is interesting is just how much effort and time we talk about sort of extracting the most out of a dollar that we spend on a token, right? But that's a race to zero. What's really important and more interesting is the $10 that we spend on a human and the $1 that we spend on a token. What do you get? And how do you make sure that that combination is $1,000? That, I think, is much more difficult.
[00:18:15] [SPEAKER_00] But it's really where the value is.
[00:18:18] [SPEAKER_02] Well, then let's start talking about agents, Vinay, right? Because agents then take the capabilities that both of you have been talking about. And as we get into this new world of MCP, which I don't know what the initials stand for, but it's kind of like putting models where people work, which is in Teams messages, it's in emails, potentially. It's in Discord. It's in Twitter. It's in wherever we are, right?
[00:18:46] [SPEAKER_02] And it brings artificial intelligence into conversations with clients where they don't care whether they're talking to a person or a model because the model is good enough to be able to give person-level answers, hopefully. But then that expands our use of tokens and it expands our use of... I think this is where you were going, Mike, before.
[00:19:05] [SPEAKER_02] It expands our use of these newfangled technologies so that it heads off other things that would have been more expensive had a person got on the call. So it does leverage that knowledge that you were talking about and scale that knowledge you were talking about in ways that we don't anticipate yet.
[00:19:31] [SPEAKER_01] Yeah, look, I think that had you told me, you know, Vinay, I'm just thinking about our roadmap and where we're going in 2027. So think about, David, the fact that we have built now probably at a minimum 21 centers of intelligence inside of our organization where a coworker, it started with a coworker that was managed by someone very, very good at that particular skill set and knowledge base.
[00:19:56] [SPEAKER_01] And that center of intelligence has grown well beyond any individual at one digital doing that job could do. So as that center of intelligence grows, we're coming to a point where instead of it just being an internal access point for everything to do with that level of intelligence, we're going to make it available directly to our clients in 2027 so they can interact with these centers of intelligence directly. And think about that.
[00:20:25] [SPEAKER_01] Our consultants are sort of like, well, what about me? Right? Where do I fit in this whole thing? But our position is our clients want answers. They want them as quickly as possible. They want to access as efficiently as possible to get to the right answer. And what we're telling our people is like, if getting to a piece of information is our value going forward, then we've got a problem because AI can get to a piece of information faster than we can.
[00:20:53] [SPEAKER_01] So how do we organize ourselves so that work gets done by the right units of work are tracked based upon what gets done best where? And that sorting out is going to be a real challenge, I think. We talk all the time inside our four walls and with our clients now that if you look at what the next year or two holds, I would say it's 80 or 90% organizational change and getting
[00:21:19] [SPEAKER_01] our people to adopt to what the future is going to look like versus a technology challenge that we're trying to figure out. We have to evolve our game. We have this discussion with our folks, literally Vinay and I on a daily basis, where we think about this sort of paradox in the marketplace where frontier models are moving so fast that it feels like it's putting us in society in danger. But yet AI in the workplace is moving too slow in our opinion for what's coming in terms of change.
[00:21:49] [SPEAKER_01] And that's what we're trying to spend our time helping our clients with.
[00:21:53] [SPEAKER_02] This is very delicate, though, because what you were mentioning before, those consultants who are saying, what am I going to do? You know, what's my role here? Knowledge work essentially become or had become commoditized before because of how many consulting firms they were doing the same thing. But each one survived because they found a different specialty that made them unique.
[00:22:18] [SPEAKER_02] And people chose to go to Deloitte or they chose to go to Anderson or that's an old reference. But they chose to go to different places because, or Accenture, sorry. They chose to go to those places because that's what they needed to feel comfortable. Do you think that our clients are going to feel comfortable even now? And think about that in the range of mom and pop shops, county governments, you know, Fortune 100 companies, whatever.
[00:22:46] [SPEAKER_02] They're coming to you to get work done. Are they going to accept that a person's not necessarily directly behind it, but kind of tangentially behind your work product?
[00:22:57] [SPEAKER_00] Yeah, I think that there's a couple of things that we need to keep in mind. I think human accountability is still very important, right? And so I think that when a firm is looking for advice or guidance from another company, that the human accountability that comes with that is valuable and worth something. But I also think that we're in, you know, if we take us to a big step back, we're in the intelligence delivery business, right?
[00:23:24] [SPEAKER_00] And the statement that we make to our people and really to the industry and the market is our intelligence is a combination of artificial and human intelligence. Now, artificial intelligence is a commodity. And what's not a commodity is the human part of it. And the human part is the one that you need to invest in. Now, the interesting thing is, is that the best investment that you can make in human intelligence is artificial intelligence. Yeah, absolutely.
[00:23:52] [SPEAKER_00] So it's the combination of them that really drive the value proposition. And I think the thing that's so short-sighted now for organizations is when they, you know, Microsoft offering 60,000 people a voluntary, you know, early retirement or separation package is just basically saying that they want to gut where the intelligence uniqueness and competitive advantage will come from in the end. Every company is going to know how to build anything.
[00:24:22] [SPEAKER_00] Knowledge is no longer a protectable asset. And soon machinery and process is not going to be a protectable asset as well. So if any company can do anything, then what is going to separate us all from each other? It's going to have to be the people because the people are going to bring their unique experiences, their unique approach to things. That will be the competitive advantage. And I think that that's where we have to do that investment. So as Mike was saying, the interesting paradox is that we need to slow down frontier development
[00:24:51] [SPEAKER_00] for safety reasons, but we need to speed up workforce adoption of AI because we need to, we have to understand how to put people at the top of that equation. If we don't, then we're going to default to cutting people.
[00:25:05] [SPEAKER_02] So do you see the work that you're doing from now on, do you see a lot of it becoming more about the, the skill development and the people development than it, than more about the investment in the technology? Is it, or is it equivalent or, you know, is there some balance there?
[00:25:27] [SPEAKER_01] Look, I would say that, you know, we, we get asked this question all the time. And in fact, Binet and I sometimes get painted with this brush of like, somehow we're pro AI and somehow we are racing toward this future of unknown that is just as potentially damaging as anything else. And it couldn't be farther from the truth. We literally started this journey because we are, we are concerned about employment, right?
[00:25:56] [SPEAKER_01] Right. And we are 100%. I, I, I, I, I, Binet's told me, told me, or heard me say this joke a bunch, but you know, I'm the product of an iron union, iron worker dad from Buffalo. And I'm like, he's looking down from heaven right now going, finally, my son's pro labor. And honestly, I am 100% pro labor, but really what is changing is that we still think in the bucket of jobs
[00:26:24] [SPEAKER_01] when really what we need to do is think about the tasks that make up jobs and the stuff that AI just does better than we do, push that to AI and then amplify our people by, by doing a bunch of things we're not doing today. We have to all go through that exercise.
[00:26:42] [SPEAKER_01] Now it just so happens that we're the industry that we're in, like we came in, we came about this journey because we're in a blinking red dot area of potential disruption to our industry. We are knowledge-based AI does that very well. We better get moving towards our future. And we absolutely have. It's different than if you're a soda shop and you serve the public and AI is going to have a different take on that particular.
[00:27:08] [SPEAKER_01] So industry verticals are different, but our view is very simple. We want to make sure that employment, optimal employment plays out in the workplace. And to do so, we're simply saying, understand what work needs to get done in what particular modality and head in that particular direction. Don't fight it. There are headwinds and tailwinds. Find the tailwinds and move in that particular direction.
[00:27:35] [SPEAKER_01] And that's really the drumbeat we're beating is like you can't fight the tailwinds that are coming into the marketplace. But there's a huge organizational change and, you know, helping your people understand. You know, Vinay and I go around the country every single day with 6,001 digital employees saying, look, we are not doing this to you. We are doing this for you. But you've got to join us.
[00:27:59] [SPEAKER_01] Meet us halfway in terms of using these tools in a way that we can free up your time to do these things that we're going to discover together. But it's a process and it's a heavy lift to get people comfortable with what's coming and where we want to go.
[00:28:15] [SPEAKER_02] Do you see any generational or ethnic or cultural reasons why people wouldn't adopt AI? Is there a distrust of the machines? You know, have people seen too many science fiction movies is where I'm going with this? Do you think that there's some kind of backlash? I mean, we know this whole data center thing in NIMBY, you know, people don't want the data centers in their backyard.
[00:28:42] [SPEAKER_02] But are there headwinds on this that are coming from the people, the workers themselves?
[00:28:50] [SPEAKER_01] Look, we do surveys of all the meetings that we do. There is definitely a Mike or Vinay don't talk enough about the environmental impact of what we're doing. And we try to talk about that as much as we can. You know, I don't know if we necessarily look, I'm a 64 year old guy, a brand new grandpa that is completely AI enabled in terms of everything I do. And so I understand how that is amplifying my capability in terms of what I can do for one digital.
[00:29:19] [SPEAKER_01] But we have people in their 20s as opposed to it as people in their 60s. And it has less to do, I think, with we do have folks that say, hey, it's technology. I'm not into technology. Right. And we're basically like, hey, you know what? You need to be because this is kind of a thing. And I don't think it's going away. But it is a broad section of people with different different attitudes about this whole thing. But there is a tremendous amount of anxiety about employment that we're trying to talk openly about.
[00:29:48] [SPEAKER_01] So I don't know, Vinay, if you have a different or expanded view.
[00:29:50] [SPEAKER_00] Yeah, look, I think that there is no doubt that there's a lot of angst amongst people. And I think data centers are a nice target for that angst, whether it's a NIMBY feeling or a lack of prospects or whatever.
[00:30:07] [SPEAKER_00] But, you know, this is not a political statement, but if we just spent a fraction of the money that we spend on data centers on educational institutions and actually helping everything from K to 12 to higher ed embrace AI and rediscover or re-understand what learning looks like in a post-AI world, I think we would have a much better chance of succeeding here.
[00:30:33] [SPEAKER_00] And I think that, you know, I have kids in, as you saw, all ranges of age. And, you know, the one that's in high school has a very sort of anti-AI environment that she operates in from teachers and staff. And, you know, my encouragement to her is use it a lot. Use it to help you think. Now, if you want to use it as a lazy machine, okay, you're not doing anything.
[00:30:57] [SPEAKER_00] But if you want to use it as a way to expand your thinking and you spend quality time with it to do that, it is going to put you in a completely different level of effectiveness and intellect than anybody else. And so I think we just got to have people embrace that in that way.
[00:31:13] [SPEAKER_02] Yeah. I don't know if you remember. You might be too young, Vinay. And that's a compliment, by the way. But when I first got into the working world, there was not a computer on every desk. And the world revolutionized because this thing... I actually was told by a company that I was a candidate for a job, for a compensation analyst job. They said, yeah, we have one shared computer in the middle of the room and you book time on it.
[00:31:39] [SPEAKER_02] You have to put your signature on a time slot. I'm like, no, no, no, no. I want a computer. I've been using a computer in college my entire life. I was in the first internet-enabled dorm at Penn State in 1985. There you go. That's wow. From a timing perspective. So we've come so far of accepting and changing our world, bringing the world of computers in. There are still people who resist computers. We know them. We work with them.
[00:32:08] [SPEAKER_02] But typically, we don't hire them because we know that you need to use computers to get things done. But isn't that the same thing as we're dealing with now? It's people getting over the fact that they have to accept this new technology as being a way of getting work done?
[00:32:24] [SPEAKER_00] I think we do need to recognize that it is different in some ways. And I think back to the offshoring trends of the 90s, right? Where somebody from India or whatever would come, watch what you do, and then take your job over there. Now, there was a different psychology there. Because at least you knew conceptually that is another human being that has value and has a life. And yes, they can be paid less overseas.
[00:32:50] [SPEAKER_00] But I understand that there's still a human being doing the job that I took pride in. Now, it's people like the stupid little chatbots doing my job. Right? And I think that we have to recognize that that's different.
[00:33:03] [SPEAKER_00] But we also need to recognize that, and this example stands with yours, is that if you are going into an educational environment or you're going into a workplace and the resistance of using AI is predominant, you're going to have a disservice to your career. Right? Knowing how to expand your thinking with AI is an economic opportunity mechanism. It's like broadband. If you don't have broadband, you're not going to get access to education or work. Right?
[00:33:33] [SPEAKER_00] That's why we invested billions, trillions of dollars into ubiquitous broadband. And we still don't have it in some corners of the country. We have to make AI ubiquitous. And we have to learn as a species, as a country, how to maximize our effectiveness as people. Maximize our joy and happiness and access to the lifestyle that we want. And that's not going to come from shine away from it. And I'm not saying that leaning in is going to be easy as well.
[00:34:01] [SPEAKER_00] Leaning in is going to be incredibly disruptive. The question is, is who's doing the leaning in? Are the people that care about technology and bottom line the ones leaning in? Or is it the people who care about people are the ones leaning in? And obviously we believe the ones who care about people should be the one leaning in. And they have to take hold of the conversation. The more that they say that it's not my problem, data centers are evil, the environment, whatever. They're leaning out of the conversation.
[00:34:28] [SPEAKER_00] And then the conversation will be led by those who don't care about people anyways. Right.
[00:34:33] [SPEAKER_01] It sounds like a multivariate equation, Vinay. I would add to this mix that I think we deal with these topics in aggregate, right? What is it going to do to employment? What is it going to do to jobs? How do you leverage AI? With almost an assumption that it's very similar to everybody and where and how they fit into the workforce today. And really, I couldn't be farther from the truth. It is such an individual journey.
[00:35:02] [SPEAKER_01] I have gone to the point where I am so intellectually amplified by working with multiple strategy coworkers, workforce intelligence coworkers, that my challenge is quieting my mind when I need to turn it off. But when I tell you, it led to Vinay and I understanding the expression of workforce intelligence and what it's going to mean to our company.
[00:35:30] [SPEAKER_01] It's facilitating us doing a startup company joint venture with a venture studio on building an entirely new company daily. It connects the dots on things I don't see. And I use this. I tell the story inside of One Digital where I never used to be on Instagram. But I went on Instagram and on Instagram, I get ideas about, I use, I get business concepts. I get philosophy discussions.
[00:35:58] [SPEAKER_01] I use all of these things on Instagram to give me ideas. And then I'll take those ideas and apply them over here. Like how, how does stoicism impact our thinking around our, you know, artificial intelligence? And all of a sudden you start getting presented with endless amounts of, of dot connecting and strategy components and whatever. And you're like, wow, how do I sort through this?
[00:36:24] [SPEAKER_01] That thing, if we could, if we could get that version of that to 6,000 people inside of One Digital to say, let's figure this out together. Let's go on these journeys based upon where you fit in the organization. But if everyone starts thinking and creating in that type of way, and we organize that for an amplification, start with your people and then move to your firm. It's going to be unbelievable what we can do. But, but we're in the middle of that. I don't know. I don't trust. How do we do this?
[00:36:52] [SPEAKER_01] And it's, it'll take years to probably get to some different version of ourselves, but that's what's happening. Absolutely.
[00:36:59] [SPEAKER_00] It's, it's, it's interesting. I didn't think that we do need to be a little philosophical about this. And I just take this from my own observations. You know, a young child, a young baby, it doesn't matter genes, you know, frankly, it doesn't matter about upbringing necessarily in that situation, but is naturally curious. They're exploring the world. And when you, when you give them Lego blocks or something, they'll start building things. They'll start doing things.
[00:37:24] [SPEAKER_00] And it is that natural curiosity that every human being has that we tend to sort of beat out of them over time. Right. And, you know, what Mike is expressing, and I think a lot of people who use AI in the way that Mike's talking about have tapped into that childlike curiosity and that childlike exploration.
[00:37:48] [SPEAKER_00] And the reason that AI is so exciting and empowering, and part of the reason why people are like, my mind's racing all the time is because it's like they've been late. Tons of Lego blocks have been laid in front of them. And they can, you know, and they can just build and create or imagine anything. And so, you know, now, not everybody is like that. Right. And some people just want to clock in, clock out, go do their thing at home. Like, there's no judgment in this. But we have to find what it means to everybody.
[00:38:18] [SPEAKER_00] And it can't mean your livelihood is going to go. So, that cannot be the option for the majority people. We have to discover other things. And we have to really understand that at an individual level. And, again, I don't think we are spending enough time on that.
[00:38:34] [SPEAKER_02] And, by the way, we could be talking about this for hours and hours because this is literally the topic of conversation for many people, not only in boardrooms, but also sitting around their kitchen tables about how they're investing their lives at work, as well as how is work investing in their lives. And I think what I might have to do is ask you guys back again because we're at 40 minutes right now.
[00:39:01] [SPEAKER_02] And, as I said, we could talk about this all day because it's just such a fascinating topic. And it impacts everybody. It impacts their paycheck. It impacts the way they take care of their children. And the way their lives are going to move going forward. So, Vinay and Mike, thank you so much for being here. I'm going to have to ask you back again because we just scratched the surface here.
[00:39:23] [SPEAKER_01] Yeah. Well, it's been an absolute pleasure.
[00:39:26] [SPEAKER_02] All right. Cool. Well, again, thank you very much. And, everybody, thank you very much for being here. Take care and stay safe. Thank you for listening to the HR Data Labs podcast. Don't forget to hit subscribe and share it with your network. You can also check out the recordings on Spotify or the HR channel, now on Roku and Fire TV. Thank you. Take care and stay safe.
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