Episode 133

John Light on the Impact of AI and Empathy in Modern Talent Acquisition Strategies

TIM: We are live on the Objective Hiring Show today. We're joined by John. Thank you so much for joining us.

JOHN: Hey, I appreciate it, Tim. Glad to be here.

TIM: And whereabouts in the world are you joining us from today, John?

JOHN: sitting just west of Houston, Texas, on the Gulf Coast, and I suppose we can debate. I think everybody got it wrong. Some in the current administration call it now the Gulf of America. Everybody else calls it the Gulf of Mexico, but in my mind, being biased for where I've grown up and lived most of my life, it is always going to be the Gulf of Texas.

TIM: I checked Google Maps in Australia, and it was sitting right on the fence about this last week. And I think I'd had all of them in brackets next to each other. So it's like you pick whichever one you prefer. We're not getting involved. Okay.

JOHN: Our preferred pronouns or whatever,

TIM: Yeah,

JOHN: For the golf, right?

TIM: Exactly. And John, you said something interesting to me just a minute ago, actually, and you said that part of your role working in talent acquisition and recruitment is that you have to be. A bit of a psychologist, I'd love to hear what you mean by that and how you've discovered this.

JOHN: Yeah, no, happy to. I've been a headhunter for over 20 years now, but I started off my professional career in accounting. Ostensibly, man, I wanted to see how business works and accounting tracks dollars. It really teaches you a lot. And you can go into operations from there and marketing or sales, all kinds of different directions. But when I stepped into the recruiting world and started thinking about, okay, why doesn't this person, why did they react this way versus this other way? In many cases, it wasn't because, oh, they're right or, oh, they're wrong. It's, oh, I didn't understand their motivation. I didn't ask the right questions on the front end. Because if you understand a candidate's priorities, you can almost, with a high level of certainty or confidence, predict what their reaction to any stimuli or curveball thrown at them, any kind of unexpected thing, is going to be, and what I've found is that we make massive mistakes on the front end because we don't try to understand what really motivates someone. So an easy example, low-hanging fruit to talk about, is money. Why do you want to make a move? I want to make more money. Okay, that's very superficial. Why do you want to make more money? Because I want to make more money. Is it prestige? Is it ego? Is it whatever? Oh, and you find out we're about to have a child, or I

TIM: Right.

JOHN: to buy a house, or I wanted this or that. And the motivation is not the money. The motivation is the thing behind that. And if you understand that, now you can talk to them about how this opportunity versus that opportunity better suits. What's really behind the curtain, so to speak, because sometimes it's not really about money. Sometimes it's not really about the title. Sometimes it's about people chasing ego. And I've, at that time in my career, it's up or out. I need to keep moving up. Maybe you don't have; have you considered other things? And so a big part of being a psychologist, in a sense, is just getting into someone's shoes. And it's measured. An empathetic process to help understand how we can help a candidate or a client navigate the process of talent acquisition. And one of the great things, Tim, that I found out along the way is that something's not right for either party. to stop it. A lot of recruiters and a lot of headhunters have a rep. I'm just going to put a butt in the chair and get a fee for it. Agree with that. I think that's shortsighted, and it's bad business. It doesn't help the candidate, and it doesn't help the client. And as someone who's been a candidate, been a client, and been the recruiter, been on all sides of this equation, sit back and think of it. I don't really want to place the right talent in there. Who's motivated for the right reasons to do the right things at the right time? But I also want to think about, does this make the most sense for their career as a next step or at least enough sense to where it's compelling? Because if it doesn't, if there's a red flag in there, man, pull the ripcord, get out of the way instead of leaving your client with someone who's flighty, and instead of putting a candidate in a position that's untenable. Man, I absolutely have grown to dislike that. I've seen it happen. I've had it happen inadvertently. But I hate it. Frankly, I think candidates deserve more respect, and I think clients and the processes they're using in many cases—and this is my opinion—you take it for what it's worth. I think most companies are still using recruitment talent acquisition processes that were first developed and implemented in the 70s and 80s. And they haven't really been modernized for how people are making decisions today.

TIM: And so this role of amateur psychologist, or amateur, should call it amateur, let's say semi-professional psychologist, is on all sides of the fence, as you say, because you're going to have to really understand the motivations of the hiring manager or other decision-makers in the process as much as the candidate as well. And I know for. If I think back to deals we've worked on that have derailed, it's where there's been some other decision maker that I wasn't really in contact with who has come involved in the process at the last moment. And then, oh, I didn't know what they were after. They were looking for different things in the candidate. Why were they looking for different things and getting people on the same page? Really understanding all of that is so important, isn't it?

JOHN: It's hard to do it, though. And I don't know how it is in Australia, but in the U.S., 98 percent of recruiters operate on what we call a contingent basis, which means they don't make a dime until they place someone into a role. So on the percentages that they generally fill those roles, they work for free 80, 85 percent of the time. And it motivates them not to chase delivery. It motivates them and incentivizes them to chase a transaction. Let's face it. We all like to get paid. There's something about getting a paycheck that makes the day easier versus not getting one, right? So these guys chase trends. I did it for years. I trained people to do it and do it really for a long time, and I just look at that model and I think it's got its place, its context. But it's not really efficient for the market today. And so part of what we did is we took this concept between contingent and the more traditional high-end retained search we call retention, where we charge a contingent-level fee and move at contingent-level speed, but we have service levels more online with retained. And since we get a retainer up front. I don't care about chasing the transaction. I care about delivering what you want in a way that makes sense. But to do that, one of the first things we do, Tim, is have an intake meeting, an onboard meeting, a job briefing meeting, or whatever in the world you want to call it. I often just call it a kickoff meeting, and I want all interested parties and interview processes present. I want them there because one of the first things I'm going to do, if they hand us a job description to say, Okay, I've read this. I may have some questions or not, but then I set it aside. Great. Put that aside for a second, y'all. me what's not on it, and you'd be amazed at what bubbles up, and you'd be amazed at the number of people hiring managers—not the internal TA people typically, but the hiring managers who go, Thank you for asking that question. And, at that point, my brain back

TIM: Huh.

JOHN: Going, yes, come on, let's dive into this because we need to understand. I think one of the most important things is understanding what is good looks like. And if what good looks like looks good to one person, is it going to look good to another person? So when you ask that question of the group, what's not on the job description, what's important, what are the three, four, or five things you want this role to accomplish in the first six months or 12 months or whatever, you've got to ask that question of everybody on the call, and you have to have everybody's participation. But when you get that man, magic can happen. When you don't get that, then your point is that there are too many constituents involved, too many. Chefs in the kitchen, however you want to put it, and you run a higher risk of not completing the assignment.

TIM: Are there typical problems that you see? So you mentioned before that companies are still doing hiring like they were in the 70s or 80s. And so that implies to me there's certain patterns that you see emerging again and again, certain typical problems companies have with hiring. Love to hear a little bit more about those.

JOHN: Yeah, maybe one of the biggest things that stands out to me is how committed they are to advertising and putting job postings out. They're committed to, hey, we need people to apply. And I think there's a time and a place and a context for that. At the same time, of it from this analogy. It's like putting a net in the water in a river, hoping the right fish swims into it. You're dropping it in and praying you get what's the right thing to come along. And a lot of times what you end up doing, number one, is you end up with hundreds of fish in the net, but you're only legally allowed to keep that one. So how do you parse it down? Let's make the gaps in the net bigger and look for bigger fish. Oh, but what if we need a smaller fish, and we need a smaller one? You're doing all these adjustments, but at the end of the day, you're just praying that you get the right person. And to get there, you've got to go through the other 499 candidates who are going in. And at the bottom of the resume, we're typing all these keywords repetitively in white font. So they didn't show up unless you know what you're looking for, but the system recognized them. Oh, wow, this is a great candidate. And what you end up with may or may not be a great candidate, but man, it had the right keyword density. With our retention model, the way we approach it differently, we don't post any ads whatsoever. We're, we take a mentality instead of putting the net in the water; our approach is to spearfish. We're going to identify what does good look like. What does a good target look like? And let's go into their living room and get them. What we found, and what keyed me on this a number of years ago, was a joint study. I don't want to say it was Harvard Business Review and LinkedIn; it may have been another couple of groups that did this. But what they found is that at any given time, maybe a quarter of the talent pool will apply for a job. There are a lot of people who never apply to anything, and there are some people who apply to everything they see. Which is why you get weight staff applying for a nuclear physicist job. I'm being extreme and silly, but you know what I'm talking about. That, a lot of people apply to jobs that absolutely make no sense. But with this idea of spear phishing, we're going to go into that living room and find them because that same study found out that 87 percent of potential candidates will take a phone call from a recruiter to talk about an opportunity that's aligned with their background and their career path. And the key thing in there is 87 percent will engage with someone reaching out with a quality opportunity that has alignment. Not this AI-generated stuff where it says, I get it every now and then. I'll get an email. Oh, you would be a great fit for this controller position. No, I would not. That was 25 years ago, man. What's wrong with you? I still get this stuff. And so our whole model is built on identifying the right people on the front end. What does good look like? And then going and talking to them. It's very efficient. And frankly, it really helps your brand and your reputation in the marketplace. But the vast majority of companies, what do they do? They scrape databases, and they post ads. And you end up in scenarios like we've been seeing here in the US. I'm sure you've seen it in Australia too, where people get frustrated. I've applied to 300 positions, and I get form letters. It's sometimes not even that. Or I had an entrance screen set up for a video call, and no one showed up. The 15-minute window or the 30-minute window came and went, and now no one's talking to me. Or we found the right person already, and we don't need to talk to you anymore. It's really making a train wreck of the marketplace, and I don't think that's right. And that's because that process, that kind of spray-and-pray net-in-the-water process, that's what people did in the 70s and 80s. And even the nineties and continuing. Why? Because it was tried and true, and it worked great for that context, but our context is so different now. People consume information so much differently. People make decisions differently than they did even a decade ago. And companies need to adjust. They need to evolve with it.

TIM: They absolutely do. And I feel like. Part of the reason the market is especially broken now is that candidates have adopted AI quickly en masse because they're individuals; it's easy for them to see the value; they're going to go, Sure, I'm going to use this tool to optimize my resume and make it look perfect for this particular job. There are also tools that allow you to actually do the applications automatically. So it can kind of cycle through tabs in your browser and apply. Each candidate who applies, it seems, is slightly less qualified in a sales sense than they were in the past, maybe also in a sense of being qualified for the role. And so that's happening. Meanwhile, it's not like the companies have adopted AI en masse on their end, though, like it seems to be fairly slow from what I've been able to glean. And so we're in this kind of weird middle stage in the market where it's just broken; it's going to be broken for a little while until companies maybe catch up until they start using. Technologies until they start using AI until they start changing their approach or philosophy. I'd love to hear a bit more about the recruitment tool you've been developing because I think it's really interesting what you guys are working on. And I'd love to hear where it's going to sit and what kind of problems it's going to solve in this.

JOHN: Yeah.

TIM: mess of ours that we have.

JOHN: No, happy to. I think you touched on something that's really important, and that is. Again, I don't know how it is in Australia, but in the U.S., most of the companies that do most of the hiring are small businesses or small or medium businesses with under 500 employees. Vast majority. The big companies that do this on mass hiring are a whole different magnitude. But there's not enough volume of those companies to make up for just the sheer, insane volume of small businesses and medium, medium businesses here. that may hire once a year, or they may hire a hundred times a year. It just depends on the business. But the big companies are hesitant in implementing full-blown, full-bore AI solutions to their problems. And a lot of them, I think, are making potentially fatal mistakes and trying to One of the biggest things I've seen come out on the market lately with AI is if somebody takes an existing ATS, Applicant Tracking System, and wraps AI around it, it ends up being a sushi roll or a burrito, and the outside of it is the AI that it moves faster, but it doesn't actually change the process and make it better. It crunches more numbers. Fantastic. AI is still at a stage where I don't think you or I would trust it to successfully order dinner tonight. Now to give me recommendations, okay. I was talking with a vice president of a company in the ed tech space a while back, and he said, I can't get it. I haven't seen AI successfully order pizza. He said, Let me give you an example. And he goes, We tested this out, and we were in a simulation and put in our faves and our not faves and our experience and all this list data and said, Hey, order his pizza. For whom? My family of four is at home. And it said it knew that I didn't like pineapple on pizza, but it ordered a Hawaiian pizza anyway but added extra mushrooms to cover it up. Oh, and by the way, I ordered 200 pizzas.

TIM: Hope you're hungry.

JOHN: You have to step back and recognize the technology is there to supplement and complement and allow us to be more of what we are, play to our strengths rather than take over the world. And when you have that change in perspective, I think it changes how you approach this whole conundrum of AI adoption. You're right, people can go in for a few bucks getting a chat, GBT, or whatever. and churn out cover letters, modifying the resume. But when you're modifying your resume, the odds are you're lying, and you create a whole other barrier of difficulty and challenge. And really, I think what needs to happen, what we're working on, I'm not interested in solving talent management. I'm not interested in solving talent retention. I'm interested in the act with talent acquisition for professionals. And that's it. And so we're taking our retention search process and building it into the machine. And that allows for a more efficient process from developing a job description to understanding what a good candidate looks like to identifying and measuring those candidates and delivering them so that an internal recruiter, for example, or a hiring manager can look and say, These are my top five candidates. That are in the geography they need to be in. Here's who I'm going to reach out to. Then reach out with your agent. And get them connected with you because people still hire people, and people still work for people. And really, in a nutshell, that's all we're trying to do. We believe if we can do that and solve that one challenge, that one problem, There are tons of companies. In North America, people around the world are going to say, We need a piece of that, because it's not taking something and wrapping AI around it. Rather, it's leveraging AI the proper way to solve a specific problem.

TIM: Which bit of the hiring process do you think will not be automated away? By AI in the next few years. What's the bit that's just going to stay there that still adds value? Is it? Is it this psychological element, for example, you're talking about before where you really need to understand everyone's motivations? Because what you've just described then is, let's say, I guess, the start of the process in terms of reaching out to the candidates and getting them aligned and what have you. But it's not then, if I've understood correctly; it's not then the presentation of the candidates to the client, or it's not working them through the process and negotiation or anything like that.

JOHN: No, you have to get the human in the loop as fast as you can.

TIM: Okay.

JOHN: I think the more AI in the process, the more valuable the human connection becomes. Do you really want to take a job where you've actually not met a person or seen a face on a screen and actually had a cogent conversation with a real flesh-and-blood human being? This is a person who gets me. What's lost with AI is true empathy. It's not there. It's a long way off at best. So how do we improve that? How do we put ourselves in a position to maximize the value of each interaction we have as the human in the loop? I think AI's big strength in talent acquisition is it comes down to the ability to crunch numbers. To look at massive amounts of data and get down to the little tranche of profiles that are going to make the most sense. But as soon as that happens, you've got to engage as a person. Would you be happy getting an automated offer for a job as a candidate?

TIM: So the great question at the moment, no But I'd say yes under two conditions: one, when I was more junior, when I knew I was one of a hundred thousand graduates going for ten investment banking roles, I feel like in that, when you as a candidate have relatively less power, the tables are a little bit turned, and I imagine that's where AI might play a more aggressive role sooner. But the other aspect is I think once maybe AI is normalized in every aspect of life, and it's, Oh yeah, I called up my doctor to make an appointment, and I spoke to an AI or whatever. There are all these other elements of AI in life. Then I guess it will be suddenly normalized to be interviewed by an AI. But that.

JOHN: People are already interviewing with AI, but what they're also finding is that AI is not always accurate in what it's asking or the answers it's delivering. And there's something so impersonal about it. I'm a big believer. I talked earlier about psychology and being an amateur psychologist. It makes you feel like Agatha Christie, or I'm on Murder, She Wrote. I'm this amateur sleuth trying to solve a murder or whatever. I sit back and look at it from the lens of the more AI is involved, the more valuable this is right here, this individual connection, person to person, this empathetic opportunity we have. And we talked about, hey, motivations. Money's my motivation. I asked, Why do you want a new job? Where's my money? Does it dig deeper? Does it understand? What's the real incentive behind it? What's real motivation? I want a bigger title. I'm ready for leadership. Why does it align those things accurately? And there's a lot of training, I think, that has to go into it to get it there. But at the end of the day, outside of specific instances like you're talking about, 100,000 people applying for 10 jobs, AI should get you down to the top 50 or 60 people you want to talk to. Not necessarily roll the whole process. But suppose you have a hiring manager who hasn't been a hiring manager before. They got to do their own talent acquisition. The situation you were in. Previously. Okay, cool. can tell you what's next in the step, in the process. Where do we go from here? But, in the end, people are going to work for you, as the hiring manager. It's pretty important, I don't care what level, that you engage with them. And there's a massive value to that. Because, if you're rolling out an AI interview process, and that's my only interaction. Let me ask you something. How would you measure loyalty?

TIM: I don't know how to do that necessarily as a human. I'm not sure the AI bot, if it's trained, would be any worse than me. Like I'd say, ask it probing questions, dig deeper, ask it about its motive, ask them about the motivations. I should say, look into their work history, see how long they've stayed on average, the average tenure. That's where my mind goes initially.

JOHN: How about this idea? When you walk out of an interview, you can remember interviews; you walked out going I don't know; let me make a pros and cons list. Let me just balance the seesaw out, right? But when you walk out of an interview, you're just feeling confident. That's our person. How do you quantify that? Do you quantify the knowledge of the offer we made is resonating with that person? and they're not going to keep looking, though they don't start for another 30 days. It's really hard to do even for a human. Okay. So there's tremendous value. And again, making that empathetic connection back and forth with a human being, not to say that AI doesn't have a place. It does. It has a prominent place. It's a massively effective tool used properly. But when people are in such a rush to use it and apply it to everything, it does none of it. And that's part of my point about why we're developing our tool. It's very specific if we try to be everything to everybody. We tend to fail. But when you try to bring in robust domain knowledge and marry it up, the technology comes up with a solution that results in or gives you better results, quantifiably better results, a higher rate, and better retention because part of what goes with a very high fill rate. We have, with our retention program, an unbelievably good fall-off rate below 4%. Why? We do the work. AI doesn't always know how to have the intellectual curiosity and empathy to know, Where do I need to go deeper? How do I make a connection and bring this person in? If you ever filled out a form on your computer, you're sitting there, you're typing away. And you're like, I just don't want to type another 800-word essay for this thing. So I'm going to give it short answers, or I might not be paying attention. Can AI correct for that? I'm sure we can say the answer's yes, but is it? And I think it's the more important question. The answer is often no. Yeah. That comes up to the person in charge, the human in the loop.

TIM: I don't disagree with you, but I feel like we should. I feel like often the discussion about AI and hiring is thinking about all the shortcomings of AI, which are true and valid now, but isn't comparing it against the current system, which is a human-based system, as opposed to it's almost like sometimes we're comparing it to perfection. Yeah. There are so many problems with human-based recruitment that I'm struggling to see how AI recruitment isn't already a net positive, even if there are some kind of pros and cons, like obviously it's going to be cheaper, it's going to be more efficient, and it's going to work 24 hours a day for almost no money. Yeah, it's going to miss out on some of these nuances. Yes, it's, we're not training it, or we haven't trained it yet to dig deep in the right way and all these kinds of things. And voice AI is imperfect. If you've used voice mode on Chachapiti, it's like it sometimes interrupts you and sometimes doesn't really let you finish these kinds of things, but I just feel like we're getting so close, and there are already so many things that could probably do better that are the end outcome almost. Inevitable.

JOHN: I said, though, AI's place is not necessarily making the decision. AI's place is to bring you to the decision point, and not be wrong. I don't think you're wrong at all to say I think there's great efficiencies to be gained by having an AI-centric talent acquisition process versus a human-centric one. But at the end of the day, the human in the loop still has to make the decision on both ends of that equation. And AI eventually will be able to solve most of the variables on either side of the equal sign. There's no doubt about it. But it's a combined hybrid effort. Because when you take the human out of the loop, you lose far more than you would otherwise have. The problem isn't necessarily that there are humans in the loop, or it's all AI, or pieces of both. The problem is that we're evolving the process, I think, so that these things align with how people are doing things. And to do that, there are pieces of the process today that AI needs to do, no doubt about it; there are pieces that humans need to do. So what I'm saying is simply that to this, no to that, but there's this amazing space in the middle where if we're smart about it, we can get the most value for all parties involved. And let's not forget the candidate side of things. You go out on LinkedIn today, and you can see post after post of people complaining and bellyaching about ghost job postings. doing hundreds of applications and not hearing anything. How many of those are out there because AI determined to put them out there because that was what was built into a system versus a person? But now I don't know. I honestly don't know. I do know I've read their peer-reviewed studies that say anywhere from 15 up to 40 percent of all jobs there are fake jobs. All these job adverts that are out there. So it's not perfect. And I certainly don't disagree. There are things that it does so much better. But again, we've got to find those areas where, how can I help us be more of our strength? rather than becoming dependent on it to get us where we're going. If that makes sense to you, because at the end of the day, every tool we've ever developed in the whole history of the human race. It's all about what? Helping us do more of what we do. Better.

TIM: I feel like my only devil's advocate to this is, though, that the thing that we perceive as our strength is, in some cases, maybe our weakness, as in the humanity, our intuition, our gut feel, our sense of whatever. The thing that's blinding our decision-making that leads to whatever the ridiculous bad higher rate is that we have that leads to the scenario where certain people set the population doesn't get the rate of opportunities as others. There are all these things that are like, Oh yeah, I applied my human intuition to it, but actually it could be a net negative, not a net positive. And in theory, an AI could do it in a less biased way.

JOHN: But like we talked about earlier today, AI has its bias. It depends on what it's been trained on and what it's ingested. It's interesting to talk about it, and I think the convergence of colloquially called human resources. In a broad, overarching sense, whether it's for corporate America or the global economy or whatever it might be. And the fact is, regardless of whatever else we do, people are people. And, like going fishing, we need to go where the ones we're after are going to be. We need to approach them in a way that's palatable to them. And we need to be able to bring them in and keep them in a context that makes sense. I think one of the big delineating factors between us and AI comes down just to empathy. Tim, that's just it. How do you define it? How do you quantify it in a way? Does it make mistakes? Absolutely. Does AI make mistakes? Absolutely. But I think again, there's this great path forward where we build on AI's strengths and ability to crunch numbers and draft data and present us with potential solutions, options, and decision points. with the person in control making the decision. And I think if we swing too far that way or swing too far this way, then we end up right back in the conundrum we're in today, which is that we have a horribly inefficient talent acquisition market. Horribly.

TIM: I'm really interested to see how the kind of voice AI agents are developing, because there seems to be almost a split whereby they use, I think, a bit more in sales at the moment than they do in recruitment.

JOHN: Yeah. Tim will basically start a conversation, saying, Hey, by the way, I just wanted to let you know I'm an AI bot. Like you're not speaking to a human,

JOHN: real or programmed

TIM: They are trying to make it so humanlike that you wouldn't know that they aren't an AI. And so that one's in that kind of uncanny valley where it's, geez, this seems like a human, but something seems off. And I don't know at the moment what the best approach is; should the AI be so humanlike, it's indistinguishable from a human? Or should it be transparently an AI? Like, I know just from using voice mode on Chachapiti, speaking of empathy, like, I get it to help me with languages. So I'm trying to learn a bit of Italian, a bit of Spanish, and a bit of Russian. And I was struck by how much they've improved just the intonation of the voice to say, Oh, you're doing a great job. Well done. I was like, yeah, I am. Thank you. Thanks for that feedback. I am doing a good job. And it felt more, way more empathetic than seeing that in writing, for example. And I don't know, it's like almost a trick to the human brain if there's just a certain voice intonation that, as long as it's close enough, we still feel as though it's empathy, even though we know deep down it's not, because it's just an AI. So I wonder if even that empathy problem might be solved eventually. Is that empathy? It's fake, but some people are fake as well, John.

JOHN: The difference is that you and I have a choice in it. Ultimately, right now, we could choose. We could go right. We could go left, forward, backward, up, down, or whatever. We can make your own choice of the right choice,

TIM: Yes.

JOHN: But it is a choice that we make consciously, and AI is not there yet. In fact, I think that point is probably one of the more terrifying potentials of AI. Because now we're talking, everybody needs to have a German shepherd around to tell us when the terminators are coming. Because I may decide something differently than what we want for this world. I think ultimately, though, we are still years away from having something that's so automated at all levels for talent acquisition that a human's not involved anywhere. And years. I'm not talking two or three years. I'm talking much longer. I had a conversation last year with a gentleman who's done a lot of development in the AI space. And we're just talking about data. And it's one of the more challenging things, I think, for us to recognize. You take a little baby, a newborn baby; they're sitting there like a little loaf of bread. They don't do a whole lot except demand to be fed, kept warm, and get clean and sleep. the first five or six months or whatever. And, but during that time, they're taking in massive quantities of data. You ever think about where that goes? Or what impact it's having on that child as they grow and change over time. as they mature every day. I'm sitting here in my office, and you're sitting in your home office too, right? And if we look out the window, there's sunshine on both sides of the world right now; yours is coming up. Mine is going down, but it's there. All the little details we take for granted that we bring in that help drive our decisions are in our subconscious, not really that we're processing with the voice in our head, but it's in the background somewhere. We're talking. each human being, an unbelievable volume of data over the course of a lifetime, over the course of, say, the first 10 years of your life. And how we view that data and how we put it in our brains, instinctively, if I can call it that, can vary because of our perspectives and our biases and what's already in there. Think about the massive volume AI still has to go. To get to the point. making decisions like a mature adult human being in a specific professional field. It's mind-boggling when you step back and really look at just the volume that's out there. Take the internet by several magnitudes. So to even get there, you also have to back up and realize there's a massive amount of physical infrastructure that needs to be put in place. There's a huge construction boom all around the world for power, cooling, and data centers. As data centers are driving it, but the power and the cooling components, you can't have the data center without it. Your compute cycles, you start adding all that up, man, it's massive. beyond anything that we're envisioning right now. And we look at some of the tycoons of industry in that space. And they're looking down the road and seeing just volumes that have to be consumed when it comes to data power and whatnot that are just—they don't exist today. So I think there's a lot of headwinds to get to where fully AI-driven throughout the talent acquisition chain and the width and breadth of it, but people are going to try to get there. But to your point earlier, and I've seen it too, using AI-assisted searches and databases. get a lot of false positives. And I get a lot of people saying, Oh yeah, this guy's qualified. And I look at the resume, and I'm like, No, they're not. And I can go through a lot of information very quickly. I can't crunch the numbers that AI does. But I don't know that I've seen a product yet that I trust 100%. That if it gives me 10 candidates that say these are the top 10 candidates, that those are indeed the top 10 candidates. We're so we're making claims and have hopes out there that I think we've got to back up. And right now, it's very hard to back up, because we're asking for something that really, I think, ultimately, most efficiently will be a team effort between humans and AI. And we're not there yet.

TIM: My only other angle of thought, if that makes sense, is if the rate of change in the technology is so profound, like it seems as though it is, is it sometimes almost hard for us to wrap our minds around how much it's changing? I imagine it's probably. Similar points in history where you went from no electricity to electricity or from no industrialization to industrialization or what have you. And that probably thinking forward to all the impacts of that is almost too much for us to wrap our minds around. So we, I also intuitively want to think, Yeah, probably there's all this human element still needed in hiring, but maybe we're underestimating it because it's changed so quickly. Maybe we can't actually. Stay on top of it. If

JOHN: Yeah, it could. Yeah, absolutely. But it's also limited, again, by the physical side of things. And the speed at which, the speed at which it moves, what's the old rule of thumb, computing power doubles every couple of years? I forget which law that

TIM: Moore's law,

JOHN: Something like that. Yeah, it increases rapidly. Look, the generation behind us, they're going to have a very different view of this. Because they'll be raised with it. But all change in the world is generational change. All the major changes. You just don't do it overnight. And I don't think we have. The dirty secret is, and you know this too, machine learning is a subset of AI, and we've been using machine learning for a long time. It's only been in the past few years that we've had the computing power and memory and data. So what not to really drive this generative AI craze that's been going on? The concept of AI, theoretically, has been around since, what, the late forties, early fifties, at least academically. I think we have probably not a dissimilar climb up that hill right now, because at what point, and if you just want to look at it really broadly, at what point. Does AI determine what's ethical versus what you think is ethical or what I think is ethical? There are all these questions, and so I think there's a big growing period, Tim. I don't know where it ends. I don't know that it does end. I wouldn't have envisioned in my lifetime that we would start wondering if we're going to get food replicators like on Star Trek. And warp capability or whatever it might be, pick your sci-fi genre du jour; I do think that regardless of what ends up, human beings are not meant to be sitting in a recliner and spoon-fed by a machine, if I can say it that way. We're meant to go do things, and we're meant to work, and we're meant to create, and we're meant to get involved and have community. And so long as we keep AI in the position of supplementing and complementing that and promoting it and allowing us to be more human, we're in great shape. When we let it cross that line in the other direction, and they start taking humanity off the board, I. e., we're not able to empathize, connect, have community, and so on and so forth, for example, then we got a problem. So when you look at it, it's really a very personal decision to interview and take a job that's offered to you, and really, from the perspective of the hiring team, I'm going to make a decision. You're going to come onto this team. And I'm betting, in a sense, some of my career success on that, on you as a new hire, right? Those are deeply personal decisions. And have to be in the loop. They have to be a piece of that. To your point, I do think we're, I do agree with you that we're just, we're leapfrogging, and something new and fresh comes out. How much does that continue? Where do you start seeing diminishing returns? I don't know. I don't know that anyone really knows, per se, but I am confident that the more AI is there, the more we're going to be like, Human race, going to be like, wait a second. want to do more human stuff. And part of that is I want to explore. I want to build, I want to grow, I want to develop, I want to do this and that, and we can never allow ourselves to be taken away from it. And that's why I think, again, the human loop is so important: your careers involve some of the most personal, difficult decisions you'll ever make.

TIM: It's been an interesting conversation. John, we've covered off a lot of different areas. I'm wondering if you could ask our next guest any question about hiring. What would you ask them?

JOHN: Okay. That's a really evil question. Would I ask? A lot of it would depend on the guest, but I would suggest I would probably ask, How are you going to deal with or how are you dealing with?" and I don't know how global this is versus in the U.S. off the top of my head, but how are you navigating the malaise that's in the market right now? Where quit rates are a traditional measure of confidence in job seekers. Are really low, but companies aren't really generating new jobs to hire into; they're very flat, and that job growth How are you as a hiring manager, a candidate, or a recruiter? How are you navigating that? I think it's one of the pressing questions. We're all dealing with it right now.

TIM: That's a perfect question that I will level at one of our guests next week, and I'll let you know what they said. John, thank you so much for joining us. It's been such an interesting conversation. We've covered off a lot of ground. It's been really great to chat with you.

JOHN: Yeah, a lot of territory, Tim. I'm grateful. And I look forward to seeing the final product on this and getting the word out in our networks. I think we've covered a lot of territory, to your point. And I'd be happy to do it again. We might pick something other than AI. We might go a little deeper in other areas and have some fun.