Resources:
- Real-time Job Postings Salary Data
- Jonathan's Book
- WhoHire
- HR Data Labs Season 7 Episode 17
- “When you’re looking at your hiring process, look at it as a two-sided coin where you really want to give as much control [as possible] to the applicant.”
- “I think in the future we’ll have a life concierge that is an AI that knows us more intimately than we know ourselves and can proactively go out and find opportunities that we’re uniquely suited for. ”
Announcer: 0:02
Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record for their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.
David Turetsky: 0:46
Hello, and welcome to the HR Data Labs podcast. I'm your host David Turetsky alongside my friend, co-host, partner, and Salary.com colleague, Dwight Brown. Hello, Dwight.
Dwight Brown: 0:56
Hello, David. How you doing?
David Turetsky: 0:58
Oh, I'm okay. How are you?
Dwight Brown:1:00
Good.
David Turetsky: 1:00
So I'm not going to talk about Meteor law. Meteor logical. Dammit, it's not spring yet! Let's just put it that way. Yeah, you'll get the joke, if you listen to the Katherine Hammond podcast, you'll understand why. I just can't speak. Anyways. So today, Dwight and I have with us Jonathan Whistman. Jonathan, how are you?
Jonathan Whistman: 1:27
I'm doing great. Nice to be back visiting with you both. And evidently, you have inner episode jokes to keep your audience tied in to the episodes.
David Turetsky: 1:37
You have to listen to a whole series in order to be able to really get the value out of these jokes.
Jonathan Whistman: 1:42
Cliffhanger episodes.
David Turetsky: 1:45
We have a few of those. We have
Dwight Brown: 1:46
We do actually! Yeah. Yeah.
Jonathan Whistman: 1:48
This might be one of them.
David Turetsky: 1:49
It could be, it could be depending upon how it goes. But Jonathan, tell us about yourself and how you got to this moment in time?
Jonathan Whistman: 1:56
Oh boy, that is a big existential question! So I'm going to limit it down to the professional world.
David Turetsky: 2:03
Yes. Remember how long this podcast goes.
Jonathan Whistman: 2:07
Well, my father met my mother, but the I'm best known for writing a book.
David Turetsky: 2:12
Oh my goodness! We're gonna go back that far!
Jonathan Whistman: 2:14
Yes, I'm going way back. I'm best known for writing a book called The Sales Boss. And it is about building high performing sales teams. How do you find the people, recruit them onto the team? How do you get the systems and processes around doing that? And it comes from my experience building and selling a couple of companies and realizing at the heart of every organization's success is the ability to build a team of people and even more importantly, to have the the sales heart of the company be you know, beating strongly because that that lifeblood feeds every other part of the organization. So that's, that's my history. I'm currently, you know, running an AI company that helps organizations find great people. So that's the area I live and breathe.
David Turetsky: 2:59
Awesome. Well, we're gonna be talking a lot about that and your book as we get into our topic.
Dwight Brown: 3:05
And unlike a lot of our guests, I've actually had the pleasure of being able to meet Jonathan in person and shared ventures together in person. So we, I don't think I've met any of our podcast guests in person.
David Turetsky: 3:20
I've met a bunch of them. A lot of them are my friends, so
Jonathan Whistman: 3:23
Well, we're out here in the desert together. And when I met David and Dwight, you know, as a sort of a precursor to the show, Dwight said para sailing? And I was like, yes!
Dwight Brown: 3:37
Paragliding!
Jonathan Whistman: 3:37
Right, paragliding, yeah, I got that wrong. So he invited me to go and I got to see a historic event take place on a dirt road out north of Phoenix.
Dwight Brown: 3:49
A historic event that could have gone really bad!
Jonathan Whistman: 3:53
That is the that is the cliffhanger part of this episode. So tune in next week!
David Turetsky: 3:58
Tune in next week, when we talk to Jonathan about what Dwight did when he went up in the air the last time.
Dwight Brown: 4:03
We're going to attach the video that Jonathan took of me botching my paragliding launch will attach it with the episode.
David Turetsky: 4:11
That's lovely.
Jonathan Whistman: 4:13
You actually handled it like a pro. I didn't even see you break a sweat.
David Turetsky: 4:18
Didn't see him break a sweat but you may have heard him cursing from 1000 feet in the air.
Dwight Brown: 4:24
Right, exactly.
David Turetsky: 4:28
Jonathan as we do for every one of our guests though, we need to ask you what's one fun thing that no one knows about you?
Jonathan Whistman: 4:34
Wow, no one knows about me.
David Turetsky: 4:36
No one and that that precludes you from using the whole thing about Dwight now.
Jonathan Whistman: 4:44
What is one thing that nobody knows about me? That's that's a that's a tough question. I would say I'm gonna I'm gonna have to go with most people don't don't know about me is that I am a complete introvert. I think nobody would guess that about me.
David Turetsky: 5:06
That's definitely true.
Dwight Brown: 5:08
We just talked about introverts and sales in the podcast we just recorded.
Jonathan Whistman: 5:13
Yeah, I just early in life discovered that if I was going to do anything, I had to become extroverted. So it's sort of a push for me to do it. And I can I can do it. But the I would, I would guess that if you polled even, like nine out of 10 of my friends, they would all peg me as an extrovert.
Dwight Brown: 5:30
Interesting.
David Turetsky: 5:32
It is interesting.
Dwight Brown: 5:33
Yeah, you learn and adapt, you have to do it become survival almost.
David Turetsky: 5:37
But now everyone knows. So Jonathan, the topic we're going to talk about today is actually one that I think a lot of people will be interested in. Because the topic of AI is pretty much on the top of most teams' minds, especially in HR, when they're talking about, well, how will it affect their lives? So today's topic is how to use AI and automation to improve the quality of your hires. So our first question is, what is wrong with the current recruiting process?
Jonathan Whistman: 6:14
I think the thing that is wrong is the the burden of the weight of just the process of recruiting. When you, the way we get introduced to companies, the way we apply to companies, people sorting through resumes, like there's just a lot of mind numbingly dull things that happen. And we're sort of looking at chance that we get that magical connection between the right person and the right role. And so I think it's almost dehumanized the process of applying for jobs, it can be disheartening for the applicant looking for meaningful work, and certainly for a company who has an opening that they need a talented, bright individual to fill. You know that mind numbingly dull work of sorting through applicants, it's just completely inefficient. So I think there's there's a ton of upside opportunities for organizations to do that in a more human way.
David Turetsky: 7:09
Well, it's difference of saying it's a more human way than a more humane way. Because for a lot of us who's who've applied to jobs within the last five years, and getting a rejection notice, five seconds after you've hit apply. We kind of wished for the days when we used to send in resumes to the New York Times, and have it actually come back maybe you know, a month later with a letter in the mail saying yes, we would like to meet with you or no, we've chosen somebody else.
Jonathan Whistman: 7:38
Well, well, that's a human problem. The human didn't use the automation delay button to say at least set it at 24 hours so it feels real!
David Turetsky: 7:47
Right! Exactly. Feels real. Exactly. Yeah. But I mean, that's one of the problems is that when you now have an algorithm that's looking through the the candidates that have applied, or the at least just let's call them applicants at that point, they're not really even candidates. But the applicants using a scoring algorithm on the back end to kind of get who the right group is to send through having the human program that or to choose or filter that's got to take training doesn't mean it may be it is the human's fault of what the AI is, is allowing through?
Jonathan Whistman: 8:25
You know, this is this is a question that's hotly debated in HR, particularly now. And here's mytake on it isthat when you can use automation correctly and use AI, and AI, you know, it's a big term that people, some people, are sort of, you know, scared of what it is, if you can just simplify it to at its core, what is AI? AI is really about recognizing patterns. And it's, you know, large mathematical models, that's all it is! I liken it to if you've ever been in an airplane, and you're, you know, taking off from the airfield, or Dwight, you know, if you're running behind the truck before your parachute lifts you off the ground. When you're when you're at a certain height, like all you can see is right around you, right, maybe the tree tops and that sort of thing. As you get a little higher, you might know what city you're in. When you get high enough. You look down at the field, all of a sudden, you see oh, those circular, you know, crops, oh, there's a cornfield there. And the higher you go, the more patterns that you can see. And that's what using AI allows an organization to do is to recognize patterns at a scale and a speed at which it's just completely impossible for humans to do sustainably. And so if you can simplify it to that, then the real question is what are the patterns that I'm unaware of? So if you have somebody you know, in your office sitting sorting through resume, they have a pattern they're using, they have a mental shortcut they're using to say this person, you know, gets an interview, this person doesn't. But you just have to get comfortable with the fact that a computer model that you know that supervised correctly, I always think there should be a human in the loop but supervised correctly, they're actually going to make a better decision most of the time. They're not coming into the office with indigestion, they didn't just have a fight with their wife, if you can set the parameters correctly, you're gonna get a very similar output every single time. Yes, you have to be careful that you're not inadvertently screening out people by race or education or, you know, by gender. But that's a very well studied area in in human resources. And I think that AI done correctly actually reduces the amount of those what I call unconscious biases, which is really just somebody unconsciously applying a pattern that they're not even aware that they're doing.
David Turetsky:10:50
But that actually gets into the job description, and who you're targeting, and what platforms you're targeting too, isn't it? It's not just the selection part
Jonathan Whistman: 11:00
Yeah, it goes all the way down the list. So if of that. you're, you know, if you're posting your jobs on LinkedIn, you're getting one sort of candidate, you're on Craigslist, you're on another, right? Just by nature of where you posted, you're introducing some bias into the hiring process in terms of what kind of candidates you're gonna get in.
David Turetsky: 11:16
Exactly.
Announcer: 11:19
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David Turetsky: 11:30
And I guess the question from there is, you know, that goes to, as we mentioned, the humans that are in control of the processes and who've programmed it or who've set it up, right? And so whether it's the hiring manager creating the job description, whether it's the talent acquisition, or the recruiter who's actually gathering those requirements, and then putting them into the platform, whatever platform is chosen, and then whomever has programmed the entire process and workflows, all those people have a hand in what are the applicants? Or what are the candidates that come out of that applicant process? Right?
Jonathan Whistman: 12:06
Yeah, that's it. And and the truth is, even without technology, they're putting a process in place.
David Turetsky: 12:11
Exactly!
Jonathan Whistman: 12:12
So where I think that the the advantages for organizations is if they can take away that just call it factory work of going through resume and sourcing candidates and, you know, trying to do the initial match, they can spend real quality time having meaningful conversations at the bottom of the funnel with the best few candidates.
Dwight Brown: 12:36
Right.
Jonathan Whistman: 12:36
And that's where I see the advantage there, there's, there's technology coming as well. You, you're probably paying attention to the world of voice AI, where it sounds very human. So imagine applicants being able to hit a website, click a button that says interview me now where they can ask all of those questions that they may. And there's a whole study in like, in the in the coaching field, I'm using the wrong word, where people are actually more transparent when talking to an AI than they are to human. Right, because there's no judgment there. And what we find as using a voice AI actually allows an applicant is to ask meaningful questions like, what do you pay, which sometimes people will wait till the third or fourth interview, just out of this false sense of I'm being polite. You're giving the candidate the opportunity right at the front end to ask all the questions they want to decide whether or not you're even a company they want to apply for. And my belief, and I write about it in my book, when you're trying to put together a team of any sort, but especially with a sales team, your goal is actually not to attract candidates, your job is to repel candidates. And let me give you an example that might sound counterintuitive. But if you you know if you're watching TV, and you see an ad for the Marines, but you got bombs blowing up, you got people chasing the for you've got some of the epic battles that have happened in World War as a recruiting ad. For me, I'm like, I'm patriotic, but I'm just checking out! There's a certain subset of people who not only are patriotic, but they raise their hand and they're on fire. They're like, that's for me. That's where I want to live.
David Turetsky: 14:21
Okay. But both of you want to not just jump out of planes, but you want to actually go up and maybe the Marines might not be your place, but Air Force might be?
Dwight Brown: 14:30
Air Force, right?
Jonathan Whistman: 14:32
Yeah, right.
David Turetsky: 14:33
Go to the Navy, they might be putting putting you in the Air Force anyway! But but I think the question I have there, though, is, and I get what you're saying. It's almost like you're trying to select people by by talking to certain groups, and not to others or talking to those groups and saying, Yeah, I don't know if this is necessarily a place for you, right?
Jonathan Whistman: 14:55
Yeah.
David Turetsky:14:55
How does AI come into that equation though?
Jonathan Whistman: 14:58
So I think that AI may not come into that equation directly.
David Turetsky: 15:04
Okay.
Jonathan Whistman: 15:05
But I'm just sharing that thought of when you when you're, you're really manufacturing your entire, you know, process when you're trying to attract candidates and filter candidates. So when the connection in my thought there is when you have voice AI, I want to be able to have a candidate that can have a conversation early on without tying up my human resources.
David Turetsky: 15:28
Of course.
Jonathan Whistman: 15:29
To ask all those questions they want, because they may discover, look, you have on calls every Thursday, and the person goes, well, I can't do on call on Thursday, because I gotta do you know, I have my kid at home. Or I can't travel or I could never work for the wage that you're offering. There's some subset of this job that isn't a fit. If you could allow candidates to really quickly sort of self select out by asking questions that at a depth that you couldn't include in your standard job app, I think you've sped up the process for both sides.
Dwight Brown: 16:00
You've got a bilateral opportunity to reject each other, basically.
Jonathan Whistman: 16:06
Yeah, and I, and that's where I would advise organizations, when they're looking at their hiring process is to, to look at it as a two sided coin, where you're, you really want to give as much control to the applicant. And today's applicant wants speed, there are large organizations or small organizations even that are completely ghosting candidates. You've gone all the way through the hiring
David Turetsky: 16:29
Oh, yeah. process and all of a sudden, you never hear from them again. Yup.
Jonathan Whistman: 16:33
So you can use AI automation to help with a ton of that, like, once you've made the decision to not gonna hire people, there's a lot of follow up, tying that off, that can be very humane, but be triggered through automation.
David Turetsky: 16:47
And I guess you're talking about having these conversations at scale and an AI or the computer can have, you know, a multitude of conversations simultaneously. It's not like you have to schedule the computer.
Jonathan Whistman: 16:58
Yeah, you can do it anytime. So a lot of my clients will have, you know, their, they're outside of maybe posting on Indeed, or the other big jobs boards, they might be trying to attract through social media channels, Right? And the plus side is you're going to
David Turetsky: 17:10
Right. get a lot of candidates, the downside is you're going to get a lot of candidates! So in order for the organization, not to be overwhelmed, and be able to have timely conversations with the people they would want to hire, there has to be some sort of pattern recognition happening and AI can help that. Let me give you an example of a real world way one company is using automations. There are assessment tools that can help predict how well somebody is going to perform in a job. There's a ton of them out there. And one of my clients is advertising on movie theaters and on like Top Golf, right?
Dwight Brown: 17:51
Oh, yeah.
Jonathan Whistman: 17:51
And they're in a blue collar industry. But they're like, what would you be? You know, how would you do as a garage door technician, it's a six figure job. Right? But people don't have that natural, you know, they're already employed, you're trying to get them. So what what they're, what they do is they scan a code, they're interacting with a questionnaire that's driven by AI that's based on real data from this company that says this person will perform well, if you invest the $30,000 in two months to train this person to be a garage door tech.
David Turetsky: 18:19
Right.
Jonathan Whistman: 18:20
But what the applicant gets back in this is all pattern recognition is three videos, it's three videos of actual employees that are working for this guy's company, that are video from them saying I love working for this company. And I'm just not going to say the name here, just not to advertise them. But I love working for this company. Right? And I was a waiter before I became this technician. So then you can chat with that technician online. That's actually that chat is an AI bot that's trained with the way that technician would have answered all of those questions.
David Turetsky: 18:55
Wow.
Jonathan Whistman: 18:55
So they can get their questions answered. And then what they're trying to do is the AI technician says, David, based on what you're sharing with me, why don't we do a video call while I'm on my truck running my route? So it automatically schedules it on their calendar. So you know, they pull up they get on a video called Why do we want on a video call? Well, we want them to see the actual job, what's happening. I want to see make sure this person is somebody we would actually put in a home, right, with our customer.
Dwight Brown: 19:23
Right.
Jonathan Whistman: 19:24
And AI has already done a bunch of work there. It's helped make that connection. It's helped screened to make sure this person is a good candidate. Now all the technician has to do is say, Hey, David, sounds like based on what you're saying. This might be of interest. Why don't you come do a ride along with me? I can give you $150 gift card we work weekends, you don't even have to take time off of your job. Just see if you even like it.
David Turetsky: 19:47
Alright, where do I go?
Jonathan Whistman: 19:48
Right?
Dwight Brown: 19:50
David's taking notes right now. This sounds better than what I've got going on.
David Turetsky: 19:53
I love garage door dudes!
Jonathan Whistman: 19:55
Think about, think about that pattern. And now if the technician decides, you know David wouldn't be a fit for our company. That technician doesn't have to carry the weight of that they can say, David, great chatting with you, hopefully, you know more, I'm going to have our HR department be in touch with you.
David Turetsky: 20:11
Right.
Jonathan Whistman: 20:12
Right? And then that sort of an automated process to kindly offload that person.
David Turetsky: 20:16
Right.
Jonathan Whistman: 20:17
What we're, what we're doing is speed to hiring. And we're making HR, traditional HR, sort of the last step in the hiring process what rather than the first screening process, because our belief is that this hiring process is a very human decision, and the closer I can put the person actually doing the job that the new employee is going to have to do. And the faster I can do that, the more I'm going to increase the odds that there's a synergy there. And as the employer, I can close the deal with the applicant. And as an applicant, I get to see really, what is it I would be doing so I'm, I'm coming into it without these, you know, rose colored glasses. That's an example of a process, you could not run without some sort of AI tools and some sort of automation and thought into that. So if you're thinking about using AI, just do the same process you've always done, I think you're sort of limiting yourself, you should be like tearing apart and saying, what are these new tools like voice AI and being able to train an AI voice that sounds like me and can answer your questions like me? How can I deploy that in a way that assists the candidate.
Dwight Brown: 21:30
So it creates an active recruiting process, basically. As opposed to the typical recruiting process where we have to imagine how somebody's going to perform in a job and the candidate has to imagine what that job is going to be like.
Jonathan Whistman: 21:44
Exactly.
Dwight Brown: 21:44
It facilitates a lot more activity without necessarily increasing the human burden, the Human Resources burden or manager burden.
Jonathan Whistman: 21:52
And yet, you couldn't run a voice AI model like that for around, fully deployed on a lot of different platforms, around $5 an hour of talk time, just like the processing costs, right, and running through your phone lines and that. And it can always be available so if you're, you know, if you want to interact with candidates, when they get done with their day job, and they want to call in at nine o'clock at night, you can fire up and have a very real conversation! I was I follow the, you know, all of the latest things in AI. So I feel like I'm fairly in tune to what's going on. And I called into my bank the other day, and I talked to an AI agent. And I was 10 minutes in before I realized I wasn't actually talking to a human. That's how good it's getting. Now, I'm also of the belief that we should never try to fool people. So you know, in my own applications, I always identify that you're talking to an AI and the way I do that, as I'm like, I'm a digital agent. Think of me like a fast pass ride to a busy Disney ride. My job is to make this easy for you and get you connected with the right person in a quick, fastest time that I can.
David Turetsky: 23:04
Yeah. Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck, we have a special offer for our listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind, go to Salary.com/HRDLconsulting, to schedule your FREE 30 minute call today. One of the things that I've seen lately is especially for what should be more customer service, or customer oriented, focused companies, where they put chats in between you and actually talking to a live person. And I'm talking on the consumer side, not necessarily on the HR side. So we're kind of getting used to talking to AI, at least initially anyways, as a, as a, as a country as a consumer base. And that's not to me, there's something wrong with that getting into the HR world, except you what you're talking about as a higher touch AI. What I'm talking about is a lower touch dumber AI, which doesn't satisfy that need.
Jonathan Whistman: 24:15
Yeah. So I think where you run into a challenge at the highest level of HR and organizations, is because they're, they're judging based on their outdated reference point. If you went back 90 days, your online chat still feels robotic, you get frustrated half of the time, in the last 90 days. You can't tell the difference, that you're not talking to a real human that is going to show up in the HR world. And the question then that's legitimate question and so how do we think about this? How do we deploy it I think the still have to come down to the the decision on who to hire is a very human decision. It affects the life of that person, and what they're able to provide for their family. When you hire somebody inappropriately, and they churn out of your organization, I feel like as an employer, you're causing damage to society, certainly to that person.
David Turetsky: 25:11
Sure and their family.
Jonathan Whistman: 25:13
And their family. Right. So it's incumbent to get it right. And you have to get comfortable being in HR, that part of your job is to say no to a lot of people.
Dwight Brown: 25:23
Yep. Right.
Jonathan Whistman: 25:24
Right. And that is not a bad thing. But how do we say no to people in in a way that is in their best interests. And there is a ton of data, you know, it's almost like if you had, I always think about, like, if you had a bunch of kids, and you were able to say, you know, the one on this end with the right opportunity and training and instruction, they could play at Carnegie Hall, and this one with the same time and instruction and all of that, they're probably going to be a karaoke singer at best. Like, we accept that there are natural talents and a lot of things like, you know, it doesn't matter how much you train at something, if you're going to be at the pro level of it, it's it's not only training and determination, but it's there's also a little bit of inherent drive and skill and affinity for that thing. And that's what you can really get out with pattern recognition is to say, this seed, this person really does well in this kind of soil, and they blossom, and they grow quickly. And you know, you think about dysfunctional companies! Dysfunctional companies have a lot of employees, and a certain subset of those employees thrive in a dysfunctional environment.
David Turetsky: 26:34
Right.
Jonathan Whistman: 26:34
Like they're best off, being able to identify employees that do well in a dysfunctional environment. And they're better off, being able to exclude the employees that don't do well in a dysfunctional environment. We all have a place that to us feels like home. And that's where AI can be helpful is to be able to get to know you, as a person, as a human, and say, You're going to thrive in this environment surrounded by a bunch of individuals like this, and these kinds of challenges.
David Turetsky: 27:05
But you're talking about it is if AI becomes the enabler in that part, to find the right fit, right?
Jonathan Whistman: 27:13
ultimately where we're going. I think at some point in the future, we'll have a life concierge that is an AI that knows us more intimately than we even know ourselves, and can proactively on our behalf go out and find opportunities that we're uniquely suited for. They will, everyone will have their own sports agent. And they'll negotiate the deals for us.
David Turetsky: 27:37
With the business partner.
Jonathan Whistman: 27:39
Yeah.
David Turetsky: 27:39
With the businesses. And it's an AI on that side, too. So it's really just an exchange of information. It's an exchange of data and an API for the AI.
Jonathan Whistman: 27:48
But not just, not just disconnected data. Probably would happen. They'd probably go to the internet Right, but really understanding why and how things work. So I can't wait for the day where I say, David have your AI call my AI and they can have lunch together. cafe, get it?
Dwight Brown: 28:09
And then I'd be like, what's this big credit card charge that my I just racked up?
Jonathan Whistman: 28:14
Keep racking up, only bottles of wine?
Dwight Brown: 28:17
Yeah, exactly. No lobster for you!
David Turetsky: 28:19
It's virtual. It's virtual lobster and virtual wine. So you know, shellfish allergy, virtual lobster, won't hurt me. But But in that case, though, what we're talking about is we're talking about probably more of a gig economy, where I'm more of a free agent. And today, I could work for XYZ and tomorrow I could work for, you know, YZX. It doesn't really matter, as long as the two are handshaking and making sure that my AI and those and the AI's for those businesses, or collectives, as they were, understand what my role is, understand what my reputation is from past gigs, and whether or not my skills will fit what they have available for me.
Jonathan Whistman: 29:00
Yeah, the World Economic Forum just released a study a couple of weeks ago, where they projected by the year 2025, which is so far in the future,
David Turetsky: 29:10
Oh my God, that's really a long way away.
Jonathan Whistman: 29:12
That 52% of work related tasks will be replaced by robotics or ai 52%. In 2025.
David Turetsky: 29:22
Sorry, I'm gonna call bullsh*t on that. Yeah, that's too close.
Jonathan Whistman: 29:25
Yeah. And I would say I believe you that you believe that, because history has always said that those kinds of changes take a long time.
Dwight Brown: 29:35
Right.
Jonathan Whistman: 29:35
But the truth is, that change is happening so rapidly. As an example, and I'll be happy to go on the record, I would say by 2025, late 2025 there will be no call centers in the US at all. The reason is, all of that can be handled today with technology that exists that can do the job better than humans and that humans would prefer to do the job through those AI's. There's there's no situation under which a corporate for profit entity is going to continue to incur the costs of massive physical structures, call centers, paying people, paying, you know, people to recruit those people, paying to train those people, trying to manage those people. They're just not going to do it. And so that industry will entirely go away. We're talking millions and millions of people. If you follow robotics right now, you look at Tesla, Tesla has been working on a humanoid robot, which is sort of still clunky after three years of being under construction. There was a company that was started 18 months ago, that is fully humanoid, that can learn human tasks from scratch without programming. So it literally just looks at the items in front of it and goes, Oh, this is an apple that's edible. These are dishes and knows how to manipulate all those.
David Turetsky: 30:58
Yeah.
Jonathan Whistman: 30:59
We tend to think that those are going to be expensive robots. They're actually not. When you look at the costs of the components that are inside of one of those robots, your average robot will come in somewhere around the size of the price of a mid size sedan here in the US, which means
David Turetsky: 31:18
But you can't take it to get to work.
Jonathan Whistman: 31:20
Yeah. But what that means is that it is likely, more than likely that even middle class families will have a humanoid robot in their homes that can learn and do any of the tasks that you need to do.
David Turetsky: 31:34
Jonathan, but it's a perfect world. And I don't buy that, that people are going to sign up to buy a robot, not yet at least, a robot in their home and trust something like that, that you know, somebody else programmed.
Jonathan Whistman: 31:50
Yeah, because that's because you're my age. Right? And that's it.
David Turetsky: 31:54
I've seen too many movies? Is that what you're Sure! saying?
Jonathan Whistman: 31:56
Yeah, no, no, yeah. You might have watched too many movies. But have you seen a young kid? Like, I've seen Because they think it's an iPad, they expect babies pick up a magazine like this a paper magazine and swipe that the world's going to respond that way. So, but we're on it? compressing what, you know, if you look at like a 50 year, timespan humans are really good at adapting over time. But we're compressing that into a five year time span.
Dwight Brown: 32:20
I would say I agree with you with the caveat that I think the capability will be there by 2025 to have everything replaced. But I do think the adoption curve is going to slow that down. Because I mean, you're right, the you, you've got babies, you got all these young people, but you still have old dogs like us!
Jonathan Whistman: 32:44
Yeah, because Chat GPT took so long for people to change their workflows. Like I would say 80% of people are running things through Chat GPT.
David Turetsky: 32:53
Yeah, but Jonathan, they still don't really understand the prompts well, they still don't get it. And a lot of the data and by the way, I've seen this and I've tested it a few times, some of the data that they're using is specious at best, as a as you know, underlying lookups to some of the information they're collecting. And so I get what you're saying, and I totally agree with you. I'm just saying it's not 2025.
Jonathan Whistman: 33:16
It's well, but if you look at, it's the pace of change. If you would have went back 90 days ago, there would have been no voice AI on the planet that would have passed for anything. 90 days later, there's voice AI that I guarantee that you could not distinguish from a human voice, or the human responses. That's a 90 day, the like the and the cost of that even like the cost of processing a GPT model. In one year, the cost of delivery of that has gone down by 40x. You don't see 40x reduction in costs in any other sort of technology at any other point in human history.
David Turetsky: 33:57
Okay.
Jonathan Whistman: 33:58
Like we're getting really close to general artificial intelligence, which, which is very, it would be very interesting, right? Like right now everything is purpose built.
David Turetsky: 34:10
It is. Except I'm not willing to say that the robots are taking over yet.
Jonathan Whistman: 34:16
I hope they don't take over so just know.
David Turetsky: 34:18
They won't. And we can pull out the plug. Yeah, there's, there's a plug and we can pull out the plug. And, you know, this isn't gonna be a Cylon situation from Battlestar Galactica.
Jonathan Whistman: 34:27
Take out a few data centers. We're good.
David Turetsky: 34:30
Yeah, yeah. Or shoot it in the battery or if it's a lithium ion battery, it's gonna blow up anyway.
Jonathan Whistman: 34:36
But you know, while this is all conjecture, right?
David Turetsky: 34:40
Yeah.
Jonathan Whistman: 34:41
These are ways that if you're leading an organization, you really have to mentally stretch yourself to think how is the world shifting and how is it changing? And what I noticed when I'm in organizations are people are hiring the same way today that they did last year. And it isn't last year.
David Turetsky: 35:02
Yeah.
Jonathan Whistman: 35:03
So much has changed. The expectations that
Dwight Brown: 35:03
Right. Right. people have around the workplace are different. The expectation around transparency that young people have as they come into the workplace are different. And the response to that, for many aged organizations are, hey these millennials don't want to work. Or they, you know, they it, there's a complaint towards
David Turetsky: 35:32
It keeps getting republished. the workforce that's coming in. And when the reality is, it's actually the workplace that hasn't really adapted to modern day reality. Our you know, our schools are the same way. Our kids grew up swiping on devices, watching, you know, dynamic
Jonathan Whistman: 35:56
With a teacher who hasn't done a bit of history on the Discovery Channel with CGI graphics, all in all of that, they can Google anything at an instance notice. And then study since they got out of college a decade ago. they show up at school. And they sit in a classroom with with a
Dwight Brown: 36:12
Right.
Jonathan Whistman: 36:12
Like, if you wonder why they're disconnected in the classroom and screwing off, it's because education printed textbook that was written 10 years ago, hasn't kept up with the pace at which young people are used to learning.
David Turetsky: 36:22
Well, my kid goes to a school that uses Chromebooks. They don't bring home any textbooks anymore. Everything they do is using Chrome and they use a lot of online resources. They use a lot of videos, they do a lot more modern things than I ever thought they would.
Jonathan Whistman: 36:38
Yeah, and they have to.
David Turetsky: 36:40
He barely takes paper home anymore. I mean, he gets some workbooks. But that's because I need him to do some extra stuff. But
Jonathan Whistman: 36:46
And I would guess in another few months, when they really, you know, get their act together, they'll be MacBooks everywhere.
David Turetsky: 36:52
I pray, Yeah.
Jonathan Whistman: 36:53
I mean Chromebooks are sort of like going back to the Dark Ages!
Dwight Brown: 36:59
Did David's eyes just lit up when you said that?
David Turetsky: 37:02
Yeah, my Apple stock took a major hit because of the department of justice.
Jonathan Whistman: 37:06
The dopamine went up. You know, there's this sort of existential question when you you know, when you ask, like, how do you come to be here? It's an interesting thing. And you've probably experienced this, as males in society, I don't know what it is for women in society. You know, just because I'm not one. But I have four sisters. But you can't be a guy, at least here in the US. And within, you know, 15 minutes max of meeting another guy, you're gonna get this question. What do you do for a living? Like, it's sort of very early in conversation. When you meet another guy, it's what do you do? It's sort of like context. It'll be interesting when we're in a situation where it doesn't take all of that human effort to
David Turetsky: 37:51
Oh, I wish I was alive during that time. But keep society going. And people are free from just working for a living like that. that's not going to be, I'm not going to have that. But I'd love that if that was true. I usually it's Hey, do you do you know about hockey? That's my first question. Do you play hockey?
Jonathan Whistman: 38:12
Yeah. Well, you are an advanced species, David.
David Turetsky: 38:16
Yeah. I don't know about that.
Jonathan Whistman: 38:18
It is sort of a very early thing, right, about your identity being so closely tied to what do you do for work?
Dwight Brown: 38:27
Right.
David Turetsky: 38:28
And it will, because one of the things that we tend to love to do is to talk to people who are either analytical in nature, we'd like to kind of bond with those people. Like we were talking about before, you know, you and Dwight like to live dangerously. I live dangerously by playing hockey or skating or whatnot. That's okay. I well, I don't like getting off my skates too high.
Jonathan Whistman: 38:51
So you're allowed to be six inches off the ground at most.
David Turetsky: 38:56
That's, that's the floor right there, get it on the ceiling, floor, whatever. But But I guess going back to what we were originally talking about, though, and I love your premise. But I have to say that in order for the robots to take over. In order for HR to allow robots to become more part of the processes, a lot of things have to happen. And one thing that we talk about all the time that Dwight and I spend most of our time talking about is that the data that underlies HR is usually crap. And in order for the AI to be able to make decisions that are well founded, it needs to have a better training dataset, and the training data sets that we're currently working with, yes, suck
Jonathan Whistman: 39:41
And I would agree with you, but why is it that most HR departments have crap data?
David Turetsky:39:47
It's because we're human, and we make mistakes. And there are mistakes that we can forgive, that don't drive payroll, and then there are some mistakes that we go, oh it's fine like the department numbers, are they not correct? It's okay, the GL stuff comes up someplace else, so we don't have to worry about payroll getting booked to the wrong department. So there are things that we allow the payroll system or the HR system to live in a state of imperfection. Because it doesn't hurt us.
Jonathan Whistman: 40:17
Well, and yeah, that would go one step deeper is that it the time and attention it takes to collect great data and analyze that data.
Dwight Brown: 40:27
Right, right.
Jonathan Whistman: 40:27
It's beyond most humans capability. And the thing that they like to do, like most people don't like that part of it, right?
David Turetsky: 40:36
Oh, yeah, audits suck!
Jonathan Whistman: 40:37
When we, when we turn over data collection and analysis and identifying what's important, and what's spurious to AI models and data models, you actually get better quality data just by the act of feeding it to that. So you as an example, if you, you know, if you go to an oncologist for cancer, here's a human that's, you know, spent $400,000 plus right to put themselves through school, eight years plus residency. And now you can take a picture with an iPhone camera, and get as accurate a diagnosis as that person can give you. Second to that, you can you can feed that picture into a database, that compares it to every source of cancer that's ever happened on the planet, and the three people that actually responded to this sort of treatment, your doctor coming out of school and going through that will never be able to keep up with that. I think at some point, it will be malpractice for a doctor not to plug into an AI for the for an understanding of what's happening with a patient. And you're gonna see that across almost every industry where the outcome is better, because you because you have an AI. That doesn't mean that it doesn't present very real, you know, challenges to the human to humans as a species, and even raise up a question of what does it mean to be human? And how do we find meaning in life? These are existential sorts sorts of questions, but you, you can't put the genie back in the bottle. And I think it's coming faster than most people recognize.
David Turetsky: 42:17
Remember, back to our, our training and our educational systems. When we learned about, you know, how did people go through stages of development, you know, fire revolutionized certain things, smelting, you know, iron into steel, revolutionize things, being able to create automobiles in mass, revolutionize things, cotton gin, other things, they revolutionized pieces of human existence. And so then we stopped focusing on the labor that needed to do those things. And then we were able to turn our attention elsewhere. So what you're mentioning now is that computers and data and connectivity, and artificial intelligence on top of that enables us to stop worrying about certain things and focus on others. So really, isn't it in getting back to the HR? Doesn't this then help HR then get out of one business and help focus strategically on other businesses?
Jonathan Whistman: 43:18
Yeah, that's exactly my point. All that filtering and mindless noise and data if they can offload that and just spend their time connected human to human in the business, I think that not only will HR professionals find that more gratifying work, it'll also have a bigger impact on the businesses that they work for.
David Turetsky: 43:48
On that note, perfect ending to a great podcast. Jonathan, thank you very much. We're gonna have to do a round two on this because there's so much more to uncover.
Dwight Brown: 43:59
We could easily gotten that five hours that we were joking about.
Jonathan Whistman: 44:04
Well, we made it past the 15 minute mark. So
David Turetsky: 44:08
30 minutes. Yeah, I think the 15 minutes actually ended your introduction. And then we got it to the meat of the podcast!
Jonathan Whistman: 44:15
Tune in to the next episode, where we shall reveal what happened to Dwight when he was running behind a large parachute and accidentally hooked onto the weather flag and flew 300 feet in the air
Dwight Brown: 44:28
And then dropped it
David Turetsky: 44:29
To be, to be continued. Bom bom bom! Anyways, Jonathan, thank you so much. That was awesome.
Jonathan Whistman: 44:38
A pleasure, gentlemen.
David Turetsky: 44:39
Dwight. Thank you. Thank you. Appreciate your being here, Jonathan. And thank you all for listening. Take care and stay safe and also pay attention. We're going to have round two with Jonathan Whistman. The podcast title of that will be the robots will be taking over in 2025!
Announcer: 44:56
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