Slop, Supply & the Search Process: Ben Zweig Breaks Down the Data

Slop, Supply & the Search Process On this episode of The Chad & Cheese Podcast, Joel and Chad sit down with Ben Zweig, CEO of Revelio Labs, economist, NYU Stern professor, and recovering quant who’s somehow raising three little girls while tracking the entire labor market. Ben walks them through Revelio’s new AI Labor Market Tracker—the data-driven, non-speculative look at how AI is actually reshaping work right now. They dig into the 28% drop in computer science majors, why companies that successfully adopt AI are growing headcount instead of cutting it, the massive friction AI is creating in the hiring process, and why 93% of the real change is happening inside jobs rather than between them. It’s economist brain meets HR reality, with just enough New York attitude and sleep-deprived honesty to keep it dangerous.

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• 1:29Speaker 03

  Hire your kids, lock the doors. You're listening to HR's most dangerous podcast. Chad Soash and Joel Cheeseman are here to punch the recruiting industry right where it hurts. Complete with breaking news, brash opinion, and loads of snark. Buckle up, boys and girls. It's time for the Chad and Cheese Podcast.

• 1:52Speaker 00

  Oh, yeah. It's the podcast your starting quarterback warns you about. It's the Chad and Cheese Podcast, everybody. I'm your co-host, Joel Cheeseman. Joined as always, Chad Soash is in the house as we welcome Ben Zweig to the show. He's CEO at Revelio Labs. He's also an economist, an adjunct professor at NYU's Stern School of Business. He's an IBM alum and frankly, putting all that other shit to shame. He's the host of the podcast, The Economics of Work Podcast. Ben, welcome to HR's most dangerous podcast.

• 2:28Speaker 04

  Thanks, guys. Happy to be here.

• 2:30Speaker 00

  Good to have you. Good to have you. So, uh, that's, that's some impressive stuff, but we always want to know who the person is that we're talking to. So let's give the listeners and viewers a little bit of Twitter bio about what makes Ben tick.

• 2:43Speaker 04

  Okay, let's see. I mean, a lot, a lot of things, a lot of things powering this ticker.

• 2:48Speaker 00

  So many layers.

• 2:48Speaker 04

  I think the economics stuff is, is pretty central. So I still kind of think like an economist, feel like an economist. So that really like, you know, was, is a core part of how I think and feel. Lifelong New Yorker, just moved out to the burbs and it still pains me every day. Got three little girls. So, you know, that's keeping things exciting.

• 3:07Speaker 00

  Oh, wow. Wow. What are the ages?

• 3:10Speaker 04

  Wow. Yeah. Five, two and a half, and seven weeks.

• 3:14Speaker 01

  Wow, dude.

• 3:16Speaker 00

  So you're sleepless in the suburbs of New York?

• 3:18Speaker 04

  In the thick of it. So if I start drooling, you know, don't take it personally.

• 3:23Speaker 00

  Man, we're gonna take it easy on you, my friend, because we both know.

• 3:26Speaker 01

  Think about that spread when they start moving into adolescence. Oh my God. Just to me, yeah, that's gonna be fun. That's gonna be fun, Ben. You're gonna like that.

• 3:34Speaker 04

  Yeah. People have been warning me about that. I mean, I feel like in some way, I'm like, how hard could it be? Like, plenty of bozos do it.

• 3:41Speaker 01

  You dumbass.

• 3:42Speaker 04

  I also thought that about, you know, being a parent to like infants and toddlers, and it's, it's really hard.

• 3:49Speaker 00

  So suburbs of New York, what are we talking? We talking deep, we're talking long, is it a borough? Is it deep suburbs like Connecticut?

• 3:56Speaker 04

  Stanford, Connecticut.

• 3:57Speaker 00

  Oh, okay. I love that a whole different state is the suburbs of a different state.

• 4:02Speaker 04

  I mean, you know, New Jersey is, you know, North Jersey is definite suburbs and Connecticut kind of, kind of makes it if you're like commutable. So it's like a 45-minute train. So we can count it.

• 4:12Speaker 01

  Okay. So question, you were a quantitative strategist at an emerging market hedge fund. You were a quant?

• 4:19Speaker 04

  I was a quant. Yeah. Yeah. Now, now people don't use the term quant anymore. Like, everyone just calls themselves a data scientist.

• 4:25Speaker 00

  Is that discriminatory? Is that sort of racist now? I don't.

• 4:30Speaker 04

  No.

• 4:31Speaker 01

  Anything to do with race?

• 4:33Speaker 00

  Well, I was thinking of the movie The Big Short where Ryan Gosling's like, my quant, look at him. He's Chinese or whatever it is in the movie. So I didn't know if quant was.

• 4:41Speaker 01

  But yeah, go ahead.

• 4:42Speaker 04

  Yeah. No, I mean, I think, I think quant for, for a while really meant like someone create, you know, creating options strategy, like, like someone deep into like the structure of like derivatives. And now it really means someone who analyzes a lot of data for trading strategies. So I think, you know, quantitative finance has become much more data oriented. So now everyone just calls themselves a data scientist. And, you know, they get a lot of people from big tech to work at these funds now.

• 5:06Speaker 00

  Gotcha. And it's a really cool pickup line for chicks in Manhattan when you say, I'm a.

• 5:11Speaker 01

  I'm a quant.

• 5:12Speaker 00

  Yeah, yeah. Quants are an economist.

• 5:14Speaker 04

  Didn't work for me at the time, so.

• 5:17Speaker 00

  It's working now, or it worked out. Three kids.

• 5:20Speaker 01

  Yeah, no kidding. I tell you what's working is this AI labor market tracker thingy is it's that, that's the technical term that you guys came out with. And we're looking at the, the July numbers. Can you give us kind of like an overview of why, why'd you come out with this in the first place? What is it? And what happened in your, your latest report?

• 5:41Speaker 04

  Yeah. Okay. So lots to unpack there. I mean, so first, first thing to, to mention, I mean, we're, we're a labor market data company. So we're, we're tracking all this employment data. And, you know, a big question that everyone's asking is how is AI affecting the labor market? And we felt like, okay, we have a lot of data to, to weigh in on that question, which is an important question today. And, you know, we wanted to create something that was specifically not speculative on what might happen in the future. We wanted to track what was happening and identifiable today, like right now, and then continue to track that and, you know, weigh in on like, you know, use that as evidence for what might happen in the future. But this is specifically like not speculative. It is, it is descriptive in what's happening today. So the way we thought about it, and the way I generally,

• 6:26Speaker 04

  like as an economist, a lot of people ask economists, what is happening with this market?

• 6:30Speaker 01

  Usually say, I have no clue.

• 6:32Speaker 04

  Right, right. Most of the time it's I have no clue. But if I feel pressed for an answer, I try to break it down into like four questions. I try to say like, all right, what's happening to supply? What's happening to demand? And of course, through supply and demand, you get equilibrium quantities and prices. So that's kind of derivative from supply and demand. So those are, you know, you have supply, you have demand, and then you have what's happening to the nature of what's exchanged. So is the actual good changing? And also what's happening to the method of that exchange? So what is happening to the search and match process that, that enables that exchange? So those are like the, the parts of a market that I think are worth analyzing. And so, you know, we, we follow that same structure in this report. So we have a section on supply factors, demand,

• 7:12Speaker 04

  quantities, prices, the content of jobs, and the, the search and match process of finding jobs, you know, employer and employee matching.

• 7:20Speaker 00

  So a customer hires you for what? Like the data to do what? Like forecast jobs? And explain to me sort of like why someone would, would hire you or why the, why the information would be advantage?

• 7:31Speaker 04

  Yeah. I mean, so, so most of our customers are, are in HR. They're, they're in corporate HR. So we, so, you know, a lot of, sometimes in TA, sometimes in like talent intelligence, workforce planning, people analytics, compensation benchmarking, employee experience, like anything where, anything that we would consider like analytical HR. So parts of HR where it's useful to kind of like have a handle on what's going on and be able to benchmark versus peers.

• 7:56Speaker 00

  Give us the state of, of that HR customer. I, I would guess they're really confused about what's going on and you have answers.

• 8:03Speaker 04

  Definitely confused. They don't have a lot of data, or, or even worse, they do have a lot of data and don't know how to use it.

• 8:09Speaker 01

  Oh God, yes.

• 8:10Speaker 04

  That's fairly common. And, you know, I mean, I used to, I used to run a people analytics team at IBM. And, you know, we would try to answer important questions. And it was worthwhile, but it took a long time. Like every question we'd want to answer was a six-month project. We had to like dig into the data, get to know things. And even at that point, we only had IBM data. We never in our wildest dreams imagined that we could like benchmark against Oracle. Like, they weren't going to give us their HR data.

• 8:35Speaker 01

  Right.

• 8:35Speaker 04

  So we're basically using a lot of public statistics, public data. So LinkedIn profiles, job postings, et cetera. And, and we're, we're trying to essentially reconstruct what the HR database looks like for a company that we have no affiliation to.

• 8:48Speaker 01

  Okay.

• 8:48Speaker 04

  So then, you know, we can allow, you know, someone to analyze a competitor. And so, you know, through that, like it's useful for benchmarking, where you're not just benchmarking against some like ambiguous industry. You're benchmarking to like this company who you want to emulate and excluding that company which you think is like not worth emulating, to say it nicely. And we also have a lot of like hedge funds that use the data for, you know, to, to track like workforce trends within companies they're tracking.

• 9:16Speaker 00

  Yeah, you do.

• 9:17Speaker 01

  So siloed data is, is really the big problem because companies only see their own data and they really need to take a look at market research, not to mention competitive data, which you, you do all of that. As we start to dig into the report, one of the biggest pieces that really caught my eye was you, you talked about supply, supply being down 28% to decline in computer science and the IT side of the house since 2022.

• 9:41Speaker 04

  Yeah.

• 9:41Speaker 01

  Talk about that because I mean, there's still a lot of, there's a lot of computer science, you know, jobs that are out there. Why, why are these rolling down? Are they just, are they just transforming into AI positions? Or what's happening? Or are they plumbers? Who knows?

• 9:55Speaker 04

  Yeah. So one is like, it's a huge increase. So 28% in, in. Like a few years is is really nuts. So students are not majoring in computer science to the same degree. I I think, and this is, this is conjecture a little bit, but but I think it's fair to say that this is in response to AI. That there is a sense from students that computer science is ripe for automation. And they are considering that major to be like potentially displaced. So so they're putting a lower premium on that training. So on the one hand, like, it's very nice to see that supply is responsive to expectations of a certain technology. So, you know, one way to think about like, will there be job displacement?

• 10:40Speaker 04

  The way I kind of think about that is there's like three determinants to that. Like, one is, is supply reactive? If if workers can basically like go wherever they want very flexibly, then we have nothing to worry about. So so if if supply is like, you know, following market needs, that's great. That should make us feel comfortable. On the demand side, like if firms are adopting very quickly, that could potentially be disruptive. If firms are basically ignoring the technology, then nothing's going to change. And we, you know, then we don't have to worry about displacement. We have to worry about productivity. It's a different story. But the third, and I think the most important is like, how quickly can the the work actually change within a job? So how quickly can the tasks of a given job kind of reconfigure to adapt to the certain technology?

• 11:20Speaker 04

  So I think the the supply being responsive is encouraging. I happen to think it's a little premature.

• 11:26Speaker 00

  Is that a data-driven opinion or is that sort of your own nuanced opinion?

• 11:30Speaker 04

  Okay, so a little bit of both. So I think the data that we're seeing from the AI lab. So we so we, you know, we we collaborate a little bit with OpenAI, with Anthropic, and now most recently with DeepMind team at Google. And they are very quickly analyzing their usage data. And at least, you know, OpenAI just came out with a paper yesterday, I think, so hot off the press, about like the transition from sort of chatbots to agentic AI. And so so that that is, that is something that is definitely happening. And I think the the thing that's interesting about chatbots is that unlike other technologies in the past, they have had such low barriers to entry. Like, anyone's grandma can use ChatGPT. It's so easy. So it doesn't, it doesn't favor the digitally native.

• 12:15Speaker 04

  And so so, you know, you can sort of like outsource, you know, question answering, like, that's very easy. But with with more agentic systems, it's pretty complicated. Like, you know, I I consider myself fairly technical and like, you know, I try messing around with OpenClaw and I'm just like intimidated. Like, you have to use a terminal, you have to, you know, it's it's like not so easy. You need to like really sit down and know it and learn it. And I kind of, you know, suspect that the people who are more capable of using these these systems are the ones who are better trained in computer science.

• 12:49Speaker 01

  Well, let's go over, I mean, just a little historical facts that we overindex on this a lot. We push kids into college. Now we need carpenters. We need plumbers. We push kids, our our kids want to start using some of the the better, newer developer languages and whatnot. And we're getting, you know, dudes that are 70 years old to be able to go back and do COBOL programming. Because nobody's doing COBOL. So it's like, it feels like we're we're going in this cycle of, oh, here we go again. Here we go again. We're over indexing on something. This is stupid. What do you feel about that?

• 13:25Speaker 04

  Yeah, no, I I think it's a real thing. I mean, now we have a shortage of radiologists right after Jeff Finn, the godfather of AI, predicted there would be no more radiologists. So, yeah, I think it's definitely a real phenomenon. I mean, one, one thing that gives me a little bit of comfort is that when we talk about like, you know, college education specifically, like, this is, this is a a change in the flow of new people, not not the stock of candidates. Also, I mean, computer science generally has the following trait where like people go in as like a software engineer, and then they're a software engineer, and then there's a lot of exits from that occupation where they end up being product managers or whatever. So there's, there's quite a bit of like outflow rates. So if there were a shortage of, you know, software engineers, I'm not terribly concerned

• 14:10Speaker 04

  because the stock is pretty high and the outflow rate is pretty high. So like, if if it were more, you know, if the market needed more software engineers, like, it would, you know, it could be just more attractive to stay in those roles relative to, you know, more of the exit options. But I do think people are overreacting a bit.

• 14:28Speaker 00

  Yeah. Whenever we get an economist, I love it because they're data-driven and they're not just sort of, this is my opinion and we'll see what happens in the future. But to ask the question of like, are are all the jobs going to go away? Are we all going to get UBI and chocolates from the government every month? And and or what I'm hearing, I think, is more of like Jevon's paradox, which I assume you're familiar with, where the efficiencies of coal meant we just had more trains and more energy and power. That's my guess, but I want to hear it from you. If someone says, what the hell is the future going to look like? Your answer is optimistic, negative, and why?

• 15:02Speaker 04

  Okay, so definitely can't say what the future is going to look like, but that's, that's very tough. But in terms of job displacement, I'm I'm more optimistic.

• 15:11Speaker 00

  If you're in Vegas and placing some bets, where would you place your bets?

• 15:14Speaker 04

  Okay, so let me, let me understand the question. Is this like, are we going to see technological displacement? Like, are we going to see job displacement or are we, or do we have less to worry about?

• 15:23Speaker 00

  I think we all agreed there will be jobs that go away. But will there be more jobs created as a result of the new technology where more different skills are required? Which I think part of what your research does, we'll get to that of of people learning new skill. But so I I think you're optimistic on where we're going. Like, you have faith that humanity will be okay with AI? Or is Terminator coming and we're all doomed?

• 15:47Speaker 01

  He is hoping.

• 15:49Speaker 04

  Yeah, yeah, I I'm franked.

• 15:51Speaker 00

  Two were born pre-ChatGPT. Just, you know, just.

• 15:55Speaker 04

  Is that right? No one? Oh, yeah, that's right. Right at the cutoff. I got one right at the cutoff.

• 16:00Speaker 00

  I know the numbers.

• 16:02Speaker 04

  Yeah, yeah, she's the control group.

• 16:03Speaker 00

  I could be an economist. I could be at NYU starting this gold business.

• 16:08Speaker 04

  Okay, so so I'm I'm more optimistic that we won't see widespread technological displacement and that we won't see big spikes in, you know, unemployment rates due to technology. Yeah, for a few reasons. I mean, one is that, let me just bring some historical context. So in in 1930, John Maynard Keynes wrote this essay, Economic Possibilities for Our Grandchildren. And he, you know, predicted, oh, if we keep growing by 2% a year, you know, we are going to have such enormous productivity that there will just not be as much to do and we'll all be working 15-hour weeks and we'll just live a life of abundance and luxury and leisure.

• 16:46Speaker 00

  You got everything right except the hours that we work, right?

• 16:49Speaker 04

  So he actually like underestimated the growth rate a little bit. So so, you know, I mean, he was like extraordinarily right on like the productivity effect. But was very wrong on what we would do to that. So I think, yeah, first of all, like, the vast majority of the world does not live in abundance. So so I think, you know, we we are very, very far from having like nothing to do and nothing to build. I I think, you know, really, anyone you ask is going to think, yeah, we haven't run out of stuff to do in the world. Like, there's plenty of useful, we have much bigger appetites than like the the economy that we have today. And that's especially true on a global scale. And this is a global phenomenon.

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• 19:32Speaker 00

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• 19:35Speaker 04

  So I think, you know, just like general, you know, growth, it just doesn't, I don't think we're running out of stuff to do, stuff to build. And, you know, there's this law in economics called Saez law, which is basically supply creates its own demand. So basically, what that really means is that the economy is constrained by its productive capacity. So if you have more productive capacity, if you have this big boost in productivity through AI, then that loosens the constraints on what we can build. And maybe in a long, long time, and you know, hundreds of years down the road, you know, maybe, maybe we'll, you know, have such abundance that we'll actually run out of stuff to do.

• 20:11Speaker 00

  More data centers, Chad. We need more data centers, more supply.

• 20:16Speaker 04

  Yeah.

• 20:17Speaker 00

  Build them.

• 20:17Speaker 01

  Oh, that's gonna, that's gonna create so many jobs, geez. So that being said, it's actually taking more jobs today, more job postings today to get to the hire. Why is that?

• 20:28Speaker 04

  Yeah. So that, I think, really, really gets at what I think is the worst thing about AI today. I think, I think, you know, there's one area where AI is actually harming the labor market. And I think that's really in the search and match process. And this is a market that you guys know really well.

• 20:43Speaker 01

  Yeah.

• 20:44Speaker 04

  You know, right now, you know, any job candidate can use these, these new automated tools to kind of auto apply for 100 jobs a minute, whatever it is. And there is a race for speed. There's so many like, you know, hiring cafe and whatever, you know, they're all kind of popping up to like, get you to be the first in line.

• 21:04Speaker 00

  Slop it up.

• 21:04Speaker 04

  Yeah. And, and, and they're, you know, creating these like, sort of, you know, AI applications that are totally signal jamming the employers. And there's some asymmetry in, in, in who can use AI, you know, in a lot of states in the US and outside the US, it's actually illegal to use AI to evaluate candidates. Now, I don't think that's actually enforced. Like, you know, a lot of, a lot of companies are-

• 21:30Speaker 01

  Not today. Not in this administration.

• 21:32Speaker 04

  Yeah. But, you know, there is some liability for, for employers using AI. So I think they've been more reticent to, to kind of deploy these tools. But, but candidates are using lots of AI. So it's very hard to determine like, who's a good applicant. And I think employers are having a very tough time, which means that the, the actual like, you know, utility of posting a job is declining quite a lot. So, so now, like, the actual like numbers of hires that you can get from a given job posting is pretty low. So, you know, in the economy at large, you know, in the US economy, job posting volume is like kind of flat. But hiring rates are very, very low. Hiring rates and attrition rates are very low. So we have this environment that's kind of like the opposite of what

• 22:16Speaker 04

  we saw in the Great Resignation, where people are just staying in their jobs. And I think it's due to this like added friction in the ability for people to match with employers. It's just gotten a lot harder.

• 22:28Speaker 01

  Well, I think there's, there are a hell of a lot less open opportunities as well. I mean, you've got the high, high churn rate for like hospitality, you know, obviously healthcare, so on and so forth. But for the most part, I mean, if you've got a job and you've got a paycheck, this is, this is the time just to sit still, be quiet, and wait for the market to open up.

• 22:48Speaker 00

  Keep your head down, do your job.

• 22:50Speaker 01

  Yeah. Okay. To your point, to your point, real quick, though, LinkedIn, as we talk about all this slop that's being out there, that's being used by, you know, by, by job seekers. I mean, LinkedIn, who was crowned the king of slop, they were pushing this on their users. And now they're asking their users to police it. The slop that they actually created. So again, another point of over indexing, where we have these platforms that are like jamming AI in for their users to use. It is backfiring on them. And they're like, holy shit. Now we have to have our users police the mess that we told them to make.

• 23:28Speaker 00

  Indeed, pausing their automated, you know, apply process as well. So people are pushing the brakes on some of these, some of these technologies.

• 23:36Speaker 04

  I think that's great. I mean, as they should. I think, you know, for such a long time, and I mean, you know, you guys know more, more about this than I do. But I mean, job boards, you know, have been basically ad businesses. And, and this is, and, you know, I think there's an opportunity for them to kind of rebrand themselves as more like clearinghouses that facilitate the match. And that, that requires some costly signal for someone to apply to a job. So if I had my way, you know, if I were running Indeed or LinkedIn, I would limit the number of applications that someone could submit per day, would maybe do some rank ordering, or would do, do something and, and, you know, try to figure out ways to facilitate more matches.

• 24:16Speaker 00

  And I think to your point about the sort of the tsunami of automated applies and lazy apply and these technologies, I think a lot of real humans have been swept up in this process where I don't know what's a real human, what isn't. And I think real humans are really struggling with applying to jobs because they're competing with robots that are applying to hundreds and thousands of jobs. One of the, the numbers in your research showed that companies that have successfully adopted AI show a 27% increase in headcount. Is that using AI to the hiring process where they're better able to filter out the real humans from the bots? Explain sort of that number and where you guys came to AI good, headcount goes up.

• 24:59Speaker 04

  Yeah. Yeah. Okay. So it's a very tough. So I think in this, in this like, how is AI affecting the labor market question, the hardest part of that is to measure AI adoption. So I think, you know, we have these measures of AI exposure, what are the jobs that AI touches, like that, that's fairly straightforward and very useful. But it's hard to measure adoption. So we had had a paper that came out with, with Ramp. So Ramp, you know, is a payment company, and they can track, you know, companies that, that are spending more on AI tokens. And the most intensive adopters are hiring a lot faster than the other adopters. So are hiring a lot faster than the not yet adopters. So importantly, you know, companies that use AI don't look like companies that don't use AI. They're, they're different.

• 25:42Speaker 04

  There's selection bias. But in this Ramp study, we were able to control for that by using, you know, firms that will adopt but haven't yet. So it's a kind of, for, for those statistic nerds out there, it's a treatment on the treated. So it's a causal effect for those who eventually get treated. And we do see an increase in hiring, even among young people, which is interesting because there's some other research that young people are being especially hit hard. And now I think we can conclusively say, maybe not conclusively, but we can for now, say that that's not the result of, you know, displacement because of adoption. Now, the problem with the Ramp study, which, you know, is that, you know, that's an anonymized set of firms, you know, they don't release their, their company data. So we have a different approach where we can't do this kind of, you know,

• 26:26Speaker 04

  isolated treatment on the treated effect. But we do look at, you know, companies that we determine are intensely using AI, you know, basically onboarding like AI integration teams. And this is, this is a method that was established in a different academic paper that works pretty well, tracks pretty nicely. And there we see an actual bigger effect of the adopters on hiring. So I think what, what to make of this is that like, you know, when companies are investing in AI, they're not, you know, they're not investing in like displacing workers, at least not yet. They're trying to build more, they're trying to change the way, the way they operate. It's something in economics called the productivity J curve. You know, you have to take this big upfront investment in order to get the payout later in adopting these technologies.

• 27:08Speaker 00

  Why do you think the layoffs are happening if they're not because of AI? Are people, we overhired in COVID, so we're sort of right sizing, we're getting rid of jobs that we don't need ever, or getting rid of high, high paid people and replacing them with lower, lower paid people that can use AI? Like, what is your opinion on why we're seeing so many layoffs, particularly in tech?

• 27:29Speaker 04

  I think we're actually not seeing so many layoffs. So I'll push back on the premise a little bit. I mean, layoffs in tech have been actually lower than, than they, than the historical averages. And most of the layoffs that we see are in manufacturing. So, so which are kind of, you know, not touching AI at all. Obviously, the layoffs in tech get a lot of publicity. And they, and I think for a while, it was very fashionable to attribute those layoffs to AI, because it makes companies look efficient. But, you know, I think, I think when I think about like Block laying off 40% of its workers, number one, it's not consistent with, with what we see in the aggregate data. So it's like an isolated case. Number two, you know, for anyone who works in a company, you know

• 28:11Speaker 04

  that like, the worst environment to implement a change is an environment where everyone's overworked and like, doesn't have the bandwidth to, to change the way they do things. So like, just a bad strategy. But also, number three, is like, even if it were to take advantage of productivity improvements, it's such a depressing state of like, the prospects for Block. It's basically saying like, we don't have anything else to do. You know, we're just gonna like, do the same amount we've always done with fewer resources and like, do less R and D or something. It's basically saying we've, we've capped out our growth. So I think it's a bad strategy, kind of inconsistent with like, the nature of technology. And I think Wall Street's not really buying it anymore.

• 28:52Speaker 05

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• 29:22Speaker 01

  Then that's who you hedge on.

• 29:24Speaker 04

  Yeah.

• 29:25Speaker 01

  So, okay, we're seeing a lot of optics flipping around. We've heard so many companies, dumbass CEOs say, we're not gonna have any, we're not gonna be hiring, we're gonna be using more AI, we're gonna be doing these things.

• 29:39Speaker 00

  Klarna.

• 29:39Speaker 01

  To only, you know, have to outsource and or bring, you know, humans back. This is something I got from a press release from, it might be one of your competitors, Global Data. And here's a quote, "In the automotive industry, Canadian Tire Corporation has a posting for the Director of Engineering Performance and AI Metrics, which points out establishing an agile and AI performance measurement practice. The initiative aims to augment traditional agile metrics with new KPIs that reflect total system health, including," this is the important part, "including human AI collaboration and automation effectiveness,"

• 30:23Speaker 01

  end quote. Is this a true transformation? Or is this just, just trying to be the virtue signaling of, you're gonna be okay, and not scare the shit out of their employees?

• 30:36Speaker 04

  It's a good question. So first of all, love global data and this is, I think, coming from LinkUp, who they acquired, which, you know, we partner with and we love them.

• 30:44Speaker 01

  Toby just left.

• 30:45Speaker 04

  Oh, is that right?

• 30:46Speaker 01

  Yeah.

• 30:46Speaker 04

  Oh, my God, I didn't realize that.

• 30:47Speaker 00

  Toby out. Yep.

• 30:48Speaker 04

  Wow. Oh, my God.

• 30:51Speaker 00

  Toby out, man. Yeah.

• 30:53Speaker 04

  Wow. End of an era.

• 30:54Speaker 01

  Yes, very much.

• 30:55Speaker 04

  No, I'm just like letting that sink in. But anyway, back to business.

• 30:59Speaker 01

  Twenty-five years. Yeah.

• 31:00Speaker 04

  Yeah, yeah. Well, I need to shed a tear. So, yeah, is that signaling? Is it real? I mean, you know, job postings in general are like, you know, they're expectations about the future. They're anticipatory. So who knows whether this will like actually pan out?

• 31:15Speaker 01

  Yeah.

• 31:15Speaker 04

  And, you know, they're useful for signaling, you know, what your intent is to the market. But they're also economic artifacts. Like, this is something that they are putting out there to get candidates who are drawn to that to some degree. So I don't think the content of job postings is virtue signaling. I think it really is the expectation that they're going to find someone who is drawn to this type of thing and it will like be more productive. Now, that's not to say it's not also like very optimistic that they can like start changing things and do a bunch of human AI collaboration and like change their, the way they operate, especially in like some big procedural company.

• 31:53Speaker 01

  Yeah.

• 31:54Speaker 04

  So I think it's going to be hard for them, but I think it's at least a slightly optimistic signal that they are at least trying.

• 32:01Speaker 00

  Sort of the big question and what your data shows that most of the change in work is happening within occupations. Say more about that. Explain what that means.

• 32:12Speaker 04

  Okay. So, yeah, this is like my pet obsession because I think public labor statistics really do not address what happens within occupations. So basically, like, sometimes when people think about, you know, the effect of AI, the effect of trade, the effect of anything, you know, they think, oh, what are the jobs that will increase? What are the jobs that will decrease? You know, will we see more of this occupation, less of that occupation?

• 32:34Speaker 01

  Right.

• 32:34Speaker 04

  And I think what gets missed is that the occupations themselves change a lot. So, you know, I think it's especially kind of obvious if we think about like the white collar workers that we know, you know, the stuff we do today is different than the stuff we did five years ago. You know, work changes and, you know, that is just the nature of change. You know, when we think about this example of, you know, bank tellers, which is like the canonical example of like, you know, what happens with AI? You know, I think the story, which I don't really buy, is this Jevons paradox story that we had, you know, this automation, this ATM, which automated bank tellers and therefore it got so much cheaper to like do, you know, banking and then now we see a chase on every

• 33:17Speaker 04

  block and, you know, bada bing bada boom, we see more demand because like it got cheaper to deliver these services. That's the Jevons paradox story. I think the story that I prefer is that, you know, the bank tellers are no longer bank tellers. It's kind of an artifact of history that they still get called bank tellers, but they're really just doing like customer relationship management stuff. And if like, you know, Sandy Weil, the chair in the Citigroup at the time, said, hey, we're going to call these customer relationship managers, we'd be having a very different conversation about, you know, the effect of AI. And that's just the arbitrariness of language. You know, the job itself transformed into something totally different. And sometimes we say that's part of the same occupation, sometimes we don't.

• 34:00Speaker 04

  But I think the reality is that like jobs themselves change pretty quickly. I mean, I mentioned, you know, I was like a quant and then data scientist and, you know, what was a quant, you know, in 2015 is different than what a quant is today. What was a data scientist in 2015 is very different than what a data scientist is today. Same with sales, same with marketing, same with so many occupations. So, you know, if we think about the total work done in the economy, if we take all the tasks, you know, that are, just sum it all up, like the total stock of work in the world, and we think of that, you know, relative to a year ago. There's different things. Sometimes, you know, industries, you know, grow and shrink. Sometimes jobs transform. Sometimes there's more occupations of this, less occupations

• 34:44Speaker 04

  of that. So there's this kind of like between occupation change and this within occupation change. And if we kind of decompose that change, we see that actually 93% of the change in the work content is attributable to changes that happen within occupations, not by some occupations growing and some shrinking.

• 35:00Speaker 01

  And that is the Zvag paradox.

• 35:03Speaker 04

  Yeah, you heard it here.

• 35:05Speaker 01

  Ben Zvag, economist and CEO at Revelio Labs. Ben, if somebody wants to connect with you, I don't know, maybe check out the tracker. Where would you send them, sir?

• 35:16Speaker 04

  LinkedIn, you know, to follow me. I'm always like constantly posting this stuff. It's a real profile, so don't worry. And reveliolabs.com, you can see the AI labor market tracker, public labor statistics. We put out a lot of newsletters, free content, just.

• 35:31Speaker 01

  Great stuff. Yeah.

• 35:32Speaker 00

  Slop free Ben, everybody. Chad, that is another one in the can. We out.

• 35:39Speaker 06

  Thank you for listening to, what's it called? A podcast. The Chad, the cheese. Brilliant. They talk about recruiting. They talk about technology, but most of all, they talk about nothing. Just a lot of shout outs of people you don't even know. And yet you're listening. It's incredible. And not one word about cheese. Not one. Cheddar, blue, nacho, pepper jack, Swiss. So many cheeses and not one word. So weird. Anywho, be sure to subscribe today on iTunes, Spotify, Google Play, or wherever you listen

• 36:19Speaker 06

  to your podcasts. That way you won't miss an episode. And while you're at it, visit www.chadcheese.com. Just don't expect to find any recipes for grilled cheese. So weird. We out.