Revelio Labs CEO Ben Zweig: 9% of firms are seriously adopting AI, freelancers are hardest hit, CS enrollment down 28%

Speaker 2: What is this? How did we get here? Anyway, we have Ben Zweig from Revelio Labs coming on the show. How are doing, Ben?

Speaker 4: Good. Good. I love that intro.

Speaker 1: Was just for you. We're testing that out for the first time.

Speaker 4: It matches the vibe. Yeah.

Speaker 2: The vibe of the labor market. Take us through a little bit on your background, how you work and then some of what you're tracking in the labor market and how it ties to your actual business.

Speaker 4: Yeah. For sure. So so I'm a labor economist, been tracking labor market data for a long time and started Rebellio Labs. So Rebellio Labs is a workforce data company. We're collecting, curating, synthesizing all labor market related data

Speaker 1: Yeah.

Speaker 4: That's out there in the world. And of course, you know, big question is how is AI affecting the labor market?

Speaker 2: Of course.

Speaker 4: Though it, know, we're we're uniquely positioned to answer that question and it's on everyone's mind. Mhmm. So we we started putting out this labor market, this is kind of AI labor market tracker which is really about answering like how is AI affecting the labor market today. So not really getting into the speculation of what might happen. Yeah. Yeah. Just today. But really like what do we know?

Speaker 2: Really quickly, what is your business model? Who who gets value out of this data? And then I also would love to know how do you go about getting more accurate data? Because I see like obviously the Census Bureau, the government has access to do polling. ADP is a very logical place to get data because they run payroll so they can see the data. But what's been your strategy there and then who's the customer?

Speaker 4: Yeah. Yeah. So I'll start with the customer. So a lot of it is hedge funds. So they're speculating on performance. Companies yeah. Nice

Speaker 1: Thank you for your time.

Speaker 4: Tier for hedge funds. Yeah. They don't get a lot of love these days, but but yeah, they they are speculating on the performance of a company that they have no affiliation to. Sure. You have to understand the, you know, what's happening in the company, the workforce dynamics, HR departments for benchmarking also, so strategic workforce planning, people analytics, talent intelligence. Mhmm. These are all like kind of segments of analytical HR.

Speaker 1: Mhmm.

Speaker 4: And academic research. So you know, they of course want to know what's going on. Sure. So these are, so basically we we get the data not through surveys, not through payroll, but really from the internet. You know, LinkedIn profiles, job postings, glass reviews, layoff notices, immigration filings, freelance platforms, like anything and everything that is in the public domain. And that information has to be you know enriched and synthesized in a smart way. Mhmm. Like there's all sorts of sampling biases, there's lags in reporting, there's like raw text so we have to classify that to occupations,

Speaker 1: to

Speaker 4: skills, seniority levels and you know importantly work activities which is more of a recent thing for us but Yeah. Important these days.

Speaker 2: And then sort Okay. Of back test against like the historical actuals to see that the model's working and then you can be more up to date?

Speaker 4: So we don't back test against financials

Speaker 2: because No. No. I mean, mean about like Yeah. Like if you ran your model on like what was the employment rate in 2021, you could look at the actual employment rate to sort of calibrate that your system is predicting employments correctly. Is that is that roughly correct?

Speaker 4: Yes and no. I mean for some models we can see what was retroactively revealed. Okay. So you know when someone changes their job they don't necessarily update that right away we can see that you know every timestamp has like more information than before. So that's like a solvable problem.

Speaker 1: Got it. Got it.

Speaker 4: But in terms of you know saying what's happening in the labor market at large Mhmm. We can kind of use BLS data. So BLS is the Bureau of Labor Statistics. Yeah. We can use that data to kind of like proxy for it but that's got issues itself. So I don't know if we want to use that as ground truth. So you know I think BLS has you know a view on what's going on from survey data, ADP has from payroll data and we have from Internet data. And they're all kind of independent in their own way. Mhmm. Kind of uncorrelated errors.

Speaker 1: Jordy. Okay. So I want to understand how how you look at your data in the context of AI diffusion. Right? So a company, an individual company, or an industry might have fluctuating labor data. Right? Maybe they're adding a lot of people. But then individually, if you look at those companies, maybe some companies are adopting AI quickly. Some companies in that sector aren't really adopting AI at all or they're doing it in a very minimal way. Let's say they just have a basic, you know, ChatGPT $20 a month subscription. So like how are you I was I was talking to John maybe was it six months ago? I was saying like, I really want there to be a firm that is just studying AI diffusion in specific industries and getting into the nitty gritty, probably doing surveys to actually understand how because every company says they're adopting AI, but we all know that there's like such a broad spectrum and then of course some people are saying that just because they wanna be in in feel like they're a part of the club.

Speaker 4: Yeah. Yeah. I think it's probably mostly those that wanna be part of the club. But I agree. Mean, so so there's a few ways to get at adoption data. So so I think adoption is the hardest part of all of this Mhmm. Because that's really a firm level, you know, piece of information whereas Mhmm. AI exposure is like more of a person level piece of Sure. So so I'll tell you the way we do it in a couple ways. So one is that we we had a partnership with, we still have a partnership with Ramp. So I know, friend of the pod.

Speaker 1: Let's

Speaker 4: go. So they can track adoption just using like AI spend. Yeah.

Speaker 5: So you

Speaker 4: can see like dollars spent on tokens, etcetera. Yeah. So that's like a pretty good way to get adoption. The problem with that is that, first of all it's like a self selected sample, you know, ramps skews toward more like tech, is fine, like that's overcomeable. Yeah. The other issue is that it's anonymized. So they can't release information at the firm level. So you know when we collaborate with them like we have you know the labor market data and they have the adoption data. So you know it's like complicated. Know we have to send data, they have to like run something, we have to do some matching. So it's like a little bit, it's got some friction. Mhmm. The other way to do it is through we use this measure which is used in a paper, a recent paper that measures adoption by like sort of hiring AI integration teams. Mhmm. So the thought is that you know if someone's like hiring AI integrators you know beyond some threshold that they're like taking it seriously.

Speaker 2: Yeah.

Speaker 4: And they're embedding it into their business processes. And by that metric we see about 9% of firms like getting very serious about AI. It's a very conservative way to measure AI adoption

Speaker 1: Yeah.

Speaker 4: But seems to be pretty good, like it's correlated with all sorts of other things.

Speaker 2: Yeah. I sort of hate that idea as a metric, but it probably makes so much sense in larger organizations that that is a great signal. But it just feels like completely the wrong way to go about actually changing a business. Like I I feel like adoption should be so much more ground up than like, oh, we're hiring a special team to do this. But that's the way businesses work. And I think you're correct to identify that and probably is very indicative of a change in the stance of the business.

Speaker 1: Where's where's an area that that AI is really good and you're seeing job loss? Because like AI AI is pretty good at software engineering now or generating code and and, you know, the companies that are adopting it the most are hiring a lot of engineers. Whereas I've heard in LA specifically, apparently, the models that do product photography, so men and women that, you know, wear a bunch of clothes for like an Old Navy Mhmm. When they're releasing a new collection. Like that work has been very impacted because that talent, they don't have a brand yet. Right? And so maybe certain companies will just say like, yeah, let's just take this shirt that we have and just generate it on 20 different AI models and you're and we're good to go. Right? It just doesn't doesn't really matter that much if they're using real talent or not. And so they choose the easier cheaper route.

Speaker 4: Yeah. I think that's a great example. I mean, for the most part, you know, the board, adoption is generally correlated with growth. But where we're seeing reductions, I mean, I think I think the creative fields are a great example. So, you know, if you need like video b roll or just like, you know, stock images or just, you know, podcast intro music, you know, you know, that that is like very easy to get from these kind of AI generated

Speaker 2: Sure.

Speaker 4: You know, creative elements. Sure.

Speaker 1: Copywriting It's fascinating because how many people yeah. It's like it's just quite interesting because when you when some of these things how many people were actually in the roles? Like, would it actually does it show up in labor data at a at a large scale at all? Right? People that are just doing, you know, stock photography and making their living that way or

Speaker 2: Yeah. And a lot of these people might have sort of sloshed around. Like, I I mean, I see I see Instagram reels from people who years ago were posting like After Effects tutorials, Premiere Pro, DaVinci Resolve, like little video editing tutorials. And now they're posting like AI enabled workflows and instead of showing you how to deal with a green screen the old fashioned way, they're just doing it the new way and they're probably still doing it for clients and the client like the client is like spec is just like I need ads that convert and they're just doing more of the work but then there's other stuff that's bleeding out all sorts of different stuff.

Speaker 4: Yeah. I mean one one kind of framing I I would put this in is that you know the we're seeing a lot of kind of automation of things are very task based. Mhmm. Things that are like really micro jobs. They aren't like full jobs at all. Mhmm. So we're seeing like declines in freelancing across the board. Mhmm. So freelancing is hurt hit pretty hard. Mhmm. But that's really an environment where people transact tasks.

Speaker 1: Not Yeah.

Speaker 3: Yeah.

Speaker 1: Yeah. This is why we were just talking about this earlier. The, you know, historically, like if you needed a really specialized website, like it's not your main site, but let's say in our case, we're doing a drop. Three years ago, we would have gone Yeah. And maybe gone to Upwork and and said like, hey, I need a simple website made and just find somebody to do that one off. And now Yeah. AI is just so good at it.

Speaker 2: Or like a basic logo for a first draft that would be like a 99 designs. Before you bring in like a real branding firm, you might just get a freelancer to mock something up for you. Now image generation can do that for sure. What do you make of the computer science shifting? There are so many opportunities for entrepreneurs, startups are growing, there's some tech layoffs, but at the same time it feels like just in general if you're if you have a computer science degree, you're probably going to be a bit better at using AI broadly. And so there's lots of opportunity and yet the number you have here is computer science enrollment is down 28% from its 2022 peak.

Speaker 4: Yeah. Yeah. So I have mixed feelings on it. First of all, it's very dramatic. Yeah. So one thing that that kind of one optimistic take is that the supply side of labor markets is actually quite responsive to changes in technology.

Speaker 1: Mhmm.

Speaker 4: And that wasn't obvious before. Mhmm. And you know, if people can reorient themselves flexibly, that's great. That means, know, we can we can be adaptive, we can have more of a dynamic economy and worry less. So I'm encouraged by that responsiveness. I think it's an overreaction for two reasons. One is that we are not seeing declines in employment you know based on the firms that are adopting a lot and that's true in engineering, it's true in tech. We're not seeing mass layoffs despite the narrative. So I think it's premature for that reason. Another reason is that I think even just a couple years ago, maybe even less, I mean time is like elusive to me. But I think you know not so long ago you know, we thought of AI as chatbots and code assistants.

Speaker 1: Yeah.

Speaker 4: And now it's more agentic tools. So it used to be such a low barrier to entry type of technology where you know anyone's grandma can use it and, you know, coders, you know, engineers were really just like, you know, replacing their work at high rates. Mhmm. Now, you know, we're seeing, you know, complicated tools like, you know, agentic systems are hard to

Speaker 2: use. Yeah.

Speaker 4: They they they kind of favor the digitally native and people who have experience with engineering.

Speaker 2: And even when

Speaker 4: you're And you're know orchestrating.

Speaker 2: There's there there are a whole bunch of like from a business from an enterprise perspective like cost trade offs, privacy, security, how how deep is this system? Like even just firing up a a coding agent today, you're hit with prompts like, do you want this to be have access to your documents folder? And that's like a question. Yeah. And and a lot of consumers are like, I don't know. And a lot of businesses are like, I don't know. So there is some sort of like capability overhang.

Speaker 4: Yeah. Yeah. And I think, you know, it's it's a different job than it was before, you know Yeah. Like people are, you know, engineers are spending less time, you know Yeah. You know, the front end engineering for a website, but they're doing more of kind of that DevOps. Mhmm. So I think it's premature and I think we'll, I mean, you know, I suspect we might have a shortage of engineers in the way that now we have a shortage of radiologists. Everyone was nervous that like radiologists were gonna be a thing of the past and and now there's a shortage and Yeah. You know, wages are super high.

Speaker 2: It's like the final boss of AI automation. AI researchers like, one day I'm coming for you radiologists. You imagine that it all started with like Just bullying fun train an AI researcher and being like, what you're doing is so useless and the AI researcher is like, I'll show you radiologist. I'm going to put you out of a job. And the radiologist is like, I'd like to see you try. And then years and years go by. Talk to me about hires to posting ratio. It's down 38.6% since late twenty twenty two. I can imagine that there's a lot of slop posts. We were debating this before. But how do you tease that out? What do you make of the hires to posting ratio dropping?

Speaker 4: So this is the thing that I get the most nervous about. Mhmm. So, you know, we're seeing some slop posts, slop job postings.

Speaker 2: Yeah.

Speaker 4: But we're also seeing a lot of slop applications.

Speaker 2: Yeah.

Speaker 4: And when a job goes up, you know, you get I don't if you guys have posted a job recently, but I I just did last week and I got, you know, a thousand applications in the first like five minutes. Mhmm. There are all these like job boards that are kind of helping people auto apply.

Speaker 2: Yeah.

Speaker 4: Even Indeed is doing this which Sure. I think is a bad move for the record but

Speaker 1: Yeah.

Speaker 4: They'll do what they want. It's, you know, so so basically employers are getting completely signal jammed. They're getting overrun with these applications that look strong

Speaker 1: Yeah.

Speaker 4: But they really have no way of verifying. So the utility of each job posting is going down, it's not it's not as good of a way to find candidates anymore. Mhmm. So employers are relying on networks, it's getting harder to hire. And in the economy at large we have this kind of low hire low fire environment where there's just not a lot of movement in the economy. And I think that is the result of, you know, AI usage in the search and match process.

Speaker 2: Yeah. You would think that, like, I I've been surprised that social media has not been that overrun with slop. Like, there's there's definitely some slop problems here and there. But the in general, the algorithmic feeds have been sort of set up to deal with this where the bad slop gets filtered out pretty quickly.

Speaker 4: Except for LinkedIn. But, yeah.

Speaker 2: Sure. But I've been I've been surprised that that there hasn't been as much of an intermediary where you put up a job post, yeah, you get hammered with a thousand applications, but the filtering is really, really good so that you're really only looking at the top 10. Maybe you dip in the top 100, but you're not at all annoyed by the bottom 900. Because I guarantee you that there are millions and millions of sloppy Instagram videos out there that would annoy me if I saw them, but the algorithm will just never show them to me. And then maybe there's one that uses AI, but it's good and it will show it to me because I still enjoy it. So it feels like, hopefully, there's people working on this. I'm sure that people are, but that feels like the next iteration to unclog this because that seems like a major problem. You need the matching in the in The US economy to be really really strong.

Speaker 4: Yeah. I mean, there's been some regulatory challenges there too. So a few years ago it became illegal for employers to That's right. You know, sift through candidates using AI. Wow. And I don't know how enforced that is. Yeah. But it's it's a liability for employers and not a liability for candidates. So there's some asymmetry in how in who can use AI.

Speaker 2: That's very interesting. I had no idea. When did that Yeah. I remember I remember that that you can't use AI to to filter out candidates. I think of it as like I I I understand where that came from on like bias based into models and like very preliminary deep barely deep learning algorithms to sort of like look at the person's name and look at their graduation date and like try and filter for that. Like I'm just thinking about like it like did the is the resume complete slop, you know? Like a complete like a pangram level that doesn't seem to impose like bias in the same ways that they were trying to avoid. So we're in this weird like knock on effect world. But that's the way these things go. Jordy, anything else?

Speaker 1: No. Come back on as This really great. Come back on as coming there's off as there's yeah. More more data that's notable. Yeah. You can tease the hedge funds a little bit. Yeah. Give them a

Speaker 4: for sure.

Speaker 2: And congrats on the progress. Thanks so

Speaker 1: much for coming on. Yeah. Great to meet you, Ben.

Speaker 3: You too.

Speaker 2: Talk to you soon.

Speaker 1: Cheers.

Speaker 2: Have a good one. Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI.