Job Titles Don’t Mean What They Used To (And That Affects Your Pay) — with Dr. Ben Zweig (Part 2 of 2)

15:38Dr. Ben Zweig:excellence fairy? Or do you think she like outlined the stuff she did? Or do you think she just chose a different title?

15:44Paula Pant:Yeah, that's a great question. I don't know.

15:45Dr. Ben Zweig:Yeah. When people ask what I do, I don't really know what to say because it's very varied. Sometimes I say I'm an entrepreneur, but sometimes I say I'm an economist, which is more of an identity than an occupation for me. Like I did a PhD in economics. I feel like in my soul, I'm an economist, but it doesn't really describe my day to day. Spend my time running a company and doing all the things that entrepreneurs do. And the fact that I have a background in economics is like not so relevant to that. I wonder if we kind of have these identities and the occupations where occupation is, and our job title, our occupation is really shorthand for the collection of things we do.

16:28Dr. Ben Zweig:Like if someone says, oh, I'm a QA analyst, then we kind of know what they do. And you're like, okay, you like testing frameworks, you can do this and that. So we maybe have some sense of the stuff they do. And if someone says, I'm an entrepreneur, then someone hears it and they say, okay, I kind of know the collection of things you do. If someone says you're a teacher, like, you know what they do. I think we can also kind of maintain this other identity that is like, oh, well, I have my professional brand and professional, I don't know, I guess just professional identity that is a little bit distinct to the day-to-day.

17:01Dr. Ben Zweig:And maybe when people ask, what do you do? Maybe there's a part of them that's asking like, how do you spend your day? Because that's a valuable question to get to know someone. But maybe a part of that question is like, how do you think about yourself professionally? I don't know.

17:15Paula Pant:It's like a tough question to answer. Yeah, yeah. I mean, and so back to what does the excellence fairy say at the proverbial cocktail party? I would imagine most people would not lead with that title because the title is not informative. So I think you would have to, going back to a job is a bundle of tasks, you would have to at that point just describe the bundle of tasks that you do. Technically, officially, my title is Excellence Fairy, but what I read, what that means is that I do A, B, C, D, E.

17:44Dr. Ben Zweig:Right. Yeah. But that like takes a while. Yeah, you do need some sort of like shorthand. Right. I mean, I think that's in some ways like why we have taxonomies generally. We have taxonomies of products and taxonomies of of the animal kingdom, like all over, we have the shorthand, which is like a mental, it's just a mental shortcut. I mentioned I have two girls in preschool and so much of raising a kid, I think is about introducing categories. I've been working on taxonomies for longer than I've had kids. So sometimes I'm just like, oh, wow, we are instilling this idea of taxonomies at such a young age.

18:25Dr. Ben Zweig:We are just like, here's farm animals, here's shapes, here's colors, Here's noise. Like we were trying to get them to a point where they think in terms of categories. And that way, you know, when we say, oh, there's a clock, we can glance at it and millisecond, like it could be a clock we've never seen before, but we know how it behaves. We know the direction things go. We know the properties that sometimes things are fast, things are slow. And that is something that we are also much better at than machines. It's very expensive. It's very computationally expensive for an AI system to recognize a clock.

19:04Dr. Ben Zweig:Our brains are wired in a way that's very cheap for us. It's like very computationally inexpensive. I think really because we have these categories. Sometimes I think that we are just taxonomical animals.

19:16Paula Pant:Right.

19:17Dr. Ben Zweig:We really need these heuristics to do kind of mental classification and association cheaply.

19:25Paula Pant:And yes, we are classification animals. Absolutely. Even the notion of an animal.

19:30Dr. Ben Zweig:Yeah, exactly. I mean, we're not birds.

19:34Paula Pant:I think a lot about the platypus because that is the animal that defies Linnaean classification, right? It's an egg-laying mammal, which, based on the rigidity of classification systems, like that should not exist. Mammals, by definition, do not lay eggs, unless you're a platypus, in which case you do. And so you have occasionally, when you have these rigid taxonomical structures, you have the occasional outlier, like the platypus, that defies those boundaries. And I don't know if there's ever been a good understanding about what to do. I feel like society has collectively just said, we're not going to talk about the platypus.

20:15Paula Pant:Next question, please. Yeah. Yeah.

20:18Dr. Ben Zweig:The way we categorize things should really be based on what's useful rather than what's like technically correct. I'm sure we all have that annoying friend who tells us that a tomato is really a fruit.

20:29Paula Pant:Yep. Yeah. Yeah. I like to say my favorite fruit salad is guacamole because avocado and tomato. Yeah, exactly.

20:36Dr. Ben Zweig:But like practically, we treat it like a vegetable. Yeah. And we categorize it like a vegetable. And that is the more convenient categorization for us. There's this really fun book. It's called Why Fish Don't Exist. And it's about the first president of Stanford University who has this crazy story. He probably murdered the wife of the founder of Stanford. Wow. It was nuts. And he was a taxonomist. So he would categorize fish and try to find a new kind of fish and like label it and name it and all that. There's been these advances in computational evolution that can sequence the genome of all these different animals, and they found that fish aren't actually a category in that they don't descend from a common ancestor.

21:18Dr. Ben Zweig:There are fish that we consider fish that are descended from reptiles or amphibians or mammals or birds or mollusks or whatever, and we consider them fish now because that just is what happens when you live in an ocean for 200 million years. You kind of become a fish. it's not actually like the evolutionarily correct way to think about how to categorize animals. We shouldn't stop using the category of fish because it means something and you can explain it to a kid and you can understand it pretty easily. And we know they behave. We know they have fins. We have all these properties that we associate with fish.

21:54Dr. Ben Zweig:In this way of categorizing things, we always have things that are at the borders. And a platypus may actually be practically at the border of a lot of different things, that's kind of always the case. So even in occupations, we have marketing and sales and it's useful to think of those. But then we have people that are kind of like between marketing and sales.

22:16Paula Pant:Yeah.

22:17Dr. Ben Zweig:And they're this like platypus type role. There's some cost to discretization by creating categories. We are discretizing, which means we are throwing away the like within category variants. We can say dogs and we know there's like big dogs, little dogs, and that is like a broad category. And we know that we're losing a lot of nuance when we just say dogs. And same thing when we say mammals, like there's weird, like there's platypuses and there's humans and there's whales and they're all really different. That's why we have hierarchy in our taxonomies. So then we have these like categories where maybe platypus is less weird, but still quite different from like primates.

23:04Paula Pant:Yeah. I mean, because you think about the classification of dog, you have everything from a chihuahua to a German shepherd or a Great Dane falls under this very broad category known as dog. You don't have that same level of variance in cats in the domestic house cat.

23:19Dr. Ben Zweig:Yeah. A cat is a small carnivorous mammal that generally adheres to a certain dimensions,

23:26Paula Pant:a certain body weight, certain dimensions. It doesn't really stray outside of this very narrow band. Yeah.

23:34Dr. Ben Zweig:And that is not the case with dogs. Yeah, it is weird that there's a lot of variance in size, but you could make the case that there's less variance in other things. So I don't have a dog, but people that have dogs, I think see a lot of similarities in themselves and other dog owners and they can relate to each other, even if their dogs are totally different types of dogs, because there's some commonalities. They're friendly and they have this positive energy and they want to please you. And well, there's all these things that identify them. And there's other dimensions that don't identify them well, like size.

24:13Dr. Ben Zweig:Whereas cats, they are identified by some dimensions.

24:18Paula Pant:But they have a great deal of personality variants.

24:20Dr. Ben Zweig:Yeah, I mean, I'll take your word for it. I don't really know anything. I like, I don't see.

24:24Paula Pant:Yeah, they have a huge degree of personality variants. Even two cats raised in the same household at the same time. Massive personality variants. Same with humans.

24:32Dr. Ben Zweig:I mean, humans don't have a lot of size variants. We are relatively all the same size, especially compared to dogs. But yeah, lots of personality variants.

24:41Paula Pant:Yeah.

24:42Dr. Ben Zweig:And we categorize ourselves in all sorts of ways. There's all sorts of like demographic categories. We categorize ourselves based on ethnicity and things like that, that are also kind of arbitrary. Exactly.

24:56Paula Pant:Yeah. I'm Nepalese. That's a definition based around a set of national borders that didn't even form until the late 1700s.

25:03Dr. Ben Zweig:Right.

25:04Paula Pant:So what would that make me pre 1700s? I don't know.

25:08Dr. Ben Zweig:I don't know. Yeah. Yeah, I mean, maybe back then, you know, revert to big five personality traits or something. I think in some ways it's a good example of like how deep we want to go based on like how familiar we are with things. So with humans, like we have such a nuanced understanding of different types of humans. You know, by saying someone's Nepalese, that's informative because it tells us about their background, how they might think about things. Like their upbringing probably carries information about socioeconomic status. There's information that we can convey, but that is relevant to us, but probably not relevant to a dog who's like looking at humans like they probably don't care.

25:49Dr. Ben Zweig:And in some way, it's a little bit similar with jobs where if you're in the finance part of an organization, you probably only care about like what's sales and marketing, what's R &D, what's this and that broad categories of occupations. But if you're in like talent acquisition, you really want to know like, oh, what's the difference between like DevOps manager and an infrastructure developer? Like things that are like really precise.

26:12Paula Pant:Right.

26:13Dr. Ben Zweig:So having this level of hierarchy, how much detail do we want to get? Like how deeply do we really care to understand something?

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30:39Paula Pant:In the world of jobs, we have this situation in which there is so much variance. in job titles, in job descriptions, in qualifications needed, in the fluidity of what that role becomes, in what types of products or services that role supports, in the size of companies. The more we talk through it, it's a wonder that a job title or description means anything at all, just given how much room for interpretation and fluidity and variance there is in the

31:11Dr. Ben Zweig:to our jobs. Sometimes when we build these taxonomies, we think, how many unique occupations are there in the world? How many unique activities? If we take the collection of things people do in their jobs, how many are there in the world? Like how many distinct things do people do? It is finite. So for job titles, let's say at about 15 ,000, you start getting like real synonyms. there's probably roughly 15 ,000 occupations. If you go deeper, you're really splitting true synonyms. And in work activities, probably like 60 ,000, which sounds like a big number. But sometimes I think about that and I'm like, oh, wow, that's actually like not that big.

31:54Dr. Ben Zweig:It's a number that we can kind of wrap our head around. And like just the collection of all the things in the world in every industry, in every geography, the whole set of things that people do in their job, 60 ,000 things like that. That's the whole economy. That's the whole labor market. It just seems not that big. Like, like sometimes I think about that and like, okay, we can understand this. Like that's not more than we can process. We can certainly like store that amount of data. We can like have a catalog that has 60 ,000 elements. It's way smaller than like a library. And yeah, maybe this isn't so hard after all, maybe understanding jobs, activities, skills, like maybe these things are actually quite doable.

32:34Dr. Ben Zweig:Maybe that does sound like a lot.

32:35Paula Pant:Yeah. And that 60 ,000 is always changing. Again, going back to how what 60 % of the jobs that currently exist didn't exist back in the early 1900s. I don't know what the rate at which they're changing and if that rate, I would imagine, although I don't know this, that rate has sped up quite a bit just in the last decade or two.

32:55Dr. Ben Zweig:I would imagine that too. It's very hard to get data on what work activities people did going back a long time. So we have good data on the occupational breakdown, which is why we do these things at the occupation level and find there are lots of occupations that didn't exist. We have social media managers and AI engineers and all that. We don't have elevator operators and switchboard operators and things like that. The better question that we really can't answer is what are people doing today that they weren't doing 100 years ago. I love to think of this example of bank tellers. There was this big article, I think in the Wall Street Journal, when the ATM came out, that bank tellers were just going to be completely devastated, demolished, completely obsolete.

33:41Dr. Ben Zweig:Now we have something like 10 times more bank tellers than we did when that article was published. So we didn't see a reduction in the quantity of bank tellers. There's a very common narrative that we hear in economics, which I think is wrong, that says that this is an example of something called Jevons paradox. So Jevons paradox is that when the price of an input falls, we can sometimes actually spend more on that input.

34:03Paula Pant:There are more use cases for it, more demand for it. Yeah, exactly.

34:06Dr. Ben Zweig:So if the price of gas to drive our car falls by half, maybe we'll drive more than twice the amount and actually spend more money on gas, something like that. It's like possible that's the case. The story goes that the price of depositing and withdrawing money became so much lower. That led to a scenario where instead of having a bank in the center of town, now you have a chase on every corner. So we just expanded the just banking as a phenomenon. The reason why I don't find that to be a satisfactory answer is that it assumes that bank telling is bank telling. And I think that's just not the case.

34:41Dr. Ben Zweig:So right now, people that are bank tellers are not primarily depositing and withdrawing cash. They're primarily doing customer support and relationship management and helping people navigate products and like different types of credit cards and things like that. And it's really much more of a customer service, customer success type role. And it's really just a different job. What could have happened where some executive at Chase Manhattan at the time had made a call that we're going to stop calling them bank tellers. We're going to start calling them relationship managers. and we'd be telling a very different story right now.

35:18The fact that we call it the same thing is just kind of unimportant.

35:23Dr. Ben Zweig:Six of one, half a dozen of the other. Yeah, yeah, it's a different job. Whether we use the same title or not is, you know, I almost wish we didn't, but sometimes we do. You know, sometimes jobs evolve gradually. I get this example where the role of statistician is totally different than it was 50 years ago. We still have the title statistician. Maybe we shouldn't. it's kind of irrelevant what it's called in a way.

35:47Paula Pant:When the role fundamentally changes so much that it is no longer recognizable to a person who had been practicing that same role, let's say 40 years in the past, it's like a tree falling in an empty forest. Is it still the same job? Yeah, exactly. It's an amoeba splitting into two. Like, is it still the same amoeba? I don't know. Yeah.

36:05Dr. Ben Zweig:I mean, maybe there is some convenience to keeping the same set of job titles, which is fine. I think that's like a good reason to keep job titles.

36:13Paula Pant:But it raises a very interesting point, which is that then casts this question mark on the conversation of, quote unquote, will jobs survive? What does it mean for a job to survive if a job title iterates the scope of work and like iterates and iterates and iterates gradually enough that it evolves to something that it didn't used to be? and that new something requires a totally different set of qualifications, experience, skills. What does it mean for that job to have survived?

36:45Dr. Ben Zweig:Yeah, I think that's right. I mean, so much of this conversation, I think, ignores that within job transformation, which is, I think, the most important part of all of this. I mean, there was this famous essay by John Maynard Keynes in 1930, which is called Economic Possibilities for Our Grandchildren. He makes a prediction that 100 years from now, And we're right around the corner, 200 years from 1930. He said that we'll be working 15-hour weeks. So that productivity will grow so much that we will run out of things to do and we'll just live a life of leisure. That's basically the prediction every generation with every technology.

37:20Dr. Ben Zweig:Sometimes I think about that essay and I think, oh, he was dead wrong. We're not working 15-hour weeks. Like he was actually right on our productivity growth. Productivity has grown actually faster than he predicted. But we have this insatiable appetite. And now we want to consume things that we didn't consume before. And we have this growing need for what we think should be done. And I think, okay, we're not going to run out of things to do. There's lots of things that we want in the world that don't exist in the world. So that kind of makes me feel like, oh, he was wrong because he didn't account for our growing appetites.

37:55Then sometimes I go back and forth on this.

37:57Dr. Ben Zweig:Sometimes I think, actually, maybe he was right. maybe we actually do live a life of leisure, but that's happening within our job. Like right now, you and I are working and like, I'm having fun, you know? So, you know, this isn't like backbreaking labor, you know, we're not in a sweatshop. And that is the reality of a lot of people's jobs now. Like jobs have gotten more social and there's more, a little more fun. There's a little more leisure. And that is the transformation of work that does give us a little bit, I mean, people are pursuing jobs based on what they want to be doing more than what just pays the most more and more.

38:37Dr. Ben Zweig:That's something we have lots of evidence for that like newer generations put a higher priority on things other than compensation, work-life balance and management and culture and all that. And that is consistent with this idea that we actually maybe are pursuing more leisure, but so much of these phenomena of job displacement, of automation, like it's happening within jobs. It's not happening between jobs. It's so important to think about what's happening within each job. How is each job transforming?

39:10Paula Pant:The advancement and the proliferation of knowledge work. I mean, that is a good point that there's a degree of leisure within the domain of knowledge work in particular, because to be able to sit in a climate-controlled environment with adequate air conditioning and adequate heat and push buttons with your fingers all day.

39:30Dr. Ben Zweig:Yeah.

39:30Paula Pant:That is certainly quite leisurely in comparison to a lot of the very dangerous jobs that millions and millions of people have had and millions still do have in other industries, which then bringing it back to AI makes it so jarring the notion that this is the first thing that has really disrupted knowledge work.

39:50Dr. Ben Zweig:Yeah, for sure. I mean, it does feel weird. I forgot who said it, but someone had some famous line that I want AI to do my laundry and dishes so I can write and make art. I don't want AI to write and make art so I can do laundry and do dishes. And it is kind of threatening the things we like about our jobs, the thoughtfulness, the deep thinking, the analytical component. I get that that's scary. The question is, what is the remainder? Is what's left over even more fulfilling or is it less fulfilling? Like, if we really were being forced into more jobs that require dexterity and things like that, I don't think we all want to be bussing tables and doing laundry, but maybe we do want to be overseeing more ambitious, exciting projects and coordinating more abstract activities that create more value.

40:48Dr. Ben Zweig:Maybe that is actually exciting in a way that AI can unlock our workflows to be more exciting and more human in a way.

40:59Paula Pant:Because there are frontiers that we haven't even approached. We're not building cities underwater yet. Yeah. It strikes me we still take trains just like we did in the 1800s. There are so many facets of life that can be innovated and iterated and new frontiers that we haven't even approached that could be developed.

41:19Dr. Ben Zweig:Totally. There's so many pockets of our economy that have had really low productivity growth. Housing, you can argue, has had even negative productivity growth. That's creating all sorts of problems for affordability and walkability, you know, things that are desirable.

41:35Paula Pant:Walkability and density of cities and how do you balance density with people? I mean, people inherently do enjoy having space, but also enjoy living in the community that comes with living in density. And how do you balance those two? We need more productivity.

41:48Dr. Ben Zweig:And it's worth kind of remembering that like the future is already here. It's just not normally distributed. We sometimes talk about like our own jobs as being, we can do more interesting, more enlightened work, but there's so many parts of the world where that's not the case, where they are still kind of working in sweatshops and that kind of productivity, like taking these innovations to other places can be really useful. Also bringing them here. We don't have bullet trains that they have in Korea or Japan. I took the Metro North today. It's fine. Super annoying. Yeah. But it's not a bullet train.

42:22Dr. Ben Zweig:Yeah. And it does feel like we as a society, I mean, me personally, probably all of us do feel a little bit impatient with how quickly we're like making progress or not making progress in certain pockets of the economy. It's like we are hungry for more.

42:38Paula Pant:Yeah. Whenever I take the trains around New York, like when I take the Metro North train or the New Jersey transit line where I go to Penn Station, I do feel a little bit like I'm living in the Gilded Age. Yeah. I feel like it's the late 1800s, early 1900s, I don't feel like I'm living in 2026. Yeah, we want more.

42:57Dr. Ben Zweig:And we'll get there. I mean, one is that we'll get there through technology, but also through combining labor with technology. In my company, I'm sure you feel the same way. I would love to have 30 more people. I know exactly what I would do. If someone can free up more of their time, I've got a long list of things I want people to work on that grows faster than our ability to work on them. Even within a company, there's just so much more that we'd like to deploy labor to do.

43:27Paula Pant:Yeah, that is true. And I think almost every small business owner feels that. Looking at the jobs report, looking at labor, labor stats from the past year, what we've seen is job growth has been slow and most of that growth has accrued to the largest companies. Looking at the labor reports, the hardest hit companies have been the smallest ones. And yet, as you and I have just talked about, both of us lead small companies and both of us are like brimming with ideas for what I know exactly what I would do with. I don't know what I would do with 30 people. I definitely know what I would do with like 10 more people.

44:04Paula Pant:Yeah, I could definitely put 10 more people full time to work tomorrow. And I'd be thrilled if I had the hiring budget for that.

44:12Dr. Ben Zweig:Yeah.

44:13Paula Pant:And the minute those 10 more people were deployed, give me six months and I would know what to do with the next 10.

44:19Dr. Ben Zweig:You're right. That employment has been slow, like way too slow for the economy at large, especially slow for small firms. It's hard to pin down exactly what's driving that. I suspect it has more to do with capital markets than with labor markets. it is harder to get financing for a small business, for a startup today than it was back in 2021. Venture capital as an industry has transformed very rapidly. First of all, it's been very unsuccessful as a business model in the last like decade or two, but also the The nature of venture capital has been much more concentrated in specific bets that attract a lot of like risk capital.

45:08Dr. Ben Zweig:And then they've been becoming more private equity-esque with the rest of their capital. So, you know, the big Silicon Valley VC firms in the last decade have basically invested like the first five to 10 years ago were basically just investing in crypto. And now they're just investing in AI. I mean, I'm exaggerating a little bit, but I think it's like true enough that they're really frothy valuations, tons of money going toward AI startups, still crypto startups now. But SaaS companies, hardware companies, science, you know, they are not getting funded. They're being funded on multiples of like earnings rather than like multiples of revenue, which is like weird.

45:50Dr. Ben Zweig:You know, we think of that as like private equity behavior.

45:52Paula Pant:Right.

45:53Dr. Ben Zweig:Which is very risk off. And trying to, you know, pressure companies to focus more on profitability, a little less on growth, on the margin. So the excitement around AI is probably funneling some capital away from growth of small, medium-sized businesses.

46:09Paula Pant:So it makes sense to the extent that the VC model prioritized growth rather than profitability because the priority was forward-looking earnings, right? the priority was, we're going to place a valuation on you based on what we think you might make five years into the future. Or 20 years into the future.

46:27Dr. Ben Zweig:Yeah.

46:28Paula Pant:And now that we're entering this AI world, forward earnings, forward revenue is so unpredictable. The value of a company is so unpredictable that the VC model can no longer place a valuation today on what a company might be worth two years in the future even because who knows what the world's going to look like two years from now.

46:51Dr. Ben Zweig:Yeah.

46:52Paula Pant:So because the future is so unpredictable, you can't discount that back to today. And therefore, you can't really place a good valuation on companies today. And so then you just have to value a company based on a revenue multiplier.

47:07Dr. Ben Zweig:Yeah. Which is the old fashioned way. I think that's exactly right. Investors, companies. I mean, we are in a high discount rate environment. we are discounting the future a lot relative to the present. We are prioritizing for the present. Some of that has to do with interest rates and the macro economy. I think a lot of it has to do with technology, like you said. I think a lot of it has to do with policy as well. There's uncertainty around like, what is the world going to look like? We have a lot more uncertainty around the geopolitical situation, around what the technological frontier is, around migration patterns.

47:41Dr. Ben Zweig:I mean, so many things that like really affect the composition of our economy. They're so uncertain right now. So I think it's creating an environment where it's very hard to invest in the future. So I think it's very rational. I don't want to blame VCs. I mean, maybe a little bit.

47:59Paula Pant:Yeah, no, it is. Yeah, it's completely rational that the valuation. And I think that it's actually probably a good thing when you're making a lot of speculative bets, placing these speculative bets on companies, not based on their current revenue, but based on expectations for future revenue, which is by definition speculative, you get a lot of busts. Whereas if you are placing valuations on companies based on current earnings or current earnings growth, when you're placing a valuation on a company based on actual numbers that they have achieved. The valuations come down, but the valuations are also rooted in something that is much more fundamental and real.

48:41Dr. Ben Zweig:In your situation, if you wanted to hire 10 people, you could accept a bad valuation on your business and raise some money and hire 10 people. But that would be very expensive. But if we were in a different environment, you would borrow money cheaply and hire 10 people if you knew that they would be deployed to product events.

49:00Paula Pant:Is it then the case that because of the changing nature of the VC model and the changing nature of how we are placing valuations on small businesses, that's why we've seen so much of the labor growth accrue to large companies?

49:12Dr. Ben Zweig:I think so. I'm sure there's other parts to the story, but that's one that I think is certainly happening. There's other frameworks to use to think through that. Maybe there are more efficiencies in large organizations. When I teach the future of work, I sometimes think, why don't firms grow to infinity? Why don't we have like infinite specialization or the opposite? Like, why don't we just have a bunch of independent freelancers like running the economy? What dictates the optimal size of firms? And I think ultimately it comes down to coordination costs. There's some cost to coordinating a small team that's lower than coordinating a big team.

49:53Dr. Ben Zweig:And there's some returns to specialization. So a big company can have specialists and they can really benefit from that specialization in a way a small company can't. I think it's this balance of coordination costs and specialization and the relative costs and benefits of those change due to technology and regulation and things like that. So I wonder if the technology we see now is reducing the coordination costs in large organizations relative to the coordination costs within small organizations. So a small company, a company of five people, like they talk to each other, like their coordination costs are the same as they've always been.

50:33Dr. Ben Zweig:But in a large company, they coordinate using technology. And as technology gets better, we get Slack and summarization and this and that and Zoom and whatever. Maybe coordinating across lots of people is becoming cheaper.

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55:17Paula Pant:This goes back to what we were talking about at the top of the show, the difference between execution and orchestration. Because that coordination cost that really speaks to orchestration and if the cost of coordination and therefore some of that orchestration comes down, that would accrue to large companies because in a small company, if you've got a team of five people, AI can help with execution, with task execution.

55:44Dr. Ben Zweig:Yeah.

55:44Paula Pant:But the orchestration of that in a team of five, a company of five is not going to change.

55:50Dr. Ben Zweig:Totally. The execution benefits accrue to small and large companies alike. That really shouldn't dramatically affect the different distribution of company sizes. I don't know if we have a lot of evidence driving the change in the returns to large companies versus small companies. It's worth thinking through.

56:07Paula Pant:Do you think this pattern will remain, this pattern of large companies being so far the winners in the AI landscape? Like, will that pattern continue to persist or will eventually small companies, which can have more niche specializations, will they be able to kind of innovate their way out of this? Yeah. Or specialize their way out of this.

56:28Dr. Ben Zweig:Yeah. I would put my bets on small firms relative to large firms. Because here's one way to think about who's well positioned to take advantage of new technology. Like we spoke about job reconfiguration and how managers really need to like reconfigure jobs. I think a lot of big companies have been successful because they have very clearly streamlined processes. and a lot of efficiency from like fine-tuning processes with a lot of structure. You know, if you think about a big bureaucracy like with an assembly line, like everyone's got a very specific job, I think it's harder for them to reconfigure the tasks in people's jobs.

57:09Dr. Ben Zweig:So I think that they will be not so well-equipped

57:12Paula Pant:to be able to restructure what people do as easily.

57:16Dr. Ben Zweig:Whereas small firms are adaptive in nature. We are constantly reconfiguring what people do. And when a new technology comes out, it's easy to use it. There's no bureaucracy to run through. I mean, I still hear big companies that are still struggling to figure out their privacy policy around using ChatGPT for their work. And they just have to use some like gated version that's super clunky and you can't export data. You know, they like can't get it together. And I think that's going to hurt them a lot. I'm also very concerned about artificial rigidities in what people do. So like occupational licensing and professional trade organizations, like if you are a phlebotomist, you're allowed to like draw blood.

58:02Dr. Ben Zweig:And if someone asks you a question that you know the answer to, you're like legally not allowed to answer that question. You have like very hard constraints on what activities you're allowed to do, what activities you're not allowed to do. There are these regulated industries that I think will also not be able to adapt to technological change. And I think that'll hurt them. I'm concerned for the future of those organizations that are typically larger, whereas the smaller upstarts that are trying something a different way can adapt very flexibly.

58:38Paula Pant:That makes a lot of sense because the more you have that occupational rigidity, the less that you are able to— we just talked about the fluidity of roles, the less you are able to adapt to the fluidity of roles, which is necessary in a rapidly changing environment. And if you can't, it's adapt or die. If you can't adapt, you die.

58:56Dr. Ben Zweig:Yeah. And our company is someone, one of our economists just wrote a newsletter about consulting firms and how consulting firms are shifting the workforce dramatically. They're taking on lots of AI roles. They're like trying to embed AI in everything they do like very aggressively. And right now, these like AI roles, like roles that didn't exist before AI, people whose job it is to use AI technologies and implement it, they now exceed the number of entry-level consultants in consulting firms. I don't know, when I saw that result, I was like, wow, this is crazy because we think of consulting firms as like the launching pad for ambitious young people.

59:35Paula Pant:Yeah, it's like the Harvard to McKinsey pipeline.

59:38Dr. Ben Zweig:Yeah, yeah, exactly. People talk about Palantir as this example of the Stanford computer science undergrad is the new Harvard MBA. I think we are seeing some of that. And consulting firms have been allowed to adapt. And I think of law firms as kind of a good parallel to consulting firms because it's a similar model. It's a similar like up or out type of model where you have the partners who are the experts really selling business. I think there's just a lot of similarities in their operating model. but law firms are bound by professional associations. There is the bar association and consulting firms are not.

1:00:16Dr. Ben Zweig:We don't see this happening at law firms, but we do see it happening with consulting firms. I think that probably is due to the bar association having imposed rigidities on what lawyers are allowed to do, how they can do it. And I think that's worrisome.

1:00:33Paula Pant:How should an individual in either of those professions deal with that impending risk? Like how should an individual who is currently a practicing attorney be thinking about that?

1:00:44Dr. Ben Zweig:For practicing attorneys, I think what the professional association is doing is sort of protecting the incumbents. So I'd be much more nervous if you were about to look for a job in law.

1:00:57Paula Pant:If you're in law, if you're like a, what is it, L3, 3L? Yeah, yeah, something like that. Yeah, the 30-year law student?

1:01:03Dr. Ben Zweig:Yeah, so I think they've been having a hard time even the last couple decades. I think the majority of law school graduates do not end up practicing law, which is crazy. But yeah, so it's been a tough market for lawyers generally. I think it'll get even tougher because there are, number one, constraints on new entrants and constraints on how productive they could be, how quickly they can implement new technology, or just like use new technologies, but also they're challenged by free and cheap substitutes for legal work. You know, if someone wants to like write up a contract, they can just do that using AI right now.

1:01:46Dr. Ben Zweig:Maybe there's a case to be made for whether that's a good idea or not. I personally use AI for a lot of contracts, but even just with this book, I wrote this book with Wiley and they sent a contract over. I just asked Chachapiti, like, what's standard? What's not standard? Like, is this a good term? What should I push back on? And I did this negotiation with essentially an AI lawyer. And I think it was fine. It was good. And who knows, maybe it'll come back to bite me. But, you know, it's better than paying a couple thousand bucks for a lawyer to look it over. I think there is competition just from practitioners having less demand for lawyers.

1:02:22Paula Pant:Especially for the kind of lower level tasks.

1:02:24Dr. Ben Zweig:Yeah, exactly.

1:02:25Paula Pant:You know, procedural, lower-level tasks, drafting up a lease for the rental of a single-family home, that type of a thing.

1:02:33Dr. Ben Zweig:I mean, most lawyers are just going to copy and paste that anyway. Exactly.

1:02:36Paula Pant:Or use the template from the state. It reminds me of a joke. A couple of friends and I, we got these invitations. The invitation said, oh, dress code is business casual. My friend looked at me and she was like, what's business casual? and my other friend was like, eh, it's when you don't read the contract too closely.

1:02:56Dr. Ben Zweig:Yeah. Nice.

1:02:58Paula Pant:So yeah, I think the new one is like, eh, funny, use AI to read the contract. Yeah, yeah, this is time and place. That's business casual. Yeah. We talked earlier about somebody who's between the ages of 25 to 30 and they're starting their career. I want to close this out with someone who's old enough that they are established, they have a network, let's say between the ages of 35 to 55, right? I guess that's a wide range, but like you're old enough that you're established, you're an incumbent. You're old enough that you don't necessarily want to retrain in a different occupation.

1:03:33Dr. Ben Zweig:Yeah.

1:03:34Paula Pant:At your age and your current stage of life, you don't want to go back to school to obtain some high barrier to entry credential, but you're also not ready to retire. So that 35 to 55 age range, what should anyone in that bucket know as we think through the future of work?

1:03:55Dr. Ben Zweig:I would almost segment it into two types of people there. Some people in that age group are working at big companies. Like you said, more and more. If you're playing a role in a big company, you're probably something like a knowledge worker. You're probably connecting things, answering emails, involved in a variety of different business functions, and your job probably does transform actively all the time. My advice to that person would be to try to pay attention to how your job is transforming. Even just like every once in a while, just look back to what you were doing three months ago and just think, oh, how did my job transform?

1:04:33Dr. Ben Zweig:What led to that? Was that because my manager wanted me to do different things? Is that because I just took on new responsibilities. I mean, there's this phenomenon of job crafting. It's this idea that people can sort of shape their own job based on what they like, what they think is more productive, what they find more meaningful. And that probably happens a lot, even when we don't explicitly think about it. So I would encourage people to just be purposeful about thinking through what you do in your job. Are there parts that you like and don't like? How would you like to transform that? And how should you interact with your manager to align that to the objectives of the business?

1:05:14Dr. Ben Zweig:If you can find a way to have a more fulfilling job and also be delivering productive output, great, go for it. Reconfigure your job such that it does that. There's other people in this age range who are in more narrow jobs that are not knowledge work. So let's say, someone's an electrician or something that is, I mean, maybe that's not a good example because that's probably not so likely to get automated, but something where your occupation might be vulnerable to automation. Most jobs don't get automated wholesale, but there have been times when that did happen. Switchboard operator, typist, things like that.

1:05:53Dr. Ben Zweig:That is concerning. So will we see AI displacement, like labor displacement? How responsive can suppliers of labor be? I think probably quite responsive. So in this example of switchboard operators, there was really very little unemployment that resulted from switchboard operators. Most people who were operating switchboards went into secretarial work. They were able to reorient what they did very quickly. And I think because the skills and tendencies overlapped, even though the work activities were different, if you do feel like your job is sort of at the cusp of what might be automated, I think I would just encourage them to take an inventory of their skills, their interests, what they're good at, what they would do if they didn't have to worry about money, which can be a good starting point to thinking about like, where is my energy?

1:06:50Dr. Ben Zweig:But sometimes what does require some adaptation, going back to the Luddites, don't smash the machines. Don't be like John Henry. It's much more productive to think of how you can sort of reorient your capabilities towards some other productive ends.

1:07:08Paula Pant:Actually, we should close on the story of switchboard operators and typists, because I think there's some hope there. Both of those job categories became obsolete. Yeah. But it actually then led to a proliferation of more advanced, more sophisticated jobs. Your mom was a typist?

1:07:30Dr. Ben Zweig:Yeah, my mom started her career as a typist. She was like trained on a specific typewriter, the IBM, whatever. And we had to get trained. And she was very proud of being able to do however many words per minute. That was her job. Now, what's interesting about being a typist is that you're not just typing. Like, you're also figuring out how to message things, how to translate information and make things concise. It's not really just hearing words and, like, typing them out. As computers became more widespread and typing got easier and easier, she kind of gradually made her way into more secretarial work.

1:08:12Dr. Ben Zweig:and then started doing more secretarial things and was in that occupation for a while. And then eventually was doing secretarial work at large companies, at law firms. She also worked at IBM for a little while, did more and more secretarial work and eventually was involved in managing a lot of information and files and categorizing things, which is kind of what people do as a secretary. and eventually just became the expert on filing documents related to, I think it was like subsidiaries of the company and like the documents for incorporating the subsidiaries of something and became this like manager of a subsidiary database for a company.

1:08:57Dr. Ben Zweig:I don't think she ever thought of herself this way. When she told me about this, I was like, oh, so you're like a database administrator. And she was like, I guess, you know, like she never thought of herself as a database administrator. But like, that kind of is what she did. And that is a role that you can hire for today. But she always thought of herself as like some hybrid of like secretary and paralegal and this hodgepodge job of like understanding and categorizing information. These things do happen gradually. The activities didn't change as much as the job titles changed, which is kind of a counter example to the bank tellers, where the activities changed a lot, and the title stayed the same.

1:09:38Dr. Ben Zweig:So yeah, I mean, you kind of have both sides of the same coin. And I think that there's quite a bit of optimism there for people who are worried about job

1:09:45Paula Pant:displacement because both the bank teller example and the typist to database administrator counter example both illustrate the changing nature of jobs, regardless of what the title is, the changing nature and then the very fluid nature of work.

1:10:04Dr. Ben Zweig:Yeah. I mean, we spoke about like how responsive is labor supply to automation? Can workers find new things to do? Another issue is how responsive is labor demand to automation, to technology. So do firms actually adopt technology? And when they adopt technology, is it fast or is it slow? And I think for better or for worse, it's a bad thing, but firms don't adopt technology all at once. they're slow, especially big companies are really slow in implementing change. On the one hand, that's not good for those companies. But on the other hand, it does give us time to adapt. Workers can find new things to do.

1:10:42Dr. Ben Zweig:They have time because firms are not going to implement new technology just like that. Yet we have a chance to kind of reconfigure work by ourselves through talking to our managers, by understanding the changing needs of the business. All of these things happen organically. And yeah, I think that is kind of a hopeful story.

1:11:02Paula Pant:Beautiful. Well, thank you for spending this time with us. Where can people find you? Yeah, thank you. They'd like to learn more. LinkedIn.

1:11:08Dr. Ben Zweig:I post a lot about labor market insights and data on LinkedIn. That's probably the easiest.

1:11:14Paula Pant:Wonderful. Well, thank you. Thank you so much. Thank you. Yeah. Thanks for having me. Thank you to Dr. Ben Zweg. What are three key takeaways that we got from this conversation. Key takeaway number one, job titles are chaos, and that affects how much you make because there are 90 million unique job titles floating around. That's messy and chaotic, but also it creates information asymmetry because if titles are inconsistent, then you can't easily compare roles. You can't easily figure out whether or not you're underpaid. So understanding your actual tasks and like how those tasks map to the market, that gives you leverage when you go in to negotiate for your pay.

1:11:58Dr. Ben Zweig:I think we see 90 million unique job titles, which is obscene. There's no way a human can understand what all those are. And so many different companies have different conventions for how they use titles. And they all need to communicate to the external market, to a job candidate who is searching for something. It's a problem that exists on the employer side and also a problem that manifests itself for employees who actually just want to be able to search for something and find something that's a good fit, that's a good match.

1:12:32Paula Pant:That is the first key takeaway. Key takeaway number two, managers become more valuable in an AI world because if AI automates execution, then the skill that is scarce and valuable is orchestration management. And so the ability to reconfigure roles, to realign teams, to rethink workflows, those orchestration types of tasks increase in value. And that means if you want job security, promotions, raises, be a great orchestrator.

1:13:08Dr. Ben Zweig:If we had to think about what managers fundamentally need to do, I think it's about job reconfiguration. I think they need to understand the evolving needs of the business, understand the people and what they're doing, and reorient, try to continuously reorient what people do in their jobs to the needs of the business.

1:13:30Paula Pant:And that's very fluid.

1:13:32Dr. Ben Zweig:it. The optimistic part of me thinks that if that's happening every day, then the emergence of a new technology may be big, but still relatively unimpactful in the general reconfiguration of work because the reconfiguration of work is so much a part of everyday business for so many people. Finally, key takeaway number three, jobs don't disappear, but they transform.

1:14:00Paula Pant:reform, automation very rarely just wipes out a role overnight. Instead, it reshapes what happens inside of that role. It reshapes the tasks that are demanded of that role. And if you're adaptable, then that's good news because it means that your upside comes from evolving with the work rather than jumping ship.

1:14:21Dr. Ben Zweig:So much of these phenomena of job displacement, of automation, like it's happening within jobs. It's not happening between jobs. It's so important to think about what's happening within each job. How is each job transforming? Firms don't adopt technology all at once. They're slow, especially big companies are really slow in implementing change. On the one hand, that's not good for those companies. But on the other hand, it does give us time to adapt.

1:14:48Paula Pant:Those are three key takeaways from this conversation with Dr. Ben Zweig. He is the CEO of Reveglio Labs and the author of a book called Job Architecture, as well as an adjunct professor at NYU Stern School of Business teaching the future of work. Thank you for being an afforder, part of the Afford Anything community. If you enjoyed today's episode, please do three things. First, share this with your friends, family, neighbors, colleagues, cousins, babysitters, teachers, dog walkers, postal workers. share it with your barista, with the guy at FedEx, with a person in your kid's carpool, share it with your former professors, share it with your favorite economist and your least favorite economist.

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1:16:02Paula Pant:Thank you again for being an Afforder. My name is Paula Pant. This is the Afford Anything Podcast and I'll meet you in the next episode.