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Afford Anything | Make Smart Money Choices
AI, Layoffs, and the Future of Your Career — with Dr. Ben Zweig (Part 1 of 2)
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- [00:00]
Paula Pant
So if you haven't seen the headlines yet, there's this company called Block. It's the company behind Square and Cash App. And afterpay, it just announced that it's laying off 4,000 employees. That's roughly 40% of its workforce. It's going to go from over 10,000 employees down to under 6,000. With this news comes the Is this a harbinger of things to come? Will we all be unemployed? And that question itself is a subset of of a much bigger question, which is, what is the future of work? To answer that, we brought in the guy who teaches a class called the Future of work at NYU Stern School of Business. His name is Dr. Ben Zweig, and he's the CEO of Revelio Labs, which is a workforce data company that uses AI to build big employment databases and track shifts in the labor market in real time. So his company analyzes millions of job postings and analyzes employee records to see what's actually changing in the world of hiring. Dr. Zweig holds a PhD in economics from the CUNY Graduate center, and he is the author of Job Architecture, which is a book that traces how we structured work in the first place, from the founders of Wall street to early management consultants to modern data scientists who are trying to make sense of the labor market. So he in that book, he argues that many of the systems around how we work were built for a different era, and AI is really exposing that. And of course, that's going to influence the future of work. So today's episode is part one of two. In today's episode, we are going to talk about which jobs are most vulnerable to automation. We're going to talk about how in particular, this affects younger workers, why hiring has really slowed for younger workers. And we're going to discuss what skills matter in the era in which AI can execute many, many tasks faster than humans can. We'll talk about what parts of your job AI can already do and what that means for you, particularly if you're early in your career. That's all in today's episode. And then in the next episode, part two, we will go deeper into the taxonomy of work and how all of that is going to likely get restructured as we move into the next few years. Oh, I should introduce myself. Welcome to the Afford Anything podcast, the show that knows you can afford anything. Not everything. This show covers five financial psychology, increasing your income, investing, real estate, and entrepreneurship. It's double I fire. I'm your host, Paula Pant. And today's episode, you know, we talk about the five pillars that the show covers. Today's episode is about that first letter, I, increasing your income because, well, any discussion around jobs, careers, around the future of work traces to that. How do we continue to earn more, to thrive in our careers in a landscape that is changing so rapidly? Again, today's episode is part one of two. Today's episode is shorter than what we usually publish. So we're going to do something a little unusual. We're going to take just a couple of minutes to hear from the sponsors who make this all possible. Today's episode is a shorter one, so I didn't want to interrupt our conversation twice. That's why we're leading with this word from our sponsors first. That way we can minimize interruptions when the interview actually begins. And so we're going to hear from them. And after that we're going to go into part one of this two part series with Dr. Ben Zweig. Most of us, we get into small business because there's some purpose behind it. There's a service or a product that we really want to get out into the world. And to be able to do that effectively and at scale, we need a team, we need people, and that requires a lot of administration. We're talking tax forms, onboarding documents. Well, Gusto handles all of that so that you can spend your time on the parts of your business that you love. Gusto is online payroll and benefits software built for small businesses. It's all in one, remote, friendly and incredibly easy to use. So you can pay, hire, onboard and support your team for from anywhere. We're talking automatic payroll tax filing, simple direct deposits, health benefits, commuter benefits, workers comp 401k, you name it. You can get direct access to certified HR experts. It's quick and simple to switch. Just transfer your existing data and you don't pay a cent until you run your first payroll. I've been using them since 2016 or 2017, which is when I hired my first employee. I've been using them the entire time. Try gusto today@gusto.com Paula and get three months free when you run your first payroll. That's three months of free free payroll@gusto.com Paula One more time. Gusto G-U-S-T-O.com Paula P A U L A I remember when I was in college, I had no credit. Wasn't bad, wasn't good. I just had no credit history. When I went to apply for an apartment for the first time, that was a problem. I was 19 years old. Going, oh man, I need to develop some kind of a credit history and I didn't know anything about it at the time. AVA is a credit building app that's designed to work fast and help your credit score where it really matters. So your credit score can start climbing with almost zero effort. Just pay your monthly subscriptions like usual with the AVA credit builder card and AVA reports your on time payments to all major credit bureaus. AVA can now report your rent, utility and phone bills automatically, even up to 24 months of past payments. So your everyday payments can help build credit faster. AVA members have improved their scores by an average of 30 points in 30 days. AVA has over 2 million downloads and is the highest rated credit building app on trustpilot with the most five star reviews. Take control of your credit today. Download the AVA app a V A and when you join using my promo code Paula, you'll get 20% off your first year, monthly or annual. Your choice. Again, grab the AVA app and use my promo code Paula, so they know you heard it from me. And you'll get 20% off any plan for up to a year. That's promo code Paula. Thanks to ava. Now go get yourself good credit.
- [06:16]
Dr. Ben Zweig
Welcome.
- [06:16]
Paula Pant
Thank you for joining us.
- [06:17]
Dr. Ben Zweig
Yeah, thanks for having me. Happy to be here.
- [06:19]
Paula Pant
Can you tell us the story? I think a lot of us learned this as kids in elementary school, but I'd long forgotten it. The fable of John Henry.
- [06:27]
Dr. Ben Zweig
John Henry is really an illustration of the Luddites. The story of the Luddites are these workers who were weavers and then the loom came out, which is this machine that can do weaving super fast, you know, a hundred times faster than a human could. They smashed the machines. The story of John Henry is the apocryphal story of that. This is such an interesting history because for so long the Luddites were considered these bozos that just like could not get a handle on technology. They were so against technology automating their jobs. And recently there's been a kind of trend to kind of reclaim the history of the Luddites and say maybe they weren't such bozos. Maybe they were really actually quite rational. And we're right to be kind of fearful of losing our jobs to automation. I feel like that's maybe gone a little too far. But yeah, I mean, this story kind of keeps coming up again and again in different versions. And we have the same idea now. We have union workers going on strike because they don't want to be replaced by automation and things like that. So even though it gets told in, like, lots of different ways. It touches on this real fear of losing our jobs to machines.
- [07:41]
Paula Pant
And how do we process that fear right now? Because we can look back at examples in history where certain technologies have come obsolete, but new technologies have arisen. You have a stat. Something like 60% of today's jobs did not exist back in 19, 20 or 30 or 40.
- [08:01]
Dr. Ben Zweig
Yeah, yeah. And I think that really understates it because those are new jobs. Even the composition of jobs change entirely. So even jobs that do exist from period to period, we do totally different things in them. A personal story. My dad was actually a statistician, and I'm kind of a statistician. You know, I'm an economist, and I analyze data, and I consider myself not a statistician. I think what he was doing in the 60s, 70s, was tabulating frequencies on punch cards to detect some anomalies and look for fraud and stuff like that. And what I'm doing is like building neural networks to create embeddings of job titles to find out what job titles are similar. And that is so different, even though the occupation that we're in might be fundamentally the same.
- [08:49]
Paula Pant
Which actually then brings me to the question, I guess we should start with, by establishing the definition of what we're talking about, what is a job? Because you differentiate a job from there, the micro tasks, and that also is differentiated from the orchestrator.
- [09:06]
Dr. Ben Zweig
Yeah. Okay.
- [09:07]
Paula Pant
So, yeah, let's go over this vocabulary. What is a job?
- [09:10]
Dr. Ben Zweig
I think the almost perfect definition of a job is a bundle of tasks. It's not quite the right definition, but I think it's a very useful step forward from thinking of jobs as the atomical unit of analysis. Sometimes people talk about automation as affecting jobs, but jobs are combinations of things. So I think it's automatically when we talk about automation, we really can't be talking about jobs, because we can go more granular than jobs, and we can think of jobs as a collection of things. Your job as a podcaster or podcaster plus, I guess, is probably a collection of different things. So you're not just doing the primary activity. You're also preparing and analyzing social media. And, like, there's a whole collection of things. And any person who has a job, they have a dozen or so responsibilities that they do in their job. The job title is really kind of a shorthand for a collection of things. So we know when someone says they're a teacher, they're doing some instruction, they're doing lesson prep, they're doing grading, they're Doing a whole set of things related to teaching. And each of those tasks can be broken down into finer kind of more detailed subtasks. That is like a way to think about what a job is to get even a little bit more precise. When we do a job, we are not simply executing on the tasks in the job, we are also orchestrating those tasks. So we're also deciding what should come first, what should come second. Who depends on this? How do I combine these tasks into a workflow to minimize the amount of work and maximize the output? Who do I need to work with? What are the connections that need to be made? So we are both executor of tasks and orchestrator of tasks. And I think that's a useful framing in a few ways, because one is that when we think about technology, is technology doing more of the execution? Is it doing more of the orchestration? So how do we complement that? That's like a big question that I think we need to grapple with. Another question is why do jobs exist? There's some discussion, there has been some discussion. There's been a trend a few years ago about maybe we should just unbundle jobs entirely and just transact in tasks. So there was this wonderful book written called Work Without Jobs by John Boudreau and Robin Suthason. Really fun book. And it basically makes this argument for unbundling work and just doing the work that needs to get done. And what do you need job titles for? I don't fully agree with that, but I think it's a really useful way to think about work because at the end of the day, the organization really cares about the work being done, the delivery of the output. And having that be delivered through a job is a second order priority, maybe not important at all. Maybe it's. Maybe it could get done through a freelancer who's like delivering on a specific task. Maybe it could be done through a technology or a vendor. So it's useful to think about the delivery of the work activities of the tasks as distinct from the jobs and the people who hold those jobs and the skills that they have. So I think it's useful. But thinking through this coordination also forces us to think through, you know, what are the costs of coordinating these different work tasks. So if you've got like a collection of things that you do as a person, it's not that hard to coordinate. Like, we can orchestrate ourselves pretty seamlessly, much more seamlessly than what it would take to coordinate a set of activities between people. So if you're Working on a team, there's more friction than just like coordinating your own activities. And if you're working between teams, there's even more friction. And between companies, there's even more friction. So we have this trade off between coordination costs, which are kind of low at the micro level and higher as you get more bigger and more abstract with specialization, which we get more of when we have bigger entities. These are just important dynamics to think of as we think through. Why does work look the way it looks and how will that look in the future?
- [13:20]
Paula Pant
Right. When we think about the execution of jobs versus the orchestration of jobs, it seems to me, and please correct me if I'm wrong, that right now these early AI models, they can execute on specific tasks, but are they currently able to orchestrate workflows and orchestrate how a bundle of tasks are performed? And if the answer is no, then are they moving in that direction, or will humans always be needed to orchestrate?
- [13:48]
Dr. Ben Zweig
Yeah, it's a really great question. It's a huge question. So let me kind of take you on this journey where I kind of changed my mind about this a little bit. At my company at Revelia Labs, we have this taxonomy of work activities, and for every activity, we want to know how vulnerable that activity is to automation. And this taxonomy of work activities is hierarchical. So you have these broad levels, which could be like analyzing financial data, and then you have like really granular levels, which is like implementing Excel macros or something like things that are much more detailed. You could think of a collection of interrelated tasks as a workflow. But what we found is that if we ask a generative AI model to score each task on the risk of it being automated, the ones at the very, very granular level have a higher risk of being automated, and the ones at the broader level have a lower risk. Which makes sense because executing on something small is easier than executing on something large and abstract and complicated. So I think that is just reality of the world for machines and for humans, doing more abstract, complex things is more complicated almost by definition. So if we can think of these subtasks as being automated, then the question is, well, if you have three tasks that are a workflow, then why don't we just train a machine or a system to just do that whole workflow? And that's kind of the promise of like agentic AI. So the idea of agentic systems is that they can coordinate, and we do have that. So. OpenAI Deep Research ChatGPT Deep Research is a good example of Find me everything. About some topic and it goes, and it does searches and it does synthesis and it's, and it does like it does a set of things to deliver like a finalized report. Claude code is another great example where you can say, build a website or, which is something you could do a more complex task. And so AI is moving in that direction of orchestrating related tasks into a workflow. Now I think there's still higher level tasks where there's not really any end in sight. So if you could do a more complex workflow, then that becomes the atomic task and then the human or whoever sits on top of that really needs to orchestrate between those subtasks. You can manage things that are more abstract and you're like kind of a higher level manager director type person or you can manage more sub level tasks. And as machines get better at performing broader tasks then we as humans, if we're playing more of this orchestration role, will be doing more high value, more abstract work. What I kind of came around to recently is this kind of divide between automation and augmentation. Sometimes in economic research we see some division between, oh, there's some technology that can automate work and others that can augment your job, augment your work and help you in doing something better. I think these are actually the same thing, just at a different level of the taxonomy. So I know I'm getting like really wonky here. Let me try to break it down. So at a micro level, if you have a work activity or a work process that has a dozen different tasks and half of those get automated, then you can concentrate in the six remaining activities. Your job has been augmented because the tasks have been automated.
- [17:34]
Paula Pant
So I think so let's say, let's just bring it to an example. You run a cupcake bakery. In the course of a normal workday, there are 12 specific tasks that you do related to, some of which are related to handling the cash register, to managing inventory, flour, sugar, eggs. If the floors are slippery because it's been snowing, there is a protocol for some employee to make sure that everything is adequately mopped up so that the customers don't slip. Like you've got all of these disparate tasks that you need to do in the management of your cupcake bakery. And so you're saying if half of those get automated, then the other half that are not automated are augmented.
- [18:18]
Dr. Ben Zweig
I would say like you as a person have been augmented. You as a person, you as a job, you as a worker, you can do more with your time. So your productivity is Enhanced. And that enhancement of your productivity happens through automation. So I'm coming around to the idea that the term augmentation is actually kind of confusing and worthless. It's actually automation of parts of what we do that make us more productive, arguably.
- [18:46]
Paula Pant
Would that also be the case? Historically, there are tasks that we used to do that have now been automated. Washing clothes, People used to wash clothes by hand. That task has now been automated by a washing machine. Washing dishes, same thing. That task has been automated by the dishwasher. Could you not make the case that what we are experiencing right now is a continuation of this historic trend in which, you know, we used to need to get ice cubes to keep our food cool, and now that has been automated by refrigeration.
- [19:19]
Dr. Ben Zweig
Yeah, yeah. It's exactly the same. There's always some part of our work process that gets automated. It's really up to us as workers and humans to reconfigure what we do. We need to adapt. And that adaptation really is a reconfiguration of your work activities. You have some composition of things that you do, and it takes up some portion of your time. If things take less of your time, you need to kind of go back to the drawing board. Sometimes you just concentrate in other things, spend more time on other things that you were doing, things that you like doing, things that are more high value. Maybe you introduce new things. All of that is in response to parts of our workflows being automated.
- [20:03]
Paula Pant
I think the fear that many people have is that we'll run out of things to do.
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Dr. Ben Zweig
Yeah.
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Paula Pant
That so many things will be automated. Because when we talk about refrigeration or the dishwasher or the washing machine, these are discrete machines that problem solve for extremely discrete tasks.
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Dr. Ben Zweig
Yeah.
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Paula Pant
So the invention of the washing machine solved for one extremely discreet task, but had no ripple effect on any other task that we did. And I think the differentiating factor with AI is that AI can automate in one fell swoop or in one new technology.
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Dr. Ben Zweig
Yeah.
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Paula Pant
Almost everything, ranging from writing poetry to processing our taxes, to researching how to train our dog.
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Dr. Ben Zweig
Yeah, yeah. It's extraordinary. In some way, this is kind of the divide between like narrow AI and general AI. And it's very clear that generative AI, what we think of as ChatGPT, is a general purpose technology and that it affects a lot of different things. Kind of like electricity is general purpose technology. It's not doing one particular thing. It powers lots and lots of different things. It won't affect. It's not like an ATM that will only affect bank tellers. It is something that will affect every job. And we can talk about the ATM example. That's like a fascinating example. But yeah, it'll affect lots of different jobs, but not entire jobs wholesale. Still, even though it is general, there are different parts of what we do that at best can't be automated. They're still uniquely human parts of our jobs. But even if we don't want to be that optimistic, at least won't be automated at the same time. Let's imagine that we have this AI that can do anything and everything in the world. As long as those get rolled out at different rates, then there's time for our jobs to kind of adapt. And that allows us to think about what our comparative advantages are to the extent we have them, which I think we do so well.
- [22:10]
Paula Pant
I think the fear is that in a relatively short period of time, and this is assuming, because right now, the big comparative advantage that humans have is dexterity, physical dexterity. But assuming that we were to get to a point where robotic dexterity could actually keep up, robotic or artificial forms could keep up with the human body, which we are very far away from right now. Yeah, but 20 years from now, that might not be the case anymore.
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Dr. Ben Zweig
Yeah.
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Paula Pant
I think the concern is once that happens, then will AI have exceeded us both in terms of intelligence and in terms of dexterity? What do we have left to do?
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Dr. Ben Zweig
Yeah, I think dexterity is a very good example of something that AI and robotics cannot do. Like you can't have a machine that busses tables, at least not yet. That is still the domain of humans. I don't think it's uniquely dexterity. So there's this wonderful paper by Isabella Luasu and Roberto Regoban called Epoch of Jobs or something like that. They have this acronym. EPOCH stands for Empathy, Presence, Opinion, Creativity and Hope. And it's these five things that are uniquely human. So. So the idea is that these are in some way not automatable. Right now I'm sure we can quibble over these things. Maybe machines can get creative, et cetera. But it is still kind of science fiction that we are going to have AI rabbis or AI psychologists, maybe podcasters, I don't really know. But teachers, I mean, teachers. Teachers are a different story because you can sort of segment like, I have two girls in preschool, Preschool teachers. I'm very sure that's not vulnerable to AI, at least not now. It takes so many of these uniquely human things, caretaker things like that.
- [24:08]
Paula Pant
Well, so Yuval Noah Harari actually has stated the opposite when it comes to AI, rabbis or priests.
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Dr. Ben Zweig
Yeah.
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Paula Pant
His hypothesis is that any text based religion, because it is text based AI can consume all of the religious text and then can answer any question that you have in order to offer that guidance. Now, so his position is that for non text based religions, that will be different, but Christianity, Judaism, Hinduism, these are all text based.
- [24:41]
Dr. Ben Zweig
Yeah, I saw that he said that. And I disagree because I think it's a misread on what we want from leaders, community leaders. I don't think the role of a rabbi or a priest is really to weigh in on religious texts in the way that it might have been in the 1800s. It is much more about building community. I mean, they are community organizers and, you know, want to inspire hope and be there for people and create bonds between families. Like, I think it's just a misread on what the occupation really is. Yeah, it's community leader. So I think it was kind of an interesting example. I mean, I agree that like reading text and deciphering text is not uniquely human anymore, but I don't think that's actually what we want in our leaders.
- [25:30]
Paula Pant
All right, well, with regard to AI psychologists, then we see programs like WOEBOT that are designed to provide AI therapy. And I know myself personally, like there are times that I've been frustrated and I've had a conversation with ChatGPT and I've left that conversation feeling much better. I've, I've kind of worked through my frustrations and understood the source and understood how to deal with the situation. And it's been a really nice. It's, it has really served an empathetic role in my life.
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Dr. Ben Zweig
Yeah, yeah, no, I think you're right. And also, so many people are confiding in AI for better or worse. They're examples where that hasn't been good. But yeah, it seems like this aversion to AI being someone that you're friendly with is less the case now than it used to be. So maybe there is room for that to kind of take over. There's two problems, as I see it for AI psychologists. One is that AI, at least right now, doesn't have good memory. So, you know, that's a problem that's being worked on. And there's improvements in increasing the context that AI can incorporate. But psychologists usually don't just meet with someone once they meet with someone over months or years or decades and really get to know a person and that presumably affects how they interact. They can tailor their therapy to what they kind of understand about the full dimensionality of a person. So maybe that will improve, but another is just human taste. I mean, my wife is a child psychologist. She sometimes tells me that being a psychologist for children is so different than like working with their parents in a way that just getting these kids to trust you and confide in you is an art. And that is something that takes social skills. We're not quite there yet with technology to actually get people to open up. I mean, some will and some won't, but there's still a set of people that feel uncomfortable opening up to AI, right?
- [27:51]
Paula Pant
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Paula Pant
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Paula Pant
everything humming right along. Call 1-800-GRAINGER clickgrainger.com or just stop by Grainger for the ones who get it done. How we led into this is the root question of as AI develops, will there be anything left for us to do? And that traces back to, really traces back to the question of what is a job, the execution versus orchestration, and then what is our role within that. So I want to take this to a practical level because for somebody who's listening to this conversation, who is between the ages of 25 to 30, relatively at the beginning of their career, trying to understand how to chart their way in this world in which everything is changing so rapidly. Many entry level jobs are disappearing and they themselves don't have at that age, they don't have the benefit of accumulated assets that they can fall back on. They don't have experience, they don't have a network. What should they know at this stage given how rapidly the future of jobs are changing?
- [29:50]
Dr. Ben Zweig
Yeah, entry level workers, young people in general, are in a much tougher spot now than they have been in a long time. One is that It's a very tough environment for hiring. So we're in this kind of low hire, low fire paradigm. And that hurts younger workers because they're usually the earliest entrance into a new hire. But it's also particularly bad in that there seems to be good evidence that the entry level jobs that are hurt the most are ones that are vulnerable to AI. So it seems like the expectation is that some of these entry level jobs may be automated or could be automated in the future. It's unclear whether firms are actually adopting technology and have less demand for younger workers because of that, or if they're just anticipating that they might have less demand for younger workers because of what they could do with this technology in the future. I think it's probably more anticipatory, but either way, younger workers are in a really tough spot. One issue is this execution and orchestration question where if AI is better at execution, then the return on coordination is bigger. Who has more skills and coordination? It's people that have managed things, that have orchestrated before. Younger workers don't really get trained in orchestration. So much of our education is based on skills and executing pretty narrow things.
- [31:23]
Paula Pant
Not in the managerial. Yeah, right.
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Dr. Ben Zweig
Yeah, exactly. I mean, even mba. So I work at New York University. I teach the future of work there. Even at a business school, the classes that are offered are very practical. They're more vocational. And I think if you had gone to business school in the 60s and 70s, that would be the next step to running a division somewhere. Now it's not now. People finish an MBA and they're a level two analyst at a consulting firm or something. Management education, I think has receded, which is unfortunate for this moment in time. So I think there probably does deserve to be a resurgence of management as a discipline because I think it can be taught. What I try to tell younger people is try to work on a project from beginning to end, take on something complicated and just try to work through the nitty gritty connections that you need to make from one process and another process. It's really hard. It's surprisingly hard. The return. The relative benefit of being a good orchestrator is hurting younger workers relative to older workers. So that's, I think the real issue. The other is just that we are in a risk off environment and younger workers generally are a higher variance bet. If employers have kind of a high discount rate and they want to prioritize the present relative to the future, it's a safer bet to find an experienced worker. They'll be more productive. Day one Younger workers are just they pay off over a longer period of time.
- [32:58]
Paula Pant
Yeah, actually that's also true. I'm thinking about overseas outsourcing. Those are often higher variance hires.
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Dr. Ben Zweig
Yeah.
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Paula Pant
You know, I was having a conversation about this with a colleague about the trade offs between overseas outsourcing versus paying more of a premium to hire somebody who's local. And really the decision came back to that variance. Right. To predictability of result.
- [33:26]
Dr. Ben Zweig
Yeah. Another piece of evidence that we're seeing that is that while we've seen declines in demand for younger workers, we haven't actually seen a reduction in wages for young relative wages for younger workers. Which is interesting because you would think, oh, decline. So we see a decline in quantity demanded, but you think, oh, declines in demand mean like lower prices and lower quantities. But we're actually just seeing those lower quantities at actually even higher prices, which is on its face a little strange. And you think, oh, maybe this isn't a demand thing, maybe it's a supply side thing. But I think it's consistent with the idea that the safer bets are the ones who are being prioritized and those safer bets are more expensive. So maybe someone will take a bet on a younger worker who's got great experience, a good pedigree, but that higher variance, less certain worker that someone might have ordinarily taken a bet on, they may not be willing to take a bet on in this environment.
- [34:26]
Paula Pant
What's going through my mind right now is the notion coming from this that if you are the owner of a small business, it actually could, depending on what industry you're in. But if you're in a type of business that needs knowledge workers, it could actually be rational to prioritize paying a premium for more highly trained, highly experienced, sure bet premium workers. It's sort of the opposite of what you often see small businesses do. Where small businesses, they run on shoestring budgets, they're often bootstrapped. Don't you have A stat, what, 10% of small businesses in the U.S. which equals about 800,000 businesses, are solo proprietorship.
- [35:07]
Dr. Ben Zweig
Yes. Yeah, that's right.
- [35:09]
Paula Pant
So if you're transitioning from a sole proprietorship to something with one full time employee outside of yourself, they better be good. Yeah. And oftentimes because you're on such a shoestring budget and you're bootstrapping the whole thing, the tendency is to want to hire somebody who's cheap and inexperienced, but it would actually be safer to do the opposite.
- [35:31]
Dr. Ben Zweig
Yeah, yeah, I think that's right. What we did at Revelia Labs is We used to hire interns. I think hiring interns, you get a better return on investment because they're more high variance. It's a long evaluation period, but it doesn't get you that benefit. You basically have to train someone and not really get any output, maybe small levels of output for the hope that they'll join in a year or whenever. It's a very tough bet to make when you're optimizing for the short term. So we stopped hiring interns. And part of me feels a little bad about that because I know you can get high returns, but sometimes in a startup you have no choice but to optimize for the short term at the expense of the long term. And that is just a choice that every business operator needs to make. Sometimes you need to weigh those costs
- [36:24]
Paula Pant
and benefits and that actually then. So for the people who are listening, who are wondering, how do I take this information and process it into the way in which I position myself as a job candidate, having more experience, more managerial experience, and then not underselling yourself, there is a signaling that if you command a premium rate, you provide premium service.
- [36:46]
Dr. Ben Zweig
Yeah. Yeah, I think that's right. I mean, there are ways to send those signals. Like, now more than ever, there is badging. There's all sorts of ways to do that. But I also think you can send those signals directly through networking and just telling people what you're doing. I mean, kind of evergreen advice for young people is to network more and just get out there, get people who know what you're doing. And maybe some will like it and maybe some won't, but at least getting that connectivity will. One, show people what you can do, but also give you some sense of what you should be learning and what your next signal should be.
- [37:30]
Paula Pant
You've just listened to part one of two in our interview with Professor Ben Zweig. He teaches a class on the future of work at NYU Stern School of Business, and he is the CEO of Revelio Labs, a company that uses AI to analyze the labor market. We'll be sharing part two of this interview in our next episode. Part two is longer, and it's going to talk through the taxonomy of jobs. And if you're thinking, why do I care about that? It's because when you go to search for a job, you need to know what you're looking for. And right now, all of those categories are shifting. So make sure that you're following this podcast and your favorite podcast player so that you can listen to part two, which we will air as our next episode. In the meantime, what are three key takeaways that we got from this conversation? Key takeaway number one, your job is a bundle of tasks. If AI can unbundle your job, it can replace pieces of you, but not all of you. So the risk isn't that AI replaces your entire job overnight. Despite what happened at Block, despite some of the more high profile layoffs that we hear about in the headlines, for most people, the risk isn't waking up to a layoff. In fact, overall, we're in a low, high or low fire environment. The risk is that AI quietly begins to automate some of the pieces of what you were hired to do. Particularly if your role is mostly the execution of tasks, then that's the part or parts of your job that are most exposed, most vulnerable.
- [39:07]
Dr. Ben Zweig
I think the almost perfect definition of a job is a bundle of tasks. It's not quite the right definition, but I think it's a very useful step forward from thinking of jobs as the atomical unit of analysis. Sometimes people talk about automation as affecting jobs, but jobs are combinations of things. So I think it's automatically when we talk about automation, we really can't be talking about jobs because we can go more granular than jobs and we can think of jobs as a collection of things. Your job as a podcaster or podcaster plus, I guess, is probably a collection of different things. So you're not just doing the primary activity, you're also preparing and analyzing social media. And like there's a whole collection of things. And any person who has a job, they have a dozen or so responsibilities that they do in their job. The job title is really kind of a shorthand for a collection of things.
- [40:02]
Paula Pant
But the elements of your job that are not pure execution of tasks, the more relational elements of your job, that's the part that's least exposed. So if you think of a job as a bundle of tasks, the most relational elements of that bundle are the elements that are harder to replace. That's the first key takeaway. Key takeaway number two, quote, unquote. Augmentation is really just automation. You'll hear companies say that AI is going to augment workers. Professor Zweig argues that that often is just automation that's happening at a smaller scale.
- [40:42]
Dr. Ben Zweig
What I kind of came around to recently is this kind of divide between automation and augmentation. Sometimes in economic research we see some division between, oh, there's some technology that can automate work and others that can augment your job, augment your work and help you in doing something. Better. I think these are actually the same thing, just at a different level of the taxonomy. So I know I'm getting like, really wonky here. Let me try to break it down. So at a micro level, if you have a work activity or a work process that has a dozen different tasks and half of those get automated, then you can concentrate. In the six remaining activities, your job has been augmented because the tasks have been automated.
- [41:31]
Paula Pant
Here's what that means in practical terms. If part of your job gets automated, then your productivity goes up, and that's great. But it also means that there's going to be some skill reshuffling because the things that make you valuable start to shift upward.
- [41:47]
Dr. Ben Zweig
Things.
- [41:48]
Paula Pant
Think about when washing machines came out. Washing machines, automated laundry. And so this task that used to take hours manually using a washboard suddenly took minutes. And that meant that the skills that made a domestic worker valuable or a homemaker valuable, those skills then shifted upward. It was no longer about banging a shirt against a washboard. It was about coordination, judgment, prioritization. These are attributes that move up the value chain. And so when we talk about augmentation versus automation, what we're really talking about is becoming more valuable because of that automation. That is the second key takeaway. Finally, key takeaway number three. Early career workers are the most exposed to risk. Because if you are early in your career, you are often hired in order to execute very narrow tasks. Untested workers typically have a very narrow scope of work, and those roles are the easiest to automate. At the same time, companies are hiring less and they are prioritizing employees that have a lower degree of variance because variance is risk. And when as a company you're facing risks externally, you can't also introduce new risks internally. So the workforce that you do hire, you want them to be more established, more experienced, more proven. And that makes this particular combination of what's happening right now particularly tough for entry level workers.
- [43:33]
Dr. Ben Zweig
Entry level workers, young people in general are in a much tougher spot now than they have been in a long time. One is that it's a very tough environment for hiring. So we're in this kind of low hire, low fire paradigm. And that hurts younger workers because they're usually the earliest entrance into a new hire. But it's also particularly bad in that there seems to be good evidence that the entry level jobs that are hurt the most are ones that are vulnerable to AI. The other is just that we are in a risk off environment and younger workers generally are a higher variance.
- [44:15]
Paula Pant
Bet those are three key takeaways from this conversation with Dr. Ben Zweig. And again, this episode is part one of a two part series. So make sure that you tune into our next episode in order to hear what next? What do we do about it? How does this affect our next steps and how does it affect the taxonomy of work? And I know taxonomy of work is not something that you, like, wake up in the middle of the night thinking about, but it's a foundational piece of understanding where you fit into this broader landscape, especially now that so much is changing. So again, make sure you tune in to part two, which is our next episode. Thank you so much for being part of the Afford Anything community. Please share this with the people in your life. I actually, as I was in the middle of recording these key takeaways, I got a text message from my best friend who's worried. We're all worried. I wrote her back right away and I was like, oh, the Jack Dorsey effect. And she was like, yeah. So if you share that same feeling, first, I thoroughly understand why. And there are millions of people who also feel the same way. And that feeling is grounded in some very real shifts that are happening. And if you do feel that way, first of all, please don't do anything rash with your finances. Don't liquidate all of your assets. Don't thoroughly change up what you're investing in. Don't dive into crypto, don't. Don't do anything rash. Remember, this is why we have investor policy statements, written statements of here's what we're going to do so that in times of fear, anxiety crisis, we can look to the guidelines that we have written out for ourselves. So if you are worried, first and foremost, please don't do anything rash with your finances. Second, remember that acquiring assets puts you in a position of power. And assets means index funds, real estate. Remember, income from rental properties is not going to be affected by AI. While it's not going to be adversely affected, it will make your job as a landlord easier by simplifying the bookkeeping, simplifying the operations. But at the end of the day, people need a place to live. There will always be rental demand. So buy assets, hold rental properties, hold index funds. Because we know that the trajectory of the US Stock market over the long term historically has always gone up. And then focus on learning about the future of work. Because you may or may not need to retrain. And if you do, and certainly and all of us, whether we have to formally retrain or not, all of us have to pivot and shift and learn new skills throughout our careers. So be nimble, be agile, and you know, in the words of Wayne Gretzky, skate to where the puck is going. Pay attention to where things are leading so that you can get ahead of it. Thank you so much for being part of the Afford Anything community. If you want to discuss today's episode, you can do so for free@affordanything.com community. It's where people in this community afforders hang out. Affordanything.com community totally free. We have a newsletter, affordanything.com newsletter where we share insights that we do not publish anywhere else. So that's affordanything.com newsletter. And again, make sure you're following us in your favorite podcast player. Open up that app, make sure you hit the follow button and while you're there, please leave us up to a five star review. Write a few words, talk about what you enjoy about the show. Thank you so much for being an afforder. I'm Paula Pant. This is the Afford Anything podcast and I'll meet you in part two of this interview with Dr. Ben Zweig about the future of work.