Jon Krohn: 00:00:00
This is episode number 443 with Jeff Wald, author of The End of Jobs.
Jon Krohn: 00:00:12
Welcome to the SuperDataScience Podcast. My name is Jon Krohn, a chief data scientist and bestselling author on deep learning. Each week, we bring you inspiring people and ideas to help you build a successful career in data science. Thanks for being here today. Now, let’s make the complex simple.
Jon Krohn: 00:00:42
Welcome to the SuperDataScience Podcast. I’m your host, Dr. Jon Krohn, and I am ever so delighted to be joined today by the brilliant and articulate Jeff Wald. Jeff is a serial tech entrepreneur with multiple successful exits, but he’s here with us today to discuss his new book on how data science, automation, and other macroeconomic factors will reshape jobs and work around the globe in the coming decades.
Jon Krohn: 00:01:10
Thanks to Jeff, this episode is packed to the brim with hard data, empirical evidence, and optimistic, soundly reasoned arguments around how human capital is likely to evolve in the future. This episode is for anyone who’d like to be able to better predict how data and automation will continue to reshape their career and the broader world. So, presumably, it’s an episode for pretty well everyone.
Jon Krohn: 00:01:35
I learned an absurd amount in this episode, and I had a lot of my presumptions about the future of work turned upside down. Join us, and perhaps many of your presumptions will be turned upside down, too.
Jon Krohn: 00:01:53
All right, Jeff. Welcome to the show. Where in the world are you calling from? I had the impression that you are in New York with me, but before the show as we were talking about, well, I was hoping to talk to you about what gym you’re going to in New York, I found out that that was going to be a question that wasn’t going to go very far. Tell us about where you are right now.
Jeff Wald: 00:02:14
Well, I will tell you that I decamped from New York City, temporarily, very, very temporarily. I will 100% be back despite people saying, “Stay down there for at least six months and establish residency. You don’t have to pay taxes.” No. I’m a New Yorker, and I will be a New Yorker and I will pay taxes in New York, but my parents do have a home down in South Florida, and given COVID, they are not doing their normal snowboard thing. While I was going for a run the other day in frigid Central Park, it occurred to me, “Wait a minute. There’s a house down there that has nobody in it. Why aren’t I in it?”
Jeff Wald: 00:02:53
So, the next day, I packed up some stuff and headed south. So, I’ve been here for 10 days. This is an elderly community. The average here in this country club is about 75. So, they’ve got very strict COVID rules, which I respect. So, I wasn’t allowed out of the house until five days and a PCR test, but as we talked earlier, I do go to the gym here at the club for the first time with masks on and only 15 people allowed in the gym. It was my first time in a gym in a year. It was so exciting. I’m going to be sore tomorrow.
Jon Krohn: 00:03:28
Nice. Have you found nice running trails? Anything like Central Park around Boca Raton?
Jeff Wald: 00:03:35
Oh, come on now. There’s nothing like Central Park. Nothing like Central Park, but this is a beautiful area, beautiful community. I’m very fortunate to be down here.
Jon Krohn: 00:03:45
I agree. I do love lapse in Central Park myself. So, we’re here to talk about your first book today. It’s called The Birthday Rules: Critical Conversations to Have with Your Children 6-16. Oh, wait. I’m taking a bad note here. Well, wait. Now, we’re here to talk about your latest book, which is The End of Jobs: Rise of On-Demand Workers and Agile Corporations. I don’t know if you want to tell us for a minute about that first book. It’s quite a pivot between the two.
Jeff Wald: 00:04:16
It’s a heck of a pivot. I mean, look, the first book was based on a lot of things that I do, which is just somebody saying something and me thinking, “Huh, that’s a good idea.” My older brother and his wife are two of the best humans that I know. They were struggling, Jon, because their oldest had come and asked for a phone. Their oldest Jonah was seven at the time. My older brother, again, best person, best parent, best partner. I mean, the two of them are just amazing. They didn’t know what to do.
Jeff Wald: 00:04:47
I thought, “Wow! These two don’t know what to do.” Aint nobody know what to do. He just made the statement. He said, “There should just be a list of when kids get what, and all parents have to follow it because I don’t want to deal with this.”
Jeff Wald: 00:05:01
I thought, “There should be that list.” So, here’s what I did, man. I hired a bunch of researchers, and we took all the data, all the reports, and all the studies about what different experts said about child and adolescent brain development. We added it all together and put together a framework, but it is a framework. It’s not designed to be prescriptive. I mean, every child’s developmental path is different. Every family’s economic capabilities are different. Every family’s value systems are different, but just because a child’s prefrontal cortex is developed enough at the age of 11 on average to handle the responsibilities of a phone doesn’t mean you should get a phone, and Jonah ended up getting a phone I think at 10. Now, he’s got one and now he doesn’t get off the phone because now he’s 15, but be that as it may.
Jon Krohn: 00:05:46
Nice. I love actually the neuroscience approach to writing that book. I wasn’t expecting that. You may not have known that I have a PhD in neuroscience.
Jeff Wald: 00:05:55
I did not know this.
Jon Krohn: 00:05:56
Yeah. So, you mentioning prefrontal cortices and that being sufficiently well-developed, that does pique my interest. I’m only going to ask one more question before we move on to The End of Jobs, but did you anything related to gender differences in brain development?
Jeff Wald: 00:06:12
We did not. We did not dive that deep because there’s so many differences even non-gender-based that when you start breaking it down, there would be … Once I broke down that subdivision, there’d be a bunch of different subdivisions to break down. So, it was just around 11, but girls certainly do develop much quicker than boys.
Jon Krohn: 00:06:33
Yeah, definitely. One of my favorite neuroscientific factoids that I like telling at parties is that boys develop an amygdala. So, your emotional side of your brain is fully adult by the age of 16, but your prefrontal cortex on average not until you’re 18. So, there’s this two-year gap where you have all the adults’ emotions but you can’t control them. That’s 16 to 18 for boys. For girls, it’s 14 to 16, and I think when you think back to a lot of high school experiences, it’s not hard to remember experiences that fit well into that neuroscience narrative.
Jeff Wald: 00:07:15
Very much looking forward to my nieces and nephews all going through that.
Jon Krohn: 00:07:19
Great. All right. So, let’s segue neatly into The End of Jobs. So, I love this book. I was so excited to be able to have you on the podcast and ask you questions about this book. I absolutely love it. So, we’re going to go through some chronology here, which follows roughly the chapter ark of the book. So, we’re going to start off with a history of work, and then we’re going to talk about some data behind where we are today and unusual things that are happening today, following history to today, and then we’re going to predict the future, and talk about how the workplace is going to be in the future with an emphasis on data, models, automation. So, let’s start off, Jeff, by talking about a history of work.
Jeff Wald: 00:08:09
Well, I’ll tell you this. Going back even a step before that, which is the why I wrote this one. I wrote it because I was getting very frustrated with people that would make predictions about the future of work that weren’t based in evidence. Look, I founded this company, WorkMarket. We raised all this venture capital. We were doing all these things. So, I got to go to all these conferences and give talks, and either on-panels are listening to other speakers, I’d hear people and it would get very frustrating because people would make predictions.
Jeff Wald: 00:08:41
I’m like, “What data are they using to come to that conclusion? That makes no sense. The data I have says that that’s completely wrong and asinine. Yet, here’s a person, a ‘thought leader’, saying it. So, maybe they have different data.”
Jeff Wald: 00:08:57
It turned out they didn’t. They just were yammering on. I abhor that. Anybody speaking in the public square without evidence I abhor, but people making predictions about the future of work is especially dangerous because people are making choices about their careers, about their family, about where they’re going to be from a community standpoint, about their companies. Then, obviously, we have societal issues. You have a responsibility to use these bodies of evidence. These bodies of evidence are history, data, and how companies actually engage workers.
Jeff Wald: 00:09:32
If you use those, you still probably are going to get it wrong, by the way. There’s no crystal ball here, but you at least have a logical reasonable, defensible model for making your prediction. You can at least back up what you’re saying.
Jeff Wald: 00:09:45
So, with that, the history of work, Jon, to make predictions about the future of work without studying history makes zero sense to me. The five most dangerous words in business that’s often said are, “This time, it is different,” because history rhymes.
Jeff Wald: 00:10:04
So, we as a society have been through mass changes in technology before. I bring that up because our big lens through which we’re looking through the future of work is the huge technological change of robots and AI. So, especially at this point in history, at this point in our development as a society, to not look at the other times when companies and workers had to come together and renegotiate their contract in the face of tremendous technological innovation, seeing how companies, workers, and society handle those changes and understanding where we’re slightly different now, but at least studying them seems like a predicate.
Jeff Wald: 00:10:50
The things that we learn from the history of work are voluminous. We could talk about it for hours, but it comes down to there’s a supply and demand imbalance. There’s always, I should say in a mass day way, there’s always more workers than there are work to be done that actually as if not being true for discrete issues in very specific points in time like blockchain analysis or data scientists now. They have a huge supply and demand imbalance the other way where there’s not enough.
Jeff Wald: 00:11:23
Traditionally, there is a huge supply and demand imbalance between companies and workers. Companies have power. Companies abuse that power, and workers have to rely on counterbalancing forces of unions, social safety net, and regulation to help get some stability in that relationship, and then the relationship continue to stable for some period, and then, boom, a new technology disrupts it.
Jeff Wald: 00:11:43
So, studying and understanding that framework, I think, is very important for thinking about how we handle this next wave of technological innovation.
Jon Krohn: 00:11:51
That makes a lot of sense to me. You’re preaching to the choir here on having data, and having some evidence behind what you’re doing. I love that that’s the focus that we’re going to have here. You even have this time is different or it’s different this time is one of the chapter titles later in the book, and I’m guessing that that’s saying that, well, maybe we shouldn’t jump ahead too far.
Jon Krohn: 00:12:15
So, a major thing that has happened in relative degrees in history that is maybe a good anecdote for how things might change in the near future is the lifetime contract, which I suspect many listeners won’t even be aware of as a practice that was common until very recently.
Jeff Wald: 00:12:38
Well, here’s the super interesting thing. They may not have seen it in terms of a colleague having gone through it, but they may have this notion of in the ’50s and ’60s there was a very different work environment. I highlighted this in the book because it’s something that when I’m at a conference and in a debate format, so I love a good debate, put me against somebody who thinks companies are screwing workers left, right, and center, and the gig workforce is an abomination.
Jeff Wald: 00:13:09
They’ll say, “It didn’t use to be this way.”
Jeff Wald: 00:13:11
I’ll say, “When? When it did not use to be this way? When did companies look after their workers? When did companies provide all of these benefits for their workers?”
Jeff Wald: 00:13:22
They’ll say, “1950s and 1960s.” I will say, “Okay. Well, let’s start with data because what does the data tell us?” The average amount of time a person spends in a job today, Jon, is 4.2 years. Now, that differs industry by industry. It differs age bracket by age bracket, but on average, 4.2 years. If we were to dial that back to the 1960s, when the Bureau of Labor Statistics started keeping track of this data, and this was the height of the lifetime employment model.
Jeff Wald: 00:13:50
Jon, I’m putting you on the spot here. What do you think the average amount of time a worker in the United States spent at a job in 1960?
Jon Krohn: 00:13:59
Well, if you weren’t posing it to me in that way, I probably would have guessed a much bigger number that I’m feeling like guessing now. So, if we hadn’t have this preamble, I might have said something like 20-30 years. I would have probably-
Jeff Wald: 00:14:17
That is the average guess?
Jon Krohn: 00:14:18
Yeah. I would have been thinking in my mind actually not above the ’50s and ’60s, I would have been thinking about maybe a little earlier with about a century ago with Ford employees, where I believe people would come to your home and make sure that you had a nice stable home before you’re even offered employment. From all that, if you’re putting all that work in to hiring these people, I just thought that if you manage to get a Ford job that was a really cushy job for a manual labor job, at least at that time, and that you have lots of benefits, lifetime pension, your family is taken care of, all these things, and that you would have that job for life like a tenured professor.
Jeff Wald: 00:14:59
That’s interesting. Henry Ford, aside from being a terrible anti-Semite and other terrible things, did a wonderful job by his workers in a number of ways. One of the things that he’s very famous of saying is, “I want to pay my workers enough so they can buy a car.” When people said, “Oh, my gosh! Why are you paying them so much?” “I want them to afford the product.” That’s a very fundamental part of why we see economic expansion is that workers are able to buy more.
Jeff Wald: 00:15:29
Here’s the thing about the data and what the data tells us. The average amount of time a person spent in a job in the United States in 1960 was five years. It’s really never changed. It goes up a little bit, it goes down a little bit. It was about 4.2 in the mid-1980s and then it started trending back up to 4.4, 4.5, then back down. That’s the reality of the US workforce, which is not to say that the lifetime model never existed. It existed, but did it exist for every worker at every company? Hard no. Absolutely not.
Jeff Wald: 00:16:08
Did it exist for some workers at some companies? Of course, it did, but we take those stories about the job at GM, the job at General Electric, the job at Dow Chemical, and the defined benefit pension plan, and all these other things. We manifest them throughout the economic experience and employment experience for every American. It just simply isn’t the case.
Jeff Wald: 00:16:28
So, it becomes a very false narrative. As a notion, it certainly existed. Did it exist as the standard for the American worker? No, it didn’t, and it becomes very important to understand that because when you’re comparing the state of the American worker today, you should be comparing it to the actual state of the American worker at some point in time, which you want to have this discussion, not some fanciful notion of what that worker had.
Jon Krohn: 00:16:54
I love it. Eliminating unnecessary distractions is one of the central principles of my lifestyle. As such, I only subscribe to a handful of email newsletters, those that provide a massive signal to noise ration. One of the very few that meet my strict criterion is the Data Science Insider. If you weren’t aware of it already, the Data Science Insider is a 100% free newsletter that the SuperDataScience team creates and sends out every Friday.
Jon Krohn: 00:17:30
We pour over all of the news and identify the most important breakthroughs in the fields of data science, machine learning, and artificial intelligence. The top five, simply five news items. The top five items are handpicked. The items that were confident will be most relevant to your personal and professional growth. Each of the five articles is summarized into a standardized, easy-to-read format, and then packed gently into a single email. This means that you don’t have to go and read the whole article. You can read our summary and be up-to-speed on the latest and greatest data innovations in no time at all.
Jon Krohn: 00:18:06
That said, if any items do particularly tickle your fancy, then you can click through and read the full article. This is what I do. I skim the Data Science Insider newsletter every week. Those items that are relevant to me, I read the summary in full. If that signals to me that I should be digging into the full original piece, for example, it’s a pour over figures, equations, code or experimental methodology, I click through and dig deep.
Jon Krohn: 00:18:30
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Jon Krohn: 00:18:56
So, in these debates, you would be coming with data and the person you’re debating against would have a few anecdotes in their mind, just like I did, and that ends up being the basis for their arguments. Yeah. I think that it’s a common flaw in human reasoning. It’s like these rules of thumb that we have for quickly coming up with an understanding of any event, including past events.
Jeff Wald: 00:19:24
I will tell you the story, which I actually had forgotten, and you just reminded me of this. I was on a debate stage. I forget who I was debating, but it was at the Economist, the Economist Magazine Future of Work Conference, and it was gig economy, good or bad. I was debating, and the person came with anecdotes about how jobs used to be and I was like, “But that isn’t true.”
Jeff Wald: 00:19:44
We went back and forth and he closed up by saying, “My son was diagnosed with cancer.” He threw the cancer thing in and how his son because he had a job at a company, had healthcare, and this and that. I lost. The Economist actually pulled the audience who thought it was good before and then who thought it was good after, and I ended up losing support to the argument.
Jeff Wald: 00:20:04
I was like, “Oh. Well, first off, my heart goes out to your son and I hope he’s okay, but, I mean, come on, man.” That’s not a debate. That is pulling on heartstrings, which, unfortunately, is very effective, not data-driven, but very effective.
Jon Krohn: 00:20:19
You sound like you know a lot about debating. Do you know what the formal … When you’re debating with somebody or there’s these flaws in logic like you could have an ad hominem attack, which isn’t what happened there, but it’s like a red herring or it’s a logical fallacy that they’re employing.
Jeff Wald: 00:20:37
I’m familiar with the words, I’m not familiar with them enough as tactics. I would probably be all the better at a lot of things I did if I actually studied and did those things. I just go into debates, I’m like, “All right. Here’s what I’m saying. Take it or leave it.”
Jon Krohn: 00:20:49
Nice. I mean, it sounds like it’s working for you, although maybe not on that one occasion.
Jeff Wald: 00:20:53
Not on that one. I got my butt kicked, not for the first time and certainly won’t be for the last.
Jon Krohn: 00:20:57
All right. Great. So, that’s super helpful. I love that you’re so data-driven. Tell us what the data tells us about where we are now. I often tell people things around not only just employment, but in terms of data, in terms of the things that make life really pleasant, whether we’re aware of it or not, so things like literacy, things like surviving childbirth, things like being able to vote on a huge range of metrics, violence, and death by violence, whether in the US or on a global scale, we are in by far the best time ever. It’s not even close. Yeah. So, I don’t have a huge amount of data on workforce stats, but maybe you can fill in the gaps.
Jeff Wald: 00:22:00
Well, I will tell you this. One of the very clear trends from the history of work is the human experience of working fewer hours, and the human experience of having evermore jobs in society, and the human experience of having a higher standard of living. Those things are very clear almost uninterrupted patterns. There’s certainly some interruptions and periods of great economic dislocation, the great depression, the financial crisis, now.
Jeff Wald: 00:22:31
For the most part, if we trendline out, an unbelievably wonderful state, to your point, I believe it was Peter Diamandis of the XPRIZE that had this term, the time of ultimate abundance. From a data standpoint, it has never been a better time. Never, never, never.
Jon Krohn: 00:22:51
Radical abundance.
Jeff Wald: 00:22:53
So, when we talk about the now, I’m going to break the now down into three different buckets, and I’m going to do that so I don’t ramble on for 20 minutes about each three, but we could break them down however you like. One is remote work, and I would talk about it pre-pandemic, through pandemic. The other is on-demand, and the third is really what the title of the book was about.
Jeff Wald: 00:23:16
The publisher loved the title, The End of Jobs, because it was provocative, and this and that, but it is somewhat misleading in as much as people think it’s the end of jobs because of robots and AI. That is not at all what I mean, and my conclusion is there will be no net job losses for robots and AI, but we’ll get into that later.
Jeff Wald: 00:23:35
The end of jobs is the end of the one office, one manager, 9:00 to 5:00 job. That job is ending, and the pandemic has sped up the end of that job. From that, we see movements on on-demand labor. We see movements on remote work. We see movements on team-based work. We see movements on task-based work. Those are a lot of the hallmarks. The one office, one manager, 9:00 to 5:00 job dying, has been dying for some time being driven by a host of factors from globalization to shareholder capitalism, to technological change, all helping to break that bone of in order to be an employee at this company, you need to be at this office, at your desk, at the manufacturing line from 9:00 to 5:00. That is the job. We have been breaking that bone and moving it away.
Jeff Wald: 00:24:29
So, the most present ways and the ways people think about it the most is the on-demand labor market, and the remote work construct, both of which help move this 9:00 to 5:00, one office, one manager job to the end state, which is a fluid, team-based, work from anywhere, always on job. That’s where we’re moving. So, that movement has been happening for 20-30 years. That is a very big hallmark of the world of work now, one office, one manager 9:00 to 5:00 to fluid, team-based, work from anywhere, always on.
Jon Krohn: 00:25:09
Nice. I am understanding that and I am glad you’re leading me into the correct interpretation of your book title. That does make perfect sense from what I saw later on the book. I just want to make sure I get those three buckets right. So, you’re saying remote work, on-demand, and the third one was?
Jeff Wald: 00:25:27
It’s this one office, one manager, 9:00 to 5:00 moving to fluid, team-based, work from anywhere, always on.
Jon Krohn: 00:25:31
Got it, got it, got it.
Jeff Wald: 00:25:34
So, we touched on the last one, and then we can dive in if you want on the on-demand labor market and remote work.
Jon Krohn: 00:25:41
Yeah. Let’s talk about those, for sure.
Jeff Wald: 00:25:43
So, on-demand labor, look, I got to know the on-demand labor market because we built this company, WorkMarket, raised about 70 million in venture capital from some of the largest VCs in the world, and we sold the company to ATP. It was a great outcome, gave me the space to finish this book, which I’ve been writing for five years.
Jon Krohn: 00:26:03
What did WorkMarket do?
Jeff Wald: 00:26:05
WorkMarket was an enterprise software platform that enable corporations to efficiently and compliantly organize, manage, and pay their freelance workforce.
Jon Krohn: 00:26:12
It sounds like you said that more than once.
Jeff Wald: 00:26:14
I’ve said that more than once. I’ve said 10,000 times at this point. If you have an freelance workforce, you need software to run it because you have efficiency issues, and you have compliance issues. We were the first and by far the largest piece of software to help companies manage their freelancers. ATP believes in a world of total talent management, which we’ll talk about a little bit later in this conversation, and in order to deliver total talent management to their clients, they needed software that helps you manage all of your workers, thus they bought WorkMarket.
Jeff Wald: 00:26:48
So, in getting to build a piece of software that help most companies manage their freelancers, we got to know a thing or two about the freelance workforce. Here’s the thing. When I started the company, Jon, in 2010, there was a statement that everybody was 100% sure was true, which was by 2020, 50% of the labor force is going to be on-demand.
Jeff Wald: 00:27:13
At the time, the on-demand labor force was somewhere between 25% and 27% of the labor force. We don’t know exactly how much because on-demand labor is very wishy-washy statistics, and there are very few data sources we can go to. We have to rely a lot on survey data, which surveys are usually 5,000 to 8,000 people in a labor world, and it’s tough to extrapolate out to 164 million American workers surveys of that size, but it’s the best we have.
Jon Krohn: 00:27:42
Yeah, especially because, presumably, some specific occupations might be just a few responses in that survey. So, it’s unlikely to be representative of the thousands or tens of thousands of people that work in that space.
Jeff Wald: 00:27:55
That’s a great point. When doing a survey of 5,000 people when you have 700+ job classifications, how many are you getting within each classification and really understanding and you very quickly get to an end that it does not give you a representative sample size.
Jeff Wald: 00:28:11
So, 25% to 27% of the labor force, and people felt that it was going to get to 50%. That was unbelievably never going to be true. I mean, there was no world in which that would have happened. Yet, that is what everybody said. By the way, now that we’ve passed 2020, thank God, you know what everyone is saying now? “Oh, by 2030, 50% of the labor force.” You’re like, “Whoa! It grew 3% in 2010 to 2020. Now, you think it’s just going to accelerate? Why? What data are you using to draw that conclusion?” because it’s never going to happen.
Jeff Wald: 00:28:53
The reality of the on-demand labor market is it has been a very large part of the labor force for a very long time. This is not some new phenomenon. This dates back to the post-war period with the Kelly girl. Kelly Services created the temp market, and freelancers go back well before then. Very large, very important part of the labor force for a very long time.
Jeff Wald: 00:29:21
The idea that it was going to suddenly accelerate, no. On the consumer side, okay, maybe. We’re replacing the delivery boy or girl from the local restaurant with Instacart, DoorDash, and all these other things. You didn’t really fundamentally changed the labor force there. A lot of dog walking happens now through apps, Rover and Wag. You know what? That created many, many, many, many new 1099 data points, but how many 1099s were being given to dog walkers before then because every dog walker was paid in cash? About zero. So, all right, we’ve got a huge amount of new data points on 1099s, but did anything fundamentally changed in the economy? We took a great market transaction and moved to the formal economy. Nothing changed.
Jeff Wald: 00:30:12
Uber drivers replacing taxi drivers, there’s no fundamental change. We’re using Ubers more than we use taxis, but there aren’t a lot of fundamental changes occurring in the labor market.
Jon Krohn: 00:30:23
1099, for those outside of the US, are formal tax forms to the IRS federal government in the US. So, basically, people going from being paid $20 cash for the dog walk to doing it through an app and then having to file taxes, we’re seeing a lot more people, yeah, filing taxes. So, we have the data. That’s a really good point. Even then, even with all of now people in the formal economy that previously might not have been, it’s still, like you’re saying, 3% of workers. It’s a splash in the pan, a drop in the pan would be the expression.
Jeff Wald: 00:31:02
These are movements. I am not saying they’re not movements, but the movements that drive another 25% of the labor force, so in the United States, 40 million workers. No, that’s not happening. That’s not going to happen. Corporations fundamentally change work streams and workflows and they haven’t, and they’re not going to. So, the idea that it’s going to hit 50% of the labor force is silly. We spend a lot of time at the book talking about the world of work. Now, in regards to on-demand workers, to give that historic context, to give the data and what the data has told us over the last 10-15 years, and to break down where those different workers are and what the consumer interaction is versus corporate interactions.
Jeff Wald: 00:31:46
It paints the picture of slow and steady growth, slow and steady growth that I think will continue, but slow and steady growth, not a doubling. That’s not going to happen because we went from 25% to 27% of the labor force to about 28% to 30% of the labor force. That’s where we are now.
Jon Krohn: 00:32:06
Yeah, over 10 years.
Jeff Wald: 00:32:07
Yup.
Jon Krohn: 00:32:09
Nice. So, that covers fluid work, on-demand work, and remote work is probably something that people are thinking a lot about right now.
Jeff Wald: 00:32:15
Oh, remote work. Let me tell you. Surprising nobody, I’m going to start with history.
Jon Krohn: 00:32:23
Great.
Jeff Wald: 00:32:23
10 years ago, 1.5% of the US workforce worked remotely. Now, definitions are super important here as they should be to all of you listeners when we talk about data structuring. Remote work means more than 50% of the time you’re working in a location that is not your office. So, if I go to the office three days a week but don’t for two days, I am not a remote worker. I have a flexible work arrangement. Totally different thing.
Jeff Wald: 00:32:52
Remote work, 1.5% of the labor force, it did double over a 10-year period. So, unlike on-demand work, which was predicted to double, no one predicted remote work doubling, but it did, and doubling happens in the labor market incredibly and frequently and only when you’re dealing with very small numbers. Remember, from 1.5% to 3% of the US labor force worked remotely pre-pandemic.
Jeff Wald: 00:33:15
The reason that the 3% was considered to be not maxing out but going to be a huge drop off in growth, if you had asked me pre-pandemic, I would have said, “Well, we’re probably going to get to 4% over the next 10 years,” because there are two fundamental impediments to the continued growth of the remote workforce. One is antiquated mindsets. We all know the manager that says, “Yeah, yeah, yeah. I know what all the studies say, that remote workers are happier, they’re healthier, they’re more productive, they have higher retention, they cost the company less, they cost the workers less. I get all that, but I think magic happens when the workers are in the office.”
Jon Krohn: 00:33:54
I’m one of those people. Yeah. Go ahead.
Jeff Wald: 00:33:59
Productivity equals presence. We all know that. Yeah, that is a point of view. I remember debating the president of a very, very, very large company and I said, “Okay. Well, you think the people have to be in the office. What data are used to support that?”
Jeff Wald: 00:34:15
He said, “Well, I don’t have data. It’s my gut.”
Jeff Wald: 00:34:19
I literally just dropped my pen. I’m like, “I can’t argue with your gut. I have study after study that tells me remote workers are all these wonderful things, and you have no reason for keeping your workers tethered to their desks, but I can’t argue against your gut because that’s insane.”
Jeff Wald: 00:34:37
So, okay. That was reason number one or impediment number one. Impediment number two, were systems, policies, and procedures. It’s one thing to say, “Okay. You guys can work remote.” It’s another to make sure that they can access all the company’s systems from outside the company’s four walls. That’s not an easy task. It’s another thing to make sure that there are policies in place that people aren’t being penalized for working remotely, that they’re not being shut out of conversations, out of meetings, that there’s a remote option for every single meeting that occurs, not, “Oh, Jon’s remote. Let’s make sure we have one,” but a default one that’s always there, that you don’t have to take a poll as to who’s there and who isn’t.
Jeff Wald: 00:35:23
Those are the kinds of things that drive remote work and make it effective. Both were significant impediments, and both had to change in March of 2020. Didn’t have a choice. Mindset had to change. We had to put in place the policies, procedures, and systems. Had to. At the height of the pandemic, 40% of the US workforce worked remotely.
Jeff Wald: 00:35:46
Now, the natural limit in the United States is 42%. That is the highest percentage of the US workforce that can work remote because, clearly, some industries can’t, manufacturing, logistics, supply chains, retail, entertainment. Many, many services can’t be performed in the home, and 42%, by the way, is the highest of any country in the world.
Jon Krohn: 00:36:11
Oh, wow!
Jeff Wald: 00:36:12
So, at the height of the pandemic, 40% of the US workforce was working remotely. It was really this Herculean task that occurred because in America, we are flexible, and we find a way, find a way to get things done, and we did as did other countries around the world.
Jeff Wald: 00:36:29
So, when we think about post-pandemic, we think about 8% of the workforce will remain remote. Again, definitions are important. Remote, meaning more than 50% of the time. So, when there’s more than 50% of the time, by the way, Jon, that means from a tax standpoint you don’t have Nexus in the office, so you’re not paying tax in New York City as a commuter or whatever it is, and below 50% of the time showing up in the office means as the office manager, I probably don’t have to allocate infrastructure to you or square footage to you.
Jeff Wald: 00:37:02
The two very, very important things of that threshold helps determine. 8% will work remote and we come to that conclusion based on a host of different survey data on what workers want and what managers want knowing that workers are going to have a little bit more of a say than they had before. Used to be a poll function with the worker who’s saying, “I want to work remote” pre-pandemic and the manager was mostly saying no. Now, the managers finally se the light, if you will. Managers will push it a little bit more, and maybe you have to find reasons to bring workers to the office.
Jeff Wald: 00:37:38
When people say, “Oh, 80%, that’s not enough,” I say, “Well, let’s remember two things. That’s 8% out of the 42%, so it’s nearly 20% of the people that can work remote will. That’s mind-blowing. Two, the definition of remote work, if you ask me, “Who’s going to have flexible work arrangements,” now we see data that points at 32% to 33% of the workforce. It’s almost 75% of everyone that could.
Jeff Wald: 00:38:04
So, you’re going to see a huge amount of people that every other Friday are in the office, every third week are in the office, and they’ll be able to work remotely, which has tremendously positive benefits for companies and for workers.
Jon Krohn: 00:38:18
As mostly an anecdote, though I do have some data to back me up, in our own company, we have about 200 people, and we did a survey at a town hall, which is something these kinds of things that we didn’t have before, but since March 2020 we’ve had regular town halls, and we do polls in them. In the most recent one, we asked, “When it’s safe to return to work, what kind of work arrangement would you like to have?”
Jon Krohn: 00:38:48
Our company, as far as I’m aware, everybody was working in an office all the time. It was unusual to be away. You’d have to have a reason. Like you’re saying, managers who’s the default answer was no. I might be wrong. There’s lots of international offices, and maybe there’s exceptions, but I think that that’s generally how it worked in our company.
Jon Krohn: 00:39:12
So, we asked 200 people in the town hall, “Would you like to return to being in the office full-time? Would you like to be working remote full-time?” or somewhere in the middle, like you’re saying. Majority in the office or majority from home, and zero people said being in the office all the time, which is crazy. You’d think that someone would at least, by accident, hit the wrong button with an n of 200. Then in terms of=
Jeff Wald: 00:39:43
How many people wanted to be remote full-time?
Jon Krohn: 00:39:48
I don’t remember off the top of my head. It wasn’t a majority. The majority want to be in the office some or portion of the time.
Jeff Wald: 00:39:57
We are social animals. Look, data is data, but human beings are human, and we have needs, and we’re not driven entirely by data and logic. We want to be around other people. I mean, I miss my colleagues tremendously. I’d love to be able to hang out. Do I want to be there all the time? No. It’s that answer of, “Who wants to be back full-time?” that gets very few respondents, but some of the time, yeah, some of the time.
Jon Krohn: 00:40:27
Yeah. So, I agree that that has changed. Then the other piece, at least from my personal experience, with my team we do scientific research and development, building models, and I thought that it would be impossible for us to do our work away from each other. Not only am I wrong, but we’re more productive than ever.
Jon Krohn: 00:40:50
Now, I do think that there’s specific, in the same way that you talk about having data about an entire economy, generalizations versus specific niche instances. So, you gave the example of blockchain analysts or data scientists being unusual relative to the rest of an economy.
Jon Krohn: 00:41:13
I think that there is a similar thing here where there are things like socialization that the employees really like, but I also think that some aspects of productivity like us being able to sit down as a team with a whiteboard and no computers, just no pads, but we’re not doing that 40 hours a week.
Jeff Wald: 00:41:35
Of course not. Of course not.
Jon Krohn: 00:41:36
So, to have a future where you set up half a day or a day or two days a week where people are in all together and you’re very specifically like, “We’re going to be doing brainstorming around how this model could be better during that specific time, and then we’ll come out with actions, deliverables from that, which shields go off and do for a day or two on your own, and I think that people are, I know now, I’ve been proved wrong. People definitely are more productive on their own doing this.
Jeff Wald: 00:42:06
To your point around the subdivisions of the data, on an aggregated basis, we have one series of conclusions. When you break things down to knowledge workers versus non-knowledge workers, to high-end professionals versus other people in the organization, they are very, very different outcomes and very different productivity curves.
Jeff Wald: 00:42:28
There are some industries, there are some job functions that, yeah, you have to be in the office for the majority of the time. There are other job functions where you never need to be there, whatsoever. There are others that there is no office. So, it’s very important when thinking about the world of work to not fall in to the trap of, “Oh, well, at my company X …” Well, your company isn’t n of one, and there are millions of companies. So, it is very, very important when thinking about the future of work to appreciate the complexity that goes in to a labor force, the size of any industrialized country.
Jon Krohn: 00:43:11
I have a lot of anecdotes, Jeff, and I have a very big gut. We’re going to lean heavily on that in our conversation today.
Jeff Wald: 00:43:17
Bring it on.
Jon Krohn: 00:43:17
Okay. You got it.
Jeff Wald: 00:43:18
Bring it on.
Jon Krohn: 00:43:19
All right. No, just kidding. We will go with data. So, speaking of data, I think the number was something like for gig economy workers, it was something like 3% of 4% of the economy today is engaged in gig economy work.
Jeff Wald: 00:43:36
Gig economy, just for your listeners, gig economy being a subset of the on-demand economy at large.
Jon Krohn: 00:43:41
Oh, I use them as synonyms.
Jeff Wald: 00:43:43
Oh, yes. This is another issue on the labor market is that we have Lexiconal issues. The on-demand labor market, as I defined it in the book, is any work that’s getting done by somebody that’s not your employee. So, that could be a temp worker. It doesn’t include vendors and outsource. It’s not like Foxconn’s employees being considered employees of Apple, but temp employees, contractors, consultants, freelancers, gig, to me, is a subset of the freelance workforce.
Jeff Wald: 00:44:20
So, gig works specifically, the Ubers of the world, very, very task-oriented, very short term tasks. A freelance might be a freelancer for years for a company, and they would not consider themselves nor would they be considered a gig worker, but they are an on-demand worker because freelance, consultant, contingent labor, temp, all of those roll together to be the on-demand workforce, and by far, the largest part of the on-demand workforce are temps.
Jon Krohn: 00:44:51
That makes a lot of sense. I will remember that. It won’t be hard because I already came up with a way to remember, which is a gig, when you’re a musician like, “I’ve got a gig tonight,” you’ll never have a gig for two years. You get a gig tonight.
Jeff Wald: 00:45:05
That’s your Billy Joel playing at the Madison Square Garden, but, yes.
Jon Krohn: 00:45:07
Right. All right. Okay.
Jeff Wald: 00:45:09
In residency, a gig residency.
Jon Krohn: 00:45:12
Well, I’m going to remember it that big way, anyway.
Jeff Wald: 00:45:16
Fair enough.
Jon Krohn: 00:45:17
Thinking about the local bar and you’re like, “I finally got a gig tonight.”
Jeff Wald: 00:45:21
There you go.
Jon Krohn: 00:45:23
Yeah. So, the on-demand economy is still a very small number. It’s still just a few percent of the entire economy.
Jeff Wald: 00:45:31
Well, the on-demand labor force in total is about 25% to 30% of the labor force.
Jon Krohn: 00:45:36
I see. This was … Right, right, right.
Jeff Wald: 00:45:39
That has been true for some time.
Jon Krohn: 00:45:42
… and has been growing by a few percent everyday.
Jeff Wald: 00:45:44
It has increased. It has taken about 3% market share over the last 10 years. It’s gone from between 25 and 27 to 28 to 30.
Jon Krohn: 00:45:54
Nice. Okay. So, with those data in hand, I can make my next point, which is that some of that growth presumably is attributable to data science models like matching models that we have in apps like Uber and Lift and TaskRabbit, and the food delivery apps. So, that’s a cool thing related to the data science world.
Jon Krohn: 00:46:18
In the future, so maybe talk about the near future first, which is probably easier to extrapolate into, how do we anticipate, how do you anticipate or how did you describe in your book how data models automation will impact not only on-demand workers, but all kinds of workers in the years to come?
Jeff Wald: 00:46:42
So, the data models and the algorithms that are driven by those data models help to make a reality, something we call total talent management. Total talent management is the, I would say concept, but it is actually in practice in a number of places. The practice of having all of your labor resources in one system so that you can see across anybody that can do an item of work for you, full-time employee, a part-time employee, a temp worker, a freelancer, a vendor, a robot, an AI system. Anything that can accomplish a task is in one system and the task sits at the top. It goes through the algorithm driven by the data of what each of those individual labor resources can do, and then the algorithm allocates the work to the most efficient piece of that labor resource plane that is available, that has the right skills, that fits in from a regulatory standpoint.
Jeff Wald: 00:47:44
Just because freelancer X would actually be the best resource to do it, you may not be able to engage freelancer X in the state of Wisconsin or in Portugal. So, there are a host of regulatory concerns that drive the algorithm. So, understanding the data model of the regulatory environment is hyper important to driving efficiency on labor allocation, but total talent management, all of your labor resources in one place. That was why ATP bought WorkMarket, to tie ATP’s or to tie WorkMarket, I should say, into ATP’s human capital management software packages.
Jon Krohn: 00:48:20
Beautiful. That was a crystal clear answer, and I love that term, total talent management. It is not one that I knew before, but I will be using a lot in the years to come. So, then looking into the more distant future, so we have the study that is always cited is Frey and Osborne, which I’m sure you’re aware of, and Michael Osborne was actually, he provided one of the … What’s it called, the beginning of the book, the recommendation of the book?
Jeff Wald: 00:48:54
Oh, yeah, the pre-roll, the advanced praise.
Jon Krohn: 00:48:57
The advanced praise, and you have a lot of them in The End of Jobs.
Jeff Wald: 00:49:01
I do. Did you read any of them? Because there’s one in there that’s very, very specific that’s very good. I’m going to hold it up to the screen.
Jon Krohn: 00:49:12
Is it your mom?
Jeff Wald: 00:49:13
It’s my mom, yeah.
Jon Krohn: 00:49:16
So, yeah, Phyllis Wald says of her son’s book, “This is the best book ever written on any subject.” She wrote a lot of books.
Jeff Wald: 00:49:27
She actually has wrote a lot of books, and the best part about that was that when she received her first copy of the book, she called me up and she wrote, “Well, Jeffrey, I didn’t say that.” I was like, “Yeah, I know. You’re very unimpressed with everything I do. I get it. I get it.”
Jon Krohn: 00:49:44
That’s very funny.
Jeff Wald: 00:49:45
“I’ll keep trying harder, mom. No problem.”
Jon Krohn: 00:49:49
Well, Michael Osborne very kindly provided advanced praise for my book Deep Learning Illustrated.
Jeff Wald: 00:49:57
Awesome.
Jon Krohn: 00:49:57
Frey and Osborn, Benedikt Frey and Michael Osborne, anytime you come across a news item that says, “This industry is going to be gutted by this year by this amount,” I mean, I haven’t done the kind of research that you’ve done or the book that you’ve done, but when I go and dig in to where that number came from, it always comes from this Frey and Osborne paper. So, this Frey and Osborne paper says things like truck driving. In 10 years, nobody is going to have that job. Whereas physiotherapy is a job that you should really get in to now because there’s going to be tons of physiotherapists in 10 to 20 years.
Jon Krohn: 00:50:37
So, that’s my perspective of where the longterm future of work is going from. Me like everyone else has a perspective that is being created by Frey and Osborne and various antidotes and gut feelings that we have from our own personal life experiences. So, maybe you can use some data to forecast those better. Actually, the second half of your book is detailed predictions from a number of market leaders around how work might be in the year 2040. So, I realized we could probably talk about this topic for days.
Jeff Wald: 00:51:19
Until 2040. I will say this. The studies that are done, when you actually read them, I think the conclusions are very different than the headlines. Headlines are Oxford University predicts 47% of jobs will go. McKinsey predicts 50% of jobs are going to go, and that’s not the predictions that any of these people doing deep dive are doing.
Jeff Wald: 00:51:51
The predictions are susceptibility automation. When we look historically, what you see are any task or I should say this differently, any job function where its component tasks include a high proportion of repetitive high volume tasks, that job gets automated away. That just happens. No question.
Jeff Wald: 00:52:17
Now, the question becomes when you look, and I believe they looked at 704 different job categorizations. When you look at the 704 different job categorizations, you can break the component tasks of that job down into repetitive high volume and not repetitive high volume. Things that are not repetitive high volume, things that involve a lot of customer interaction, things that involve a lot of empathy, design, creativity, those don’t get automated.
Jeff Wald: 00:52:48
Then jobs that have 100% of their tasks that are repetitive high volume tasks, the same thing over and over again, think about the assembly line and the guy turning the screw. Well, eventually, the guy turning the screw got replaced with a robot just doing the screw. Yeah, that was going to happen. That is always going to happen.
Jeff Wald: 00:53:08
The issue I have, I guess two issues. One is that in our social media-driven world, the headline makes a way around the world like, “Oh, my God! These venerable institutions and venerable people predict all half the jobs are going to go. We’re all screwed.” That’s problem number one. That’s not their fault. Problem number two is just because a job is susceptible to being replaced by automation, it doesn’t mean that it’s going to be in the near term.
Jeff Wald: 00:53:36
We can dive into the anecdote of truck drivers, which I’ve been doing a lot of thinking about recently because, are truck drivers at some point in human history are going to be automated? 100%. That is going to happen. Are all of them? No. We never see every single job go. We still have people that are blacksmiths. They’re artisans, but we have blacksmiths. We still have candlemakers.
Jon Krohn: 00:53:58
I like that, the artisan truck driver.
Jeff Wald: 00:54:00
Yeah. Look, there will still be those edge cases and specific reasons that you want to keep some around, especially in the medium term. In the longterm, maybe I can get my mind around, “No, no one will ever do that again.”
Jeff Wald: 00:54:12
Here’s the reality. The simple logic is autonomous vehicles are coming, therefore, truck drivers are going to go. There are three million professional truck drivers in the United States. That is just light semis and semis, the class six to class eight, the big rigs and smaller trucks, not delivery trucks, not Uber drivers or taxi drivers, not bus drivers. Those are all different. There are three million light and heavy truckers.
Jeff Wald: 00:54:44
Just because autonomous vehicles are coming, it doesn’t mean that they’re coming now. Autonomous vehicles have been 90% road-ready for the last seven years. We’ve made almost no progress on that last 10%. That last 10% is incredibly hard because as your audience knows, those are all made up of edge cases. The edge cases here are huge and have huge implications about life and death, mind you. This isn’t the edge case of, “Well, what if somebody goes to that part of the site and the button doesn’t work?” Nobody gives a shit about that. These are serious edge cases.
Jeff Wald: 00:55:19
So, I would argue that there is a case to be made that autonomous vehicles will never be road-ready. We never get to the point of making them road-ready. Now, I think that that’s a very low probability, but it’s not an impossibility. It has to be considered as a probable outcome.
Jeff Wald: 00:55:40
The best case that I hear from people at Waymo and at Tesla and at various other places doing autonomous vehicles is, “Our best case is five years from now,” best case for the car to be road-ready. Okay.
Jon Krohn: 00:55:52
Yeah, at companies where people are incentivized to be optimistic.
Jeff Wald: 00:55:56
Yeah. Best case is five years. So, let’s give them that. Let’s give them the five years, which I don’t think. Once the vehicle is road-ready, the road itself has to be ready. You’re not just unleashing these things. You need censoring technology all over the place. You need repair infrastructure. What happens when a tire blows? Who’s repairing that tire? There needs to be a place for that autonomous … It can’t just pull in to its local station and be like, “Beep, beep. Repair me.” There need to be specific repair infrastructure, and then the regulatory framework. What happens when that truck hits somebody? What happens when the delivery is late? Who’s responsible? What happens if it gets hijacked? There are so many things that need to be put in place from a regulatory standpoint.
Jeff Wald: 00:56:41
As difficult as it was to get the car road-ready, getting the road-ready, I’ll give you my best case and that is 10 years. Let’s say that happens. Let’s say that happens. Now, we have to buy the trucks. Knight-Swift is the larges trucking company in the United States. It has 18,000 tractor trailers. It has a capex of $500 million a year, most of which is not dedicated to truck replacement, but let’s say half of it was, and let’s say the average truck costs 150,000, both of which I think are ridiculous assumptions. I think the truck itself is much more expensive and I think they spend much less on truck replacement.
Jeff Wald: 00:57:20
Even in that scenario, it takes them 10 years to replace their entire fleet. So, now we’re at 25 years out. So, when you start to put those together and you start to realize those are best cases, the reality is that there is not a reasonable case in the near term or even really in the medium term that truckers are going to be out of jobs. The reality of truck driving in the United States is that there are shortage of truck drivers. This is a good middle class earning job that somebody without a college education can get. Yet, we are pushing people away from it because, “Oh, that job is going to go.” That is an, in my view, inappropriate series of conclusions that have unfortunate consequences for people that could be earning a very good wage. So, there’s my anecdote and rush on truckers.
Jon Krohn: 00:58:13
No, that was great. I feel enlightened. Yeah. I’ve been way too gutty on my feelings about the future. I need to, yeah.
Jeff Wald: 00:58:26
Well, it’s one of those things, Jon, that we learn from history is that people always do this. It happens every single time with technological innovation. “Oh, my gosh! All the jobs are going to go,” and then they relate that forward to, “Oh, my gosh! It’s the fall of society,” and every time we end up with evermore jobs, higher standard living, working few hours. That is not to gloss over, by the way. Another important lesson from history, which is that those transitions are tough. They’re tough because jobs do go. We look at those 704 different job classifications and we do the math as to 50%, 75% lose about half their jobs, 75% to 100%. I’m talking about the component task being repetitive high volume tasks again.
Jeff Wald: 00:59:10
If 50% to 70% are repetitive high volume tasks, about 50% of the jobs go. If 75% to 100% of the tasks are repetitive high volume tasks, about 100% of the jobs go. We do the math on those 704 job classifications. 10% to 15% of jobs in industrialized economies are going to go.
Jeff Wald: 00:59:26
Now, more jobs will be created or I should say about the same, about 10% to 15% will be created, so we’ll have no net job losses, but you need to move in the United States, 25 million workers from one job, one industry, one function, one geography to another. We do not do that well. History’s lessons here are very clear. We do not look after those workers. We don’t provide the retraining necessary. We do that to our peril.
Jon Krohn: 01:00:00
Yeah. I couldn’t agree more on that front. Even my gut was in agreement. Yeah, and that one, even if you just think about how much, even if we had all of the infrastructure in place that would allow people to retrain well, there’s a mindset in humans. I think we are creatures of habit like any other animals. If coal mining has been the thing that you’ve been doing and your parents did and you live in a coal town, it’s hard to see that things are changing, “No, I need to take a computing class on the weekends.”
Jeff Wald: 01:00:36
Yeah. You’re 100% right. Look, we went from one million coal workers in the United States in the 1950s down to about 200,000 as we get into the ’80s, down to about 80,000 now, and it’s got nothing to do with environmental policy. It’s got nothing to do with trade policy. Machines rip coal from the ground. So, what do people in those communities do? This recording is January 18th, so the president for two more days, “I’m going to save coal jobs.” It was not a possibility to save coal jobs.
Jeff Wald: 01:01:09
What we should be talking about is not saving the coal job, but helping the coal miner to get to a job in industries and segments that are growing because that person we owe something to. We don’t owe something to the coal on the ground and getting it out. We don’t owe anything to that. We owe something to those people and to those communities to help them.
Jon Krohn: 01:01:29
For sure. What a beautiful way to end talking about The End of Jobs. I just have one last question for you. Well, I have two last questions for you, but you’ll see why the last question isn’t really much of a question at all. So, my penultimate question for you is, do you have any book recommendations? I mean, maybe even related to The End of Jobs, but it could be anything.
Jeff Wald: 01:01:54
Well, the first thing that comes to mind is if you’re going to read one thing about the future of work, well, actually, if you’re going to read one thing, you should read my book. I’m going to make a book recommendation. What am I doing here? I’d recommend my own book. That’s what you should read. Okay. If you’re going to read two things about the future of work … Sorry. I should have thought that one through. If you’re going to read two things about the future of work, I would say the World Economic Forum’s report that they put out almost two years ago now, but I still think it is brilliant. It is thoughtful.
Jeff Wald: 01:02:24
Their analysis on the near term, the next five years, and what’s going to happen industry by industry, geography by geography, job function by job function is incredibly thoughtful and is based not on some study in a classroom, but it’s by having conversations with the C-suites, the companies all over the planet. I think it is the best thing one can read on the future of work, and I drew very heavily from it. Very close contact with the team of the World Economic Forum, and think they are thinking about this issue more intelligently than most.
Jon Krohn: 01:03:00
Beautiful. We’ll try to link you to that in the show notes. Do you remember the title of it or it’s just World Economic Forum?
Jeff Wald: 01:03:06
World Economic Forum’s Fourth Industrial Revolution Future of Work.
Jon Krohn: 01:03:10
Nice. That will make it very easy for us to find it and link to it. Thank you. All right, Jeff. Then the last question is how can people stay in touch with you? Should they follow you on Twitter, follow you on LinkedIn? How can they hear more great data and insights about how things have been and how things are going to be?
Jeff Wald: 01:03:28
If you want some tweets about economist articles and various studies in companies that are helping to shape the future of work, Twitter is the one place that I go by Jeffrey. I actually could not get Jeff Wald, but LinkedIn is where I spend most of my time, and certainly anyone can reach out on either platform. I’m always happy to talk about the future of work and how we can use the history of work, the data in the world of work, and how companies actually engage workers to get a better sense and a more thoughtful series of predictions about the future of work.
Jon Krohn: 01:04:03
Beautiful. It’s so interesting. In the data science community, LinkedIn is definitely the social medium of choice. Guests on the program always say, “I do use Twitter,” or in some cases they say, “I don’t use Twitter at all,” but everyone has a LinkedIn profile and recommends that. Either way, they get in touch. I thought that maybe you’d be different, but no. Perfect.
Jeff Wald: 01:04:23
No Instagram, no Snapchat, none of that. Too much to do in the real world.
Jon Krohn: 01:04:29
All right. I’ll just cross off the next question about your TikTok handle. Great. All right. Thank you so much, Jeff, for being on the program. I learned a ton. I am 100% confident that our audience members did, too.
Jeff Wald: 01:04:42
This was so much fun. Thank you so much for having me, and I look forward to staying in touch.
Jon Krohn: 01:04:52
Holy moly did I ever learn a lot in that episode. I have a feeling you did, too. Jeff has so, so, so many well-informed statistics and arguments under his belt based on historical and contemporary data, and then he communicates his points flawlessly. We covered how we currently enjoy more jobs, more free time, and a higher quality of life than ever before, trends in remote work, on-demand work, and the fluidity of employers, how repetitive high volume work will continue to disappear as it always has over the past centuries.
Jon Krohn: 01:05:27
We discussed how previous industrial revolutions before now, the current revolution driven by data science and automation is likely to result in more net jobs, not fewer. Although, on the way, there’s likely to be some social upheaval. Plenty of food for thought. You can get way more detail from Jeff Wald’s newly released book, The End of Jobs.
Jon Krohn: 01:05:50
As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, and URLs to Jeff’s LinkedIn and Twitter profiles, as well as my own LinkedIn and Twitter profiles at www.superdatascience.com/443. That’s www.superdatascience.com/443.
Jon Krohn: 01:06:12
When you add us on LinkedIn, it might be a good idea to mention you were listening to this SuperDataScience Podcast episode so that we know you’re not a random salesperson. If you enjoyed this episode, kindly leave a review on your favorite podcasting app or on YouTube, where you can enjoy a high-fidelity video version of today’s program. It sure is nice to be able to put smiling faces to all the laughs we had today.
Jon Krohn: 01:06:35
I also encourage you to tag me in a post on LinkedIn or Twitter to let me know your thoughts on this episode. I’d love to respond to your thoughts in public and get a conversation going. All right. It’s been so great. Thank you for listening. Looking forward to enjoying another round of the SuperDataScience Podcast with you very soon.
