Speaker 1: From executive search to talent strategy, leadership development, rewards and
Speaker 1: succession planning. Corn Fairy can help you realize the full
Speaker 1: potential of your people so you can take your business
Speaker 1: where it wants to go up. Learn more at corn
Speaker 1: Ferry dot com slash up. There's a lot of talk
Speaker 1: about how robots are coming to take our jobs, how
Speaker 1: in the future near or far, our jobs will be
Speaker 1: replaced by gleaming machines that will work tirelessly, precisely and efficiently.
Speaker 1: But let me tell you something. The robots aren't coming
Speaker 1: to take our jobs. They're already here. This is game plan.
Speaker 1: I am Sam Grobert. I'm a writer at Bloomberg Business
Speaker 1: Week magazine. I am joined here by We're back in Greenfield.
Speaker 1: I'm a reporter at Bloomberg where I cover the workplace,
Speaker 1: and today we're going to talk about automation and what
Speaker 1: it means for all of us at work today, tomorrow,
Speaker 1: and even beyond them. So, Becca, you've written a story
Speaker 1: about this topic. Do you want to tell our dear
Speaker 1: listeners a little bit about what you found. Yeah, So,
Speaker 1: automation has been in the news recently because of our
Speaker 1: President elect Donald Trump saying that he is going to
Speaker 1: keep factory work here in the US and brokering that
Speaker 1: dear deal with Carrier a plant in Indiana, that BLOCKBUSTERR deal, Yes,
Speaker 1: saving a round a thousand jobs human jobs. This brought
Speaker 1: up a conversation about automation. I know that as a
Speaker 1: reader of business news, there are so many articles about
Speaker 1: the robots are coming for your jobs, but I tend
Speaker 1: to gloss over them because a lot of them are
Speaker 1: just basically saying that and little else. Yeah, it's exactly,
Speaker 1: it's not very relatable. But now in this case, people
Speaker 1: are pointing out that often the jobs that have been
Speaker 1: lost in the manufacturing industry aren't being replaced by cheaper
Speaker 1: laborers in Mexico or China, but much cheaper robots who
Speaker 1: are much more productive and do a lot of the
Speaker 1: work that human used to do. So there are kind
Speaker 1: of some startling statistics about this. If I may, oh,
Speaker 1: you may, indeed, I want to hear them. So America
Speaker 1: has lost more than seven million factory jobs since manufacturing
Speaker 1: employment peaked in nineteen seventy nine, but we now produce
Speaker 1: far more. We've more than doubled our production in the
Speaker 1: same time span. Right, so making more stuff with fewer people. Right,
Speaker 1: So America is the second biggest manufacturing producer in the
Speaker 1: world after China. And another study that came out last
Speaker 1: year found that trade, which is the big Donald Trump
Speaker 1: talking point, accounted for about of America's lost factory jobs,
Speaker 1: which is you know, has been accelerated in the last
Speaker 1: decade and people really do feel that, and obviously that's
Speaker 1: why his message did resonate with so many people. But
Speaker 1: of those jobs were taken by robots. And if you
Speaker 1: even think about it, there was a story out in
Speaker 1: the l A Times by our former colleague Natalie at
Speaker 1: Drop who wrote about warehouse jobs. So warehouse jobs have
Speaker 1: brought a lot of human jobs to the US, but
Speaker 1: robots are quickly taking a lot of that work. I
Speaker 1: remember a few years ago Amazon had this Kiva robot, right,
Speaker 1: I've seen the Kiva robot in action. Actually, I was
Speaker 1: in Devon's, Massachusetts once at a company called Quiet Logistics,
Speaker 1: which has the Kiva robots themselves. In the way that
Speaker 1: the warehouse was organized is there was one zone for
Speaker 1: the humans and then there was another zone that you
Speaker 1: didn't go into for the robots. Because the robots are
Speaker 1: moving around rather quickly and they're carrying very heavy things,
Speaker 1: and so it wouldn't be very safe to go into
Speaker 1: the robot zone. But the idea was that there was
Speaker 1: a bit of a division of labor there. The robots
Speaker 1: would go and retrieve items from way back in the warehouse,
Speaker 1: you know, an eighth of a mile away, and bring
Speaker 1: them to a human who could then box it, packaging
Speaker 1: label something a robot is not as good at yet,
Speaker 1: not yet yet, that's the word. That's what my story
Speaker 1: is about. It's about manufacturing in jobs that we can
Speaker 1: see robots doing. But robots are coming, as you mentioned,
Speaker 1: for other jobs that we never really thought about already.
Speaker 1: Already there are robots replacing financial analysts, for example, and
Speaker 1: these are robots more in the digital sense. They're not
Speaker 1: physical robots, but they are bots in manipulating data. Yeah,
Speaker 1: and so what are we going to do when all
Speaker 1: those people are mad about their jobs being gone? Or
Speaker 1: what will their jobs look like are they're going to
Speaker 1: be gone? Those are some of the questions that I've had,
Speaker 1: and then I try to answer in the story. Well,
Speaker 1: I do suggest that everybody go and read that story.
Speaker 1: But while you're listening here, we are going to be
Speaker 1: joined by someone who can help answer those questions right
Speaker 1: here in your headphones. We are joined here by Professor
Speaker 1: David Demming from Harvard's Graduate School of Education. Hi David,
Speaker 1: Hi Sam, how are you great? Thank you. I was wondering,
Speaker 1: as we're talking here about the role of robots and
Speaker 1: labor and how we feel about them coming to take
Speaker 1: our jobs and so forth, could you possibly give us
Speaker 1: a brief description of the current state of the art
Speaker 1: of robotics in the workplace, Like where are they most prevalent,
Speaker 1: what are they doing well, what can't they do well yet,
Speaker 1: and what needs to happen for that to change? Sure? So,
Speaker 1: I mean, I think robotics are pretty prevalent in the workplace,
Speaker 1: but mostly in manufacturing. So um. One of the things
Speaker 1: that's been in the news a lot recently is the
Speaker 1: decline of manufacturing jobs. And so you might think that
Speaker 1: manufacturing itself is in decline, but actually that's not true.
Speaker 1: Manufacturing output has continued to grow. This is actually sort
Speaker 1: of a boom time for manufacturing output. It's just that
Speaker 1: it's robots doing the job instead of humans. Robots are
Speaker 1: also prevalent in other sectors of the economy. But the
Speaker 1: thing that robots and machines and computers really still can't
Speaker 1: do very well at all is socially interact with people
Speaker 1: or with each other. Alright, So so social reaction just
Speaker 1: means the non routine, hard to script out process of
Speaker 1: having a conversation with another human being, figuring out if
Speaker 1: you and I are going to work together on a team,
Speaker 1: what are you going to do? What am I going
Speaker 1: to do? Being flexible, being empathetic. That's something that at
Speaker 1: least right now, we still don't have a good way
Speaker 1: to program to automate, and I definitely want to get
Speaker 1: to that. But there's this paper that you site, and
Speaker 1: I've seen a lot about how cent of total US
Speaker 1: employment is at high risk of automation over the next
Speaker 1: one to two decades. So you're saying manufacturing work has
Speaker 1: already been gutted. Basically, what is the next frontier of automation.
Speaker 1: That's a great question. I mean, I guess it's really
Speaker 1: hard to predict the future. But I think one of
Speaker 1: the reasons why you see a lot of um newspaper
Speaker 1: articles that I would call our other people also would
Speaker 1: call automation anxiety kind of pieces, it's because you see
Speaker 1: automation increasingly UM grabbing onto what we might think of
Speaker 1: is white color or professional work. So one example is
Speaker 1: ecovery in the legal world. If you're if you're an
Speaker 1: attorney or you have friend who attorneys already know about this.
Speaker 1: This is you used to be an attorney's job to
Speaker 1: comb through reams of paper looking for bits of information
Speaker 1: that might be relevant to a case or to a deposition.
Speaker 1: Now we've got software to do that, and so that's
Speaker 1: kind of been really great for UM for legal work.
Speaker 1: It's made legal work much more productive, but it's putting
Speaker 1: lawyers out of work. And you see some of that
Speaker 1: with automation of maybe you guys know this short kind
Speaker 1: of newspaper articles. Fact based articles are being automated, are
Speaker 1: automatically produced recaps of sports games, things like that. And
Speaker 1: so when you start to see that happen, those are
Speaker 1: the types of changes that are affecting the people that
Speaker 1: we all know, people that journalists know, And so I
Speaker 1: think you see a lot more articles about it. But
Speaker 1: I think, in fact, the progress of automation has been
Speaker 1: pretty continuous over the past several decades. David. When I
Speaker 1: think of manufacturing robots, I I suppose, like many people
Speaker 1: imagine those big sort of arms in a say auto plant, right,
Speaker 1: kind of assembling a car body. Um, those have been
Speaker 1: around for some decades now in one form or another.
Speaker 1: What kinds of robots are being built or will be
Speaker 1: built that can work presumably alongside humans? What? What? What
Speaker 1: do they start to look like? Yeah? Exactly. So when
Speaker 1: you when you think of robots in your mind, you
Speaker 1: have this image, at least I do, growing up in
Speaker 1: the in the eighties. I think of like the Transformers, right,
Speaker 1: stuff like that. But actually, automation is not just robots,
Speaker 1: is any process that can be scripted out in advance.
Speaker 1: And so you know, you don't think of things like
Speaker 1: I don't know, Siri or basically um computer programs that
Speaker 1: can like maybe customer service, like if you call a
Speaker 1: company and you want to get help with your credit card,
Speaker 1: your credit card gets stolen or you want to call
Speaker 1: and complain. You don't think of the voice on the
Speaker 1: other end that's not a human as being a robot.
Speaker 1: But that's actually the same manifestation of robotics, meaning scripting
Speaker 1: out what used to be human performance in a predictable way.
Speaker 1: So I think that's been going on for a long time.
Speaker 1: Those things are also robots in some sense, and you're
Speaker 1: going to see more of that, robots being more and
Speaker 1: more human like. One of the more modern expressions of
Speaker 1: that are these messenger based bots. Yes, you can chat
Speaker 1: bots that you can like say I want to flight
Speaker 1: to Florida, and it'll be like, I see that you
Speaker 1: want to flight to Florida. These are your options. I'll
Speaker 1: take that one. And then it makes a joke about
Speaker 1: Florida or something. Right. Yeah, they're path yeah, they they're
Speaker 1: getting picked here all the time. And that's the thing
Speaker 1: that's kind of the at least of my understanding the
Speaker 1: frontier is we're getting better at natural language processing. You
Speaker 1: think about think about Sirie. Okay. I don't know how
Speaker 1: much you guys, you Siri, but you have to say
Speaker 1: things in a certain way to Siri to get the
Speaker 1: kind of response. Otherwise you get the you know, searching
Speaker 1: the web for right. Yeah, and that's Sirie being not
Speaker 1: understanding natural language. Right. So I'll give you an example. Um,
Speaker 1: my children, maybe as many children do, love to play
Speaker 1: with my phone and with Siri, and so they were
Speaker 1: asking Siri, Sirie, what color is my water bottle? Okay,
Speaker 1: which Siri could actually has the technology to figure that out.
Speaker 1: Siri could turn its camera into the water bottle and
Speaker 1: say your water bottles red. But series you stupid to
Speaker 1: figure that out? So Siri says, searching on the web
Speaker 1: for what color is my water bottle? But if you
Speaker 1: ask Siri, what's thirty seven times fifty nine, as I
Speaker 1: encourage my daughter to do, Syria can do that right away.
Speaker 1: So I said to her, why do you think, Siri?
Speaker 1: Can you know this is a cognitive process. It's really
Speaker 1: most people can't just do thirty seven times fifty nine
Speaker 1: in their head. Syria can do it instantaneously. And that's
Speaker 1: because the problem space is defined. You have a database
Speaker 1: where you say, these are all the multiplications. You know,
Speaker 1: do this, and Syrie can do that really quickly. But
Speaker 1: common sense tasks like telling you what color your water
Speaker 1: bottle is, which are very simple and natural for humans
Speaker 1: to do, is really hard. Actually, a program up happens
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Speaker 1: You get a partner who truly understands people, leadership and
Speaker 1: the new landscape of work, a partner who knows how
Speaker 1: to take your business up. Learn more at corn Ferry
Speaker 1: dot com slash up. So all of us talk about
Speaker 1: robots taking our jobs is scary, but I found some
Speaker 1: comfort in your working paper the growing importance of social
Speaker 1: skills in the labor market, probably as somebody who uses
Speaker 1: social skills in her job a lot, but you found
Speaker 1: that there are some skills that are going to be
Speaker 1: valued in the labor market because robots can't do them.
Speaker 1: So can you talk about that and maybe what kinds
Speaker 1: of jobs are the safest? Sure? So, the way you
Speaker 1: want to think about how technological progress affects the labor
Speaker 1: market is that there's really two things going on, and
Speaker 1: we tend to focus on one of them, which is
Speaker 1: how technology takes away jobs what economists would say substitutes
Speaker 1: away for for human labor. So you've got robots or
Speaker 1: computers substituting away for human labor, and that's the scary side.
Speaker 1: But there's a friendly side too, and that is it
Speaker 1: also makes people who aren't substituted away much more productive. Right, So,
Speaker 1: for example, you used to have to do complex mathematical
Speaker 1: calculations by hand. You might hire an accountant to do this.
Speaker 1: Let's say you own a small business. Now you've got
Speaker 1: tools like Microsoft Excel that allow that process to be
Speaker 1: automated and very quick and very customizable. Right, so we
Speaker 1: focus on the putting the account and out of business,
Speaker 1: but the other side of it, if you make business
Speaker 1: decisions for a living, you're much more productive. So the
Speaker 1: lesson that we've learned from the study of how technological
Speaker 1: change affects labor markets is that anything that is not
Speaker 1: directly replaced by technology is generally complimented by it, which
Speaker 1: is an economist way of saying makes you more productive.
Speaker 1: So you have to look at the labor market and say,
Speaker 1: what are the things that technology still can't do very well?
Speaker 1: And those things, those tasks, those job tasks are going
Speaker 1: to be made more productive and thus going to be
Speaker 1: more important. And that's kind of the thesis of the paper,
Speaker 1: which is that, look, at least right now, we don't
Speaker 1: know about the future, but right now, machines can't have
Speaker 1: an unscripted conversation within a human being or with each
Speaker 1: other in any kind of meaningful way. They can't socially interact,
Speaker 1: they can't work on a team, they're very inflexible. And
Speaker 1: so people who can do that are going to be
Speaker 1: more valuable in the workplace than they were before because
Speaker 1: we've got technology to do all the other things you
Speaker 1: want to hear a really left field example of what
Speaker 1: you're talking about. Okay, So I was done in Florida
Speaker 1: last year at Florida Atlantic University, and there's a team
Speaker 1: of engineers who are developing these um robot boats, basically
Speaker 1: water based drones that can inspect bridge pilings underwater. And
Speaker 1: this was a job that people used to have to
Speaker 1: do themselves. They had to go underwater with a diving
Speaker 1: rig and it was actually pretty dangerous and it took
Speaker 1: a really long time, and you can only send out,
Speaker 1: you know, one person at a time. And now what
Speaker 1: they're doing is the robots are going around and constantly
Speaker 1: just floating around these bridges and looking at them underwater
Speaker 1: with their cameras. The people who used to have to
Speaker 1: go into the water stay on land at a place
Speaker 1: where they're watching what the robots are looking at and
Speaker 1: making decisions about, oh, now we have to go fix that.
Speaker 1: So their job is safer, and they actually get to
Speaker 1: cover more territory because the robots don't ever stop work.
Speaker 1: You have to ask yourself. The U is a great example.
Speaker 1: You ask the question, why don't we automate the final step,
Speaker 1: which is telling the you know, telling the robots what
Speaker 1: to do once they get there. It's because it's unpredictable, right,
Speaker 1: and so people are just very easily and naturally able
Speaker 1: to say, oh, look, I see the problem, let's do
Speaker 1: this instead. But if you're going to write a kind
Speaker 1: of a script or a computer program to do that,
Speaker 1: you have to be able to anticipate anything that might
Speaker 1: possibly happen. And some situations are really uncertain, and people
Speaker 1: are still much better than machines at reacting to uncertainty.
Speaker 1: So these skills, I've seen them called a couple of
Speaker 1: things like empathy, labor, non cognitive skills, social skills, social labor.
Speaker 1: They also are happening in high skilled labor. I now
Speaker 1: we're talking a lot about manufacturing and transportation. Do you
Speaker 1: think that our education system is pushing these enough? Are
Speaker 1: we teaching this well enough? Are these are this? Is
Speaker 1: this valued enough? Because I get a sense I'm just
Speaker 1: going to say that it's not because we're hearing so
Speaker 1: much about the push for STEM, and you know how
Speaker 1: everybody needs to be a coder and learned to code,
Speaker 1: but you never hear people raising their hand for learn
Speaker 1: to be more empathetic. It's a great question, um, and
Speaker 1: I'm glad you brought up STEM because it does tie
Speaker 1: into that. You do see a lot of discussion of
Speaker 1: you know, we need more STEM jobs, and I think
Speaker 1: that is not untrue. The way I would say it
Speaker 1: is that you want people to have technical skills because
Speaker 1: they need to understand how the machines work and how
Speaker 1: things are happening under the hood, so to speak. But
Speaker 1: you also want them to have these social skills. It's
Speaker 1: just not enough to have one and not the other.
Speaker 1: I would predict, for example, that twenty years from now,
Speaker 1: you'll see many fewer jobs where you just write software
Speaker 1: code all day, but you'll see a lot more jobs
Speaker 1: where you have to do something like that as part
Speaker 1: of many other things you do on the job. So
Speaker 1: I think the workplace is just becoming more integrated in
Speaker 1: terms of which task keep to do in and less
Speaker 1: predictable in part because of this change that anything that's
Speaker 1: predictable and and and can be scripted, we can automate,
Speaker 1: and so you need people to be able to um
Speaker 1: flexibly rotate between many different things in a kind of
Speaker 1: unpredictable way. And so I talked about this in the paper.
Speaker 1: I mean there's a kind of way to formalize this,
Speaker 1: but essentially it's like, look, if you don't have any
Speaker 1: technical skills, you don't know how to do anything, then
Speaker 1: the fact that you're you have good social skills is
Speaker 1: not that useful actually, because there are a lot of
Speaker 1: people who can do that. There are a lot of
Speaker 1: people who can who can empathize with others. But what
Speaker 1: there aren't a lot of people who can do is
Speaker 1: people who have the technical skills who understand how everything
Speaker 1: is working. But also those people kind of tend to
Speaker 1: be people, tend to be one or the other. And
Speaker 1: so I think what you see very clearly in the data,
Speaker 1: and what I talked about in the paper is that
Speaker 1: you see increasing labor market returns for people who have
Speaker 1: both types of skills what kondom is often called cognitive
Speaker 1: skills or hard skills and soft skills or social skills.
Speaker 1: I wouldn't be doing my job as a business journalist
Speaker 1: if I didn't refer to Steve Jobs probably one severy
Speaker 1: week or so. But what you're talking about really reminds
Speaker 1: me of one of his keynotes where he said that
Speaker 1: the key to his success or Apple's success was the
Speaker 1: marriage of technology and the liberal arts. That it couldn't
Speaker 1: just be numbers and engineering, but it also had to
Speaker 1: have the inspiration and the artistic and cultural influences of
Speaker 1: literature and music and all these other things. And so
Speaker 1: the two brought together is what really makes for interesting products,
Speaker 1: interesting workers, and so forth. Uh, you can't just have
Speaker 1: the one. I think that's right, and I think what
Speaker 1: he's really talking about is the marrying of human creativity
Speaker 1: with all the things that are possible with technology. You know,
Speaker 1: you can't just if you're just training yourself, or we're
Speaker 1: training students in schools to just be like the machines,
Speaker 1: then we're not gonna be able to make full use
Speaker 1: of these technological advances. It's really putting the two together
Speaker 1: that's so important. David, thank you so much. This has
Speaker 1: been a really eye opening conversation. Really appreciate it, my pleasure.
Speaker 1: It's great to talk to you guys. So David scared
Speaker 1: us obviously because the robots are coming for all the jobs.
Speaker 1: He even mentioned journalism right. One thing that we didn't
Speaker 1: get to talk to him about that I thought was
Speaker 1: interesting that he and I had talked about when I
Speaker 1: talked to him for my story was about the gender
Speaker 1: component in all of this. Manufacturing obviously tends to be
Speaker 1: a very male dominated field, and a lot of empathy
Speaker 1: jobs that are really growing in this country tend to
Speaker 1: be female dominated jobs like nursing, home health AIDS, physical therapists, teaching,
Speaker 1: and these are skills that he's saying are going to
Speaker 1: be valued more and more. But I do wonder a
Speaker 1: lot of them aren't very well paid now, and you
Speaker 1: still don't see a lot of men going into nursing,
Speaker 1: even though it's a high growth field that does pay well.
Speaker 1: I'm wondering how that will change, and if more men
Speaker 1: do go into these fields, that they will become higher paying,
Speaker 1: because that has happened historically. Once men go into a field,
Speaker 1: it tends to be more like we need some more
Speaker 1: money here. This is ridiculous. Which happened with computer programming
Speaker 1: is one big example. But I remember there was a
Speaker 1: study that I cite that talks about um how janitors
Speaker 1: make a lot more than house cleaners, for example, or
Speaker 1: HR people tend to be female, men make less than
Speaker 1: someone with a similar skill set in the office, things
Speaker 1: that don't make sense at all except for gender. It
Speaker 1: is interesting when you think about sort of the progression
Speaker 1: of automation that it occurs across different categories. So we
Speaker 1: start with blue collar jobs, and then it moves to
Speaker 1: white collar jobs, and then people like us in the
Speaker 1: media start to get really scared because we go, oh, wait,
Speaker 1: it's for me, which just proves the sort of bias
Speaker 1: that's in the implicit in all of that. But then
Speaker 1: there's also the gender. So it's like blue collar jobs,
Speaker 1: which often have been male dominated jobs. So it'll be
Speaker 1: I don't know, the next fifty years will be Yeah,
Speaker 1: we'll see if quote unquote pink collar jobs stay pink
Speaker 1: color or will that become more prestige work right, and
Speaker 1: just the jobs that everybody needs and wants to have,
Speaker 1: because they're the ones that actually are left. I was
Speaker 1: talk to a lot of people who said managers are
Speaker 1: going to be more valued in the future because managers
Speaker 1: tend to have to work with people, promote cross teams,
Speaker 1: and again, those are skills that are tend to be
Speaker 1: associated with women. I guess I should go be a manager.
Speaker 1: I know it's sorry everyone, I know nobody. Nobody wants
Speaker 1: to hear that like that idea at all. Anyway, if
Speaker 1: you don't want to row about to take your job,
Speaker 1: get some social skills, I would have to say that.
Speaker 1: But I do love that we're standing up for social skills.
Speaker 1: I have relied on social skills more than intelligence or
Speaker 1: talent for about twenty years now. So if that's the
Speaker 1: way the future is going, I am well set for it,
Speaker 1: all right. And now it's time for half bake takes.
Speaker 1: Half fake takes. Half bake takes are not fully formed thoughts, ideas,
Speaker 1: opinions that Becca and I have had and discussed. Becca,
Speaker 1: do you want to lead us off with your half
Speaker 1: bake take? So my half big take I think will
Speaker 1: be controversial. Good. This morning I had a really horrible
Speaker 1: subway commute where the four train that I take was
Speaker 1: just crawling, got stuck with so slow it was crowded,
Speaker 1: I had to stand in a weird position. My back
Speaker 1: hurt terrible. Generally subway commuting is better than driving community,
Speaker 1: but I think getting stuck on a slow train where
Speaker 1: you think the worst thing is happening is on par
Speaker 1: with being stuck in traffic while driving. It's really bad.
Speaker 1: And being stuck in traffic totally terrible. It happens more
Speaker 1: often if you're a driving community, But being stuck on
Speaker 1: that train it's not. I know, you get to read
Speaker 1: and stuff, but you run out of reading material and
Speaker 1: it's like you don't have any personal space. I'm going
Speaker 1: to disagree, kind of disagree with you here. Also, I
Speaker 1: don't take a fancy train to work where you get
Speaker 1: a seat right, No, no, no, agreed. But here's the
Speaker 1: thing about getting stuck in traffic. When you're in a
Speaker 1: car and you and you're driving, right, it's exhausting. No,
Speaker 1: it is because you actually have to pay attention. I know,
Speaker 1: I know, but I just there is. It's I think
Speaker 1: generally subway communing is better, I really do. But this
Speaker 1: running was really rough and I think as bad as
Speaker 1: my old traffic. You know what, you know what I'm
Speaker 1: going to do the thing that I think. I'm sorry
Speaker 1: you had a bad community. Thank you. That's the important
Speaker 1: thing here is your half baked take. Well, my half
Speaker 1: baked take is also kind of travel related, mobility related.
Speaker 1: Um ladies and gentlemen, if you are out there and
Speaker 1: you have not signed up for t S A pre
Speaker 1: Check or Global Entry, you are playing yourselves. This is
Speaker 1: like the best thing that's happened. Okay, you recently just
Speaker 1: got it because my credit card paid for it, and
Speaker 1: it is really amazing. You can get to the airport
Speaker 1: so late and you get to go through so quickly.
Speaker 1: One thing I would say is that you know, we
Speaker 1: have some t S A pre check privilege. Not everybody
Speaker 1: can afford it or also we'll get approved. Maybe not gonna.
Speaker 1: I gotta say, as far as the affordability is concerned,
Speaker 1: if you do any traveling at all, I think it's
Speaker 1: what like for five years. Okay, yeah, it's I guess
Speaker 1: if you're buying a plane ticket, you can probably afford it.
Speaker 1: But not everyone gets approved. And I was felt kind
Speaker 1: of weird giving my fingerprints to the government. Right. Um,
Speaker 1: there is t S A pre Check which works domestically.
Speaker 1: Global Entry is another program that allows you also to
Speaker 1: skip the line when you're returning from a foreign country
Speaker 1: at immigration, um and includes pre check. I think it's
Speaker 1: like twenty more so it's actually the better deal. You know,
Speaker 1: maybe I should have done that. Uh. The t s
Speaker 1: A itself is really interested in getting as many people
Speaker 1: to sign up as possible because it produces the standard
Speaker 1: line at their already overtax security checkpoints. And so they've
Speaker 1: actually been setting up sort of temporary or mobile registration sites.
Speaker 1: I do suggest you go online see if there's one
Speaker 1: near you, make the time to go over there. It's
Speaker 1: like flying in nineteen, like seven again, you just kind
Speaker 1: of like, oh, yeah, here's a metal detector. I'm breezing through.
Speaker 1: That's a reason to do it. You don't have to
Speaker 1: go through the rapist scan machines. So that's what they're called. Okay,
Speaker 1: and this has been half break takes, half baked takes.
Speaker 1: Thank you for listening, and thank you again to Professor
Speaker 1: David Demming from Harvard School of Graduate Education. You've been
Speaker 1: listening to game Plan. Game Plan is produced by Liz
Speaker 1: Smith and Magnus Hendrickson. The head of Bloomberg Podcast is
Speaker 1: Alec McCabe. If you like the show, head on over
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Speaker 1: am at sam grow Part and I'm at ours Greenfield.
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