Don’t Worry, the Robots Can’t Do Your Job—Yet

Summary

Podcast Summary

The New Yorker Radio Hour

Episode: Don’t Worry, the Robots Can’t Do Your Job—Yet
Date: December 12, 2017
Host: David Remnick

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Overview

This episode delves into how advanced automation and robotics are transforming the American workforce, particularly in industries reliant on manual labor. Sheila Kolhatkar, New Yorker staff writer, investigates whether robots and smart machines are fueling job losses and driving increasing income inequality—issues that tangled themselves up in the 2016 presidential election. The episode features insights from economist Andrew McAfee (MIT), robotics expert Stephanie Tellex (Brown University), Chris Gahagan (CEO of Symbotic), and contextual reflections on historical and policy solutions. The tone is thoughtful, analytical, and occasionally wry, balancing both marvel and caution regarding what automation means for jobs, communities, and the future of work.

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Key Discussion Points and Insights

The Changing Nature of Automation and Its Effects on Jobs

• Traditional vs. Modern Automation

  • Historically, new technologies destroyed some jobs but created new, better ones ([01:04]).
  • Current automation, powered by AI and robotics, seems different, possibly eliminating more jobs than it creates; this may drive income inequality.

• Economist’s Anxieties

  • Andrew McAfee (MIT): CEOs privately express concern that their own tech investments may eliminate vast numbers of jobs.

    "I can identify the technologies that we're investing in right now that are going to put very, very large numbers of people in my organization out of business. Do not quote me on this." — Andrew McAfee [01:46]

State of Current Robotics

• Advancements: Smarter, More Dexterous Machines

  • Sheila Kolhatkar visits Professor Stephanie Tellex, who focuses on robotic dexterity and object recognition ([02:50]).
  • Robots are much better, but still limited:

    "If I am a 90% pick success rate and I'm dropping 10, I am not a cost effective robot." — Stephanie Tellex [03:23]

  • Adapting robots to new objects and environments remains a major challenge.

• Real-World Industry: Warehousing Automation

  • Symbotic, a company near Boston, is automating warehouse tasks (e.g., stocking and pallet-building) almost entirely, reducing the need for human workers ([04:43]-[07:36]).
    • Robots stack goods in store-preferred order, improving efficiency, saving not just on wages but also on operational costs like lighting (i.e., "dark warehouses").

      “When we build the pallet robotically, we build it in the order that the store wants it. ... That product is taken immediately from the truck out to the store for restocking.” — Chris Gahagan [06:48], [07:17]

    • Installing such systems is expensive, but returns through efficiency gains and labor savings can be significant.

Societal Implications & Political Underpinnings

• Job Market Transformation and Geographic Disparity

  • McAfee notes that though the US economy continues to add jobs, these are often lower-paying, less stable, and lack the classic middle-class security ([08:50]):

    "Instead of turning out lots of really solid middle class jobs... the job creation engine is kicking out lots of lower middle class jobs that are lower paid, they're more precarious, the hours are all over the map, and they're just not as good a job." — Andrew McAfee [08:50]

  • Automation may not be a “hot button” in Washington, but it's at the root of anxiety about diminished opportunity—a feeling that played a role in the last presidential election.

    “These job market challenges brought on by technology and globalization are not the full explanation for Donald Trump as our president. However, it is really hard for me to imagine that a message of isolationism and nationalism... would have played to the electorate in 1996, in 2000.” — Andrew McAfee [09:58]

• Personal Reflection: The Technologist’s Dilemma

  • Professor Tellex expresses anxiety about her own role in potentially fueling inequality and labor shifts ([11:07]):

    “If those problems are really caused by me, by robotics, that's bad… if we don't tell the story right, someone else is gonna tell it for us and I'm not necessarily gonna like how that story goes.” — Stephanie Tellex [11:07]

Potential Solutions and Societal Responses

• Universal Basic Income vs. Meaningful Work

  • Universal Basic Income (UBI) is discussed as a safety net but viewed skeptically.

    “I don't think lack of money is the great problem. It's a lack of dignity, glue for a community, meaning, purpose in life, stuff to do to fill up the hours of the week, things like that, things that a job is really, really good at, giving to people.” — Andrew McAfee [13:08]

• Need for Big Policy Shifts

  • The history of response to technological surges (i.e., creation of universal K-12 education in response to electrification/internal combustion) suggests America can adjust—but isn't preparing now.

    “My huge frustration is I just don't even see the will or the desire or the awareness of the need to come together and to do big things.” — Andrew McAfee [15:09]

• Geographic and Educational Issues

  • Economic fate is becoming zip code-dependent, and both the innovative economy and jobs are geographically concentrated.
  • Mobility rates are falling, making adjustment harder ([14:23]).

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Notable Quotes and Memorable Moments

• On Robots’ Limitations:

  "It's not going to take very many drops before you're like, don't want you robot. I'm going to go get my water bottle myself." — Stephanie Tellex [03:23]

• On the Realities of Displaced Workers:

  "Adding 2 or 3 million more of those people to the ranks of the unemployed is not a recipe for stability. I don't think." — Andrew McAfee [11:49]

• On Societal Denial:

  "Automation doesn't ever seem to come up in Washington. Members of Congress never talk about it. ... But it's the future. There are a lot of things we could be doing to prepare for it, but the first step we need to take is... to just acknowledge that it's happening." — Sheila Kolhatkar [15:37]

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Timestamps for Important Segments

• Automation and Job Loss — Introduction: [01:04]
• MIT’s Andrew McAfee on CEOs’ Concerns: [01:41]
• Robotics Dexterity Limits (Tellex at Brown): [02:50]-[04:09]
• Real World Warehouse Automation (Symbotic): [04:43]-[07:36]
• Economic and Political Impact — Election, Inequality: [08:50]-[10:37]
• Technological Change and Solutions (UBI, Education, Policy): [11:49]-[15:37]
• Final Thoughts on Denial and Preparation: [15:37]

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Conclusion

The episode paints a nuanced picture of technological advancement’s double-edged sword. On one side, robotics and automation hold promise for efficiency and economic gains; on the other, they threaten stability and shared prosperity by eroding the types of jobs that built and sustained the American middle class. The discussion underscores that while robots can’t do all our jobs yet, they are changing work and society—and public dialogue and policy are lagging behind.

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Further Reading:

• Sheila Kolhatkar’s article, “Welcoming Our New Robot Overlords,” available at newyorkerradio.org

Transcript

[Narrator/Announcer]

Floor 38.

[Andrew McAfee]

These are just anecdotes, but it's building up into something more coherent.

[Sheila Kolhatkar]

I think it'd be interesting to really try to unravel what his ties.

[Andrew McAfee]

There's this sort of country city divide.

[Fabio Bertoni]

They're inconvenient, and it's not clear where it goes next.

[Narrator/Announcer]

From one World Trade center in Manhattan, this is the New Yorker Radio Hour, a co production of WNYC studios and the New Yorker.

[David Remnick]

Welcome to the New Yorker Radio Hour. I'm David Remnick. Staff writer Sheila Kolhadkar covers a lot of business stories for the New Yorker, and she's been looking at a tremendous economic change going on in our country right now. Sheila's been reporting on how automation is making its way into industries that are still depending very much on human labor, with robots that are much more advanced than what you might imagine in an automotive factory, for example. And every time robotics makes another leap forward, economists start worrying with good reason. Here's Sheela Kolhatkar.

[Sheila Kolhatkar]

Well, I first became interested in this because income inequality is one of the defining issues of our time, and it's one of the major reasons that President Trump won elected office. It was one of the strong motivations of many of his voters. And it occurred to me that technology and automation might be partly to blame. And this actually defies conventional wisdom because in the past, tax technology has eliminated certain kinds of jobs, but it was largely seen as creating new and better jobs. But that seemed to not be happening anymore. So I wondered what was going on.

[Andrew McAfee]

I have really interesting experiences.

[Sheila Kolhatkar]

Over and over I talk with Andrew McAfee, who's an economist at MIT, because.

[Andrew McAfee]

When I give my talks or engage in discussions about this phenomenon, very often afterward, I have fascinating hallway conversations with CEOs who will come up to me and say, hey, where are all the jobs going to come from? And I say, aren't you the job creation engine? And they say, I can identify the technologies that we're investing in right now that are going to put very, very large numbers of people in my organization out of business. Do not quote me on this.

[Sheila Kolhatkar]

So if it's true that robots and automated machines are replacing human workers in large numbers, we could be on the verge of one of the biggest waves of unemployment we've ever seen. So why are things changing now? Well, robots are getting smaller and faster and more precise in their movements, and most importantly, they're getting smarter. They can problem solve on the fly better than they ever could before. I talk with a professor named Stephanie Telix. She Runs a computer science lab at Brown University. She and her students are at the cutting edge of research to make robots more dexterous and smarter.

[Stephanie Tellex]

I'm personally more interested in manipulation scenarios where the robot can pick things up and put things down. It can move not only itself, but other objects in the environment.

[Fabio Bertoni]

Can I have the metal object over there?

[David Remnick]

This one?

[Sheila Kolhatkar]

No.

[Fabio Bertoni]

The other spoon.

[David Remnick]

Final answer you wanted.

[Fabio Bertoni]

Object 6.

[Sheila Kolhatkar]

The way Stephanie sees it, robots are a long way away from actually replacing people. We know so much and we do so much intuitively, and robots have to be taught how to do absolutely everything.

[Stephanie Tellex]

If you imagine that the robot, all it can do is deliver objects. So I'm saying, get me the water bottle, give me the food, get me the Kleenex box, give me the phone. And my success rate is 90%, which is probably more or less where state of the art is at recognizing the object. That means 10 times in a day I'm going to fail to follow the command successfully. It's not going to take very many drops before you're like, don't want you robot. I'm going to go get my water bottle myself because I don't want you to drop it. And on a factory line it's the same thing. Like if I am a 90% pick success rate and I'm dropping 10, I am not a cost effective robot.

[Sheila Kolhatkar]

It's not just the physics of picking things up. Robots aren't great at recognizing objects they haven't encountered before.

[Stephanie Tellex]

So what we've done is created an approach for perception that allows the robot to automatically adapt itself to the environment and the objects that it's asked to manipulate. And so you can imagine that that factory robot, like if it's on an assembly line, it's doing assembly tasks. There's like 10 things maybe it's got to pick up. You can imagine that by adapting itself, it can reach a much higher level of precision at recognizing objects and reach the rates of reliability that are needed to be successful in those tasks.

[Sheila Kolhatkar]

Those kinds of advances are having a huge impact in places like warehouses, like the order fulfillment centers that service Amazon and other E retailers. Warehouses have been a really bright spot in the economy for workers who don't have college degrees, who perhaps couldn't find jobs in manufacturing. But I visited a robotics company called Symbotic just outside Boston, and they're working on making warehouses much more efficient and practically human free.

[Chris Gahagan]

Welcome to the machine.

[Sheila Kolhatkar]

Chris Gahagen is the CEO.

[Chris Gahagan]

So this is the, we call it the ITC Integrated Test center.

[Sheila Kolhatkar]

They have all sorts of robots of different sizes, Including a fleet of adorable little race cars that zoom around big cages.

[Chris Gahagan]

Each vehicle weighs about little less than £300. They travel at 25 miles an hour. They kind of look like a little go kart. We have to be green.

[Sheila Kolhatkar]

So in a symbolic warehouse vehicle, Big stacks of goods come in. Robots unpack them. The goods get stored all around this giant storage cage that looks like something out of the matrix. And little robot cars zoom around gathering up the goods Whenever there's an order. And they repack everything at the end onto these pallets, which are these tall kind of stacks of crates of goods. So here's where we can see the kind of research Stephanie tellex is doing playing out in the real world. The part that's revolutionary Isn't just the robot's ability to quickly grab stuff. Stuff. It's their brains. It's the computer's ability to recognize and handle A wide variety of objects.

[Chris Gahagan]

That wouldn't have been possible without the advent of kind of much more powerful computers, really inexpensive sensors. And a lot of those sensors were made because of what's happened with smartphones and other computer and consumer electronic devices. That just wasn't possible 10 years ago.

[Sheila Kolhatkar]

Most warehouses are built for human workers, and the pallets are usually stacked in the order that's best for humans. So taking into account worker safety, Minimizing exhaustion, the potential for human error. So the stuff never arrives at the stores in the order that the stores would like.

[Chris Gahagan]

The difference is, when we build the pallet robotically, we build it in the order that the store wants it. So when the pallet arrives, Instead of a human being having to downstack that pallet onto carts, they can take that pallet directly to the floor and start restocking the store and what that allows.

[Sheila Kolhatkar]

So what Chris gahagan is talking about Might sound like a minor point, but if you run a store, Especially a chain of stores, and you're waiting for stacks of goods, and the robots have already arranged everything in exactly the order that you need it in, that's actually a really big deal.

[Chris Gahagan]

And what that allows a store to do Is be more efficient with their labor, but more importantly, reduce outages in the store so they can start to replenish the store much faster. Because now when that truck arrives, there's no work that's happening in the back of the store. And literally, that product is taken immediately from the truck out to the store for restocking.

[Sheila Kolhatkar]

A symbotic system is really expensive to install, but afterward, a company saves a Lot of money, not just in wages and benefits, but the company also saves on lighting and heat. People in the field talk about dark factories. Well, this would be like a dark warehouse.

[Chris Gahagan]

Lights are only on in the front of the system where human beings are running the system. And we've done kind of initial analysis. Compared to a manual based warehouse that has fork trucks and people in it, we are about 30% more energy efficient.

[Sheila Kolhatkar]

So what's going on at Symbotic reflects what's going on all around the country. It eliminates the need for many types of jobs, but it does create one new class of jobs, that of the system operator, which is a higher skilled, higher paid job that requires more education, but there's only one of them. In this political climate, many of Symbotic's clients don't want people to know they're even installing this system. Every time the minimum wage goes up, or might go up, the company gets more calls. Higher wages make automation more attractive. Andrew McAfee says that we can't look at America as one economy anymore. There are different economies for different classes of workers.

[Andrew McAfee]

We. We also should keep in mind that we have added jobs month by month, without fail, in this country for well over 80 months in a row. So we are not at peak jobs. The job creation engine of the American economy is still positive, is still kind of working. What I think is going on though, is that that job creation engine has kind of kicked into a lower gear. And instead of turning out lots of really solid middle class jobs like it was doing back in the 60s and 70s, the job creation engine is kicking out lots of lower middle class jobs that are lower paid, they're more precarious, the hours are all over the map, and they're just not as good a job in a lot of different ways.

[Sheila Kolhatkar]

So are you. Did you see any signs of some of these tensions in the presidential election and the voting patterns? I'm sorry to ask you.

[Andrew McAfee]

I'm sorry, is that a serious question? No. Okay, so nothing rang any bell at any point in time?

[Sheila Kolhatkar]

All right.

[Andrew McAfee]

I have no idea.

[Sheila Kolhatkar]

You sound a little naive, but I'm sure it's hard to avoid the well, does this explain Trump question.

[Andrew McAfee]

These job market challenges brought on by technology and globalization are not the full explanation for Donald Trump as our president. However, it is really hard for me to imagine that a message of isolationism and nationalism and really pretty naked ethnocentrism would have played to the electorate in 1996, in 2000. That just doesn't work for me. So I Do think there is something about this feeling of diminished opportunity, people suddenly getting a raw deal when they signed up for a fair deal.

[Sheila Kolhatkar]

That brings us back to Stephanie Telex, the computer scientist at Brown. For most of her career she was really excited and optimistic about technology. She told me she was inspired to go into this from watching the Jetsons as a kid. Then during the presidential election, Stephanie learned that her parents were planning to vote for Trump and that his argument that job losses in the US were due to immigration had really resonated with them. She knew that income inequality was a huge factor in the election and she started to wonder if her work was contributing to the problem.

[Stephanie Tellex]

If those problems are really caused by me, by robotics, that's bad. Like it's bad. They don't understand it's bad. They're being told lies. It's bad because they're not like if that they're gonna apply the wrong solutions. And I also felt like also if they figure out that it's robotics, you know, if we don't tell the story right, someone else is gonna tell it for us and I'm not necessarily gonna like how that story goes.

[Sheila Kolhatkar]

One scenario people are worried about is the trucking industry and the question of what will happen to truck drivers once self driving technology really takes. Some economists estimate that over the coming years, 2 million truck drivers could lose their jobs.

[Andrew McAfee]

I don't worry about the raw numbers at all. The reason not to worry about that is that every month in America there are more than 1.5 million layoffs. More than 1.5 million people lose their jobs in America every month. So the notion that we can't absorb an additional 2 or 3 million layoffs is just dead flat wrong. We could absorb that with hardly a hiccup, especially if it occurs over a decently long period of time. However, it's the kind of people doing those jobs that are a concern because like they tend to be less well educated, middle aged, white working class. Adding 2 or 3 million more of those people to the ranks of the unemployed is not a recipe for stability. I don't think.

[Sheila Kolhatkar]

So. What are the solutions? One idea that comes up is ubiquitous universal basic income. This is the idea that the government would give every citizen an amount of money that would cover basic living expenses. It's a controversial idea because it sounds sort of socialistic, very un American that you'd get money to sit around and do nothing. But recently economists have started to talk about this more and more and even the celebrity co founder of Tesla, Elon Musk, has Been promoting this idea, but Andrew McAfee is really skeptical.

[Andrew McAfee]

When I read about and when I go look around the communities where work has gone away, I don't see people starving. I don't think lack of money is the great problem. It's a lack of dignity, glue for a community, meaning purpose in life, stuff to do to fill up the hours of the week, things like that, things that a job is really, really good at, giving to people. What we need to do is give the innovators and the entrepreneurs the best possible playing field to come up with new things for people to do. Job creation has always been a really decentralized activity. It's done by companies, by entrepreneurs, by innovators who want to go build a good or a service or put a product out there and they need people to do that. That game is not over.

[Sheila Kolhatkar]

It has become very geographically, what's the word?

[Andrew McAfee]

Concentrated.

[Sheila Kolhatkar]

Yes. I mean that's it. I mean there was just a study released that was saying that basically your zip code tells you everything you need to know about what your economic fate is and if you're in the wrong place and those innovative types of jobs are not there or all the McDonald's in town have introduced their kiosks and there's nothing else for a service worker to do. That is a big problem.

[Andrew McAfee]

One of the challenges associated with that is a people just don't like to move willy nilly, just chasing jobs all over and B, the evidence is pretty clear that there are fewer moves, that geographic mobility among American workers is going down, not up. One of the, I think clearest lessons from history is that big technology changes. Big surges in technology necessitate big societal adjustments. For example, the last time we had a surge of technology this big was probably 100 years, about 100 years ago when we had the one, two punch of electrification and the internal combustion engine. One of the things we did in response to that was set up universal, compulsory, universal, free K12 education. That was extremely controversial when people first started talking about it. It was going to be too expensive. Some kids were not smart enough to handle a high school education. But we came together and we decided as a country to do this thing. My huge frustration is I just don't even see the will or the desire or the awareness of the need to come together and to do big things.

[Sheila Kolhatkar]

Automation doesn't ever seem to come up in Washington. Members of Congress never talk about it. You never hear the President mention it. It didn't come up at all during the tax debate. Automation isn't the kind of emotional issue that gets voters riled up, like immigration or outsourcing automation just sounds kind of boring. But it's the future. There are a lot of things we could be doing to prepare for it, but the first step we need to take is, is to just acknowledge that it's happening.

[David Remnick]

Sheila Kolhatkar is a staff writer at the New Yorker, and you can find her article welcoming our new robot overlords. That's if you need a little pick me [e-mail] Foreign I'm David Remnick, and I'm here with Fabio Bertoni, who plays a key role as the New Yorker's lawyer. He's deeply involved in our reporting every week and every day, especially our investigative pieces. And he keeps us out of trouble about 20 times a day. And when I start to feel like my job is stressful, I just wander down the hall to Fabio's office and I feel a little bit better. I wanted to find out how he takes his mind off of work on the rare occasions when he's not at work.

[Fabio Bertoni]

So it is Toscanini's 150th anniversary of his birth. There's a new biography of Toscanini.

[David Remnick]

So I'm looking at the book that's here. This is Harvey Sachs's biography of Toscanini. Yes. I'm lifting it up and getting half a hernia.

[Fabio Bertoni]

Yes. It's gigantic.

[David Remnick]

And so what made you dip in?

[Fabio Bertoni]

Well, actually, I was invited to a concert given by musicians from La Scala, and it was just. It's interesting to hear about him. He's anti Fascist. He left Italy, who refused to conduct for Mussolini. He came to New York and became the conductor for NBC. So it's an interesting story.

[David Remnick]

These are the days when the networks.

[Fabio Bertoni]

Had classical music and he had the 30% of the American public listening to classical music on the radio every week.

[David Remnick]

That's amazing.

[Fabio Bertoni]

I just made up that statistic. But it was a big number. So I'm reading this book and I'm watching YouTube videos, and there's one from 1948 of him conducting the Embassy Symphony Orchestra in Beethoven's Symphony Number nine. And he's just very dramatic. He has incredible facial expressions as he conducts. He makes all these gestures. Some of them don't appear to be actually conducting gestures, but they're like Italian hand gestures that I remember from my childhood. There's no.

[Sheila Kolhatkar]

There's a point.

[Fabio Bertoni]

Well, he does shake his head at the musicians a couple times, but there's a point where he touches the side of his nose, which is a gesture that, you know, in my experience means like, be careful here, like, pay attention. So there's this quiet moment here. He touches. It's just a warning. He just wants to put them on notice.

[David Remnick]

He looks fabulous with that salt and pepper mustache and those big arm movements, the flowing white hair. He's fantastic.

[Fabio Bertoni]

Yeah. So my next pick is, I have a friend, he recently had to get rid of his smoker, so he gave his smoker to me. And now I'm burdened with the grave responsibility of learning how to barbecue. And in one of those wonderful fortuitous moments, I discovered on our book table here, a book called Franklin Barbecue in Austin, from Austin, Aaron Franklin of Franklin Barbecue did a book, and it's. And it's a fantastic book. He's got a lot of history of barbecue there, the science of Barbecue. So I am now learning how to barbecue.

[David Remnick]

You know, I tried to get into Franklin's. I got there at 11 o' clock in the morning in Austin, and the line was said to be. It was two hours long. And I now regret it, that I didn't do it, but I. I blew it. So while you're listening to Tuscanini conduct Beethoven, the smoke will slowly be imbuing the meat with all sorts of good things.

[Fabio Bertoni]

Applewood smoke.

[David Remnick]

Apparently you have more for us.

[Fabio Bertoni]

I have one more, and it's Lucia Battisti. And Lucia Battisti was a pop star in Italy in the 70s.

[David Remnick]

Well, I think we should play a little bit of this, but only if you sing over it as if we're riding along in your car.

[Fabio Bertoni]

As your lawyer, I advise you against.

[David Remnick]

All right, well, at least let's hear a little the music.

[Fabio Bertoni]

Yeah. And, and, and, you know, you need to appreciate this completely. Making noises unironic.

[David Remnick]

That's the best way to do it.

[Fabio Bertoni]

It's a beautiful song. Yeah.

[David Remnick]

Got a little synthesizer keyboard there.

[Fabio Bertoni]

And it gets more.

[David Remnick]

Wow, that's a sound that. That has disappeared from the earth a little bit. Here we go. Oh, what I wouldn't pay to hear you sing this in the car.

[Fabio Bertoni]

And it get. It gets loud at the end with.

[David Remnick]

Barbecue sauce all over. Fabio. Thank you very much. Fabio Bertoni, the New Yorker's legal counsel. I'm David Remnick, and you're listening to the New Yorker Radio Hour. Next week on the New Yorker Radio Hour, staff writer John Lee Anderson was granted a rare interview with the president of Venezuela, Nicolas Maduro, who could be on the way to making himself the dictator of Venezuela. What would it take for democracy there to end altogether? It's an essential story and I hope you'll join us.

[Narrator/Announcer]

The New Yorker Radio Hour is a co production of WNYC Studios and the New Yorker. Our theme music was composed and performed by Meryl Garbus of Tune Yards, with additional music by Alexis Quadrado. This episode was produced by Alex Barron, Emily Botin, Ave Carrillo, Rhiannon Corby, Jill Duboff, Karen Frillman, David Krasnow, Sarah Nix, Michael Rayfield, Mythali Rao, and Steven Valentino, with help from Corey Schreppel, Johnny Vince Evans, Eric Malinsky, Emily Mann, and Jessica Henderson. Our story on automation was produced with help from the Frontline Dispatch, a new podcast from Frontline. The New Yorker Radio Hour is supported in part by the Tsarina Endowment Fund.