January 20, 2026 • 43 mins
AI is automating thousands of jobs. But humanity's fear of technology replacing us is nothing new. Journalist and podcaster Jacob Goldstein tells Kal the story of the original Luddites during the early 1800s Industrial Revolution. They talk about how technology changes, how those changes reshape our work, and who historically gets protected and who gets left behind.
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Speaker 1 (00:00):
So like literally a week and a half ago, hadn't
logged down to Zoom for maybe maybe two weeks. Right,
it was around Thanksgiving, and then I was traveling and
I had the Zoom meeting, and I open it, I
click on the link, you know, a minute before it
starts because I'm early, I am professional, And of course
it starts updating itself because it needed a software update.
(00:20):
And I'm frantically texting everybody who I'm supposed to be
meeting with, like blaming Zoom as if it's Zoom's fault. Oh,
of course, now Zoom decides to do a software update.
Speaker 2 (00:28):
Blah blah blah. No, I knew in my heart that this.
Speaker 1 (00:33):
Was a me issue, right, You knew you had a
Zoom meeting log on ten minutes early because it might
need a software update. But that's like how young and
old I am simultaneously, right, sold that I'm trying to
blame Zoom for a thing that's clearly my issue, and
young enough that, like, you know, we all had roommates
who did dial up the porn because that's what the
nineties were like. But as we think about things more
(00:57):
and more in a society, if you want to use
the word progresses. We've all thought about AI as well
as an actor, of course, terrifying the idea that robots
and computers could take all of our work. But it's
not just actors. No matter what you do for a living,
the humanity of your work, the interpersonal skills, the taking
pride in your job, even if that means you're in
a cubicle all day, whatever it is that you're doing.
(01:19):
The fear that maybe technology could replace us is like
a real thing.
Speaker 2 (01:23):
It's not just creepy.
Speaker 1 (01:24):
It feels like it's getting out of control, and it
feels like the heart of the humanity that we all
have is being questioned right now. So wanted to have
a very interesting guest on who could help us sort
through all of that, and that is journalist Jacob Goldstein,
who tells me that we've been here before, and.
Speaker 3 (01:41):
It started in the cloth business.
Speaker 4 (01:43):
So these original a lot ofites were like the first
people really to be impacted in a significant way by
the industrial revolution, the first people to face this thing
that we are talking about that we are afraid of
essentially losing their jobs to technological change.
Speaker 1 (01:58):
In my conversation with Jacob, we're going to go all
the way back to the eighteen hundreds, to the original Luddites,
and I want to hear about their story. And we're
going to talk about how technology changes, how those changes
reshape our work, and then frankly, who historically gets protected
and who gets left behind?
Speaker 2 (02:16):
Here we go again again again.
Speaker 1 (02:21):
Okay, Hey, I'm cal Pen and this is here we
Go again, a show that takes today's trends and headlines and.
Speaker 2 (02:28):
Asks why does history keep repeating itself?
Speaker 3 (02:31):
Here we go?
Speaker 1 (02:47):
Hey, good morning, good morning, how you doing. Thanks for
today I'm speaking with Hi.
Speaker 3 (02:53):
I'm Jacob Goldstein.
Speaker 1 (02:54):
He was a previous host on the NPR podcast Planet Money,
so if you recognize his voice maybe from that. He's
the current host of the podcast What's Your Problem and
Business History, and he's the author of the book Money,
The True Story of a Made Up Thing. Before we
fully dive in, I was very excited to talk to
you about all these topics because a couple of reasons.
(03:16):
I generally know very little about technology. I'm forty eight,
so as my college friend who does work in tech,
always reminds me like, we're that generation that grew up
fully analog, but we're still kids when the digital wireless
world kind of came around, and so it's rare, that
rarity of knowing how to use both. But I'm now
getting old enough from like I just want to rotary phone,
(03:37):
like when I was nine. So since you are smart
and thoughtful and very knowledgeable about all of this, just
the nerd part of me was really excited to talk
to you. When did you know you wanted to be
a journalist and focus on the things that you do.
Speaker 4 (03:52):
So I became a journalist in my late twenties. I
was a newspaper reporter, and it was around the time
of the two thousand and eight financial crisis. I was
a reporter covering healthcare at the Wall Street Journal, and
like everybody else, I wanted to understand what was going on.
(04:13):
It was confusing to me, you know, I knew about healthcare,
but even though I was at the journal, I wasn't
covering finance.
Speaker 3 (04:18):
And I have an aunt who.
Speaker 4 (04:19):
Was an MBA who is like my go to kind
of business and money person when I don't understand things,
and you know, there had been this collapse obviously of
the stock market and of real estate prices, and I
asked her, like it seemed like people were talking about
like a trillion dollars disappeared from the stock market, right,
So I had this maybe dumb, maybe not question, where
(04:40):
did the money go?
Speaker 2 (04:41):
Yeah, that's not dumb, that would be my question.
Speaker 3 (04:44):
And she said it wasn't there in the first place.
Money is fiction. Oh yeah, And I was like, ooh fiction.
I had been an English major, I knew about fiction.
I was like, this is interesting to me. And that
was sort of the gateway.
Speaker 4 (04:57):
And so not long after that, I went to work
for this podcast called Planet Money. It's like covering the
economy but in a very sort of narrative storytelling way,
and through that I got into kind of the history
of money and the history of economics and the stuff
we're going to.
Speaker 3 (05:11):
Talk about today.
Speaker 2 (05:12):
Amazing.
Speaker 1 (05:12):
I had an AHA moment like that when I was
hosting a show just before COVID terrible title called This
Giant Beast that is the Global Economy for Amazon Prime.
And I had a great time working on it, but
that was, you know, I was the host through which
the audience gets to experience things, so I knew very
little going in and my mind was blown. It sounds
like not dissimilar to your two thousand and eight phone call. Yeah,
(05:34):
as a journalist, I'm going to jump ahead a little
bit to just sort of what people are thinking about today.
And I'll preface it by saying, as an actor and
a writer, we just came off of two very protracted
labor disputes where we were forced to go on strike
by these big media companies in large part because of
AI proposals and provisions. As a journalist, are you at
(05:57):
the place where you're thinking through or or are you
afraid that technology is going to replace people in your
profession or has it already?
Speaker 3 (06:07):
Yes?
Speaker 4 (06:07):
And yes, I mean if you look at employment in
newsrooms over the past twenty years, which is to say
my career, it has gone down and down and down
because of technological change. Right, It's not exactly AI doing reporters' jobs.
It's people substituting away from newspapers. But yes, looking forward,
(06:30):
I am worried that AI will be able to do
what I do. And fortunately I'm not that young and
I don't have to make it that much longer. But
like I would caution someone starting their career of trying
to be a journalist in the traditional sense, right, I mean,
perhaps interesting question is, like what is the combination, right, Like,
(06:54):
we'll AI take our jobs is a valid question, But
in the meantime, how can we use AI to do
our jobs better? Is perhaps a more practical question.
Speaker 2 (07:04):
And how do you feel like you're able to use
AI to make your job better?
Speaker 4 (07:09):
I mean it helps me with research, is the short answer.
And I will say there's an interesting cultural thing, certainly
among journalists and writers. I suspect it's not as much
in other fields. But because large language models are writing,
they feel like a direct threat to you know, journalists
in a way that they perhaps do not to people
in other fields. So I don't know, there's a feeling
(07:30):
among journalists that using AI is cheating.
Speaker 2 (07:34):
Right.
Speaker 4 (07:34):
People will sort of abashedly admit to using AI, and
I think that's unhealthy, right. I think using AI is
like using the Internet, which by the way, is like
you know, Google searches are driven by AI, of course.
So for example, I host this show called Business History,
and just this morning I took a script that one
of my colleagues had written that was like a great narrative,
but I felt like it could use some big ideas,
(07:56):
and I put it into a large language model and said, like,
what are some big I can ideas that you think
sort of emerge from this, what are some themes? And
it's not like I copied and pasted the answer. It
suggested some ideas, and I went and researched those ideas, right,
So that's an example.
Speaker 1 (08:10):
So when you research the ideas after the prompt, are
you researching that outside of the AI models? And here's
basically what I'm getting at is like, how as a journalist,
how do you make sure that the sources themselves are accurate?
Knowing that, however, AI has learned that may not be
whatever the inputs were may not be a real thing.
So how do you fact check that when you take
(08:31):
the next step in research?
Speaker 4 (08:33):
I mean that part is the same as always, right,
Like I don't I never take the answer from the
AI as a reliable answer. I go and I look
it up elsewhere, and I look at the source and
I evaluate the credibility of the source material. And you know,
certainly ayes hallucinate some, but like overall, it's useful, and
(08:53):
like once in a while it's like, oh, that doesn't
make sense, but quite often it's like, oh, that's a
good idea. And like just this morning there was like
particular monetary policy shift in Germany and the nineteen forties
that it pointed me to that was in fact real
and interesting in the history of the Volkswagen.
Speaker 3 (09:09):
Beetle, which is the story we're working on.
Speaker 1 (09:10):
But okay, I want to go back a little bit
to history because the same friend who I mentioned, the
tech savvy college friend, often likes to tell me that
I'm a Luddite.
Speaker 2 (09:21):
And I don't think he's necessarily wrong.
Speaker 1 (09:22):
The way that we throw around the word today technology
doesn't necessarily get me as geeked as some of my friends,
although although I'll obviously have my moments. And so just
in looking back, right, I get that people have always
had this fear that newer technology is going to replace
their jobs. So what you've talked about is that we've
been here before, and all this palpable fear started back
(09:43):
in eighteen hundreds, and so can you take us to
the eighteen hundreds and tell us what happened, Like, take
us back to that moment in history for somebody who
doesn't know anything.
Speaker 2 (09:51):
About the Luodites.
Speaker 4 (09:53):
Yeah, so the original Luddites were quite different. Just to start, then,
the word we have to the word Luddite today. I mean,
it's just like somebody who doesn't like technology because they
don't like it. Right, So, the original Ludites were cloth
workers in England in the first part of the eighteen hundreds,
like eighteen eleven, eighteen twelve. Around that time, and for
(10:15):
essentially all of human history, there was very little technological change.
Right this world do we live in where you just
assume that technology changes generation after generation, that things get
more efficient. That was not the nature of the world
until the Industrial Revolution, which started in the second part
of the seventeen hundreds in England, and it started in
(10:35):
the cloth.
Speaker 3 (10:36):
Business, right.
Speaker 4 (10:37):
So these original Lutdites were like the first people really
to be impacted in a significant way by the Industrial Revolution,
the first people to face this thing that we are
talking about that we were afraid of, essentially losing their
jobs to technological change. The cloth business was actually a
huge business for England at the time, and the.
Speaker 3 (10:56):
Luddites were skilled artisans. Right.
Speaker 4 (11:00):
We think of, you know, factory work as terrible in
the eighteen hundreds, and it was, but before the Industrial Revolution,
it was like something you did kind of in your home.
It was farmed out and so you know, there were
all these steps to making fabric, and different people specialized
in different parts of it.
Speaker 3 (11:17):
So the croppers would take a rough piece of.
Speaker 4 (11:20):
Fabric and they would have these giant shears, like kind
of giant metal scissors, and they would crop that. I
guess the nap I don't know about fabric. They'd crop
something off.
Speaker 3 (11:30):
The wool to like make it smooth and nice.
Speaker 4 (11:32):
Okay, So that was their job, and for the time
it was a pretty good job. Like they worked for themselves.
They set their own hours, very poor by the standards
of today, which was an important point, but in relative
terms at the time they were doing well. And then
along came the industrial Revolution, which started out as machines
(11:52):
to make cloth. Right, So somebody invented a thing to
spin a raw fiber into threadbody invented a loom, and
then somebody invented a shearing frame, right, a machine to crop,
a machine to do what the croppers had done. Right,
And so this is the thing we are talking about.
This is AI taking our job, but for the croppers,
(12:14):
but for the skilled clothmakers.
Speaker 1 (12:16):
I'm curious the all those inventions. What's the do you
know the range of time? Like did this all happen
in a year or was it over a period of
like fifty years.
Speaker 3 (12:24):
Decades? The order of magnitude is decades.
Speaker 2 (12:26):
So slower than slower than right now. Well maybe maybe not.
Speaker 4 (12:31):
I think it didn't feel that way if you were
a cropper right like, there wasn't the spread of information
like there is today. They didn't necessarily you know, if
you were a cropper in eighteen hundred, you didn't know
that somebody was going to invent a shearing frame and
that it was going to show up.
Speaker 1 (12:46):
You know.
Speaker 3 (12:46):
I think it came as a surprise.
Speaker 2 (12:49):
And once it was there, there's a guy named Ned Lud.
Speaker 3 (12:52):
Yes, so what starts happening? Does one curse on this show?
Speaker 2 (12:57):
Oh? Yes, please feel free.
Speaker 4 (12:58):
So these people who have these good jobs, who have
these skills, see the machines coming and taking their jobs,
and they essentially think like fuck this, like no, like
we're not gonna do it this way. And so what
they start doing is going in the middle of the
night and attacking these new machines like literally physically, like breaking.
Speaker 3 (13:18):
Them with sledgehammers.
Speaker 4 (13:19):
It's like, oh, you're gonna do this with the machine. No,
I'm going to break your machine with a sledgehammer. And
then you can come back to me and I'll keep cropping.
Speaker 3 (13:26):
And in I.
Speaker 4 (13:27):
Believe it's eighteen eleven, it starts to get more organized.
So these have been just kind of random, ad hoc
kind of what we would call today, like maybe mob.
Speaker 3 (13:35):
Sure it might be a word people would use today.
Speaker 4 (13:37):
But in eighteen eleven it starts to feel more organized,
and there start to be these letters from this self
titled General General ned Lud, And he's actually holed up
in Sherwood forest, like Robin Hood, who has kind of
similar vibes.
Speaker 3 (13:56):
Right, and these letters are.
Speaker 4 (13:59):
Taking on the tones of Civil war frankly, right. He
has this title General ned Lud and at one point
they call his his Army of Redressers, right, like a
redress of grievances. The main thing you need to know
about ned Lud there was no ned Lud he was.
He was a myth.
Speaker 3 (14:17):
He was a myth. He was like Robinhood. He was
like Robin Hood.
Speaker 4 (14:19):
I mean, there may actually have been a guy named
ned Lud decades earlier who was like a framebreaker, but
like this General ned Lud, this guy leading the revolt,
he was an invention, which is kind of genius, right, like, yes,
it's a genius way for a people with no political power,
for a group of people with no political power to.
Speaker 3 (14:38):
Create a movement. Right, you invent a figurehead, a mythical
general hold up in the forest who does not exist.
Speaker 4 (14:46):
And so it's this idea that the workers are not
just randomly breaking machines, they're organizing to fight back. And
one thing that's interesting, there's this historian who has called
what they were doing collective bargaining by riot because there
were no unions, Like they couldn't even vote, right, there
(15:07):
wasn't a mass suffrage in England at the time. There
were certainly no units. They basically didn't have power in
any organized way. So they were seizing power and it's
this collective kind of.
Speaker 3 (15:17):
Ad hoc way.
Speaker 4 (15:18):
And these attacks get more systematic. There's this one, particularly
dramatic one where these guys they all mass at this
bar and they're going to attack this mill.
Speaker 3 (15:33):
But the mill owner knows.
Speaker 4 (15:34):
Like the Luddites that you know, have been attacking around
this region, He's been preparing to defend himself. So the
owner is actually like sleeping there. He's hired some people
with rifles to defend the factory. He actually has like
a that of I think sulfuric acid.
Speaker 3 (15:51):
He's going to like pour down on them.
Speaker 4 (15:52):
Yes, it's very medieval, right, it feels very like medieval castle.
He's made this factory like a fortress, and so the
Ludights march on it is it is very it's like
proto civil war, right. This is an armed organized attack
on a heavily defended factory. And they get in and
there's like actually an exchange of gunfire, and ultimately they retreat.
(16:13):
So there's two Ledites dead. They have failed in their attack.
And around this time Parliament, you know, the British government
realizes that this is getting out of hand, and they
have passed a law making attacking machines punishable by death.
Speaker 2 (16:31):
Amazing, I am amazed.
Speaker 1 (16:33):
It was almost ninety nine percent confident that this would
not go in the favor.
Speaker 2 (16:37):
Of the people.
Speaker 1 (16:39):
Tell me something about British history that I didn't already know. Okay,
go ahead.
Speaker 4 (16:44):
So after this attack where two Ledites are killed, there's
like this round up. Essentially, the government fights back. A
bunch of Leedits are arrested, throwing in jail their triede
and several of them are in fact given death sentences,
and they are publicly hung. They actually make the gallows
twice as high as usual so that you can see
(17:04):
it from even farther away. You know, there's like, you know,
a crowd of people witnessing the hanging. And this basically
defeats the Luddites, right, Like, this is basically the Luddites lose. Right,
the government rounds them up and kills them, and they
stop attacking the machines, and there are no more rich croppers,
(17:25):
or they were never rich, but there are no more
like relatively well off croppers after this, Right, It's just
the machines are, in fact, a better cheaper way to
make cloth. The Ludites don't have any political power and
they are out of luck.
Speaker 1 (17:45):
That all makes me wonder, like if you put the
Luddite story next to conversations that we're having about AI today,
in what ways do you think that they're similar and
how are they different? And the one stat that I
remember when we were researching for this that came out
up is like Goldman said that by twenty thirty, AI
could replace the equivalent of three hundred million full time jobs.
(18:08):
Forbes said it would replace two million manufacturing jobs by.
Speaker 2 (18:12):
The end of next year alone.
Speaker 1 (18:15):
And so when I when that Light eight story especially,
it's got everything, It's got public policy, it has a clear,
very simple explanation of who really wins in the short
term and capitalism, the power dynamics, all of that. So
then that just makes me wonder if you put that
story next to today's AI conversations, like, how do you
see them being similar or different?
Speaker 4 (18:36):
Yeah, I mean, certainly workers being potentially replaced by machines
is in fact similar like that in some places you know,
call centers.
Speaker 3 (18:50):
It's already happening, clearly. I Mean.
Speaker 4 (18:52):
One thing that I think is an important difference is
political economy, right Like it's and for people to say, oh,
ordinary people have no power in today's economy, it's all
the rich people. And like, certainly rich people have a
lot of power, but relative to the Luddites, ordinary people
do have more power today, right Like unions were illegal,
(19:13):
the Luddites literally could not vote, right, And so it
will be interesting to see who is losing jobs to
AI and when and how politics and the government respond, right,
And it's an interesting moment now because for a long time,
(19:34):
technological change threatened lower skilled workers, right like twentieth century
automation hollowed out.
Speaker 3 (19:42):
The middle to a significant degree.
Speaker 4 (19:43):
Right, There was this phrase, the hollowing out of the middle,
where like physical labor actually for a while did okay,
and if you were sort of highly educated you did okay,
but if you were kind of in the middle of
the distribution, it was bad for you. Now it's kind
of across the board, and strikingly, people like lawyers and
journalists are threatened. And so those are people who traditionally
(20:05):
have had more political influence, perhaps, right, And so I
think how will the government respond? Is super interesting and
super unclear.
Speaker 1 (20:15):
Yeah, do you have a sense of which other types
of jobs are projected to be affected? I mean the
manufacturing jobs obviously that's well documented and also follows a
pattern of technological change throughout history.
Speaker 2 (20:28):
I think you're right, the.
Speaker 1 (20:29):
Doctor lawyer thing is relatively new, jarring for a whole
different economic class of people. What are the ones we're
not thinking about?
Speaker 3 (20:38):
I don't know. I mean, I'm wary of making.
Speaker 4 (20:42):
Like AI is insane right now, Like it is like
I don't you know, there is one thread of the discourse,
and maybe this has died down. I don't know that
is like, Oh, it's just hype from AI companies when
A companies are like AI is going to be crazy,
Like I don't think that is true. It is interesting
to think about where are the bottlenecks, right, I do
(21:03):
think there might be bottlenecks in adoption, right, Like in
a superficial.
Speaker 3 (21:08):
Way, AI looks really good, but when you actually try
and get it to do stuff, it's kind of a
pain in the ass.
Speaker 4 (21:16):
And like there's this interesting guy, Dwarkish Patel, I believe
who he writes about AI and he interviews a lot
of the really smart AI people, and he made the
point that he's been trying to use it for his
own work, but that it's not good at like learning incrementally,
Like it can do an okay job, It can do
a five out of ten job, But a human being
(21:38):
that starts out a five out of ten at your company,
you can kind of get them up to eight out
of ten, and getting the AI to eight out of
ten getting it to learn on a particular task is
actually still doesn't work. And like getting an AI to
actually do a job for you is still hard. And
so I don't know, I really don't know how it's
going to roll out, and I don't know what the
(21:59):
political responsible base.
Speaker 1 (22:01):
Do you feel like it's too early to speculate what
the differences are compared to previous technological changes or advances.
Speaker 4 (22:12):
I mean, I think one thing that is important to
remember is, at least so far, every time people have
lost jobs to technology, new jobs that people couldn't.
Speaker 3 (22:27):
Imagine before emerged, right, So.
Speaker 4 (22:30):
Like some number of people are displayed, some number of
people are worse off in the short.
Speaker 3 (22:36):
To medium and sometimes long term, but always so far
there have been more new jobs.
Speaker 4 (22:42):
People's wants and willingness to pay for things is insatiable,
and I suppose this may be the end of that,
but I wouldn't bet against that, you know. I mean,
I feel a little silly that making podcasts is my job, right,
trust me?
Speaker 2 (22:57):
It is security I have about my jobs.
Speaker 4 (22:59):
Yes, I understated, and that's because you know, we have
all these machines that make really cheap clothes and cheap
food and all of the basic things. You know, Like
if you just look at farming, right, like in eighteen
hundred something, they have rough numbers. Eighteen hundred and ninety
percent of Americans were farmers in nineteen hundred and fifty percent.
Speaker 3 (23:18):
Of Americans were farmers. Ish, maybe forty.
Speaker 4 (23:20):
Two thousand two percent of Americans were farmers, right, maybe
the greatest displacement of labor you can think of because
of the reaper and the tractor. Right, But like everybody
went to working factories and then they went to work
as you know, personal trainers and bank tellers and other things.
Speaker 1 (23:35):
It also makes me wonder like the the implications up
to your point about like maybe there is no next
widespread job thing that people have, Like what do you
retrain for when it's just a computer taking everything? And
I wonder like in terms of the TBD for what
government does, if it's even government that does this is
(23:57):
like just taking the US. We live in a country
that can barely agree on the federal level that we
should have just the basic social safety net in place, right,
compare us to other industrialized, civilized countries. And so then
jump to you know, well, what about universal basic income
and people who are really touting universal basic income? And
(24:20):
if you look at today's political climate, in what fucking
reality are you going to get these two generally right
of center, if you're looking at global standards, two political
parties to agree that UBI is going to be a
thing that we can do. And I don't mean to
be a cynic about it, but it concerns me greatly
that our politics in the democracy are designed to move slowly.
Speaker 2 (24:40):
That's a given.
Speaker 1 (24:42):
But if the technological displacement of jobs is moving faster
than our public policy, what happens.
Speaker 4 (24:49):
I mean, that's definitely a possible bad outcome. I mean,
the short answer to what happens. So what you're imagining
is like lots of people lose their jobs because of
technological change, and the government doesn't too very much to help
you because we have more truly.
Speaker 1 (25:01):
Darius benefiting from all this, and they just don't want
to give up their money.
Speaker 4 (25:05):
It's possible, I mean, I mean, obviously it's the case
that you know, Northern European countries have much more robust
safety nets than we do. It's also the case that
like Medicare and social security are politically totally untouchable and
have broad support, right, and so like maybe there's like incrementalism, Right,
maybe you can get social security and medicare ten years earlier. Right,
(25:27):
Like that seems like a plausible outcome. It is, in
fact interesting that the people talking the most about universal
basic income. Are AI people and tech people who actually
think this might happen. Right, you know, there is this
essay that John Mayer Kines, you know, of Kynesian economics
fame wrote in nineteen thirty, I think called economic Possibilities
(25:51):
for our Grandchildren, and I think that was actually where
the term technological unemployment was first used. Right, so, you
know thirty, right, the world's going into the depression, but
he's thinking generations ahead, and he's thinking about technological change
and robots taking our jobs as we would sort of
colloquially say today, and he's imagining, like, well, what if
(26:14):
our grandchildren are just working fifteen hours a week. He's like,
first of all, it's hard not to work at all, right,
we're sort of wired to want to do something, but
maybe you don't have to work that much. And people
sort of a look at fifteen hours a week now
and laugh like, oh haha, how charmingly wrong he is.
But I was looking as I was thinking about this interview,
and if you look at hours a week, it was
like sixty hours a week in nineteen hundred and fifty
(26:38):
hours a week in nineteen thirty when he wrote that,
we sort of plateaued at forty in this country, but
in the Netherlands they're like down to thirty two hours
a week now, like all these days off with all
the right, with a lot of same mondays thrown in
in the MiGs. And so I don't know, like, yes,
there are many if we think of possibility space, there
are definitely bad outcomes. I'm not entirely pessimistic, like, look,
(27:03):
it is the case that there is a progress aspect
to this, right, Like, yes, a lot of things could
be bad, and a lot of things can go wrong,
and maybe we're not in the best possible timeline. Certainly
we're not in the best possible timeline.
Speaker 2 (27:13):
But like.
Speaker 4 (27:15):
Having machines do stuff instead of people, it does mean
there is more abundance, right, And so there is this
problem that you're pointing to of like sharing the abundance,
making sure that it doesn't all go to six people, right,
which is a real thing to worry about.
Speaker 3 (27:29):
But there will be more to go around, I guess
if it's.
Speaker 1 (27:33):
Structured, right, yes, And you know I am not I'm
not such a louttite that I don't acknowledge that we
you know, the advances in medical research and science and
exploration and astronomy, and all of those things are are
going to be incredible. I was just talking with a
doctor who I ran into last night, who was touting
how exciting it is in her field for AI research
(27:56):
in medicine, and I have the exact opposite viewpoint as
an artist.
Speaker 4 (27:59):
Right, let me ask you a question. I've been curious,
do you ever use AI for work?
Speaker 1 (28:04):
I have tried because I was curious, and thank god,
AI does a piss poor job at writing jokes. I'm
sure one day it will learn. Part of that, obviously,
is that comedy is so subjective.
Speaker 2 (28:18):
But no.
Speaker 1 (28:19):
In fact, what I've used it for and what I
find to be helpful is that as a writer an actor,
my brain is usually very scattered. Like you know, people
like me think of the ten things that you're not
supposed to say out loud, but like that's constantly in
our brain, and so you're you always have a filter
on your brain depending on the setting you're in. Like
(28:40):
if I'm on stage doing a stand up routine, that
filter is off and generally it works and sometimes it
will get you in trouble. But in normal day to
day interactions, there's like this weird filter you have to
put on. And so when I'm writing a what by
normal people's standards would be like a professional document or
a professional email or a professional text, my mind has
(29:01):
nine paragraphs of things to describe what I want to say,
and I just need two sentences. So I have found
that something like that is a helpful tool, but I
have not found that in my actual professional life that
there's there's anything that I have that I've benefited from.
That said, I know it's all coming.
Speaker 3 (29:19):
So you're saying it's good for taking all the creativity
out of your.
Speaker 2 (29:23):
Life so far.
Speaker 3 (29:23):
Yeah, Anti, yeah, yea, yeah.
Speaker 2 (29:25):
That's been my experience.
Speaker 1 (29:26):
But again, the you know, I read about all of
the things that AI companies are working on to replace actors,
to clone our voice, our performance is coming up with
just completely new actors slash characters or personalities. So those
are the things that I don't you know that I
hope won't take my job. Like you you said this earlier,
I am of a certain age where like I'd love
(29:48):
a good twenty to twenty five more years in my career.
But man, I'm glad I'm not eighteen going to drama school.
It's a it's a whole different ballgame.
Speaker 4 (29:57):
I mean, I feel like if you're eight and going
to drama school, you've got to think of using the tools, right,
Like that a classic technology thing is you don't want
to be competing against the tool the technology. You want
to be using the technology. And you've seen that. I
mean to get back to the sort of historical arc,
like that was a thing that happened in factories, right,
(30:18):
Like US factories in many instances are high tech. Right,
There's like CNC machining was that computer Numerical control machining.
Speaker 3 (30:25):
Like they're sort of like tech jobs for kind of.
Speaker 4 (30:29):
Skilled workers, right, And even like when I worked at NPR,
Like there were the engineers in the studio who were,
you know, running the board, but then there was the
guy who was the manager of those guys, who was
like very much like unengineer at heart, and he loved
doing the stuff, but he was also like building the
systems and like running the servers and like he was
(30:49):
the guy who figured out how the reporters could self up,
could record ourselves, right, And so that's the guy you
want to be, right, You want to be the guy
using the tools. And like I haven't given up on that, Like,
in the long run, I assume AI will be better
at doing everything that I do than I am, but
hopefully that'll take a while. There's some number of people
(31:09):
who are like used to hearing me and like I
can sort of use AI to maybe work faster or
be smarter, right to do to figure out in five
minutes what it would have taken me an hour of
Google searching to figure out, and obviously still vet it,
but like, for the medium term, that's what I'm banking on,
and I do think that's like a healthier relationship to
(31:29):
technology in general.
Speaker 1 (31:37):
I was invited to an AI exhibit recently, an AI
art exhibit I'm putting art in air quotes by a
friend who is a tech bro, and he's like, hey,
I've been working for the last eighteen months on these
art pieces, these AI art pieces. Will you come to
the gallery opening? And I'm like, I love you. There
is nothing that I would rather do less on a
Tuesday night than come and see the product of you
(32:01):
getting high in front of your laptop and pressing buttons.
Speaker 3 (32:05):
No, sorry, man, I wanted that I'd stay home.
Speaker 2 (32:08):
I would stay home and do it myself. But yeah,
I'm not a fan of that.
Speaker 1 (32:14):
I don't want to get too off drag and I
want I want to ask a comparison question about the
Ludites and today. In the lud eight era, you write
that productivity went way up, wages for regular workers barely
moved for decades. That's something that we're obviously used to.
You see that time and time again. You also talked
about how the generations after the Ludites benefited from the
(32:34):
machines themselves that replace them.
Speaker 2 (32:36):
So I'm curious, like, what are the big.
Speaker 1 (32:38):
Lessons from that period about who benefits from new technology?
What do the people who are in the generation that
are directly impacted by machines supposed to do. I know
we talked about that a little bit, but all that's
leading to right now with AI knowing how much diversity
of opinion there is, how much panic and excitement there is,
what should our outlook be.
Speaker 3 (33:00):
That's super hard.
Speaker 4 (33:02):
I mean, you know, the I think the closest thing
in recent times that comes to my mind is workers
who've lost their job to foreign competition. Right Like, in
the kind of early part of the aughts, there was
what's come to be called the China Shock, which was
you know, China entered the World Trade Organization in like
two thousand. It's funny not that long ago, right, China
(33:25):
used to be a super poor country, was not a
major competitor, and then bam they entered the WTO. And
there were places in the United States that were making
things that competed with Chinese imports, like clothes, and they
got obliterated.
Speaker 3 (33:42):
The United States as a whole did well. And I
mean ordinary people, right, Like, it is the case that like.
Speaker 4 (33:50):
For working people, when clothes get cheaper, that is good, right.
It means they have more money in their pocket, more
money to spend on basic things. And I think it's
easy to overlook that part. But the people and the
towns that were competing against China were worse off, right,
So overall everybody was better off, And it's important to
say that, Like, it wasn't just rich.
Speaker 3 (34:09):
People who got better off.
Speaker 4 (34:10):
It wasn't just the owners of capital, right, And I
think that is likely to be true here as well. Right,
it is a competitive world, and I do think that
there is a universe where overall people are better off
from AI. The really hard question is how do you
help people who are clearly losing their jobs because of AI, Right, Like, well,
give them money seems like a good part of an
(34:31):
answer to me, Will it happen politically? I don't know
for the reasons you said, but if I were waving
a wand give them money in a way, though it
seems like I don't want to say the easy part
because it would be great if it happened politically it
would be hard. But there is this more complicated thing
for everyone, right, and Cain's talked about it one hundred
years ago. It's like work is meaning for a lot
(34:52):
of people, right, Work is purpose is just an organiz
principle in life. And so if we project forward, I
don't know what's going to happen. I don't think all
jobs are going to be gone in five years. Maybe
I'll be wrong, but I doubt that. But if in
fact lots of people are losing their jobs because of AI, yes,
please give them money. Of course there will be more
(35:14):
money to go around. There should be money for those people.
But like, what do we do about everything else? How
do we like help them find meaning in their life?
Speaker 2 (35:22):
Like that?
Speaker 4 (35:23):
It feels handwavy to me, but like might be my
own problem in three years, right, Like yeah, my identity
is my work to some significant area, and like a
technology might put me out of a job. You know,
it's easier for me to talk about now that it's me.
I don't just sound like some asshole talking about other
people in a condescending way, like this is me, Like truly,
I could put me out of a job before I
want to be out of a job, and like it
(35:45):
will be hard in many ways if that happens. Not
just the money.
Speaker 1 (35:49):
I worry that not just in the interim, but when
we're when we're dead the next you know, the next
eighty years or eighty years.
Speaker 2 (35:56):
Down the line.
Speaker 1 (35:57):
Yeah, if you have new new classes of people that
are so completely diametrically like they don't mix. You've got
this AI job class of folks who are working. And
then if there's even success at something like UBI, people
who have been pushed out, who are then economically locked,
whose children are economically locked in a particular scenario, does
(36:20):
that breed a type of class resentment that's even more
extreme than what we see today. You know, you see
scapegoating of immigrants. The fear that I have about how
that could be exacerbated without the right public policy. Once
the people who have lived it have died off is
also very scary.
Speaker 2 (36:39):
I don't have to worry.
Speaker 1 (36:39):
About that because I'll be dead, but it is something
that I think, you know, that worries me. My question
for you is literally the exact opposite, because I hate
wrapping up on a doomsday scenario. I think it's very
easy to do. I'm not a rage beaita. When you
imagine the next ten or twenty years, what are kind
(37:00):
of the most realistic ways that technology might change our
jobs that don't fit into either a doomsday scenario or
a utopian story.
Speaker 3 (37:11):
Yeah, that's nice. So like it kind of the middle
of the probability distribution.
Speaker 1 (37:15):
Right, Like, if I took AI to tell me to
take the emotion out of that question, that's what it
would have given me.
Speaker 3 (37:21):
Yeah.
Speaker 4 (37:22):
No, it's interesting to think about, right, because it's more subtle,
So people, it doesn't make so much of a story. Okay,
so let's think about that. So one thing, one thing
in that story is the capability for AI really to
actually do people's jobs.
Speaker 3 (37:39):
It emerges rather slowly.
Speaker 4 (37:41):
Right, It is constrained for reasons partly technological but partly
also institutional, Like companies have all this sort of tacit information, right,
Like people at companies know how to do all these things,
but they never wrote it down right, and so AI
can't just know that like, oh you got to call
John and accounts receivable when this happens, like that kind
of thing. So it's slow, right, That's one important thing,
(38:04):
is like if you can see the transition coming, if
you know that it's not tomorrow, like you know, kids
don't become a journalist if you're twenty maybe, or if
you do, learn how to do it with AI, because
it's going to change.
Speaker 3 (38:17):
So it's slow, right.
Speaker 4 (38:19):
Two, So the extent happens, it will increase output, right, like,
it will make us more productive and more efficient, and
like at a basic level that is.
Speaker 3 (38:29):
Good and I think it is underappreciated. Right.
Speaker 4 (38:32):
It will increase material abundance. It will help us whatever
you want, more clean energy, certainly we can get better
at doing clean energy, get better at doing battery storage
of clean energy, better medicine, Like there are happy things
that will happen. The economic policy in the government is
a complicated one. I mean, if you want more redistribution,
like when there is more wealth, you can generally have
(38:52):
more redistribution right, you could the people are having crazy
capital gains windfalls, perhaps you could raise the capital gains
tax and use that to help the people who are
being put out of work, or help people go on
Medicare at fifty five instead.
Speaker 3 (39:03):
Of sixty five. Right, That would be a very popular
political program.
Speaker 4 (39:07):
Still, you will have people who are losing their jobs
because of AI, who, because of the political realities, may
well be worse off financially, who even if they are
better off financially, lose a sense of meaning in their life.
Speaker 3 (39:21):
Probably.
Speaker 4 (39:22):
You know, often jobs and job types are clustered, so
it won't be just an individual losing their job. You
will have communities impacted, you know, communities having this economic.
Speaker 3 (39:33):
Disaster. Perhaps if it's bad, right, like like with the.
Speaker 4 (39:36):
China shock, and so, you know, you might see this
kind of heterogeneous outcome. And I don't want to make
it as simple as rich and poor. I don't want
to make it as simple as like the trillionaires get
more trillions than everybody else is screwed. I think, in fact,
a likely outcome is more subtle than that and more varied.
And there are some people who are working class, middle
class who learn how to use AI who get more
(39:57):
money because they're being more productive.
Speaker 3 (40:00):
Others who are totally screwed.
Speaker 1 (40:02):
Then final question for you, when, because we opened with this,
when you look at this long history of people who
have been afraid that machines would take their jobs, and
many cases where they did, what still worries you when
you look ahead? And what gives you the most hope?
Speaker 4 (40:17):
So I guess what still worries me when I look
ahead is the rate of change of AI because when
you look at these instances and the alternatives, right, so,
like the Ludites are this very dramatic story where these
people who had a decent life lost their jobs and
machines and their lives got worse. On a much vaster
scale was the automation of farm work, right, that was
(40:40):
like almost everybody worked on a farm, and then almost
nobody works on a farm now. But there wasn't really
I mean, you know, farmers organized politically in various ways,
and they wanted different policies. But you never had a
kind of lud Eite moment for farmers, in part because
it was gradual, in part because you had industrialization alongside it,
so people could leave the farms and go work in
a factory. So it's if it's really sudden, if a
(41:04):
huge amount of people lose their jobs really fast, like
that just feels super politically dangerous and unstable, right, Like
it could be violent, it could be bad. I mean,
the hopeful thing fundamentally to me is that people have
been worried about technological unemployment for two hundred years, and
indeed pockets of people have really suffered from it for
a long periods of time. But like today, the employment
(41:27):
to population ratio, the share of working age people.
Speaker 3 (41:30):
With jobs is like as high.
Speaker 4 (41:32):
As it has ever been, right, much higher than it
was fifty years ago, when far fewer women were working,
despite incredible amounts of innovation, technological change, you know, technological
job laws. So like, I think we really do underrate
the extent to which we are good at coming up
with new jobs. Jobs we cannot imagine today, And like
(41:53):
I actually think that'll happen in the long run. It'll
be maybe embodied things, you know, like maybe I'll go,
I don't know, be a meditation.
Speaker 3 (41:59):
In instructor or something. And yes, you could have aib
a meditation instructor.
Speaker 4 (42:02):
But there are some things I think people who will
be richer overall will pay for a human being to do.
Speaker 3 (42:09):
I just don't know what those things will.
Speaker 1 (42:10):
Be, So forget podcasting. We will have other jobs that
we just can't even think of right now.
Speaker 4 (42:16):
I believe that we you and I I don't know,
but people will have jobs, our grandkids.
Speaker 3 (42:22):
Yeah, I mean it could happen to me. Yeah, it
will be super interesting.
Speaker 2 (42:27):
Yeah, for sure. What a time to be alive to
witness it.
Speaker 3 (42:30):
You know, what a fucking time to be alive.
Speaker 1 (42:34):
I was journalist Jacob Goldstein to hear more of what's
in Jacob's brain, Listen to his podcast What's Your Problem
and Business History, and read his book Money, The True
Story of a Made Up Thing. Here we go again
as a production of iHeart Podcasts and Snafu Media in
association with New Metric Media. Our executive producers are me
(42:58):
kalpen at Helm's, Mike fa Alissa Martino, Andy Kim, Pat Kelly,
Chris Kelly, and Dylan Fagan. Meghan tan is our producer
and writer. Dave Shumka is our producer and editor. Our
consulting producer is Romin Borsolino. Tory Smith is our associate producer.
Theme music by Chris Kelly logo by Matt Gosson. Legal
(43:18):
review from Daniel Welsh, Caroline Johnson, and Meghan Halson. Special
thanks to Glenn Bassner, Isaac Dunham, Adam Horn, Lane Klein,
and everyone at iHeart Podcasts, but especially Will Pearson, Carrie
Lieberman and Nikki Etour. Thanks for listening. Everybody, tell your
friends write a review. All of this helps. I appreciate
(43:38):
you listening, and until we go again, I'm Kel Penn
So like literally a week and a half ago, hadn't
logged down to Zoom for maybe maybe two weeks. Right,
it was around Thanksgiving, and then I was traveling and
I had the Zoom meeting, and I open it, I
click on the link, you know, a minute before it
starts because I'm early, I am professional, And of course
it starts updating itself because it needed a software update.
(00:20):
And I'm frantically texting everybody who I'm supposed to be
meeting with, like blaming Zoom as if it's Zoom's fault. Oh,
of course, now Zoom decides to do a software update.
Speaker 2 (00:28):
Blah blah blah. No, I knew in my heart that this.
Speaker 1 (00:33):
Was a me issue, right, You knew you had a
Zoom meeting log on ten minutes early because it might
need a software update. But that's like how young and
old I am simultaneously, right, sold that I'm trying to
blame Zoom for a thing that's clearly my issue, and
young enough that, like, you know, we all had roommates
who did dial up the porn because that's what the
nineties were like. But as we think about things more
(00:57):
and more in a society, if you want to use
the word progresses. We've all thought about AI as well
as an actor, of course, terrifying the idea that robots
and computers could take all of our work. But it's
not just actors. No matter what you do for a living,
the humanity of your work, the interpersonal skills, the taking
pride in your job, even if that means you're in
a cubicle all day, whatever it is that you're doing.
(01:19):
The fear that maybe technology could replace us is like
a real thing.
Speaker 2 (01:23):
It's not just creepy.
Speaker 1 (01:24):
It feels like it's getting out of control, and it
feels like the heart of the humanity that we all
have is being questioned right now. So wanted to have
a very interesting guest on who could help us sort
through all of that, and that is journalist Jacob Goldstein,
who tells me that we've been here before, and.
Speaker 3 (01:41):
It started in the cloth business.
Speaker 4 (01:43):
So these original a lot ofites were like the first
people really to be impacted in a significant way by
the industrial revolution, the first people to face this thing
that we are talking about that we are afraid of
essentially losing their jobs to technological change.
Speaker 1 (01:58):
In my conversation with Jacob, we're going to go all
the way back to the eighteen hundreds, to the original Luddites,
and I want to hear about their story. And we're
going to talk about how technology changes, how those changes
reshape our work, and then frankly, who historically gets protected
and who gets left behind?
Speaker 2 (02:16):
Here we go again again again.
Speaker 1 (02:21):
Okay, Hey, I'm cal Pen and this is here we
Go again, a show that takes today's trends and headlines and.
Speaker 2 (02:28):
Asks why does history keep repeating itself?
Speaker 3 (02:31):
Here we go?
Speaker 1 (02:47):
Hey, good morning, good morning, how you doing. Thanks for
today I'm speaking with Hi.
Speaker 3 (02:53):
I'm Jacob Goldstein.
Speaker 1 (02:54):
He was a previous host on the NPR podcast Planet Money,
so if you recognize his voice maybe from that. He's
the current host of the podcast What's Your Problem and
Business History, and he's the author of the book Money,
The True Story of a Made Up Thing. Before we
fully dive in, I was very excited to talk to
you about all these topics because a couple of reasons.
(03:16):
I generally know very little about technology. I'm forty eight,
so as my college friend who does work in tech,
always reminds me like, we're that generation that grew up
fully analog, but we're still kids when the digital wireless
world kind of came around, and so it's rare, that
rarity of knowing how to use both. But I'm now
getting old enough from like I just want to rotary phone,
(03:37):
like when I was nine. So since you are smart
and thoughtful and very knowledgeable about all of this, just
the nerd part of me was really excited to talk
to you. When did you know you wanted to be
a journalist and focus on the things that you do.
Speaker 4 (03:52):
So I became a journalist in my late twenties. I
was a newspaper reporter, and it was around the time
of the two thousand and eight financial crisis. I was
a reporter covering healthcare at the Wall Street Journal, and
like everybody else, I wanted to understand what was going on.
(04:13):
It was confusing to me, you know, I knew about healthcare,
but even though I was at the journal, I wasn't
covering finance.
Speaker 3 (04:18):
And I have an aunt who.
Speaker 4 (04:19):
Was an MBA who is like my go to kind
of business and money person when I don't understand things,
and you know, there had been this collapse obviously of
the stock market and of real estate prices, and I
asked her, like it seemed like people were talking about
like a trillion dollars disappeared from the stock market, right,
So I had this maybe dumb, maybe not question, where
(04:40):
did the money go?
Speaker 2 (04:41):
Yeah, that's not dumb, that would be my question.
Speaker 3 (04:44):
And she said it wasn't there in the first place.
Money is fiction. Oh yeah, And I was like, ooh fiction.
I had been an English major, I knew about fiction.
I was like, this is interesting to me. And that
was sort of the gateway.
Speaker 4 (04:57):
And so not long after that, I went to work
for this podcast called Planet Money. It's like covering the
economy but in a very sort of narrative storytelling way,
and through that I got into kind of the history
of money and the history of economics and the stuff
we're going to.
Speaker 3 (05:11):
Talk about today.
Speaker 2 (05:12):
Amazing.
Speaker 1 (05:12):
I had an AHA moment like that when I was
hosting a show just before COVID terrible title called This
Giant Beast that is the Global Economy for Amazon Prime.
And I had a great time working on it, but
that was, you know, I was the host through which
the audience gets to experience things, so I knew very
little going in and my mind was blown. It sounds
like not dissimilar to your two thousand and eight phone call. Yeah,
(05:34):
as a journalist, I'm going to jump ahead a little
bit to just sort of what people are thinking about today.
And I'll preface it by saying, as an actor and
a writer, we just came off of two very protracted
labor disputes where we were forced to go on strike
by these big media companies in large part because of
AI proposals and provisions. As a journalist, are you at
(05:57):
the place where you're thinking through or or are you
afraid that technology is going to replace people in your
profession or has it already?
Speaker 3 (06:07):
Yes?
Speaker 4 (06:07):
And yes, I mean if you look at employment in
newsrooms over the past twenty years, which is to say
my career, it has gone down and down and down
because of technological change. Right, It's not exactly AI doing reporters' jobs.
It's people substituting away from newspapers. But yes, looking forward,
(06:30):
I am worried that AI will be able to do
what I do. And fortunately I'm not that young and
I don't have to make it that much longer. But
like I would caution someone starting their career of trying
to be a journalist in the traditional sense, right, I mean,
perhaps interesting question is, like what is the combination, right, Like,
(06:54):
we'll AI take our jobs is a valid question, But
in the meantime, how can we use AI to do
our jobs better? Is perhaps a more practical question.
Speaker 2 (07:04):
And how do you feel like you're able to use
AI to make your job better?
Speaker 4 (07:09):
I mean it helps me with research, is the short answer.
And I will say there's an interesting cultural thing, certainly
among journalists and writers. I suspect it's not as much
in other fields. But because large language models are writing,
they feel like a direct threat to you know, journalists
in a way that they perhaps do not to people
in other fields. So I don't know, there's a feeling
(07:30):
among journalists that using AI is cheating.
Speaker 2 (07:34):
Right.
Speaker 4 (07:34):
People will sort of abashedly admit to using AI, and
I think that's unhealthy, right. I think using AI is
like using the Internet, which by the way, is like
you know, Google searches are driven by AI, of course.
So for example, I host this show called Business History,
and just this morning I took a script that one
of my colleagues had written that was like a great narrative,
but I felt like it could use some big ideas,
(07:56):
and I put it into a large language model and said, like,
what are some big I can ideas that you think
sort of emerge from this, what are some themes? And
it's not like I copied and pasted the answer. It
suggested some ideas, and I went and researched those ideas, right,
So that's an example.
Speaker 1 (08:10):
So when you research the ideas after the prompt, are
you researching that outside of the AI models? And here's
basically what I'm getting at is like, how as a journalist,
how do you make sure that the sources themselves are accurate?
Knowing that, however, AI has learned that may not be
whatever the inputs were may not be a real thing.
So how do you fact check that when you take
(08:31):
the next step in research?
Speaker 4 (08:33):
I mean that part is the same as always, right,
Like I don't I never take the answer from the
AI as a reliable answer. I go and I look
it up elsewhere, and I look at the source and
I evaluate the credibility of the source material. And you know,
certainly ayes hallucinate some, but like overall, it's useful, and
(08:53):
like once in a while it's like, oh, that doesn't
make sense, but quite often it's like, oh, that's a
good idea. And like just this morning there was like
particular monetary policy shift in Germany and the nineteen forties
that it pointed me to that was in fact real
and interesting in the history of the Volkswagen.
Speaker 3 (09:09):
Beetle, which is the story we're working on.
Speaker 1 (09:10):
But okay, I want to go back a little bit
to history because the same friend who I mentioned, the
tech savvy college friend, often likes to tell me that
I'm a Luddite.
Speaker 2 (09:21):
And I don't think he's necessarily wrong.
Speaker 1 (09:22):
The way that we throw around the word today technology
doesn't necessarily get me as geeked as some of my friends,
although although I'll obviously have my moments. And so just
in looking back, right, I get that people have always
had this fear that newer technology is going to replace
their jobs. So what you've talked about is that we've
been here before, and all this palpable fear started back
(09:43):
in eighteen hundreds, and so can you take us to
the eighteen hundreds and tell us what happened, Like, take
us back to that moment in history for somebody who
doesn't know anything.
Speaker 2 (09:51):
About the Luodites.
Speaker 4 (09:53):
Yeah, so the original Luddites were quite different. Just to start, then,
the word we have to the word Luddite today. I mean,
it's just like somebody who doesn't like technology because they
don't like it. Right, So, the original Ludites were cloth
workers in England in the first part of the eighteen hundreds,
like eighteen eleven, eighteen twelve. Around that time, and for
(10:15):
essentially all of human history, there was very little technological change.
Right this world do we live in where you just
assume that technology changes generation after generation, that things get
more efficient. That was not the nature of the world
until the Industrial Revolution, which started in the second part
of the seventeen hundreds in England, and it started in
(10:35):
the cloth.
Speaker 3 (10:36):
Business, right.
Speaker 4 (10:37):
So these original Lutdites were like the first people really
to be impacted in a significant way by the Industrial Revolution,
the first people to face this thing that we are
talking about that we were afraid of, essentially losing their
jobs to technological change. The cloth business was actually a
huge business for England at the time, and the.
Speaker 3 (10:56):
Luddites were skilled artisans. Right.
Speaker 4 (11:00):
We think of, you know, factory work as terrible in
the eighteen hundreds, and it was, but before the Industrial Revolution,
it was like something you did kind of in your home.
It was farmed out and so you know, there were
all these steps to making fabric, and different people specialized
in different parts of it.
Speaker 3 (11:17):
So the croppers would take a rough piece of.
Speaker 4 (11:20):
Fabric and they would have these giant shears, like kind
of giant metal scissors, and they would crop that. I
guess the nap I don't know about fabric. They'd crop
something off.
Speaker 3 (11:30):
The wool to like make it smooth and nice.
Speaker 4 (11:32):
Okay, So that was their job, and for the time
it was a pretty good job. Like they worked for themselves.
They set their own hours, very poor by the standards
of today, which was an important point, but in relative
terms at the time they were doing well. And then
along came the industrial Revolution, which started out as machines
(11:52):
to make cloth. Right, So somebody invented a thing to
spin a raw fiber into threadbody invented a loom, and
then somebody invented a shearing frame, right, a machine to crop,
a machine to do what the croppers had done. Right,
And so this is the thing we are talking about.
This is AI taking our job, but for the croppers,
(12:14):
but for the skilled clothmakers.
Speaker 1 (12:16):
I'm curious the all those inventions. What's the do you
know the range of time? Like did this all happen
in a year or was it over a period of
like fifty years.
Speaker 3 (12:24):
Decades? The order of magnitude is decades.
Speaker 2 (12:26):
So slower than slower than right now. Well maybe maybe not.
Speaker 4 (12:31):
I think it didn't feel that way if you were
a cropper right like, there wasn't the spread of information
like there is today. They didn't necessarily you know, if
you were a cropper in eighteen hundred, you didn't know
that somebody was going to invent a shearing frame and
that it was going to show up.
Speaker 1 (12:46):
You know.
Speaker 3 (12:46):
I think it came as a surprise.
Speaker 2 (12:49):
And once it was there, there's a guy named Ned Lud.
Speaker 3 (12:52):
Yes, so what starts happening? Does one curse on this show?
Speaker 2 (12:57):
Oh? Yes, please feel free.
Speaker 4 (12:58):
So these people who have these good jobs, who have
these skills, see the machines coming and taking their jobs,
and they essentially think like fuck this, like no, like
we're not gonna do it this way. And so what
they start doing is going in the middle of the
night and attacking these new machines like literally physically, like breaking.
Speaker 3 (13:18):
Them with sledgehammers.
Speaker 4 (13:19):
It's like, oh, you're gonna do this with the machine. No,
I'm going to break your machine with a sledgehammer. And
then you can come back to me and I'll keep cropping.
Speaker 3 (13:26):
And in I.
Speaker 4 (13:27):
Believe it's eighteen eleven, it starts to get more organized.
So these have been just kind of random, ad hoc
kind of what we would call today, like maybe mob.
Speaker 3 (13:35):
Sure it might be a word people would use today.
Speaker 4 (13:37):
But in eighteen eleven it starts to feel more organized,
and there start to be these letters from this self
titled General General ned Lud, And he's actually holed up
in Sherwood forest, like Robin Hood, who has kind of
similar vibes.
Speaker 3 (13:56):
Right, and these letters are.
Speaker 4 (13:59):
Taking on the tones of Civil war frankly, right. He
has this title General ned Lud and at one point
they call his his Army of Redressers, right, like a
redress of grievances. The main thing you need to know
about ned Lud there was no ned Lud he was.
He was a myth.
Speaker 3 (14:17):
He was a myth. He was like Robinhood. He was
like Robin Hood.
Speaker 4 (14:19):
I mean, there may actually have been a guy named
ned Lud decades earlier who was like a framebreaker, but
like this General ned Lud, this guy leading the revolt,
he was an invention, which is kind of genius, right, like, yes,
it's a genius way for a people with no political power,
for a group of people with no political power to.
Speaker 3 (14:38):
Create a movement. Right, you invent a figurehead, a mythical
general hold up in the forest who does not exist.
Speaker 4 (14:46):
And so it's this idea that the workers are not
just randomly breaking machines, they're organizing to fight back. And
one thing that's interesting, there's this historian who has called
what they were doing collective bargaining by riot because there
were no unions, Like they couldn't even vote, right, there
(15:07):
wasn't a mass suffrage in England at the time. There
were certainly no units. They basically didn't have power in
any organized way. So they were seizing power and it's
this collective kind of.
Speaker 3 (15:17):
Ad hoc way.
Speaker 4 (15:18):
And these attacks get more systematic. There's this one, particularly
dramatic one where these guys they all mass at this
bar and they're going to attack this mill.
Speaker 3 (15:33):
But the mill owner knows.
Speaker 4 (15:34):
Like the Luddites that you know, have been attacking around
this region, He's been preparing to defend himself. So the
owner is actually like sleeping there. He's hired some people
with rifles to defend the factory. He actually has like
a that of I think sulfuric acid.
Speaker 3 (15:51):
He's going to like pour down on them.
Speaker 4 (15:52):
Yes, it's very medieval, right, it feels very like medieval castle.
He's made this factory like a fortress, and so the
Ludights march on it is it is very it's like
proto civil war, right. This is an armed organized attack
on a heavily defended factory. And they get in and
there's like actually an exchange of gunfire, and ultimately they retreat.
(16:13):
So there's two Ledites dead. They have failed in their attack.
And around this time Parliament, you know, the British government
realizes that this is getting out of hand, and they
have passed a law making attacking machines punishable by death.
Speaker 2 (16:31):
Amazing, I am amazed.
Speaker 1 (16:33):
It was almost ninety nine percent confident that this would
not go in the favor.
Speaker 2 (16:37):
Of the people.
Speaker 1 (16:39):
Tell me something about British history that I didn't already know. Okay,
go ahead.
Speaker 4 (16:44):
So after this attack where two Ledites are killed, there's
like this round up. Essentially, the government fights back. A
bunch of Leedits are arrested, throwing in jail their triede
and several of them are in fact given death sentences,
and they are publicly hung. They actually make the gallows
twice as high as usual so that you can see
(17:04):
it from even farther away. You know, there's like, you know,
a crowd of people witnessing the hanging. And this basically
defeats the Luddites, right, Like, this is basically the Luddites lose. Right,
the government rounds them up and kills them, and they
stop attacking the machines, and there are no more rich croppers,
(17:25):
or they were never rich, but there are no more
like relatively well off croppers after this, Right, It's just
the machines are, in fact, a better cheaper way to
make cloth. The Ludites don't have any political power and
they are out of luck.
Speaker 1 (17:45):
That all makes me wonder, like if you put the
Luddite story next to conversations that we're having about AI today,
in what ways do you think that they're similar and
how are they different? And the one stat that I
remember when we were researching for this that came out
up is like Goldman said that by twenty thirty, AI
could replace the equivalent of three hundred million full time jobs.
(18:08):
Forbes said it would replace two million manufacturing jobs by.
Speaker 2 (18:12):
The end of next year alone.
Speaker 1 (18:15):
And so when I when that Light eight story especially,
it's got everything, It's got public policy, it has a clear,
very simple explanation of who really wins in the short
term and capitalism, the power dynamics, all of that. So
then that just makes me wonder if you put that
story next to today's AI conversations, like, how do you
see them being similar or different?
Speaker 4 (18:36):
Yeah, I mean, certainly workers being potentially replaced by machines
is in fact similar like that in some places you know,
call centers.
Speaker 3 (18:50):
It's already happening, clearly. I Mean.
Speaker 4 (18:52):
One thing that I think is an important difference is
political economy, right Like it's and for people to say, oh,
ordinary people have no power in today's economy, it's all
the rich people. And like, certainly rich people have a
lot of power, but relative to the Luddites, ordinary people
do have more power today, right Like unions were illegal,
(19:13):
the Luddites literally could not vote, right, And so it
will be interesting to see who is losing jobs to
AI and when and how politics and the government respond, right,
And it's an interesting moment now because for a long time,
(19:34):
technological change threatened lower skilled workers, right like twentieth century
automation hollowed out.
Speaker 3 (19:42):
The middle to a significant degree.
Speaker 4 (19:43):
Right, There was this phrase, the hollowing out of the middle,
where like physical labor actually for a while did okay,
and if you were sort of highly educated you did okay,
but if you were kind of in the middle of
the distribution, it was bad for you. Now it's kind
of across the board, and strikingly, people like lawyers and
journalists are threatened. And so those are people who traditionally
(20:05):
have had more political influence, perhaps, right, And so I
think how will the government respond? Is super interesting and
super unclear.
Speaker 1 (20:15):
Yeah, do you have a sense of which other types
of jobs are projected to be affected? I mean the
manufacturing jobs obviously that's well documented and also follows a
pattern of technological change throughout history.
Speaker 2 (20:28):
I think you're right, the.
Speaker 1 (20:29):
Doctor lawyer thing is relatively new, jarring for a whole
different economic class of people. What are the ones we're
not thinking about?
Speaker 3 (20:38):
I don't know. I mean, I'm wary of making.
Speaker 4 (20:42):
Like AI is insane right now, Like it is like
I don't you know, there is one thread of the discourse,
and maybe this has died down. I don't know that
is like, Oh, it's just hype from AI companies when
A companies are like AI is going to be crazy,
Like I don't think that is true. It is interesting
to think about where are the bottlenecks, right, I do
(21:03):
think there might be bottlenecks in adoption, right, Like in
a superficial.
Speaker 3 (21:08):
Way, AI looks really good, but when you actually try
and get it to do stuff, it's kind of a
pain in the ass.
Speaker 4 (21:16):
And like there's this interesting guy, Dwarkish Patel, I believe
who he writes about AI and he interviews a lot
of the really smart AI people, and he made the
point that he's been trying to use it for his
own work, but that it's not good at like learning incrementally,
Like it can do an okay job, It can do
a five out of ten job, But a human being
(21:38):
that starts out a five out of ten at your company,
you can kind of get them up to eight out
of ten, and getting the AI to eight out of
ten getting it to learn on a particular task is
actually still doesn't work. And like getting an AI to
actually do a job for you is still hard. And
so I don't know, I really don't know how it's
going to roll out, and I don't know what the
(21:59):
political responsible base.
Speaker 1 (22:01):
Do you feel like it's too early to speculate what
the differences are compared to previous technological changes or advances.
Speaker 4 (22:12):
I mean, I think one thing that is important to
remember is, at least so far, every time people have
lost jobs to technology, new jobs that people couldn't.
Speaker 3 (22:27):
Imagine before emerged, right, So.
Speaker 4 (22:30):
Like some number of people are displayed, some number of
people are worse off in the short.
Speaker 3 (22:36):
To medium and sometimes long term, but always so far
there have been more new jobs.
Speaker 4 (22:42):
People's wants and willingness to pay for things is insatiable,
and I suppose this may be the end of that,
but I wouldn't bet against that, you know. I mean,
I feel a little silly that making podcasts is my job, right,
trust me?
Speaker 2 (22:57):
It is security I have about my jobs.
Speaker 4 (22:59):
Yes, I understated, and that's because you know, we have
all these machines that make really cheap clothes and cheap
food and all of the basic things. You know, Like
if you just look at farming, right, like in eighteen
hundred something, they have rough numbers. Eighteen hundred and ninety
percent of Americans were farmers in nineteen hundred and fifty percent.
Speaker 3 (23:18):
Of Americans were farmers. Ish, maybe forty.
Speaker 4 (23:20):
Two thousand two percent of Americans were farmers, right, maybe
the greatest displacement of labor you can think of because
of the reaper and the tractor. Right, But like everybody
went to working factories and then they went to work
as you know, personal trainers and bank tellers and other things.
Speaker 1 (23:35):
It also makes me wonder like the the implications up
to your point about like maybe there is no next
widespread job thing that people have, Like what do you
retrain for when it's just a computer taking everything? And
I wonder like in terms of the TBD for what
government does, if it's even government that does this is
(23:57):
like just taking the US. We live in a country
that can barely agree on the federal level that we
should have just the basic social safety net in place, right,
compare us to other industrialized, civilized countries. And so then
jump to you know, well, what about universal basic income
and people who are really touting universal basic income? And
(24:20):
if you look at today's political climate, in what fucking
reality are you going to get these two generally right
of center, if you're looking at global standards, two political
parties to agree that UBI is going to be a
thing that we can do. And I don't mean to
be a cynic about it, but it concerns me greatly
that our politics in the democracy are designed to move slowly.
Speaker 2 (24:40):
That's a given.
Speaker 1 (24:42):
But if the technological displacement of jobs is moving faster
than our public policy, what happens.
Speaker 4 (24:49):
I mean, that's definitely a possible bad outcome. I mean,
the short answer to what happens. So what you're imagining
is like lots of people lose their jobs because of
technological change, and the government doesn't too very much to help
you because we have more truly.
Speaker 1 (25:01):
Darius benefiting from all this, and they just don't want
to give up their money.
Speaker 4 (25:05):
It's possible, I mean, I mean, obviously it's the case
that you know, Northern European countries have much more robust
safety nets than we do. It's also the case that
like Medicare and social security are politically totally untouchable and
have broad support, right, and so like maybe there's like incrementalism, Right,
maybe you can get social security and medicare ten years earlier. Right,
(25:27):
Like that seems like a plausible outcome. It is, in
fact interesting that the people talking the most about universal
basic income. Are AI people and tech people who actually
think this might happen. Right, you know, there is this
essay that John Mayer Kines, you know, of Kynesian economics
fame wrote in nineteen thirty, I think called economic Possibilities
(25:51):
for our Grandchildren, and I think that was actually where
the term technological unemployment was first used. Right, so, you
know thirty, right, the world's going into the depression, but
he's thinking generations ahead, and he's thinking about technological change
and robots taking our jobs as we would sort of
colloquially say today, and he's imagining, like, well, what if
(26:14):
our grandchildren are just working fifteen hours a week. He's like,
first of all, it's hard not to work at all, right,
we're sort of wired to want to do something, but
maybe you don't have to work that much. And people
sort of a look at fifteen hours a week now
and laugh like, oh haha, how charmingly wrong he is.
But I was looking as I was thinking about this interview,
and if you look at hours a week, it was
like sixty hours a week in nineteen hundred and fifty
(26:38):
hours a week in nineteen thirty when he wrote that,
we sort of plateaued at forty in this country, but
in the Netherlands they're like down to thirty two hours
a week now, like all these days off with all
the right, with a lot of same mondays thrown in
in the MiGs. And so I don't know, like, yes,
there are many if we think of possibility space, there
are definitely bad outcomes. I'm not entirely pessimistic, like, look,
(27:03):
it is the case that there is a progress aspect
to this, right, Like, yes, a lot of things could
be bad, and a lot of things can go wrong,
and maybe we're not in the best possible timeline. Certainly
we're not in the best possible timeline.
Speaker 2 (27:13):
But like.
Speaker 4 (27:15):
Having machines do stuff instead of people, it does mean
there is more abundance, right, And so there is this
problem that you're pointing to of like sharing the abundance,
making sure that it doesn't all go to six people, right,
which is a real thing to worry about.
Speaker 3 (27:29):
But there will be more to go around, I guess
if it's.
Speaker 1 (27:33):
Structured, right, yes, And you know I am not I'm
not such a louttite that I don't acknowledge that we
you know, the advances in medical research and science and
exploration and astronomy, and all of those things are are
going to be incredible. I was just talking with a
doctor who I ran into last night, who was touting
how exciting it is in her field for AI research
(27:56):
in medicine, and I have the exact opposite viewpoint as
an artist.
Speaker 4 (27:59):
Right, let me ask you a question. I've been curious,
do you ever use AI for work?
Speaker 1 (28:04):
I have tried because I was curious, and thank god,
AI does a piss poor job at writing jokes. I'm
sure one day it will learn. Part of that, obviously,
is that comedy is so subjective.
Speaker 2 (28:18):
But no.
Speaker 1 (28:19):
In fact, what I've used it for and what I
find to be helpful is that as a writer an actor,
my brain is usually very scattered. Like you know, people
like me think of the ten things that you're not
supposed to say out loud, but like that's constantly in
our brain, and so you're you always have a filter
on your brain depending on the setting you're in. Like
(28:40):
if I'm on stage doing a stand up routine, that
filter is off and generally it works and sometimes it
will get you in trouble. But in normal day to
day interactions, there's like this weird filter you have to
put on. And so when I'm writing a what by
normal people's standards would be like a professional document or
a professional email or a professional text, my mind has
(29:01):
nine paragraphs of things to describe what I want to say,
and I just need two sentences. So I have found
that something like that is a helpful tool, but I
have not found that in my actual professional life that
there's there's anything that I have that I've benefited from.
That said, I know it's all coming.
Speaker 3 (29:19):
So you're saying it's good for taking all the creativity
out of your.
Speaker 2 (29:23):
Life so far.
Speaker 3 (29:23):
Yeah, Anti, yeah, yea, yeah.
Speaker 2 (29:25):
That's been my experience.
Speaker 1 (29:26):
But again, the you know, I read about all of
the things that AI companies are working on to replace actors,
to clone our voice, our performance is coming up with
just completely new actors slash characters or personalities. So those
are the things that I don't you know that I
hope won't take my job. Like you you said this earlier,
I am of a certain age where like I'd love
(29:48):
a good twenty to twenty five more years in my career.
But man, I'm glad I'm not eighteen going to drama school.
It's a it's a whole different ballgame.
Speaker 4 (29:57):
I mean, I feel like if you're eight and going
to drama school, you've got to think of using the tools, right,
Like that a classic technology thing is you don't want
to be competing against the tool the technology. You want
to be using the technology. And you've seen that. I
mean to get back to the sort of historical arc,
like that was a thing that happened in factories, right,
(30:18):
Like US factories in many instances are high tech. Right,
There's like CNC machining was that computer Numerical control machining.
Speaker 3 (30:25):
Like they're sort of like tech jobs for kind of.
Speaker 4 (30:29):
Skilled workers, right, And even like when I worked at NPR,
Like there were the engineers in the studio who were,
you know, running the board, but then there was the
guy who was the manager of those guys, who was
like very much like unengineer at heart, and he loved
doing the stuff, but he was also like building the
systems and like running the servers and like he was
(30:49):
the guy who figured out how the reporters could self up,
could record ourselves, right, And so that's the guy you
want to be, right, You want to be the guy
using the tools. And like I haven't given up on that, Like,
in the long run, I assume AI will be better
at doing everything that I do than I am, but
hopefully that'll take a while. There's some number of people
(31:09):
who are like used to hearing me and like I
can sort of use AI to maybe work faster or
be smarter, right to do to figure out in five
minutes what it would have taken me an hour of
Google searching to figure out, and obviously still vet it,
but like, for the medium term, that's what I'm banking on,
and I do think that's like a healthier relationship to
(31:29):
technology in general.
Speaker 1 (31:37):
I was invited to an AI exhibit recently, an AI
art exhibit I'm putting art in air quotes by a
friend who is a tech bro, and he's like, hey,
I've been working for the last eighteen months on these
art pieces, these AI art pieces. Will you come to
the gallery opening? And I'm like, I love you. There
is nothing that I would rather do less on a
Tuesday night than come and see the product of you
(32:01):
getting high in front of your laptop and pressing buttons.
Speaker 3 (32:05):
No, sorry, man, I wanted that I'd stay home.
Speaker 2 (32:08):
I would stay home and do it myself. But yeah,
I'm not a fan of that.
Speaker 1 (32:14):
I don't want to get too off drag and I
want I want to ask a comparison question about the
Ludites and today. In the lud eight era, you write
that productivity went way up, wages for regular workers barely
moved for decades. That's something that we're obviously used to.
You see that time and time again. You also talked
about how the generations after the Ludites benefited from the
(32:34):
machines themselves that replace them.
Speaker 2 (32:36):
So I'm curious, like, what are the big.
Speaker 1 (32:38):
Lessons from that period about who benefits from new technology?
What do the people who are in the generation that
are directly impacted by machines supposed to do. I know
we talked about that a little bit, but all that's
leading to right now with AI knowing how much diversity
of opinion there is, how much panic and excitement there is,
what should our outlook be.
Speaker 3 (33:00):
That's super hard.
Speaker 4 (33:02):
I mean, you know, the I think the closest thing
in recent times that comes to my mind is workers
who've lost their job to foreign competition. Right Like, in
the kind of early part of the aughts, there was
what's come to be called the China Shock, which was
you know, China entered the World Trade Organization in like
two thousand. It's funny not that long ago, right, China
(33:25):
used to be a super poor country, was not a
major competitor, and then bam they entered the WTO. And
there were places in the United States that were making
things that competed with Chinese imports, like clothes, and they
got obliterated.
Speaker 3 (33:42):
The United States as a whole did well. And I
mean ordinary people, right, Like, it is the case that like.
Speaker 4 (33:50):
For working people, when clothes get cheaper, that is good, right.
It means they have more money in their pocket, more
money to spend on basic things. And I think it's
easy to overlook that part. But the people and the
towns that were competing against China were worse off, right,
So overall everybody was better off, And it's important to
say that, Like, it wasn't just rich.
Speaker 3 (34:09):
People who got better off.
Speaker 4 (34:10):
It wasn't just the owners of capital, right, And I
think that is likely to be true here as well. Right,
it is a competitive world, and I do think that
there is a universe where overall people are better off
from AI. The really hard question is how do you
help people who are clearly losing their jobs because of AI, Right, Like, well,
give them money seems like a good part of an
(34:31):
answer to me, Will it happen politically? I don't know
for the reasons you said, but if I were waving
a wand give them money in a way, though it
seems like I don't want to say the easy part
because it would be great if it happened politically it
would be hard. But there is this more complicated thing
for everyone, right, and Cain's talked about it one hundred
years ago. It's like work is meaning for a lot
(34:52):
of people, right, Work is purpose is just an organiz
principle in life. And so if we project forward, I
don't know what's going to happen. I don't think all
jobs are going to be gone in five years. Maybe
I'll be wrong, but I doubt that. But if in
fact lots of people are losing their jobs because of AI, yes,
please give them money. Of course there will be more
(35:14):
money to go around. There should be money for those people.
But like, what do we do about everything else? How
do we like help them find meaning in their life?
Speaker 2 (35:22):
Like that?
Speaker 4 (35:23):
It feels handwavy to me, but like might be my
own problem in three years, right, Like yeah, my identity
is my work to some significant area, and like a
technology might put me out of a job. You know,
it's easier for me to talk about now that it's me.
I don't just sound like some asshole talking about other
people in a condescending way, like this is me, Like truly,
I could put me out of a job before I
want to be out of a job, and like it
(35:45):
will be hard in many ways if that happens. Not
just the money.
Speaker 1 (35:49):
I worry that not just in the interim, but when
we're when we're dead the next you know, the next
eighty years or eighty years.
Speaker 2 (35:56):
Down the line.
Speaker 1 (35:57):
Yeah, if you have new new classes of people that
are so completely diametrically like they don't mix. You've got
this AI job class of folks who are working. And
then if there's even success at something like UBI, people
who have been pushed out, who are then economically locked,
whose children are economically locked in a particular scenario, does
(36:20):
that breed a type of class resentment that's even more
extreme than what we see today. You know, you see
scapegoating of immigrants. The fear that I have about how
that could be exacerbated without the right public policy. Once
the people who have lived it have died off is
also very scary.
Speaker 2 (36:39):
I don't have to worry.
Speaker 1 (36:39):
About that because I'll be dead, but it is something
that I think, you know, that worries me. My question
for you is literally the exact opposite, because I hate
wrapping up on a doomsday scenario. I think it's very
easy to do. I'm not a rage beaita. When you
imagine the next ten or twenty years, what are kind
(37:00):
of the most realistic ways that technology might change our
jobs that don't fit into either a doomsday scenario or
a utopian story.
Speaker 3 (37:11):
Yeah, that's nice. So like it kind of the middle
of the probability distribution.
Speaker 1 (37:15):
Right, Like, if I took AI to tell me to
take the emotion out of that question, that's what it
would have given me.
Speaker 3 (37:21):
Yeah.
Speaker 4 (37:22):
No, it's interesting to think about, right, because it's more subtle,
So people, it doesn't make so much of a story. Okay,
so let's think about that. So one thing, one thing
in that story is the capability for AI really to
actually do people's jobs.
Speaker 3 (37:39):
It emerges rather slowly.
Speaker 4 (37:41):
Right, It is constrained for reasons partly technological but partly
also institutional, Like companies have all this sort of tacit information, right,
Like people at companies know how to do all these things,
but they never wrote it down right, and so AI
can't just know that like, oh you got to call
John and accounts receivable when this happens, like that kind
of thing. So it's slow, right, That's one important thing,
(38:04):
is like if you can see the transition coming, if
you know that it's not tomorrow, like you know, kids
don't become a journalist if you're twenty maybe, or if
you do, learn how to do it with AI, because
it's going to change.
Speaker 3 (38:17):
So it's slow, right.
Speaker 4 (38:19):
Two, So the extent happens, it will increase output, right, like,
it will make us more productive and more efficient, and
like at a basic level that is.
Speaker 3 (38:29):
Good and I think it is underappreciated. Right.
Speaker 4 (38:32):
It will increase material abundance. It will help us whatever
you want, more clean energy, certainly we can get better
at doing clean energy, get better at doing battery storage
of clean energy, better medicine, Like there are happy things
that will happen. The economic policy in the government is
a complicated one. I mean, if you want more redistribution,
like when there is more wealth, you can generally have
(38:52):
more redistribution right, you could the people are having crazy
capital gains windfalls, perhaps you could raise the capital gains
tax and use that to help the people who are
being put out of work, or help people go on
Medicare at fifty five instead.
Speaker 3 (39:03):
Of sixty five. Right, That would be a very popular
political program.
Speaker 4 (39:07):
Still, you will have people who are losing their jobs
because of AI, who, because of the political realities, may
well be worse off financially, who even if they are
better off financially, lose a sense of meaning in their life.
Speaker 3 (39:21):
Probably.
Speaker 4 (39:22):
You know, often jobs and job types are clustered, so
it won't be just an individual losing their job. You
will have communities impacted, you know, communities having this economic.
Speaker 3 (39:33):
Disaster. Perhaps if it's bad, right, like like with the.
Speaker 4 (39:36):
China shock, and so, you know, you might see this
kind of heterogeneous outcome. And I don't want to make
it as simple as rich and poor. I don't want
to make it as simple as like the trillionaires get
more trillions than everybody else is screwed. I think, in fact,
a likely outcome is more subtle than that and more varied.
And there are some people who are working class, middle
class who learn how to use AI who get more
(39:57):
money because they're being more productive.
Speaker 3 (40:00):
Others who are totally screwed.
Speaker 1 (40:02):
Then final question for you, when, because we opened with this,
when you look at this long history of people who
have been afraid that machines would take their jobs, and
many cases where they did, what still worries you when
you look ahead? And what gives you the most hope?
Speaker 4 (40:17):
So I guess what still worries me when I look
ahead is the rate of change of AI because when
you look at these instances and the alternatives, right, so,
like the Ludites are this very dramatic story where these
people who had a decent life lost their jobs and
machines and their lives got worse. On a much vaster
scale was the automation of farm work, right, that was
(40:40):
like almost everybody worked on a farm, and then almost
nobody works on a farm now. But there wasn't really
I mean, you know, farmers organized politically in various ways,
and they wanted different policies. But you never had a
kind of lud Eite moment for farmers, in part because
it was gradual, in part because you had industrialization alongside it,
so people could leave the farms and go work in
a factory. So it's if it's really sudden, if a
(41:04):
huge amount of people lose their jobs really fast, like
that just feels super politically dangerous and unstable, right, Like
it could be violent, it could be bad. I mean,
the hopeful thing fundamentally to me is that people have
been worried about technological unemployment for two hundred years, and
indeed pockets of people have really suffered from it for
a long periods of time. But like today, the employment
(41:27):
to population ratio, the share of working age people.
Speaker 3 (41:30):
With jobs is like as high.
Speaker 4 (41:32):
As it has ever been, right, much higher than it
was fifty years ago, when far fewer women were working,
despite incredible amounts of innovation, technological change, you know, technological
job laws. So like, I think we really do underrate
the extent to which we are good at coming up
with new jobs. Jobs we cannot imagine today, And like
(41:53):
I actually think that'll happen in the long run. It'll
be maybe embodied things, you know, like maybe I'll go,
I don't know, be a meditation.
Speaker 3 (41:59):
In instructor or something. And yes, you could have aib
a meditation instructor.
Speaker 4 (42:02):
But there are some things I think people who will
be richer overall will pay for a human being to do.
Speaker 3 (42:09):
I just don't know what those things will.
Speaker 1 (42:10):
Be, So forget podcasting. We will have other jobs that
we just can't even think of right now.
Speaker 4 (42:16):
I believe that we you and I I don't know,
but people will have jobs, our grandkids.
Speaker 3 (42:22):
Yeah, I mean it could happen to me. Yeah, it
will be super interesting.
Speaker 2 (42:27):
Yeah, for sure. What a time to be alive to
witness it.
Speaker 3 (42:30):
You know, what a fucking time to be alive.
Speaker 1 (42:34):
I was journalist Jacob Goldstein to hear more of what's
in Jacob's brain, Listen to his podcast What's Your Problem
and Business History, and read his book Money, The True
Story of a Made Up Thing. Here we go again
as a production of iHeart Podcasts and Snafu Media in
association with New Metric Media. Our executive producers are me
(42:58):
kalpen at Helm's, Mike fa Alissa Martino, Andy Kim, Pat Kelly,
Chris Kelly, and Dylan Fagan. Meghan tan is our producer
and writer. Dave Shumka is our producer and editor. Our
consulting producer is Romin Borsolino. Tory Smith is our associate producer.
Theme music by Chris Kelly logo by Matt Gosson. Legal
(43:18):
review from Daniel Welsh, Caroline Johnson, and Meghan Halson. Special
thanks to Glenn Bassner, Isaac Dunham, Adam Horn, Lane Klein,
and everyone at iHeart Podcasts, but especially Will Pearson, Carrie
Lieberman and Nikki Etour. Thanks for listening. Everybody, tell your
friends write a review. All of this helps. I appreciate
(43:38):
you listening, and until we go again, I'm Kel Penn
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