As we enter a world of artificial intelligence, the question of what should be automated looms before us. Models need clear, objective metrics to train on. But, can jobs really be distilled to data points? In her book, The Last Human Job: The Work of Connecting in a Disconnected World, Prof. Allison Pugh asserts many jobs have a relational component that can’t be caught in the metrics. In this episode, Prof. Pugh warns that devaluing connective labor leads to automation that overlooks the core issues and leaves us more isolated.
Topics:
- Connective Labor
- Undervaluation of Connective Labor
- Automation of Connective Labor
- Role of Data in Education
- Educational Inequality and Standardized Testing
- Artificial Intelligence and Relationships
- Growing Demand for Connection
- "What books have had an impact on you?"
- "What advice do you have for teenagers?
Bio:
Allison Pugh is a Research Professor of Sociology at Johns Hopkins University, and the author of four books, most recently The Last Human Job: The Work of Connecting in a Disconnected World (Princeton 2024). The 2024-5 Vice President of the American Sociological Association, Pugh was faculty at the University of Virginia for 17 years before moving to Hopkins this summer. She is a former journalist, and her writing has appeared in The New Yorker, The New York Times, The New Republic, and other outlets. She served as a US diplomat in Honduras, cofounded a charter school in Oakland, waited on tables at the US Tennis Open, packed salmon roe in Alaska, and was an intern at Ms. Magazine.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:10):
As we enter a world of artificial intelligence, the
question of what should beautomated looms before us.
Models need clear, objectivemetrics to train on.
But can jobs really bedistilled to data points, or is
there a more emotional andrelational side to effective
labor?
This is the Aiming for the Moonpodcast and I'm your host,
Taylor Bledsoe.
(00:30):
On this podcast, I interviewinteresting people from a
teenage perspective.
In her book, the Last Human Jobthe Work of Connecting in a
Disconnected World, ProfessorAllison Pugh asserts many jobs
have a relational component thatcan't be caught in the metrics.
Allison Pugh asserts many jobshave a relational component that
can't be caught in the metrics.
Pugh warns that devaluing whatshe calls connective labor leads
to automation that overlooksthe core issues and leaves us
(00:52):
more isolated.
Allison Pugh is a researchprofessor of sociology at Johns
Hopkins University and theauthor of four books, most
recently the Last Human Job.
She is also the 2024-2025 VicePresident of the American
Sociology Association.
Pugh was faculty at theUniversity of Virginia for 17
years before moving to Hopkinsthis summer.
(01:15):
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Substack lessons frominteresting people and all right
with that.
Sit back, relax and listen in.
(01:37):
Well, welcome, Professor Pugh,to the podcast.
Thank you so much for coming onthe show.
Speaker 2 (01:44):
Thank you for having me.
Speaker 1 (01:45):
So you wrote a fascinating book named the Last
Human Job, the Work ofConnecting in a Disconnected
World.
Speaker 2 (01:56):
And to kind of dive into our conversation.
What is connective labor?
That's the name I came up withfor a kind of practice that
happens all over the economy.
We have all experienced it andmaybe all of us have done it.
What it is is the moment ofreflecting back to the other
person, the person that you see.
(02:19):
It's the act of seeing theother and being seen.
It's reciprocal, mutual,interactive and it involves some
emotion, emotional sensing ofthe other.
Therapists call this attunement.
But it happens not just intherapy, although that's the
most iconic example.
(02:40):
It happens in teaching, ithappens in the law, it happens
in management, it happens inhigh-end sales, it happens in
medicine.
It's wherever someone has toconvince you that you're being
seen.
For whatever they do to workTeaching is a really powerful
(03:03):
example, I think they do to workTeaching is a really powerful
example, I think, especially foryoung people.
Speaker 1 (03:12):
It's really interesting to think about
connective labor because, as youhighlight throughout your book,
it's something that you don'ttend to think about when you
think about specific jobs.
When you think about doctors,for example, you talk about did
they prescribe the rightmedicine, did they diagnose the
patient correctly, and you don'talways.
You talk about bedside mannerperhaps, but it's more of this
means to an end of kind ofgetting a patient treated.
(03:32):
Why is that?
Why do we not value this typeof work as much?
Is that a problem too?
Speaker 2 (03:39):
I think it's such a problem and you could say that's
really underlying the wholebook.
It's why I wrote this book,because we don't value it and so
we're doing things to it tomake it harder, and actually
it's on its way to being widelyautomated, and so I wrote this
book to point it out and tohopefully reveal some of its
(04:00):
value.
I think the reason why we don'tsee it is because it's not what
we pay for.
You know, we want a child tolearn algebra.
We think, or many people mightthink, that a lot of these
things are about informationdownload.
Like just you know, here arethe download.
(04:22):
Like just you know, here arethe you know 10 things you have
to know about algebra.
But actually we know this acrossmany occupations, people won't
hear the thing, the informationthey need to download, unless
(04:43):
they feel seen.
This is a human fact, and sodoctors have told me, you know I
interviewed many primary carephysicians and they've told me,
you know, I know, you know thatsomeone needs to stop drinking
or that they need to lose weightor stop having so much sugar or
all the things that everyonedoes, and they also know that
it's the relationship that willkind of nudge the person towards
(05:08):
better behavior and that thepatient won't hear them, won't
hear any of this advice unlessit comes from a place of I see
you.
I understand your situation andI see you is not just you drink
too much, it's this is yoursituation.
And I see you as not just youdrink too much, it's this is
your situation, and I get what'shappening here and I get what
(05:32):
your goals are.
It's a really interactive thingand seeing is at the basis of
so much of economic activitythat we actually value actually
value.
Speaker 1 (05:47):
A big part of having conversations like this and a
big part of their disruptionseems to be the value of data we
put in a lot of these jobs,which one of the other questions
that I want to get to later onis.
That balance is specificallywith science jobs, where you
need data to assess whethersomeone is becoming healthier,
for example, but there's alsothis connection where, when you
valued the data and theanalytics, you're missing a big
(06:08):
part of maybe why you wereoriginally even assessing this
and you cite schools as a bigfactor.
Could you discuss that a bit?
Speaker 2 (06:15):
I mean, if your listeners are teenagers, they
know this backwards and forwards.
Many schools, in part, you knowto be kind, they are kind of
nudged or pushed in thisdirection by their, you know, by
their societies, by thesuperintendent, by the district,
(06:37):
by the legislature, butnonetheless, schools have become
places where we count studentsmore than we see them and so we
are counting how many people youknow are attending and how many
people are graduating and howmany people are maybe passing.
(06:58):
You know English, but we're notreally seeing the kids
underneath the numbers.
And that started or that gainedreal traction with in the
United States, with the no ChildLeft Behind Act, which put real
punitive measures in againstschools that you know didn't
(07:25):
meet certain metrics, but whatit did, what that did, was
really that became the enginebehind the counting revolution,
so that there's actually, youknow, people have, you know, an
administrator has recently wasquoted as saying you know, an
administrator was quoted assaying I know kids by data more
(07:46):
than I know them by faces, andthat's a corruption, that's a
serious degradation of teachingand learning, and most teachers
will tell you that.
Speaker 1 (08:00):
What would you say to the argument that this?
Well, first off, this argumentmight be not true, so I am
curious about that as well.
But the argument that says,well, going the data-supported
route, the kind of the analyticsroute, is what produces the
most efficient and best students, for example.
So in order to make sure thatyour students know math, know
(08:22):
English, at a standard level,for example, you need the ACT,
the SAT and tests that kind ofcreate that, I guess, analytical
description of the world.
And so by that we need to be asefficient as possible and, for
the sake of the student, providethem these skills, even if it
means we don't have as much of apersonal connection.
Speaker 2 (08:45):
So what you're really getting at is isn't data worth
something?
And you know, standardizedmetrics can have their use and I
agree with both of those.
So I'm not like anti all data.
I'm a sociologist.
This book is based on data, youknow like it's qualitative data
but nonetheless it is.
(09:06):
You know, the gathering of alot of information and then the
studying it for particularpatterns.
So I understand data and I, youknow, have I appreciate it.
I think it's useful.
You know what it is is like,kind of gathering the
information before you make adecision, make an analysis, make
an argument.
So yeah, but what this book isreally arguing is that data, for
(09:32):
data's sake, has really takenover many spaces where actually
relationships are vital andthey're kind of moving
relationships aside, they'reshouldering relationships out of
the picture.
And I actually want us toprioritize relationship and see
(09:53):
if we can fit data in, and rightnow we're prioritizing data and
seeing if we can fitrelationships in.
So I think that you know, yes,standardization, standardized
tests, I think, have a place,you know, for schools, for
admissions, you know,considering, you know who have
50,000 applicants, say, and haveto figure out some way to sift
(10:17):
through them, but moreimportantly, really for the
student themselves to be able tosay like.
More importantly really, forthe student themselves to be
able to say like huh, it lookslike I'm stronger in English
than in math or whatever, butbeyond that it's not going to
tell you much, not going to tellyou how you learn, not going to
tell you you know, kind of,what tactics to take to master a
(10:38):
particular field.
So it's pretty modest what it'sgetting people and it's
certainly not worth, in myopinion, all the inordinate
attention that it gets in ateenager's life.
Speaker 1 (10:53):
You know I was reading about an example of in
the book Weapons of MathDestruction and I believe the
preface or the introduction theauthor discusses I believe it
was the DC schools who basicallydecided, in order to improve
the quality of teachers, theywere going to assess whether to
rehire them based onstandardized test scores and we
(11:14):
were discussing that and shediscussed how the author was
saying that's a terrible ideabecause of all the other factors
, as your book connects anddiscusses in great depth and I
was talking to one of myteachers about that.
He was like I would quit on thespot, like that is a terrible
means and the interesting thingabout that is that system lost
great teachers because of thealmost.
(11:36):
Just.
It's the data encroaching onthe thing you're going after
which is really interesting tome.
Speaker 2 (11:44):
And also there's a kind of really important
inequality component to thatthat so much of standardized
testing is actually justmeasuring socioeconomic status
and privilege or advantage, youknow, built in over time.
It's not actually measuringwhat teacher grabs this set of
kids in September and ends upwith this set of kids in June,
(12:07):
and so it's not really a measureof teaching.
It's a measure of what kind ofsocial environment the kids have
came into school with.
And yeah, so it's a terriblesign, it's a terrible signal for
a society to send to teachersthat you know, we have this, we
(12:28):
have this one hammer andeverything is a nail, and
instead teachers are aboutteaching, is about relationship,
and I'm not saying that wedon't know what's good and bad,
and I'm not saying it doesn'tmatter what's good and bad.
In fact that was anothermotivator for this work thinking
about this kind of deeplyinterpersonal, humane service
(12:50):
and how do we scale it up soit's not only available for the
rich or the lucky, like we allas a society worry or should be
concerned about that.
How to spread good teaching,doctoring, therapy, counseling,
et cetera, you know.
And so that was a kind ofinitial motivator for figuring
(13:14):
out how to you know, for mydoing this research, and the
people that were really alsothinking about that were in AI,
and so I came into this project.
I would say more agnostic aboutthat than when I left it or,
you know, after I, you know,wrote the last word, I ended up
(13:36):
more cautionary about the AIproject than when I started.
Speaker 1 (13:43):
That was actually the area I wanted to dive into next
, because a big part of thesummer, at least for me and my
interviewing schedule has been,like, very specific about AI.
What's the effects of AI onsociety?
And also, how do you build AIand AI technologies in a way
that doesn't not in the way assome people talk about, as it
(14:03):
has the least amount of harm,but actually helps people as
well?
What do you think the balanceis there?
Because when we talk aboutscience, for example, artificial
intelligence providesincredible connections across
research and even in likeinterpersonal connections with
doctors.
Scribes perhaps it's scribingso the doctor can look more at
(14:23):
the patient.
What's the balance betweengetting data from interactions
and also the connection?
Speaker 2 (14:32):
Well, if we can draw a kind of line from the argument
that we just were talking aboutwith regard to data and the
argument that I want to makeabout AI, there's some
consistency there, because whatI really want us to do is
prioritize the relationship.
So AI has a you know wealth ofkind of applications, many that
(14:57):
I'm sure we have not, you know,even conceived of yet.
So it is a you know kind of acornucopia of offerings for us.
But engineers are kind of, youcould say, like throwing a lot
of spaghetti at the wall hopingsomething sticks, and one of the
spaghettis strands that they'rethrowing is about automating
(15:19):
relationship, is aboutautomating relationship.
So the scribe, for example,who's, you know, for your
listeners, someone who's writingdown the or usually updating
the electronic health records sothat the doctor can actually
have eyes on the patient andhave a, you know, direct, you
know emotional, thoughtful,reciprocal, connective labor
type experience with a patientthoughtful, reciprocal,
(15:40):
connective labor type experiencewith a patient.
That's not, you know,automating relationship.
That's actually doing somethingbetter.
That's actually arguably that'sallowing the doctor to have
this relationship, and so that's, I would argue, that's, you
know, kind of AI or AI plus, youknow, kind of in service to
(16:06):
relationship.
But there are many instanceswhere engineers are like, huh
well, what if we just automatethe discharge nurse?
What if we just automate thepalliative care consultant?
What if we?
These are I'm not making theseup, these are actual cases where
engineers are kind of tryingout you know kind of agents, ai
(16:27):
agents that stand in the steadof actual humans who would have
relationship, or you know kindof connected labor, a lot of
(16:49):
uses but engineers who aretrying everything they can are
also aiming at automatingrelationship, and that's gone
too far, that's AI run amok.
So those are cases, for example, where you have automated
palliative care consultants,automated discharge nurses,
automated therapists and theargument and I explore this in
(17:09):
my book because I want to kindof inoculate the reader against
these arguments technologistsadvocate for the use of these
because it's better than nothing.
You know, oh, places that don'thave therapy.
Or, oh, students that can't getgood teachers, you know, et
cetera, and I worry that we'rekind of turning to technology to
(17:32):
solve things, solve problemsthat we are not willing to solve
with just providing betterstaffing.
Or, you know, improving ourcredentialing system.
Or you know improving ourcredentialing system.
Or you know, trying to paypeople more so that it attracts
them into the business.
You know like there are othersolutions, hard politically
perhaps, but technology is notgoing to solve these problems.
(17:56):
Because what happens if we usetechnology to solve better than
nothing problems is we are, youknow, running towards a future
in which rich people get youknow kind of this interactive
human experience and low incomepeople or less advantaged people
(18:17):
get that, get that workautomated, get you know, they
get to have the teacher, that isthe bot, whereas the wealthy
person gets the person that youknow in the human who is devoted
toward to having a relationshipwith that student.
So it's just that utterinequity where the human contact
(18:40):
becomes a luxury.
That's what we really need toavoid.
Speaker 1 (18:46):
It's a really interesting dystopia there,
because it's almost flipped themodel on its head of what you
would usually see across likesci-fi things, where rich people
have servants who are robotsand like Alfred is a robot or
something like that.
It's flipped, it's on its headand actually the more I guess
traditional or non-technotopiasociety would be at the higher
(19:09):
class, for example, which Ihadn't ever considered and it
was super interesting to me.
One of the other things that'sconcerning is doing more
research into so backing up.
Some listeners know that somelisteners don't.
I love computer science andprogramming, especially like
natural language processing, butit's concerning where some of
(19:30):
this stuff is being applied.
So there are great ways to applynatural language processing in
the study of algorithms in humanlanguage, but also some areas
where that's a little creepy,such as Instagram's now, like
chatbots that are replacingpeople and like saying I can
talk to you, I can be yourfriend, I can talk to you about
anything, and you're like well,there's multiple things.
(19:52):
One, you're replacing a personthere.
That's that's creepy.
That's a computer.
Two, aren't you also tellingreally personal things to big
corporations Like that's?
That's the other aspect thatyou discuss in your book that we
assume computers are these safespaces but they're not and it's
(20:25):
yeah.
Speaker 2 (20:25):
Could you discuss that a little bit more too?
That analyzes why thatbasically says they.
You know the the web is wherethey have privacy from their
parents and their.
That is worth more to them thanprivacy.
Speaker 1 (20:44):
What was the thing that?
What was the thing thatteenagers had more of it.
Just cut out that one word.
Speaker 2 (20:50):
Teenagers want privacy from their parents and
are willing to.
They find that in the internet,they find that in social media,
they find that out there in theworld, even though they are
kind of giving up privacyvis-a-vis the corporation and so
(21:11):
traditionally they care moreabout getting privacy from their
parent than privacy from thecorporation.
But at the same time I talk toyou know, undergrads all the
time and they are aware and, youknow, kind of aren't thrilled
about corporate surveillance.
So, yes, what you're describingis really disturbing and
(21:31):
actually we're going to see moreand more of that, because I've
seen, you know there's a toythat's getting a lot of
attention and, I think, somesignificant purchasing called
Moxie.
Have you ever heard of it?
It's like an AI companion forkids.
Heard of it?
(21:57):
It's like an ai companion forkids and I think it you know
it's it's being sold again,better than nothing.
So it's being sold by peoplewho sold on the premise of like,
this will help your childpractice friendship and it's
supposed to be for kids, maybe,uh, making friends or something
like that.
But it's the same principle.
It's the to be for kids, maybemaking friends or something like
that, but it's the sameprinciple, it's the same issue.
(22:20):
These kind of better thannothing arguments are a way to
get the AI into the household,you could say, and it's
replacing real humanrelationship, and that's the
kind of AI I want us to reallydraw the line about.
Speaker 1 (22:47):
Like a hundred years ago you might not have seen the
same connection problems.
And it's kind of the sad thing.
Now we're using our technologyto kind of retrograde and go
back and say, well, that problemwe didn't have a hundred years
ago.
Well, you can just fix it withtechnology, and it's really
unique, I guess.
Look at the world.
I'm not entirely sure why, youknow.
Speaker 2 (23:12):
I'm not entirely sure why, you know, I'm not sure,
I'm not a historian, but I'veread histories of the motion et
cetera, and I'm not sure that Iwould agree 100% with what you
just said, because I actuallythink this is kind of an
interesting moment.
I believe that there's evidenceto suggest that we actually are
(23:33):
searching for greater connectionnow than ever before, that
there's a lot of pressure we'reputting on each other to truly
see the other.
Those kinds of demands of beingseen and being seen, those
weren't really expectations, say, 150 years ago.
So it's almost like theseproblems that we have are in
(23:56):
part because of this kind ofgreater need for attention, sure
, but also for a deep emotionalconnection.
And yes, we're not that good atit and yes, we're looking to
technology to solve that problem, but also it's kind of, I think
, a relatively new problem.
These are very we haveessentially very high
(24:18):
expectations for ourrelationships and for our
capacity to be seen in thisworld.
I'm all in favor of thosepretty much, and so that's what
this book is about, is aboutvaluing connection and about how
to get more of it, etc.
But I also do think that it'spretty modern, I'll say.
Speaker 1 (24:41):
Well, that's great.
I hadn't heard that argumentbefore, and that's a much more
hopeful and optimistic view ofthe world versus the terrible
downfall of humanity that youusually hear with social
connection.
Speaker 2 (24:54):
I think they're always getting worse or whatever
Right.
Speaker 1 (24:56):
Exactly.
Speaker 2 (24:57):
So that's great, yeah , I mean I actually behind a lot
of you know I come to this from.
I started as a familysociologist and that's really
what we know about marriage, forexample.
Like marriage, now you hearlike, oh, you know, half of
marriage is in end of divorce.
But really what it is is you'reeither going to be married for
(25:19):
50 years and it's.
You know there's a bifurcationof marriage.
But our expectations ofmarriage is much, much greater
because of this intensificationof emotional kind of action.
So it is a positive, but italso makes things more fragile
and more fraught, so it has somenegatives.
Speaker 1 (25:39):
Well, that's great, honestly great, Like I'm
definitely going to be thinkingabout that idea more, because
it's usually it's doom and gloom.
Speaker 2 (25:46):
Yeah.
Speaker 1 (25:47):
So, backing up and asking the last two questions,
we ask all of our guests whatbooks have had an impact on you.
Speaker 2 (25:54):
So such a fun question.
For me, the best books, thebooks that I discovered I loved,
were books that have the craft,because you want to be
astounded by the writing, havethe plot so that you can go
(26:15):
through.
It's someone who can tell agood story and also have the
heart Like where do they grabyou?
Do you love the people?
I need that actually, and sofor me a pinnacle story here
would be Bel Canto by AnnPatchett.
If you haven't read it, run andget it.
(26:37):
It's about um.
It's about an opera singer in aLatin American country and um
and a takeover by terrorists andby the end you have great
sympathy for everybody involvedand it's a wonderful story.
(26:58):
It's an amazing story.
I think it's the best thingshe's ever written.
I actually think it's myfavorite novel of all time.
But also, I actually read a tonof YA.
I have three kids, read a tonof YA.
I have three kids and I'm a bigYA reader.
(27:19):
So something I've read probably10 times or more has been the
entire Tenere series written byNaomi Novik, who's a really
fabulous YA writer, and that hastaught me a lot about kind of
integrity and coming to knowourselves and just trying to be
true to the person you nowunderstand yourself to be, and
(27:43):
also masculinity and theBritish-French wars of the early
19th century, and dragons,sorry.
Speaker 1 (27:57):
Hey, that's very eclectic.
That first book soundsabsolutely fascinating, like the
heart and soul of it.
Plus, the country gets takenover by terror.
So well, that's a thriller inits own right, and but I love
those books that combine, as yousaid, the plot in the heart and
also have these kind of likerevealed deeper truths about the
world and society and people.
Speaker 2 (28:15):
Yeah, exactly, me too .
Speaker 1 (28:17):
Our last question is what advice do you have for
teenagers?
Speaker 2 (28:22):
Yeah, I actually so, as I have three kids and walked
them, you know, walked alongsidethem as teenagers, and it
wasn't until my last teenagerthat I figured it out or that we
figured it out together.
So I believe that the way theUS does high school, we do high
(28:45):
school really badly, I think,and we do college really well.
So high school is a story ofyou can't really choose anything
and everything is really highstakes and so it's.
You know, lack of choice andintense stakes makes for a rat
(29:08):
race.
That is really appalling anddeadening to the soul.
Maybe I'm being too negativebecause you look very happy.
High school is a rat race, butcollege is actually a place of
great autonomy, great choice andactually not that high stakes.
An individual grade is not asconsequential except, of course,
(29:32):
if you're going to pre-med, buteverybody else is just trying
to learn and trying to do,trying to figure out what
they're interested in.
So college is actually a greatplace.
But for teenagers, my daughterand I discovered something we
call the joy metric and weborrowed it from Marie Kondo.
(29:53):
I don't know if you rememberMarie Kondo.
She was the one who was allabout how to make things tidy
and it was all like you have tothrow things out if they don't
give you joy.
If things don't spark joy,throw them out.
And that is we actually appliedthat for my daughter applied
that was, you know, in someintense calculus and you know,
(30:14):
do I stay with field hockey ordo I, should I retake this exam?
You know, like all theseterrible questions that you have
to answer as a college, as apre-college person, in high
school, and we just started tobe like what gives you joy?
So she, she dropped, for exampleI tell this story to people
because she dropped band in theone year that the band was going
(30:38):
to London and then rejoined itthe next year when they went to
Gatlinburg and I was just likeyou missed London, you know.
But that's the, you know,that's the parent, the
over-involved parent, the ratrace speaking, and she was
really happy and she droppedfield hockey.
And then she picked up like Iforget cross country and just
(31:00):
kind of did and it sounds thesesound like minor choices and at
the time they were her life, sothey were major for her.
But I understand they soundlike minor choices, but the good
news is she still uses it today.
She's now 23, and she has asense.
(31:22):
She has an inner sense that shehas developed of being able to
hear what sparks joy, what she,what, what what sparks joy, and
if you can hear it, that'sthat's something.
That is that's something.
You have to develop thatantenna and that's what I
(31:43):
recommend for teenagers to do ittoday Develop your own joy
metric.
You won't have a choice foreverything, because we don't
give teenagers a lot of choice,but whenever you have a choice,
try and hear it and live the joymetric, because developing your
own sense of hearing, your ownantenna, will serve you for the
rest of your life.
Speaker 1 (32:03):
Well, thank you so much, Professor Pugh, for coming
on the podcast.
I really enjoyed our discussion.
We went from automation torelationships to, of course,
books and advice.
It was a great discussion.
Thank you so much for coming on.
Speaker 2 (32:17):
Thanks.
Thanks so much for having me.
I really appreciate it.
I loved the conversation.
As we enter a world of artificial intelligence, the
question of what should beautomated looms before us.
Models need clear, objectivemetrics to train on.
But can jobs really bedistilled to data points, or is
there a more emotional andrelational side to effective
labor?
This is the Aiming for the Moonpodcast and I'm your host,
Taylor Bledsoe.
(00:30):
On this podcast, I interviewinteresting people from a
teenage perspective.
In her book, the Last Human Jobthe Work of Connecting in a
Disconnected World, ProfessorAllison Pugh asserts many jobs
have a relational component thatcan't be caught in the metrics.
Allison Pugh asserts many jobshave a relational component that
can't be caught in the metrics.
Pugh warns that devaluing whatshe calls connective labor leads
to automation that overlooksthe core issues and leaves us
(00:52):
more isolated.
Allison Pugh is a researchprofessor of sociology at Johns
Hopkins University and theauthor of four books, most
recently the Last Human Job.
She is also the 2024-2025 VicePresident of the American
Sociology Association.
Pugh was faculty at theUniversity of Virginia for 17
years before moving to Hopkinsthis summer.
(01:15):
If you enjoyed this episode,please rate the podcast and
subscribe.
You can follow us ataimingthenumber4moon on all the
socials to stay up to date onpodcast news and episodes Check
out the episode notes for linksto our website,
aimingforthemooncom us at aiming, the number four moon on all
the socials to stay up to dateon podcast news and episodes.
Check out the episode notes forlinks to our website, aiming
for the mooncom and our podcast.
Substack lessons frominteresting people and all right
with that.
Sit back, relax and listen in.
(01:37):
Well, welcome, Professor Pugh,to the podcast.
Thank you so much for coming onthe show.
Speaker 2 (01:44):
Thank you for having me.
Speaker 1 (01:45):
So you wrote a fascinating book named the Last
Human Job, the Work ofConnecting in a Disconnected
World.
Speaker 2 (01:56):
And to kind of dive into our conversation.
What is connective labor?
That's the name I came up withfor a kind of practice that
happens all over the economy.
We have all experienced it andmaybe all of us have done it.
What it is is the moment ofreflecting back to the other
person, the person that you see.
(02:19):
It's the act of seeing theother and being seen.
It's reciprocal, mutual,interactive and it involves some
emotion, emotional sensing ofthe other.
Therapists call this attunement.
But it happens not just intherapy, although that's the
most iconic example.
(02:40):
It happens in teaching, ithappens in the law, it happens
in management, it happens inhigh-end sales, it happens in
medicine.
It's wherever someone has toconvince you that you're being
seen.
For whatever they do to workTeaching is a really powerful
(03:03):
example, I think they do to workTeaching is a really powerful
example, I think, especially foryoung people.
Speaker 1 (03:12):
It's really interesting to think about
connective labor because, as youhighlight throughout your book,
it's something that you don'ttend to think about when you
think about specific jobs.
When you think about doctors,for example, you talk about did
they prescribe the rightmedicine, did they diagnose the
patient correctly, and you don'talways.
You talk about bedside mannerperhaps, but it's more of this
means to an end of kind ofgetting a patient treated.
(03:32):
Why is that?
Why do we not value this typeof work as much?
Is that a problem too?
Speaker 2 (03:39):
I think it's such a problem and you could say that's
really underlying the wholebook.
It's why I wrote this book,because we don't value it and so
we're doing things to it tomake it harder, and actually
it's on its way to being widelyautomated, and so I wrote this
book to point it out and tohopefully reveal some of its
(04:00):
value.
I think the reason why we don'tsee it is because it's not what
we pay for.
You know, we want a child tolearn algebra.
We think, or many people mightthink, that a lot of these
things are about informationdownload.
Like just you know, here arethe download.
(04:22):
Like just you know, here arethe you know 10 things you have
to know about algebra.
But actually we know this acrossmany occupations, people won't
hear the thing, the informationthey need to download, unless
(04:43):
they feel seen.
This is a human fact, and sodoctors have told me, you know I
interviewed many primary carephysicians and they've told me,
you know, I know, you know thatsomeone needs to stop drinking
or that they need to lose weightor stop having so much sugar or
all the things that everyonedoes, and they also know that
it's the relationship that willkind of nudge the person towards
(05:08):
better behavior and that thepatient won't hear them, won't
hear any of this advice unlessit comes from a place of I see
you.
I understand your situation andI see you is not just you drink
too much, it's this is yoursituation.
And I see you as not just youdrink too much, it's this is
your situation, and I get what'shappening here and I get what
(05:32):
your goals are.
It's a really interactive thingand seeing is at the basis of
so much of economic activitythat we actually value actually
value.
Speaker 1 (05:47):
A big part of having conversations like this and a
big part of their disruptionseems to be the value of data we
put in a lot of these jobs,which one of the other questions
that I want to get to later onis.
That balance is specificallywith science jobs, where you
need data to assess whethersomeone is becoming healthier,
for example, but there's alsothis connection where, when you
valued the data and theanalytics, you're missing a big
(06:08):
part of maybe why you wereoriginally even assessing this
and you cite schools as a bigfactor.
Could you discuss that a bit?
Speaker 2 (06:15):
I mean, if your listeners are teenagers, they
know this backwards and forwards.
Many schools, in part, you knowto be kind, they are kind of
nudged or pushed in thisdirection by their, you know, by
their societies, by thesuperintendent, by the district,
(06:37):
by the legislature, butnonetheless, schools have become
places where we count studentsmore than we see them and so we
are counting how many people youknow are attending and how many
people are graduating and howmany people are maybe passing.
(06:58):
You know English, but we're notreally seeing the kids
underneath the numbers.
And that started or that gainedreal traction with in the
United States, with the no ChildLeft Behind Act, which put real
punitive measures in againstschools that you know didn't
(07:25):
meet certain metrics, but whatit did, what that did, was
really that became the enginebehind the counting revolution,
so that there's actually, youknow, people have, you know, an
administrator has recently wasquoted as saying you know, an
administrator was quoted assaying I know kids by data more
(07:46):
than I know them by faces, andthat's a corruption, that's a
serious degradation of teachingand learning, and most teachers
will tell you that.
Speaker 1 (08:00):
What would you say to the argument that this?
Well, first off, this argumentmight be not true, so I am
curious about that as well.
But the argument that says,well, going the data-supported
route, the kind of the analyticsroute, is what produces the
most efficient and best students, for example.
So in order to make sure thatyour students know math, know
(08:22):
English, at a standard level,for example, you need the ACT,
the SAT and tests that kind ofcreate that, I guess, analytical
description of the world.
And so by that we need to be asefficient as possible and, for
the sake of the student, providethem these skills, even if it
means we don't have as much of apersonal connection.
Speaker 2 (08:45):
So what you're really getting at is isn't data worth
something?
And you know, standardizedmetrics can have their use and I
agree with both of those.
So I'm not like anti all data.
I'm a sociologist.
This book is based on data, youknow like it's qualitative data
but nonetheless it is.
(09:06):
You know, the gathering of alot of information and then the
studying it for particularpatterns.
So I understand data and I, youknow, have I appreciate it.
I think it's useful.
You know what it is is like,kind of gathering the
information before you make adecision, make an analysis, make
an argument.
So yeah, but what this book isreally arguing is that data, for
(09:32):
data's sake, has really takenover many spaces where actually
relationships are vital andthey're kind of moving
relationships aside, they'reshouldering relationships out of
the picture.
And I actually want us toprioritize relationship and see
(09:53):
if we can fit data in, and rightnow we're prioritizing data and
seeing if we can fitrelationships in.
So I think that you know, yes,standardization, standardized
tests, I think, have a place,you know, for schools, for
admissions, you know,considering, you know who have
50,000 applicants, say, and haveto figure out some way to sift
(10:17):
through them, but moreimportantly, really for the
student themselves to be able tosay like.
More importantly really, forthe student themselves to be
able to say like huh, it lookslike I'm stronger in English
than in math or whatever, butbeyond that it's not going to
tell you much, not going to tellyou how you learn, not going to
tell you you know, kind of,what tactics to take to master a
(10:38):
particular field.
So it's pretty modest what it'sgetting people and it's
certainly not worth, in myopinion, all the inordinate
attention that it gets in ateenager's life.
Speaker 1 (10:53):
You know I was reading about an example of in
the book Weapons of MathDestruction and I believe the
preface or the introduction theauthor discusses I believe it
was the DC schools who basicallydecided, in order to improve
the quality of teachers, theywere going to assess whether to
rehire them based onstandardized test scores and we
(11:14):
were discussing that and shediscussed how the author was
saying that's a terrible ideabecause of all the other factors
, as your book connects anddiscusses in great depth and I
was talking to one of myteachers about that.
He was like I would quit on thespot, like that is a terrible
means and the interesting thingabout that is that system lost
great teachers because of thealmost.
(11:36):
Just.
It's the data encroaching onthe thing you're going after
which is really interesting tome.
Speaker 2 (11:44):
And also there's a kind of really important
inequality component to thatthat so much of standardized
testing is actually justmeasuring socioeconomic status
and privilege or advantage, youknow, built in over time.
It's not actually measuringwhat teacher grabs this set of
kids in September and ends upwith this set of kids in June,
(12:07):
and so it's not really a measureof teaching.
It's a measure of what kind ofsocial environment the kids have
came into school with.
And yeah, so it's a terriblesign, it's a terrible signal for
a society to send to teachersthat you know, we have this, we
(12:28):
have this one hammer andeverything is a nail, and
instead teachers are aboutteaching, is about relationship,
and I'm not saying that wedon't know what's good and bad,
and I'm not saying it doesn'tmatter what's good and bad.
In fact that was anothermotivator for this work thinking
about this kind of deeplyinterpersonal, humane service
(12:50):
and how do we scale it up soit's not only available for the
rich or the lucky, like we allas a society worry or should be
concerned about that.
How to spread good teaching,doctoring, therapy, counseling,
et cetera, you know.
And so that was a kind ofinitial motivator for figuring
(13:14):
out how to you know, for mydoing this research, and the
people that were really alsothinking about that were in AI,
and so I came into this project.
I would say more agnostic aboutthat than when I left it or,
you know, after I, you know,wrote the last word, I ended up
(13:36):
more cautionary about the AIproject than when I started.
Speaker 1 (13:43):
That was actually the area I wanted to dive into next
, because a big part of thesummer, at least for me and my
interviewing schedule has been,like, very specific about AI.
What's the effects of AI onsociety?
And also, how do you build AIand AI technologies in a way
that doesn't not in the way assome people talk about, as it
(14:03):
has the least amount of harm,but actually helps people as
well?
What do you think the balanceis there?
Because when we talk aboutscience, for example, artificial
intelligence providesincredible connections across
research and even in likeinterpersonal connections with
doctors.
Scribes perhaps it's scribingso the doctor can look more at
(14:23):
the patient.
What's the balance betweengetting data from interactions
and also the connection?
Speaker 2 (14:32):
Well, if we can draw a kind of line from the argument
that we just were talking aboutwith regard to data and the
argument that I want to makeabout AI, there's some
consistency there, because whatI really want us to do is
prioritize the relationship.
So AI has a you know wealth ofkind of applications, many that
(14:57):
I'm sure we have not, you know,even conceived of yet.
So it is a you know kind of acornucopia of offerings for us.
But engineers are kind of, youcould say, like throwing a lot
of spaghetti at the wall hopingsomething sticks, and one of the
spaghettis strands that they'rethrowing is about automating
(15:19):
relationship, is aboutautomating relationship.
So the scribe, for example,who's, you know, for your
listeners, someone who's writingdown the or usually updating
the electronic health records sothat the doctor can actually
have eyes on the patient andhave a, you know, direct, you
know emotional, thoughtful,reciprocal, connective labor
type experience with a patientthoughtful, reciprocal,
(15:40):
connective labor type experiencewith a patient.
That's not, you know,automating relationship.
That's actually doing somethingbetter.
That's actually arguably that'sallowing the doctor to have
this relationship, and so that's, I would argue, that's, you
know, kind of AI or AI plus, youknow, kind of in service to
(16:06):
relationship.
But there are many instanceswhere engineers are like, huh
well, what if we just automatethe discharge nurse?
What if we just automate thepalliative care consultant?
What if we?
These are I'm not making theseup, these are actual cases where
engineers are kind of tryingout you know kind of agents, ai
(16:27):
agents that stand in the steadof actual humans who would have
relationship, or you know kindof connected labor, a lot of
(16:49):
uses but engineers who aretrying everything they can are
also aiming at automatingrelationship, and that's gone
too far, that's AI run amok.
So those are cases, for example, where you have automated
palliative care consultants,automated discharge nurses,
automated therapists and theargument and I explore this in
(17:09):
my book because I want to kindof inoculate the reader against
these arguments technologistsadvocate for the use of these
because it's better than nothing.
You know, oh, places that don'thave therapy.
Or, oh, students that can't getgood teachers, you know, et
cetera, and I worry that we'rekind of turning to technology to
(17:32):
solve things, solve problemsthat we are not willing to solve
with just providing betterstaffing.
Or, you know, improving ourcredentialing system.
Or you know improving ourcredentialing system.
Or you know, trying to paypeople more so that it attracts
them into the business.
You know like there are othersolutions, hard politically
perhaps, but technology is notgoing to solve these problems.
(17:56):
Because what happens if we usetechnology to solve better than
nothing problems is we are, youknow, running towards a future
in which rich people get youknow kind of this interactive
human experience and low incomepeople or less advantaged people
(18:17):
get that, get that workautomated, get you know, they
get to have the teacher, that isthe bot, whereas the wealthy
person gets the person that youknow in the human who is devoted
toward to having a relationshipwith that student.
So it's just that utterinequity where the human contact
(18:40):
becomes a luxury.
That's what we really need toavoid.
Speaker 1 (18:46):
It's a really interesting dystopia there,
because it's almost flipped themodel on its head of what you
would usually see across likesci-fi things, where rich people
have servants who are robotsand like Alfred is a robot or
something like that.
It's flipped, it's on its headand actually the more I guess
traditional or non-technotopiasociety would be at the higher
(19:09):
class, for example, which Ihadn't ever considered and it
was super interesting to me.
One of the other things that'sconcerning is doing more
research into so backing up.
Some listeners know that somelisteners don't.
I love computer science andprogramming, especially like
natural language processing, butit's concerning where some of
(19:30):
this stuff is being applied.
So there are great ways to applynatural language processing in
the study of algorithms in humanlanguage, but also some areas
where that's a little creepy,such as Instagram's now, like
chatbots that are replacingpeople and like saying I can
talk to you, I can be yourfriend, I can talk to you about
anything, and you're like well,there's multiple things.
(19:52):
One, you're replacing a personthere.
That's that's creepy.
That's a computer.
Two, aren't you also tellingreally personal things to big
corporations Like that's?
That's the other aspect thatyou discuss in your book that we
assume computers are these safespaces but they're not and it's
(20:25):
yeah.
Speaker 2 (20:25):
Could you discuss that a little bit more too?
That analyzes why thatbasically says they.
You know the the web is wherethey have privacy from their
parents and their.
That is worth more to them thanprivacy.
Speaker 1 (20:44):
What was the thing that?
What was the thing thatteenagers had more of it.
Just cut out that one word.
Speaker 2 (20:50):
Teenagers want privacy from their parents and
are willing to.
They find that in the internet,they find that in social media,
they find that out there in theworld, even though they are
kind of giving up privacyvis-a-vis the corporation and so
(21:11):
traditionally they care moreabout getting privacy from their
parent than privacy from thecorporation.
But at the same time I talk toyou know, undergrads all the
time and they are aware and, youknow, kind of aren't thrilled
about corporate surveillance.
So, yes, what you're describingis really disturbing and
(21:31):
actually we're going to see moreand more of that, because I've
seen, you know there's a toythat's getting a lot of
attention and, I think, somesignificant purchasing called
Moxie.
Have you ever heard of it?
It's like an AI companion forkids.
Heard of it?
(21:57):
It's like an ai companion forkids and I think it you know
it's it's being sold again,better than nothing.
So it's being sold by peoplewho sold on the premise of like,
this will help your childpractice friendship and it's
supposed to be for kids, maybe,uh, making friends or something
like that.
But it's the same principle.
It's the to be for kids, maybemaking friends or something like
that, but it's the sameprinciple, it's the same issue.
(22:20):
These kind of better thannothing arguments are a way to
get the AI into the household,you could say, and it's
replacing real humanrelationship, and that's the
kind of AI I want us to reallydraw the line about.
Speaker 1 (22:47):
Like a hundred years ago you might not have seen the
same connection problems.
And it's kind of the sad thing.
Now we're using our technologyto kind of retrograde and go
back and say, well, that problemwe didn't have a hundred years
ago.
Well, you can just fix it withtechnology, and it's really
unique, I guess.
Look at the world.
I'm not entirely sure why, youknow.
Speaker 2 (23:12):
I'm not entirely sure why, you know, I'm not sure,
I'm not a historian, but I'veread histories of the motion et
cetera, and I'm not sure that Iwould agree 100% with what you
just said, because I actuallythink this is kind of an
interesting moment.
I believe that there's evidenceto suggest that we actually are
(23:33):
searching for greater connectionnow than ever before, that
there's a lot of pressure we'reputting on each other to truly
see the other.
Those kinds of demands of beingseen and being seen, those
weren't really expectations, say, 150 years ago.
So it's almost like theseproblems that we have are in
(23:56):
part because of this kind ofgreater need for attention, sure
, but also for a deep emotionalconnection.
And yes, we're not that good atit and yes, we're looking to
technology to solve that problem, but also it's kind of, I think
, a relatively new problem.
These are very we haveessentially very high
(24:18):
expectations for ourrelationships and for our
capacity to be seen in thisworld.
I'm all in favor of thosepretty much, and so that's what
this book is about, is aboutvaluing connection and about how
to get more of it, etc.
But I also do think that it'spretty modern, I'll say.
Speaker 1 (24:41):
Well, that's great.
I hadn't heard that argumentbefore, and that's a much more
hopeful and optimistic view ofthe world versus the terrible
downfall of humanity that youusually hear with social
connection.
Speaker 2 (24:54):
I think they're always getting worse or whatever
Right.
Speaker 1 (24:56):
Exactly.
Speaker 2 (24:57):
So that's great, yeah , I mean I actually behind a lot
of you know I come to this from.
I started as a familysociologist and that's really
what we know about marriage, forexample.
Like marriage, now you hearlike, oh, you know, half of
marriage is in end of divorce.
But really what it is is you'reeither going to be married for
(25:19):
50 years and it's.
You know there's a bifurcationof marriage.
But our expectations ofmarriage is much, much greater
because of this intensificationof emotional kind of action.
So it is a positive, but italso makes things more fragile
and more fraught, so it has somenegatives.
Speaker 1 (25:39):
Well, that's great, honestly great, Like I'm
definitely going to be thinkingabout that idea more, because
it's usually it's doom and gloom.
Speaker 2 (25:46):
Yeah.
Speaker 1 (25:47):
So, backing up and asking the last two questions,
we ask all of our guests whatbooks have had an impact on you.
Speaker 2 (25:54):
So such a fun question.
For me, the best books, thebooks that I discovered I loved,
were books that have the craft,because you want to be
astounded by the writing, havethe plot so that you can go
(26:15):
through.
It's someone who can tell agood story and also have the
heart Like where do they grabyou?
Do you love the people?
I need that actually, and sofor me a pinnacle story here
would be Bel Canto by AnnPatchett.
If you haven't read it, run andget it.
(26:37):
It's about um.
It's about an opera singer in aLatin American country and um
and a takeover by terrorists andby the end you have great
sympathy for everybody involvedand it's a wonderful story.
(26:58):
It's an amazing story.
I think it's the best thingshe's ever written.
I actually think it's myfavorite novel of all time.
But also, I actually read a tonof YA.
I have three kids, read a tonof YA.
I have three kids and I'm a bigYA reader.
(27:19):
So something I've read probably10 times or more has been the
entire Tenere series written byNaomi Novik, who's a really
fabulous YA writer, and that hastaught me a lot about kind of
integrity and coming to knowourselves and just trying to be
true to the person you nowunderstand yourself to be, and
(27:43):
also masculinity and theBritish-French wars of the early
19th century, and dragons,sorry.
Speaker 1 (27:57):
Hey, that's very eclectic.
That first book soundsabsolutely fascinating, like the
heart and soul of it.
Plus, the country gets takenover by terror.
So well, that's a thriller inits own right, and but I love
those books that combine, as yousaid, the plot in the heart and
also have these kind of likerevealed deeper truths about the
world and society and people.
Speaker 2 (28:15):
Yeah, exactly, me too .
Speaker 1 (28:17):
Our last question is what advice do you have for
teenagers?
Speaker 2 (28:22):
Yeah, I actually so, as I have three kids and walked
them, you know, walked alongsidethem as teenagers, and it
wasn't until my last teenagerthat I figured it out or that we
figured it out together.
So I believe that the way theUS does high school, we do high
(28:45):
school really badly, I think,and we do college really well.
So high school is a story ofyou can't really choose anything
and everything is really highstakes and so it's.
You know, lack of choice andintense stakes makes for a rat
(29:08):
race.
That is really appalling anddeadening to the soul.
Maybe I'm being too negativebecause you look very happy.
High school is a rat race, butcollege is actually a place of
great autonomy, great choice andactually not that high stakes.
An individual grade is not asconsequential except, of course,
(29:32):
if you're going to pre-med, buteverybody else is just trying
to learn and trying to do,trying to figure out what
they're interested in.
So college is actually a greatplace.
But for teenagers, my daughterand I discovered something we
call the joy metric and weborrowed it from Marie Kondo.
(29:53):
I don't know if you rememberMarie Kondo.
She was the one who was allabout how to make things tidy
and it was all like you have tothrow things out if they don't
give you joy.
If things don't spark joy,throw them out.
And that is we actually appliedthat for my daughter applied
that was, you know, in someintense calculus and you know,
(30:14):
do I stay with field hockey ordo I, should I retake this exam?
You know, like all theseterrible questions that you have
to answer as a college, as apre-college person, in high
school, and we just started tobe like what gives you joy?
So she, she dropped, for exampleI tell this story to people
because she dropped band in theone year that the band was going
(30:38):
to London and then rejoined itthe next year when they went to
Gatlinburg and I was just likeyou missed London, you know.
But that's the, you know,that's the parent, the
over-involved parent, the ratrace speaking, and she was
really happy and she droppedfield hockey.
And then she picked up like Iforget cross country and just
(31:00):
kind of did and it sounds thesesound like minor choices and at
the time they were her life, sothey were major for her.
But I understand they soundlike minor choices, but the good
news is she still uses it today.
She's now 23, and she has asense.
(31:22):
She has an inner sense that shehas developed of being able to
hear what sparks joy, what she,what, what what sparks joy, and
if you can hear it, that'sthat's something.
That is that's something.
You have to develop thatantenna and that's what I
(31:43):
recommend for teenagers to do ittoday Develop your own joy
metric.
You won't have a choice foreverything, because we don't
give teenagers a lot of choice,but whenever you have a choice,
try and hear it and live the joymetric, because developing your
own sense of hearing, your ownantenna, will serve you for the
rest of your life.
Speaker 1 (32:03):
Well, thank you so much, Professor Pugh, for coming
on the podcast.
I really enjoyed our discussion.
We went from automation torelationships to, of course,
books and advice.
It was a great discussion.
Thank you so much for coming on.
Speaker 2 (32:17):
Thanks.
Thanks so much for having me.
I really appreciate it.
I loved the conversation.
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