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There's been a discussion for quite some
time that artificial intelligence was
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going to have an impact, potentially
negative, on the workforce.
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Especially when you're talking about jobs
like blue-collar jobs.
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We could see robots
replacing humans in many
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of the tasks that require
some level of labor.
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But there is now more and more thought
that there will be somewhat of a
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blue-collar transition or revolution in
terms of the types of jobs that humans
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will still need to be called on for in
this new AI world.
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Pleasure to be joined by Lynn Wu,
who's an Associate Professor of Operations,
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Information and Decisions here at the
Wharton School.
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Lynne, great to catch up again.
How are you?
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I'm good, thank you for having me.
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Thank you.
All right, so I'm one of those people.
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I've heard all of this conversation
going on.
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There's obviously been a level of concern.
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Where should we believe this?
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Because seemingly this idea of a
blue-collar revolution or readjustment or
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however you want to— is coming into play,
isn't it?
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It is.
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I think it's an exciting time,
because for the longest
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time we worry about blue-collar
workers, and we still do.
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And now with the general rise of this type
of super incredibly capable artificial
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intelligence tools, it can enable a lot of
blue-collar work as well.
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What are the types of jobs then,
if humans are going to be replaced by
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robots in some areas,
what are the types of jobs
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that humans will still
need to be counted on for?
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Absolutely, that's a great question.
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So let me just be clear.
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A lot of the digital AI tools we use today
are in the virtual world.
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They're not co-existing, occupying the
same space and time as we do.
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When that happens, when robots have the
type of intelligence accelerating the way
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that digital tools in our computers,
in our laptops, in our phones are doing,
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then there's a different story.
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For currently, there's a lag between the
physical AI and the digital AI.
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So that means in short terms, medium
terms, that physical manipulation in the
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unstructured environment.
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Like, you know,
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people who do the last mile of
delivering your product to your door,
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delivery workers, or healthcare workers
who lift you out of the bed, doing
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services for you when you're bedridden,
right?
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These kinds of work are still going to be
very much in demand.
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And furthermore, the
type of job that we care
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about is people who can
use AI really effectively.
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I mean, it's how do I use AI to solve
really difficult problems?
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And problems with, you know— under
certainty and easily verified,
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that, AI can do already.
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But problem solving
under very high uncertainty
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and verification can
take a long period of time.
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That requires human judgment and
responsibility.
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And that's where humans can shine.
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And lastly, you know,
interpersonal social
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intelligence is not something
AI can ever replace.
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- Right.
- At least yet.
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- Right. And do you expect that
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even with this shift you talk
about in the medium term,
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are these changes that are going to be
going on,
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are they going to be more long term or is
there kind of a shelf life to them?
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There might be a shelf life to them just
because of scaling law, in that the ability
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of artificial intelligence has been grown
exponentially.
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Like in 2022, when ChatGPT
first released, we didn't think
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it could do the coding kind of
task that we're seeing today.
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So we generally tend to under— we
generally do not understand exponential as
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a human brain because we are linear
thinkers.
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We're not designed to think exponentially.
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So I would say in the long term,
this could continue.
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And then robotics,
physical AI, will be entering
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a scaling law just as
we see with digital AI.
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And then all of this will be rethought
again.
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Right. But right now, at least in
the medium five, ten years, we're
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not seeing that yet because
it is a difficult problem to solve.
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Now, you mentioned healthcare a moment
ago.
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I would think there are certain
sectors where there's almost
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kind of a platform to how much
we can expect AI to contribute.
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Like in healthcare, I don't think there's
much doubt that doctors and nurses,
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the physical need
for those jobs is still
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going to be there for
a long period of time.
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Absolutely.
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I think healthcare is a good example,
and this applies to all jobs.
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We have to rethink about what jobs are
actually encompassing.
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What is it encompassing?
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So the doctor's jobs have already changed.
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If you think about
radiologists, right, they don't
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spend time reading,
detecting cancers anymore.
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But they spend a lot of time communicating
the findings to the primary care or
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whatever the cancer doctors, to formulate a
diagnosis or treatment plan.
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And their jobs are shifting in the sense
the job they did before may not be the job
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they're doing right now, but
the radiology job will always
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be there, just like most of
the jobs classes we see today
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will be there, just what we're doing
within that job will be changed.
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Does this then change the thought process,
and this is maybe a larger scale question
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about AI and the workforce moving forward,
of how the younger generation may need to
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think about what their career might want
to be 10, 20, 30 years down the road?
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I think it's never too
early to think about what
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you want to do with life,
and it's okay to change.
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And I think right now for
young people, this is a really
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exciting time because the
rule book was thrown out, right?
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And you got to redesign your own rule
book.
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This generation of people gets to redesign
it.
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They don't have to play
the rules that we set up
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50, 100 years ago since
the Industrial Revolution.
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So it's a really
exciting time for you to
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control what you want
to do with your career.
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And how do you do that?
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You got to use AI.
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You got to use AI, daily life. Use it, how
to do what you want to do more effectively
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and more efficiently.
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And that's where you're going to have the
biggest edge.
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And I think it's interesting also,
I saw a study the other day, but it seems
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like there's already a recognition by
younger generations about some of this.
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Because the report said that you've got
about 13 or 14% of undergrad students who
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are already rethinking their major because
of artificial intelligence.
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So the younger
generation we know is more
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tech savvy than some
of us older individuals.
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But it truly is an understanding,
it seems like already, that you need to
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really look at what you want to do because
of the impact that AI is going to have.
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Exactly. So if I were a young 20-something,
I absolutely would seize the opportunity.
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I will use AI and see whatever I can do
with it.
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You know, redesign your rulebook.
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You get to redefine what's going on next
50-100 years.
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That's a really exciting time for this
generation.
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Overall then, how do
you think the workforce is
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going to be impacted as
we move forward here?
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I think there will be a very strong
short-term disturbance.
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I mean, a lot of traditional jobs
will be changed dramatically,
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including my job as a research
scientist, as a professor.
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Including you as a media content creator,
right?
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So it doesn't mean that professor's job
will be gone, research jobs will be gone,
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and media content creators' jobs will be
gone.
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It's just, what we do within that job class
will be very, very different.
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And you already see that with software
engineering, right?
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So these days, most advanced coders,
software engineers, they don't code.
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They use AI to code, right?
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What they do is they think about the
higher-level artifacts.
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Like, how do I design this thing?
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What does architecture look like?
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What is client requirements?
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So they're doing a lot
more higher-level jobs than
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maybe the project managers
were traditionally doing.
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So it just means that software engineers
will still play a very important role.
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It's just going to be very different,
just like we are.
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Like, what I'm going to do
is going to be different 10
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years from now or five years
from now than I would do now.
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And that could be exciting for some people,
and that could be scary for some people.
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So it depends on how you look at it.
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So since you mentioned it, how is your job
as a professor going to change?
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Do you see it?
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Oh, I think it's going to change
dramatically for several ways.
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Number one, as a research scientist,
like, how we research is going to change.
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Some of the stuff I do, like, you know,
that would take months to do,
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Now I can use Claude Code, and it'll be
done within a couple hours, in a week,
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in a day, right?
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So it brings a lot of thinking about,
"Well, maybe, you know, this idea
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generation and verification is going to be
fundamentally changed."
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So how do we think about hypotheses?
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Because now we can test hypotheses really
quickly, potentially.
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So we've got to think
about, like, this loop
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between creating a problem
and solving a problem.
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- Right.
- And that's exciting.
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And as a teacher, as a, you know,
professor to undergrads and BA students,
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I think fundamentally how we teach is
going to be changed quite a bit.
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Like, you know, why would I come to your
class when I can have a ChatGPT
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to, you know, ask all the questions,
right?
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So I think that has to be thought about.
How do we manage the classroom experience
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so we can effectively use AI as opposed to
just, you know, being replaced by AI.
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Lynn, always great to talk with you and
get your insight.
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Thanks again for a few moments today.
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Thank you so much.
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Thank you. Lynn Wu, who's
Associate Professor of Operations,
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Information and Decisions
here at the Wharton School.
