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Dan Loney: Well, of course,
artificial intelligence has been one of the big stories in 2024. Companies
are investing millions of
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dollars right now to figure out
how it can help their operations improve. And then there is the
public at large. They are seeing
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how it can change and improve
their day to day lives. Pleasure to talk AI in 2024, about what we
can expect next year and beyond,
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with Lynn Wu, who's an Associate
Professor of Operations, Information and Decisions here
at the Wharton School. Hi Lynn.
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Great to see you again.
- Same here. Great to have you with us. Look, I think my first line in
there kind of sets the stage for
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this. We've had artificial
intelligence around, but it feels like this year— maybe to a
degree a little bit in 2023, but
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this year, specifically— it has
just been this explosion. How have you viewed what we've seen
play out around AI this year?
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Well, I think this year we have seen a
tremendous amount of progress on both technology and also just
general adoption by wider
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part of the society. Like, not
just the tech firms, but small, medium sized businesses are
starting using AI. Big firms are
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having— developing AI strategies
around the operations, marketing and a variety of work functions.
So we see definitely a wider use
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adoption. Also just general use. That component of both large and
small businesses using AI
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I think is one of the unique
aspects of it, because a lot of people would probably say, okay,
larger business has the
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resources to be able to make the
investment, but there are avenues that small businesses
can really dig deep into and
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really benefit from. Oh yeah, absolutely. I like to
think of the example just like, you
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know, like search ads back in
like, 20 years ago. Remember, it kind of democratized advertising to
some extent. - Sure, yeah.
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- Because I think things like Gen AI
democratize some of the knowledge, certain knowledge.
You know.
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Or knowledge search. And also general use of it for writing, for
coding, for development projects,
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for players that wouldn't—
weren't able to do this in a large scale before.
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One of the areas that was talked
about for a while, and I think we're seeing it, you know, play
out maybe in a different path,
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was how it was going to impact
labor. I mean, the discussion point was, "Well, it's going to
take jobs away. It's, you know,
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we're going to see new dynamics
at play." It feels like that it's a little bit different.
That there is a complementary
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aspect to AI that's maybe taking a bigger role, at least
right now. Well, I think the complementarity is
going to be key in the future
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and also the present. There are
lots of things Gen AI can do, and there are lots of things it
does terribly and does it wrong.
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And you've seen the
hallucination examples, right, and the things they're not very
good at. And I think finding
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that complementarity, figuring
out what humans are really good at and what machines are really
good at, and how the machines
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and the humans can work together
on a variety of different tasks in a variety of different industries,
that's going to be key. And that
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is not just about figuring out
complementarities. Once you figured out that working
relationship, you also need to
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redesign the workforce, the work
processes around it, to support that complementarity.
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I think the belief by a lot of
people is, or at least the expectation, is that AI will be
perfect. We know that's not the
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case, because it's still humans
that are doing a lot of the behind the scenes work. So what
should our expectations be
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around AI and its usage and its
capabilities, maybe going forward? I think one good rule of thumb
is that if you already good at
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this, AI is going to help even
more. But if you're not good at this already, or have a very,
very rudimentary knowledge about
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something, I would use AI
cautiously. Because it does hallucinate. It will tell you
things. Sounds very true, but in
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fact, it's not true. In fact, they
confidently say the wrong thing. And that is where it's difficult
for somebody who do not have in-
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depth knowledge about the field
to disambiguate. Is this right
or is it wrong? So that becomes an important
dynamic for businesses to have
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that understanding and to kind
of factor that in to the operational piece of
understanding that AI is going
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to be great, but it won't be
100% you know, proof, in terms of what it can bring to the
table.
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And it'll never be, with the
state of art today, right? It will never be for the next couple
years. So I think it's really
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important for all business to
mitigate the risks of these type of using these type of tools, in
a variety of settings.
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I saw a paper that you had
talked about, about how AI was going to impact innovation. Take
us into your thoughts about
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where AI will play a role in
that area specifically. Yeah. I think AI is going to really
change how products are being—
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being developed. I think one key
thing we've discovered in our recent research is that AI
really turbo charges prototyping.
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Right? Before, you can— you have to
make an ad yourself. Right? Im— figure out image,
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which actor to use. Now these
are all just generated automatically using
generative AI, so you can just
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decide to have 10 different
copies. - Right. - Right? And with— with which— whatever
actor you want,
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and then play it out. So that rapid prototyping is going to be
huge, right? And that's— that's—
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that's not just for, you know,
digital products. - Right. We also see in physical products too.
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AI guides prototypes.
Like, AI gave you ideas on where you actually
should build a prototype. - Right.
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And how you should release in the
wild, and collect data, creating this positive feedback loop
where AI guides you to develop a
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product, and a product— when the
consumer using the product, those data are being fed back to
the machine and continuously
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improve your product. So that
really expedited prototyping, and that can dramatically change
the way products even develop in
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both small firms and big firms. What do you say to the people that—
obviously there are some
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concerns out there about bad
actors and what they can bring forward by using AI. And I think
the question of how we
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mitigate some of the negatives
out there becomes an important component to this as well.
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I think this is— this is huge,
and for a variety of reasons. You mentioned bad
actors. I think that's where
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regulation could potentially be
helpful. You know, just like, you know, we— you can have a —
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you can have a saw, right?
And a saw can be used to kill somebody. A saw
can also be used to—
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an amazing tool to help you, you know, cut down a
tree, build furniture, right? AI is just a tool. But you need to
have regular— you need to have
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uses on where it should be used
and where it shouldn't be used. And also, I want to also caution
that as firms are widely using
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these generative AI tools, these
tools are actually very, very expensive. - Right. - Right?
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And it won't become
cheaper anytime soon. You would generally
consider the exponential growth
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like, you know, Moore's law, we'll
continuously be making technology cheaper and cheaper. - Right.
- But we also see generative AI is
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a highly concentrated industry,
with a few players each level of the AI stack. So it's never going to
be that cheap because of the,
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you know, because of
concentration industry. What about the— what about the
component of AI with the general
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public and how it impacts their
day to day operations of doing projects? And I know part of the
conversation for me started here
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at Wharton, with professors
talking about how they were going to deal with generative AI
use in their classes and with
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testing and such. How do you see
that playing out? Oh, you mean AI in education, or AI—
- Well, AI
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in just use with the public, I
think in general. In general. Well, I think AI is a general
purpose technology.
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Just think of the
internet, right? Internet can
be used for a variety of things,
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variety of reasons, right? And I
think for a general public, it's also same thing, right? You
should, as individuals, should
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try to do everything you can—
you're doing, and see if AI can do it. Right? And then see where
things that AI is really good
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at, like, that can help you
expedite the work you do. And also see where is it— it can
go astronomically wrong, right?
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Then, that's the area you should
focus on putting more time and effort into. I think this is
very individual for— for people,
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and also individual for firms to
figure out what the best use cases for you, specifically, it's
going to be. And that is going to
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be key to unlock the benefit of
AI for you. Well, part of that conversation is
almost generational, like
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younger generations versus older
generations, and how they accept and use AI. It feels like, right
now, and you can shed light on this
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that a lot of generations are
trying to, you know, get their feet wet and understand the
impacts of of generative AI.
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Younger generations may be able
to just run with this as we go in future years and be able to
use it in even, you know, even
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greater uses in the years ahead. I actually think older people
can use generative AI pretty
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well. Maybe my sample is small.
I see my parents using it all the time to say, "Hey, this is
the medical report. What does it
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really say?" - Yeah.
- Right? I think it can definitely, it's— because the usage
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itself is pretty—
relative easy as a conversational bot, in many—
that's where— how most people are
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using it. So I actually think it
lowers the— the typical, you know, technology requirement for
older people to use. - Right.
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Than— than, you know, some— some
other technologies out there. Which will be great, then, for
all generations.
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Exactly. I actually worry about
young people using these tools too
much. - Why so?
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Well, because you don't
know how to write anymore. You don't know how to—
- Well, that's true too. Yeah.
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You don't know
how to do the basic things, and you use this tool to substitute it.
And I think that could be a problem.
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What do you think, then, going
back to the component of innovation, what are the
biggest challenges of
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incorporating AI to enhance
innovation moving forward? Oh, there's lots of challenges
out there.
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So number one, one thing you really have to be
aware is that all these AI, guided prototyping, all these
AI general innovations, tend to
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be recombinational and
incremental, right? These are great innovations, right? Big—
77% economic growth come from
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combining things in a new way,
improving existing things incrementally, right? But the
13% or 19% is radically new
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innovation, right? And those are
actually becoming more important, because they
immediately get fed back, fed
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into AI as they further enable AI
to recombine that into existing technologies and improve it in some
new way. So I think for firms
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that are doing recombination
incremental innovation, this is— the— this is amazing, right? You
can really turbocharge it.
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Well, and if you, if you have that
recombination, then from a business aspect, you're— you're
kind of taking the next step,
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but maybe making a different
level of investment than maybe you would have if you're doing
just radical innovation.
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Absolutely, absolutely. But for radical innovation
people, right—so, GenAI may or may not help you. It
may help you, but it's not
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clear. It's not that clear how—
to the extent it can help you. Then what do you do, right? So I've
produced this amazing
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technology. If I release it to the
wild, I am a name, I create a lot of positive spillover to
others who can use my technology
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to combine something in a new
way or improve it better than I could.
- Right. But then what do I
get out of it? Right? So
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there's— there's a bit of
incentive change, because one part of innovation,
recombination and incremental,
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got much, much cheaper. - Yeah.
- It elevates, actually, the value of the radical innovation. And we
actually need to protect that radical
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innovation more— more—
more than before. What do you think, then, 2025
means for
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the growth of AI? Ooh, that's a really tough question. I think it can go both ways, and
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let me explain that to you. So this pace of where AI technology is improving is exponential.
And it's still riding an
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exponential curve, okay? But
there's also limitation that can constrain that exponential
growth.
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Number one is that we
already ran out of high fidelity data. Which is,
you know, the
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human-written, you know, articles
and writings. Because remember, OpenAI had made— and others— had
made exclusive access to <i>Wall</i>
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<i>Street Journal</i> and others,
right? Because we need human to write these these texts. - Sure.
- Right? And these data has mostly
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trained on internet data
produced by humans. - Right. But now this data is actually being
produced by machines now.
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So it gradually degrades the
technology output, because machine-generated data, when
they fed into AI algorithm
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produce less good stuff than
human data. So there's a limitation on data. - Right.
And also, I mentioned before,
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these things are still going to be
very, very expensive. - Sure. Right? So when—so we're going
to see exponential growth
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and concentration, industry
concentration makes it much more expensive. Then adoption will be
diminished. - Right.
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So you're going to see the two forces
going on. Like, which one, which
one's gonna win out?
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It's going to be a big one.
So it's unclear. Yeah. Lynn, great to see you again.
Thanks very much for your time.
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Great to have you here. Lynn Wu,
Associate Professor of Operations, Information and
Decisions
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here at the Wharton School.