You've written an article, co-author, an article in Foreign Affairs, specifically
The Coming Economic Revolution. So you've thought through these things.
First of all, start with what the challenges are that we have in terms of
size of workforce and productivity that perhaps I could help us on the way.
A lot of what I think will be called secular headwinds to growth.
So we lived for three decades, David, in a world that produced massive amounts of
incremental productive capacity made mainly in the emerging economy.
As a result, we had a deflationary kind of pattern in the economy.
Inflation wasn't much of a problem and all that.
That's that force is fading rapidly. And then you've got aging in the global
economy. More than 75% of the global economy is
in a serious pattern of aging. You know, declining workforces in some
cases, you know, big changes in labor market behavior.
And you have shortages in all the major employment sectors in the American
economy, the big ones. And then and then, you know, you have
geopolitical tensions and shocks coming from multiple sources like pandemics,
climate and the pattern of diversification initiated both by
businesses, you know, in pursuit of kind of resilience and longer term
profitability, but also now by by economic policy in multiple
jurisdictions. And when you combine all those forces
and the declining productivity trend, which has been fairly dramatic in the
last decade, then you've got a world that switched relatively rapidly from,
you know, demand constrained to supply constrained growth patterns.
And so we're looking for solutions to the supply side issues that are that are
creating not only problems for growth, but, you know, problems for the
re-appearance of inflation, you know, for higher interest rates, higher lower
valuations, higher costs of capital and so on.
And we're doing it in an environment where we have to make enormous
investments in things like sustainability.
So it's when I stand back and look at it, it's a you know, it's a big
challenge. And and so that led me and others to try
to start looking for, well, what tools and technologies do we have to try to
solve this problem? We all lived through something of a
digital revolution in the late nineties, in the early aughts, in the move to the
Internet. And there was a real growth impetus from
that, as I recall. In what ways does this new move the
digital revolution through? I compare to that maybe it's similar to
or maybe different from. So we did get a productivity surge.
It came, you know, in the late 1990s after everybody got access to the
Internet and the World Wide Web came in, you know, into its own.
And that lasted into the early 2000. And some of that is the ongoing, you
know, the digitalization of supply chains and so on.
That was pretty powerful. And it it had
noticeable effects. It produced a decline in middle class
jobs and in the advanced economies like like ours.
It produced what David Autor calls job and income polarization.
There was a lot of automation that, you know, we automated things like, you
know, the keeping of the general ledger. Most of the most of the information
systems that a corporation runs on are now sort of digital and fairly automated
and so on. So, you know, it was powerful.
But but it feels to me at least very different from the current round.
One of the things you point out in your paper in Foreign Affairs is the effect
that this new gender reveal could have on the so-called knowledge economy,
which is different from the automation you referred to from the Internet.
That may well affect at different strata of jobs, as I understand it.
Yeah, I mean, you know, the knowledge economy, when you think about it
carefully, kind of runs through and across the entire economy.
So even even in what we think of as sectors that are sort of largely blue
collar, you know, there's a fair amount of knowledge, you know, that goes into
these things skills, knowledge and so on.
Take something like nursing. You know, nurses are pretty highly
trained people. They have to respond in intelligent ways
to the signals they get from, you know, the equipment that their patients are
are using and they're looking after and so on.
But, you know, but yes, I mean, the CEO of Google, when asked, you know, where
is this going to have its immediate impact that's going to be in the
knowledge economy. But but I think, you know, we tend to
think of that as sort of white collar work.
And and there's an element of truth in that.
But I think it actually does run right across the entire economy, maybe not to
the same depth in every sector, but but nevertheless, it's a very broad based.
Do we have a sense. And I understand this may not be
determinate. I mean, that is to say it may differ
based on what we do, but do we have a sense of the extent to which this
general will displace jobs? They will go away as opposed to
transform jobs? Look, I mean, we're in a period of
intense exploration and experimentation. So, you know, people often say, you
know, that it's important to acknowledge, you know, real uncertainty
when you're in the early stages of this. And that's certainly characteristic of
this. Having said that, James and I our best
guess is that generative AI is mainly not exclusively, but mainly going to
going to turn out to be, you know, a powerful digital assistant.
One of the points you make in the paper is that, as I say, this is not
predestined. To some extent, how this ends up depends
upon us and what we decide to do about it.
Explain to us about what you think needs to be done or should be done so that we
get the most good out of generative AI. And as a sub point, how much of that can
be trusted to the markets as opposed to governments?
So, you know, I think the markets and government and attitudes and what and
biases are all part of the mix. So I think the first order of business
is to is to avoid what Eric Burdon of some calls in an influential paper,
The Turing Trap. So Alan Turing proposed that we evaluate
progress with digital machines by using the following tests.
Can we build a machine that interacts with the human?
And the human thinks that it's interacting with another human.
It was a fairly small step, and this is common in the world to evaluate progress
in AI by determining how well machines do relative to human performance.
So we do that in language recognition, we do it in image recognition, which is
a huge breakthrough in the, you know, in the last six, seven years.
And we do it, you know, with the generative AI does the generative I do
better than the average human on the LSAT, the law school aptitude test.
That's okay. But there's a very small step from that
to thinking that, you know, well, once the machine parses the human mind and we
get rid of the human, and that leads you to what you might call the automation
bias. And that I think we do have to resist.
And that means that's not government, that's not business, that's not any
particular sector. It's just the way we think about it.
So I think that's step one. Step two is, you know, you can influence
the direction of technology and development.
And I think, you know, with incentives and so on.
And you should be influenced in the direction of collaboration and
augmentation. And then third, with the way James and I
thought it, but we don't want to repeat that pattern of, you know, sort of high,
high adoption rates and very low adoption rates that we saw in earlier
rounds of digital, you know, adoption. And so there I think the government has
a very distinct role to get, you know, to at least think carefully about
programs and activities that, you know, increase the likelihood of diffusion
across the entire economy to small and medium sized businesses.
And for me, as an economist, this is not a just a nice to have or a
distributional issue if we believe there's a potential productivity surge
yet for the entire economy, we're not going to get it.
You know, if we can count on the fingers of one hand the sectors that have moved
ahead in using this. So so.
So I think it's important from a kind of macroeconomic performance point of view
to to think carefully about what we can do
to maximize the sort of accelerate the diffusion and adoption rates across the
economy.