As I say, it's really early going and we don't know a lot.
But what do we think we might know about productivity and growth?
Because that's what investors seem to be most interested in.
Thanks very much for inviting me. What you said is correct.
We don't know very much about the productivity consequences.
It's easy to project enormous potential gains, but we would have made the same
projection about the computer era of the last four years.
And in fact, growth has been relatively disappointing, especially for the last
15 years. So I think there's enormous upside
potential, but sometimes that potential doesn't turn into reality.
And, you know, we will spend some of the productivity of AI dealing with the
problems that it creates, including a hallucination, including vulnerability
to cyber attacks. And so part of that gain, that
so-called, you know, dividend, will actually be squandered on air defense.
Talking about productivity and growth, women are sort of talking about whether
the pie gets bigger overall. What about how we divide up the pie that
is distribution, particularly in the labour market, because you've done some
work there based on past history. But what we think might happen with
jobs, who gets better jobs? Who gets worse jobs?
So the period we're concluding now, the computer era, the traditional computer
era, has been one of rising inequality. And we think that computing has had a
lot to do with that because it's been very complimentary to decision makers,
you know, to lawyers and doctors and marketers and people who basically use
information and analysis to make high stakes one off decisions.
It's displaced a lot of workers from production work, from office work, and
sort of pushed them down into less expert jobs in food service, cleaning,
security, entertainment, recreation. And those are socially that's socially
valuable work, but it's paid poorly because it doesn't require specialised
expertise. And so computing, as we understand it,
has actually not been that great for growth and
really created a lot of inequality.
I think A.I. has the potential to work quite, quite
differently. As you talk, it sounds to me there's
another distributional potential effect here.
People, for example, receiving medical care, which is far from equally divided
in this country and goodness knows around the world.
Is it potentially that more people will have access to better health care in the
United States and around the world? Great question.
So I do think there's a lot of super expensive services that are expensive,
specifically because they're provided by highly paid experts.
So that would be an education, that would be in health care, that would be
in architecture and design, that would be in software development.
And I could make those things more accessible.
I think that's going to help. That has potentially enormous benefits
both in rich, industrialized countries like the United States and potentially
even more so in the developing world where that type of expertise is even
more scarce. On how we do that, of course, depends a
lot on our institutions, right? So the good scenario is the price of
medical care comes down, it becomes more broadly available that we, you know,
allocate it more efficiently. The bad scenario is basically if you're
wealthy, you see a highly paid doctor, and if you're not wealthy, you see a
machine. So we shouldn't count on the technology
to solve our problems. For us, it opens possibilities.
But how we use them is really a societal choice.
And just to give you a very stark example of this, you know, in the 1940s,
scientists figured out how to harness controlled nuclear fission that has two
really powerful uses. One is for energy generation and the
other is for a nuclear weapons. North Korea is a country that has lots
and lots of offensive nuclear weapons, but no nuclear power plants.
Japan, the only country against which offensive nuclear weapons has ever been
used, has no nuclear weapons and dozens of nuclear power plants.
So II is a bit like nuclear energy, but in some ways more powerful and certainly
more applicable. We can use it for really good things and
for really destructive things. And already both of those are current.
And finally, first, let's come back to where we started, the uncertainty about
AI right now, because one of the things I've learned from you and from others is
this is not just a matter of degree, but perhaps of kind.
That is to say basically with automation, we people who are really
expert in this know how it works. They know what the rules are.
The computers are following a I generally, as I understand, we literally
don't know how it works and never will know how it works.
It doesn't know how it works itself. What does that say about our ability to
predict where it's going and how to manage it?
So your point is an excellent one. Just to to slightly clarify, we
understand or, you know, computer scientists understand mathematically
what it's doing, but in any given instance, it's it's like a child.
It's it's learned some lessons. What do I actually you know, it's read
some things. It's encounters.
And things we will actually do on any given occasion is is extremely hard to
predict. You'd have to know everything it's ever
been exposed to. And so that makes it actually a lot like
human experts in some ways because we're ourselves unpredictable in the sense.
But it means that when we interact with AI, we need to learn how to treat it not
as authoritative but as a guide or support decision making.
And that's really critical. A professor does that and.
Necessarily lead to regulation. That is to say the government telling us
when we should and shouldn't use A.I. because we don't want to use it the
wrong way. I think it leads to a couple of types
regulation. One of them is safety regulation, but
we're actually pretty good at that. You can't buy a toaster oven that
doesn't meet energy standards and fire proof new standards.
And so when we're using A.I. in specific applications, like in
aircraft or in medicine or in cars, the government should and I think will
regulate that. And so absolutely.
But there's two other forms of regulation that I think are much harder.
One is I, you know, desperately, you know, chips away or let me put it
differently, I really threatens the foundation of our
intellectual property system, our copyright laws.
They just weren't built in anticipation of machines that would absorb and then
memorize and not exactly reproduce, but pretty much replicate what's already
there. So that's an issue.
And I really think there's a real threat to, you know, to newspapers, to crude,
to illustrators, to artists, to actors. That needs to be negotiated and set
properly law in bargaining. And we've already seen the Screenwriters
Guild do that. The Actors Guild do that.
But that's only the very beginning. So that's one type.
It could also be used for, you know, invading privacy, for
exploiting and monitoring workers. That's something else we need to
regulate. And then I think finally, we need to
direct A.I., not just prevent it from being bad, use badly, but help to use
it. Well, we've talked a lot about medicine.
Health care is, you know, almost 20% of our economy.
And most of that money actually is ultimately government money.
The government pays for at least 40% of our health care, meaning we pay for it
through taxes. It means government has a lot of
leverage to help shape the way it develops, to say, hey, let's use this
technology to democratize access to reduced professional boundaries and
allow more people, skilled people, up and down the line to do better decision
making and deliver health care more efficiently.
The same is true in education. I can be a supplement to teachers.
I don't think it's going to be a replacement, but it's not easy to use
well, and again, a lot of educational expenditures, ultimately government
expenditure, not just in case you 12 schools, but also in universities and
state colleges and universities and even in private, not for profit universities
like the one I have the privilege of working in.
So again, the government should not just be trying to control and prevent misuse,
but try to say how can we use this better to deliver services more
effectively and to improve the quality of work for people, the majority of whom
do not have a college degree, who have really done pretty poorly in the United
States over the last four decades, and potentially who could be enabled, could
be supported to do more valuable work by having access to better tools.