Episode Transcript
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Speaker 1 (00:00):
Bloomberg Audio Studios, podcasts, radio news. Tech leaders, including Sam Altman,
the head of open Ai, which created chat GPT, have
been brimming with optimism about the future of artificial intelligence.
A lot of the things that people are starting to
experiment with now, you know, sort of super cheap energy,
(00:22):
virtual reality, genetic editing, really great AI. You know, these
things are going to transform the world in very fundamental ways.
And Meta CEO Mark Zuckerberg shares Altman's enthusiasm.
Speaker 2 (00:34):
The next five to ten years AI is going to
deliver so many improvements in the quality of our lives.
Speaker 1 (00:40):
They've successfully sold the promise of AI its transformational power
to many investors. The titans of Silicon Valley have poured
billions of dollars into research and development, and the share
prices of their companies have risen in kind. But the
recent arrival of a Chinese competitor called deep Seek made
investors question some of the prevailing narratives that had emerged
(01:01):
around this buzzy technology. Deepseek says it created a rival
to chat GPT maker open AI's model that can perform
human like reasoning at a fraction of the cost, and
that's raised some new questions about where the frenzy surrounding
AI is going to lead and who the winners and
losers in the AI era are going to be. It's
something Tom Orlick, that chief economist at Bloomberg Economics, has
(01:24):
been wrestling with.
Speaker 2 (01:26):
So if we look at the grand sweep of history,
hundreds of years, thousands of years, it's really clear that
the tech visionaries have it right. The plow, the windmill,
the textile factory, the electric motor, the automobile, the PC,
(01:46):
the Internet, all of these have driven increases in prosperity.
And that's the claim that the AI visionaries in Silicon
Valley and China shen Jen are making about the large
language models that there.
Speaker 1 (02:01):
But Tom says, lives are not lived over the span
of hundreds of years or millennia. Lives are lived, he says,
over years and decades, and with the development of AI,
it seems like time is moving even faster.
Speaker 2 (02:14):
Technology. Powerful technology can have positive impacts on the people
who invent it and the people who own it, but
also significant negative impacts on workers who find themselves displaced
and unable for whatever reason to retrain, reskill, relocate, and
get a foothold back in the labor market.
Speaker 1 (02:37):
I'm David Gerat and this is the big take from
Bloomberg News Today. On the show, Tom lays out three
cases for what AI will mean for the economy, companies
and investors, and for you and me. Tom Orlick says,
the first scenario he and his colleagues at Bloomberg Economics
considered for how AI will transform our lives has an
(02:59):
hour that's pretty rosy.
Speaker 2 (03:02):
If we think about the revolution in robotics and automation
which swept the manufacturing sector in the nineteen nineties and
early two thousand's. Well, the promise there was that we
would have machines that could do the work of factory
workers better, faster, cheaper. Of course, that was good news
(03:24):
for the folks that owned the factories and the machines,
not such great news for many of the workers who
lost their jobs. Still, in the grand sweep of history,
doubtless a positive. What's the promise of AI. Well, the
promise of AI is that it can do something similar
for the white collar workers. Right, you're a lawyer, you're
(03:47):
an accountant, you're an economist. Well, AI can supercharge your productivity,
enable you to get your job done more quickly.
Speaker 1 (03:58):
That best case is productivity goes up and a lot
of people are going to benefit from that. Do I
have that right?
Speaker 2 (04:03):
That's right, David even podcast hosts.
Speaker 1 (04:07):
God Will I. What is the second scenario that you're considering?
Speaker 2 (04:13):
So the second scenario is that AI turns out to
be more of a parlor trick than a paradigm shift. Yes,
these chatbots look pretty impressive. It's fun that we can
ask chat gpt to draft a legal document in the
style of a Shakespeare tragedy and it does it in
(04:36):
a couple of seconds. But maybe the downsides of AI
turn out to be more important. Maybe AI stumbles on
the path from the lab to the market and it
just can't do the job, and so the booster productivity
is there, but it's not a game changer.
Speaker 1 (04:53):
The final scenario that you weigh is the most worrisome,
and I wonder if you could lay that out for us.
Speaker 2 (04:58):
The last path is kind of a dystope in path,
and that's one where AI is powerful. It can do
the job of accountants and lawyers and economists, and it
can review X rays and it can write architectural plans.
But instead of supercharging productivity for individual workers, that ends
(05:19):
up just replacing a vast swath of the workforce, and
white collar workers face the same challenge in the twenty
twenties and twenty thirties that blue collar workers faced in
the nineteen nineties and the early two thousands. Massive job losses,
lost income in miseration.
Speaker 1 (05:40):
Leads me wondering sort of how all of this is
going to shake out.
Speaker 2 (05:42):
So one of the things that's happened in the last
week is that the sudden appearance of deep Seek has
suggested that developing leading edge AI models could just be
much cheaper than we previously thought. What it also suggests
is that the competition between Chinese AI champions deep Seek,
(06:03):
Ali Baba and others and the US champions is going
to get more intense. And as we saw in the
Cold War in the technology race, the space race between
the US and the USSR, when you have those sharp
geopolitical incentives, well that can amp up investment accelerate progress
past the technology frontier. And both of those things, cheaper
(06:24):
AI and sharper incentives, more competition between the AI champions
both suggest the moment at which we find out if
AI is going to be a game changer for productivity
and how that cake is going to be divided up,
that moment of kind of revelation is going to come forward.
Speaker 1 (06:46):
So how will we know when that moment of revelation
has arrived. We'll get to that next. We've talked about
this question of how AI is going to impact productivity,
and I'm curious how economists measure that.
Speaker 2 (07:05):
So that's a really good question. And adding to the
sort of the complexity and the confusion here is the
fact that it's actually rather hard to measure productivity. So
if we think about productivity gains at the economy wide level,
or if we think about what drives growth at the
economy wide level, well, it's how many workers you've got,
(07:25):
it's how much capital you've got, and it's how smart
you are at combining those workers in that capital. And
that's the kind of productivity piece. How do we measure that, Well,
we observe where growth is, we subtract what we know
about the labor force, we subtract what we know about
the capital stock, and productivity is the residual. Right. So
(07:46):
productivity is already kind of a bit mysterious, right, It's
measured based on what we can't explain from anything else.
Add to that the fact that GDP numbers growth numbers
are very odd and significantly revised, and what you've got
is a situation where measuring productivity gains, especially in real time,
(08:07):
is pretty hard to do.
Speaker 1 (08:09):
Are there any unique challenges to trying to measure productivity
in the context of AI. I think just perhaps given
the kind of speed of uptake that we're seeing here,
doesn't make the job of calculating productivity harder.
Speaker 2 (08:20):
So, first of all, it's not a surprise that we
don't see the AI productivity gains in the GDP data. Yet,
if you think about technology and its impact on the economy,
the eureka moment for the inventor is a necessary, but
not a sufficient condition for the positive economic impact. You
(08:40):
need that eureka moment, but you also need time for
the new innovation to be diffused through the economy. You
need time for all the factories to go from steam
power to electric power. You need time for all the
companies to work out how to use PCs and how
to integrate them into their workflow. These things take time.
(09:01):
So the fact that AI is not present is not
showing up in the productivity data yet, isn't a huge surprise.
Speaker 1 (09:07):
What can we learn from the impact of past technological innovations.
So you can go back to the cotton gin if
you want, or to stee empowered locomotives. But what if
we just look at, say the impact that computers had
or the Internet had.
Speaker 2 (09:19):
There's a few things to point to, right. So the
first thing is it takes time for new technologies to
show up in higher productivity. Solo a Nobel Prize winning economist,
he said, indeed, not hand Solo, the the Jed.
Speaker 1 (09:38):
The Jedi.
Speaker 2 (09:39):
The Jedi famously said in nineteen eighty seven, we can
see the computer age everywhere apart from in the productivity data.
And it wasn't till a decade later that Alan Greenspan,
then the FED Chair, led a kind of statistical effort
to find the evidence of productivity gains from the computer.
(10:00):
So it takes time for new technologies to show up.
The second thing to say is if you allowed decades
to pass, new technologies raise prosperity for everybody. We're all
better off because of electrification, we're all better off because
of the internal combustion engine. We will all be better
off because of computers and the Internet. But in the
(10:22):
kind of more short period of time. In the years
and decades after a new technology is introduced, the gains
very often are not broadly shared. And the reason for
that is that workers who are displaced by new technologies, well,
for them, the losses often outweigh the gains.
Speaker 1 (10:40):
As we go forward, what are you going to be
watching for? What are other economists going to be watching for,
is they try to assess the impact that AI is
going to have on productivity.
Speaker 2 (10:48):
We're going to be looking at the technology and the
advances in capability for chat, GPT, LAMA, deep seek and
the other models. We're going to be looking at the
case studies, the early evidence of how AI boost productivity
or doesn't boost productivity, and how those gains are allocated
(11:08):
at a micro level, at a company level. Now, where
can we see evidence of a productivity boost from AI? Well,
not so much in the macro numbers, not so much
in the GDP numbers, but if we look at case studies,
we do see some pretty striking results. It's been a
bunch of case studies thinking about whether using AI can
(11:29):
make coding faster, for example, or help people in call
centers deal with calls faster and get better results, and
those case studies they're kind of micro, right, they're looking
at a tiny slice of the labor market, but they
are pretty encouraging to answer the big question, is there
an economy wide productivity boost? Well, I think that's a
(11:51):
question which is still going to take years, maybe decades
to answer.
Speaker 1 (11:56):
The answer to that question is going to be incredibly
consequent whenever we get it. If AI helps everybody, or
if the technology's benefits are not evenly distributed, and we
see the disappearance of rafts of white collar jobs that
Tom says would have a huge effect on our society
and on the balance of political power.
Speaker 2 (12:17):
If we do see the cake being divided up in
such an unequal way, that's going to raise some important
political questions. We've just seen Donald Trump get elected for
a second time as US president. Why has he been
elected a second time as US president? Well, people talk
about China and Mexico and trade and what that did
(12:39):
to US jobs. But guess what. US jobs didn't get
replaced just by Chinese workers and Mexican workers. They also
got replaced by machines. Well, if that's what happened when
blue collar jobs get replaced by machines. I wonder what
would happen if white collar jobs are replaced by machines.
(12:59):
I'm not advocating for my fellow economists to print out
their Excel spreadsheets, mold them into papier mache pitchforks, and
start marching on the data centers of Arlington. But in
a dystopian scenario, that's a possibility.
Speaker 1 (13:16):
Tom was a pleasure. Thank you very much, my pleasure, David.
This is the Big Take from Bloomberg News. I'm David Gura.
This episode was produced by David Fox. It was edited
by Patty Hirsch and Rachel Metz. It was fact checked
by Adrian A. Tapia and mixed and sound design by
Alex Sagura. Our senior producer is Naomi Shaven. Our senior
(13:37):
editor is Elizabeth Ponso. Our executive producer is Nicole Beemster Boor.
Sage Bauman is Bloomberg's head of Podcasts. If you liked
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