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