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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Tech leaders, including Sam Altman,

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Speaker 1: the head of open Ai, which created chat GPT, have

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Speaker 1: been brimming with optimism about the future of artificial intelligence.

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Speaker 1: A lot of the things that people are starting to

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Speaker 1: experiment with now, you know, sort of super cheap energy,

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Speaker 1: virtual reality, genetic editing, really great AI. You know, these

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Speaker 1: things are going to transform the world in very fundamental ways.

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Speaker 1: And Meta CEO Mark Zuckerberg shares Altman's enthusiasm.

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Speaker 2: The next five to ten years AI is going to

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Speaker 2: deliver so many improvements in the quality of our lives.

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Speaker 1: They've successfully sold the promise of AI its transformational power

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Speaker 1: to many investors. The titans of Silicon Valley have poured

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Speaker 1: billions of dollars into research and development, and the share

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Speaker 1: prices of their companies have risen in kind. But the

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Speaker 1: recent arrival of a Chinese competitor called deep Seek made

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Speaker 1: investors question some of the prevailing narratives that had emerged

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Speaker 1: around this buzzy technology. Deepseek says it created a rival

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Speaker 1: to chat GPT maker open AI's model that can perform

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Speaker 1: human like reasoning at a fraction of the cost, and

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Speaker 1: that's raised some new questions about where the frenzy surrounding

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Speaker 1: AI is going to lead and who the winners and

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Speaker 1: losers in the AI era are going to be. It's

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Speaker 1: something Tom Orlick, that chief economist at Bloomberg Economics, has

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Speaker 1: been wrestling with.

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Speaker 2: So if we look at the grand sweep of history,

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Speaker 2: hundreds of years, thousands of years, it's really clear that

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Speaker 2: the tech visionaries have it right. The plow, the windmill,

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Speaker 2: the textile factory, the electric motor, the automobile, the PC,

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Speaker 2: the Internet, all of these have driven increases in prosperity.

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Speaker 2: And that's the claim that the AI visionaries in Silicon

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Speaker 2: Valley and China shen Jen are making about the large

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Speaker 2: language models that there.

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Speaker 1: But Tom says, lives are not lived over the span

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Speaker 1: of hundreds of years or millennia. Lives are lived, he says,

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Speaker 1: over years and decades, and with the development of AI,

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Speaker 1: it seems like time is moving even faster.

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Speaker 2: Technology. Powerful technology can have positive impacts on the people

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Speaker 2: who invent it and the people who own it, but

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Speaker 2: also significant negative impacts on workers who find themselves displaced

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Speaker 2: and unable for whatever reason to retrain, reskill, relocate, and

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Speaker 2: get a foothold back in the labor market.

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Speaker 1: I'm David Gerat and this is the big take from

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Speaker 1: Bloomberg News Today. On the show, Tom lays out three

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Speaker 1: cases for what AI will mean for the economy, companies

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Speaker 1: and investors, and for you and me. Tom Orlick says,

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Speaker 1: the first scenario he and his colleagues at Bloomberg Economics

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Speaker 1: considered for how AI will transform our lives has an

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Speaker 1: hour that's pretty rosy.

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Speaker 2: If we think about the revolution in robotics and automation

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Speaker 2: which swept the manufacturing sector in the nineteen nineties and

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Speaker 2: early two thousand's. Well, the promise there was that we

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Speaker 2: would have machines that could do the work of factory

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Speaker 2: workers better, faster, cheaper. Of course, that was good news

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Speaker 2: for the folks that owned the factories and the machines,

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Speaker 2: not such great news for many of the workers who

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Speaker 2: lost their jobs. Still, in the grand sweep of history,

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Speaker 2: doubtless a positive. What's the promise of AI. Well, the

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Speaker 2: promise of AI is that it can do something similar

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Speaker 2: for the white collar workers. Right, you're a lawyer, you're

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Speaker 2: an accountant, you're an economist. Well, AI can supercharge your productivity,

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Speaker 2: enable you to get your job done more quickly.

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Speaker 1: That best case is productivity goes up and a lot

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Speaker 1: of people are going to benefit from that. Do I

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Speaker 1: have that right?

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Speaker 2: That's right, David even podcast hosts.

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Speaker 1: God Will I. What is the second scenario that you're considering?

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Speaker 2: So the second scenario is that AI turns out to

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Speaker 2: be more of a parlor trick than a paradigm shift. Yes,

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Speaker 2: these chatbots look pretty impressive. It's fun that we can

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Speaker 2: ask chat gpt to draft a legal document in the

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Speaker 2: style of a Shakespeare tragedy and it does it in

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Speaker 2: a couple of seconds. But maybe the downsides of AI

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Speaker 2: turn out to be more important. Maybe AI stumbles on

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Speaker 2: the path from the lab to the market and it

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Speaker 2: just can't do the job, and so the booster productivity

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Speaker 2: is there, but it's not a game changer.

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Speaker 1: The final scenario that you weigh is the most worrisome,

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Speaker 1: and I wonder if you could lay that out for us.

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Speaker 2: The last path is kind of a dystope in path,

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Speaker 2: and that's one where AI is powerful. It can do

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Speaker 2: the job of accountants and lawyers and economists, and it

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Speaker 2: can review X rays and it can write architectural plans.

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Speaker 2: But instead of supercharging productivity for individual workers, that ends

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Speaker 2: up just replacing a vast swath of the workforce, and

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Speaker 2: white collar workers face the same challenge in the twenty

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Speaker 2: twenties and twenty thirties that blue collar workers faced in

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Speaker 2: the nineteen nineties and the early two thousands. Massive job losses,

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Speaker 2: lost income in miseration.

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Speaker 1: Leads me wondering sort of how all of this is

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Speaker 1: going to shake out.

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Speaker 2: So one of the things that's happened in the last

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Speaker 2: week is that the sudden appearance of deep Seek has

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Speaker 2: suggested that developing leading edge AI models could just be

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Speaker 2: much cheaper than we previously thought. What it also suggests

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Speaker 2: is that the competition between Chinese AI champions deep Seek,

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Speaker 2: Ali Baba and others and the US champions is going

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Speaker 2: to get more intense. And as we saw in the

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Speaker 2: Cold War in the technology race, the space race between

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Speaker 2: the US and the USSR, when you have those sharp

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Speaker 2: geopolitical incentives, well that can amp up investment accelerate progress

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Speaker 2: past the technology frontier. And both of those things, cheaper

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Speaker 2: AI and sharper incentives, more competition between the AI champions

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Speaker 2: both suggest the moment at which we find out if

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Speaker 2: AI is going to be a game changer for productivity

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Speaker 2: and how that cake is going to be divided up,

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Speaker 2: that moment of kind of revelation is going to come forward.

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Speaker 1: So how will we know when that moment of revelation

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Speaker 1: has arrived. We'll get to that next. We've talked about

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Speaker 1: this question of how AI is going to impact productivity,

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Speaker 1: and I'm curious how economists measure that.

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Speaker 2: So that's a really good question. And adding to the

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Speaker 2: sort of the complexity and the confusion here is the

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Speaker 2: fact that it's actually rather hard to measure productivity. So

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Speaker 2: if we think about productivity gains at the economy wide level,

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Speaker 2: or if we think about what drives growth at the

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Speaker 2: economy wide level, well, it's how many workers you've got,

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Speaker 2: it's how much capital you've got, and it's how smart

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Speaker 2: you are at combining those workers in that capital. And

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Speaker 2: that's the kind of productivity piece. How do we measure that, Well,

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Speaker 2: we observe where growth is, we subtract what we know

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Speaker 2: about the labor force, we subtract what we know about

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Speaker 2: the capital stock, and productivity is the residual. Right. So

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Speaker 2: productivity is already kind of a bit mysterious, right, It's

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Speaker 2: measured based on what we can't explain from anything else.

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Speaker 2: Add to that the fact that GDP numbers growth numbers

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Speaker 2: are very odd and significantly revised, and what you've got

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Speaker 2: is a situation where measuring productivity gains, especially in real time,

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Speaker 2: is pretty hard to do.

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Speaker 1: Are there any unique challenges to trying to measure productivity

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Speaker 1: in the context of AI. I think just perhaps given

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Speaker 1: the kind of speed of uptake that we're seeing here,

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Speaker 1: doesn't make the job of calculating productivity harder.

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Speaker 2: So, first of all, it's not a surprise that we

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Speaker 2: don't see the AI productivity gains in the GDP data. Yet,

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Speaker 2: if you think about technology and its impact on the economy,

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Speaker 2: the eureka moment for the inventor is a necessary, but

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Speaker 2: not a sufficient condition for the positive economic impact. You

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Speaker 2: need that eureka moment, but you also need time for

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Speaker 2: the new innovation to be diffused through the economy. You

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Speaker 2: need time for all the factories to go from steam

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Speaker 2: power to electric power. You need time for all the

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Speaker 2: companies to work out how to use PCs and how

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Speaker 2: to integrate them into their workflow. These things take time.

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Speaker 2: So the fact that AI is not present is not

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Speaker 2: showing up in the productivity data yet, isn't a huge surprise.

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Speaker 1: What can we learn from the impact of past technological innovations.

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Speaker 1: So you can go back to the cotton gin if

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Speaker 1: you want, or to stee empowered locomotives. But what if

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Speaker 1: we just look at, say the impact that computers had

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Speaker 1: or the Internet had.

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Speaker 2: There's a few things to point to, right. So the

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Speaker 2: first thing is it takes time for new technologies to

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Speaker 2: show up in higher productivity. Solo a Nobel Prize winning economist,

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Speaker 2: he said, indeed, not hand Solo, the the Jed.

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Speaker 1: The Jedi.

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Speaker 2: The Jedi famously said in nineteen eighty seven, we can

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Speaker 2: see the computer age everywhere apart from in the productivity data.

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Speaker 2: And it wasn't till a decade later that Alan Greenspan,

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Speaker 2: then the FED Chair, led a kind of statistical effort

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Speaker 2: to find the evidence of productivity gains from the computer.

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Speaker 2: So it takes time for new technologies to show up.

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Speaker 2: The second thing to say is if you allowed decades

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Speaker 2: to pass, new technologies raise prosperity for everybody. We're all

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Speaker 2: better off because of electrification, we're all better off because

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Speaker 2: of the internal combustion engine. We will all be better

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Speaker 2: off because of computers and the Internet. But in the

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Speaker 2: kind of more short period of time. In the years

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Speaker 2: and decades after a new technology is introduced, the gains

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Speaker 2: very often are not broadly shared. And the reason for

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Speaker 2: that is that workers who are displaced by new technologies, well,

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Speaker 2: for them, the losses often outweigh the gains.

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Speaker 1: As we go forward, what are you going to be

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Speaker 1: watching for? What are other economists going to be watching for,

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Speaker 1: is they try to assess the impact that AI is

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Speaker 1: going to have on productivity.

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Speaker 2: We're going to be looking at the technology and the

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Speaker 2: advances in capability for chat, GPT, LAMA, deep seek and

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Speaker 2: the other models. We're going to be looking at the

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Speaker 2: case studies, the early evidence of how AI boost productivity

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Speaker 2: or doesn't boost productivity, and how those gains are allocated

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Speaker 2: at a micro level, at a company level. Now, where

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Speaker 2: can we see evidence of a productivity boost from AI? Well,

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Speaker 2: not so much in the macro numbers, not so much

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Speaker 2: in the GDP numbers, but if we look at case studies,

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Speaker 2: we do see some pretty striking results. It's been a

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Speaker 2: bunch of case studies thinking about whether using AI can

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Speaker 2: make coding faster, for example, or help people in call

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Speaker 2: centers deal with calls faster and get better results, and

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Speaker 2: those case studies they're kind of micro, right, they're looking

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Speaker 2: at a tiny slice of the labor market, but they

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Speaker 2: are pretty encouraging to answer the big question, is there

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Speaker 2: an economy wide productivity boost? Well, I think that's a

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Speaker 2: question which is still going to take years, maybe decades

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Speaker 2: to answer.

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Speaker 1: The answer to that question is going to be incredibly

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Speaker 1: consequent whenever we get it. If AI helps everybody, or

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Speaker 1: if the technology's benefits are not evenly distributed, and we

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Speaker 1: see the disappearance of rafts of white collar jobs that

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Speaker 1: Tom says would have a huge effect on our society

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Speaker 1: and on the balance of political power.

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Speaker 2: If we do see the cake being divided up in

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Speaker 2: such an unequal way, that's going to raise some important

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Speaker 2: political questions. We've just seen Donald Trump get elected for

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Speaker 2: a second time as US president. Why has he been

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Speaker 2: elected a second time as US president? Well, people talk

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Speaker 2: about China and Mexico and trade and what that did

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Speaker 2: to US jobs. But guess what. US jobs didn't get

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Speaker 2: replaced just by Chinese workers and Mexican workers. They also

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Speaker 2: got replaced by machines. Well, if that's what happened when

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Speaker 2: blue collar jobs get replaced by machines. I wonder what

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Speaker 2: would happen if white collar jobs are replaced by machines.

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Speaker 2: I'm not advocating for my fellow economists to print out

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Speaker 2: their Excel spreadsheets, mold them into papier mache pitchforks, and

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Speaker 2: start marching on the data centers of Arlington. But in

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Speaker 2: a dystopian scenario, that's a possibility.

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Speaker 1: Tom was a pleasure. Thank you very much, my pleasure, David.

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Speaker 1: This is the Big Take from Bloomberg News. I'm David Gura.

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Speaker 1: This episode was produced by David Fox. It was edited

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Speaker 1: by Patty Hirsch and Rachel Metz. It was fact checked

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Speaker 1: by Adrian A. Tapia and mixed and sound design by

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Speaker 1: Alex Sagura. Our senior producer is Naomi Shaven. Our senior

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Speaker 1: editor is Elizabeth Ponso. Our executive producer is Nicole Beemster Boor.

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Speaker 1: Sage Bauman is Bloomberg's head of Podcasts. If you liked

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Speaker 1: this episode, make sure to subscribe and review The Big

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Speaker 1: Take wherever you listen to podcasts. It helps people find

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Speaker 1: the show. Thanks for listening. We'll be back tomorrow.