How AI could help the middle class
Link (Click) : How AI could help the middle class
SYLVIE DOUGLIS, BYLINE: This is PLANET MONEY from NPR.
(SOUNDBITE OF COIN SPINNING)
GREG ROSALSKY, HOST:
It's been about six months since ChatGPT was released to the public. And basically, from the moment that happened, it felt like this seismic shift.
+ seismic : 지진의, 지진에 의한, (규모가) 엄청난
NICK FOUNTAIN, HOST:
Because all of a sudden, people everywhere realized just how powerful artificial intelligence already is. They began using this AI chatbot to do all sorts of things - to write raps, to take the bar exam, to identify bugs in computer code and fix them.
+ all of a sudden : 갑자기
+ bar exam : 사법고시
ROSALSKY: I mean, all that stuff is pretty cool. But at the same time, there's been all this doom and gloom about AI. Will it take our jobs? Will it derail democracy? Will it kill us all?
+ gloom : 어둠, 우울
+ derail : 탈선하다(시키다)
FOUNTAIN: And these aren't, like, off-the-wall questions. Like, serious people are asking these questions right now.
+ off the wall : (재미있게) 특이한, 약간 미친(제정신이 아닌)
ROSALSKY: Yeah, it's kind of easy to fall into this, like, doom spiral these days. But then, a couple weeks ago, I saw something that gave me, like, this little glimmer of hope. It was a study that looked at this customer service department of a big software company, and they started using ChatGPT to help workers get better at their jobs. And interestingly enough, it worked. Like, it made the less skilled workers at this company much more productive. And at the same time, it didn't do much for workers at the top.
+ doom spiral : 운명의 소용돌이
+ glimmer : (희미하게) 깜박이는 빛, 희미한 기미, (희미하게) 깜빡이다(빛나다)
FOUNTAIN: So basically, AI narrowed the productivity gap between lower-skilled workers and workers with more skills. And, Greg, I think it's fair to say you read a lot of economic studies.
ROSALSKY: Probably too many.
FOUNTAIN: And yet you have been telling me - you've been telling all of us - that this finding felt really big to you because it's different from how we usually understand the way technology affects workers.
ROSALSKY: Yeah, there's a whole generation of research looking at the effects computers have had on the labor market. And over and over again, what economists find is that, for decades now, computers have been this major force for increasing inequality. What this study shows is that AI could be different. And when I saw that, I was like, you know what? I want to talk to David Autor.
FOUNTAIN: David Autor, professor at MIT, widely regarded as one of the greatest labor economists in the world.
ROSALSKY: Autor led a lot of that research that found computers were this force for a shrinking middle class. And I wanted to find out if he thinks, maybe this new technological era we're in is going to be different if maybe AI could be a force for greater equality.
+ shrink [ˈsriŋk] : 줄어들다(오그라지다), 줄어들게(오그라지게)하다
FOUNTAIN: Right. So hello, and welcome to PLANET MONEY. I'm Nick Fountain.
ROSALSKY: And I'm Greg Rosalsky.
FOUNTAIN: And, Greg, today's show is going to be a little different. We found your conversation with David Autor so interesting, so illuminating, so prescient that we're just going to run it.
+ prescient [ˈpre-sh(ē-)ənt] : 선견지명[예지력]이 있는
ROSALSKY: Today in the show, the American middle class has been shrinking for more than 40 years. Could AI help reverse that trend?
(SOUNDBITE OF ANTON SYCH ET AL.'S "VIRTUAL MACHINE")
ROSALSKY: When David Autor thinks about how AI will affect the future of work, he actually looks to the past. He sees two major turning points when technology fundamentally changed our economy. The first turning point was a long time ago. We're talking about the Industrial Revolution, when machines began to replace work that had previously been done by hand.
DAVID AUTOR: So prior to the Industrial Revolution, there was a lot of artisans, people who did all the steps in making a product - right? - so whether it's a piece of clothing or, you know, building house or a tool. The era of mass production created an alternative way of making things. And it was basically breaking things down into a series of small steps that would be, you know, accomplished in sequence often by, you know, machines, managers and pretty low-skill workers, right? And so a lot of artisanal skill was displaced. I mean, the Luddites rose up for a reason.
+ artisan [ˈär-tə-zən] : 장인, 기능 보유자 (=craftman)
+ in sequence : 차례차례로
+ displace : 대신(대체)하다 (= replace), (살던 곳에서) 쫓아내다, (평소의 위치에서) 옮겨 놓다
ROSALSKY: Autor says that, at first, the factory jobs that displaced the artisans required less skill and also paid less - so kind of a bummer. But then, machines got more complex, and so did the things they could make. You know, we're talking automobiles instead of textiles. And so factory owners started to need workers with more skills.
+ so did the things (that) they could make : 도치 문구 (ex : so do I)
AUTOR: Over time, that work became more skill-demanding because people had to follow formal rules. And if you're using a lot of expensive equipment and making precise products and using expensive inputs, you need people who are kind of - can follow those rules well. So this created what you might think of as the kind of middle skill, what I would call mass expertise, right?
ROSALSKY: This is, like, the golden era that we hear a lot about in the United States, this time when people could graduate from high school with basic reading and math skills and then go out and find gainful employment, you know, jobs on factory floors or jobs in offices, where workers had to understand how to, you know, compile paper records or deal with basic financial transactions.
+ gainful : 돈벌이가 되는
+ factory floor : 작업현장
AUTOR: For people who didn't have four-year college degrees, these were the relatively better-paid jobs, right? They're better-paid than, for example, food service, cleaning, security and so on. And the reason is that food service, cleaning, security - they're valuable pursuits. They do, you know, important things in the world, but most people can do them. And so they're not going to be well remunerated. For work to be well paid, especially in an industrial economy, it needs to be expert work of some sort.
+ pursuit : pər-ˈsüt (sut 발음 주의) 추구, 일
+ remunerate : 보수를 지불하다
By expert, I mean, one, you need a certain body of knowledge or competency to accomplish that - a thing. That thing must be worth accomplishing, right? And not everyone can do it. And so it is the case that the kind of industrial era helped really grow the middle class. It created this tail wind where people with a reasonable amount of education - all of a sudden, it made them highly productive in offices, highly productive in factories, highly productive in sales. And so, yeah, it created this huge rising tide that, you know, was relatively equalizing. Now, I don't want to say it's only technology, right? There are institutions that went with this. There's democracy. There was obviously the system that educated people. But the technology helped.
+ competency[ˈkäm-pə-tən(t)-sē ] : = competence (능숙함), 권한, (특정한 일을 하는 데 필요한) 기능
+ body : 많은 양(모음)
+ tail wind : 개선된 상황으로 진전을 발전시키는 힘, 또는 영향력
ROSALSKY: So, OK, the Industrial Revolution, it killed off jobs for skilled artisans. But then, it created a whole bunch of new jobs for middle-skill workers, jobs that gave opportunities to Americans without a college degree. That's turning point No. 1. The second big turning point is computers. This is what a lot of David Autor's research has focused on. He finds that in the computer era, starting around 1980 or so, all of those middle-skill jobs that emerged from the Industrial Revolution, they started getting automated away. Think robots taking jobs on assembly lines or computer software taking jobs from administrative office workers. At the same time, computers made higher-skill workers much better at their jobs. This elite group benefited a bunch from using email, building spreadsheets, searching the internet - I don't know - like, trading stocks and information instantaneously all over the world.
+ trade stocks : 주식을 매매하다
+ instantaneously : 순간적으로, 즉석으로, 동시에
AUTOR: So if you're a highly educated worker, you know, if you're a doctor or an attorney or a marketer or researcher, those people are highly, strongly complemented by this sort of automation of these information processing and routine tasks. On the other hand, if you are someone who does, like, dexterous, manual work, like food service, cleaning, security, entertainment, recreation, there's really not much complementarity there at all, right?
+ complementarity : 상보성(서로 모자란 부분을 보충하는 관계에 있는 성질)
+ dexterous : 손재주가 비상한, 솜씨 좋은
ROSALSKY: Mmm-hmm.
AUTOR: It doesn't make you much better, doesn't make you worse. However, you have lots of people in the middle, who are now being pushed out of those middle-skill occupations. And it's just not very easy to move up, right? If you're an adult and you're displaced from your manufacturing job, it's very unlikely you're going to get a law degree or medical degree. So you're going to more likely end up driving a truck, working in a restaurant, working as a security guard. And so the computer era actually devalued that mass expertise and massively amplified demand for elite expertise, which has been really not so great, right? It just means (ph)...
+ occupation : 직업, 심심풀이
+ devalue : 평가 절하하다
+ expertise : ˌek-(ˌ)spər-ˈtēz (끝에 '티즈' 발음 주의)
+ amplify : 증폭시키다
ROSALSKY: Not great if you're not a...
AUTOR: That's right.
ROSALSKY: ...Elite worker...
AUTOR: Right.
ROSALSKY: ...'Cause it's pretty great if you're an elite worker.
AUTOR: It's true. It's been a lot - it's been a great four decades for elite workers especially in the United States.
ROSALSKY: But to put it in crude words, technological change over the last few decades has increased inequality.
+ crude : 대충의, 대강의, 대충 만든
(ex : To put in crude words : 조잡하게 표현하자면)
(ex : in clude terms : 대충 말해서, 거칠게 말해서)
AUTOR: Sure.
ROSALSKY: And now it feels like maybe, like, just maybe, we're in a new era. Like, you know, I was already starting to think this. And then, this new empirical study came out by Erik Brynjolfsson, Danielle Li and Lindsey Raymond and - that looked at what happened to a software company and its workers after the company adopted an old version of ChatGPT. And they basically find that this AI system makes their workforce much more productive. But more interesting to this conversation, they found that only some workers benefited from it, and it was actually the less experienced, lower-skilled workers that benefited from use of the technology. And the more experienced, higher-skill workers saw little or no benefit. And to me, that kind of - it seems to be, like, reversing what we've been seeing, where it's complementing the bottom and not really doing much for the top. And I just want to get your reaction to those findings.
+ empirical : 경험(실험)에 의거한, 실증적인 (<-> theoretical : 이론적인, 이론에 근거한)
AUTOR: Sure. And actually, you know, my students Shakked Noy and Whitney Zhang also have a paper where they did a sort of a related experiment working with people doing writing tasks. And these were people who are college-educated and do, like, advertising copy and so on. And some used ChatGPT, and some didn't. And basically, they found that using the large language model - it made everyone more productive by saving them a lot of time. But it pulled up the bottom very considerably. So the people who are only pretty poor writers on this scale became average, and people who are excellent became a little better. And so it reduced productivity inequality. So it's very consistent with the paper by Brynjolfsson and Li and Raymond.
+ pull up : move up
+ consistent : 일관된, 한결같은, ~와 일치하는
ROSALSKY: So that's interesting. I didn't know about that study. So now we have two empirical studies that are showing that it's pulling the bottom up...
+ empirical : 경험(실험)에 의거한, 실증적인
AUTOR: Yes.
ROSALSKY: ...And maybe doing a little for the top, but maybe...
AUTOR: Right.
ROSALSKY: ...Not doing much.
AUTOR: Right.
ROSALSKY: So there's a big implication there. Yes?
+ implication : 영향(결과), 함축, 암시
AUTOR: There's a big possibility there. So the good scenario is one where AI makes elite expertise cheaper and more accessible, right? So right now, you know, if you want to do a lot of medical procedures, you need a medical degree. That takes a decade, right? And that makes those people scarce, expert and expensive. But you can imagine that with the right tools, you could devolve some of those tasks to people who have - know something about medicine and health care, but they don't have to have that level of education. And then, they could do much more. And, you know, we already have an example of that, right? So the nurse practitioner - a nurse practitioner's a nurse who has an additional master's degree. They...
+ expertise : (tez 발음 주의) 전문 지식(기술)
+ scarce : 부족한, 드문, (부사) 겨우, 간신히, 거의 ~않다
(부사 ex : I can scarce remember him)
+ devolve : (권리, 의무, 직분을) 양도하다, 맡기다, 지우다, (재산 등이) 이전되다, 귀속하다, 의존하다
+ nurse practitioner : 실습(견습) 간호사
ROSALSKY: My sister's a nurse practitioner.
AUTOR: OK, great. And so nurse practitioners are well paid, right? The median pay is about $150,000 a year. And they do many of the things that only medical doctors were allowed to do, right? They diagnose. They prescribe. They treat, right? And, you know, how is that kind of passable? Partly, it's a change in, you know, medical norms and scope of practice boundaries. Partly, they're enabled by technology, right? There's a machine that says, don't put those two prescriptions together; you know, that would be a problem. And, you know, this set of symptoms is associated with this constellation of diseases; check the following. And you can imagine many ways in which people with foundational skills in something could use AI to make that expertise go further. So the good scenario is basically where AI lowers the cost of elite expertise, makes it more available, and increases the value of basically the middle-skill workers of the future. That's my good scenario.
+ diagnose : ˈdī-ig-ˌnōs '다' 발음 주의
+ treat :
(1) (특정한 태도로) 대하다(다루다, 취급하다, 대우하다) -> ex : to treat people with respect / consideration / suspicion
(2) 여기다, 치부하다 -> to treat his mark as a joke (그의 말을 농담으로 치부하기)
(3) 처리하다, 논의하다
(4) 치료하다, 처치하다
+ disease : di-ˈzēz '지즈' 발음 주의
+ median : 중간의, 중간값
+ passable : 그런대로 괜찮은
+ constellation : 별자리, 성좌, 패턴, 배열
ROSALSKY: So to translate - potentially, potentially, AI - good for the middle class...
AUTOR: Could be.
ROSALSKY: ...Good for rebuilding the middle class. That's, like, the...
AUTOR: Could be.
ROSALSKY: That's, like, the hope.
AUTOR: That's the good scenario.
ROSALSKY: That's, like, the headline.
AUTOR: And not just hope - we got to make it happen.
+ have got to = got to = gotta : ~해야 한다
ROSALSKY: That's the headline right there. Like, David Autor hopes...
AUTOR: Yes.
ROSALSKY: ...That AI is good for the middle class.
AUTOR: No, no, no. Let's use AI to reinstate the middle class.
+ reinstate : 복귀시키다, 회복시키다
(SOUNDBITE OF ANTON SYCH ET AL.'S "TRICKY QUIRKY")
ROSALSKY: What it will take to make that happen and also the other scenario David Autor imagines, the one that doesn't go so well for workers - that's after the break.
(SOUNDBITE OF ANTON SYCH ET AL.'S "TRICKY QUIRKY")
ROSALSKY: So for years, David Autor has been looking at the effect of technology on the labor market. And he finds that computers made elite workers better at their jobs and much richer. But at the same time, computers also made a bunch of good middle-class jobs disappear. Autor thinks maybe AI could help reverse that trend, lift a whole bunch of workers back into the middle class by helping them get better at writing or research or - I don't know - creating complicated legal documents. Basically, AI could allow them to do jobs currently reserved for the upper echelon of the labor market. That's what David Autor calls the good scenario.
But even in this good scenario, know there's going to be a disruption of people who are currently making - I don't know - a hundred to $200,000 a year or something like that. All of a sudden, it doesn't make as much sense to pay those people as much anymore because you have a whole pipeline of people who can now do that job.
+ echelon [ˈe-shə-ˌlän] : 계급, 계층, 직위
+ disrupt : 방해하다, 지장을 주다
AUTOR: That's correct. It's possible that, basically, you will see some expensive expert work just less in demand, that you will need fewer managers for certain types of decision-making, that, you know, more, like, legal work will be done by machines as opposed to by lawyers, and that you'll have lawyers, but they're supervisory, and there are, you know, fewer of them. So, yeah, I think it's possible. But, you know, in the long run, that means fewer people will have to go to a college (laughter), which is expensive.
+ supervisory [ˌsü-pər-ˈvī-zə-rē ]: 'z'발음 주의
ROSALSKY: Yeah.
AUTOR: And it also matters, right (ph)? This is not a zero-sum game, right? If it makes us all more productive, we're wealthier as a result of that, right? So even if it displaces some of what you do, but then the rest of what you do you do it 10 times as fast, that's a gain in productivity.
+ you do it 10 times as fast : 구문 순서 주의
ROSALSKY: So you're hopeful in the labor market thing...
AUTOR: Yeah.
ROSALSKY: ...Contingent on smart government policy, essentially.
+ contingent : (어떤 행사에 참석한, 특히 출신지가 동일한) 대표단, 파견대, (~의) 여부에 따라
(ex : All payments are contingent upon satisfactory completion dates)
AUTOR: Smart government, smart private sector, smart philanthropy, smart universities.
+ philanthropy : 자선활동
ROSALSKY: And so maybe that will involve some disruption...
AUTOR: Yeah.
ROSALSKY: ...Of people at the top.
AUTOR: Yeah.
ROSALSKY: But you know what? They've been doing so well for so long...
AUTOR: Yeah.
ROSALSKY: ...That maybe, you know, you got to crack some eggs to make an omelet.
AUTOR: That's right. And I don't think they're just going to be thrown out of the top...
ROSALSKY: (Laughter) I did not think you were going to say, that's right. I love that, though.
AUTOR: I don't think they're all going to be just, like, thrown out of the top floor of office buildings.
ROSALSKY: Yeah.
AUTOR: I - you know, this is - these things happen so gradually.
ROSALSKY: Yeah. So that's the optimistic scenario. I just want to bounce off with the dystopian kind of - and maybe this is - I mean, the truly dystopian is they become sentient and kill us all. But the dystopian economic...
+ bounce off : ~에 대한 (사람의) 반응을 살피다
+ sentient [ ˈsen(t)-sh(ē-)ənt] : 지각이 있는, 극히 예민한 (sh발음도 되고 t발음도 가능)
AUTOR: Or we - actually, the more likely dystopian is some - we use it to kill one another.
ROSALSKY: Yeah.
AUTOR: Right?
ROSALSKY: But - OK, so (laughter) putting that aside, putting human existence aside, I'm focusing...
+ put aside : ~을 제쳐놓다
AUTOR: Right.
ROSALSKY: ...On the (laughter)...
AUTOR: Right.
ROSALSKY: ...On the economics.
AUTOR: Other than that, how was the play, Mrs. Lincoln? Yes.
+ other than that = except
ROSALSKY: (Laughter) I can still imagine, like, a narrative or potential future where it's actually...
AUTOR: Right.
ROSALSKY: ...AI is inequality-increasing. So...
AUTOR: Sure.
ROSALSKY: One scenario - obviously, like, companies who own these systems will get insanely rich. But then, there's the - also, like, the downstream effects where there's a whole bunch of industries where a bunch of people used to do the job, but now only you need one or two people to do it.
AUTOR: Yeah.
ROSALSKY: So what do you think about that sort of pessimistic potential future?
+ pessimistic : 비관적인
AUTOR: I don't want to rule it out. I mean, you can - so you can imagine a world where, you know, you just need a few superexperts overseeing everything, and everything else is done by machines, right? So that's one possibility. Another possibility is one where, like, no one's labor scarce, right? That's not a good world because then we have lots of productivity, but nobody - who owns it? Just the owners of capital, right? Then, we have to have a revolution and blah, blah, blah. It's not going to work out well, right? Those things never work out well. So I don't view those scenarios as highly likely.
+ rule out : 제외시키다, 배제하다 (= exclude, eliminate)
+ scarce : 부족한, 드문, 겨우, 간신히, 거의 ~ 않다
+ work out : (건강, 몸매 관리 등을 위해) 운동하다, (일이) 잘 풀리다(좋게 진행되다)
+ likely : ~할것 같은, 그럴듯한, 그럴싸한
One thing to recognize is that we are actually in a period of sustained labor scarcity because of demographics, right? We have very low fertility rates. We have large populations who are retiring. And we have radically restricted immigration. And so the U.S. population is growing at its slowest rate since the founding of the nation. And most industrialized countries - and China as well, by the way - are facing this problem of they're getting smaller and older, or their populations are not growing. That's a world where we need a lot more automation actually to enable us to do the things we need to do including care for the elderly. So I'm not worried about us running out of work and running out of jobs to do. I am worried about the devaluation of expertise.
+. scarcity (스케얼 발음 주의) : 부족, 결핍
+ demographics : 인구 통계 (자료)
+ fertility : 비옥함, 생식력
+ radically : 근본적으로, 철저히, 극단적으로
+ elderly : 연세가 드신, 연세가 드신 분들, 어르신들
ROSALSKY: Wait. Just for clarity, though, 'cause you just said, I am concerned about the devaluation of expertise.
AUTOR: Right.
ROSALSKY: But also, though, it sounded like you were excited about the devaluation of expertise...
AUTOR: No, no, of highly...
ROSALSKY: ...Because then anybody could do it.
AUTOR: OK, sorry. Let me be clear. I'm worried about a world where no one's labor scarce. But let me give you, like, an example of what I mean by this. Like, for example, you might say, oh, Waze makes everyone an expert driver, right? But, no, actually, it doesn't. It doesn't make anyone an expert driver. It has the expertise, right? So there was a time when London taxicab drivers needed to know all the highways and byways of London, which is a - it took years to master, right? It was an incredible feat of memorization. And then, that made them really expert. They could get you around London better than any other driver. Well, now, you don't need to know that. You just need a phone, right? And that's good for passengers. It's good for consumers. But it devalues the expertise that those drivers...
ROSALSKY: Would you call that de-skilling, essentially? De-skilling?
+ deskill : (특정 직종에 필요한 기술을) 단순화하다
+ byway : (통행이 별로 없는) 샛길, (주제의) 사소한(부차적인) 부문
+ memorization [ˌme-mə-rə-ˈzā-shən]
AUTOR: I would say it devalues the expertise. So I realize that's not - didn't meet you halfway there. But what I mean is...
ROSALSKY: (Laughter) Well, it sounds like de-skilling to me because they used to have a skill that was - oh, I guess it was a valuable skill...
AUTOR: Yeah, I guess...
ROSALSKY: ...And now they still have that skill.
AUTOR: They still have the skill. It's just not needed.
ROSALSKY: But it's just less valuable. Yeah.
AUTOR: It's not scarce, right? So the expertise of, you know, being a London cabbie has - you know, has been substantially devalued.
+ substantially : 상당히, 많이(=considerably), 주로, 대체로
ROSALSKY: OK. So, yeah, that's one of Autor's big worries - that what happened to London cabbies kind of happens to the entire labor force - that AI makes human expertise kind of irrelevant. It devalues it. But David Autor doesn't actually think that will happen, at least not for all workers and not any time soon. He says people still have all of these advantages over AI. Like, we're more adaptable. We have more common sense. We're better at relating to other people. Not to mention - we have bodies. We have arms and legs, and we move around in the world. Like, there's a bunch of things about being a human that still have advantages in the marketplace. So AI raises all of these different possibilities - some more promising, some kind of scary, some very scary. And I just want to end by getting sort of the big-picture gut check from David Autor.
+ irrelevant : 무관한, 상관없는 (여기서는 의역해서 '무의미한'으로 해석)
+ adaptable : (새로운 환경에) 적응할 수 있는
+ common sense : 상식
+ promising [ˈprä-mə-siŋ] : 유망한
+ gut check : 평가, 테스트
I'm curious where your head is at, like, 'cause - where are you? 'Cause you seem hopeful that it could rebuild the middle class if we channel it. But then, it's also like there's all these other who-knows-where-this-is-going.
+ channel : (돈, 감정, 생각 등을) ~에 쏟다, (돈, 도움 등을) (~을 통해) 보내다, (물빛 등을) 나르다(보내다)
AUTOR: Yeah. I mean, I think there's an optimistic - there's a positive scenario for the labor market. But that's the labor market side. I think there's many reasons for concern about how AI could be misused in all kinds of other ways - right? - from misinformation to control of critical systems to surveillance and monitoring and coercion to very, very dangerous weapons, smart weapons that can autonomously do all kinds of terrible things. So I think there's lots of reasons to be scared about how it can be used. The irony is the labor market is the least scary part of this at the moment in my mind.
(LAUGHTER)
+ sirveillance : 감시 (= observation)
+ coercion [kō-ˈər-zhən] : (무력, 협박에 의한) 강제(강압)
+ autonomously : 자체적으로, 독자적으로
+ irony : 아이러니, 역설적인 점(상황), (흔히 농담조의) 비꼼, 반어법
ROSALSKY: Well, thank you very much. I really appreciate it.
AUTOR: Sure thing, Greg. It was a pleasure speaking with you. Thanks. This was really a lot of fun.
(SOUNDBITE OF AARON SCHULTZ ET AL.'S "PLAYING THE GAME")
ROSALSKY: If you enjoyed this episode, we've got more AI content on the way. Next week, we will be launching a three-part series where we try to figure out whether we can replace all of PLANET MONEY with AI. Yeesh (ph). If you just want more insightful PLANET MONEY content about the economy from a real-life human being, subscribe to the newsletter that I write. You can find it at npr.org/planetmoneynewsletter.