Adapting To Massive Technological Change
rising from a small rocky island in the San Francisco Bay at the top of a two-story Victorian building is a lighthouse called East brother it's actually the oldest lighthouse inside San Francisco Bay Tom but is the head of a nonprofit group that has maintained the historic structure since 1980 when the coast guard abandoned it they did with e brother what they did with a lot of lighthouses they just boarded it up and walked away from it but thanks to the efforts of locals and preservationists the historic Lighthouse is now Immaculate a thriving Guest House business supports its upkeep and on a recent volunteer day a motorboat fed a dozen passengers to the island two inke keepers live at East brother and run the guest house but it's volunteers who handle minor maintenance to work in the garden so come here minute so I brought you can use anything in here there's the paint peeling on the fog horn engine room picket fence post leaning over the rust spots on the White Walls that wrap the Light Tower this place really takes a beaing out here I'll tell you nevertheless the lighthouse has stood since it was built 150 years ago it tells the story of a critical time in Maritime history and a story of how quickly things change you know with modern navigation tools a lot of these things are essentially obsolete but back when they built this lighthouses were one of the main ways that Mariners could navigate into the bay and navigate up the Sacramento River although lighthouses still guide ships all but a few were automated many years ago the fact that the light vog horn could become automated that's happened at all the lighthouses all over the country probably all over the world that's a huge change because the original plan of the coast guards we understood it was basically to tear these buildings down and put the light up on a pole when East brother changed hands from the Coast Guard to volunteers its new operators had a dilemma they wanted to keep the human element of the lighthouse alive even though there was no longer any clear way to pay for it we needed to find a way to create a revenue stream so we wouldn't have to keep going out and looking for Grants after some brainstorming they came up with the idea for the guest house as a way to make money and keep people on the island today that idea provides more than enough Revenue to cover costs including the salary of two inke keepers a job that attracts a lot of attention whenever it's posted lighthouses have always had a sort of a Romanticism about it people I can't tell you why people just really into lighthouses advancements in technology will upend our world and when new Innovations replace human labor how do we adapt Susan ay the economics of Technology professor at Stanford Graduate School of Business studies these shifts and what they mean for workers businesses and Society whenever anybody says oh we're not going to have enough jobs that's actually not a well defined statement because there's a lot of jobs that are things people could do that would be valuable the real question is whether the receivers of those Services can pay for them how we navigate these shifts depends on the choices that we make if we act wisely Ai and other Innovations can serve society and create opportunity how we design these transitions is our Focus today this is if then a podcast from Stanford Graduate School of Business where we examine research findings that can help us navigate the complex issues facing Us in business leadership and Society I'm Kevin Co senior editor at Stanford [Music] GSB can you start by talking about a study that you did in Sweden that dealt with the impact of job losses there so with a couple of collaborators from Sweden we studied the impact of about 22,000 layoffs that occurred over a period of a couple of decades and that type of study had been done before people had showed that layoffs had negative effects on people but the new wrinkle that we added in our study was to use machine learning techniques that I'd recently developed in order to ident identify in a datadriven way exactly which groups of people were most impacted by layoffs and that can help in turn direct policy and we were also interested in how long those impacts last and so what we found was that the most impacted bottom 10 or 20% were really impacted badly so their earnings were down 40 to 45% in the first year and their earn ings remained down by about 15% 10 years later so that's quite a long-term negative shock then we try to say well all right what's the characteristics of these people and we look at a whole bunch of different characteristics but if you had to pick a couple of things age and education together accounted for a lot of the difference so that suggests that there may be groups of workers that are really not going to recover from such a layoff and especially if they're older and towards the end of the career we may want to think about different kinds of policies than for the younger workers where really helping them transition into something new may be feasible and possible one thing that seems to be consistent in a lot of your work Susan is that there is a high value on lowcost highly scalable Solutions is that what the goal should be in these sorts of situations the promise of digital technology especially for helping the poor developing countries vulnerable workers is it's scale economies MH I wanted to take those same insights and apply them to something that might not be funded purely for profit but if you can cover the fixed costs that you could scale them more broadly and so yes that's absolutely part of the thesis to figure out how you can do lowcost interventions another reason I was thinking about that specifically in the last few years I started some of this work just before or during the pandemic and that was a time when especially you know online learning and people's participation in online learning had a a step change up so one of the other things that we saw during the pandemic is some governments were looking at trying to do online learning non-standard credentials while people were home and so we started working with with corsera corsera in particular was interested in sharing some of their resources with people in developing countries some of them are offered for a fee they were interested in scaling those up more broadly and we looked into possibly measuring the impact of such training on a larger scale one big question we had was if you did get a corsera non-standard credential would it even make a difference and is this really a good use of time if put it on your CV is anybody going to pay attention so we ran a study with corsera where we created a new product feature that helped people put a corsera credential on their LinkedIn profile in two clicks and we studied that specifically for people in developing countries or people without a college degree and then we wanted to see well like at a larger scale can we actually help people get jobs so over for a six-month period we had about 800,000 people who didn't have a college degree or were from a developing country who finished corsera courses in a bunch of areas like related to data science and it other things like that and so we took each person at the point they finished their course and we randomly gave them access to the feature that made it easy for them to post their credential on LinkedIn I see so some people got the feature and some people didn't with the feature you just clicked two buttons and then the credential would get posted on your LinkedIn profile so it wasn't just something you wrote on your profile but it actually clicked through to a description of the certificate and what it meant so it sort of automatically vetted it at the same time exactly it was a credible credential yeah yeah and so we randomized 800,000 people into 50/50 treated control over a 6-month period and then we watched how they got jobs and we found that indeed posting the credentials did have an impact on getting a job and we tracked the jobs not by surveying people which has a very low response rate but by watching their LinkedIn profiles and seeing who posted a job and again this effect persisted over many months so policy makers who might have been considering the possibility of say subsidizing training for folks to get an online certificate now we have at least some evidence that if it's paired with something like the link feature that you describ then it probably is worth it exactly and as soon as corsera saw our results they actually rolled out the feature platform wide so now you know millions of people are enjoying the feature so in that study what's the lesson for people who aren't in a situation where they are being automatically given that prompt there's a big benefit to showcasing the credentials you have and especially having them on your digital profiles for industries that do a lot of digital recruiting now I want to caveat that actually that the world is changing rather quickly there so AI is getting better and better at reading resumés and also AI is getting better and better at writing resumés so the way that a particular thing on a resume gets read could be a moving Target in that environment but I think generally the prospect for having a verifiable credential still look quite good because it can be sort of validated and an AI resume screening tool in principle should be able to pick that up sure now if everybody has a credential it also doesn't make you stand out right so putting a credential on your CV is going to still have impact that's related to supply and demand and so you do have to signal that you have something scarce in order for it to have value mhm what does it mean if we have ai on both ends of the transaction we have a I writing the cover letters and producing the resumés and we have ai evaluating the cover letters and the resumés what does that world look like that's a great question and it's something I think about a lot I used to get emails maybe you know every other week of someone who wanted to work for me somewhere in the world and they would maybe a high school student a college student a grad student all sorts of people email professors and they would tell me about how their interest matched up with my research perfectly MH now I get one of these like every other day and many of them are written by chat jpt clearly and so it's basically removed the signaling value of taking the time to write a thoughtful email so now I I mostly delete all those emails there's too many of them I can't answer them I can't read them so basically taking the cost away of sending the email removed a friction that was playing an important role right it's a differentiator right yeah having to incur a friction is a differentiator so this is not a new problem but in the employment context it's going to be a very important problem and employers actually had gotten there first with automated screening tools but now with Chachi PT the employees have caught up a friend of mine was telling me about how they were thinking about you know how you would design a new Marketplace to really lean into this idea that the robots were talking to each other it will be different though because you have to do something about the fact that people if I have a robotic agent that's applying on my behalf and it's having an interview with a robotic hiring agent the fact that I can apply to infinite jobs and conduct infinite interviews in parallel changes the game sure in a variety of ways but of course you're wasting the time of another robot so and we don't want to do that so so the time cost maybe isn't the problem but some point the person needs to incur some real cost to show that they're interested and that they're a good fit there are a lot of concerns I think in the general population about what AI will mean longer term in terms of its disruption to employment and so on what is your view about both what we should be wary of with respect to Ai and what the potential is that would either mitigate those concerns or create new opportunities and new Pathways that don't exist now that actually might make the world better easier cheaper so whenever anybody says oh we're not going to have enough jobs that's actually not a well- defined statement because there's a lot of jobs that are things people could do that would be valuable the real question is whether the receivers of those Services can pay for them mhm and so there's a few things that kind of scale with the size of the population so Child Care Elder Care Health Care you know seeing a theme there's a bunch of you know personal services in general that some of it can be provided by technology maybe an AI coach on my phone is is good for helping me do exercise but some things you're still going to want a person for and I think there's enough thing things that we have valuable work for most of the population imagine how much healthier you would be if you could actually talk to your doctor like once a month or if they actually could follow up with you and fine-tune your treatment or make sure that things were working for you most of us are way undertreated and we're just guessing and checking in just very periodically think about obesity think about General metabolic Health there's a bunch of stuff where with some concerted effort we could make people healthier which would lower costs in the long run so it be a good investment but somebody has to pay for this so people can pay for it themselves if they have good jobs and if the services aren't too expensive or the government can provide them and a lot of these things are either subsidized or funded like teachers and child care are funded by the government so's a pretty big role for the government in all of this and of course Elder Care largely funded by the government so in that kind of a world what can AI do AI can help the service provider be a better and safer service provider even if they don't have a lot of training and so I can give an example of another study we did in Cameroon one problem in Africa is there's not enough educated nurses so for the nurses we designed a tablet application that helped them ask the right questions of the patients in order to give targeted information back to the patient that was customized to their specific concerns and situations and that was consistent with best medical practice that had a huge positive impact and also the nurses loved it so those types of programs they're easy to build the problem going forward with AI is that there may be too easy to build there's going to be a whole proliferation of them so we need actually some vetting of them we need a a modest number of them that are actually good and give the right answers but if we had those things and they were vetted and tested then we could actually have a lot more people be nurses and teachers and coaches and elder care providers so those are examples where a little bit of investment like a very modest amount of investment something that hundreds of people could build you know we're not talking massive massive amounts of money because it's like building software um you could help service providers be better service providers and then you'd have to combine that with the government funding the provision of those Services unless the economy is doing so well that you know lots of people can just afford to buy those on their own yeah so some mix of public and private investment could actually help us transition to an economy where there's lots of participation where actually the technology has allowed a bunch of people to do better jobs and make everybody healthier and reduce healthcare costs so that all sounds great what am I worried about on the flip side is if we have any localities that are very focused in something like there's a major employer or set of employers that are doing the same kind of jobs and those get automated all at once so my favorite example for the last 10 or 15 years has been call centers and one reason that's an example is that there are lots of parts of the world that were former manufacturing centers or mining centers right that where those have already been shut down and so then the next thing those regions did is they invested in call centers because you had these very low wage workers in these very low cost of living areas no jobs so put a call center there but the call center my prediction was once they get automated they're all going to kind of get automated together and one reason is that the call centers are all using software already to help the workers answer the calls and they already have been connected to the relevant databases of information and they're already recording all of those calls so if you take all of that data plus the it infrastructure that exists and throw now modern AI at it with very little effort you can make a lot of those work work obsolete yeah and so we are starting to see that already the technology is here and the adoption is happening and so you could kind of shut down for the second time say a former mining town or region of a country that was focusing on call centers and it's very unclear what those people are going to do next yeah and if they're low wage people in poorer areas and so on those effects then because it's in a concentrated area are even larger than they might be they spiral down to the restaurants the hairdressers everything the real estate so those areas can collapse and so I think there are parts of the US that could be vulnerable to this I think the US is probably better able to diversify and recover but I think some other countries might have more trouble with that you know I think if we can preserve our Economic Institutions it's certainly feasible with a very strong and a very effective government intervention at both the state and National level that the US can come through this transition fine if on the other hand you know we have trouble with governance people decide they don't like redistribution if we have trade Wars or things like that that pile on top of the technical shocks basically if we have any unforced errors then I become more concerned what is it like for you to be thinking about all of this it just seems so massive in terms of how to chip away at it trying to tackle the problems that technology will create for society can definitely be overwhelming and every day I get up and I ask myself you know am I spending my time in the right way so over the last 5 to S years I was trying to build out case studies in advance of examples where technology could be beneficial with the understanding that if we found some especially those that would help workers or that would help some of these really big problems then we would be prepared to counterbalance any negative effects of Technology as they come in but then there's a whole another set of policy type of questions like how should we think about macroeconomic impacts what should we do about open source models and how do we weigh the potential National Security concerns against the massive economic benefits of having low prices right right I just bounce back and forth between wanting to solve these big picture kind of economy-wide problems and these building projects where the building projects are very timec consuming each project takes a huge amount of time on the other hand doing the building projects helps give confidence to understand what is going to happen in the future from the policy perspective so i' I've basically just de Ed not to choose and continue to do both well one of the great things about being associated with the GSP and I would actually extend this to Stanford and any research institution is that it's an optimistic Enterprise it seems to me are you optimistic cautiously optimistic cautiously optimistic okay I think the thing that concerns me most actually is that in advance of figuring out some of the societally beneficial applications of Technology before we've really got that project yeah done we figured out how to get people very angry very quickly yes through communication so I do think it's hard to manage through crises and change when everybody's angry at everybody else and in the end of course this is a problem as old as time and propaganda and Sensational headlines and all of that have been a problem forever and they've been utilized forever but the ability to really get people's emotions going in a very targeted very personal way like all of us have something that will make us mad yeah and the triggers are at the ready everywhere yeah yeah wouldn't it be nice to live in a society where we all felt like we were on the same team and we're willing to make investments to make the communities around us better that's actually my biggest concern I think that economically it's perfectly possible to manage these transitions but I'm worried about our ability to pull together and row in the same [Music] [Music] direction if then is a podcast from Stanford Graduate School of Business I'm your host Kevin cool our show is written and produced by making room and the content and design team at the to GSB our managing producers are Michael McDow and Elizabeth weik Stern executive producers are soral husbands Den Holz and Jim kogan sound design and additional production support by Mumble media and H Ash and a special thanks to Tom butt from the East brother Lighthouse for more on our faculty and their research find Stanford GSB online at gb. stanford.edu or on social media at Stanford GSB if you enjoyed today's conversation consider sharing it with a friend or colleague and remember to subscribe to if then wherever you get your podcasts or leave us a review it really helps other listeners find the show we'd also love to hear from you is there a subject you'd like us to cover something that sparked your curiosity or a 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2025-04-05 23:26
