Stanford Leadership Forum 2026: Rewiring the Workforce in the Age of AI

0:03 Good morning, everybody. My name is Paul Oyer. I'm a professor of economics here at the Stanford gsb and I'm merely a moderator and I'm doing this because I do teach about AI. I don't know enough about it like these people do, but we're going to learn a lot. So welcome to the session. Certainly very timely. And I'm guessing a lot of you in the audience are heavily invested in this issue. Many of you are actually work at the companies that build the technology we're going to discuss.

0:32 And some of you are probably building AI products that are going to affect the labor markets. So I want to. We definitely want to. Hopefully this will be great. So we're in the interesting position of discussing a transformation while it's happening and with an audience that includes some of the people that are involved. So I think that's a really exciting thing. So I want to introduce the panel real quick. At the end is Susan Athey, the economics of technology professor in the economics group here at Stanford.

1:01 She brings economic rigor and a long track record on technology and labor markets. She is deeply embedded in the Stanford tech policy world and used to be the chief economist for the Antitrust division of the Justice Department. Susan is a world renowned economist. I won't listen the awards she's won and the societies of which she's a member and officer. Let's just say it's a long list. And most importantly, when I asked Claude about this to describe our panel, Claude said that Susan is, quote, the bridge between economic rigor and the actual boardrooms of big tech, which I thought was a pretty good description.

1:42 I thought they did a pretty good job with that one. I'm going to go skip across to. Neila is ADP's chief economist and she and her team have provided a lot of valuable insights through her access to real payroll and employment data from tens of millions of workers. If you listen to NPR's Marketplace like I often do, you'll often hear Neila commenting there. She knows what's happening in labor markets right now more than almost anyone else.

2:11 And her role in the panel, per Claude, it might have been Gemini, now that I think about it, but I don't remember. Is quote, the person who sees the real time heartbeat of the American workforce. Again, pretty good, don't you think? I love it pretty well on that.

2:26 I'm going to get a T shirt

2:27 made

2:29 and then Tamay Besaroglu. How'd I do? Good. Okay. Is the co founder of Mechanized and that's company A venture explicitly trying to automate knowledge work at scale. Tamay has also done serious research on AI progress and economic impact. He provides an interesting perspective here because based on his outspoken belief that AI will displace many workers and is backing that up through a direct commercial stake in displacement.

3:03 Tamay did a really interesting talk here on campus recently at the Digital Economy Lab. And, and I can tell you it was extremely provocative. And according to whichever AI tool it was, his role in this panel is to be, quote, the one tracking the exponential curve of the machines themselves. So we're looking forward to that. Tamay. So let's just start. I thought it would be most interesting because Tamay is often seen as really provocative to let him start by explicitly, your company is explicitly trying to automate knowledge work.

3:39 And so if you wouldn't mind starting by giving us your honest assessment of the timeline and how you think the labor market, how you think about the labor market consequences of what you're building, that would be a great launching off point. And then we'll let the others jump in.

3:54 Sure. So for context, I co founded this company called Mechanize. We build environments for training and evaluation where we teach models how to do knowledge work. We focus on software engineering right now, which is the domain that the AI labs are competing most intensely over. And so we work closely with the leading AI labs and help them advance the model's capabilities at doing software engineering. And in the future we want to just support the capabilities needed to automate knowledge work.

4:31 And so my assessment of the timeline is that I think over the next maybe one to five years we're going to see this continuation of this kind of gradual automation of tasks that knowledge workers are doing, in particular in software engineering and engineering. And that's been really exciting, but also more broadly in other domains of knowledge work, in consulting and, and finance and banking and oil and gas and whatnot.

5:02 I think I don't expect a very drastic amount of acceleration in automation over the next one to five years. I expect that in the next one to three decades we'll probably have very widespread automation. So I think over the next one to five years I might expect maybe a couple single digit percentage points of jobs being displaced or automated away possibly. But over one to three decades I expect that to be, maybe the majority of work in the US economy ends up being automated.

5:34 And so maybe one way of making a concrete prediction is economists pay attention to the labor share of the economy, which tells you the fraction of output that's paid out in wages to workers And I expect within one to three decades that we'll see more money spent on running AI workers in the economy than we will spend on human workers. And I expect that to be pretty likely within one to three decades.

6:06 Great, thanks. So, Susan, what do you think of the timeline that Tamaya has laid out and how does it square with what you're hearing? You talk to a lot of policymakers, executives, and of course, I know you're in the middle of teaching MBA students as we speak.

6:21 Yeah. So let me start from more of an academic perspective and then circle back to what I think, how that squares by what I'm seeing. I think a principle that I'm seeing

6:32 across

6:34 everything I'm doing is that it can be very helpful to separate technological capability from the implementation and the change that actually occurs. So we can have great technological improvement, but that improvement only translates into impact when it's combined with an existing system or an existing organization. And to reason about both the timing and the impact, I find that I need to really dig into the first principles of the scenario and then pop up from there.

7:13 So just take an example of drug discoveries. We can really widen the pipeline of drug discoveries and somebody can show you how they can come up with new molecules and it'll blow your mind. But then you have to think, okay, well, what about the human trials? How are we going to make the human trials go faster? How are we going to make the FDA in the US Go faster? And of course, if we are piling up amazing drugs, eventually we will figure out how to invest in the capacity of the human trials and we will change the fda.

7:45 But there are things like maybe that requires an act of Congress and that requires political parties to be aligned. So there are things that can stop that even when the economic returns are very high. And then at a micro level, something probably all of us can relate to now is that we can see that it just got a lot easier to write emails. Great. I can write my emails really fast. But what did that do to your inbox and what did that do to productivity?

8:15 Actually, you know that everybody is getting spammed and, you know, my students preparing a slide deck for me, but now it's all done by Claude and it's like not editable easily. And you know, they. And it looks beautiful. And I can't actually tell until I read it that they didn't think about it. So it takes time. We're going to have to change how we communicate. We could have a whole session about just how we're going to Change communication and business.

8:43 And we will figure it out. But it might be bumpy along the road. I think that's a pattern of every general purpose technology is that there's a technology change, but then there are micro complementary innovations. The innovation can be like an organizational innovation or a norm. And then there are sectoral factor reallocations and it just one prods another prods another prods another. And it's iterative and it's slow. So that's. And I think we're going to unpack this theme further.

9:15 So I'll stop on that point. Now I want to come back to answer your question of what am I hearing? I think actually students, I have two kids in college right now. I mean fear, big fear in MBA class, you ask who is afraid? What is your most popular topic of conversation right now? Passionate. It's like, oh my God, I have to choose my career and everything I knew is wiped out. So this is terrifying. And I also think it's, that filters down to the high school kids and the parents.

9:45 So there's a lot of fear. And that's something that also has huge political and geopolitical implications. In terms of the firms I worked at DOJ where I helped with a leadership team of, of hundreds of people doing white collar work. And I also, we have a lot of professional services coming through Stanford and I work very closely with some of them. Everybody's trying to figure out how to reorganize right now. If you ask any attorney, they're, oh my gosh, how are we gonna train our new attorneys?

10:16 What's the world look like? The organizational change is pretty profound that's needed to fully take advantage of this. So going from the pilot of like, oh great, I can search my company documents and get a summary to, I'm going to redo my workflow. I think people are just at the beginning of that. And I'll leave with one very interesting thing the MBAs told me last week was that the most spammed job opportunity they were getting on from LinkedIn, they were getting the most LinkedIn spam to be a forward deployed product manager.

10:52 So we can come back to that too.

10:54 Let me just add, let me add one anecdote to your point about like, you know, we're trying to figure all this out and the junior labor market stuff is really complicated. I love to use this. I'm going to use this in my class in a few weeks if any of you are in my class. Bloomberg had an article about how OpenAI was trying to train people to be junior investment bank, train their models so that they wouldn't have to hire junior investment bankers.

11:21 And then literally three days later they had an article about how the investment bankers were using the junior investment bankers to teach them how to use AI, which, you know, I mean, maybe they're talking about replacing them, but you couldn't help but think the demand for the junior people was even going to go up because of the AI. So we'll see how that plays out. Neela, what's your take on Tomy's and Susan's assessments of where we are and where we're going?

11:45 And you can either talk about that broadly or if you're ready to certainly bring in some ADP data and what you're seeing on whether or not we have any real displacement through AI yet.

11:57 Well, thanks first of all for inviting me to be on the panel and thanks to my co panelists for giving me so much to respond to. I hope over the next 45 minutes I can touch on three different themes, context, sentiment and applications. I'm already stacking the deck in terms of the questions, but let's start with the context because I think it matters. AI is not happening in a vacuum. I would love to level set on the context in which this transformation is taking place.

12:28 I can do so from the vantage point of ADP, which pays over 42 million workers around the world. It's tax day. ADP will issue over 80 million W2s in the course of a quarter. And what it means is that I get to see about 1/5 of the US workforce in real time. We've started sharing that on a weekly basis with everyone. And what I can say, short answer is I'm not seeing a big effect on the labor market right now, but we can unpack that a little bit later.

13:00 Here's what we are seeing. If you look at the last two years, three out of every four US jobs that were newly created came from one super sector, education and healthcare. And if you unpack that a little bit further, you'll find that the biggest occupation, the biggest sector that was seeing those job gains were home health care aids. Why not because of AI? Another a that is way more dominant in the labor market which is aging.

13:32 We are looking at mass retirements of the boomer population and it is the wealthiest generation in history and they are choosing to age in place. And there is yet to be an AI robot that can make their do their laundry or make them sandwiches. And that is what the customer ultimately wants. And we are far from that. So where the jobs are coming and where the tech is going are totally different. Let's add on to that because the US is not unique.

14:02 In fact, if you look at Europe, they're 10 years ahead in terms of the aging demographic. China, Japan, I just got back from Vietnam, the Southeast Asia. Southeast Asia is at their demographic sweet spot in terms of the labor market. And yet they're only going to be in that sweet spot for about 10 years because of fertility policy. It's already ticking, that time clock in which they can take advantage of having more workers than dependent individuals.

14:30 It won't be forever. And so where is the growth coming from? Africa. Three fourths, and I know Susan can talk a lot about this through 3/4 of the working age demographic is going to come from a continent who's not part of this conversation on tech. So that's the context. And so that does color my perception of where the tech is going and this idea of diffusion or how it gets to application. I'm going to touch on the other two really, really quickly for sentiment and then application and I hope we can jump back to those sentiment.

15:06 Yeah, I have one college age, one about to be college student. They're on a predestined path. AI is not going to affect it much. But I will say there is a lot of insecurity out there. In fact, when we survey about 40,000 workers in 36 countries, only one in four say that their jobs are safe from elimination. They're probably in low paying home health care occupations, a lot of them. In the United States it's 28%.

15:39 What our research does show though is that if you invest in people by upskilling them, that safety number increases by five times. This is not like the weather. We don't just need to carry an umbrella or have a bigger jacket. We can actually change the course of the environment we're in just by investment. And that leads me to application. When you think about what the frontier research is saying, it's not macro. And I don't think anyone's a macro economist on this panel.

16:11 So we can. We don't have to deal with that.

16:14 I was in charge of choosing who was on the panel.

16:16 Oh, you did? Excellent, excellent sidestep. S and P. Is that a whole time? Okay, we don't have to talk about that.

16:25 No.

16:25 AI works at the task level. This idea of occupation, man, that's going to be an old concept after a while. We're not going to be talking about being an economist. We're going to be talking about the kinds of tasks, skills and activities that define our work. And that's going to change because AI will change it. It's going to be a lot more fluid. I joked with Eric Brunjolfsson, who's our partner at the on the ADP employment report and also runs the Stanford Digital Economy Lab, that one day we're not going to be talking about a jobs report, we're going to be talking about a task report, the number of tasks created and destroyed.

17:07 And I said this in jest four years ago to an audience similar to this one. And Eric was in that audience and he said we can do that. So that's what we're going to do. We are going to redefine how we think about jobs and occupations and we're going to do it for the best betterment of the worker because if we do that, then we get the promise of growth that is inclusive and shared.

17:29 So let me ask one quick follow up and then we'll move on. Regarding Eric, who you I knew you worked with and when you said you're not seeing much in the data, that strikes me as a little bit in conflict with the paper of Eric's and the others that's gotten a lot of attentionaries in the coal mine. So how do you reconcile that? For those of you who don't know, Eric and a couple of co authors have a paper suggesting that junior young software engineer the job market really has been negatively impacted by LLM.

18:02 So that is one of two papers using ADP data that use the word canary. So I think canary is an adp and the reason why Eric was able to do this research using ADP data is because it's so granular. So it says multiple things, not just one thing. What they were able to do is use ADP's job title taxonomy, which we've been working on for a very long time, several years and classified jobs in terms of AI exposure, both AI exposed careers like software developer or customer service agent and also AI non exposed careers like that home health care aid I told you about.

18:43 And what they show is that during the rollout of ChatGPT and afterwards in October 2022, you see a distinct drop in employment for early career people between 22 and 26 in AI exposed careers like software developers and customer service agents. It also shows that for more complex jobs related to more tenured workers, older workers, you see a ramp of it, of, of employment.

19:13 So I would say that the data does suggest that there is an automation of work and the Trick for the HR professionals is not okay. Those jobs just went away. It's upskilling those young workers to more complex tasks. The augmentation role of AI is what's really what that paper talks about, not the automation work. If you look at augmentation, if you look at people older in their careers doing more complex jobs, what you see is employment growth, growth.

19:44 And that's not a foregone conclusion. I thought AI would just get rid of all the old expensive people. Right, right. Didn't you guys think that like, especially

19:55 because the junior wants to know how to use AI.

19:57 Exactly. AI is not getting rid of me, it's going after my kid who's going to graduate into a role that no longer exists. And that is what we should be concerned about, upskilling the youth to take advantage of the tool which they are natively born to do. Apparently instead of a silver spoon, they have Claude. But we need to upskill them into the new job, the new tasks that are coming, not the old ones that we have educated them for.

20:27 I'm glad you qualified with upskilling the youth because the history of upskilling the older people who are threatened by technological change is not, I like to say it's unblemished by success. If we look historically at upskilling older people who've lost their jobs. Let me move the conversation forward and if from the context of your follow ups you want to come back to anything, feel free. So is this wave of automation, do you feel like it's qualitatively or quantitatively different than the spreadsheet, the electricity, the plow, compared to mechanization, computerization, offshoring, what pattern matching do you see that's appropriate?

21:06 And what do you think is different from prior technical changes? And why don't you start us off tonight?

21:11 Yeah, I guess one thing to say about junior software engineers, we had mechanize are actually like aggressively hiring junior software engineers. And we're looking to hire maybe 50 junior software engineers this year. So we're, we're like, I see some of these claims that like, oh, you know, junior software engineers are, you know, not going to get jobs. And then my own experience here is that I'm like really aggressively trying to hire them. But the skills that we look for is very different than it was a couple years ago where we look for people's, we explicitly evaluate during the interview process people's ability to use things like cloud code and make a lot of progress, produce a lot of code and still understand the code that the Models produce and being able to orchestrate models in parallel and things like that.

21:58 And, and we find people who are very good at that to just be enormously productive and they just generate a lot of value. And so we want to hire quite a lot of them. And if there are any software engineers here who are interested,

22:13 you have raised hands here.

22:18 So is this qualitatively different? I mean, I think in software engineering there has been many decades of automation that looks fairly similar to what I'm seeing with AI today. Where first you had compilers that automated the writing of machine code. Previously engineers would hand write machine code that got automated. With compilers you had higher level programming languages like Python and so on that abstract away assembly code.

22:51 And so we've had this long run trend of higher and higher levels of abstraction in software engineering where you have in the 2000s you had web developers spend many hours write boilerplate code that an import statement in Python can do in a second. So we've had this long run trend of higher levels of abstraction. And it seems like AI is this continuation of this trend where instead of writing a lot of the code, software engineers are now just orchestrating agents to do a bunch of the engineering and deciding which things test and how to instruct the models, and noticing the weaknesses with models and maybe manually stepping in when there are things which models just struggle with doing, for example, testing UX and so on, which the vision abilities of coding models are not that great and they have some limitations there.

23:50 So they have to figure out when to step in and do that. And so I see this being this continuation of this long run trend towards higher and higher levels of abstraction. And it seems fairly similar to previous automation. Now the thing that is pretty different is that compilers and high level programming languages don't promise to be able to fully automate everything that an engineer can do. And AI, I think eventually we're able to automate everything that software engineers can do.

24:21 I think that'll take a couple decades, but I think we'll eventually get there. And so that is a very big difference compared to prior technologies.

24:28 Does that mean you foresee a world where we just have excess workers relative to what we need to get the job done? When you talk about this displacement in one to three decades, are we talking about unemployment or you haven't really?

24:45 Yeah, I'm talking about unemployment there. So it would just mostly be done by agents.

24:51 But that's a big difference from the past, obviously.

24:53 Yes, that would be a big difference. So eventually we'll get to A place where there's a lot of unemployment.

24:59 Okay, Susan, either picking up where tamay or just going back to prior waves. I'd love to hear your input.

25:06 Sure. Let me start with just a comment about timescales. Personally, I'm trying to focus more on the next 10 years because I feel like if we actually survive in our current form to that world that you described, we're going to be very lucky. We can figure some of that stuff out when we get there. But to me it's seeing a path to get there is is so not that it's not interesting to think about that future, but a lot of things have to happen first that we should be focused on in my view.

25:41 I wanna first just respond to the what do young people do? Question. Something I'm thinking about a lot from teaching and also my kids and from in my work. Right now I'm working on legal and professional services. And it's already pretty much a documented fact that with some effort, not off the shelf, but with some effort, AI is better than the most junior people at the most routine tasks. And so that is just that is a wave that's happening and it's happening fast.

26:15 And people are already thinking about how are they going to bill and how are they going to restructure from that. What do the young people do though going back to this forward deployed product manager. My son's a STEM major, he's math, physics. Never a lot of coding. First a year ago he wasn't really. He was in the worried camp. Then he got involved interning with a startup and when he just got his offer letter it was a forward deployed engineer.

26:42 I don't think he knew what a forward deployed engineer was, maybe not even until he got his offer letter. But that's what he was doing. And they wanted him not because of the skill, but because he was a problem solver. So he was a. And nothing he learned in his math or physics classes, but just the breaking down of a problem into components and solving it. And there's a huge demand for that right now. Because if you have an idea about how to conceptualize and solve a problem and break it down and measure and test, then you have these tools that can make that idea a reality.

27:18 But in terms of like what are the people going to do? If you think about all these people in India or other places that were like they were doing some stuff before for big IT firms, if the whole world is going to start digitizing and solving problems, it's at least a 10 year journey to digitize and redo the IT stack of every organization in the world and create IT stacks for every kind of organization in the world.

27:47 So this feels like just like your company, like this is a big expansion before it contracts. Now let me come back to your first question, which was how is this wave different? So let me contextualize. One of my projects for the last year is I'm one of the faculty advisors. There's two of us for the World Development Report 2026 on artificial intelligence. So we've got about 50 people working. I'm traveling all over the world to create a huge report that's going to inform World bank policy for developing countries.

28:18 So I've been hearing, listening all different countries, Africa particularly, and we've been trying to customize this based on data. And there's just a few facts out of, we haven't written it yet, but there's a few facts that really focused my mind that you all might find helpful as well. That in the developing world there's, depending on the country, between 50 and 70% of people are working in one person or one family firms. So they are retail and agriculture are two of the biggest ones.

28:47 And so when you think about the employer is the worker for the majority of workers, that kind of reframes everything. Because now it's not about you're not firing yourself. Of course, the family farms might not be family farms. The retail might get taken over by a chain. So those firms might not be like that in the future. A little bit further out. But already a lot of them are in small towns and stuff. They're in rural areas.

29:16 So it's not really going to all become chains immediately or get gobbled up by private equity in these countries. So then you're really thinking if you think that there will be a lot of small firms going forward, then this automation is just making them more productive. It's allowing them to grow. And many of them are supplying things that, that actually are in where there were capacity constraints before. More local food, more local services, more goods. So you're in that stage of development.

29:47 This is just productivity enhancing. The second piece is there's another category of workers that scales with the size of any country. And I think Neela set me up really well for this. It's health care, education, elder care. The first two in developing countries are more important than the elder care, but they've got that too. Those scale with the size of the economy. And if you look rich to poor, they're way under supplied. In poor countries, your nurses don't have a high school education, your teachers are teaching science without any science education.

30:22 The ability to upskill those service workers who then have positive externalities through your whole economy. And if you have agricultural workers, if you can all make them 5% healthier, that goes one for one into output. So I think those opportunities are huge. And so going back to pattern recognition, a lot of the development economists are like, okay, here's another technology that's going to widen the gap between rich and poor.

30:52 To pick up on Neela's earlier point, it could, it absolutely could, but it doesn't have to. And this is one of the few times in my lifetime where like a relatively small amount of dollars with relatively high ROI can actually improve the lives of like billion of people. So this is like the opportunity of a lifetime. We may not take it and we may fail to execute, but it's profound and it's different.

31:22 I think.

31:23 Can you be a little more specific about for that opportunity to take who's using AI to do what to make that happen?

31:32 So I think there's two for the small business just focusing on the small business, there's two vectors. One is that app developers using low cost software development tools of high quality will have AI powered products delivered to small business owners through their mobile phones or through low cost hardware, sensors, cameras, whatever. So you've got cameras watching stuff go in and out of your little retail shop.

32:02 So now you have inventory, you have a bot in your WhatsApp, and now you suddenly have customer orders and you have supply inventory and order management without doing a thing. So I think like the advice five years ago is if you want to digitize, you have to do A and then B and then C and then D. And each of them is expensive and by the time you do it, maybe it's obsolete and like just why bother now?

32:27 It's like the AI makes the adoption in principle so much easier. I think looking at WeChat is a great inspiration for what we're going to see. But even WeChat, a lot of that stuff is not as AI powered as it could be. So that's the first vector is like things that come and meet the small business owner, where they are in their context, in their language, with tailored customized advice, bringing in local data and overcoming all the frictions they face in their life and integrated in their workflow.

32:57 Huge upside. Farmers are way inefficient right now. There's not enough agricultural extension agents either. So we can empower the farmer, we can empower the agricultural extension agent. The agricultural extension agent becomes a technological extension agent and, and makes the farmers more productive. That's one vector. The second vector is. So that's a private sector vector, but it needs infrastructure, it needs mobile and it needs an ecosystem. It needs financing and so on. It's got too many pilots and not enough implementation.

33:26 The second part is government services provision. Government plays a role in education, health and all that stuff, especially in developing countries. So that's like philanthropy, NGOs and governments delivering. The problem is actually getting it across the finish line. You talk to a government health official now, they tell you they've got a thousand vendors pitching them pilots. No shortage of pilots because of the software coding. They can all. Everybody. I can write software. Everybody can write software.

33:54 So now we've got a thousand pieces of software, but nobody's getting better health care. So that's the problem there.

34:01 Okay, thank you. That's helpful, Neela, picking up on any of those themes. But especially. Well, if you want to add ADP's perspective on this versus history, that would be great. But if you. Wherever you want to go with it.

34:14 Okay. I hope the takeaway from this day and anytime someone speaks about AI is it could, but it doesn't have to. I think that's perfectly put. I also like Tamay's comment about, yeah, we're still hiring software developers, just different ones than we're looking for, different skills than we used to. I think that's more realistic than not looking for them at all. When I talk to leaders who are like leading companies big and small, 44% of the US workforce works in a company with under 50 employees.

34:50 Three, four work in a company with under 250 employees. And then you scale that around the world. This is a small business economy and that's not often appreciated. But they're struggling to hire people because they're looking for people who will work a 12 hour shift. Who wants to do that anymore? There are jobs available, but we live in a very fragmented labor market. I'll give you something that really captured my attention.

35:23 I was looking at the median age of an H Vac worker, a plumber and an engineer. The median age of an H Vac worker critical to build a data center has fallen by five years and six years. That's really fast from a demographic perspective. And it's not because there's a bunch of young people who want to do H Vac. It's because there's so many people retiring from that field. Wherever there is an infrastructure, not a digital, digital application but an infrastructure application, whether it's in finance, there's not enough accountants, it's in caregiving, there's not enough babysitters or caregivers or educators or health care, or whether it's construction, there's not enough specialized trades.

36:10 You have gaps and you have supply gaps. And what AI does is it's funneling all of that, it's papering all over all of that and saying this is the only thing an economy can produce. And the biggest problem I have with it is for what ends. Because if you destroy the worker, you destroy the consumer. And we're still a consumer led economy. So I have to question the business model of destroying your customer base, because that's very different than the industrialized path that has given wind to AI.

36:47 That's what makes me hopeful. Because if you take out any of these scenarios to their ultimate. This is why I'm not a philosophy major right now. I realized when I was a philosophy major, if I picked any kind of canon, any kind of philosophy, the ultimate end was nihilism. And you can say the same thing about AI. If you take the worst or even the best case scenarios, you end up with no workers and no consumers and no economy because, and in my view, no society, because society is based on transactions and commerce and relationships.

37:26 That's not, in my view, a very likely outcome. I think the outcome is we have to give up this notion that knowledge work is the only kind of work and that AI is destroying knowledge work. Maybe knowledge work was on a path to extinction way before Chat and Claude came into the picture. Maybe we've set up our youth for an economy that was about to disappear and be extinct in 20 years anyway. Because that's what economies do.

37:54 They constantly evolve, they constantly change. And AI is just one more ramification of that change. Sorry to get philosophical for a moment. I think really the task at hand is to define the task of AI. And I don't think we've done that really well from a humanist perspective. My goal in our research and our data is to take jobs, break them up into occupations. I have a housing background and we did something very simple in the olden days of statistics, econometrics.

38:29 No one says econometrics anymore, but that used to be a thing. What we did is we looked at millions of houses and we did something called Hidan regression. Everybody remember that? And that allows us, through millions of observations of housing listings, to actually provide the value of that fourth bedroom or the finished basement or the powder room in Palo Alto. It's got to be like $500,000 to

38:54 have a powder room in them.

38:56 You're able to do that because you saw that millions of times. And at adp, we kind of do the same thing. We see millions of job listings, millions of occupations, and we see millions of wages. And our goal is to use that same practice, hedonic regression, to break down the elements of a job, the elements of a software developer, and price the value of the tasks so that we can tell an employer, employer like yourself, what are the higher value tasks, what are the lower value tasks, and how are those tasks changing over time?

39:32 So that we can go to a college campus, maybe not Stanford, maybe my alma mater, Indiana University, and say these are the tasks and skills that you will need to be competitive in this industry over time, that we can be very clear and specific with hiring managers and with workers on where these tasks are heading. I think there's a lot of empowerment in AI, but there's also a lot of blind spots on what the labor market really is right now.

40:01 So we're trying to elicit that with the data.

40:04 Great, thanks. So let me pick up on a theme that Susan and Neela just got to, which is inequality. And so if we look historically over the last few waves of technology, that's always put pressure on the, you know, it's increased the level of inequality. And what worries me, personally, I don't know about the rest of you, is that's led to a lot of political turmoil within the United States. So let's focus on the United States for a few minutes.

40:31 You started talking about other countries. Let's bring it back to the U.S. you know, as you look over the near term, because, you know, we won't go out to three decades yet. How do you feel about, you know, the distribution of skill and wealth and how AI is going to affect that? Who's most at risk, who's least. And then if you want to speculate on political issues, that's great, and if not, that's reasonable. Susan, why don't we start at that end and move our way back this way?

41:00 Yeah. So I think we've touched on a lot of the foundations of your question already that from a. I do believe that the small business can be a great beneficiary, and that's very important for the political part of it as well. That is out of the narrative. And so I think we had a famous politician visit campus a few weeks ago, Bernie Sanders, who gave a speech, he met with some of us privately as well.

41:31 And that really focused me on, you know, he was, he was really talking about what he was hearing from his constituents. And the way that he phrased it, we met was with that, you know, all the tech companies are making headlines by saying this. Many jobs are going to get replaced. And I can kind of understand why they want to do that. Because it's clickbait and people want to read it, they're interested in it. And that also shows your technical leadership because your technology is so good, it'll get ahead, that makes sense.

42:00 But at the same time it's riling up fear. And that has political consequences. And it's apparently not as clickbaity to talk about how it can help people. If there was more public conversation about how it could help people, that might galvanize us to here in the US and in other countries to be putting pressure on our politicians. Why am I not? Why is this technology so great? But I don't have good health care, I don't have good education.

42:33 The things that are important to me as a person, I'm not getting. And I'm powerless in this economy to get them. I have to afford a bigger house to move to a better school. And I can't do that. This is what grandma can't get a doctor's appointment, I can't get a doctor's appointment. These are the things that are, that are making people fearful and unhappy. And if we look back at previous shocks like trade shocks, immigration, Covid the fears that people have when people get scared of change, when things change and people are scared of change, they react and they react strongly.

43:12 And it doesn't always lead to long term investments that solve the problem. And so I am very, very nervous that the fear gets ahead of the benefits. And then we incapacitate ourselves to solve global problems through electoral choices we make. So that's really where I'm coming from. And just to put like a little bit more of an economic lens on it, when I do this micro analysis, I really think hard for every impact on what are the bottlenecks and what gets in the way.

43:53 One thing that gets in the way of benefits is that you need to test, you need to make sure it's safe. It's good. You have good training data, you've evaluated your impact. If you are destructive, if you are a destructive force, whether you are a weapon, like a military weapon, or you are a terrorist, or you are a cyber criminal, the trust and safety department doesn't slow you down. Okay? Legal doesn't have to review. You just do it.

44:21 And a lot of those are very nimble already digitized organizations that adopt new technologies very quickly. So the fast harms are coming. If you've got leaders saying you're all going to lose your jobs and tangible harms like grandma just lost her life savings from a fraud and cyber hack or whatever, that's a recipe for not doing a good job investing in the benefits.

44:46 So it's a great point. I mean, AI is incredibly unpopular. It's almost as unpopular as most politicians right now. And then to your point, I think everybody in this room, I would guess, would agree with the following two statements. That is we need to regulate AI, but if we do, we'll probably there's a good chance we make it worse rather than better in the the ideal thing is to regulate it really well, but it's going to be very hard to do that.

45:11 Can I just. When I was at DOJ in the antitrust division, one of the things I needed to do was comment on AI policy in US and the world. So I spent two years redlining and putting comment bumbles on documents about AI regulation that had said all the same thing. This may impede new business formation. This may be bad for turnover. This is going to benefit incumbents and reduce competition in if you reduce competition, you won't get the benefits that this regulation is trying to create.

45:42 And for the developing countries it's even bigger because they feel helpless. So they want to protect themselves and have their values be respected, which they don't feel like happened in the past. But then they may not have anybody serve them, in which case then they have no leverage and their laws aren't enforceable and they don't get the goals they wanted. So you're kind of between a rock and a hard place in this problem.

46:08 Yeah, Tamai.

46:10 Yeah, I mean I definitely think that there'll be a lot of really large benefits accruing to people winning and people also losing. I mean, one thing that I see is that I'm kind of struck by how large the variance is in how well people can use AI tools and how productive people can be.

46:30 Unbelievable.

46:31 So one interesting thing is we have a part of the interview that we do where we give people take home and we actually give them the ability to use whatever AI tools they want. And there's a thing you can do which is you can just give the take home to an AI model and just let it do it and submit whatever that produces. And the majority of candidates do worse than just letting AI do everything and submit that and that's quite shocking, what's going on here.

47:03 I mean, I think people just fail to appreciate how good AI systems are and they kind of interfere and they kind of hobble or constrain what the model is doing and are very prescriptive about what it should do, but, like, bake in bad design choices. And so I just see that there's this huge variance in how well people can use AI tools. And then that results in this huge amount of these large differences in how productive people are.

47:33 And so I think there's going to be this greater kind of inequality. Seems pretty plausible to me. I would say that automation in general does broadly benefit people, even though there are these transition costs associated with this transition and displacement and so on. If you just look at the history of automation and electricity and the combine harvester and things like this and the computer, those have just been broadly very useful to people.

48:07 And I expect AI to also be broadly very useful.

48:11 Thanks. After Neela, I'm going to start calling on people. So if you want to come up to the mic, please do. And Neela, take us away on this topic.

48:19 I think the concept of value add is really important here. And what value the AI is adding, not just, okay, it can do cool things, but to what end is it doing those things? That's a really important question. And who's the ultimate customer? Is the customer other AI firms or is the customer the worker? Consumer. So I'd like to go back to that primary economic unit being the worker, the consumer. In our data, we look at how people use AI and people who use AI daily, it's very interesting.

48:57 They feel very much more engaged in their work. They're excited about it. It gives them passion. Not surprisingly, they tend to be in tech, but they also feel disconnected from their teams and they feel less productive, not more. Why? Because the things that make us feel productive, answering emails, going on sales calls, doing these routine things, actually are being done by AI. So what's left?

49:28 The hard stuff. Figuring out the business plan, figuring out the new market, figuring out how to make money on all this investment that we put on AI when we don't have any customers because we killed all the workers. That's the hard seller stuff that makes us feel less productive. And I am unjust. I don't think we're going to actually kill workers. We have to change our measurement of productivity. We have to change our measurement of engagement.

49:55 And the missing piece, the heartbeat in which you announced my bio of the worker is trust. And the way to a worker's heart is through investment and upskilling and reskilling. If we could do that smartly, we actually serve both ends of the economic spectrum, the worker and the ultimate consumer. That's value add. And I think that should be top of mind. That helps with the inequality issue as well.

50:20 Thanks. Let's take a start over here.

50:23 Okay. So I wanted to dig deeper on Paul's question about the political consequences of this. You know, Susan, you mentioned the trade shock that happened. You know, it's been started 35 years ago, and it overall made the United States wealthier, but it created a lot of losers. And I think most people would agree that us failed in handling the transition costs for huge amounts of people. And that's had extraordinary political consequences.

50:55 It seems to me that this is going to be the disruption will be bigger, will be faster, and may have more variance. And you know, Neela, you mentioned nihilism. I think if this is generationally concentrated or concentrated in other ways, what sorts of social safety net and transition assistance should the United States provide? This seems to me to be a real leadership question for all of us because we failed it once before and we got to figure out policies that can address this challenge.

51:32 If I could take that one. The great news is we can all see it coming. It's not like it's a pandemic level surprise. We have data and we have AI tools that actually could help with better outcomes. We can use AI to solve the issues of AI. But I would be remiss, without mentioning tax policy that incentivizes capital over labor. Let's start there. That is something that we should have a conversation with about the incentive structure of AI investment relative to labor investment, because that's tilting the deck away from the worker.

52:05 And if that's what society wants, okay, let's acknowledge it. But if there is an incentive that actually leads to scaling up the workforce, let's talk about that as well.

52:17 I should say, especially if a country is importing the automation technology, then there's a big externality. They could hire workers, they could import an automation technology. When the firm's indifferent, the country is not indifferent. And so that's, you know, keeping the money in the economy, keeping and getting the positive benefits of your workers learning and everything else is there. So I think you put that very well on top of what you said. I think political movements come when there are concentrated groups of people getting impacted.

52:52 Maybe a good or bad news, we'll see which it is, is that some of this is concentrated on richer people. They're more politically powerful but also have more of a built in safety net. I think that's going to be interesting to see. Then if the robots come, if what we're doing right now turns into faster physical automation, which I think is a few years out, then it starts looking like the China shock, plus a lot more.

53:22 I'm fearful of that. I've been doing research to try to use digital tools to help people make transitions. I think we should be investing more in that enterprise because it's going to change from year to year what transitions people need to make. So we need to be good at the business or the practice of doing transitions, not just one one off programs.

53:46 Making predictions is, is difficult, particularly about the future. So I'm interested in whether you think a we should be doing more scenario planning and what types of scenarios you think we should be preparing for and what's an example of something we could do so that if we lived in that future scenario, you know, we would have greater resilience. Can you just clarify who you mean by we should be when you say we? Well, why don't we start with the

54:17 US I can answer this already. I mean, so the scenario planning in general is the tool that I'm using in a lot of my business consulting and in my teaching. And I think there's many ways you can interpret this. But part of it is that I think people's minds kind of explode when they think about all the changes and they can't really get them all at the same time. So then they fixate on one thing which may not be the right thing.

54:47 So to me, scenario planning is partly is narrowing down what scenarios are actually plausible. And in my view you can rule out a lot of scenarios. Like when I was in Washington, everybody was thinking about the scenario where we could close down open weight models. That was a massive waste of time. I told them that many people said it was a massive waste of time, but other people said it was possible. Very senior famous technical people said it was possible to close down open models.

55:17 And so we wasted a lot of time thinking about that when that was just a waste of time because of course China was going to make one. It was 100% certain that somebody would. So rule out those scenarios and focus your attention on what is important. But the biggest insight I bring to scenario planning is to think about the timing. And that's why I have been obsessing on thinking about bottlenecks and blockers and what goes fast and what goes slow.

55:42 Because actually I do sit in Scenario planning sessions that are focused on the wrong scenarios, and they're also focused on decision irrelevant scenarios. Like if you want to sit around and say, what are we going to all do on the beach to make ourselves feel meaning of life when the drones stop us? Daiquiris. I don't think that's a good use of time because if we get to the beach and drones are dropping us daiquiris, we'll have plenty of time to figure out how to make it meaningful.

56:07 But what I'm worried about is on the path to the drones dropping daiquiris, some physical input is needed and somebody's got a choke point on that physical input and we have a war or blow ourselves up trying to control it. So that's the thing I'm worried about, not what we do when we get to the beach. And so the scenario planning should be around, like, what is the timing of good and bad? What is the timing of decisions and what scenarios help us think about the decisions we're making today.

56:36 Today that we really need to figure out.

56:39 I think also in addition to those types of scenarios, the daiquiri one would be my favorite of all time. We should run scenarios that are a little more introspective. What if our data's bad? What if we're told the wrong thing? What if our assumptions were not well played? What if our assumptions have changed? Those kinds of introspective scenarios are missing, I think, in the discussion.

57:10 I also think we should spend a lot more time on worker transitions and using that data, high quality data, and looking at how workers can transition over time along with the tech, not just tech by itself.

57:23 So we're out of. I'm so sorry, I'm being told we're out of time. And I just want to give each of you a chance to wrap up with, like, what is. If the optimists are right, what does the world look like down the road? Make us hopeful. What can the world look like? And you can pick your scenario, whether it's two years, 10 years, whatever you want. We'll go that way. Neela. And it has to be quick because we're out of time.

57:48 The worker is empowered. The worker has several skills. The worker skills are adaptable, agile and resilient and that the growth and prosperity that is experienced by the world is shared.

58:01 Yeah, I guess I am excited about just rapid technological progress and medical innovation and economic growth and for people to just broadly be a lot better off.

58:14 I think people's core needs getting met efficiently, cheaply, conveniently, when and where they want. It goes a huge way if you can keep your family safe, fed, clothed, educated, part of the community. Another thing I think has been, I don't want to say like a waste of time, but like disproportionate. In all of these panels and discussions I go to is everybody's talking about universal basic income, which like most countries can't afford at a good enough level and is not.

58:43 It doesn't actually make people happy. It's like goes along with the daiquiris on the beach. Instead, we need to be thinking about meaning and participation in society and the fact that there still will be work to do, but it's more like, do we want to be like a king and serfs or do we want to have a society that there's still lots of work to do? It's just people don't have a very good life in one of those visions.

59:09 But it's not that people are sitting around on the beach and so the question is, can they participate meaningfully, economically, socially and politically in this advancement?

59:21 Thank you very much.