AI is eating into entry-level jobs

0:00Now, what I would like to do today is talk about what we're learning about how artificial intelligence may impact the labour market. there's been so many words spilt over the past decade and more about what it might mean for jobs creating this lovely portmanteau the job apocalypse and we're looking for data for evidence of that and this week my friend eric brinyolfsen and two of his collaborators came up with a new paper which i think is really robust and very very interesting eric is a professor at stanford University, where he runs a digital economy lab, where I'm a digital fellow as well. And in this paper, Eric and his collaborators, Bharat Chandar and Ryu Chen, analysed payroll data from ADP, which is a really large payroll processor globally, but very, very strong in the US, handling payrolls for millions and millions of American workers.

1:02And they were able to look at this data correct for various confounders like COVID and seasonality, and they identified a really, really interesting finding. And that finding was that early career employees, people between the ages of 22 and 25, in the most AI-exposed roles, roles exposed to artificial intelligence, such as customer service or software development, experienced a 13 % relative decline in deployment from 2022 onwards. So this is a really important key finding. It's a substantial decline in employment for those early career workers. And it was really focused, concentrated on AI-exposed occupations.

1:56In contrast, for workers who had more experience in those particular professions and areas, employment actually went up. You know, mid-career software developers, you saw a 10 % increase in employment. In the case of these early careers, employees 20 to 25, there was a 13 % decrease. So it's really, really meaningful, that bifurcation. And if you looked at less exposed occupations, so you looked at sales and marketing or health aids or stock clerks, you didn't see the same suppression of employment, particularly for that early career employees. So if you look at health aids, for example, over that period of time, there was roughly a 20 % increase in hiring for early career workers.

2:45We see that there is a pattern which appears in jobs where AI automates rather than augments work. It appears in jobs where today's AI tools can actually be used in meaningful, meaningful ways. The thing that is really consequential is that while this isn't causal proof, the researchers have done a really, really sterling job ruling out obvious alternatives. So trends that existed before artificial intelligence, COVID-19, education differences, firm and industry shocks. So where they end up is the most likely explanation is the impact of artificial intelligence. So the question is, what's going on in here?

3:32What's this picture? There's a reasonable intuition, which is reflected in the paper, which is that artificial intelligence is supplanting the bookish knowledge that a 22 or 23-year-old has. Those workers have learned through high school and university formal technical skills, but what they haven't necessarily established is practical skills to work, what the workplace is like, and specifically the tacit knowledge that exists not just in work in general, but in specific firms and specific departments within specific firms. So that tacit knowledge is something that isn't in your degree syllabus for the moment.

4:16It isn't in the company manual that you get when you get a job offer. it's something that you learn over time that shows up when you go through the job selection process through the interview process and i think you can you can see some additional evidence support for that theory because in the highly exposed occupations that is occupations that were really exposed to ai older cohorts added jobs six to nine percent so that delta is 13 percent down for first early career workers and maybe 9 % up for more experienced workers. So that seems to be a strongish sign that judgment and experience is valued.

5:01And in a sense, that fits what we might have seen elsewhere and what our intuitions might have been, that artificial intelligence is taking over the production side of the equation. Large language models operate at scale today. They draft 30 % of Microsoft's code, processing billions of lines a day through tools like Cursor and other coding tools. They're starting to automate the creation of slides for management consultancies. And I think that model of junior workers generating output and senior workers judging that output, it is no longer one that makes as much sense in a world of AI, because you get the AI to generate the output and then you go off and judge it.

5:47And when you think about, if you've worked with software development teams and engineering teams, a really large part of working effectively in that team is knowing how that team works, right? When do you do a pull request? How do you note up your code? When do you go and talk to your senior developer or your engineering manager. These are all softer skills that you build with judgment and with experience. So Eric and his collaborators frame this research as a canary in the coal mine. You know, we used canaries in, well, we, our forebears, used canaries in the 19th century in the coal mining industry because the canary was really susceptible to the leak of, you know, poisonous gases.

6:34Canary keels over, miners know it's time to get out of that mine. So I think that there is something really suitable with that analogy. But I also, as I'll discuss over the next 10 or 15 minutes, think that we're not quite out of oxygen because it does take time for firms to reconfigure around a new technology. It's only August 2025. This fieldwork was done for a couple of years, running for the earlier part of this year, it's very, very early days for large numbers of companies to do anything but dabble in the technology. And at these moments in time, it's much easier to do non-hires, that is stop hiring people.

7:21And where do you stop hiring? You stop hiring in the graduate entry funnel than it is to get rid of people. A firing is much harder than a non-hiring, especially at these moments where things are quite turbulent. And so I think one of the things I've been thinking about is that we're quite early in this. So both this survey and the data they used being incredibly robust. And I mean, we're lucky to have this sort of depth of the survey and piece of research at this point in time, but also a recognition of where we are in the cycle. So with that, I just want to dig into how I had been critically analyzing and assessing this report.

8:01And I did have a chance to congratulate Eric by WhatsApp and have a little bit of a back and forth with him, but I won't represent what he said to me in that WhatsApp discussion because I may not do justice to it. But here is some of the things that I think are worth adding as not perhaps not qualifications, but just lenses on the research and what it could be telling us. The first is, who does ADP serve? I mean, ADP is this big, well-established business, and it really has a middle market focus, serving smaller and regional firms with strengths, particularly in the northeast of the US. So these may well be firms that some of you have heard of, but many of us won't because they aren't the big global franchises necessarily that we think of, the Starbucks or the Microsoft.

8:49They are, by and large, smaller and more regional firms. So these findings may reflect a different tier of the labor markets. And what do I mean by that different tier? A lot of the focus on hiring, obviously, is around nameplate firms who we all recognize. It just makes life a lot easier. But this may not be reflecting those class of firms. it may also be reflecting companies that are sufficiently small that they outsource their payroll right they don't run these capabilities themselves and it made me wonder about the extent to which companies like this are actually quite sensitive to cost savings compared to really big firms that have big funnels of hiring right how sensitive would they be shaving off a few new headcount may make a meaningful difference for those companies.

9:45They also might be able to respond more quickly. I mean, this is a canary in the coal mine moment, right? It may be that a mid-sized regional firm can respond and retool around AI far faster than a big, huge, regulated national company that's just going to take a little bit longer, in which case what we are seeing is a pattern that could get repeated across the economy. I've been thinking a little bit about the geographic concentration, that geographic issue, and what it tells us, and the extent to which what we're seeing is a large number of mid-market, mid-sized firms coming to a similar conclusion about how they should think about youth workers in the space of AI enabling higher productivity levels with people who are more experienced.

10:31So I'm still playing around with that idea. I think the second point I would make is the two main sectors that were impacted, because of their exposure to AI were customer service and software development. And those two are quite different classes of work. Customer service is very much closed-end class of tasks. You know, you have a range of customer service issues. We already know that AI systems can, through chatbots, answer 80%, 90%, 95 % of queries. But you have a very different class of work because you're ultimately in a kind of responsive mode to customer input. Whereas software development is a much more open-ended task, right?

11:18There's no company that doesn't have a backlog of development projects that they will never finish because as they finish one piece, they add a new requirement. And so there's a question there about whether the same underlying dynamics are at play for customer service as are at play for software development. So it could be that it's the same dynamic. And that is a kind of complicated position for us to find ourselves in, because the argument there would be that artificial intelligence tools are sufficiently generalized. They know about everything from the world series to the MSCI world benchmark and everything in between.

12:00And so what we're seeing is the generalizability of that technology impacting two very different classes of work. Or it might be that we're seeing two different etiologies, two different parts to affecting customer service versus software development hiring. And I think it'll be interesting to unpick that. Because one speaks about a tidal wave that comes in and sort of affects all boats. And another speaks about much, much more tactical sector level granular effects that we need to unpick and understand. I did wonder why companies reduced hiring rather than cutting graduate salaries, right? A typical economic analysis would be, well, if you get into this situation, you have two strategic choices.

12:48You could hire the same number of people and offer them lower salaries, or you could just keep the same salary and hire fewer people, which is what firms seem to have done. That's completely understandable in a sense, right? Internal pay structures are a little bit rigid, but I think there's a really interesting dynamic, which is that it allows firms to exhibit preference for higher quality, smaller workforces over pure cost optimization. So let's think about this, right? If firms are acting in a rational way when it comes to their hiring, they will very much try to hire the best people that they can.

13:25And let's say their selection process is perhaps not perfect, but very good at determining who's a great candidate, who's an average candidate, and who's just a kind of good enough candidate. This result might suggest that what companies are able to do is slough off that bottom decile or bottom two deciles of new hires they might have otherwise done, because they feel they can achieve their commercial and business objectives with a smaller workforce, which through this selection process will end up being the higher potential group of people who get the offers or not. And that I think is quite an interesting shift.

14:06It's an interesting dynamic. I mean, of course, like you always have bottom, top and bottom quartiles, but what I'm suggesting is that it could be the case that the absolute capability of the bottom quartile rises when you reduce your hiring and you can therefore be a little bit more selective. A quick note, if you want to support us in bringing more of these conversations to the world, please consider subscribing to the show. I think there's another question, which is really about what can we say this early into the deployment of AI technologies in companies. I've always argued that what history has shown is that you start with relatively low hanging fruit.

14:49And it takes time to re-engineer your processes. And my favorite example of this is to think about electricity back at the turn of the 20th century. So when electricity shows up, lots of factory owners end up buying into electricity early. I mean, they weren't slow, but buying in meant you put some pendant light bulbs in your workshop so that workers could work longer hours. You could perhaps extend the working day. But you didn't change the fundamental power delivery, which was a sort of steam-driven power delivery, or in the car industry, very artisanal working, looking not too dissimilar to a Coachworks or a Blacksmith's workshop.

15:29And it took time before you re-engineered and rewired processes, Henry Ford in the car industry being the best example of somebody who believed in electricity and built a production flow that could only work with electricity. And so if we're at that early stage where this is about buy-in rather than being able to deliver belief, where existing firms, particularly mid-sized firms, which generally may be able to make decisions more quickly, but they don't have the working capital, they may not have the talent depth and the leadership, they may not be able to make the investment for a full re-engineering and reorganization, what we might be seeing is the effect of low-hanging fruit of simple productivity and quick cost savings rather than a fundamental pattern of what might happen as artificial intelligence comes in and helps people rethink what work looks like.

16:31And this tacit knowledge piece is quite important for all of that. Because, you know, tacit knowledge is often the je ne sais quoi that makes firms do what they do, be who they are, be as good as what they are in the sectors they are and serving the customers that they do. And that experiential workplace wisdom may explain why younger workers are going to be vulnerable. But ultimately, younger workers are cheap. They're probably very willing to learn. And they may actually be more AI au fait than older workers. So in my mind, there's also a question about, does this pattern hold over a few quarters, or does that pressure on higher paid, more experienced workers start to show up?

17:20There's another dynamic that I want to introduce here. So Darren Asamoglu, Nobel laureate, and his long-term collaborator, Pascal Restrepo, talk about something that is probably best described as business stealing. So when they looked at automation over a few decades, this would have been robotic automation in factories and so on. What they identified was that firms who, which went early in automation, actually ended up growing their labor forces. Jobs that were lost as a consequence of automation were lost in firms that didn't automate. And I write about this in my first book. You can see it over my shoulder there, where my finger is.

18:02And what I describe essentially is my hypothesis around this is that to get involved in automation is a difficult, complicated thing. It's not just about understanding the technology. It's also about being able to manage that change and communicate it to your workforce, get them motivated. All of those things are signs of a good, great management. And so you could say that a proxy for great management might be firms that can roll out automation very, very well because those are difficult projects. And so where we end up is essentially through Restrepo and Asimoglu, and this is my interpretation rather than their conclusion, is that firms who are successful in automation are successful partly because of the automation, but also partly because they have better leadership.

18:48And so in traditional competitive dynamics, we'll outperform firms that have worse leadership. And I think this is a little bit relevant to what we might be seeing as we broaden the lens from Eric Brynjolfsson and his collaborators very tight and very very well evidenced paper to asking how this might ultimately play out. Now there are obviously some really clear challenges you know history rhymes it doesn't repeat and so let's talk about I think one of the hardest clear challenge which is that the technology is improving rapidly, rapidly, rapidly, and maybe rapidly-er and rapidly-est. And you think about a deep search, deep research query you might put to Claude or ChatGPT.

19:38That's 20 minutes of work for the AI agent. It's sort of dozens of hours of work for a human. And it can generally complete that work unattended. And that 20 minutes is going to turn into 200 minutes and then 2 ,000 minutes, and you can parallelize the agents to go off and do that work. And it will go beyond deep research, which feels like it's increasingly a solved problem, into other domains. And so that, I think, is a differentiator in this technology compared to previous automation waves and things like electricity, which we go back to, because we have to think about what happens in a workforce where you can go off and get that level of work done.

20:19Most employees can manage to think about a three to four hour task. But when you start to think about three to four month tasks or three to four year tasks, that's where you get involved in senior management ultimately and leadership. Today, the AIs can't be led in that way, but at some point perhaps they might be. So that adds a degree of uncertainty. So I know we've got a question coming in about universities lagging behind providing a useful state of the art education. And I'm going to touch on that, because I think that gets to this key idea that actually what matters is policy solutions to all of this, right?

20:58What's very helpful with a paper like this and some other papers that have come out recently is that it gives us an indication as to what we are diagnosing, and therefore what is the policy that is required. There is a trap here. There is a market failure. Firms have shown to some extent they're disinclined to hire graduates and train them and give them that tacit knowledge that makes them useful. Why are they disinclined? Maybe it's an investment they can't afford. Maybe they'll make that investment and then they're worried that those workers will leave. But it's just too risky for them. So there is a kind of policy market failure here that will probably require interventions that are both carrots and sticks.

21:47So, you know, hire people, train people, get some kind of a tax credit is the type of thing that you might be thinking about. What is the education that workers need to come out with? They will clearly need earlier access to tacit knowledge, whether that comes through internships or apprenticeships, whether that comes through formal university education. I have faith that universities can teach difficult, complex, structured and unstructured things. I mean, they have done for centuries. So I mean, there's no reason to believe they can't teach elements of these tacit skills. And I think there could be an issue for this cohort, which is that they may have graduated before ChatGPT was everywhere, they may not be as AI native.

22:32And therefore, as they go into the job market now, where employers are expecting that, they're falling down both on AI nativity and on tacit knowledge. Again, to be clear, I'm making an assumption about the AI nativity as it was. So it does feel that we will need a different mechanism to train workers, whether it's universities or something else. Of course, not everyone goes to university. The costs of university are very, very high. And I can imagine that splitting in a couple of dimensions. So dimension number one would be really understanding that tacit knowledge. And the challenge of tacit knowledge is that it's often not codified.

23:14So, you know, can you go out and codify that knowledge in some useful way that allows you to bring people up to speed? And I've invested in a company, sugar work it's called CEO Vanessa Liu that attempts to do that to identify and crystallize the tacit knowledge within within companies and and and then if you can do that can you then have that be that something that young people can get either through diversity or through their internships I think the second thing is how good are people really at using the AI tools and using them to be able to develop and strengthen the business case for hiring them.

23:55And again, this early cohort probably hasn't had a chance to formally learn it. And what are those skills anyway? You know, is it just about prompting or is it about meta-prompting? Is it about understanding critical thinking and analysis and more formal analysis so that you can ask the right questions? It's probably a mix of all of those. I think there's another difficult pattern to all of this, which is that if you think about internships and how do you gather tacit knowledge and experience in the world, well, there we run into a real equity problem, which is that ultimately people who come from particular backgrounds where perhaps there's space in the family home to discuss business will in the background implicitly develop that knowledge.

24:38They may be the ones who can afford to take unpaid internships. And so it raises another thorny question about how this is going to bifurcate across the workforce with a different cut. So it was a great paper. It's made me think a great deal. I've spent a lot of time thinking it through and talking to people in California when I was there earlier this week. That is all we have time for today. Thank you so much for showing up. I'd encourage you to read the paper. We will put it out, of course, with some more analysis across Exponential View over the coming weeks. But in the meantime, I wish you all a wonderful weekend.

25:14Thanks for listening all the way to the end. If you want to know when the next conversation is released, just hit subscribe wherever you're listening. That's all for now, and I'll catch you next time.