Chapter 1: What are the key findings about AI's impact on entry-level jobs?
Now, 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 Brynjolfsson 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 Chandan and Ryu Chen,
analyzed 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. And they were able to look at this data, correct for various confounders like COVID and seasonality, and they identified a really, really interesting finding.
Chapter 2: How does generative AI affect job losses among younger workers?
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.
Chapter 3: What factors contribute to job declines in AI-exposed roles?
In contrast, for workers who had more experience in those particular professions and areas, employment actually went up. Mid-career software developers, you saw a 10% increase in employment.
Chapter 4: Why don’t firms simply reduce salaries instead of hiring less?
In the case of these early career 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 aides or stop clerks,
Chapter 5: What historical parallels can we draw from electricity's impact on jobs?
you didn't see the same suppression of employment, particularly for that early career employees. So if you look at health aides, for example, over that period of time, there was roughly a 20% increase in hiring for early career workers. We see that there is a pattern which appears in jobs
Chapter 6: How does leadership influence job losses in the age of AI?
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 have
Chapter 7: What are the implications of AI for policy and education?
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? What's this picture?
Chapter 8: How can we address the equity challenges in the job market due to AI?
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. It 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 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, 6% to 9%. So that delta is 13% 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.
And 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.
And 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 Our forebears used canaries in the 19th century in the coal mining industry because the canary was really susceptible to the leak of poisonous gases.
Canary 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.
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