When are robots going to take my job?
Some important questions to consider
We find ourselves in interesting times in 2024. There continue to be some impressive advances in AI and robotics. But are these technologies actually ready to take our jobs?
Here are some of the recent developments that have stuck with me the most:
AI agents taking on increasing responsibilities: from handling up to 2/3 of customer service inquiries in some cases, to doing highly technical work such as writing and deploying (simple) web applications, human-level data analysis tasks, and aiding in the discovery of whole new classes of antibiotics.
Early signs of generalist humanoid robots that can understand voice commands and do household tasks. Will they soon be able to (economically) do what these cleaning robots cannot yet? (It should be noted that the time it took for the robot to do this was 8-10x longer than a human, so the trajectory of improvement here will be something to watch)
Accelerating investment: NVIDIA stock has doubled since my previous exploration of AI trends only 5 months ago, becoming the 3rd most valuable company in the world by market cap while branching out into robotics as well.
On the other hand, critical flaws have become increasingly clear, with no easy fixes in sight:
Looseness with facts: from generating false legal citations to offering customers a Chevy Tahoe for $1 when prompted the right way, the internet is full of (often funny) stories of AI fails.
Impressive benchmark results ≠ real-world impact: many common AI benchmarks that make systems seem really smart effectively just measure the ability to memorize information, a domain where computers have long vastly exceeded human ability.
Less automation than advertised: seemingly autonomous systems like robotaxis still rely on an appreciable amount of human intervention.
In spite of these challenges, the LLM-fueled advances of the early 2020s appear to open a lot of doors for AI agents and eventually robots to take on a lot of work. But with U.S. unemployment still near record lows and layoffs still well below their historical average in March of 2024, it’s clear that robots haven’t been throwing many people out of work yet. However, productivity is on the rise again in spite of only 5.5% of firms reporting use of AI to the Census. Given that earlier surveys have suggested AI use by individuals may be higher, it’s quite possible that there is a substantial amount of “secret” AI use by employees is that is creating efficiencies that aren’t yet captured in productivity statistics.
Past performance is no guarantee of future results of course. While economists who have extensively studied the history of the labor market like Daron Acemoglu think AI and computer vision abilities will automate less than 10% of tasks in the economy, studies like this one from Goldman Sachs in April 2023 estimate the level of automation as closer to 30%. Some technologists are even more aggressive in predicting the arrival of an Artificial General Intelligence (AGI) so smart that it exceeds human ability in all areas by 2030. But will speculation about AGI being just around the corner seem just as foolish as those who predicted fully self-driving cars by 2019?
One thing that has bothered me about many predictions of AGI is how much they tend to oversimplify intelligence as a single variable, even as the definition of what AGI means is hotly contested. Yes, it is hard to speculate about the specifics of something that doesn’t exist yet, and whether scaling up raw computing power alone will solve problems is an area of active debate. Papers like this one from NBER do account for the existence of tasks of greatly varying complexity and the likelihood of some jobs staying “nostalgically” or legally human. And even my own previous analysis, which looked at the impact of the most transformative AI I could imagine from the perspective of working hours, was far simpler than I would have liked.
A few years ago, I did a deep dive in Census occupational data, building off this Fed analysis that looks at how much jobs have changed in each decade going all the way back to 1860.
I would love to see a chart that projects this distribution of jobs forward to the year 2120. But even a 20-40 year prediction could capture many of the hypothesized impacts of current and future generations of AI. McKinsey has published a good snapshot of what the current state of occupational change looks like, and some rough estimates extending further into the future (see figure 8 here). My goal over the coming months is to play around with a few different scenarios in a way that more ordinary readers (especially students who are making big decisions about their careers) can appreciate.
This is a story with many parts to it— I will keep these links updated as I continue to explore this subject:
At a high level, how can we measure how much jobs are changing? How do the 2020s compare to history? And how can we best communicate this to the public?
Is a robot or a recession more likely to take my job? How does technological change compare to other factors?
If robots were about to take our jobs, where would we see the first signs where that was about to happen?
Where have previous official predictions of job change failed?
Which jobs should we be most worried about in the 2020s? Which ones might grow even more than expected and still exist for a long time to come? And which ones should we keep the closest eye on?
What are the implications of self-driving technology on the job market?
How might AI reshape education and job training?
Are jobs becoming more or less satisfying over time?
What is the role of unions in shaping the job market?
Taking likely job changes into account, what does the future of the workweek look like?
The beauty of these topics is that they are all relevant questions for the full range of reasonable scenarios of technological advancement. In thinking about all of these various facets of the future of work, I am struck by how complex and specialized each occupation is. Context is that which is scarce, as
puts it. The AIs of the future will have a lot to learn, as will we in figuring out how much we can trust them.