The three types of jobs we will see for many years to come
What sort of work might still exist in the 2070s, when today's high school students are retirement age?
Opinions about how much AI will shift the job market of the future vary considerably, from economists like Daron Acemoglu who think that only 5% of jobs are likely to be automated to investors like
who suggest that we are on course to have AI powerful enough to do any job that can be done remotely by 2027. Others with a close pulse on the latest trends like argue that there is a lot of disruption to come even from existing technology, so we should estimate a high level of uncertainty for any predictions about the long-term future of work, difficult as it may be for anyone who needs to forecast 10 years (or more) out.And yet if you are making decisions about what to study, or what career to focus on, thinking about what sort of job you might have in another 10, 20, or even 50 years is unavoidable. In a world with such a hazy future, what jobs might still be around in the 2070s and beyond?
After spending the past few years studying how much jobs have changed in each of the past 15 decades, and keeping a close eye on 6 key AI variables, I think it’s helpful to think of the jobs of the future belonging to at least one of these 3 (or maybe 4) broad categories:
#1 Inherently human jobs
Think of these as all jobs where having a human is inherently valuable to the customer.
Whether this is a matter of trust, rapport, motivation, nostalgia, signaling that one has enough money to hire a person rather than a robot, or even a moral preference, these jobs are already a significant fraction of the economy and may be more resilient than many expect, especially given how adoption of AI might actually prove to be unpopular. These “inherently human jobs” span a range of fields, from therapists/counselors to fine dining restaurant staff to babysitters.
It’s worth noting that the three sectors that contain the highest proportion of these jobs comprise about 30% of overall employment and have been rising over time:
#2 Knowledge verification jobs
This is primarily technical work where personal accountability for factual accuracy is required.
Whether AI hallucinations can ever be fully “solved” is a hot topic of debate among AI researchers. But even if they become exceedingly rare based on benchmarks like SimpleQA, we are still likely to require subject matter experts. Someone will need to verify that AIs are indeed still providing us with accurate information that reflects an updated knowledge of the world, filling in any gaps or vulnerabilities that may prove unexpectedly difficult to seal off in real-world situations, especially in fields like law where an extremely high level of accuracy is often required. There is a trust component to this as well— decision-makers may inherently trust a fully accountable individual more than a loosely accountable machine. It’s been demonstrated that people set a higher bar before trusting a robot, and that trust is more easily broken.
Many technical jobs in law, medicine, science, engineering, technology, and auditing fall in this category of “knowledge verification”. The late 2020s and early 2030s will likely see an ongoing reassessment of the jagged frontier of when an answer from an AI might suffice vs. which problems require an accountable human. What happens to software engineering jobs, where the largest part of the corpus of operational knowledge is already in AI training data, will be an important leading indicator which we can track via a mix of job postings data and industry employment data. While I would be surprised to see this occupation disappear entirely, I wouldn’t be too surprised by either a modest growth (if new tools make knowledge-intensive work cheaper and induce more demand) or a modest decline if exisiting human expertise paired with AI is sufficient.
What is certain is that education and training for these roles will need to be changed significantly to better complement new technology as it evolves— after all, developing subject matter expertise involves a heavy personal investment and it’s important to do it right, especially if more frequent adaptation is required. And if more “knowledge verification jobs” disappear than expected, it may be because these evolve into management roles as the scope of managerial duties is set to increase in an era of powerful AI.
#3 Managerial jobs
This includes any work that requires highly consequential decisions to be made by a directly accountable individual.
Just like the two types of work above, this will take many forms, from jobs required to exist by regulation to entrepreneurs who might employ a mix of people and AI for their ventures. Similar to knowledge verification work, the core principles supporting the continued existence of these jobs include accountability for decision-making and desire for control, though some technologies might reduce the need for line supervisors and middle layers of management over time.
Of course, management jobs are rarely entry-level. If you are starting your career today, your journey will likely start in you specializing in an “inherently human” or “knowledge verification” job, or possibly though another fourth category: “other physical work”.
Other physical jobs (#4?) is an honorable mention here. This includes jobs that require physical work, specific expertise, and need to be performed in unpredictable environments, but don’t fall in any of the other above categories. Some types of work that fall under this such as driving may be significantly automated by self-driving vehicles within the next decade or two. Other types of work such as construction and repair may not be significantly impacted until cost-effective, skilled humanoid robots become available, but that would require developing robots that have a very wide range of physical skills and even after that there are maintenance concerns to consider. It’s possible that more investment may be made in developing complementary robots for some types of construction and repair work.
In the meantime, job prospects in this field are likely to be solid over the next few decades, especially in data center and electrical infrastructure construction as well as semiconductor production. The major caveats are that exact levels of employment will be sensitive to interest rates, and advancements in humanoid robots are somewhat of a question mark over the very long run.
The bottom line
Good analyses of the automation potential of different jobs often include a percentage estimate, which I think is an important way to think about this. Take restaurant workers for instance. Fast food restaurants, where customers care more about a cheap and tasty final product could be largely automated in the not-too-distant future. But sit-down restaurants might only be partially automated, based on the tastes of their customers. This might in theory eliminate half of food service jobs.
But 50% automatable doesn’t necessarily translate into 50% fewer jobs. If the same technology that causes this automation also causes society to become wealthier as a whole (assuming gains are shared reasonably equitably, another big if), the demand for the remaining human-operated sit-down restaurants could go up. If this demand ended up doubling, it would bring us right back to where we started. On the other hand, the rise of economical domestic robots that can do home cooking might cause these restaurants to take a hit. Will such robots be capable of self-repair, or as
points out, would more robots mean more jobs open up in servicing these machines?Thus we return to having to accept a high level of uncertainty about the future of work, at least over the very long run. If there is one thing that makes me optimistic, it’s that we are capable of tracking the early signs of technological employment more closely than ever before. Hopefully a mix of better predicting the impact of new technology on jobs, rethinking re-skilling, and thoughtful policies aimed at keeping inequality in check can help guide us towards a future where people are able to spend more time doing what they love.