It's an interesting exercise to think about jobs, or tasks within jobs, that could in principle be replaced by automation but for some reason aren't. Often, the reason isn't the state of technology. Sometimes it is the state of technology, but not in a way that is obviously related to where technology is progressing today. To see what I mean, come with me on my morning commute to work.
On the way out of my building I say hi to the doorman, just a quick hi if I'm busy, or exchanging a few sentences if I'm not. We, or rather our landlord, could choose to not have a doorman and instead have access cards and perhaps cameras with facial recognition. I'm happy we have a doorman.
Often, there are some maintenance workers in or around the lobby, given that there's always something that goes wrong in a building where several hundred people live. Maintenance work involves lots of tricky manual manipulation in unique configurations, because everyone furnishes their apartment differently. Changing the drain pipes looks simple, but somehow is not so simple when I attempt it myself.
Turning the corner, one of the first places I pass is my son's daycare. His teachers are lovely. That's not just great, but necessary: otherwise we would not entrust them to take care of our son eight hours a day. Letting a machine take care of him is obviously not something we will ever consider.
There's a lot of retail where I live, on the border of SoHo and Greenwich Village. Grocery stores, delis, and big names like Nike and Apple. There's even a (small) Target. There's a bunch of small and unique stores, and some very fancy and pricey high-fashion boutiques. I guess most of what these stores sell could be bought online, and we do get much of our groceries delivered. But it's nice to go shopping in person. Browsing store aisles enables different serendipity than browsing websites, whether it's for books, clothes, or steak cuts. It’s social, and you don’t have to wait for delivery.
Automate grocery retail? It’s been tried many times, starting many decades ago. In the 1960’s, Stockholm had the world’s largest vending machine, with 1500 different items. It closed as soon as the law changed so that stores were allowed to be open on weekends and evenings.
There are also plenty of restaurants. I know, you can cook and eat at home. What can I say? Even in the post-scarcity hyper-automated utopia of Star Trek: Deep Space 9, where you can extract food from replicators, Captain Sisko’s father runs a Creole restaurant in New Orleans.
Behind me on Bleecker Street there are some live music venues that stubbornly refuse to be outcompeted by Spotify, or the Walkman, or the Gramophone. There's also a nail studio, a waxing studio, and a fortune teller. Somehow I don't think the fortune teller will be replaced by better prediction algorithms. Further behind me is my doctor's office. I want my doctor, and whoever he refers me to, to use the best available technology to diagnose and treat me. But I also want him to make the judgments. I like him and trust him.
Crossing Bleecker Street and walking out onto Broadway, there are lots of taxis and probably even more delivery bikers. The latter have an attitude to traffic rules that is relaxed even by New York standards. Will these drivers and bikers be replaced by self-driving cars and delivery robots? Maybe, eventually? Good luck with the Manhattan traffic, though. And for quite some time, I expect that delivery robots will be more expensive than whatever recently arrived immigrants from Haiti or Venezuela get paid. You may say that the last sentence is cynical, but I disagree. I believe recently arrived immigrants appreciate having a way to make money.
I take the F train from Broadway-Lafayette down to Jay Street in Brooklyn, where my lab is. The F train has a human driver. Why does the New York subway have human drivers, while the metro systems of Copenhagen and Singapore are driverless? Probably because the latter were designed to be driverless from the ground up. The New York subway doesn't have barriers with doors separating the platforms from the train, and the signaling system is about a hundred years old. For real, 100 years. I also wonder how much savings there is to be had from making the trains driverless - only a small fraction of MTA's 70 000 employees are train drivers. I bet we will continue to have train drivers for quite a while.
In New York, the subway is often the fastest and most practical way of getting from one place to another, regardless of how rich you are. Because traffic. So you really do meet (or at least share a train with) all kinds of people on the subway. The F train passes next to the financial district, and Downtown Brooklyn has a fair number of financial institutions in its own right. So, probably, many of my fellow passengers have job titles like Senior Software Developer, Director of Data Science, Key Account Manager, VP of Sales, Compliance Analyst, HR Specialist, Lead Investor, or Prompt Engineer.
I wouldn't claim to know what all these people actually do all day, even though I actually like hearing people describe what their job titles concretely mean. My impression is that each of these jobs has lots of different tasks, and that these tasks are ever-changing. Many of these tasks involve reading lots of text and producing new text, or program code. These are the kind of problems where current AI can be very helpful. The degree to which it can help varies, from doing the whole task, to providing useful feedback, to being utterly useless. Knowing what AI can help with and how to make it do so requires plenty of specialist knowledge. The same goes for knowing when the task is correctly done. Sure, the models are getting better, but that just means we can attempt more and harder tasks. It's not like we are running out of problems to solve..
All of these jobs are ultimately about trust and responsibility. Not only does the task need to be done, someone needs to take responsibility for what was delivered. Someone the organization trusts, so that everyone in it can get on with their part of the job. This responsibility is ultimately what the white-collar worker gets paid for. The buck always stops with a human.
Most of these jobs are also about communication. All those meetings where you try to figure out what needs to be done, who should do it, how you should coordinate it, and overcome all the myriads of little roadblocks you encounter along the way. Like data access, compliance with all kinds of rules, not stepping on someone's toes. Some people love to complain about how meetings are getting in the way of doing their job, but arguably the meetings are the most important part of the job. The more your job is about meetings, the less automatable it is.
A homeless man enters the subway car, and starts a short and well-worn spiel about his predicament. He just needs a few dollars to buy something to eat. Most of my co-passengers look at their phones and pretend not to hear him. This man's "position" actually should be "eliminated" so he can have a better life, but automation is not the answer here.
Where I get out of the Jay Street subway station there is usually some police presence, because Brooklyn. The police do many different things, and we love to argue about which ones they should do more of and which ones they should not do at all. The policemen and women you see around Jay Street mostly seem to stand around, but I guess that's because they're less visible when they do other things. Given that people do get robbed in the area, and there have been incidents of the local high school kids bringing guns to school, having police just stand there and be visible seems motivated. I guess many police officers would appreciate AI help in writing and editing their reports. But… automate the police? Replace police with algorithms and robots? That's a staple of sci-fi stories, from Robocop to Minority Report. Let's just say that it's never portrayed as a good thing.
On my way into my office I pick up a coffee. I know, I could make coffee myself. But then I would have to make sure to have fresh milk in the office, and coffee beans, and… you know what, I'm not going to make any excuses. I don't need to explain myself to you. I buy coffee from the coffee shop because I like to. The coffee is good and the barista knows my name. Automate that.
Then we get to the calmest part of my day. I'm at my desk, with a good coffee, waiting for my first meeting to start and thinking about the day in front of me. So let's think about my job. What do I actually do, and could I be replaced by technology?
I always tell those of my PhD students who consider a faculty career that the transition from graduate student to faculty member is rough. A PhD student is mainly concerned with their own research project, whereas even a new assistant professor has what feels like 10-20 jobs. Often jobs they are not prepared for, including obscure committees, department politics, and complaining students. The only way I know to get through this is to slice your day into slivers, context-switch often, decide which two or three of these jobs you are going to do well, and half-ass the other ones. Let's focus on the two "jobs" (types of tasks) that I consider to be the core tasks of a faculty member at a research university: lecturing and research advising.
Lecturing is by no means an optimal mode of knowledge transfer. It was supposed to have been made obsolete by massively online open courses, and before that it was supposed to have been made obsolete by lectures over TV, radio, VHS, or even by books. Personally, I generally prefer reading books. Nevertheless, the lecture persists. I think it's largely because of the ritual, where a real live human gets up in front of you and speaks to you, forcing you to at least pretend to pay attention. Afterwards, you can say you attended a lecture. I wrote a post about this recently.
When it comes to research advising, it's a curious blend of knowing the technology, knowing the literature, knowing the personalities that dominate the research field, feeling where the wind is blowing, seeing patterns, sensing opportunities, having a vision, being reasonable, being unreasonable, counseling, friendship, and navigating bureaucracy. Also: having an opinion, giving a damn. It takes different shapes with each student, because each advisor-advisee relationship is different. It is crucial for the advisor (me, in this case) to admit that they don't know very much about anything in particular. I'm never on top of the literature, I don't know any maths, and I've forgotten how to program. My sessions with my PhD students often consist in them teaching me things, and me asking questions. I'm pretty good at asking questions, partially because I'm good at admitting when I don't know things, and partially because I have interesting interests. Because life experience.
Could a PhD student talk to an LLM instead of me, and still produce good research? Sure. They could also simply read the relevant papers themselves. People do that all the time, and there are many good self-taught researchers. Still, the evidence seems unambiguous that having a good and compatible advisor/mentor helps you become a better researcher. I have modeled myself on and learned much from my mentors and advisors, and also sometimes intentionally decided to be less like them in some manners.
Recently, my friend Georgios and I published the second edition of our textbook on AI and Games. Writing down everything you know about your own field of expertise? This would seem like begging to be replaced. Anyone could now just read our book instead of talking to me. However, it's quite the opposite. In practice, the more people read things I've written, the more they want to talk to me and even collaborate with me. I would actually be worried if it was the other way around. So, freely giving away everything you know is a good way to stay relevant. Knowledge work is not a zero-sum game, as simplistic ideas of labor replacement would have it.
Looking at the various professions I have encountered on my way to work, it is tempting to divide them into on the one hand low-status jobs which deal with human communication, handling physical objects, or just being there, and on the other hand high-status jobs which require hard cognitive or creative work. Then you could conclude that the "fancy" professions are the ones facing an automation threat. But I think that would be simplistic. Most jobs are actually some mix of these. The doorman solves plenty of cognitive problems, as people keep coming to him with their problems, or sometimes try to sneak past him, and often he observes patterns, such as a tenant using their apartment as an AirBnB. The maintenance workers similarly need to come up with creative solutions to any number of tasks, alike but never identical. And we haven't even gotten started on the complexity and amorphousness of what the police do. At the same time, all us are to some extent customer service agents and virtual doormen and maintenance workers of our professional domains. We talk to people to figure out what needs to be done, convince people that something needs to be done, lead, trust, engender trust, take responsibility, problem-solve, sanity-check.
Another reflection is that many of the jobs where people worry about being replaced by automation are jobs that their grandparents would never have heard of, and perhaps not their parents either. This makes me wonder whether there's a Lindy Effect for jobs: the longer a profession has been around, the longer it is likely to persist. Many of the jobs mentioned in the Bible still exist and are even reasonable career choices, including preacher, carpenter, goldsmith, fisher, teacher, baker, merchant, politician, and musician. In comparison, novel professions such as SEO specialist, social media manager, and drone operator might be less likely to be known to your grandkids.
Finally, the idea that a job or task would be "replaced" because a machine can do it is quite weird, when you think about it. My parents and many other of my family members are visual artists. Some time ago, I showed my mother some image generation models. She wondered why anyone would be interested in this and how it had anything to do with her profession. Even without machine-generated images there is a near-infinite richness of images around, because there are eight billion humans in the world and many of them produce images. What difference would another source of images make, especially if there is no personal experience behind them? For her, the personal experience is what makes the image interesting.
Your mileage may vary. This is what I see around me. Perhaps you live in a suburb, work from home, and generally avoid seeing people. In which case, that's your problem prerogative. I still don't think your job is likely to be replaced, although many tasks in it may be transformed.
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