You can’t make AI go away. How can you live with it?
Or more precisely: How can you make a living in an AI world?
Here are a couple standard answers: “Learn to use AI.” Or figure out an AI-proof career, one that can’t be automated away. Maybe something with your hands.
Ethan Mollick doesn’t think that will work.
Mollick is a Wharton management professor who has become one of the more influential guides to what AI can already do — and what it will mean for people who have to work alongside it. His new book, “Co-Existence,” argues that the models are improving much faster than most people understand, and the only way to deal with their rapid acceleration is to learn how to live with them.
That means acknowledging that a lot of what you can do today will be less useful in the future, and concentrating on the attributes you already have that AI can’t replicate.
I talked to Mollick about what AI means for your job, your kids’ future, and what happens when the systems that train workers start to break down on my Channels podcast. The following is an edited excerpt of our conversation.
Peter Kafka: People worried about their jobs, or their kids’ jobs, are being told to become AI experts — or to find lines of work that are AI-proof, like plumbing or nursing. Is that useful?
Ethan Mollick: I don’t think it’s very useful. First of all, that’s great until PlumBot 6000 comes along. I’m not a robotics expert, but the robotics experts I know went from, “LLMs will never … this AI boom is completely unrelated … robots are hard” to, “Actually there’s a thing here.”
I don’t know what the future holds. I don’t think anyone does, which is either comforting or very upsetting to hear from somebody who’s an AI expert.
Look at the most changed field, which is coding. We already know coding is changing. [But] we aren’t even clear whether there are going to be more or less jobs in coding. We know what you do as a coder is going to change and keep changing.
Writing error-free code is now less important than conceiving of what you want to have built. A lot of coders are happier than they’ve ever been before. Some are sadder. These are evolving jobs.
In the book, I talk about four things that give you at least some advantage. Deep knowledge: knowing an area really well, because that expertise lets you outcompete the AI. Wide knowledge: knowing about lots of different things.
One thing I see in companies is they say, “Our coders are doing design work, our designers are doing coder work, and the marketers are doing business plans.” The more you know about a lot of little things, the more AI can help you.
Then there’s taste. AI produces a lot. What prevents it from being slop? Your taste about which things to pick.
And finally, agency. I don’t think “learning to use AI” means very much right now. We’ve been testing all the methods we use to teach people how to use AI. None of those tricks work anymore.
Prompting has started to look a lot more like managing. You don’t have to do magic words anymore to make the AI do something. Instead, you specify: Here’s the product I want, here’s how I know it’s good, here’s how you should test it, here’s what you show me partway. Give me five different mockups that I can pick among.
That seems like good news for someone mid-career, who has experience, taste, and has gotten good at their job. But if you’re starting out, how do you develop that stuff in a world where AI says, “I’ll do the research. I’ll write it for you.”
There are two problems. One is people as individuals, and the other is the institutions we have for teaching people.
As individuals, we love to cognitively offload stuff. That’s what we’re built to do. Technology has always been about replacing annoying labor. No one pines for washing dishes by hand once they have a dishwasher.
No one wants to churn their own butter.
My kids don’t learn cursive. I don’t know how to use a slide rule.
But we’re bad at picking which things we want to offload. That’s a problem with everyone cheating on homework.
And then there are the institutions. There are two sets of learning institutions for learning how to do a job. One is college and high school education. They’re screwed up right now, but we’ll figure that out. AI is actually a really good tutor, and we know how to test people without AI: You put them in a classroom with a blue book or have a conversation. We’ll have better education as a result, with AI tutoring outside of class and active learning inside of class.
What I’m worried about is the other method, which is apprenticeship.
I send people to a bank, consulting company, audit firm, or car dealership, and they learn the same way everyone always has.
They do grunt work, poorly paid. They might get abused a little bit. The trade is that they learn skills and contacts, and they learn how to do stuff because we force them to do it.
And middle managers get somebody to do their crap work. Even if you’re a bad mentor, people still learn the ropes. If they show they can do a good job, they advance. You get the next generation of good employees. It has worked out wonderfully for a few thousand years.
That all broke because the incentives are no longer aligned. As a junior employee, your best bet is to use AI for everything because it’s better than you. Junior people are much worse at using AI because they dump everything into Claude and give you Claude’s results. How could they tell you whether it’s a good report or bad? They haven’t built taste.
And managers realize: “Why would I go to an intern when the AI will solve the problem for me, at a better rate?” And company leaders say: “Why would I want to slow this train down? Why would I want worse work from interns?”
The training mechanism was this virtuous cycle, and it is completely shattered.
Either we will have to make more formal education happen — a lot of light, white-collar work will have to look more like welding or plumbing, with a trade-school aspect—or companies are going to have to get serious about mentorship as a real thing they do. It takes time and costs productivity, but you gain the benefit of having junior people.
My fear is that companies will say, “We’ll just hire senior people.” That’s the dangerous area. The talent pipeline ends. There are no replacements.
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