MIT economics professor Sendhil Mullainathan says it is in humans’ power to put AI on a path to help us rather than replace us
“[WSJ] What’s wrong with how AI tools are being developed and deployed?
[Mullainathan] Every time Anthropic or OpenAI or Google releases a new model, you’ll notice they always talk about, oh, we did better on these benchmarks. That’s the way they keep score. In many ways those benchmarks dictate what these models are asked to be good at.
We pick an area and then we say, “Can this thing do this as well as people?” So we’re building algorithms with a strong capability for automation. And when we say they’re getting better and better, we mean their capabilities for automation are getting better and better. If you look at the standard benchmarks, there is nothing in them that would make you say, “Oh, here’s a metric for helping a person do something better.” [..]
[WSJ] How does AI as a “bicycle for the mind” fit in?
[Mullainathan] Imagine that you’re looking for a job, and you wanted some help from an algorithm that would help you decide where you should apply. The where-should-I-apply question is inherently a bicycle-for-the-mind question. It requires combining some things the person knows—what kind of jobs do they like, where are they willing to live, etc.—with some stuff that the algorithm is better suited for: Given your résumé, where are you likely to get an interview? Where are you likely to get an offer?
So you’ve got these two different kinds of information. The algorithm understands, given your résumé, what your opportunities may look like. You understand your preferences. If some communication could happen, a lot could get unlocked.
[WSJ] A lot of this seems to come down to AI is much better than us working with the data, but it doesn’t see anything outside of the data.
[Mullainathan] And there are just so many problems where what’s not in the data is as important as what’s in the data. [..]
[WSJ] Your work shows that scarcity—scarce time, scarce money—basically steals mental capacity. How could AI help us deal with scarcity issues at work?
[Mullainathan] A product I think would fundamentally transform the nature of work is one that helps me really just make better decisions about what I take on and don’t take on. It seems a bit mundane, you already have Google Calendar, you have automatic schedulers. But while those things all solve the logistics of scheduling, they’re not solving the core time-management problem which we all have, which is not about, can this meeting fit in here. The core problem is we’re not managing bandwidth very well. We’re not thinking about, “Oh, man, if I take all these meetings, I’m going to be overwhelmed.”
Notice this has two elements we’ve mentioned. The algorithm has access to a wealth of understanding about your calendar, your past meetings, about known psychological biases. You have a wealth of understanding of what you’re trying to accomplish, what has worked for you, what does not work for you, what has made you nervous. If we could combine these two things, I think we’d have a totally different way to approach time management.”
Full article, J Lahart, Wall Street Journal, 2025.4.14