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Start free trial →The Developing Dev · Software Engineering & Infra · November 2026
Asked how to pick scientific problems worth solving, which he says is the part AI does not do, Ramakrishnan describes the trade-off between important and feasible.
…courses because I didn't really know anything. And so I knew what a ribosome was, so I wrote to them and they offered me a fellowship to go to Yale. And that was the start of my sort of ribosome career in 1978. And I sort of, you know, basically, you know, it lasted my whole life, actually.
…you were saying that even if AI accelerates the problem solving, the picking what problems are interesting, picking what problems are important, that at least today is uniquely human and valuable. Do you have any thoughts on how to pick interesting scientific problems to solve and how do you pick?
Well, you know, there are big problems like the origin of life, or, you know, how does the body develop from a single cell into a complicated animal, you know, complicated body, or, you know, how, well, what happens to the brain? And how does the brain work? How does it even make decisions? What's the basis of your feeling of sentience? Things like that. So those are big, complicated questions, but you can't answer them overnight. You know, you have to say, okay, that's a big question. Where do I start? And you have to sort of carve out a path that gets you closer to what you want to do or what you want to answer. And I think that's where the judgment comes in. You have to combine something that's important with something that's feasible. You can work on something important, but it's completely premature. The tools aren't there. Even the conceptual framework isn't there. You're just wasting your time. On the other hand, if you just do some little incremental thing, just wandering around and not getting towards some larger goal. So you have to somehow combine the two. You have to have an overarching vision. And then you have to break it up into steps that are…
I see. If I recall correctly, at the time when you started deeply getting involved in ribosomes, that space was not as booming?
Oh, it had gone through a boom in the 50s and 60s when people had discovered the ribosome. They found out what it did. They found out who the players were. They discovered mRNA. They discovered tRNAs and how all these things come together on the ribosome and make protein. And then the people who…
So when you were picking that unfashionable field, did you have a sense that the technology was advancing enough so that you could solve it, or that was just pure happenstance?