Dario Amodei

If you just kind of like, and this is totally unscientific, but if you just kind of like eyeball the rate at which these capabilities are increasing, it does make you think that we'll get there by 2026 or 2027. Again, lots of things could derail it. We could run out of data. You know, we might not be able to scale clusters as much as we want.

Like, you know, maybe Taiwan gets blown up or something. And, you know, then we can't produce as many GPUs as we want. So there are all kinds of things that could derail the whole process. So I don't fully believe the straight line extrapolation. But if you believe the straight line extrapolation, we'll get there in 2026 or 2027.

I think the most likely is that there is some mild delay relative to that. I don't know what that delay is, but I think it could happen on schedule. I think there could be a mild delay. I think there are still worlds where it doesn't happen in a hundred years. Those were the number of those worlds is rapidly decreasing.

We are rapidly running out of truly convincing Brockler's truly compelling reasons why this will not happen in the next few years. There were a lot more in 2020, um, Although my guess, my hunch at that time was that we'll make it through all those blockers.

So sitting as someone who has seen most of the blockers cleared out of the way, I kind of suspect, my hunch, my suspicion is that the rest of them will not block us. But, you know... Look, at the end of the day, I don't want to represent this as a scientific prediction. People call them scaling laws. That's a misnomer, like Moore's law is a misnomer.

Moore's law, scaling laws, they're not laws of the universe. They're empirical regularities. I am going to bed in favor of them continuing, but I'm not certain of that.

Yeah, yeah.

Well, let me start with your first questions and then I'll answer that. Claude wants to know what's in his future, right? Exactly. Who's it? Who am I going to be working with?

So I think one of the things I went hard on when I went hard on in the essay is, let me go back to this idea of, because it's really had an impact on me, this idea that within large organizations and systems, there end up being a few people or a few new ideas who kind of cause things to go in a different direction than they would have before, who kind of disproportionately affect the trajectory.

There's a bunch of kind of the same thing going on, right? If you think about the health world, there's like trillions of dollars to pay out Medicare and other health insurance. And then the NIH is a hundred billion. And then if I think of like the few things that have really revolutionized anything, it could be encapsulated in a small fraction of that.

And so when I think of like, where will AI have an impact? I'm like, Can AI turn that small fraction into a much larger fraction and raise its quality? And within biology, my experience within biology is that the biggest problem of biology is that you can't see what's going on. You have very little ability to see what's going on and even less ability to change it, right?

What you have is this, like... From this, you have to infer that there's a bunch of cells that within each cell is 3 billion base pairs of DNA built according to a genetic code. And there are all these processes that are just going on without any ability of us as unaugmented humans. to affect it. These cells are dividing.

Most of the time that's healthy, but sometimes that process goes wrong and that's cancer. The cells are aging. Your skin may change color, develop wrinkles as you age. And all of this is determined by these processes, all these proteins being produced, transported to various parts of the cells, binding to each other.

And in our initial state about biology, we didn't even know that these cells existed. We had to invent microscopes to observe the cells. We had to invent more powerful microscopes to see below the level of the cell to the level of molecules. We had to invent x-ray crystallography to see the DNA. We had to invent gene sequencing to read the DNA.

Now, we had to invent protein folding technology to predict how it would fold and how these things unfold.

bind to each other uh you know we had to we had to invent various techniques for now we can edit the g the dna as of you know with crispr as of the last uh 12 years so the the whole history of biology a whole big part of the history is is basically our our our ability to read and understand what's going on and our ability to reach in and selectively change things

Um, and, and my view is that there's so much more we can still do there, right? You can do CRISPR, but you can do it for your whole body. Um, let's say I want to do it for one particular type of cell and I want the rate of targeting the wrong cell to be very low. That's still a challenge. That's still things people are working on. That's what we might need for gene therapy for certain diseases.

And so the reason I'm saying all of this, and it goes beyond, you know, beyond this to, you know, to gene sequencing, to new types of nanomaterials for observing what's going on inside cells, for, you know, antibody drug conjugates. The reason I'm saying all this is that this could be a leverage point for the AI systems, right?

That the number of such inventions, it's in the mid-double digits or something, you know, mid-double digits, maybe low triple digits over the history of biology. Let's say I have a million of these AIs, like, you know, can they discover a thousand, you know, working together? Can they discover thousands of these very quickly?

And does that provide a huge lever instead of trying to leverage the, you know, two trillion a year we spend on, you know, Medicare or whatever? Can we leverage the one billion a year that's, you know, that's spent to discover, but with much higher quality? And so what is it like, you know, being a scientist that works with an AI system?