Because uh, to, to me, uh, the results are impressive, both from a, the perspective of the, you know, the model and also just the effort that went into the validation by a large team.

First of all, I, I think I should tr- start saying is that both when we were at MIT and Tomy and Nicholas and working at Barzilay's lab, as well as at Bolts, uh, you know, we are not a, we're not a biolab, and, you know, we are not a therapeutic company.

And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our, um, team to do the experimental validation.

One of the things that really Hannes and the team pioneered was the idea, okay, can we-... go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and, and trying to validate.

But how can we test these models across a very wide variety of different settings so that, you know, anyone in, uh, in the field and, you know, protein design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across, uh, many different tasks? And so he basically put together, you know, I think it was something like, you know, twenty-five different, you know, academic and industry labs that committed to, you know, testing, uh, some of the designs from the model, and some of this testing is, is still ongoing.

And, you know, giving, uh, results kind of, uh, back to us in exchange for, you know, hopefully getting some, you know, new great sequences for, uh, their task.

And, and he was able to, you know, coordinate this, you know, very wide, uh, set of, you know, uh, scientists.

Is that a direction that you guys are pushing?

And so how are you able to sort of set the vision that so many other exceptional people were excited to follow?

So we really started working on the Boltz model soon after AlphaFold3 was published.

AlphaFold3 was a particularly important moment for the field.

On the one hand, on the positive side, because it set a new standard in terms of the performance of our ability to model biomolecular interactions.

On the other hand, it was really the first time that really the state of the art was no longer accessible by most people in the And so Jeremy, I, and Saro started working together around this time on Bolt.

And, you know, Bolt's one, we came together to create Bolt's one, which was the first fully accessible open source model to approach half a volt free level of accuracy.

And, you know, that created a lot of excitement in the field.

Where, in your view, do you see AI having an impact in helping, you know, remove or at least, you know, improve those bottlenecks? Is it going to help with the predictability that a molecule behaves in a monkey? Will it help with the predictability that a molecule behaves in a human? Help us develop a little bit of intuition around how AI will ultimately help in the ultimate endeavor of making a medicine available to patients.