And that whole process from end to end can be immensely expensive, cost billions of dollars and take, you know, up to a decade to do that. And in many cases, it still fails. You know, there’s countless diseases out there right now that have no vaccine for them, that have no treatment for them. And it’s not like people haven’t tried, it’s just, they’re, they’re challenging.

And so we built the company thinking about: how can we reduce those timelines? How can we target many, many more things? And so that’s how I kind of entered into the company. You know, my background is in software engineering and data science. I actually have a PhD in what’s called information physics—which is very closely related to data science.

And I started when the company was really young, maybe a hundred, 200 people at the time. And we were building that early preclinical engine of a company, which is, how can we target a bunch of different ideas at once, run some experiments, learn really fast and do it again. Let’s run a hundred experiments at once and let’s learn quickly and then take that learning into the next stage.

So if you wanna run a lot of experiments, you have to have a lot of mRNA. So we built out this massively parallel robotic processing of mRNA, and we needed to integrate all of that. We needed systems to kind of drive all of those, uh, robotics together. And, you know, as things evolved as you capture data in these systems, that’s where AI starts to show up. You know, instead of just capturing, you know, here’s what happened in an experiment, now you’re saying let’s use that data to make some predictions. 

Let’s take out decision making away from, you know, scientists who don’t wanna just stare and look at data over and over and over again. But let’s use their insights. Let’s build models and algorithms to automate their analyses and, you know, do a much better job and much faster job of predicting outcomes and improving the quality of our, our data.

So when Covid showed up, it was really, uh, a powerful moment for us to take everything we had built and everything we had learned, and the research we had done and really apply it in this really important scenario. Um, and so when this sequence was first released by Chinese authorities, it was only 42 days for us to go from taking that sequence, identifying, you know, these are the mutations we wanna do. This is the protein we want to target. 

Forty-two days from that point to actually building up clinical-grade, human safe manufacturing, batch, and shipping it off to the clinic—which is totally unprecedented. I think a lot of people were surprised by how fast it moved, but it’s really… We spent 10 years getting to this point. We spent 10 years building this engine that lets us move research as quickly as possible. But it didn’t stop there.

We thought, how can we use data science and AI to really inform the, the best way to get the best outcome of our clinical studies. And so one of the first big challenges we had was we have to do this large phase three trial to prove in a large number, you know, it was 30,000 subjects in this study to prove that this works, right?

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