It's about enabling mission outcomes in environments where speed, accuracy, and trust are critical.When the COVID-19 pandemic hit, the need for faster scientific workflows for national security concerns like pandemic preparedness became immediate.

There was a lot of effort going on in the science community to understand how to come up with rapid responses to help stop this pandemic, and one approach was developing antibodies, and that was all work that can be done largely computationally.

At the end of the day, the result was they went and actually did some experiments in the laboratory using synthesized molecules to understand, did what AI pointed us to and what simulation told us was possible actually work? And that was an eye-opener to me because it's like, wow, that is something that would've taken years and years of manual effort to do, and now we're able to do this with the help of AI assistance in a matter of weeks and months.

And thinking about that in the context of other science problems, the possibilities are almost limitless.

That effort required the same balancing act now facing Genesis, broad collaboration, national scale, and work that ultimately had to function inside environments where access could not be taken for granted

We had to have a unclassified analogy to that so that we could partner with people who didn't have the clearances, but at the end of the day, this had to work in the classified environment.

We solve a lot of the infrastructure problems in the unclassified network.

We come up with similar problems to the kinds of things we're solving that are classified that we can talk about openly