Interviews that captures how someone thinks, how they communicate, how they solve problems at scale.

We like to say we're doing the work of consequence.

And of course the work, the consequence of failure, of course, in this world is pretty significant.

Like you can have your car fail or your plane, you know, like these are just catastrophic scenarios.

And an example I like to talk about, even if you go back to something as simple as the CrowdStrike thing that, you know, you may remember, took down a lot of the air traffic control and many different flights.

That was just like one line of code that ultimately kind of broke.

And that wasn't even like a very hard thing.

Um, and, um, I think on top of that, you've got a lot of historical systems written in legacy code bases that, you know, uh, many engineers don't even know anymore right and they have to kind of like go back to learn um and probably they'd never you know but without ai they probably would never when you know have the ability to be rewritten and you know more efficiently and run on you know lower cost hardware and things like that so um tell us a little bit you know obviously we see like you know huge major um you know defense contractors on your website and obviously we don't want to discuss anything confidential but you know tell us tell us like a good use case of you know where where a company came in and You know, they applied Code Metal and they were able to kind of, you know, take old legacy systems and implement, you know, them in a new way.

Yeah

... uh, that experience.

So look, I think, you know, coming into an environment which we're in now, which is this kind of AI world that we're in, I really just saw this la- large opportunity to work with really a lot of smart people.

A lot of, you know, MIT Lincoln Lab engineers, um, like our co-founder, uh, or sorry, founder and CEO, Peter Morales.

And really it was, you know, I had all this great experience, and I'm very grateful for my time at Salesforce and Tableau and, and the things before that.

But it was like, how do I take all these things that I've learned over, you know, 19, 20 years and apply them to, you know, a new industry, but also an industry and a, and a company specifically like CodeMetal where I could make a bigger impact? And what do I mean by that? Well, it was, you know, make a difference, make an impact.

Because ultimately there's so many, there's a lot of AI noise, of course.

Yeah.