Why Are Teams Only Aligned During a Crisis? | CircleCI & MongoDB

**Ben Lloyd Pearson** (0:08)
Welcome to Dev Interrupted. I'm your host, Ben Lloyd Pearson.

**Andrew Zigler** (0:11)
And I'm your host, Andrew Zigler. In today's news, we're talking about why startups are leaving big corporations in the dust when it comes to AI, why building a simple calculator app is actually a complex puzzle, and what to do now that AI has made your whiteboard interviews completely useless. Ben, what do you want to talk about first today?

**Ben Lloyd Pearson** (0:30)
Well, I feel like since everything seems to be about AI anymore, maybe we just start with a one story that's not AI this week.

**Andrew Zigler** (0:37)
Oh, I love the calculator story. So this calculator story that we're going to include in the roundup is a deep dive into something that we all take for granted every day. We all open up a computer or our phone and do a simple calculation on the calculator app. But did you know that under the hood, this is actually a lot more complex than you would have thought? This is because of how we represent numbers in computers in the abstract ways in which we have to do so. Then when you start introducing calculations, you start sacrificing accuracy for precision. And in this article, it's a really interesting deep dive. I will say I'm not the biggest math nerd in the world, so I can't do it justice. But for those that are, I know they'll find this a fun ride behind the engineering journey and the complexity of a calculator app.

**Ben Lloyd Pearson** (1:21)
Yeah, I wanted to include this story because it is just a fun look at something that, like you said, we all take this for granted. In fact, one of the biggest mistakes that my teachers taught me in high school and elementary was that I wasn't always going to have a calculator in my pocket, so I had to learn math. But it turns out, for my reality, that the opposite was true. I do always have a calculator in my pocket. And actually, the entire reason I got into software development was because I hate how much I hate math. So I'm really glad that people like this exists because it enables people like me. You know, I think my distaste for math really took off when I learned about the quadratic equation. And I literally learned how to write software on my calculator that did my homework for me. And, you know, technically teachers might call that cheating, but it actually sparked a whole interest in me. You know, I would learn the formulas enough to write an application that solved it for me. But yeah, just to say, like, I don't understand it either. I'm really glad that there are people in this world who can.
And if you're someone who is, like, really geeky about math, it is a pretty fascinating breakdown on how this, like, very standard technology works today.

**Andrew Zigler** (2:34)
And another thing as well on this one that was interesting to me is how when you solve a problem, like a calculator app, you know, you might be able to solve 99% of it really, really easily. But getting that last 1% so that it's perfect and putting in that blood, sweat and tears to make something foundational works incredible. That's what great technology is all about.

**Ben Lloyd Pearson** (2:55)
Yeah, so I want to talk about one of the stories that I read this week, and that's about how there's this like two-tier AI economy that's emerging between startups and corporations. So large organizations really kind of appear to be falling behind startups in terms of AI adoption. Startups move very quickly. They're able to innovate rapidly. They try new things frequently. But, you know, big corporations conversely have a tendency to get stuck in like red tape. And they may have money and resources, but pouring money and resources into this AI challenge isn't really what is solving the challenges, you know. Startups are experimenting really fast with these. And I think they're able to do it a lot more efficiently. So you're seeing AI adoption take off within startups but lag behind quite a bit in larger corporations. We work for a small company.
We're already using agentic workflows with AI to optimize some of the stuff we do for the show, for pulling in research. We're using this new workflow we just built to collect research for newsletter articles that will be coming out over the next months and weeks. This is true from our perspective. We work at a startup and we are very rapidly innovating with AI.

**Andrew Zigler** (4:10)
Indeed. And it's about being able to iterate very quickly. Sometimes, if you're in a larger organization, your cycles are just so much longer naturally. That can be hard to get the momentum needed to build something that's changing so quickly like AI. And I'm excited for what these kinds of things will unlock for us to help us work more efficiently. I also think it calls out the real root of where the AI innovation comes from, which is scrappy teams making resourceful decisions with what's available to them in the time they have. And I think it's going to cause us to see a lot more diversity.