How Every's Team Used AI to Ship Its Biggest Launch Ever

**Dan Shipper** (0:00)
We generated the largest subscription revenue increase in every history last week. We did it by launching this thing called the Builder Pack. Basically, we partnered with a bunch of the AI tools we use every day, and subscribers to the Builder Pack get discounts and credits to those tools. So $1,000 in codex credits, 12 months of free cursor access, all that kind of stuff. The Builder Pack's awesome, but I think the actual story behind it, like how we did it, why we did it, how it generated so much revenue, is really interesting. For this episode, I had the team who worked on the Builder Pack. Everything from conception all the way to the full executionary launch. Come to the show to share how we get it and what we learned. I hope you like it.

**Brandon Gell** (0:35)
Hello, everyone, and welcome to Ai & I. This is a slightly different version of the pod today, where we've got myself, Yash, Austin and Douglas here, and we're missing Daniel.
We're going to be taking you on a journey today, introducing you to All Access and the Builder Pack. And this is a new tier at Every that we decided to launch recently. And we would just want to talk about why we decided to build it, why it's important, how we use it, and what it means to be a builder today, all of which, all of us are builders and use these tools every day. So yeah, hopefully by the end of this, there'll be like one nugget that you can take out of this and use in your every day. So I guess like to introduce us or to introduce this pod, first I'm going to introduce All Access. We launched it Tuesday on the 14th. It was a big launch for us, big endeavor across the whole company. All Access is a new tier and every it's a $625 annual plan that gives you access to the Builder Pack, which is 10 benefits, exclusive benefits from all AI companies that you know and love that enable you to go build and adopt some of these tools in ways that you may not have been able to before. And All Access includes a couple other things. It includes access to office hours that we're hosting, that are exclusive to All Access members, unlimited use of Quora and Spiral. And we have a couple other things up our sleeve that we're going to be launching in the next few months as well.
But we really want to talk about the Builder Pack, because that's probably the meat of All Access. And the Builder Pack is 10 soon to be more exclusive benefits to AI tools that the Every Team uses every day, so that you can get access to these tools and use them for free for a while and get comfortable with using them in your stack. It's early access, exclusive access to the Every Stack. This is the team that brought it to life. All right, Yash, how do you use the Builder Pack?

**Yash Poojary** (3:03)
Yeah, context, I'm Yash. I run one of the products, like, I'm just, yeah.

**Brandon Gell** (3:09)
Who are you?

**Yash Poojary** (3:11)
Who am I? Well, I ask myself that every day, where I run one of the products at every call Sparkle, and I also work on the growth team now.
A large part of my work is using Claude. I know at every, we are like Codex stands, but I'm still very Claude. I've been using Claude for the last one year. And this involves, like, I built out the entire product, like, I think 15, rebuilt Sparkle 15 times using Claude, and PostHog. So those are the tools I reach out for the most. And recently, I joined the growth team at Every. And growth meant a lot of A-B testing, a lot of segmentation and other stuff. And again, I started optimizing PostHog, but I find it really annoying to open, like, dashboards and set a lot of these things up. And that's where my setup with Claude helps a lot, where I have, like, custom skills built out. And essentially, my plan is, in the next few weeks, to automate the entire A-B testing pipeline. Like, a lot of the work that I spent doing for the first one month at the Grow team, I'm like, this is very boring because it's very straightforward. Like, I should be working on more interesting problems and I should not be clicking buttons and figuring out what's the right audience size and stuff like that. Like, Claude is so much better at that. I have to prompt it. I have to figure out, okay, what's the right audience size? When should an experiment go from 10% AB testing cohort to 50%? Like, why should I be doing that manually?