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

AI makes building easy. The hard part is knowing where to start. Today, Every’s Yash Poojary, Austin Tedesco, and Douglas Brundage share how they turn ideas into products. OpenAI staffers offer a practical Codex playbook. Spiral general manager Marcus Moretti shares a no-nonsense strategy for having Fable delegate tasks to cheaper models. And we debut the daily driver, a running list of the models the team is using this week.

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‘AI & I’: The tools the team uses—and their tips for new builders

Last week we had the biggest monthly recurring revenue gain in Every’s history—roughly $9,000 in two days—from launching All Access, our new $625-a-year membership tier. On this week’s AI & I, I handed the mic to our COO Brandon Gell, who sat down with three of the builders behind that launch—growth engineer Yash Poojary, head of growth Austin Tedesco, and head of marketing Douglas Brundage—to talk about the tools they use most, how they build with them, and their tips for new builders getting started.

All Access subscribers get the Builder Pack, which includes $7,000 in credits and unlimited access to the AI tools Every uses every day.

Watch on X or YouTube, or listen on Spotify or Apple Podcasts. You can also read the transcript.

AI redistributes work to the harder problems. Yash spent a month manually running A/B tests—clicking through dashboards, calculating audience sizes—before deciding to hand the whole thing to Claude. “It’s a lot of fake work,” he says. So he automated it. He’s now doing the same for the entire testing workflow, which will free him to spend more time on what he enjoys: coming up with ideas and deciding what to test next.
It speeds up the process from idea to execution. Austin describes the ideal way to work with agents as “at the top and bottom of the AI sandwich”: You frame the problem and review the output. Everything in between can be delegated to AI. For example, during the Builder Pack launch, Yash flagged in Slack that the team should email users who’d shown intent to purchase but hadn’t converted to a paid membership during the early-bird discount window. Austin took a screenshot of the thread, dropped it into Codex with a “Can you do this?,” and headed to the gym. When he came back, Codex had defined four audience segments and written the copy for emails to send to each one. Austin only needed a few minutes to tweak the headlines before sending. By the next morning, the emails had generated $25,000 in revenue.
Working with AI agents is like conducting an orchestra. Doug finds it interesting that Codex, Haiku, Sonnet, and Fable are all literary forms, while directing agents is called “orchestration.” Instead of playing every instrument yourself, you learn to conduct a set of AI agents that can each play a part. “As long as you know what the piece needs to sound like, you can be conducting a lot of different orchestras at the same time,” he says. But using agents still requires strategic thinking upfront: “You have to put in a lot of work upfront, in terms of doing some metacognition and figuring out: How do I think about this? What is my process?” Once you’ve codified your process, he says, “These tools can run with it. You can ask them for help if you don’t know why something happened.”
How to get started. Brandon advises new builders to pick a product you already use and love and build a simpler version. Soon, you’ll identify features you don’t like and things you want to change. “Very quickly, it’s not duping,” he says. “It’s like inspiration, and you’re off making your own thing.” Austin recommends building something you’d be excited to text a friend about. For him, that was a movie app: the Fandango for indie movies. “I would encourage people to not necessarily make the most complex app for the sake of complexity, but to make something you don’t think you can, because you’re going to learn so much through that process.”

Miss an episode? Catch up on Dan’s recent conversations with Anthropic head of product Mike Krieger; the team that built Claude Code, Cat Wu and Boris Cherny; the team that built Codex, Thibault Sottiaux and Andrew Ambrosino; Vercel cofounder Guillermo Rauch; podcaster Dwarkesh Patel; and others to learn how they use AI to think, create, and relate.—Miriam Partington

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How OpenAI builds with Codex

Codex is powerful and versatile, which can make deciding how to start using it an overwhelming experience.

In the following video, three OpenAI staffers break down how they use Codex to make their jobs more efficient—and provide a playbook so you can do the same.

Romain Huet and Dominik Kundel, who work on developer experience for Codex, show how they brainstorm feature ideas and run deep research in ChatGPT, then have Codex use the whole thread as its brief to start building.
Kyle Kober, who works in product finance, demonstrates how he used Codex to build a system for reconciling OpenAI’s monthly compute costs, a process that used to take five days. Kyle’s Codex system completes most of it in about five hours, after which the finance team reviews the results and finishes the remaining work.

Here’s how you can get started:

Select a task you do every week or month.
Give Codex access to the files you normally use, an example of a finished result, the steps and rules you follow to achieve that result, and any checklists you use to catch mistakes.
Supervise the first run.
When Codex can handle part of the process reliably, save those instructions as a reusable skill so it can follow the same procedure next time.

Ready to put this playbook into practice? Upgrade to Every All Access and redeem $1,000 in Codex credits through the Builder Pack on new and existing ChatGPT Business accounts.