How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman

**Alex Lieberman** (0:00)
Creating content has afforded me so many opportunities, but I am capped on the amount of time that I can spend creating content every day. And so I've been basically thinking about how can I re-engineer my content process to be AI native or AI assisted? The other problem I've been trying to solve is how do I make it as easy as humanly possible for employees to be creators while they have full-time jobs? And so that was kind of the genesis of this content machine idea was optimizing for these two problems.

**Claire Vo** (0:26)
I do read your posts, they're quite good. So they don't strike me as slop, but prove it live. How would we go through this process?

**Alex Lieberman** (0:32)
So the first step of the content machine is called the Oracle. The Oracle just does a scan of the last seven days of information from all of the channels that I'm in. And it ranks a short list of ideas. And it has like a whole scoring system for what it defines as a good content spike, gives it a positive score. I basically get 15 content spikes.

**Claire Vo** (0:53)
AI is just out of the box pretty terrible at writing. How you get it to not write slop.

**Alex Lieberman** (0:59)
The only time in my view that the content machine actually produces slop is more of an indictment of the person not sharing good enough ideas during the interview step than the AI writing bad stuff.

**Claire Vo** (1:09)
Uh oh, so what you're saying is we are actually the slop.

**Alex Lieberman** (1:12)
My take is that AI slop is hilariously people just pointing the finger at themselves and saying, I'm not intelligent enough.

**Claire Vo** (1:23)
Welcome to How I AI. I'm Claire Vo, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, I have Alex Lieberman at Tenex and he's going to show us how he climbs Cringe Mountain, aka builds a content machine at his company by using AI and guess what? He doesn't get AI slop out. This is an awesome workflow for anybody trying to market their company and get an edge on distribution. Let's get to it. Quick word from today's sponsor, Firecrawl. If you're building with AI agents, you've probably hit the same wall. Your agent needs data from the web, but the right pages are difficult to find, buried in JavaScript or blocked behind logins. Firecrawl is a web data API that lets agents search, scrape and interact with the web at scale and get that clean structured data they can actually use. Over a million developers, including myself, build on it. It's open source and it's free to start. Stop fighting the web for data and start powering your AI agents and apps with Firecrawl at firecrawl.dev. Use code howiai to get 10,000 free credits today.
Alex, I am excited about this episode because I tell everybody, in order to make it, certainly as a bootstrapped entrepreneur, you have to do one thing and it's not vibe code. It is climb Cringe Mountain. You have to post. You have to post on X, you have to post on LinkedIn, you have to talk about yourself. And I will say, as a true engineers engineer, I will build literally anything to avoid talking about this.
People are surprised about this. So please tell me, how have you started to use AI? Why did you start to use AI to build the content engine at your company?

**Alex Lieberman** (3:16)
Well, first of all, thanks so much for having me. So I, first of all, I totally ascribe to climbing AI Cringe Mountain.
I probably move on like the totally opposite side of the spectrum, which is like, I am happy to be cringey and just like, shoot as many shots on goal to sell kind of what I'm doing, because my belief is people who believe that, like, if you build it, they will come. I just in a world in which distribution is more important than ever before. I don't think that's necessarily the case or isn't most of the time. So basically, there have been two problems within the context of Tenex that I've been trying to solve. And just for folks that don't know what Tenex is, Applied AI Company helping mid-market and enterprise companies go through their AI transformation. And we do everything from strategy to for deployed engineering. So the first is, I have been creating content on the Internet for the last ten years, basically since I started Morning Brew when I was in college. And I think creating content has afforded me so many opportunities, but I am capped on the amount of time that I can spend creating content every day. Like I would say 25% of my time can be spent creating content. And so I've been basically thinking about, how can I maximize the value of that 25% of my time? And what I've thought a lot about is, how can I re-engineer my content process to be AI native or AI assisted while making sure that I'm not putting out AI slop because I think that's what creates so much aversion to AI for people, especially around creating content, is that it's just going to spit out just really mid stuff that sounds like everything else that's been said on the Internet. The second is, every business I build, whether it's a media company or non-media company, I think we need to think more about building media companies on top of our businesses in the future and the reason for that is, in a post-AI world where technology gets more commoditized than ever before, there are fewer emotes in business and I believe that trusted distribution is one of them, so overall I am building a media company on top of Tenex and I think that I'm going to do all the obvious things like bring full-time creators onto staff, writers, we're going to have a newsletter, all these things, but I don't think enough companies think about turning their own employees into creators if they want to do that and so the other problem that we've been trying to solve is how do I make it as easy as humanly possible for employees to be creators while they have full-time jobs? And so that was kind of the genesis of this content machine idea was optimizing for these two problems.