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 co-founded Morning Brew in college and grew it into one of the most-read business newsletters in the world before selling it to Business Insider. Now he’s the co-founder and co-managing partner of Tenex. In this episode, Alex explains why distribution is becoming a durable moat, why founders and teams need to “climb Cringe Mountain,” and how he rebuilt his content process around AI without letting it produce generic slop. He walks us through every step of his Content Machine live: an Oracle that scans internal systems and the internet for content spikes, an interview panel that pulls out his real ideas, voice and style files that keep drafts sounding like him, an editorial council that scores and revises posts, and a lessons loop that learns from his feedback. What you’ll learn: Why the blank page is the biggest friction point in content creation, and how an AI Oracle eliminates itHow to map your current workflow before you add any AIHow Alex built a six-step Content Machine in Claude that goes from idea spike to publishable postWhy the interview step (not the drafting step) is where AI slop actually comes fromHow to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s averageWhy your employees are your most underleveraged marketing channel right nowHow the Tenex Creator Cup turned content creation into a team sport with a $5,000 prize pool— Brought to you by: Firecrawl https://firecrawl.dev/?utm_source=newsletter&utm_medium=partner&utm_campaign=how_i_ai—Power AI agents with clean web data Customer.io https://www.try.customer.io/paid/how-i-ai?utm_medium=ads&utm_source=how_i_ai&utm_campaign=ads_q3_2026_how_i_ai_podcast&utm_content=newsletter_placement_2&utm_term=demand%20gen—Build customer engagement campaigns from a single prompt — In this episode, we cover: (00:00) Introduction to Alex Lieberman (02:35) Why Alex built a content machine (06:56) Alex’s thoughts on AI slop (09:00) Mapping the workflow from scratch (13:24) The six-step Content Machine setup (23:11) Live demo: Oracle, Interview Panel, and Writer’s Council in action (30:38) Employee advocacy: the Tenex Creator Cup and $5K prize pool (36:45) Lightning round: great engineers, AI use cases, slop fixes — Tools referenced: • Claude / Claude Code (Anthropic): https://claude.ai https://claude.ai/ • Wispr Flow (voice-to-text transcription): https://wisprflow.ai https://wisprflow.ai/ • Notion: https://notion.so https://notion.so/ • Linear: https://linear.app https://linear.app/ • Slack: https://slack.com https://slack.com/ — Other references: • Morgan Housel: https://www.morganhousel.com https://www.morganhousel.com/ • David Perell: https://perell.com https://perell.com/ • Shaan Puri / My First Million podcast: https://www.mfmpod.com https://www.mfmpod.com/ • Gary Vaynerchuk: https://garyvaynerchuk.com https://garyvaynerchuk.com/ — Where to find Alex Lieberman: X: https://x.com/businessbarista LinkedIn: https://www.linkedin.com/in/alex-lieberman/ Tenex: https://www.tenex.co/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

• 0:00Speaker 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.

• 0:26Speaker 01

  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?

• 0:32Speaker 00

  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 good content spike, gives it a positive score. I basically get 15 content spikes.

• 0:53Speaker 01

  AI is just out of the box, pretty terrible at writing. How you get it to not write slop?

• 0:58Speaker 00

  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.

• 1:09Speaker 01

  Uh oh, so what you're saying is we are actually the slop.

• 1:12Speaker 00

  My take is that AI slop is hilariously people just pointing the finger at themselves and saying, I'm not intelligent enough.

• 1:23Speaker 01

  Welcome to How I AI. I'm Claire, product leader and AI obsessive here on a mission to help you build better with these new tools. Today I have Alex Lieberman at 10x 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.

• 2:03Speaker 01

  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 finding 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.

• 2:48Speaker 01

  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 engineer's 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?

• 3:16Speaker 00

  Well, first of all, thanks so much for having me. So I, first of all, I totally ascribe to climbing AI cringe mountain. I, 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, 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, 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 10x that I've been trying to solve. And just for folks that don't know what 10x is, Applied AI Company,

• 4:01Speaker 00

  helping mid-market and enterprise companies go through their AI transformation. And we do everything from strategy to forward deployed engineering. So the first is I have been creating content on the internet for the last 10 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?

• 4:45Speaker 00

  Because like, 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 kind of just really mid-stuff that sounds like everything else that's been said on the internet. The second is, you know, every business I build, whether it's a media company or a 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 moats in business and I believe that trusted distribution is one of them. So overall, I am building a media company on top of 10x.

• 5:28Speaker 00

  And I think that, you know, 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 of 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 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.

• 5:56Speaker 01

  I love this because one, I, I went through this myself. You know, I am a at Chief Product Officer on TikTok. I went through this phase where I was like, you know what? Part of my job as an executive, when I had those jobs, was to be the marketing face of the company. And, you know, I used to say in SaaS, you were, you weren't buying the product, you were buying the roadmap. And what I would tell my team is they're not buying the roadmap, they're buying the team, which is like, they believe this team can execute on the vision. And so they want to bet with either a subscription or an engagement or whatever on the team. And I talked to a lot of CEOs right now that are really unsettled by the current state of marketing where existing channels have diminishing returns.

• 6:40Speaker 01

  They're not seeing the old stuff really work. And I'm, every single one of them, I tell your number one marketing channel should be your employees. And so I love that you're investing in this. Now, my, my big concern is, and I have a little bit of a, you know, content distribution engine going as well with some AI in it, is AI is just out of the box, pretty terrible at writing compelling copy. And I think we've all seen this, we're all really critical of it, but it seems like you figured this out. So I'd love to hear just a little bit of your process, where you think AI fits, where it doesn't, and how you get it to not write slop.

• 7:14Speaker 00

  Yeah, totally. So I have a few thoughts here. We were talking about this before, which is Luluma Surya has talked about this idea of, it's a shame when the best writers in the world use AI because they cap their ceiling. And so her view is, is AI will raise the floor of bad writers, but then cap the ceiling of great writers. I think I agree with that with nuance. The, the first thing I'll, I'll say is that writing and creating content is a multi-step process which we'll go through. And I think just saying that using AI in any part of the content process is going to lower your ceiling is not necessarily true.

• 7:58Speaker 00

  I think if we're to say for the actual drafting or editing part of the content process, using AI lowers your ceiling, I can buy that and I can understand if you're a gifted writers in the top 1% of writers, how basically just banking on a model that's been trained on the entire corpus of the internet, and so by definition will be average versus you being a above average or an acceptable writer, why would be worse? Totally get that. But there are many parts of a content process. And I think some of these parts, you can use AI and still continue to actually raise your ceiling, not lower it. The other piece of it is, I always think about like alternatives. So the alternative for, you know, our team at 10x, and, and we can talk a little bit in a, in a bit

• 8:42Speaker 00

  about how we're incentivizing people to create. But the alternative before this content machine was people just not creating content. And so it's like, if I have two options, get people to create content around their expertise and the things that they're doing in work or not create, of course, I'm going to take the first every single day of the week. And so what I can do is kind of take you through how I thought about my content process and then how I transformed it. And even though we're talking about it in the context of using AI for content, our, our view at 10x and my view is like, you kind of use this kind of process mapping and then rebuilding for any process in your work. I, this is just kind of the world that I know.

• 9:22Speaker 00

  Okay, so this is, when I started this process, I basically just drew out exactly how the content process works today. So for anyone creating content, you basically start by finding inspiration. And historically speaking, the hardest thing for not even just people who are trying to create content for the first time, but like any, I would actually say especially people who are creators, is finding new ideas that feel novel and interesting and just don't feel like tired regurgitation of what's been said is really hard. So first step is finding an idea. Second step, or like one B is if it is an idea related to a lived experience where you feel for fully informed about the idea, then you can skip the research step. But for a lot of ideas, you'll actually have to research.

• 10:07Speaker 00

  Then the next step is you're going to get all of your thoughts out on paper, where it's kind of like the, the, the mental vomiting of your thoughts around the idea. Next step is you decide what is kind of the anchor format that you want to create a piece of content around, whether it's long-form website, LinkedIn, newsletter, et cetera. Then you write the piece, then you review a draft, you revise it. So you go through the editing process, you publish it. And then as kind of Gary Vee talks about in this idea of the content pyramid, you take your anchor piece of content, you turn it into micro content and you distribute it across all social platforms or platforms where people consume.

• 10:48Speaker 00

  So this is the way, and whether it's video or writing, this is how the process of creating content goes. And so I started with this to basically be like, this is what things look like today. Where does it make sense for AI to live in this process? And then should AI be the driver? Or should AI be the co-pilot?

• 11:06Speaker 01

  I would love to just pause here because let's put content aside. This is the exact process I tell everybody to go through when trying to figure out how to apply AI in a function, in their day to day, in their company. It is like, taking this workflow-first approach is just the most tractable way to get AI into a system. And so I'm like, literally write it out.

• 11:29Speaker 00

  Yep.

• 11:30Speaker 01

  And then make those decisions of like, human, co-pilot, automation. The other thing that I recommend to people is, I say like, don't workflow what you do now, given your current constraints. Workflow what you would do in an ideal world, given no constraints. Like, given no constraints, of course you would cut all this content into multi-channel, like little micro-content. Of course, in given no constraints, you'd have a researcher always on, looking and mining for ideas. And so I think those two things, writing out your workflow and then also like removing existing constraints from how you design the workflow is really important. We have this episode with Matt, the CEO of Suzy, and he does this in his marketing team,

• 12:14Speaker 01

  where he's like, he takes a call transcript and he's like, how much can I extract out of this? Not how much can my team extract out of it, but like, in theory? And then kicks off, you know, 100 different things off of one piece of content.

• 12:27Speaker 00

  Yeah, 100%. And what we've found in kind of doing this process mapping process with companies and with clients is, first of all, you end up realizing that a lot of efficiencies are found outside of even thinking about using AI, because most people have not actually done the process of mapping their processes before. And AI was just like the force function to get them to do it. And then they find all of this waste in their process. So yeah, I totally agree with this. And this is like the unsexy thing to do, but I actually don't know how you reimagine the work you do if you don't start here. It's also why, like, when we talk about job loss and all of these things with AI, the only reason I was able to do this is because I've done the process of creating content,

• 13:12Speaker 00

  you know, thousands of times. And so it shows you the importance of like, being a subject matter expert and having vertical expertise. That knowledge, at least right now, is really needed. Otherwise, you can't reimagine the work. Okay, so basically, I took this, and the next step in the process was like, again, assuming no constraints, rebuild the entire thing. And so what came out of that was this thing called the Content Machine. And I'm going to just show you kind of how it works, just as like a visual. And then I'll take you through like, me actually running the machine. So basically, here is how the Content Machine works. So the first step of the Content Machine is called the Oracle. And the whole idea is that the Content Machine, which is just a directory of skills,

• 13:56Speaker 00

  it is a plugin in, you could either do it through the CLI or through Claude Code or CoWork, is, it is connected to all of my systems of record at 10x. So my Slack, my Notion, my meeting notes, Linear, Git, Gmail. It is connected to all of these things. And basically what happens is, 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 that it believes are good content spikes. And it has like a whole scoring system for what it defines as good content spike. Like, if there's a story or an anecdote related, it gives it a positive score. If there's a strong point of view, it gives it a positive score.

• 14:40Speaker 00

  If there's specific examples, it gives it a positive score. And so every day, I basically get 15 content spikes. Half of them are from the Oracle, looking at my internal systems. And then the other half of them are doing what's called internet reading, where I have provided the Content Machine a list of the accounts that I follow on X, LinkedIn, and just the websites I go to. And it will go and look at all of the content that's been created by these people over the last seven days, and share those as spikes as well. So like, you're one of the people, my internet reader. It will look at everything that you've posted in the last seven days. And when it thinks there's a good content spike, for either me to do a quote retweet on one of your posts, or respond, it will share that in the list. So that's the first step in the process.

• 15:25Speaker 00

  That on its own is maybe the most helpful thing in the entire, like, in this entire system. And going back to the point of, even if you believe that AI will create AI slop and you don't want it drafting your content, just helping you go from blink page to concrete idea, I think lowers the friction so much that you'll actually get in the habit of creating. Okay, so then you select an idea, and then you go into something called the interview panel. And basically, what the interview panel is, is I had six world-class interviewers, codified each as skills, where there's Tim Ferriss, Joe Rogan, Michael Barrow, Barbara Walters, Howard Stern.

• 16:09Speaker 00

  And they just, one by one, will ask questions about this topic to you, until they feel like they've gotten enough information to create a piece of content. One other thing I'll add here that's just not here is, it goes straight from Oracle to the interview panel. If it is a topic, and I'll show you what this looks like in a minute, actually, in Claude. But if you pick a topic that you don't actually feel like you have all the information on already, you can run the research assistant, and the research assistant will give you a full brief into the topic. So, okay, we go through the interview panel. Basically, what I do is I yap to text all of my answers using Whisperflow. Then it drafts in my voice.

• 16:50Speaker 00

  And so the way that this works with the Content Machine is, basically, the entire process, like, all of these steps are what live in the Content Machine repo that everyone on our team has kind of pulled down onto their computer. But separately, all of us have a personal folder that has codified our voice, our style guide, and like, our sources that we want to pull ideas from. And so when a voice is actually drafted, or when a piece is actually drafted, what's happening is it's actually going into my specific voice and style guide file, and using that to create the piece of content. And so just to show you what that looks like. So my style guide here is, you know, this is who Alex is, this is his role in the company,

• 17:34Speaker 00

  these are the owned assets that he's going to want to promote, these are his main anchor types of content, here are his key integrations, here are the social handles that he follows, you see Claire's there, you know, people who give enterprise and exec lens. So that's the first. And then the second is the voice guide. And so what this is, is basically when I launch the Content Machine, it's studied. So this is, all of my top-performing X posts and LinkedIn posts were studied, has executive summary with voice and tone, hook formulas, content structures, latent patterns, topic categories. And so this breaks down the top 10 best-performing X posts I've had of all time, my best-performing LinkedIn posts, what my core DNA is, my number-one rule,

• 18:18Speaker 00

  which is writing like you're texting a friend, and then examples of, like, the language that I use, self-deprecating confidence. Like, it really goes into depth into this file. Like, this is my full voice codified. And then, we'll get to it in a minute, but the other really important piece of this whole process is, when a piece of content is drafted by the Content Machine for me, I will give it feedback, and it will have a reinforcing loop from that feedback. And so one other file that's in the Content Machine is the Content Lessons Markdown file. It is all of the things that I've said it messed up when drafting my content in the past. It logs that as lessons.

• 19:00Speaker 00

  So around tone, where I've said the tone wasn't right, structure and flow, when that wasn't right. And so it will always check the Content Lessons Markdown when editing a piece of content to make sure it's not making the same mistake again.

• 19:13Speaker 01

  And is this generated as you go through the editing process with a skill? Like, is it like, if you identify some piece of feedback, are you manually populating that? How is that getting created?

• 19:23Speaker 00

  Yeah, so the way that the Content Machine works is basically, after a piece of content is drafted, and then it goes through the editing process, which I'll show you in a second, I get the final piece of content. What I will do is, I will give it feedback on that final piece of content. Once it's all done, I'm like, this is good to go, this is good to publish. There's basically a job that's kicked off as part of, like, kind of this, these like, chained-together skills. And it's the lessons loop. And the lessons loop is, let me look at what the diff is between the original piece that we provided to Alex versus the new piece. What are abstractable lessons we can take from that? And then we ask Alex, do you want these lessons to be added to the lessons file? If he says yes, then we add them in. So that's how that works.

• 20:07Speaker 00

  And then just to finish taking you through the process, and then I can kind of show you in practice, that's how my voice is drafted. Then the next step is, so I've shared, I've picked an idea, I've yapped to text all of my thoughts around the idea, as I'm interviewed by this panel of interviewers. Then I pick, say I want to create a LinkedIn post. It's drafted in my voice using my style guide, my voice markdown file, and my Content Lesson markdown file. Once the draft is done, it goes through the Writers Council. So similar to how there's the six interviewer personas I created, there's six writer personas I created. So it's like David Perel, Sean Purry, Morgan Howson, the AI slop allergist.

• 20:49Speaker 00

  And they go through, they read the content and they score it one to 10. If the aggregate score is under a nine out of 10, it runs a revision loop until it scores a 10 out of 10. And then it's done. And then, once the piece of content is done, then I can repurpose and distribute, which means there's just a step where I can say, I want to repurpose this. The content machine asks, what do you want to repurpose it into? I could say, three short tweets, two long LinkedIn posts, and it will go look at what my format of writing in these different post types was in the past and write in that style.

• 21:24Speaker 01

  I think, again, this is just a reflection of a couple of things I would say. One, map your workflow. Two, with AI, you can bring in basically a council of experts without having a council of experts. So again, it's like, in this ideal world, I would be interviewed by the best of the best. Well, guess what? You can approximate that in your process. I think finding the right reinforcement loops on, as I'm building this process, how do I iteratively make it better? Because you can make sense of inputs and outputs of unstructured data in a way you weren't able to do before is really useful. And then, you know, AI is like multimodal. And so, and I mean that not just in like image to text

• 22:08Speaker 01

  or text to voice or voice to text. I mean it in like text to mini text, text to long text. And so, using all those kind of components of LLMs, I think makes this particular use case a good one. This episode is brought to you by Customer I/O. You're here because you'd rather use AI than talk about it. With Customer I/O, you describe the campaign you want to build, and the AI agent creates it for you. The audience, the messages, and the timing. You review it, make any changes you want, and launch. Instead of spending hours stitching together tools and workflows, you can focus on the work that actually drives growth. Every campaign is tied back to results,

• 22:50Speaker 01

  so you can see what's working and what to do next. More than 9,000 brands use Customer I/O to turn the data they already have into messages customers remember. Visit customer.io/howai AI to try it today. Customer I/O, more impact from every message. I did tell you before we got on, I do read your posts. They're quite good, so they don't strike me as slop. But prove it live, do it live. How would we go through this process?

• 23:20Speaker 00

  Let me bring up my Claude, and I'll just show you one example of how it already ran, and then we'll do it live also. And I will happily embarrass myself if this goes terribly wrong. So, okay. So basically, what we're looking at is the content machine running. And just so you can see, because this may be helpful, like, the content machine lives as a plugin in 10x's, our internal Claude, and basically, everyone has added it to their machine. Anytime I'm basically pushing an update to it on Git, people can just auto-update it right from here. So I was trying to figure out something that was, for anyone who is not technical in the business,

• 24:03Speaker 00

  was palatable enough to, as we push updates, to benefit from those updates. And we can talk about it in a second, like, how sharing this with everyone in the business is getting us closer to what, I think it was the CEO of Sendbird, who went through like their internal Quest thing with you, like, how we're getting closer to that. But so, let me go back to this for a second. So basically, what this did is it first looked at my sources, internal sources. It pointed out spike ideas. So this was like a single-player versus multiplayer AI framework that we talked about a lot. I talked to someone at a bank who's working with McKinsey, and McKinsey gave them an idea and was like, this is going to take six months to build. He said he rebuilt it in a week with Claude code. Me saying to someone on a sales call

• 24:45Speaker 00

  that I may be the world's worst salesperson, that sounds like a fun one. After, I said to someone on a call, after I sold Morning Brew, I was miserable, like, that's a good content spike. So like, you can see it's really latching on like specific stories. And then, from the internet reader, it pulled from the different sources that I've given it in my profile file. So, one was, you know, Amazon putting a billion dollars behind FDEs. Aaron Levy versus Ethan Malik talking about like, are FDEs the solution to every enterprise's problems versus not? Everyone's a builder now, shipping is the hard part. And so then, I would just select a content spike that I want to actually build out. And just so you know here, the other thing in the content machine is,

• 25:27Speaker 00

  while I'm going to select one idea here to build out into a piece of content, there are several ideas here that I think are worthy of building out. And so, part of the content machine is when everyone at the company sets it up for the first time, what's created is the vault. And the vault is literally just a Notion database. And every idea that is generated by the Oracle, even if you don't use it, is added to this vault. So you can go into that in the future to pull ideas out of. Okay, so I picked Spike 13. Spike 13, I thought was interesting. Aaron Levy saying like, FDEs are God's greatest gift to Earth right now. Malik being like, no, it's not going to solve all the problems. And so, I picked this, and I wanted to do more research on the topic around, like, what are the different arguments around FDEs?

• 26:11Speaker 00

  Why do Aaron versus Ethan have different perspectives? So then, the research step was started up. And you can see, basically, a research brief was created for me about their takes, what's been said, what are potential contrarian or novel angles that I can take here? What are open questions that I should try to answer? So I read this beforehand. I can also, as I'm doing the interview, if I think there are like really good quotes from this, I can pull that into my answers as well. So then I go in, and the interview starts. So, I forgot Larry King was in here also. So Larry King is like, Levy says FDEs win because deploying agents is more technical than people think. Malik says they'll disappoint because the real problem is org structure and expertise, not code.

• 26:54Speaker 00

  And he said, you said they're both right. Here's what they're missing. So in one or two sentences, what are they both missing? I provide my thoughts on like, are FDEs the solution to all? Why do I agree or not with Aaron and Ethan? And then, the follow-up question is just playing off of my answer. So now, it asks, and the interview panel is really trained to pull as much specificity and examples out of me as possible. So, Tim Ferriss is like, give me the engagement where this was painfully true. Build your team nailed technically. So it asked me to like, give a customer story. So I did that. So we go through, I was asked five questions. Then it says, interview's a wrap, and voice and lessons locked in. So this is where it looks at my content lessons and voice MD file.

• 27:39Speaker 00

  And then, LinkedIn draft MD is created. So here, two of the smartest people I follow just publicly disagreed about the hottest job in AI. They're both right, and that's exactly the problem. By the way, if I was to give feedback on this, I'd be like, this feels kind of AI cringey. They're both right, like the two, the punt, yeah, this, that is just, is like the newest M-dash. And so, I'd give that feedback, and I'd say, add this to content lessons. This week, Amazon committed a billion dollars to a forward-deployed engineering org, OpenAI. And Anthropic already did versions of the same thing. Google is hiring hundreds. And so, just, it includes basically all of my thoughts here. Like, one of my answers was, what do companies need to set themselves up for success for AI transformation? And it took my exact words, where it was like,

• 28:22Speaker 00

  so if you're staffing AI transformation, you need these things. One of the reasons that this doesn't write slop mostly is because I have basically trained it to only use my words. So I think that's a big thing also, is like, this whole content machine is built on the fact that the interview I do with the panel is just turned into a large markdown file of the transcript. And the transcript really should be the only words that are used other than maybe the hook and the conclusion. And the job of the writer is really to almost be this like, like shaping the clay of the content, not inventing net new things. And so, the only time, in my view, that the content machine actually produces slop

• 29:07Speaker 00

  is more of an indictment of the person not sharing good enough ideas during the interview step, than the AI writing bad stuff.

• 29:14Speaker 01

  Uh-oh, so what you're saying is, we are actually the slop. That is, we are.

• 29:19Speaker 00

  My take is that AI slop is hilariously, people just pointing the finger at themselves and saying, I'm not intelligent enough. And so, and then, the other thing it does, and I just actually recently added this in as a content lesson, is, I was like, start giving me more examples of hooks, because I want to be able to choose and compare. So it provides hooks and closings. And so, what I would do here, oh, so it didn't even run the Writers Council yet? So let's just even do that, run the council. So what it's going to do now is it has the draft, it has the hooks and closings. It's going to run the editorial council. If it's above a nine out of 10, it'll give it to me as is. If it's not, it'll run a revision loop. And then what I would do after, when I get it, is I will give it feedback, like the feedback I gave you around that, like if this, then that line being cheesy.

• 30:04Speaker 00

  I'll go through, I'll make sure they're added to the content lessons markdown file. And then I'll take a final pass and I'll post this. And then my view, and this is something I want to add to the content machine, is right now, the content machine is not, when I run the oracle, it's not pulling previous pieces of content that I should, that should think about repurposing. That should be part of the run I do every single day. And so, see, they're, they're all grading right now and I can already tell it's not going to be a nine out of 10. And so they're going to run a full revision loop.

• 30:32Speaker 01

  Oh, my gosh, I love it. And then you're going to get this live.

• 30:35Speaker 00

  Yep, so like I will, right after we are done with this, I will post this live.

• 30:38Speaker 01

  Well, and then what I want to say, you know, maybe what I want to cover after we've, we've wrapped this flow, which you've kind of showed us the end to end of like, how, how do we discover a good idea? How do we extract via, again, our friend Hillary Gridley, via the Yappers API, all of our best ideas? How do we shape that in our voice and then get some critical feedback and ship it? But you're also, it's not 100% AI. You also have your team and your changing team behaviors to all embrace this new model of content creation. So can you just walk us through some like the non-AI parts of, of your content machine and some things you're experimenting with at the company?

• 31:15Speaker 00

  So this actually, this content, first of all, I do not want to claim to be the inventor of employee advocacy. This has been around for a very long time. But I just think that the internet and the platforms on top of it have enabled it in a way that I don't think companies have caught up to it. Or they worry so much about the risks of giving everyone in your company a megaphone that they don't end up doing it. And so I tested this for the first time at one of my other businesses called StoryArb, where we did a campaign called Own the Internet. And the idea was that it was going to be a six or eight week campaign. Everyone at StoryArb had to post on LinkedIn, or not had to, they were encouraged to. And there was an opportunity to win money, like I think it was like 5,000 bucks the winner would get.

• 32:00Speaker 00

  And the only rule was that 50% of the content you create has to be within the world of what StoryArb does. And it ended up driving so much traffic for the business. It drew in the quarter that we ran Own the Internet. It drove 40% of all of our inbound leads. It drove a ton of top of funnel for hiring. And so I just knew that I would do this, like any opportunity I have to do this, I will do it moving forward. And so we literally just started yesterday, 10X's version of this. And so I'll just share like what the details are behind it. So it's called the 10X Creator Cup. And it's a month long challenge to get everyone at 10X posting. And the way you play is you just post on LinkedIn or X.

• 32:44Speaker 00

  And then we have a Slack channel internally called Reply Guys. And that's where everyone can just see everyone's posts throughout the Creator Cup. And the way it works is every time you post, you get 10 points. Every time you engage with someone else in the company's post, you get three points. Every week, my co-founder and I will do an editor's pick of the best post of the week. You'll get plus 50. And there are weekly games and then there's month long games. And the whole point here is we want to encourage people to post where it's not just a race for impressions, where one person ends up crushing it with impressions. It's like, we don't want people feeling like they don't have a chance. And we also want it to feel palatable for people, where just like playing the game is, is going to get you something.

• 33:28Speaker 00

  So even example this week is the week long game this week is called Full House, where if at least 70% of everyone in the company posts at least once this week, then we unlock the one, the week one prize, where everyone gets a lottery ticket. And we pull one person and they win a couple hundred bucks this week. And so we're giving out $5,000 of prizes this week. But the interesting thing here is like, we were talking about this earlier. And, and I said, 10X, we're playing the game on hard mode. And what'd you say, you're playing the game on honest mode? Yeah, like I think, so 10X is fully bootstrapped. And I think it requires us to have to be creative with certain things.

• 34:09Speaker 00

  And one of those things is, we truly have the best engineers that I've ever worked with in the world. And there's, there's a few reasons for that. But we do not have like the gravitas yet of a lab, right? Like OpenAI or Anthropic. And because we haven't raised a boatload of money also, we don't have like the visibility of being on TechCrunch and being mentioned by all of these top tier investors. And so the question becomes, how do we attract top talent when we do not have all the advantages that a venture backed company would have? Part of my view is, is the way you do it is have your top talent show versus tell the cool work that they're doing. And so to me, even if the 10X Creator Cup does not generate any leads for our business,

• 34:53Speaker 00

  if it just leads to more engineers knowing about 10X and applying to 10X. And in a perfect world, if we hire one engineer from this whole campaign, well, then the $5,000 we would have paid at, we're paying out for 10X Creator Cup is worth it so many times over. Cause you think about what you would pay a recruiting agency to get one engineer. It makes it entirely worth it. And so, yeah, like, I just believe that like we, we should lean into the bootstrapping thing, which is like have an underdog mentality, use guerrilla marketing, us all in this together to kind of create this aura around 10X.

• 35:27Speaker 01

  I just, as another bootstrap founder, could, another bootstrap founder that's leaning on distribution as an edge, I, I completely, I completely agree. I think the other thing is we really underestimate what a employee teammate value proposition it is to say like, we actually encourage you to have a platform. So many companies are like short sighted. Because I, I actually hear CEOs saying, well, I don't want that person talking about how awesome they are because somebody's going to come recruit them. And I'm like, that is the number one thing that's going to jump them out of this company is their inability to talk about all the amazing stuff that they're doing and build their own personal brands. And so I think that is just totally, totally spot on. And again, I love to see another bootstrap founder do it.

• 36:11Speaker 01

  As I say, not on, I mean, maybe on hard mode, but definitely on, on honest mode.

• 36:15Speaker 00

  Totally. And, and even one of the things that I think about is like, if I think about what even Anthropic has done really well on the internet is they've basically been incredible, whether planned or not, at employee influencer. Like if you think about everyone surrounding Claude Code and cowork, whether it's Tariq or Boris or Felix, right? Like they are extensions of the brand now. And I trust their product more because they're talking about it and not just the company.

• 36:44Speaker 01

  Totally, completely.

• 36:46Speaker 00

  Yeah.

• 36:46Speaker 01

  Well, Alex, this was awesome. This has given me so much inspiration. I have a similar kind of content engine, Open Claw backed, his name is Max. But you know, same, same thing. Well, I would love to wrap with quick lightning round questions and then we will get you out of here. One, you've said that you work with the best engineers in the world, or at least the ones that you've worked with. What do you think makes for a great engineer right now?

• 37:10Speaker 00

  Someone who has like, the ability to understand systems at a really deep level, like the foundational things that have made engineers always good, but who have the malleability to fully lean into making their workflows agentic. Like I think you have a lot of really young engineers who are fully AI pilled, but don't have the foundation of knowledge that, you know, a staff or senior level engineer has. And then I think you have a lot of veteran engineers that are not malleable enough to change the way in which they work. And, and I think the other thing that comes to mind is like, this idea of the forward deployed engineer is more important than ever before,

• 37:50Speaker 00

  because AI transformation is only going to happen by understanding a business's context. And so, yeah, that's one piece. And the other piece to it is, I think the best engineers in the world want to go along themselves. I think they want to bet on their abilities. And that's why like, we pay our engineers like salespeople at 10X. People think we're crazy for doing it. But my view is I want to self-select for people that are willing to bet on their own abilities.

• 38:14Speaker 01

  I agree. You do know how long I've said like PMs and engineers on variable comp? Forever, forever. I think it's the best.

• 38:21Speaker 00

  It, it's incredible. And we still like, again, there's a ton of people who give a shit about it. And look, there are trade offs, but we have thought about all of them. And it has kept all of our people close to the frontier, because the way they get leverage is by understanding what's possible with the technology.

• 38:37Speaker 01

  Well, and I will hype up your people. We've had two other 10X folks on the podcast, both, both amazing. And you should go, go check out those episodes with CJ and JJ. Now, do you hire people that don't have two letter?

• 38:50Speaker 00

  I was going to say, I really, I need to change my name to AJ. I really messed up the flow of the 10X guests.

• 38:55Speaker 01

  Well, I'm married to an EJ. So if you, if you need another, another J guy on the team, we can do that. Okay, second question, which is, you know, you showed us your, your creative process. We've seen, you know, cowork with JJ, we've seen coding with CJ. What are the personal use cases? Like what is an AI use case that you use in your personal life that you think is really unique?

• 39:18Speaker 00

  So my, my wife was recruiting for jobs for a period of time. She's worked in tech and she's recruiting for jobs for a while. And I think one of the more interesting use cases that I built for her, that I'm surprised a company hasn't done yet, is the recruiting process is brutal and finding roles that are right for you is brutal. But I think we're in this age, like people have always talked about when we were working on Morning Brew, like someone's going to build the one of one newsletter, like the newsletter, like everyone has their own customized newsletter in this world. But basically what I built was a hiring board customized for her, where every single day she got an email.

• 39:58Speaker 00

  And the email was 10 curated jobs based on her previous job, like her LinkedIn and what she's looking for, and scored on that. And then she could click apply from the email and it'd auto fill out the initial application to the job. So that's like one specific use case. The other one in this, I feel like this is like the classic one, is building a custom children's book. We have an 11 and a half month old at home. And, you know, the idea that our, our sweet Brooke can have stories that actually relate to her family. To me, it's such a cool use case as well.

• 40:33Speaker 01

  I love it. You're going to have to join our How AI AI parent roundtable, because we have so many good use cases. All right, last one, I ask everybody, when your AI oracle creative council is giving you straight slop, what is your prompting technique? Do you get mad? Do you cry?

• 40:57Speaker 00

  I would say the honest answer is I will do, it basically depends on how frustrated and how much time I have.

• 41:05Speaker 01

  I like how flustered you are. You're like, uh-oh, should I tell the truth?

• 41:08Speaker 00

  No, no, no. So either I will just literally say, screw it, and I'll just write the thing by hand. Like that, that, I think that's like my typical outers. I'll just be like, screw this, it's, it's taking more time than it's worth. I'll write the thing by hand. Or the other thing I'll do is like, I will actually lean and spend a ton of time into this being like, this is shitty. These are all the ways that these, these things are shitty. I'm pretty sure these were content lessons already. Why is this not registering as a content lesson I've already taught you? So I really should be kinder to it, given that the AI overlords will be my boss at some point. But honestly, I am sometimes an asshole to it.

• 41:44Speaker 01

  So the, the class look like, no, why are you the way you are?

• 41:48Speaker 00

  I've not asked that yet.

• 41:51Speaker 01

  Alex, this has been so helpful, truly. Going to steal all these ideas, which is a secret reason why I do, do these interviews. So thank you for sharing with us, with our audience. Where can we find you and how can we be helpful?

• 42:03Speaker 00

  Yeah, I am business barista on X, Alex Lieberman on LinkedIn, and 10X is our company. So if you're a big business and you need help with AI transformation, hit us up at 10x.co.

• 42:16Speaker 01

  And if you're an awesome engineer, Alex is hiring all of them.

• 42:20Speaker 00

  Literally all of them.

• 42:21Speaker 01

  As far as the content machine is telling me, you are hiring all of them. Well, thanks for joining How I AI.

• 42:28Speaker 00

  Thanks so much.

• 42:30Speaker 01

  Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiipod.com. See you next time.