Modern Creator
Leveling Up with Eric Siu · YouTube

The AI Content Machine That Drives $100M+ Leads

Alex Lieberman demos the 8-step Claude skill directory he spent 50 hours building — and shows how it cut a 30-hour post down to four.

Posted
3 days ago
Duration
Format
Interview
educational
Views
821
30 likes
Big Idea

The argument in one line.

An AI content machine that interviews you in the voice of six world-class journalists, drafts from your own words, and iterates via a writer's council eliminates AI slop while cutting production time by 80%.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…
  • You publish content regularly and spend most of your time drafting or repurposing rather than thinking and ideating.
  • You run a small team and want every employee to be able to publish without content creation becoming a full-time role for each person.
  • You use Claude Code or Claude Projects and want a structured repeatable system rather than ad-hoc prompting.
  • You are trying to drive B2B pipeline through content and want a framework for which platforms convert enterprise buyers versus engineers versus general audiences.
SKIP IF…
  • You want a plug-and-play tool — this system took 50+ hours to build and requires ongoing investment to iterate.
  • You want automated AI content you can set and forget — the system requires your voice, your interview time, and your editorial sign-off at every cycle.
TL;DR

The full version, fast.

Alex Lieberman built a multi-step Claude skill directory called the Content Machine: it scans Slack, Notion, Gmail, and a curated internet feed daily to surface content spikes (the Oracle), interviews you using styles modeled on Tim Ferriss, Joe Rogan, Larry King, Barbara Walters, Michael Barbaro, and Howard Stern to extract your actual words, drafts from that raw material only, then runs the draft through a six-person writer's council — Morgan Housel, Tim Urban, Greg Eisenberg, David Perell, Sean Puri, and a slop detector — that loops on revisions until it scores 9/10. A learning loop diffs your final edits against the council draft and stores the lessons permanently. The result: a 2,000-word post that once took 20-30 hours now takes 3-4.

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Voices

Who's talking.

00:00hostEric Siu
01:00guestAlex Lieberman
Chapters

Where the time goes.

00:00 – 03:15

01 · Intro: the hype-to-real gap

Eric frames the session: most AI content on the internet is about wild demos, not business revenue. Alex frames the problem: executives want to see the magic before believing in it.

03:15 – 06:15

02 · Alex's role at 10x

Alex explains his two-job structure: CRM for the company and co-creating long-term strategy. 50% of his time is content; the other 50% is fires and field research. No direct reports except one full-time creator.

06:15 – 10:10

03 · Content Machine overview

Gary Vee's content pyramid as the original inspiration. The machine as an assembly line from raw material (idea) to finished product (distributed content). Screen share of the custom website documenting the pipeline.

10:10 – 16:00

04 · The Oracle and the Vault

Live demo: Oracle scans Slack, Notion, Gmail, and internet reader, scores spikes, surfaces top 10. The Vault saves every spike to Notion so all creators can browse unused ideas. Alex explains why deduplication matters.

16:00 – 26:00

05 · Interview Panel live demo

Six interviewer skill files (Ferriss, Rogan, King, Walters, Barbaro, Stern) each with distinct questioning styles. Live demo: context management topic chosen, Tim Ferriss asks first, Joe Rogan follows up. WhisperFlow used for voice input. Raw markdown saved as anchor.

26:00 – 36:00

06 · Writer's Council and Learning Loop

Six editor skill files review the draft and score it. If below 9/10 and missing info, returns to interview panel. If editorial only, runs revision loop. Learning loop diffs final approval against council draft and saves lessons. Demo shows repurposing engine output.

36:00 – 43:00

07 · Reach, 30 Days of AI, platform breakdown

30 Days of AI series: 10,000 new followers and 1.5M impressions from day one. LinkedIn is enterprise pipeline; X is street cred and engineers. Total monthly reach: ~2.5M on X, ~1.5M on LinkedIn. One post confirmed a $100M+ business prospect.

43:00 – 48:00

08 · Enterprise AI patterns and ROI

Where companies get stuck: focus shifting to ROI but most AI work doesn't have clear attribution. Three ROI-clear categories: engineering (PR merge rate), customer support (ticket deflection), sales (harder — revenue per rep is the only real metric).

48:00 – 51:06

09 · Productivity metrics and wrap

10x engineers paid on story points with minimum guarantee — natural inflation from AI improvement. Eric poses the compounding question: true or false, Alex using AI for 12 months makes Eric unable to catch up on content. Wrap and where to find Alex.

Atomic Insights

Lines worth screenshotting.

  • The reason most AI content is slop is that the AI is not using your words — grounding every draft in your own interview answers is the anti-slop mechanism.
  • A 50-hour upfront investment in a content system pays back permanently when a 30-hour post drops to 4 hours on every future cycle.
  • Content quality compounds because of the learning loop: every editorial diff you make gets stored as a lesson and applied to every future draft.
  • The Gary Vee content pyramid still applies today — AI just removed the 20-person team requirement to execute it at scale.
  • 10x's entire enterprise revenue has come through LinkedIn; X drives street credibility and engineer hires, not B2B deals.
  • 1.5 million impressions from one LinkedIn post translated into a confirmed $100M+ business lead the very next day.
  • Making employees into creators requires friction to publish to be near zero — a shared skill directory is the infrastructure that makes that possible.
  • Every successful AI transformation follows the same diagnostic: map the process end-to-end, then ask where the human is truly irreplaceable.
  • Naming a series (30 Days of AI) turns individual posts into a story arc people follow; the container compounds attention more than any single piece.
  • Engineering AI ROI is easy to prove with PR merge rate and headcount; sales AI ROI takes more than 6 months — start with the easy wins to fund the harder ones.
  • A centralized company skill dojo compounds across teams: one exceptional skill built by one person lifts every person who uses it.
  • Engineers paid on story points with a minimum guarantee create natural inflation — AI getting better raises their floor without management doing anything.
  • Standardized folder and file conventions are one of the highest-leverage context management practices in Claude Code — they prevent wasted tokens searching for information.
  • Starting a fresh task with only the memory file you need is the right default — not continuing an existing context window that has accumulated unrelated tokens.
  • Bootstrapped companies with no brand history cannot compete on legacy against McKinsey — content authority that demonstrates expertise is the only available moat.
Takeaway

The assembly line that makes every employee a creator

WHAT TO LEARN

The only way to scale creator output without scaling headcount is to strip the process to its assembly line and automate everything except the three moments only you can fill.

02Alex's role at 10x
  • Structuring your role around the two or three highest-leverage activities — and trusting a cofounder to own everything else — is a deliberate choice, not a default.
  • Reserving five hours a week for field calls with practitioners, then publishing what you learn, is a repeatable source of both ideas and credibility.
03Content Machine overview
  • Treating content creation as an assembly line with defined raw materials, steps, and finished products makes it possible to automate each step independently.
  • The Gary Vee content pyramid still applies today — AI eliminated the team size requirement, not the structural logic.
04The Oracle and the Vault
  • Connecting your idea-generation system to your actual work data (Slack, Notion, email) produces ideas grounded in what you actually know, not generic takes.
  • Archiving every idea you surface — not just the one you use today — turns daily brainstorming into a shared team resource that compounds across time.
05Interview Panel live demo
  • Identifying where you are truly irreplaceable in a workflow — and showing up only there — is the prerequisite to any AI-native process worth building.
  • The interview panel step is the anti-slop mechanism: it forces every piece of content to be grounded in your spoken words before any drafting begins.
06Writer's Council and Learning Loop
  • A revision loop that scores content against a defined rubric and blocks publishing below a threshold is the operational difference between a quality bar and a vibe.
  • A learning loop that diffs your final edits against the AI draft and stores the delta as permanent lessons means the system gets better with every piece you publish.
07Reach, 30 Days of AI, platform breakdown
  • Naming a series creates a story arc that compounds attention over time; unnamed individual posts do not accumulate the same way.
  • LinkedIn drives enterprise pipeline; X drives credibility and engineer recruiting — treating both channels identically wastes one of them.
  • Distribution at scale is sales infrastructure for a bootstrapped business competing against legacy consulting brands — not a vanity metric.
08Enterprise AI patterns and ROI
  • The ROI of AI in engineering is immediately provable through PR merge rate and headcount math; the ROI in sales takes more than 6 months — start with the easy wins to fund the harder ones.
  • Every company's AI ROI question starts the same way: map the process end-to-end, then ask where a human is truly necessary.
09Productivity metrics and wrap
  • Paying engineers on story points completed rather than hours worked aligns incentives with output — AI improvement becomes personal income growth, not a threat.
  • Building a content system like this takes 50+ hours upfront and ongoing iteration — anyone treating it as a weekend project will abandon it before the compounding begins.
Glossary

Terms worth knowing.

The Oracle
The first active step in the Content Machine: a Claude skill that scans multiple company data sources (Slack, Notion, Gmail) and a curated internet feed, then scores and surfaces the top 10 content spikes from the past 24 hours.
Content spike
A scored content idea the Oracle identifies as likely to resonate with the creator's specific audience, based on recency, relevance to current conversations, and a proprietary scoring rubric.
The Vault
A Notion database where every content spike the Oracle generates gets archived — not just the one the creator used — so any team creator can browse past ideas and pick one up later.
Interview Panel
A step in the Content Machine where six skill files modeled on different interviewer styles (Ferriss, Rogan, King, Walters, Barbaro, Stern) take turns asking the creator questions about their chosen topic to extract specific, opinionated answers in their own voice.
Writer's Council
Six editorial skill files modeled on prominent writers (Housel, Urban, Eisenberg, Perell, Puri, plus a slop detector) that review each draft, score it out of 10, and either send it back to the interview panel for more input or run a revision loop until the score reaches 9/10.
Learning Loop
The final step of the Content Machine: it diffs the creator's approved final version against what the writer's council submitted, extracts lessons from the changes, and saves them to a content-lessons.md file so future drafts avoid the same mistakes.
Skill dojo
A centralized company repository of approved, tested AI skills (also called a skills academy or skill directory) where any employee can find or contribute workflow automations, preventing individuals from building duplicate lower-quality versions in silos.
Software factory
An engineering workflow model where AI handles most code generation and review, humans oversee and accept output, and the primary productivity metric becomes PR merge rate rather than lines of code written.
Story points
A unit of engineering work complexity used at 10x as the basis for compensation: engineers have a minimum guarantee and earn more based on the number of accepted story points they complete per period.
WhisperFlow
A voice-to-text transcription tool the guest uses to answer interview panel questions by speaking rather than typing, keeping the input natural and conversational.
Resources

Things they pointed at.

01:00product10x.co ↗
05:00productUltrathink (10x newsletter)
06:30linkGary Vee Content Pyramid
25:50toolWhisperFlow
35:50toolRemotion
16:15toolClaude Code ↗
16:15toolCodex
17:40toolNotion
17:40toolSlack
17:40toolHubSpot
18:20channelClaire Vaux — How I AI podcast
18:25productSendbird skills dojo / quest model
21:40productGumroad public bounty model (Sahil Lavingia)
1:19:45toolJuicebox (AI sourcing tool)
Quotables

Lines you could clip.

10:20
“I need to be involved in the first mile and the final mile. Everything else should be handled by AI.”
Clean one-liner that defines the entire system philosophy — no setup needed→ IG reel cold open↗ Tweet quote
27:45
“When we talk about AI slop, typically slop comes from you not actually using your words.”
Direct diagnosis of the most common AI content failure — quotable and shareable→ TikTok hook↗ Tweet quote
51:00
“I am sure we are gonna get at least a $100,000,000-a-year-plus business that comes out of the post I made yesterday.”
Specific, audacious claim with a named dollar figure — visceral for B2B audiences→ newsletter pull-quote↗ Tweet quote
43:10
“Every company in their AI journey should probably start with something that can very clearly map to revenue generation or cost savings — very clearly.”
Actionable framing for enterprise AI skeptics — tight enough to clip cold→ LinkedIn short-form↗ Tweet quote
51:00
“If you truly want all 40 team members to become content creators, they're going to need a system like this because they can only spend two hours a week max putting content online.”
Frames the scale argument for a shared skill dojo in concrete headcount terms→ newsletter pull-quote↗ Tweet quote
Topic Map

Where the conversation goes.

00:00 – 03:15sparseIntro and framing
03:15 – 06:15steadyFounder structure and focus
06:15 – 10:10denseContent Machine architecture
10:10 – 16:00denseOracle and idea sourcing
16:00 – 26:00denseInterview Panel and anti-slop mechanism
26:00 – 36:00denseWriter's Council, revision loop, learning loop
36:00 – 43:00denseDistribution, reach, platform strategy
43:00 – 48:00denseEnterprise AI patterns and ROI
48:00 – 51:06steadyMetrics, productivity, wrap
The Script

Word for word.

Read-along

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metaphoranalogy
00:01Fix that. Yeah. Try try to go for it.
00:04If can't fix it, that's okay. But I know we're live right now. We're we're it looks good.
00:08Okay. Alright, guys. Sweet.
00:09We're live we're live right now. And the those of you that are coming in right now, just type in where you're coming from. Alex and I will get started in about a minute or so.
00:17But today, we're gonna be talking about just how you can actually drive more business revenue using AI because a lot of people that share this stuff on, like, Twitter and the the the ones that share it on YouTube as well. It's about more like, oh, this is the craziest thing.
00:32Look at what this this app I built. Oh, this this is the craziest agent right now, and nobody really talks about driving business revenue, which is weird to me. Right?
00:40Because everyone's talking about the AI build out. So I think it's incumbent on on people like you, Alex, um, to be able to share what's actually happening because you're actually in the trenches right now. So, um, maybe those that I I hate to do these intros, but I again, Alex and I, we've known for each other, but you should intro your yourself.
00:55I'll do the the intro for you real quick from my side, at least. But Alex and I have known each other for for a bit.
01:01Um, he founded, uh, cofounded Morning Brew, um, and then now he's got 10 x. And, uh, and they I'm just gonna call you guys an AI transformation company, and he's been producing a lot of great content. He's moving at a very fast pace right now.
01:14Um, and we thought we'd do, like, a a podcast here, and then next week, I'll I'll I'll be on on his show. But we're just gonna kind of, um, just nerd out on what's actually happening with AI in business. We'll talk about the latest with autonomous models, uh, sorry, autonomous agents, the latest models, and all that.
01:29So, Alex, um, I don't know if I missed anything. You can yeah. Feel free to add to that.
01:33No. You did great. Uh, pumped to do this.
01:36Uh, I think the number one thing like, I have you're actually in it, but, uh, I have this text group with some executives who are, um, trying their best to be on the frontier of this technology and building with it.
01:49And I think the number one frustration I hear from people in the field right now is that, like, the hype to what's real gap feels massive right now.
02:01Right? Like, you have all of these labs that are coming out with their new models. You have AI companies that are raising an insane amount of money, But then you go into, like, a mid market company or an enterprise, and you ask, like, what do they actually have in production, whether it's, like, single player workflows or, like, cross functional workflows that have truly moved the needle for their business.
02:23It's like crickets. And so a lot of these executives I talked to are just like, I wanna see where the magic is. Like, I wanna see the magic so I can believe in the magic so then I can, like, put resources into building it for myself or for my company.
02:35And so I love that you're doing this, um, and I'm trying to do the same type of stuff, which is just, like,
02:39share the most valuable workflows that are actually creating leverage within your work and within your company and then learn from each other while doing it. Yeah. Yeah.
02:48The the and maybe we can talk about these these text groups and also, um, in person groups as well. But, um, I think when when you and I spoke maybe a couple weeks ago, you you really only have two major roles, I think, at your company right now.
03:01So just if you can explain kind of the reporting structure for you because you you cofounded this business, but Yep. You are only focused on very specific things right now, and I think it's good for everyone to know and and learn how that works. Yeah.
03:11Yeah. Yeah. So I cofounded
03:1310x with, uh, Arman Hezarkhani, uh, about fourteen months ago. Um, we had met right around the pandemic.
03:21Uh, he previously, before 10x, had started a AI fintech company called Partheon, and, uh, I had invested in it.
03:29I was an adviser to the company. So, like, we together developed a lot of our views on AI prior to starting Tenx together. And the way we kind of think about the business is I I basically focus my time on two things.
03:46One is being the CRM to the company, and two is cocreating our strategy and long term vision with Arman.
03:56And the reason for that is I think when I was cofounder at Morning Brew, I had this, like, ego about needing to do everything and feeling like my worth was directly correlated with how many things I was able to do as an entrepreneur.
04:15It looks totally different in kind of this next phase, and I think that's for two reasons. One is at some point, I was like, this is stupid. I should just do the stuff that's highest leverage and most enjoyable to me.
04:26But, also, I I think that part of the reason you find a great cofounder is so that you trust that they're gonna do amazing work on the things you're not spending your time on.
04:40And so the way it works is, like, I am deeply focused on driving enough demand for 10 x such that we will never have to pay for a dollar in marketing or build a sales org. My cofounder, Arman, is incredible at running delivery and sales for the organization. And then together, uh, both of us like, our goal is to work on this company for the next twenty years of our lives.
05:00And so we will pan out to talk about, like, building this modern day Bell Labs, which is kind of our vision for the business. But, yeah, I would say 50% of my time right now is spent creating content myself and also building out kind of, like, the media operation of 10 x.
05:16So I'd obviously draw a lot of inspiration from MorningBroom building that. And so kind of my and and the one other thing I'll add that I think is helpful context is we have not raised a dollar at 10.
05:29We are completely bootstrapped. And so I think what I'm basically thinking about is how do we build a small but mighty content operation here that the outside feels like we have a 100 person media company.
05:41But then when you actually come, like, behind the scenes at ten x, really, it's just a few creators, Alex, the employees of ten x, and then really good infrastructure that gives us leverage to create content.
05:53And so one of the things I'm gonna take you through today is this content machine, which is the most frequent, uh, directory of skills that I use in my work. But the plan is also to use it as this centralized system that any creator at Tenex can use as well to create leverage for us.
06:08Yeah. I think that's a good starting point. So maybe, um, maybe before we we we jump into that, um, so you you mentioned because what
06:15I'll call it here is this. You look at Dharma Shah, CTO, cofounder of HubSpot. He has no direct reports.
06:20Okay? And I look at my podcast cohost for marketing school, Neil. He has no direct reports.
06:24That's how he started the business. Right? And Neil, specifically, he's just the distribution.
06:27I'm saying just, but it's a very big thing. He's a distribution machine. Yep.
06:30And he does go and kinda handle strategic work and and play free safety, but the rule is no direct reports. And so is that kinda what you have to so you're you're driving distribution.
06:38Do you have no direct reports, or do you have any? The only direct report I have, uh, is our one creator who's full time on staff right now. So I think ultimately,
06:47the only direct report I think I'll have long term is if we build out the media operation and end up having, like, any sort of kind of person running our media. They'll report to me, then all creators will report into them.
06:59Yeah. And then what's the other 50% of your time going to? Is it mostly, like, strategic?
07:03It's yeah. It's bay basically, whatever the biggest fire is at the time, that's where, like, I'm focusing my time. So whether that is a client fire, whether that's a fire related to one of our partnerships, it's that.
07:16But, honestly, most the other so the other part of the so the 50% is creating content, but then the way I think about it is in order to create great content, I need to both be building and be out in the field. So it's actually if I think about it, probably 75% of my time is related to the content to content surface area, which is me either trying stuff out in codex or in CloudCode or with whatever tools, uh, exist.
07:40Um, and then it's also literally, I try to reserve, I don't know, five hours a week minimum to just getting on calls with people who are in the trenches and have them just teach me what they're doing within their work.
07:53And then if you've looked at, like, my ex or my LinkedIn before, that usually turns into a post about all the things, like field notes of everything I've learned from this person during our call. Yeah. That's good stuff.
08:02Alright. Well, I mean, people came here for the the the sauce. So what's the the sauce around the the content machine?
08:07Because you shared this with me, and I'll I'll talk about kinda what I did with it afterwards because you're great graceful to share it with me. Yeah. Totally.
08:13So, basically okay. Here here was the the inspiration.
08:18I would say ever since Morning Brew and even before this, I've always thought about how do you take one piece of content and turn it into as many pieces of derivative content as possible to ex to basically maximize the surface area of of content.
08:34And the way I actually I think the the way the framework that really stuck with me was Gary Vee posted, like, I think, like, yeah, yeah, like, fifteen years ago, the content pyramid. And, basically, it was this presentation still online.
08:48You can go look it up where, basically, he shows, like, he has an anchor piece of content. You know? An example for him would be, like, an hour long keynote he did in Germany.
08:57And then he shows these levels of every distribution channel, so every social media channel, email, uh, podcast, etcetera, and the system he has to distribute content natively in all these channels.
09:09And the thing I've always thought about is pre AI, you need to have a massive team to do that. So, like, Gary has a massive content org that supports this effort. With AI now, you don't need that massive content org.
09:21The other thing I've thought about is if I could build a really good, um, AI native process for creating content, there's two things I get out of it.
09:30One is, like, I'm patient zero. So if it works well for me, I can then have it used for anyone in my company who wants to be a creator. But the other thing is as we are doing AI transformation in companies, it kind of is all the same process, which is, like, understanding a process step by step from beginning to end and then starting to ask yourself, if we were to burn this to the ground and build it, uh, from the ground up, where am I absolutely necessary?
09:54And we'll kinda talk through that. So before I actually, you know, work on it and Claude, just put together a an h t like, just a website that kind of walks through it.
10:03So I'm gonna show that quickly. Um, screen, content machine.
10:07Can you see this? Yes. Okay.
10:11Cool. So, basically, the idea of the content machine is it is one pipeline that runs end to end or step by step in the process of creating content. And what you're gonna see is every step in the content machine is just the next step.
10:25If I think about any process as an assembly line and you have raw materials that you start with and you have finished product that you end with, you have all these steps in between. Every process, can think of as an assembly line. I think about content the same exact way.
10:40So, basically, the way that this pipeline runs is a new addition even since we spoke is creator selection. And the reason for that is I want to I I've always said to the team internally, I wanna turn 10 x into WWE for nerds. I want every full time I want every full time employee here to be a creator.
10:58And the only way to do that is it can't be a full time job for them, so we have to reduce the friction of them putting out great content to as close to zero as possible. So here, you can select the creator you are. So these are four examples right now, Alex, JJ, who's our full time creator, Arman, my cofounder, CJ, who's one of our engineers, and Sammy, who's one of our strategists.
11:16And when you select yourself, like, when you select that you are the creator who's working the content machine, you have a profile markdown file, a style guide markdown file, and a content lesson markdown file. So, basically, what this does is it identifies, like, who you are, what your voice is, and feedback that you've given to the content machine in the past that it should keep in mind for any future content you create.
11:38Any questions on that before we go to the next step?
11:41No. I think that makes a lot of sense. And what's interesting to me is, uh, JJ, I think you hired him as a community manager, and then now he's I guess it's like, hey.
11:48You're a community manager. You might as well just be a full time content creator. I I didn't really pick that up.
11:52That's that's cool. Yeah. And the way we think about content creators at 10 is interesting, which is historically in a media company, to underwrite a content creator, basically, you have to believe that you are going to be able to sell enough ads against them or drive enough subscriptions against them to justify their salary.
12:10At ten x, it's a little bit different because because there is so much appetite for AI trainings and workshops, and that is a fair bit of what we do at least, uh, in the early days of relationships with customers. JJ has become, like, a five tool player where a lot of time outside of creating content and building that he does now is teaching, like, leading workshops.
12:30And so there's this interesting model which is if we have enough demand for companies to pay, let's just call it, 25 to $50,000 for a workshop, you can very quickly pay back the cost of a creator. And then anything they create and any leads they drive on top of that is effectively free marketing. Um, so that's creator selection.
12:49Step one in the process now so I've selected myself. I'm Alex. The next is what I call the Oracle.
12:54And I think this is one of the biggest value ads in the content machine, which is helping you find ideas. One of the hardest things that any creator will tell you that they struggle with is day after day thinking of content ideas. So right now as we're recording this, I just started a new series called thirty days of AI, where every day, I'm gonna share an insight from the field.
13:14Coming up with content ideas for thirty days straight is hard as hell. The Oracle is what helps me here. And so I'll I'll show it in a minute.
13:22But, basically, what the Oracle is is I have my content machine hooked up to all of my data sources at 10 x. So Slack for internal comms and client comms, Notion for call recordings and, like, our, uh, internal documents, Gmail for emails, and then also Internet reader.
13:39So Internet reader basically goes and looks at a curated feed of, uh, x accounts and websites that I've told it will have valuable information around AI. It goes through all of these sources, and then it provides a a list of 10 spikes of what it thinks are good content ideas to work with.
13:58So just to like, I can just show you what it looks like in practice. Let me share this screen.
14:07Here we go. Uh, window, Claude. Okay.
14:11So if I just do do new task and I do, um, run the content machine So what it's gonna do first is, again, it's gonna go through the first step, which is, like, asking me the question of which creator are you. So we'll answer that in a second.
14:26And, again, as new employees join 10 x, they can literally just be added to the content machine, and we can have it start by their content. Here we go. Which creator is this run for?
14:35Alex, what do you wanna do? So to ask me like, so say I already had a content idea, so I didn't need the Oracle.
14:42I could just do develop my own idea, but I want an idea, so we'll run the Oracle. So, basically, what the Oracle is gonna do now, like I mentioned, is it is going to look at all of the different data sources we have at 10, and it has a score for scorecard for what it considers to be a good content spike.
14:59And a spike is literally what's an idea that it thinks would be interesting to my audience. And then it's gonna provide a list of the top 10 spikes from the last twenty four hours that it found. And someone just asked, is all of your work through Claude, or do you have any agent harnesses?
15:14This is all through Claude. There are no agent harnesses here. Like, again, this is yeah.
15:20I guess I get it I guess it is a harness. You're right. Um, and so it's searching all of these sources.
15:26The other cool thing is something I've thought a lot about is when it shares these different content ideas, if it shares 10 of them, there's only gonna be one that I talk about today. But there could be four other good ideas that I wanna use in the future. So what I also built into the content machine is something called the Vault.
15:44And I'll show it in a second, but the Vault is basically just a Notion database where every content spike from the day gets added into this Vault. See here, it says, let me dedupe against the Vault and ground a couple of the freshest angles. Then what can happen is any of the creators at 10 x can then go into the vault and sort search for a good idea even if I only used one of the 10 that I was served.
16:08Got it. That makes a lot of sense. And this is cool.
16:10It's, um, by the way, think I I want I it's it's good to call out for those of that that can't see this. I mean, Alex is sharing a skill file right now that's pretty portable. It doesn't have to live within CloudCowork.
16:19You could bring it to something else if you're you could use it in Codex as well. Um, and that's that's what I think is really powerful. And even in that chat group this morning, I I think the fact that we were just kinda nerding out on how it's really important to have a Skill Academy or Skill Dojo, um, you could talk about how you guys are doing that too because I think it it democratizes these processes for people.
16:37Because fact of the matter is when you're a company, people talk about processes and repeatability. Nobody reads the processes, but people are forced to use these skills in a sense, um, if if, um, if you really wanna get everyone up to speed with AI, I think. Yeah.
16:50A 100%. So there is this article, uh, or not this article, this video interview that Claire Vaux, who has the podcast How I AI, was interviewing
16:58the CEO of Sendbird. And so I was watching that episode, and, basically, what they have at Sendbird is because, uh, one thing I think about a lot is how do you turn AI into a compounding asset in your company versus a lot of people just building in silos because that is what it is in most companies right now.
17:17And I think one of the ways you have to do it is with what Erica is talking about, which is the skills dojo or, the the guide Sendbird has, like, a similar type of thing, which is what is a central repository that has, um, approved skills for your company?
17:34So let's just say your company has 30 salespeople, and there's a seller that has built an exceptional skill for, I don't know, uh, sending follow-up emails or, uh, scanning, um, you know, scanning interactions in Slack to measure, uh, client health or anything else.
17:50What usually happens in the company right now is it's not centralized. Other salespeople build their own versions. They're not as good, and they're getting less performant skills.
17:58So the idea is a central repository that has your skills. What the the folks at Sembr did that I think is really interesting is they have these things called quests where any employee in the company can post a process that they have an issue with or they just think is not good.
18:16And then what happens is anyone can go in, any engineer or any person in the company can go in and accomplish a quest, like, basically build the agent, the skill, the plug in that solves the problem that the person posted. And then when that quest is complete, uh, a few things happen.
18:32One is that it's then posted into the repository, into the dojo, um, so anyone can use it. Anyone can improve the thing in the future. So, like, future quests can just be add ons to previous quests, and the company built kind of, like, governance, uh, data controls, and security around it.
18:49So even if you're not an engineer who doesn't understand, like, you know, concerns around data leaking or cybersecurity, it's been hardened so anyone can build and kind of this, like, package is wrapped around anything that anyone builds within kind of the central repository.
19:06Yeah. I I think the walk and the run before you go through the screen over here on on that one is, I I was just talking to my team this morning about gamifying it. Right?
19:12So you can have the quest, and then you can upvote it almost like it's, like, Reddit. And then the the ones that get the most upvotes, like, you you you you put out, like, a bounty on it. And then I I can imagine that you do these micro payments using stable points or something, where the higher the the the upvotes, the higher the challenges of something, the more someone gets paid out.
19:28And then you can see a leaderboard of how much money people made. So I I could see the future of organizations as being super gamified. Yeah.
19:34Totally. I mean, it kinda reminds me of what Sahil did, uh, Sahil Lavinia did at Gumroad for a period of time where, like, their entire,
19:41uh, Trello board or whatever was effectively public and kind of any engineer could just basically take down a bounty and get paid for it, yeah, I think a similar thing can happen for sure. Cool. Alright.
19:51Let's get into this. Okay. So, basically, what happened is the Oracle ran.
19:54It scanned Slack. It scanned Notion pages or recent, uh, recent pages and meeting notes. It scanned my Gmail, so sent an important inbound.
20:02It did the Internet reader, so which is the live x watch list and any news, uh, from, like, the labs, um, from, uh, Wall Street Journal, McKinsey, Bain, BCG, etcetera. And then it ran these are the spikes.
20:16So here's an example. One was around Anthropic just filing to go public. So then it shares what the idea is, why it matters, what the hook could be.
20:24Another one, and this is what I'm planning on doing for my thirty days of AI idea today, is talking about context management and how to do good context management and why it's so important. And then just to show you what happens, again, we have the vault hooked up. So if I just, uh, stop sharing this and I share the vault, basically, all of these spikes are then shared here, right, in this vault.
20:51And so then what happens is any of the creators at ten x who are looking for a good idea, they can come into the vault and find an idea that works for them. So here's one.
21:00Turn every employee into a creator, and it's just, like, me talking about this idea of distribution as a moat and how you turn people into creators. Anyone can pick up this idea.
21:12So let's go back to the next step in the process.
21:15You're Yeah. I I think when you showed me this stuff, you are like, for the LinkedIn post, it's not you writing it. Is it writing in your voice?
21:21Is is or is it like a is someone from your team taking, taking, like, like, an an AI skill of your your voice and writing it? Or, like, how how is that working for you? Because, like, like, if you're not doing it.
21:30Yeah. Yeah. Yeah.
21:31Yeah. So the whole the whole beauty of this and the reason I think content machine
21:36solves this issue and this complaint about AI slop is my general view on any kind of AI enabled process in the future, but especially this one is I need to be involved in the first mile and the final mile.
21:51So the first mile is I need to provide the direction and the idea that I choose. I need to provide the thoughts or the context, and then I need to be involved in the QA. Everything else should be handled by AI.
22:05So let me just I'm gonna just show you what the next step in the process is, and I'll demo it quickly just so you can see what it looks like. Um, and it'll answer your question about how is the content actually written. So if we go back to the the breakdown of the content machine so we just did the order Oracle.
22:21What happens now in the Oracle is, like, if I select an idea let's just say I selected the idea around the Anthropic IPO, and I, like, am like, I don't actually know all that much about the Anthropic IPO. I can run a research step where it builds a research brief before step two, which is where I get interviewed by a world class panel.
22:41So this is kind of the key of not creating AI slot. Because what happens is when I move on to step two, a panel of world class interviewers ask me questions and pushes me to be more specific, to steel man arguments, to, you know, provide frameworks.
22:58And so when I do that, that is acting as the meat for any content that we're actually drafting moving forward. So just to show you what that looks like, I basically constructed this panel of who I think are six of the best interviewers in the world.
23:11Tim Ferriss, Joe Rogan, Larry King, Barbara Walters, Michael Barbaro, and Howard Stern. Each has a different style.
23:19And within the content machine directory of skills, there are each interviewer here has its own skills. So Tim Ferriss has a skill.
23:27Joe Rogan has a skill. Larry King has a skill. And those skills basically define what is the interview style of these interviewers, what do they typically push on, what are the questions they ask.
23:37And so I'll show you in a minute. But when I pick the content ID I wanna go with and I wanna get interviewed, you'll see different members of the panel will come in at different times, and they will push on the things that they are most known to push on.
23:49Nice. I like it. So
23:51here, I'll just show you what the next step looks like. So let's share this.
23:58Okay. So so I'm gonna go with context management.
24:06Let's just go with that. And and we don't have to go through the whole thing, but let's say let's go forward with spike one about context management, and let's move to the interview panel to talk about this spike.
24:24So now what's gonna happen is it's gonna skip the research step step since I said I wanna just go straight to the interview panel. And so this panel is gonna start asking me questions about context management. And the idea is I'm gonna provide all these thoughts.
24:39Those thoughts are then gonna get saved as a raw markdown file. And so then when we get go to create the the first piece of content, it is pulling from everything I've said.
24:51So it's never using to create the content things that I did not say. And I think that's the key. Like, when we talk about AI slop, typically, slop comes from comes from you not actually using your words.
25:06Okay. So let's see. Forget the abstraction for a second.
25:10Walk me through the most recent time This week, you sat down with an AI agent to do real work.
25:17What was the task? What exactly do give me the actual It actually interviews you. Oh, yeah.
25:21It interviews you. And and if I don't like a question, I can be like, you know what?
25:27I don't love this question. I wanted to really focus on the best, um, tactics and best practices for doing smart context management and context hygiene.
25:37Can we reorient the interview around that, please? And so what I'll do is, like, I'll get it asked these questions, and I'll probably spend, I don't know, fifteen minutes on it just yap to texting.
25:48And once I do that, like, that is all I need for a good anchor piece of content. Okay. So now see it, reoriented it.
25:54Let's go straight to the tactics. Give me your top three context hygiene best practices, the specific repeatable moves. So I'll just be like, okay.
26:02First of all, I know a lot of people get worried around compaction. So when the context window gets filled up, whether it's in Claude code or codex, people worry that when compaction happens, you lose important information. I think these models have gotten so good where you should not be worried about compaction, and the important essence is actually kept.
26:20That's number one. Number two is having really standard conventions for how you set up your folder and file system is one of the best ways for good context management because then you can easily point a model to the right folder or, uh, kind of the right file, and it doesn't need to waste tokens looking for a diamond in the rough.
26:44And then the final piece is for any kind of net new workflow, you should think about starting a new task and then having kind of, like, a file that stores the memory of a separate task, but you need to relate back to in this new task.
27:02So you start with a fresh context window, and the only thing that the context fills up with is the file that you want it to read. And you're using WhisperFlow for this? Yep.
27:12Just using WhisperFlow. And so then what you'll see is I talk through it, and it'll think through.
27:18The next interviewer won't be Tim Ferrissett again. It'll be one of the other interviewers, and it'll specifically ask about the thing I answered.
27:25Yeah. I think it's important to call this out too, but before this shows up too. Like, the the fact of the matter is you're not just handing it off.
27:31You're actually spending the fifteen minutes to cocreate the content versus it like, it's it's it's not just the, hey. Like, just YOLO and go. So that's important.
27:39Yep. Exactly.
27:41And so, I mean, what we can even do is, like, um, go to, like, kind of what is the next step in the process look like.
27:50Um, and I can even use what I the thirty days of AI, uh, day one yesterday was yesterday, and I and I did it about software factories and, like, this concept of a software factory. And so I can show how the next step works.
28:02So let me go question here.
28:04Yeah. What was the difference of the output from before this automation to now?
28:09So, for example, one post a day to day five. Do you understand the question?
28:15I I think I understand the first one. What was the difference of output from before this automation? Now Yeah.
28:19If if you're probably referring to quality. Yeah. So, I mean, the short answer is the quality is markedly better, and the reason is twofold.
28:28One is having this interview structure basically just pulls all of my thoughts onto paper.
28:38And so when content is created, it is taking my words. And so, generally, people do not believe AI is AI slop when when your own words are being used.
28:50Yeah. The the second thing is, and this is helpful for me because I have a lot of content online, is, like, this voice the the voice m d file that I have that's part of, again, my profile as a creator in the content machine is has ingested all of my podcast interviews, all of my x posts, all of my LinkedIn posts.
29:10So there's just, like, a lot of, like, rich information there. But the other reason this is so much valuable so valuable and so much better than what I have before is let me just show one piece to this.
29:22And my view is, like, any good, and I think we talked about this, Eric. Uh, any good AI system has like, completes a full loop.
29:33And so what that looks like for me is okay.
29:37So, basically, I was interviewed by the interview panel just so you guys know what happens next. Then I would go to write a piece of content.
29:45And when I go to write a piece of content, it asks me basically what is the anchor piece of content you wanna create, whether it's a newsletter, a long form LinkedIn post, a YouTube script. It gets written, and then there's a writer's council.
29:58So these are six of the best writers that I look up to. So Morgan Hassell, Tim Urban, Greg Eisenberg, David Perrell, Sean Purry, and then a slop detector. And what they do is they look at the initial draft that is written by the content machine, and they score it.
30:12And if it scores below a nine out of 10, there are two possible paths. One path is if as they score it, they say, Alex didn't provide enough information here, here, and here, it sends it back to the interview panel where they ask me for more information about the missing information. If they feel like it is just not written well, so it's all editorial changes, then they do a revision loop until the council scores above a nine out of 10, and that is when they show me the final piece.
30:41I love that. That's great because here's the thing. I've been using these eval panels and but the thing is it's auto scoring its own, like, AI created content without my voice.
30:51The the major thing is your voice is included, and it's going back back and forth with you. And I think most people get it wrong. Like, I've gotten it wrong because I just rely on the AI Discord AI.
31:00So A 100%. And so then what happens is, like, the final piece of content's created. And then step seven is
31:06go gets to, like, the Gary Vee content pyramid, which is we got to the final anchor piece of content. Now it's gonna create derivative content.
31:14So short form video, script, x article, LinkedIn article, one to two short posts, one to two short posts, and a playbook for 10 x's website. And for each one of these formats, there's a a prior of what what that format looks like that I actually created.
31:30So, again, similar to how there's a voice dot m d file, there's a format dot m d file for each of these formats based on what I've created in the past. The final piece to this is and just so you know, like, I haven't finished building this part in, but the next the final piece here is I'm building in the distribution piece where every piece of content that's created gets UTM'd.
31:48I can schedule it from here. It gets attached to HubSpot, and we can do attribution straight from the content machine. Um, but the final piece that's really important is where is it?
32:00One second.
32:06Where is it? Um, there is here.
32:12Lessons. Okay. So, basically, what you see here is contentlessons.md.
32:20Let me see if there's another place that okay. This is it. The learning loop.
32:23So, basically, what happens is after I approve a piece of con or after I get the final piece of content from, like, the editorial committee who's reviewed the draft, did the revision loop to get it to a nine out of 10, I look at the content, and I make my final edits. I feed the final version, post my edits back to the machine.
32:44And what it does is it looks at the diff between what the editorial committee gave me and what I provided it. It extracts lessons from the feedback I provided it, and it saves those lessons into a content lessons dot m d file.
32:57So in the future, we loop back up. When it is reviewing my content, it has this content lessons dot m d file. So it is looking for any of its previous failures from my perspective of where the writing wasn't sharp, and it's making sure it's not making those mistakes again.
33:12Yeah. Dude, this is great because one of my friends, he has a we did, like, a AI mastermind a couple weeks ago, and then he has a skill that that helps him find real estate deals and a real evaluates real estate deals.
33:23I'm like, how long did it take you to make this skill? He's like, dude, hours and hours and hours. Right?
33:26I have to, like, I have I'll I'll just ask you straight up. How long did it take you to make this skill? Because I think a lot of people are like, oh, I can probably do this in, like, you know, a couple minutes or so.
33:34But how long did take for you? I probably spent
33:38minimum fifty hours on this. I've spent minimum fifty hours. And the reason, again, I think it's worth the fifty hours is now I really only focus my time in three places.
33:50Picking the idea, being interviewed about the idea, and providing feedback on the final draft.
33:58And my view is is everything else gets taken care of for me. Oh, sorry. And the final the the fourth thing that I spend my time on is improving the system.
34:05And then because of that, not only am I spending way less time on content like, the the thing I post about the software factory yesterday, previously, that would have taken I don't know.
34:19Um, that would have taken, like, probably twenty to thirty hours to write. It took me, like, three to four hours. And the way I view it now is, like, I'm also kind of like as AI helps to write content, I almost think of myself as, like, a disc jockey, like a DJ of remixing a lot of stuff versus just creating a lot of net new stuff.
34:38The final piece is is if I truly want all 40 team members here to become content creators, they're going to need a system like this because they can only spend, let's call it, two hours a week max putting content online.
34:50Yeah. And so, by the way so so let's call it, like, a 80 to 90% reduction, but it still takes a good amount of time, three to four hours. By the way, it used to take three to four hours maybe for, like, a, like, a, you know, 1,500 word, 2,000 word blog post.
35:01But even then, like, if you really wanna go deep, you you would spend the time that you're talking about. Uh, how much reach do you think you're getting right now across the board with your content per month right now? Yeah.
35:10I mean, I'll just use the example of just yesterday.
35:13Yesterday, I posted about, again, thirty days of AI, and I can talk about why I'm doing it. Like, there's Yeah.
35:19There I regardless, I would have posted about AI every day for the next thirty days. But when you name something and you make it a contained series, people love following along the journey. So I posted about thirty days of AI yesterday, and I got 10,000 followers in the last twenty four hours.
35:34And that is just because people people love story arcs.
35:40People love following journeys, especially journeys where there's something in it for them. Um, so just in the last day from that post and my day one post about thirty days of AI, that is just 1,500,000 impressions in the last twenty four hours.
35:52And so this goes back to, like, why do I spend all my time on content? My view is 10x is competing.
36:00One one way to think about it is we are competing against some of the biggest management consulting firms in the world.
36:06McKinsey, Bain, BCG, you know, Accenture, Deloitte, you know, keep the list going. We do not have fifty to a hundred fifty years of history that will just, like, automatically make a CEO reach out to us.
36:19We don't have like, we're not I'm not a partner at McKinsey who has twenty years of history with the CEO of a Fortune 50 business. The only way we are going to build trust and legitimacy with these firms is either through partnerships, like the partnership we have with Anthropic, referrals from our existing customers, or content that shows that we are an authority on doing AI transformation.
36:39And so the reason I spend all my time on this is I believe the 1,500,000 impressions that I, uh, drove yesterday, that's less important to me than knowing I am sure we are gonna get at least $100,000,000 a year plus business
36:53that comes out of the post I made yesterday. That is a real prospect that we can end up, uh, you know, doing business with. Yeah.
36:59The thing is I I think we we you can you choose if you wanna reveal this or not, but I I think it's a you know, we've we've talked about the what x is good for, what LinkedIn is good for, what other channels are good for, right, in terms of driving pipeline. And I by the way, I I found what you said to be true about x being really good at bringing in engineers.
37:17So that that's been good. But, like, I I think okay. Let's say the next post gets 1,500,000.
37:22Are you are you cross posting it as well to other channels to to drive pipeline? Yeah. Yeah.
37:26Yeah. So my whole thing is thirty days of AI, I want to exist on all platforms. So I posted on x and LinkedIn yesterday.
37:33Um, my plan is so starting today, I'm gonna post on short form also. So, like, I'm using the repurposing engine, um, on the content machine where, basically, you took my software factory post, which was, 2,000 words, and it was like, okay.
37:47This is the part of the post that you should focus on with short form. And I thought it picked the exact right part for a short form video, so I'm gonna record those today. The other thing I'm gonna do is I'm gonna turn it into an email newsletter where it's a thirty day email newsletter that people can follow along and get an email every day with my insight from the day.
38:04And then what we'll do is we'll drive a lot of emails from that. Ten x already has a newsletter called Ultrathink that people naturally gravitate to once the email kind of explodes and the the series is done.
38:15How how long do you think you're spending on content per day right now? Four, five, six, seven hours?
38:22Yeah. I would say five hours minimum.
38:25Okay. Minimum. Wow.
38:26Okay. And because you're talking it's like, I I kinda feel like I I like the DJ analogy because I do feel like in what like, I'm making a a YouTube long form. All of sudden, I'm recording a webinar with you, And all of sudden, I'm, like, chopping things up for shorts as well I'm repurposing.
38:38Like, that's that's what I feel like I'm doing. So Yeah. And and, also, one the the reason I also view myself as a DJ right now is, like, when I wrote this software factory piece yesterday,
38:48I do not claim to be a lifelong engineer who has built like, who has built his own software factory.
38:55And so I am also approaching this entire thing as kind of like a like a just a student of the industry, almost like talking to the smartest people I know to get their perspectives on things and then collating it in a way that makes sense to people.
39:14So, like, how did I approach the software factory thing yesterday? One is I used the research feature like, function of the content machine.
39:21Then I went to all of my engineers at 10 x, asked them, what do you think about a software factory? And then I and then I also had the Oracle find all tweets related where, like, people like Chamath or Ryan Carson have mentioned a software factory.
39:36And so it's like, I was almost, like, pulling all of these things into a patchwork that I thought told a cohesive story to the nontechnical person about what a software factory is and why you should care from the smartest voices in the space.
39:48Yeah. This is awesome. And and by the way, because you come from such a strong newsletter background, like, how does this newsletter play in right now?
39:54Like, are you guys running a bunch of paid ads for acquisition? Like, what's the deal? No.
39:57We're not running we're not running any paid ads. Again, it's not a huge newsletter yet. So the 10, uh, Ultrathink is a, um,
40:05we have 12,000 subscribers. It's around a 45%, uh, open rate, um, and it's weekly.
40:12We are gonna up that at some point to I think we'll probably get to three times a week. Right now, JJ, our creator, who's, like, the man that does it all, is also writing our newsletter, and he has his own set of skills to write Ultrathink. But but my view is this is, like, I think all these other channels we have so thirty days of AI, if we end up doing webinars, when I bring back my show and I'm the like, you know how you're going live right now?
40:38The thing I'm gonna do is, like, I'm gonna go live and ruin my show, but I'm gonna still make them events so that we capture leads for events before going live. That's gonna all funnel into Ultrathink. And then the other thing that we plan to do is I'm gonna actually sell ads against Ultrathink and any of the media we do so that any content hire we make is basically paid for by our ads.
40:58And so all, like, marketing we do is zero CAC.
41:01Yeah. I love it. That that's awesome.
41:03Um, we'll love once you figure that out, we we we can do another one of these. And by the way, everyone watching right now, you guys should take once this is on YouTube or whatever channel, you should just take the transcript of this and then ask it to Skillify. Just ask it to interview and then figure out how you can Skillify this and make your own.
41:18Totally. And one thing I would say is, like,
41:22this whole process of turning my content process into kind of, like, an AI native workflow, it is not a point in time thing. It is, like, a constant job. Right?
41:31Even since you and I spoke, Eric, I added in the ability for someone to, uh, like, for creators to have, like, their own file in the structure. I added in the Internet reader thing where it's curating the Internet and not just our in internal stuff. And so I'm constantly thinking about what are ways that the content machine can be leveled up.
41:49Like, one thing I'm thinking about, and I don't know if it's gonna be possible because the tech isn't there yet, is, like, if I just stepped up to the the camera to record a short form video script that the content machine recorded for me, could I have a remotion skill built into it that does, uh, a short form video edit using the content machine?
42:07Like, I'm constantly thinking about any friction or work that exists, how do I take that to zero
42:12so I'm only doing the things I have to do? Yeah. It's it's constant upgrades.
42:15I don't know if you played this is nerdy stuff, but I used to play Warcraft and can keep upgrading your your town hall. Same same thing. Same thing.
42:22In, like, your castle. Right? So Yeah.
42:24That's great. We're just a few more questions that just before we wrap up here. So, yeah, I mean, maybe going back to the reach thing for a second.
42:31So yesterday, 1,500,000 on just that expo. Like, how much how how many impressions do you think you're getting per month right now? We're talking, like, twenty, thirty, 50,000,000?
42:39No. It's not that much yet. Let me let me look.
42:43Here. I'm just gonna look at Creator Studio as we're talking. So last last four weeks, I'm at 2,500,000.
42:57And then last three months, I'm at 8,000,000, and that's on Twitter. To to also to the point you were saying before, it has been my experience that x is, like, where you get street cred and where you attract engineers and where you learn.
43:15LinkedIn is where you drive business. Like, all basically, all of 10x's revenue has come through LinkedIn.
43:25All of our AI strategists, which are, like, our technical consultants, have come through LinkedIn. A lot of our engineers have either come through, um, x or through Juicebox, which is like an AI, uh, sourcer.
43:40They're Juicebox.
43:42Yeah. Yeah. Yeah.
43:42So and then the only, like, business opportunity that's come through x is our partnership with Anthropic originally originated with someone
43:54yeah. Yeah. So, like, that that is huge, but that is, like, the one thing.
43:57So, yeah, it's been interesting to see kind of which platforms provide value. So let let's say x is, like, two, three million a month, something like that, uh, in terms of impressions. What do you think it is for LinkedIn?
44:07Let me see.
44:10And then I I as you pull it up, the reason why I'm asking for it is is because to get these types of compounding results, one, takes a long time, but also the amount of time Alex putting into his five plus hours a day, like, that's what it takes. If you honestly, it's probably, like, seven plus hours or so.
44:24But I just Totally. Want to give you a sense of of what volume you can achieve.
44:27Yeah. Yeah. Yeah.
44:28So so LinkedIn last twenty four hours was a 120,000.
44:38LinkedIn and I and I have 210,000 followers on LinkedIn.
44:43By the way, followers just doesn't matter anymore as it relates to, uh, social. Can I I'm having trouble, like days?
44:53Yeah. That's what I'm trying to do. Let's see.
44:59My guess I I can't get it right now, but my guess is, uh, last, like, last month, it's probably a million and a half.
45:08What I'll also say is Mhmm. I and this is this is something I constantly think about is I generally want my impression number to increase, but I also when I think about who 10x's customer is, it's a very specific person.
45:24It is basically a CEO, c COO, CIO, or CTO at a $100,000,000 a year business or up.
45:32So a lot of times, I'm thinking about how different is the content I create for that person versus, say, like, just the typical knowledge worker who's trying to navigate AI. And I think there's a Venn diagram. Like Yeah.
45:45When when we're talking about how to build, like, a directory of skills like the content machine, I think that sits in the middle of the Venn diagram. But there may be things like, um, how to think about governance as it relates to AI that I should that I should probably talk about that probably won't get as many impressions, but isn't relevant to, you know, the second audience I just mentioned.
46:06Yeah. It'll get it'll get the right people ultimately. Yeah.
46:08Exactly.
46:09Cool. And so because you see a lot of these business right now, what what do you think is the primary thing?
46:14Like, where where are most people getting stuck with AI? Like, what's the what what are the patterns that you're seeing?
46:20Yeah. I think so I think there's a few things.
46:25One is, like, everyone right now is it it the the focus is really starting to turn to ROI. Like, every company that we talk to is, we're spending a shit ton on this. Our revenue is not necessarily going up.
46:41We have to, like, start really going through with a fine tooth comb more. And and so this is why I think it's interesting. Like, I think that every company in their AI journey should probably start with something that can very clearly map to, uh, either revenue generation or cost savings and, like, very clearly.
47:02Because my view is is, like, there's gonna be a lot of AI work you do in a company where it is not super obvious what the ROI is. Just like for a long time, me creating content online was not super clear what the ROI was.
47:14And so to me, there's a there's a few of these things that, like, very clearly can tie to ROI. One is getting your engineering work to be more closer to a software factory where, basically, the amount of PRs that are merged and and didn't have to go through human review or at least humans reviewing it.
47:33It was the only step, and they weren't writing write writing the code. The way to measure that is you just look at what are the average PR number of PRs merged by engineers before and after.
47:44What's the average cost of an engineer? How many fewer engineers do you have to hire now because of it? Very clear.
47:49Uh, customer support, also very clear. Like, number of tickets that, uh, that were closed by your customer support agent, and what is the number fewer of customer support people that you need in your business, also very clear.
48:05Sales gets a little wonkier because, like, you go into a sales org, typically, sellers spend 50% of their time not on kind of, like, revenue generating activities, like talking to customers. If you can show that you're getting more of their time spent on revenue generating activities, that's a plus. But unless, like, their actual revenue that they are driving is going up, it's harder to show.
48:26But, basically, my view is is you have to focus on ROI driving activities, basically, to win more budget for things that are not necessarily gonna show ROI over, you know, a three to six month period.
48:38Yeah. I'll I'll give you the the last question. I I think we can go on for a very long time.
48:41But Yeah. So we'll we'll do another one of these. But how are you measuring kind of productivity right now?
48:46Are you looking at obviously, the people like to talk about token maxing, but I think you gotta pair it with another metric such as PRs, for example. So how are you looking at it? Is it is it you're talking about tokens, uh, consumed and also something else paired with it.
48:57How are you thinking about it?
48:59Yeah. I mean, for us at 10 x, it's, like, super easy with our engineers. I can't remember if I told you this, but, like, we pay our engineers, like, salespeople.
49:06So, like, our engineers at 10 x all have, like, a minimum guarantee, and they are paid based on story points they complete. So, basically, the way we measure it is how is an engineer's number of story points they complete for a complete and are accepted by clients going up over time.
49:23And in theory, almost like there's inflation of the US dollar, there should be natural inflation of story points. Because, like, if you think about it, if if AI keeps getting better, engineers should be able to do more just by the AI getting better, the then the alpha that they get is them improving their own systems and their harnesses for working with it.
49:43So that's how we measure our engineers. It's like, what is their growth of story points complete over time?
49:48Yeah. I'll I'll throw in a fun question here for you. So do you think this is true or false?
49:52If, uh, Alex is using AI and Eric is not using AI and Alex continues to compound for twelve months or so, Eric will be unable to catch up.
50:01True or false?
50:06With anything or specifically with content?
50:09Let's go on content.
50:12I think it is true if the output that I'm putting out is actually better than a pre AI world.
50:23If the output's not better, then it is false. And, actually, I don't think Alex will be able to catch up with Eric.
50:28Yeah. Okay. So so here's that that's actually good because then you can apply the law of compounding with content also to business and anything else, really.
50:35Yep. And then I I think it it's it's it's true. Okay.
50:37So that's a good place to end. So, Alex, what is the best place for people to find you online and learn more about 10x? Yeah.
50:43Uh, on x, Business
50:44Barista. LinkedIn, I'm Alex Lieberman. And 10x.co.co is our website, and always down to chat about it.
50:52I'm I'm definitely not the expert.
50:55I don't know everything, but I'm just trying to learn as much as possible on this journey.
50:59Cool. Great. And guys, I'll be on Alex's next week so you can join us over there.
51:03But thanks so much for joining, Alex, and thank you all
The Hook

The bait, then the rug-pull.

The stream starts with a live audio glitch — two operators troubleshooting in real time before diving in. What follows is 51 minutes of one of the more honest on-screen demonstrations of an AI content workflow that actually exists, runs in production, and has a confirmed $100M+ lead attached to it.

CTA Breakdown

How they asked for the click.

MENTIONED ON CAMERA
01:00product10x.co ↗
16:15toolClaude Code ↗
Frame Gallery

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