Alex Lieberman sold the majority stake in Morning Brew to Insider in 2020, when the media company born in a college dorm was valued at $75 million. He now runs 10x, an AI-transformation consultancy, while keeping up high-frequency content output on X and LinkedIn. In an interview with How I AI, the podcast under Lenny’s Newsletter, he revealed the system behind that output: a Claude-based “content machine” where AI participates in everything from ideation to drafting to review, and yet the output has “no AI slop.”

This breakdown is compiled from the full transcript of that interview, walks through the content machine’s operational stages, and finally distills what this system means for ordinary creators.

Why build a content machine

Lieberman says he faced two problems:

The first is time constraints. He has been creating continuously for a decade since founding Morning Brew in his dorm, and content has brought him many opportunities. But the time he can dedicate to creation each day is only about 25%. He wanted to know how to maximize the value of that 25% while guaranteeing he doesn’t produce AI slop.

The second is employee creation. He believes that in the future business world, as technology becomes increasingly commoditized, the real moat is “trusted distribution channels,” and turning your company’s employees into creators is a distribution resource many companies haven’t fully tapped. His goal is to make “employees becoming creators while holding a full-time job” as simple as possible.

His approach is to refactor the content production process to be AI-native.

Before getting into the details, it’s worth understanding Lieberman’s basic stance on AI content. The host mentioned a writer friend’s view that AI will raise the floor for mediocre writers but cap the ceiling for great ones. Lieberman agrees with that assessment but says it needs refinement. If AI only participates in drafting and editing, it might indeed cap the ceiling for top-tier writers’ output. But content creation is a multi-stage process; using AI for inspiration gathering, material organizing, and format conversion doesn’t mean abandoning quality. More important is comparing against the baseline—his team’s alternative before the content machine was simply not creating at all. Between having employees produce content around their expertise and having nobody produce any content, he’d always choose the former.

He also mentions a counterintuitive observation: the popularity of AI slop is actually creators “pointing at themselves and saying I’m not smart enough.” When a person feeds AI vague ideas, they naturally get vague writing back. The problem lies in the input stage, not in the AI itself. This view runs throughout the interview and explains why his system spends enormous effort on ideation and interviewing, not writing.

Step 1: Map the existing process first, then tear down constraints and rebuild

Lieberman emphasizes that the starting point of the whole transformation isn’t introducing AI, but drawing a process diagram. He breaks the entire content-creation process down from beginning to end: finding inspiration, judging whether it’s worth writing, drafting, editing, publishing, distributing. He says most people, before doing process mapping, have no idea how much waste is in their own process—AI is just the push that forces them to complete this step.

Host Claire Vo adds a key piece of advice: don’t design the process around current constraints; design it around the ideal state. Unconstrained, of course you’d want an always-on researcher digging up topics for you, and of course you’d split one piece of content into multi-platform micro-content. First map the ideal process, then decide which stages go to AI and which stay with humans.

Step 2: Oracle scans seven days of information and produces fifteen post ideas

The content machine’s first component is called Oracle. It connects to all of Lieberman’s information sources in Tenex, including Slack, Notion, meeting notes, Linear, Git, and Gmail, plus a custom list of internet information sources he configured himself. Oracle scans the information from the past seven days every day, scores potential “content spikes” (specific topics identified from the information stream that have potential to be written about) against a scoring rubric, and finally outputs roughly 15 candidates.

This design stems from a fear of the “blank page.” Lieberman thinks the biggest friction point in content creation is facing a blank page, and Oracle’s role is to fully automate that stage. Creators open the machine each day and face not a blank page, but fifteen topic candidates loaded with specific details.

The real-world example shown in the interview is vivid. Oracle grabbed several lines from internal sources: a conversation with a banking client, where the client said McKinsey’s advice would take six months to land while he rebuilt it in a week with Claude Code; his self-deprecating line on a sales call that “I might be the worst salesperson in the world”; and his pain after selling Morning Brew. Oracle flagged these concrete stories as high-potential content spikes. From external sources, it picked up Amazon’s $1 billion investment in FDE and the disagreement among experts over whether FDE is a cure-all.

The key point is that Oracle isn’t grabbing generic topics, but specific moments from someone’s lived experience. Lieberman says AI-generated slop usually isn’t because the AI writes badly, but because the person didn’t feed good enough material at the sourcing stage.

Step 3: Six interview personas—“say it out loud” instead of “write it down”

Once topics are selected, the content machine moves to the interview stage. Lieberman created six interviewer personas that take turns asking him questions. He responds to these questions out loud, using speech-to-text tools like Wispr Flow to shape the raw draft material.

The intent behind this design is to turn creation from “facing a blank document” into “answering questions.” What you say out loud is more conversational and more genuine than what you write, and closer to his actual voice. This stage determines the information density of the content and is, in Lieberman’s view, the watershed for whether the whole system produces slop. He repeats one judgment throughout the interview: whether AI-generated content has an “AI smell” depends mainly on how good the ideas are that a person shares in the interview stage. If the input is vague opinions, the AI naturally outputs vague text; if the input is specific moments from lived experience, the AI outputs details nobody else could fabricate.

Step 4: “Encode” personal style with Markdown files

Lieberman’s writing style is decomposed into two types of files.

One kind is a voice markdown file, recording his sentence preferences, word choices, and expressive traits. The other is a content lessons markdown file, recording the lessons distilled from each round of editing—like “don’t use clichés like ‘if X, then Y.’”

When drafting, the AI writes the first draft in his voice according to these two files. Each time he gives feedback on a final draft, the system runs a “lessons loop”: comparing the differences between the raw material and the final published version, extracting lessons that can be abstracted and reused, and, after his confirmation, appending them to the lessons file. This way the system increasingly matches his standards, effectively solidifying his personal aesthetics into a searchable asset.

Step 5: A writers’ committee scores the draft—below 9 and it gets rewritten

After the first draft is done, it moves to the review stage. Lieberman created six writer personas, including well-known writers like David Perell, Morgan Housel, and Shaan Puri, plus one persona specifically allergic to “AI smell.” The six reviewers each read and score the piece from 1 to 10. If the total score is below 9, it automatically enters a revision loop until it scores 10 before being released.

In a live demo, the draft was initially scored low by the reviewers and the system immediately entered the revision loop; Lieberman joked, “I guess it won’t get a 9.” The whole review process is visible and auditable—the author can see each reviewer’s score and comments. In essence, this mechanism uses AI to simulate an editorial committee, providing an outside perspective without a real human editing team.

Step 6: Reuse and distribute after publishing

Once content is finalized, the machine also handles distribution. Lieberman can specify what formats to break it into—for example, three short tweets plus two long LinkedIn posts—and the system auto-generates the multi-platform versions in his style.

He says one step is still missing from the process he runs daily: Oracle should also remind him which historical content is worth repackaging. That’s the feature he plans to add next.

The tech foundation: a catalog of Claude Skills

This content machine isn’t custom software; it’s built on Claude’s plugin ecosystem. Lieberman describes it as “a catalog of skills,” running in Claude Code or Cowork environments, and team members can pull updates from the company’s internal code repo. He deliberately made the update process simple enough that non-technical colleagues can sync to the latest version with one click.

This choice is worth noting. He didn’t develop a dedicated product for the content workflow; instead, he decomposed the process into a set of composable skill files hung onto a general-purpose AI programming tool. The benefit is extremely fast iteration—a lesson discovered today can become a shared skill update for all colleagues tomorrow.

The numbers: production time dropped from 20–30 hours to 3–4 hours

On the system’s effectiveness, the figures circulating outside the interview say per-post production time dropped from 20–30 hours to 3–4 hours, and building the system itself took over 50 hours. Lieberman didn’t directly give either number in the interview, but the live demo throughout it confirmed the process’s efficiency—picking a topic from that day and completing drafting, review, and revision within minutes.

It should be noted that efficiency gains don’t equal quality compromises. He repeatedly stresses that the premise of this system is his own decade of content-creation accumulation—running thousands of content-creation processes—before he could decompose the workflow finely enough. This also answers his stance on whether AI caps the creative ceiling: AI won’t turn mediocre people great, but it can free excellent creators’ time from repetitive labor to invest it where real judgment is needed.

Employee creation: using gamification to turn everyone into a content source

The content machine solves “how the founder creates efficiently himself,” but Lieberman also addresses, at the company level, “how to get employees willing to create.”

He believes employees are the most-underrated marketing channel for most companies. Claire Vo adds during the interview that she’s seen many CEOs worry that employees who stand out too much on social media will get poached, but what actually makes employees leave is their lack of opportunity to show off valuable work. Companies willing to encourage employees to build personal brands have a clear advantage in the talent war.

At his previous company StoryArb, he ran an event called Own the Internet: over six to eight weeks, everyone was encouraged to post on LinkedIn, with the only rule being that at least half the content had to be work-related, and the final winner got $5,000. That quarter, the event contributed 40% of the company’s inbound leads and brought a lot of recruiting-side exposure.

The version Tenex just launched is called Creator Cup: for a month, everyone posts on LinkedIn or X—10 points per post, 3 points for engaging with a colleague’s post, an extra 50 points for the best post each week chosen by the founders, plus weekly and monthly leaderboards. The company opened a Slack channel called Reply Guys where everyone reads and comments on each other’s posts. The whole design turns content creation into a team sport, using points and leaderboards to create engagement.

Lieberman says employee advocacy has existed for many years; he just thinks the internet and social media have lowered the barrier to an unprecedented low, while most companies are still stuck in the stage of worrying about risk. He also shares an observation from the interview: as technology becomes increasingly commoditized in the AI era, business moats are getting rarer, and “trusted distribution channels” are one of the few barriers that can compound over time. Turning your company into a media company and making employees into content sources is, in essence, building that moat.

Anthropic is a counterexample: several engineers related to Claude Code keep producing on social media, and readers trust the product more because they trust the individuals. These people have become an extension of the brand, and their personal output and the company’s product form a mutually reinforcing loop.

What ordinary creators can learn

The entire system can be abstracted into a four-step methodology that doesn’t depend on Morning Brew’s resources or Tenex’s engineering capabilities.

First, map the process before talking about AI. Break the creative process into stages, find the bottlenecks and waste; this is the starting point of any AI transformation. Lieberman says in the interview that process mapping alone exposes a huge amount of efficiency problems—AI is just the catalyst that pushes you to get it done.

Second, topics come from real experience. Oracle’s value isn’t “automatically generating topics” but fishing concrete stories out of the person’s own conversations, meetings, and emails. Without real raw material, no AI can write content with real information density.

Third, style can be encoded. A voice guide plus a lessons file is, in essence, sedimenting “I don’t think this writes well” into searchable rules, so AI understands you better the more you use it.

Fourth, externalize the review. The six-persona committee simulates “getting an expert to review your draft,” providing an objective feedback loop without an editing team.

At the end of the interview, Lieberman is asked “what do you do when AI produces slop.” His answer is pragmatic: either write it by hand, or spend time scolding it and confirm whether those lessons have already been written into the lessons file. He admits he sometimes treats AI poorly—“given that AI will eventually be my boss, I should really be nicer to it.”

Finally, it’s worth noting his take on engineers: the best engineers have both system-level understanding and the willingness to make their entire workflow agentic. This standard applies equally to content creators: understanding how the whole system works while being willing to delegate repetitive labor to AI is the most valuable combination of abilities in this transformation. The value of this content machine lies precisely in turning that combination of abilities into a replicable process.

Sources: This breakdown is based on the full transcript of the How I AI podcast interview with Lenny’s Newsletter, “How the founder of Morning Brew built a Claude content machine that never runs out of ideas” (Lenny’s Newsletter), and related reporting on Insider’s acquisition of Morning Brew (Axios, Business Insider).