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 shares his motivation for creating a 'content machine' that leverages AI to generate high-quality content efficiently. He discusses his approach to mapping out his current content creation processes to identify where AI can be integrated as a co-pilot rather than a replacement. The process involves a six-step system including the Oracle (which scans internal and web sources for content ideas), an interview panel (where AI conducts structured interviews to extract ideas), voice and style guides (to ensure AI drafts in his personal tone), and a writer’s council (that scores and revises drafts). The goal is to prevent AI slop by relying heavily on structured human input and iterative feedback loops, and the entire process emphasizes process mapping and removing constraints to maximize AI’s effectiveness.

Lieberman demonstrates his AI-powered content process using Claude, showing how the system automatically identifies content spikes from internal data and external sources, then facilitates structured interviews to extract unique insights. The system drafts content in his style, using his voice and content lessons, and scores the output through an editorial council. He highlights how this setup allows fast iteration, personalization, and alignment with his style. He notes that 'AI slop' often stems from insufficient idea sharing during interviews rather than AI quality, emphasizing the importance of structured human input and feedback to keep drafts on point.

The episode explores how employee advocacy can be a powerful marketing channel, illustrated by Lieberman's 'Creator Cup' initiative that encourages employees to post and engage on social media. He discusses how this not only increases visibility and engagement but also helps with talent attraction for a bootstrapped company like 10X. The approach involves gamification, prizes, and fostering a culture where employees actively contribute to the company's narrative. Lieberman advocates for companies to view employee content as a strategic asset rather than a risk, emphasizing that encouraging employees to share their achievements enhances employer branding and organic distribution.

Lieberman emphasizes the qualities of great engineers—deep system understanding, flexibility to adopt AI, and a proactive mindset to leverage technology for business. He explains their unique compensation model (variable pay based on performance) to attract self-motivated talent willing to bet on their abilities. On a personal level, he discusses innovative AI applications, such as a customized job alert system for his wife and a personal storytelling tool for his child, illustrating how AI can solve real-world problems in everyday life.

Lieberman candidly describes his approach to prompting AI: depending on time constraints and frustration levels, he either manually edits, gives direct instructions, or vents his frustrations at the AI. He admits to being sometimes harsh, recognizing that managing AI output involves patience and iterative refinement. His openness highlights that even experienced AI users find value in straightforward, sometimes tough, feedback loops to improve results.