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Lenny's 🏦 Vault

Media Businesses Need Founder Content Creation

by Dan Shipper on July 17, 2025

Dan Shipper's company Every operates at the bleeding edge of AI adoption, showcasing principles that will likely become standard across organizations in the coming years.

Core AI-First Operational Principles

  • Dedicated AI Operations Role

    • Employ a head of AI operations who focuses on identifying repetitive tasks and building prompts/workflows
    • This person helps everyone on the team automate as much as possible
    • Separating automation work from day-to-day operations makes adoption more likely
    • Focus on behavioral changes to ensure people actually use the AI tools

  • Compounding Engineering

    • For every unit of work, make the next unit easier
    • Example: Instead of writing PRDs manually each time, create prompts that transform rough thoughts into structured PRDs
    • Build libraries of prompts that make processes more efficient over time
    • Store these in shared repositories (GitHub) for team access

  • Multiple AI Agents for Different Strengths

    • Use different AI models for different tasks based on their unique capabilities
    • Recognize that AI agents have different "personalities" and strengths
    • Example: Using Claude for certain writing tasks, GPT for others, specialized agents for code review

  • Zero Manual Coding

    • Engineers don't write code directly - they manage AI agents that write code
    • Engineers still need to understand code to review and guide AI effectively
    • The workflow becomes: requirements → prompts → AI generates code → review → refine

Organizational Structure and Culture

  • Small Teams with High Output

    • 15 people running multiple products, daily newsletter, and consulting
    • AI enables small teams to accomplish what previously required much larger teams
    • Example: Quora product built by just 2 engineers plus "15 Claude code instances"

  • Generalist-Focused Teams

    • AI enables people to work across multiple domains effectively
    • Team members can handle diverse responsibilities rather than specializing
    • AI handles specialized knowledge, allowing humans to focus on connections and vision

  • Skills That Matter in the AI Era

    • Management skills become universally important (the "allocation economy")
    • Evaluation, taste, and vision become more valuable than execution
    • Knowing when to dive into details vs. delegate to AI
    • Ability to communicate problems clearly to AI

Adoption Patterns That Work

  • CEO Must Be an Active AI User

    • The #1 predictor of successful AI adoption is whether the CEO actively uses AI tools
    • Leaders must set the example and have realistic expectations about capabilities
    • "If the CEO uses ChatGPT or Claude daily, it's going to work out"

  • Create Visibility and Momentum

    • Hold weekly meetings where people share prompts and use cases
    • Send weekly emails showing usage stats and highlighting innovative users
    • Identify and elevate the 10% of early adopters who will figure things out first

  • Focus on Leverage, Not Replacement

    • Most successful companies use AI to "go further faster" with existing teams
    • The goal is increased output and capabilities, not reducing headcount
    • Companies see AI as enabling them to do more with the same resources
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