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D

Dan Shipper

Co-founder and CEO

Every

🎯 Product Strategy (1)⚡ Execution (1)👥 Team & Culture (1)

Key Takeaways

  • 1.Hire a 'Head of AI Operations' whose sole KPI is to audit internal workflows and build prompts/automations to remove repetitive tasks for the team.
  • 2.Shift engineering metrics from 'lines of code' to 'agent management'; at Every, engineers no longer write code manually but instead manage suites of agents (like Claude Code) via detailed PRDs.
  • 3.Utilize 'Local Agent' workflows (e.g., Claude Code, Gemini CLI) to process sensitive local files (meeting notes, codebases) for deep analysis without uploading to a web chat interface.
  • 4.Adopt the 'Service Unbundling' strategy for product ideation: Identify expensive services (legal, chief of staff, copy editing) and build AI wrappers that democratize access to them.
  • 5.Implement 'Compounding Engineering': Every unit of work should produce a prompt or automation that makes the next unit of work faster (e.g., a prompt that writes PRDs based on rough notes).
  • 6.Foster AI adoption through 'Social Proof Loops': CEOs must use the tools visibly, hold weekly 'prompt share' meetings, and publish internal usage stats to gamify adoption.

Methodologies(3)

🎯 Product Strategy

A framework for ideation that focuses on taking expensive, exclusive human services (lawyers, therapists, chiefs of staff) and democratizing them via AI wrappers. Instead of inventing new behaviors, it automates existing high-value behaviors that were previously cost-prohibitive.

Core Principles

  • 1.Identify 'Luxury' Services: Look for services only rich people or large companies buy (e.g., on-call lawyers, professional copy editors, chiefs of staff).
  • 2.Internal Dogfooding: Build a 'wrapper' (simple interface over an LLM) to solve this problem for your own team first.
  • 3.The Vibe Check: Evaluate success based on internal 'vibes' and high-frequency usage by your own team, rather than external market research.
  • +1 more...

"There are a lot of expensive services that rich people and big companies are paid for right now... what cheap intelligence does is it makes those kinds of things affordable for small companies and individuals."

#service#unbundling#strategy
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⚡ Execution

A development workflow where engineers stop writing code manually and start acting as 'Agent Managers'. The focus shifts from syntax to architecture, requirements gathering, and code review.

Core Principles

  • 1.Prompt-Driven Architecture: Engineers spend their time writing detailed PRDs and instructions in English rather than writing functions in Python/JS.
  • 2.Multi-Agent Deployment: Use specific agents for specific tasks (e.g., one instance of Claude Code for coding, an 'Agent Charlie' for GitHub PR reviews).
  • 3.Compounding Prompts: Build a library of prompts that turn rough notes into technical specs, ensuring that the setup for the agent gets faster every time.
  • +1 more...

"No one is manually coding anymore. Organizations like ours, people who are playing at the edge, we're doing things that, in three years, everybody else is going to be doing."

#agentic#engineering#execution
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👥 Team & Culture

A change management framework to transition a company from 'AI-curious' to 'AI-native' by leveraging leadership behavior and social proof.

Core Principles

  • 1.The CEO Benchmark: The CEO must visibly use AI daily. If the leader delegates AI usage, the team will view it as 'low status' work.
  • 2.The 'Tobi Memo' Strategy: Send a company-wide memo declaring the company AI-first, explicitly stating 'I wrote this email with ChatGPT, and you should too.'
  • 3.Weekly Prompt Shares: Host a weekly meeting dedicated solely to team members showing off a prompt that saved them time.
  • +1 more...

"If the CEO is in it all the time... everybody else is going to start doing it. If the CEO is like, 'I don't know, this is for someone else,' no one else is going to be able to lead that charge."

#ai-first#cultural#rollout
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