Chase Watermelons on the Ground

The "Fast Thinking vs. Slow Thinking" Model for AI Transformation

Howie Liu, co-founder and CEO of Airtable, describes how he restructured his company to thrive in the AI era by creating a dual operating system that balances rapid innovation with thoughtful infrastructure development.

The Fast Thinking vs. Slow Thinking Framework

• Inspired by Daniel Kahneman's cognitive model, Airtable reorganized into two distinct groups:

  • Fast Thinking Group (officially "AI Platform"): Ships new capabilities on a near-weekly basis
  • Slow Thinking Group: Focuses on deliberate, longer-term infrastructure bets

• The Fast Thinking team:

  • Prioritizes rapid experimentation and iteration
  • Focuses on creating "jaw-dropping" user experiences
  • Ships quickly to learn what works through real usage
  • Operates with high autonomy and entrepreneurial thinking
  • Builds excitement and creates top-of-funnel growth

• The Slow Thinking team:

  • Makes deliberate, premeditated bets
  • Builds complex infrastructure that can't be prototyped quickly
  • Enables initial adoption seeds to grow into larger deployments
  • Creates scalability for enterprise-level usage
  • Turns initial excitement into durable growth

The "Watermelons on the Ground" Principle

• In the AI era, there are countless high-value, low-effort opportunities
  • "We have all these fruit trees and there's so many crazy low-hanging fruit... you've got literally massive watermelons sitting on the ground"
  • "All you have to do is walk over 20 feet and pick it up instead of having to climb the really tall coconut tree"
  • Focus on finding and attacking the biggest opportunities first

The IC CEO Approach

• CEOs and leaders need to become individual contributors again in the AI era:

  • Get hands-on with the technology (use AI tools hourly, not just daily)
  • Understand capabilities through direct experimentation
  • Be the "chief taste maker" by experiencing the product firsthand
  • Cut standing meetings to focus on timely, urgent topics
  • Reduce layers between leadership and product development

• Why this matters:

  • AI is evolving so rapidly that you can't understand it from a distance
  • "To really understand the solution space of what's possible, you kind of have to be in the details"
  • "You can't taste the soup without participating in at least some part of creating the soup"

Role Evolution in the AI Era

• The most successful people in any role are those who can cross boundaries:

  • PMs need to become "hybrid PM-prototypers with good design sensibilities"
  • Engineers need product thinking skills
  • Designers need technical understanding
  • Everyone needs a minimum baseline in all three disciplines

• Breaking down silos extends beyond product teams:

  • Marketing teams should be able to execute campaigns end-to-end
  • Sales people need to understand the product deeply like solutions engineers
  • The goal is to collapse dependencies so individuals can drive outcomes

Experimentation Over Planning

• Shift from deterministic planning to experimentation-driven development:
  • For novel AI features, start with "vibes" before formal evaluations
  • Throw ideas against the wall to see what works
  • Only formalize testing once you understand the use cases
  • Prioritize prototypes over decks and PRDs
  • Let people cancel meetings to experiment with AI tools

The Refounding Principle

• Ask: "If you were literally founding a new company from scratch with the same mission, how would you execute using a fully AI-native approach?"
• Evaluate whether your existing assets give you an advantage or hold you back
• If you can't leverage your existing business effectively, consider selling and starting fresh

The Play Principle

• Encourage "play" with AI tools rather than just task-oriented usage
  • Create personal projects to force deeper engagement
  • Experience a wide range of AI products, not just your own
  • Share your experiments to normalize this behavior
  • Block out days or weeks for pure exploration