Software Products Must Be Refounded for AI Era

The "IC CEO" Model for Leading in the AI Era

Howie Liu, co-founder and CEO of Airtable, describes how leaders must transform their approach to build successful products in the AI era. This model emphasizes hands-on engagement, rapid experimentation, and cross-functional capabilities.

The Refounding Imperative

• AI represents a paradigm shift requiring companies to "refound" their products
  • "Every software product has to be refounded because AI is such a paradigm shift"
  • Unlike predictable shifts (desktop to mobile), AI evolves rapidly with each model release
  • Each evolution implies novel form factors and UX patterns to capitalize on new capabilities
  • Leaders must 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?"

Fast Thinking vs. Slow Thinking Organization

• Restructure teams into two complementary modes:

  • Fast Thinking Group: Ships new AI capabilities on a near-weekly basis

    • Focused on creating "jaw-dropping" value and experiences
    • Operates with high autonomy and entrepreneurial thinking
    • Prioritizes experimentation and rapid iteration

  • Slow Thinking Group: Makes deliberate, premeditated bets

    • Handles complex infrastructure and architecture requiring careful planning
    • Enables initial adoption seeds to "sprout and grow into much larger deployments"
    • Provides stability and scalability for enterprise-grade requirements

The IC CEO Approach

• Leaders must become individual contributors again

  • Get hands-on with the technology, not just reviewing reports
  • Understand product capabilities through direct experience
  • Become the "chief taste maker" by participating in creation
  • "To really understand the solution space of what's possible, you kind of have to be in the details"

• Practical implementation:

  • Cut standing one-on-ones in favor of timely, insight-driven meetings
  • Focus on urgent topics rather than routine check-ins
  • Supplement with high-quality in-person relationship building
  • Create weekly sprint check-ins focused on AI execution

Maximizing AI Exploration

• Encourage "play" over structured learning

  • "If you wanna cancel all your meetings for like a day or for an entire week and just go play around with every AI product that you think could be relevant to Airtable, go do it"
  • Psychological play state enables deeper learning and discovery
  • Lead by example by sharing experiments and prototypes

• Become a power user of AI tools

  • Use AI tools hourly, not just daily
  • Experiment aggressively with compute resources
  • "I take pride in being the number one most expensive in inference cost user of Airtable AI"
  • Create personal side projects to force deeper engagement with tools

• Prioritize experiential learning over theoretical understanding

  • Build actual prototypes rather than writing documents
  • Test capabilities directly rather than reading about them
  • "It's hard to taste the soup without participating in at least some part of creating the soup"

Cross-Functional Skill Development

• Success requires "polymathism" across traditional roles

  • PMs need to become "hybrid PM-prototypers with good design sensibilities"
  • Engineers need product thinking capabilities
  • Designers need technical understanding
  • "There's a strong advantage to any role who can kind of cross over into the other two"

• Collapse role silos throughout the organization

  • Marketing teams should be able to execute entire campaigns independently
  • Sales representatives should develop SE (Sales Engineering) capabilities
  • Everyone should become more "full stack" and outcome-oriented
  • "The ideal person may be specialized and deep in one dimension but well-rounded enough to be dangerous on the other two"

Experimentation Over Planning

• Prioritize vibes before evals

  • Start with open-ended exploration before defining metrics
  • "For a completely novel product experience or form factor, you should actually not start with evals and you should start with vibes"
  • Throw ideas against the wall to discover what works
  • Only formalize testing once you've identified promising use cases

• Ship to learn

  • Get capabilities into users' hands to discover use cases
  • "The best way to get AI value out there is experientially"
  • Prioritize PLG (product-led growth) to enable widespread experimentation
  • Focus on showing value in the product, not telling in presentations