How we restructured Airtable’s entire org for AI | Howie Liu (co-founder and CEO)
Howie Liu, co-founder & CEO of Airtable, discusses reinventing his decade-old business for the AI era. He shares how CEOs are becoming ICs, restructuring into "fast" and "slow" thinking groups, and the critical skills product teams need to thrive in this rapid transition.
Deep Dive Analysis
12 Topic Outline
Introduction to Howie Liu and Airtable's AI Transformation
Addressing the 'Airtable is Dead' Viral Tweet Controversy
The Trend of CEOs Becoming Individual Contributors (IC CEOs)
AI's Paradigm Shift and Continuous Evolution in Product Development
Airtable's Organizational Restructure: Fast-Thinking and Slow-Thinking Teams
New AI Form Factors and Airtable's Vision for App Building
Empowering Teams Through AI Tool Experimentation and Play
Developing Cross-Functional Skills for Product Managers, Engineers, Designers
When to Use 'Vibes' vs. Formal Evals for AI Product Development
Key Strategies for AI-Driven Success and Company Reinvention
Counterintuitive Startup Wisdom: Staying Close to Product Details
Advice for Aspiring Engineers and Designers in the AI Era
5 Key Concepts
IC CEO
An 'IC CEO' refers to a CEO who acts as an individual contributor, getting deeply involved in coding, building, and leading initiatives directly. This approach is crucial in the AI era to stay relevant and continuously refine product-market fit by understanding product details intimately.
Fast Thinking Group
This is an organizational structure focused on rapidly shipping new, high-value AI capabilities, ideally on a near-weekly basis. The goal is to create 'jaw-dropping' value and generate top-of-funnel excitement and new use cases.
Slow Thinking Group
This group focuses on more deliberate, premeditated planning and execution, typically for foundational infrastructure or complex data systems that cannot be shipped quickly. It complements the fast-thinking group by allowing initial adoption seeds to grow into larger, more durable deployments.
Vibe Coding / App Building with AI
This concept describes the magical experience of using AI to build apps by simply describing what you want. As AI models improve, new form factors emerge, allowing users to generate complex code or full-stack applications agentically.
LLM Map Reduce
This is a capability that allows processing of large content corpuses that exceed an LLM's context window. It breaks content into chunks, performs an LLM call on each chunk, and then aggregates those results with another LLM call.
7 Questions Answered
CEOs should become 'IC CEOs' again, getting into the code, building things, and leading initiatives themselves, as AI necessitates being deeply involved in product details to continuously refine product-market fit.
Companies can restructure into 'fast-thinking' teams focused on shipping new, high-value AI capabilities weekly, complemented by 'slow-thinking' teams for deliberate, foundational infrastructure work.
Individuals in these roles need to become more cross-functional, developing a baseline proficiency in all three areas (product, engineering, design) to think full-stack and operate with more autonomy.
For novel product experiences, start with 'vibes' and open-ended experimentation to discover what works, then introduce formal 'evals' once a clear use case cluster has emerged.
Avoid stepping away from the product details and the core passion that founded the company, even as it scales, to maintain holistic vision and drive non-incremental innovation.
Actively play and experiment with various AI products, build personal side projects, and leverage AI tools themselves as expert tutors to bolster skills in engineering, design, and product management.
Using AI tools hourly and aggressively, even if 'wasteful' in terms of inference cost, provides invaluable strategic insights and a deep understanding of what's possible, far outweighing the cost.
28 Actionable Insights
1. Re-found Mission AI-Native
If your existing company cannot execute its mission with a fully AI-native approach, consider selling it and starting a new, AI-native version of that mission.
2. Clean Slate AI Mission
For existing companies, take a clean-slate approach to your mission by imagining how you would execute it using a fully AI-native approach if starting from scratch today.
3. CEOs: Become ICs Again
CEOs should revert to an “ICCO” (Individual Contributor CEO) role by getting hands-on, building, and leading initiatives, especially in the AI era, to understand product details intimately.
4. Structure for Fast & Slow AI
Restructure your company into a “fast-thinking group” for rapid AI capability development and a “slow-thinking group” for deliberate, foundational work, to accelerate AI investments and ensure durable growth.
5. Embrace AI-Native Urgency
Adopt the intensity and urgency of an AI-native company, constantly asking if you are executing as fast and leveraging new capabilities as effectively as they are.
6. Aggressively Invest in AI Compute
Aggressively invest in AI compute cycles for high-value problems, even if seemingly costly, because the strategic insights gained can provide invaluable returns.
7. Break Down Role Silos
Break down role silos across all departments (e.g., marketing, sales) by encouraging individuals to become more “full stack” and outcome-oriented, reducing dependencies and increasing self-sufficiency.
8. Cultivate Hybrid Unicorn Skills
Encourage product managers, engineers, and designers to develop cross-functional skills, becoming “hybrid unicorn types” who can understand and contribute to adjacent disciplines.
9. Baseline Proficiency in All Roles
Aim to be at least “minimally good” at all three core product development roles (PM, engineering, design), even if specializing in one, to foster a multidisciplinary approach.
10. Embrace Growth Mindset in AI
Individuals across all roles (PM, engineering, design) should embrace a growth mindset and proactively learn new skills to become more effective and versatile in the AI era.
11. Stay Connected to Product Details
As a founder or leader, prioritize staying connected to the product details and the core work you love, even as responsibilities expand, to maintain passion and drive.
12. Explore AI Products & Side Projects
Continuously use a wide range of AI products (even outside your company’s offerings) and create small side projects to gain experiential understanding of new capabilities and form factors.
13. Experience New AI Releases
Stay constantly abreast of new AI product releases and capabilities by experiencing them directly, as the pace of innovation is weekly, not yearly.
14. Approach AI with Play & Curiosity
Encourage a mindset of “play” and curiosity when exploring AI tools, rather than just checking a box, to foster deeper learning and discovery.
15. Build Fun & Useful Projects
Choose personal or work projects that are both useful and enjoyable to build, as this intrinsic motivation will drive deeper learning and skill development in AI tools.
16. Build Your Own Projects
To truly learn and develop product sensibilities, actively engage in building your own projects through trial and error, rather than just observing others’ work.
17. PMs: Become Hybrid Prototypers
Product Managers should develop hybrid skills, becoming prototypers with strong design sensibilities to thrive in the AI era.
18. Prioritize Interactive AI Demos
Prioritize interactive prototypes and demos over static decks or PRDs when evaluating AI product ideas, as the real proof and feel of an AI product comes from direct interaction.
19. Embrace AI Experimentation & Iteration
Shift from deterministic, timeline-driven execution to a model of constant experimentation and iteration, especially for AI product development.
20. Start with Vibes, Not Evals
For novel AI product experiences, prioritize “vibes” (open-ended, broad testing) over formal evals initially, to discover what works and understand the range of possibilities before narrowing down.
21. Use Evals for Iterative Improvement
Once a product’s basic form factor and core use cases are established, use evals for iterative improvement and empirical testing (like A/B testing) to refine performance.
22. Ship Rapidly, Cohesively
Ship major new product capabilities on a near-weekly basis, aiming for a cohesive product experience that moves at a breakneck pace, like AI-native companies.
23. Block Time for AI Play
Encourage employees to block out dedicated time (a day or a week) to play with various AI products relevant to the company’s domain, fostering curiosity and learning.
24. Reduce Standing 1-on-1s
Reduce standing one-on-one meetings to free up time for more timely, urgency-driven, and insight-informed discussions, fostering agility and responsiveness.
25. Prioritize Urgency-Driven Meetings
Prioritize meetings that are urgency-driven and informed by timely, high-value insights (“real alpha”) to maximize their impact and avoid unproductive discussions.
26. Sell If No AI Advantage
If your existing product or business doesn’t offer a clear advantage for an AI-native approach to your mission, consider selling the company and re-founding the mission with a fresh, AI-native start.
27. Use AI as an Expert Tutor
Leverage AI tools like ChatGPT as an “expert tutor” for learning new skills (e.g., software engineering, design), asking open-ended questions to understand how to build things.
28. Cultivate Humility & Gratitude
Approach life and work with a spirit of humility and gratitude, as this mindset fosters open-mindedness, attracts opportunities, and can become a self-fulfilling prophecy for positive outcomes.
6 Key Quotes
Regardless, I think that now we're entering this moment where like every, certainly every software product in my opinion has to be refounded because like AI is such a paradigm shift.
Howie Liu
I'm proud to say like I am, I'm pretty sure I'm still the, I just checked this recently, but like I take pride in being the number one most expensive in inference cost user of Airtable AI, not just within our own company, but I think for a long time I was globally across all our customers as well.
Howie Liu
If you want to 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.
Howie Liu
I think for a completely novel product experience or form factor, you should actually not start with evals and you start with vibes.
Howie Liu
Don't step away from the details that both you love.
Howie Liu
Everyone can learn how to be a versatile, you know, kind of unicorn, like product engineer, designer hybrid in the AI native era. And, and like the only thing stopping you is like just going out and doing it.
Howie Liu
1 Protocols
Howie Liu's AI Learning & Experimentation Protocol for Employees
Howie Liu- Cancel all meetings for a day or an entire week to create dedicated time.
- Go play around with every AI product that you think could be relevant to the company.
- Approach experimentation with curiosity and a spirit of exploration, not just to check a box.
- Share findings, links, and screenshots of what you're doing and learning with others.
- Prioritize building actual interactive demos and prototypes over writing extensive documents or PRDs.
- Focus on continuous experimentation and iteration rather than rigid, deterministic timelines for execution.