Weekly AI Sprint Check-ins Drive Urgency

In Howie Liu's view, the AI revolution demands a fundamental rethinking of how companies operate, requiring leaders to become hands-on builders again rather than distant managers. This shift isn't just about adding AI features—it's about reimagining your entire company as if you were founding it today in an AI-native world.

Liu believes the most successful leaders in this era will be those who immerse themselves in the details and actively use AI tools daily. He personally spends hundreds of dollars on inference costs to extract insights from sales call transcripts, viewing this as trivial compared to the strategic value gained. For Liu, this hands-on approach isn't micromanagement but rather the only way to truly understand what's possible and make holistic decisions that connect different parts of the organization.

To accelerate Airtable's AI transformation, Liu reorganized the company into two distinct groups: a "fast thinking" team focused on shipping new AI capabilities weekly, and a "slow thinking" team handling more deliberate, infrastructure-focused work. This structure allows for both rapid experimentation and thoughtful foundation-building, with each complementing the other.

Liu encourages his team to "play" with AI tools rather than just using them functionally. He tells employees they can cancel meetings for a day or even a week just to experiment with AI products, believing this exploration is essential for understanding possibilities. He's shifted from standing one-on-ones to more timely, insight-driven meetings that can immediately apply new learnings.

For product teams, Liu sees the boundaries between roles blurring. The most successful people will be those who can cross traditional boundaries—PMs who can prototype, engineers with design sensibilities, designers who understand technical constraints. He advises everyone to become "minimally good" at adjacent disciplines while maintaining depth in their specialty.

When developing new AI capabilities, Liu recommends starting with open-ended experimentation before moving to structured evaluations. He believes you must first understand what's possible through creative exploration before you can define what "good" looks like for systematic testing.

The core lesson from Liu's journey is not to step away from the details that made your product successful in the first place. While scaling requires new skills and responsibilities, maintaining connection to the product itself—especially during transformative periods like the AI revolution—is essential for continued innovation and finding new forms of product-market fit.