Executive Dinner & Discussion

Experimenting with AI: How Product and Engineering Leaders Are Moving Faster

Thursday, May 21, 2026  ·  6:30 – 9:30 PM

A small, invitation-style dinner for product and engineering leaders in San Francisco. We'll get into the real conversations: how teams are using AI features and A/B testing together to learn faster, ship smarter, and stop guessing.

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Accelerate growth
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Venue

The Workshop at The Progress

A private dining room above one of SF's most celebrated restaurants. Handmade furnishings, original artwork, a library lounge, and windows throughout. The kind of space that makes people want to stay and talk.

24+ native SDKs designed for performance with 50% smaller footprints and zero network calls.

24+ native SDKs designed for performance with 50% smaller footprints and zero network calls.

24+ native SDKs designed for performance with 50% smaller footprints and zero network calls.

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Cozy restaurant interior with wooden tables and chairs, set with plates, glasses, and flowers, illuminated by ceiling lights.

What to Expect

This is a small, curated gathering, not a conference. You'll sit down with a group of peers who are actively working through the same challenges around AI rollouts, experimentation culture, and product velocity.

Agenda

Dinner, drinks, and a featured customer story from a team that's doing it well (TBA). Then open discussion.

6:30 PM - Arrival & cocktails - informal networking
7:00 PM - Dinner begins - family style
7:30 PM - Featured customer story
8:00 PM - Open discussion - facilitated but not scripted
9:30 PM - Wrap up - take the conversation to the bar if you want

Who Should Attend

Senior product and engineering leaders at growth-stage or enterprise companies who are navigating AI adoption and experimentation.

VP / Director of Product
Head of Engineering
Growth & Experimentation Leads
CTO / CPO
Staff / Principal Engineer

Topics on the table

AI feature rollouts

How to safely ship AI features without flying blind, using flags and staged rollouts to manage risk

Measuring AI impact

What metrics actually matter when you're testing an AI feature vs. a traditional product change

Speed vs. rigor

How fast-moving teams are maintaining experimentation discipline without slowing everything down