Everyone Builds, Ships, and Sells. Winners Do It Differently. | Kimberly Tan, a16z Investing Partner

Kimberly Tan is an Investing Partner at Andreessen Horowitz, focused on early-stage enterprise and applied AI, with investments including Decagon, Prepared, Mem, and Sola. In this conversation, Kimberly breaks down why building AI is a different game, and the companies that prove it: - Building: the AI products that win aren't the best demos, they're the ones that go to the customer and forward-deploy. She saw it early in Prepared, which knew the 911 market better than anyone and was acquired by Axon for $640M. - Selling: enterprises don't buy AI on promise, they buy proven ROI. That's how Decagon, which she backed before it even had a name, grew into a $1.5B company. - Automation: AI won't do everything, and the winners know where a human stays in the loop, like Sola, her bet on automating the back office without taking people out of it. Enjoying our video? Now find us in our magazine. → https://www.eomag.io/?utm_source=youtube&utm_medium=description&utm_campaign=midroll 00:00 Intro 02:27 Build Your Moat at the Customer's Desk 07:24 Meet EO Magazine 08:02 Don't Sell AI, Sell Proven ROI 11:23 Know Where Automation Should Stop 13:48 On Your Side, Not on Your Back EO is a global media brand for builders. We tell the defining stories of founders shaping the future: people who see what others don’t and build what they believe in. Subscribe to EO: https://www.youtube.com/@eoglobal EO Magazine: https://www.eomag.io Instagram: https://www.instagram.com/eostudio.official/ X: https://x.com/eostudi0 LinkedIn: https://www.linkedin.com/company/eo-studio EO Studio: https://eo.team/ Business inquiries: partner@eoeoeo.net Build what you believe in.

Kimberly Tanguest

Sep 3, 202617mWatch on YouTube ↗

EPISODE INFO

Released: September 3, 2026
Duration: 17m
Channel: EO Studio
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Kimberly Tan is an Investing Partner at Andreessen Horowitz, focused on early-stage enterprise and applied AI, with investments including Decagon, Prepared, Mem, and Sola. In this conversation, Kimberly breaks down why building AI is a different game, and the companies that prove it:
Building: the AI products that win aren't the best demos, they're the ones that go to the customer and forward-deploy. She saw it early in Prepared, which knew the 911 market better than anyone and was acquired by Axon for $640M.
Selling: enterprises don't buy AI on promise, they buy proven ROI. That's how Decagon, which she backed before it even had a name, grew into a $1.5B company.
Automation: AI won't do everything, and the winners know where a human stays in the loop, like Sola, her bet on automating the back office without taking people out of it.
Enjoying our video? Now find us in our magazine. → https://www.eomag.io/?utm_source=youtube&utm_medium=description&utm_campaign=midroll 00:00 Intro 02:27 Build Your Moat at the Customer's Desk 07:24 Meet EO Magazine 08:02 Don't Sell AI, Sell Proven ROI 11:23 Know Where Automation Should Stop 13:48 On Your Side, Not on Your Back EO is a global media brand for builders. We tell the defining stories of founders shaping the future: people who see what others don’t and build what they believe in. Subscribe to EO: https://www.youtube.com/@eoglobal EO Magazine: https://www.eomag.io Instagram: https://www.instagram.com/eostudio.official/ X: https://x.com/eostudi0 LinkedIn: https://www.linkedin.com/company/eo-studio EO Studio: https://eo.team/ Business inquiries: partner@eoeoeo.net Build what you believe in.

SPEAKERS

Kimberly Tan
guest
Investing Partner at Andreessen Horowitz (a16z) focused on AI and application-layer software investing.

EPISODE SUMMARY

In this episode of EO Studio, featuring Kimberly Tan, Everyone Builds, Ships, and Sells. Winners Do It Differently. | Kimberly Tan, a16z Investing Partner explores enterprise AI winners win on customer proximity, ROI, and deployment rigor Kimberly Tan explains why early “GPT wrapper” critiques underestimate the engineering, workflow, and deployment work required to turn base models into enterprise-ready AI products.

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