Everyone Builds, Ships, and Sells. Winners Do It Differently. | Kimberly Tan, a16z Investing Partner
In a Nutshell
Enterprise AI success demands deep customer immersion and industry-specific knowledge, as base models alone cannot capture the tacit business context, regulations, and workflows needed for production value. The most effective founders embed directly with customers—through forward-deployed teams and vertical focus—to translate unspoken requirements into AI systems that deliver measurable ROI, such as higher resolution rates in support or faster emergency response. Non-deterministic AI requires sophisticated model routing, human oversight, and a clear pilot-to-production path; winners combine technical excellence with relentless execution and patient, founder-aligned investors.
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GPT wrapper was a very pejorative term used to describe a lot of the early AI application companies. The capabilities out of the box are not the same as the capabilities needed to actually show value to an end enterprise buyer. There's so much work between the base model and the end client that needs to get done. A lot of people underestimated just how much work there needed to be done.
There's so much knowledge that exists in people's heads that are not on paper, they're not ingestible by the models, that you just need someone to sit down and talk to the customer and understand what it is they're trying to do and then map that out. The only people who will understand it are the founders or the employees of startups who go to the customer site and take the time to actually learn these things.
Early-stage founders building enterprise AI should fly to their customers, sit next to them, and really understand to the depth that you can. Being the industry-focused solution gives you a ton of leverage in knowing exactly what you need to build for them. Being able to build your product in a very specific way that works exactly for their needs has a lot of value.
Kimberly Tan is an investing partner at Andreessen Horowitz. She's been at the firm now for over 6 years, all early-stage B2B software investing, today focused a lot on applied AI. She's had the privilege of working with a lot of companies including Decagon, Prepared, Menlo, and Solo.
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