Episode 2 | Digging Your Moat: Customer Discovery + PLG for AI Startups with Rodrigo Liang (CEO/co-founder, SambaNova)

Idea-stage investors are still excited about AI, but the savvier ones are looking for social proof and tangible takeaways from the customer discovery process. Before they commit, these investors are seeking evidence that the idea/team they’re considering won’t melt like snow in the rain the next time a major player improves their model.

With that in mind, I interviewed Rodrigo Liang, co-founder and CEO of SambaNova Systems, for Episode 2. Launched in 2017, SambaNova raised more than a billion dollars in its first three years to create a full-stack LLM platform.

Our conversation covered a lot of ground, including his thoughts on product-led growth strategy for AI startups and the importance of aligning the needs of customers with the technology you’re developing.

Here’s a breakdown:

• SambaNova's founding, market dynamics, and early product development (1:22)
• AI in enterprise environments, developing an ideal customer profile (4:23)
• how to ID different customer classes (6:47)
• developing a cohesive marketing strategy from Day 1 (10:09)
• keeping product development aligned with the needs of your customer (14:11)
• the pitfalls (and payoffs) of creating a feature for a single customer (17:13)
• customer transparency: can you/should you share your product roadmap? (20:28)
• overcoming AI adoption challenges and controlling costs (24:24)
• understand your customer's journey to deliver value quickly (28:21)
• "we really are focused on getting those quick proofs of success” (30:09)
• Rodrigo's advice for early-stage AI startup employees (35:45)
• early-stage fundraising should be about milestones, not runway (38:17)

Fund/Build/Scale is sponsored by Mayfield. If you have a fundable idea for an AI-first startup at the cognitive plumbing layer, email aistart@mayfield.com.

The podcast is also sponsored by Securiti, pioneer of the Data Command Center.

Follow Fund/Build/Scale on Substack and LinkedIn.