In this episode of Terminal Value, I’m joined by Karl Alomar, Managing Partner at M13 and former Chief Operating Officer of DigitalOcean, to discuss where value may accrue as AI models become cheaper, why proprietary context and data matter, and what makes an application difficult to replace.
We cover DigitalOcean’s developer-first strategy and just-in-time capacity planning, the GPU shortages that gave neoclouds their opening, inference efficiency, model pricing, real-time data infrastructure, and AI products embedded in core business operations. Karl also shares his view on AI-enabled microbusinesses, the value of operating experience in venture capital, the tension between company-building and secondary liquidity, and the identity and security layers AI agents may need.
Terminal Value: https://www.youtube.com/@Terminal-Value-Podcast
Chapters
0:00 Why the real AI moat sits outside the model
0:35 Introduction to Karl Alomar and M13
1:30 Why DigitalOcean succeeded against larger cloud providers
3:50 GPU scarcity, neo-cloud economics, and capacity planning
13:50 How software can dramatically improve GPU efficiency
20:55 Open-source models, pricing pressure, and application-layer moats
31:45 Real-time data infrastructure and AI embedded in business workflows
38:40 How AI could reshape employment and create millions of micro-businesses
42:40 Why operator experience matters as venture funds get bigger
48:15 Private AI valuations, secondary markets, and the challenges of going public
57:40 How experience shapes investors and the value of ambitious young founders
1:01:05 Why AI agents will need identity, verification, and security
1:02:50 Closing takeaways
Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.