On Saturday, my good friend Chidi Erike (in partnership with Duke University - The Fuqua School of Business) organized an event at his home in LA representing some of the most talented companies and tech leaders in our space today.
AI was naturally a major topic of conversation, especially among companies building genuine AI products. A recurring discussion revolved around choosing between open-source and closed-source LLMs, and their respective pros and cons. After several insightful dialogues, I wanted to share my perspective.
Open-source represents the long game. It offers strategic ownership, cost control at scale, extensive customization, and enhanced data privacy. You control your infrastructure, and your technology stack can become a significant differentiator and a competitive advantage. However, the initial engineering investment and ongoing resource commitment can be substantial.
Closed-source (proprietary) solutions offer a faster route to market, exceptional performance, simplified deployment, and fewer infrastructure concerns. However, costs escalate rapidly at scale, and there's significant dependency on external providers, including vendor lock-in and limited control over data security and compliance.
My CTO Vishaal captured it well when he said, "Hybrid is the move. Proprietary makes sense for speed, reliability, and excellent user experience. Open-source works best when strategic control, sensitive data handling, and long-term cost management matter most."
From my viewpoint, successful AI companies will increasingly adopt this hybrid approach, balancing quick market entry with sustainability and immediate user needs with long-term strategic advantages.
I'm interested to hear how others are tackling this choice. Are you opting for open-source, closed-source, or a hybrid strategy?
And great discussing these topics with others leaders in the house including Chibuzor “Obi” Ugokwe, MBA, Trevor Fay, Rick Lucas, and Nate Mitchell!
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