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Long Only Funds

I had the good fortune of sitting in on Abdiel Capital's info session on campus. Abdiel is a hedge fund that only takes long positions, and they are one of the rare, deeply concentrated value-oriented firms with an exceptional, tight-knit team. They focus heavily on understanding product development, founder mission, and the underlying mechanics of a business. Their track record speaks for itself. If you invested one million dollars as an LP nineteen years ago, you would be sitting on roughly twenty-five million today.

I have been increasingly drawn to long-only investing as a discipline. The detail orientation, the iterative discovery, and the sustained time horizon required to truly understand a business are incredibly appealing. Duncan came across as especially methodical. We barely talked about valuation and instead spent our time discussing what it means to truly understand the organisms that companies are. He told the story of Abdiel’s work on Spotify, where the team built e-commerce stores on Shopify, BigCommerce, and Volusion to test and deeply understand the ecosystem before investing. I appreciated how much thoughtfulness and intellectual honesty that reflects.

The session also made me think about how aligned Abdiel's approach felt with my work this past summer at ICONIQ. Even though ICONIQ is growth-oriented and Abdiel is long-only public, both approaches hinge on the same core question: what company deserves to be number one fifteen years from now and why. Both emphasize deep expert calls, understanding products at a first-principles level, and forming a conviction that survives multiple layers of pressure testing. That overlap made the conversation even more meaningful for me. It felt like two versions of the same underlying craft, just at different stages of the company lifecycle.

The conversation also left me with a broader question. Could a long-only investor, applying the same evaluation principles they use for companies, objectively conclude that committing to a long-only investing career is the right choice in today’s climate. It is a decision that requires long-term conviction, clarity of edge, and an honest assessment of opportunity cost.

A couple of things Duncan mentioned stuck with me:

  • "Misjudgment of people is a bigger issue than overpaying."

    • My takeaway is that this might be true in practice, but it is also structurally easy to attribute mistakes to misjudging people. It can become a blanket explanation that is harder to rigorously diagnose than whether you simply overpaid. It makes me think about how to build processes that avoid hiding errors in that category.

  • "We have not seen AI explicitly improve companies’ processes or outputs yet."

    • My takeaway is that I agree on the near-term. But saying that AI has not materially improved performance today does not necessarily imply that it will not have massive long-term implications. A lack of impact today should not be used as the reason we are not worried about tomorrow. If anything, that is when fundamental shifts tend to sneak up on people.

Overall, the session made me more interested in long-only investing, and it pushed me to think more deeply about what real edge looks like, how rigorous true understanding must be, and what it would mean to make that bet at a career level.