Scale via sample size and systematic collection

Check out Carbon Arc here: https://www.carbonarc.co/

Kirk McKeown, founder and CEO of Carbon Arc and former senior investor-facing operator across Glenview and Point72, on how alpha migrates as market structure, tooling, and competition evolve. What most investors misunderstand about “edge” is that it is rarely static and often lives in process design, information capture, and interpretation of small narrative inflections. Why hit-rate systems, decision trees, and data structure matter now as models commoditize and the marginal advantage shifts toward differentiated inputs and synthesis.Kirk started his career at Tudor Investments during the late-1990s cycle, then worked at Glenview Capital under Larry Robbins where he built and led primary research capabilities supporting a concentrated, long-horizon portfolio process. He later spent 8.5 years at Point72 supporting a multi-manager environment optimized around catalyst-driven, variant-view investing, high at-bat volume, and repeatable organizational process. Across these seats, he worked directly with investment teams on improving idea generation, hit-rate, and conviction through compliant information collection, supply chain and value chain work, and rigorous feedback loops.

In this episode we cover:- Why alpha “moves” over time and how competitive advantage migrates with market structure and tooling- Hit-rate vs slugging frameworks across concentrated portfolios and multi-manager platforms- A research function’s only mandate: lift idea flow, hit-rate, or conviction without contaminating decision-making- Building edge via compounding domain knowledge, field research, and leading indicators before consensus data prints- “Main Street becomes Wall Street”: model-driven decisioning, data decimalization, and pricing data like a utility- Inventory as the core causal variable behind boom-bust cycles in fundamentals and supply chains- Factor frameworks as a scaling mechanism for research: market structure, business model, and decision-tree priors
Timestamps:(00:00) Intro(04:47) Tutor vs Glenview vs Point72: how edge differs(12:29) How to build “lift” for PMs: at-bats, hit-rate, sizing(18:44) Building research edge: outwork, read, fieldwork(27:16) Personal moat in 2026: analogs, history, decision trees(40:08) “Main Street becomes Wall Street”: what that actually means(44:30) Carbon Arc thesis: “decimalization” of data market structure(46:43) Why the edge migrates to data plus domain context(51:00) How to win in commoditized research: sample size beats anecdotes(01:03:26) Factorizing everything: themes, market structure, business models(01:08:37) Pruning decision trees: signals, scale points, inventory dynamics(01:14:18) Contrarian 2026 take: hedge funds launching enterprise AI labs(01:23:32) Final question: one habit to build career alpha

Follow Kirk McKeown:LinkedIn – https://www.linkedin.com/in/kirk-mckeown-400607214/