Fund.py

Hedge Fund Manager David Orr (@orrdavid) on why he’ll back a delivery guy over a Harvard grad, as long as the track record is real. David Orr runs Militia Capital, a ~$480M hedge fund. ~50% annualized, Sharpe 2. “If you didn’t go to Harvard, they’re not going to give you money. They’re gonna lead you on for years.” We cover: - Edge is just reps. He's placed a few hundred independent bets every year since 2019, more in one year than a lot of hedge fund managers get in a decade. - The two-hour rule: if he can't decide on a stock in an hour or two, he skips it. When a trade goes against him, he quits. "The market said I'm wrong, I'm wrong." - 500 positions in his partition and zero notes. Some of his best shorts are down 40% and he's forgotten what the company does. - The Gogo lesson: he spent 100 hours on a short and was right on the fundamentals (it's now $2), but it ripped to $20 first. All that research made him stubborn on the way up. - The AI risk system: it groups the book into custom baskets like "Banks Done Right" and "yield traps," recalculated daily. A 25% bet can quietly become 45% in two weeks. - How Militia recruits PMs: 95% of track records are junk, and one cherry-picker ran 30 accounts and sent the best one. The PMs who make it include a game show winner, a Spanish government worker and a programmer, with 0.15 cross-correlation. - The best short seller in the world, quietly: 40% a year short only for years, and he still gets a tiny allocation at his fund. - Why multi-managers' real business is marketing. Collect $2B a year in fees on $100B and it's "mission accomplished," whatever the returns. - The long/short tax trick: harvest losses on losing shorts, and never cover one that's down 90%. No tax is due until it's delisted, which can take 10 years. - $150M raised from one email asking LPs for $100M. He says he could raise a billion but won't, and admits they "must have gotten at least somewhat lucky." - His AI stock map: hyperscalers locked customers into take-or-pay deals at ~35% return on capital. If compute prices crash 80%, the end customers get wiped out, not the hyperscalers. - Is investing 99% talent? For big alpha, almost entirely. Pod shop alpha is teachable, maybe for 1 in 50 people rather than 1 in 10,000. Highlights: 00:00 Intro 00:44 How AI custom factor baskets track dynamic correlation in levered portfolios 03:24 Sourcing non-consensus PM talent and verifying live Sharpe track records 07:24 Structuring carry and tax deferral to attract elite proprietary traders 12:50 Why institutional multi-manager platforms fail to back pure alpha 20:22 The two-hour research limit: Avoiding sunk-cost bias in short selling 27:58 Momentum screening vs deep value: Why top longs hit new highs 29:40 Why traditional finance books fail and what operator biographies teach 37:16 Why rapid technological adaptation beats traditional economic moats 39:26 Analyzing the AI trade: Why hyperscalers offer superior downside protection 44:57 Internalizing stock borrow desks to maximize net-of-fee fund compounding 55:25 Avoiding confirmation bias and why shorting revenue growth fails 01:01:42 Pod shop leverage factories vs raw discretionary alpha generation 01:07:12 Unplugging from market narrative feeds to preserve independent conviction