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

"A lot of game theory is very technical, but it really can be reduced to one thing: play weaker players. Don't get into the ring with Mike Tyson." Alix Pasquet (@alixpasquet) is the Managing Partner & portfolio manager of Prime Macaya Capital Management, a behavioral long/short fund a decade in, built on refusing to fight the pods on their terms. "We don't try to compete with the pods. They're richer than we are, smarter than we are, move faster than we are, and in many cases better looking." We cover: - Strategy is trade-offs: no required management meetings, no alt data, no Wall Street research. "Let them have that, we'll be right here" - The time-horizon inefficiency small funds still own: 6-18 month dynamics the pods have abandoned - Pods are interviewing college sophomores at $300K+, so he waits for the analytical turnover and hires them after - What a top quant fund taught him: behavioral exploitation at scale, data scrubbing, transaction costs, and temporal stops (if the trade hasn't paid in a set time, get out) - When the fund wouldn't share its models, he mapped where its employees went to school and studied those departments' sciences - Idea generation: Phil Fisher found close to 80% of his best ideas came from other investors. Build the network, track the 13Fs - The top-100 CEO list, five years of crashing the same conference, and the "female test" that surfaced Lululemon - Why cheap stocks are cheap for terrible reasons now: the information environment that broke old-school value investing - Your network is your moat, because most hedge funds have restaurant-level failure odds and no terminal value - Margin of safety as a life principle: 3-5 years of personal cash, and the multibillionaire who still flies coach Highlights: 00:00 Intro 00:54 Three edges: analytical, informational, and behavioral advantage 09:18 Sponsor break 09:48 Reading for edge: the canon, timing, and change investing 16:40 What a top quant fund taught a fundamental PM 23:24 Data skepticism, alt data, and the quantification fetish 26:50 How small funds compete with multi-manager pods 30:51 Idea generation: networks, 13Fs, and colliding themes 44:44 People as indicators: bull-bear debates and cross-referencing 51:00 Why technical analysis works: a behavioral lens 58:52 The future of the analyst job in the AI era 1:06:54 AI blind spots: wicked environments and non-stationary markets 1:14:34 Personal competitive advantage: energy, power pairs, network as moat 1:23:41 Margin of safety as a life strategy