I think the small, concentrated, high-conviction fund thesis is increasingly hard to square with what has actually happened in the industry. LP dollars have been moving toward passive, systematic, market-neutral and multi-manager structures while traditional LO/Tiger/mid-net L/S has steadily lost share. More importantly, the success of the pod model has already shown that fundamental investing is not some intractible problem. You can break the process into forecasts, exposures, risk budgets, sizing rules while reducing many of the behavioral and portfolio construction problems inherent to the traditional Tiger model. Acadian's work ( supports these same findings: concentrated portfolios have not produced consistent alpha, while more systematic processes can scale through greater breadth. If quarterly fundamental alpha can be industrialized this way, I don't see why longer-horizon fundamental alpha should remain permanently exempt, particularly when prop firms are spending billions on compute, data, domain-specific transformer architectures while continuing to push their forecast horizons outward. The chart below makes the concentrated portfolio argument even harder because the market has become materially more factor driven through most of recent history. A 10-20 name portfolio can therefore very easily amount to a few large latent factor bets with idiosyncratic noise layered on top. This is also where I think the idea that the answer is a smaller fund with more conviction starts to break down. If the data show that concentrated fundamental portfolios are not producing durable excess returns in an increasingly factor-driven environment, then the natural advantage shifts toward firms capable of extracting many smaller signals, controlling their common exposures and combining them systematically rather than relying on a PM's ability to identify 15 exceptional stocks. I also strongly disagree that fixing this is as trivial as onboarding Arcana and building an automated factor hedging layer. The software interface may be easy; building a risk system that is actually useful in production is not. Off-the-shelf models have materially underpredicted realized volatility in 2026, which pushes you into needing custom factors and covariance estimation and the much harder question of whether the factor structure you are using in your off the shelf model is even correct. Axioma itself distinguishes estimation error from specification error and shows how basic choices around estimation window, frequency, weighting, outliers and autocorrelation materially change the resulting exposures. Then you still need to turn those forecasts and risk estimates into positions. Portfolio optimization is a separate technical discipline involving constraints, turnover, estimation error, transaction costs and the interaction between all of them. None of that is trivial, particularly inside concentrated fundamental funds where many PMs remain skeptical of factor models in the first place. The capacity problem compounds this. Passive and pod capital are heavily concentrated in liquid large caps, so the obvious place for a small fundamental fund to look for less competed longer-horizon alpha is further down the capitalization and liquidity spectrum. But a 10-20 stock portfolio deploying meaningful capital into small/mid/micro caps is going to move price against itself both entering and exiting positions. At that point market impact and optimal execution are part of the alpha model rather than implementation details because they determine how much of the forecast survives into realized PnL. MOSEK treats transaction costs, market impact and portfolio constraints as explicit parts of the portfolio optimization problem for exactly this reason. So the concentrated fundamental shop ultimately ends up needing custom risk models, portfolio optimization, impact modeling and execution research anyway. Which are precisely the capabilities where the large prop firms have spent years building an industrial advantage, which is why I think the more likely endpoint is that they continue moving outward in horizon and subsume more of the longer-horizon fundamental alpha rather than leaving it permanently protected for small discretionary funds. Just my take though, would be interested in @TheStalwart @tracyalloway @__paleologo to opine as well

1/2 Lets think about this from the ground up. You are a modal L/S HF and you want to make sure you will be around in 10 years, so you decide you need to make some serious changes. You probably have never hired anybody technical or you have less than a handful of technical people of questionable quality on staff to do this (maybe a DS/SDA + a SWE). You will have to outlay significant upfront cost to build infra + hire expensive technical talent in a market where you are much more likely to be outbid for high quality talent (and mid to low quality is arguably worse than nobody here). While prop firms have a longer time horizon to do this, you will need to convince LPs and GPs substantially invested in the fund that it is worth the firm's time and energy to undergo this transition and expense + you will have to convince your LPs that you will be able to pull this off without it seeming like a red flag and strategy drift. Say you do all that, you hire 1-2 actual QRs, 1-2 actual SWEs, nice to haves would also be a dedicated risk quant + microstructure quant/qt instead of relying on just the manual execution trader(s) you probably have. Now you have to go about the process of building the commercially useful things without burning too much time + adding value to the desk so your LPs and GPs maintain faith in what you are doing. You will need to build or buy a risk model and a portfolio optimizer but what's this, if you want to do optimization correctly you will need to calibrate a market impact model because you are probably overtrading and have never really thought about TCA, temporary impact, and permanent impact so you will need to procure or generate a dataset of your trades and the market's response to them only to find out that your long horizon alphas would have a better transfer coefficient if you overlaid short term execution alphas. But then you run into a few problems: 1) doing research at the microstructure scale requires much more complex infra, larger and more expensive datasets, 2) Do do this effectively you essentially need a team dedicated to stat arb style signal research but you are just a MT/LT fundamental investor, 3) even if you were able to put a small team on optimal execution signal research + monetization you will come to the realization that you are trading against counterparties at this frequency that can outcompete you on both speed and cost fronts since they are market makers/HFT firms with rebates and colocation you are unable to afford or procure. What this optimal path ends up looking like is exactly a prop firm and why I believe they will outcompete even the Citadels/Millenniums/Balys of the world