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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 (acadian-asset.com/investment-ins…) 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
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
I have been of the view that the exact opposite will happen.View quoted post on X
@pmje73 recently tweeted a view that he believes concentration will tick back up, and I couldn’t agree more.
Experienced fundamental investors will know the degree to which your tier 2 and tier 3 ideas act as a tax on your best 10-15 best ideas. How good is your 67th idea? Generally pretty crappy. Yet the idio-based risk models have demanded breadth for risk management purposes, so many stock pickers have been forced to play that game. I lived this personally going from a seat where I ran 6x12 (and consistently generated double-digit spread) to seats where I had to run 30x70 to make the risk model happy (and could never figure out how to scale research depth needed to have alpha in my 67th best idea).
Strip out a talented stock picker and just have them focus on one thing: give me your 15 best ideas. I can’t logic through how that approach to portfolio construction doesn’t get meaningfully more compelling given the evolution of tool set and current state of equity markets:
1) the ability to build scaled research systems distilling then tracking the 15 best ideas from 8,000 stocks has been transformed with AI…a single stock picker / PM can truly now have close to institutional-grade research intelligence, which was never the case before
2) historical periods of index concentration have been compelling starting points for bottom’s up stock picking / and with more asset classes facing high valuations and low forward return prospects, the ability to find 15 great equity ideas that can CAGR 15%+ for 15 years is a more compelling value proposition to LPs on a relative basis. There are 26.6 quatturodecillion (!) 15 stock portfolios I could construct from 8,000 underlying global equities, and, definitionally, roughly half of those will outperform a broad global index. AI can’t and won’t change this fundamental reality of dispersion (will probably make overshoots and factor/thematic violence worse, a benefit to the longer duration investor). People will do this in regional banks, Nordic small caps and thousands of other approaches that aren’t necessarily owning AI winners. There’s always a bull market somewhere.
3) Agree with @0xFaust12 point on risk systems, but the friction to onboard a tool like Arcana and build an automated AI factor hedging system is now fairly trivial and with relatively little effort can take that 15 stock portfolio that probably looks like a giant factor bet into a tapered idio-primary portfolio. The bottleneck is more awareness than technology.
There are lots of reasons there hasn’t been this explosion of talented stock pickers starting $100-$350m 15 stock concentrated long biased funds (coming from a guy who thought about building one in 2018, i can list all the reasons in detail). Specifically, many of the most talented stock pickers have had a durable bid from the multi-complex with compelling guarantee terms.
To me, what the Jane Streets (and many others) will be able to do is to start industrialize the broad alpha of the discretionary multi-manager. The same way that quant firms compressed alpha signal in alternative data, quant firms will compress alpha signal currently harvested by swarms of pod teams. Except for the fact that the large multi-managers are run by the sharpest, most adaptive people in our economy, to me the “peak pod” argument becomes more real over the next 3-10 years. And, multi-teams will be able to do with 8 investors what they historically did with 12. What do the 4 who attrit do?
@chamath had this idea a few years back where he gave small portfolios to single analysts/PMs. It was a super intriguing idea but the time and tooling wasn’t ripe. I think it’s ripe now.
Probably some centralization makes sense (build the AI and data pipelines centrally), whether it’s sponsored by a quant firm with a buy-side or by an LP directly. Absorb the displaced multi-manager talent and just ask them for 15 best ideas
