Fund management is undergoing active restructuring

A structural change is brewing in fund management. Post internet, we saw “Democratization of Data” as every listed company’s filings, financials and management commentaries became available at zero or near-zero cost to every market participant. Now due to AI’s gradual adoption, we are seeing an “Abundance of Intelligence” bringing the same zero or near-zero cost advantage to research & number crunching that previously took days. We have tools that let you interact with bulky PDFs within seconds, do exhaustive research on any topic, turn raw data into visuals or even build financial models directly in Excel. I argue that this will diminish the pricing power of fund managers in public markets.

Pricing power of fund managers in public markets has anyway been shrinking. In the US, the average expense ratio of an equity mutual fund has fallen by 62% between 1996 to 2024 (Investment Company Institute). A manager running an active portfolio with fundamental analysis is competing with math driven factor strategies such as momentum or momentum-quality and is likely underperforming them over the long-term despite charging a higher fee. In fact, if our benchmark were not Nifty 500 but Nifty 500 Momentum 50, which has given a CAGR of 20.1% over the past 15 years, hardly any Indian equity PMS/AIFs or mutual funds would outperform. This is trailing data, but going forward we can expect Abundance of Intelligence to expedite this further. Forget quant models, even if the competitor is a high-agency, AI-native retail investor or a leaner low opex team picking stocks with fundamental research, it would be on level playing field compared to a large fund’s manager who may have only a marginal advantage except perhaps his experience/wisdom in the markets.

This will only solidify going forward. We already have AI-native funds such as AlphaFundAI that during backtesting found themselves in the top 0.1 percentile of hedge funds in the US. These funds with leaner teams will unleash an expense ratio war that traditional shops will inevitably lose unless they radically modernize, cut costs and compete on pricing.

Goldman Sachs estimates $500 billion in capex on AI in 2026, most of it going into data centers that train models powering both consumer and enterprise AI. Come 2026, public equity alpha is no longer a product of information; it is a product of computation. Consequently, pricing power is shifting to where “The Screen” cannot reach: Private Markets

Here, I use “Private Markets” as an umbrella term for private equity, real estate, infra and credit. I purposely refrain from using the term “Alternate Investing” as that includes public equity focused PMS or AIFs in the conventional legal framework.

Private Markets have different building blocks. One, “Network Asymmetry”. Unlike public markets, you cannot invest in any private company or infra project at a single click but rather require access to the deal which is a function of your network. Two, “Brand Asymmetry” as a poor brand reputation as a fund or fund manager means the company can turn down your capital even if you agree to their terms, especially in primary rounds. Three, “Value-Addition Asymmetry” which is what your fund brings to the table in addition to capital such as your ability to unlock doors with potential clients, other investors, senior operators or government, which is again ultimately a function of network. Four, “Information Asymmetry” as two funds investing in private companies may have vastly different data banks allowing one to make superior decisions over the other consistently. This applies to real estate, infra, and private credit too where data isn’t just “unstructured”, rather it’s “unrecorded” in public domains. Five, “Pricing Asymmetry” that creates a “Human in the Loop” necessity for negotiating complicated deal structures, terms, valuations etc. and navigating any special opportunities which effectively lead to a differentiated entry/exit price or XIRRs for different investment funds.

These asymmetries in private markets allow for a “complexity premium”, which is the ability to charge higher management + performance fees than funds investing exclusively in public markets.

Though I don’t see most private market funds (including VC/PE) outperforming N500M50 (the new benchmark) on a Gross IRR basis, the fact is that large institutions, family offices, sovereign wealth funds, etc. will continue to invest in these for diversification into different risk profiles anyway – and the asymmetry led “complexity premium” I spoke of will create fatter paychecks for their fund managers and teams.

Thanks to AI, private market teams will get leaner too which will allow them to offer better pricing to Limited Partners without eroding margins. However, public market funds will see razor thin pricing and turn into compute or index driven funds that will be an evolution over today’s passive strategies. Meanwhile, conventional fundamental research talent will move from public to private markets.

END OF ARTICLE