Private markets are entering a new phase of concentration. Capital is flowing into an increasingly smaller group of large-scale managers, fundamentally reshaping the competitive landscape.
Over the last year, the top-10 private equity funds took the largest share of US fundraising in more than a decade, accounting for 46% of all US private equity capital raised.
As private market allocations continue to expand globally, an outsized proportion of that growth is being directed towards these so-called “megafunds”.
This marks a structural shift in how private markets operate. Scale now brings operational advantage, and operational capability has become a deciding factor in capital allocation.
Operational standards are rising
Private market allocations are set to surge 70% to nearly $24 billion by 2030. A huge portion of this growing pool of capital is being directed to megafunds.
Megafunds have invested heavily in institutional-grade operating models. They have centralised data architectures, automated workflows, integrated portfolio monitoring systems and real-time reporting capabilities. This infrastructure enables faster decision-making, more robust valuations and greater transparency for limited partners (LPs).
LPs are now placing greater emphasis on operational due diligence, data governance and reporting transparency. Allocations are increasingly influenced not only by track record, but by a manager’s ability to demonstrate institutional resilience and scalability.
For smaller managers, this shift raises the competitive baseline.
Many still rely on spreadsheets and PDF-driven reporting, manual NAV calculations and fragmented systems across service providers. These processes are error-prone, difficult to audit and don’t offer the transparency and analytical depth LPs now expect, leaving smaller managers susceptible to being screened out before performance is even assessed.
In today’s environment, operational weakness can prevent a fund from reaching the performance discussion at all.
Data transparency becomes a competitive necessity
As LP scrutiny intensifies, data quality is emerging as one of the most pressing operational challenges in private markets. Inconsistencies across reporting, valuation and portfolio data are no longer viewed as minor inefficiencies, but as indicators of operational risk. Managers that cannot present clear, coherent data often face longer due diligence processes and more searching questions during fundraising.
This also means that GPs can only offer limited transparency as to where capital is actually invested, pushing data standardisation and governance to the top of the agenda for LPs.
In a concentrated market, this is a material competitive disadvantage.
For smaller managers, the objective is not to match the scale of megafunds, but to put the right operational building blocks in place. Consistent data definitions, aligned reporting frameworks and stronger governance enable managers to meet rising LP expectations and engage more confidently during due diligence. Without this clarity, even compelling performance stories can struggle to cut through.
Managers that delay modernising their data infrastructure risk falling behind as megafunds continue to raise investor expectations around transparency and valuation accuracy.
AI and automation distinguish market leaders
For many of these smaller firms, AI could hold the operational key to competing in a more concentrated private markets landscape. AI can speed up due diligence, improve forecasting, strengthen valuation accuracy and help teams do more with fewer resources.
AI’s ability to analyse vast volumes of information across inconsistent formats and turn it into usable insight is unparalleled. Deploying it compiles more reliable datasets for investment teams, supports stronger decision-making, and enables more accurate and transparent reporting back to LPs.
However, technology alone does not solve structural weaknesses. AI systems are only as effective as the data underpinning them. Firms with fragmented or poorly governed datasets will struggle to extract reliable insights. Without disciplined data architecture, automation initiatives risk becoming costly experiments rather than strategic advantages.
To make full use of AI, firms must ensure they’re building AI initiatives on the entire picture of asset data and using consistent formats.
Modern operating models are now essential
For smaller managers, there are only two paths forward. They must either modernise their operating models to meet rising institutional standards or differentiate through deep specialisation that justifies structural differences.
Those that invest early in data governance, scalable systems and operational resilience can compete on equal footing, regardless of fund size. Those that delay risk being marginalised because they fail to meet the new baseline expectations of capital allocators.
In an era defined by scale, transparency and institutional discipline, modernisation has become the price of admission.
Gulsey Torenli is Senior Director of Private Equity & Private Debt at Vistra Fund Solutions
