Both Sides of the Algorithm

Artificial intelligence has crossed the threshold in private markets from conference-panel abstraction to operational reality. Limited partners are no longer asking whether their managers use AI; they are asking how, and increasingly treating digital competence as a fundraising criterion. Nearly every LP surveyed this year expects AI in dealmaking, and enhanced, faster, more transparent reporting has overtaken returns as the single most-cited investor demand.

The adoption is concentrated where the work is most document-intensive. Due diligence leads, with roughly a third of managers reporting meaningful integration; deal sourcing, portfolio monitoring, and investor reporting follow at varying speeds. The productivity gains in narrow tasks are real and large — confidential information memorandum extraction compressed from days to under an hour, quality-of-earnings work completed nearly by half faster, first-draft investment committee memos cut from a working day to a couple of hours.

But 2026 also delivered the other side of the ledger. AI now sits inside private portfolios as an exposure, not just a capability. By regulators’ own count, AI-linked companies accounted for more than a third of private credit origination last year, up from roughly one in six over the prior half-decade. When a single product launch reignited fears that software business models could be disrupted faster than they can be underwritten, the equity of the largest alternative managers sold off sharply — a reminder that the market now prices AI as a credit factor.

For asset managers, wealth advisors, private banks, and family offices serving the United States, the U.S. offshore channel, and Latin America, the dual nature of AI defines the agenda. The opportunity is to deploy these tools to compress cost, sharpen sourcing, and meet rising client expectations for transparency. The discipline is to understand AI as a portfolio risk — concentration, correlation, and disruption that conventional models were not built to capture. This paper maps both, and closes with what it means for the region’s practitioners.

I. From Talking Point to Table Stakes

For three years, AI in private markets lived mostly in the future tense. That tense has changed. The defining feature of 2026 is not enthusiasm but expectation: limited partners have moved from curiosity to scrutiny. In this year’s LP surveys, roughly 99% of respondents said they want general partners to integrate AI into dealmaking, with 66% expecting it specifically in due diligence, 48% in sourcing, and 45% inside the investment committee process itself.

That expectation is paired with a parallel demand. Enhanced transparency and reporting is now the leading LP requirement, cited by roughly three-quarters of investors — ahead of fees, ahead even of returns in some surveys. Where allocators were content a year ago with quarterly statements, they now expect near-real-time information and treat the speed, accuracy, and timeliness of data as a proxy for a manager’s operational maturity. AI is the only plausible way most firms can meet that bar at scale. In practice, the two demands have fused: proof of digital competence has become part of the fundraising conversation, and managers that cannot demonstrate it are finding it harder to win commitments in a still-difficult capital-raising environment.

The result is a quiet reordering of competitive advantage. The edge is migrating from who has the best relationships to who can convert proprietary data and unstructured documents into decisions fastest. That is a different kind of moat, and it favors managers who started building data infrastructure early.

II. Where AI Is Actually Working

The honest picture of adoption is uneven, and the gap between narrative and practice matters. S&P Global’s 2026 survey of private equity managers found due diligence to be the most integrated use case, with about 31% reporting AI somewhat or fully embedded. Yet majorities still rated AI as ineffective for the harder, judgment-laden tasks — 64% for deal sourcing and 75% for portfolio monitoring. Adoption, in other words, is real but narrow, clustered in document-heavy work where the technology genuinely excels and thinner where it must substitute for judgment.

Within that narrow band, the productivity gains are striking. Analyses of generative AI in transaction workflows document the compression at each stage: extracting the contents of a confidential information memorandum, once a 10-to-40-hour task, now takes under an hour; quality-of-earnings and diligence work is completed roughly 46% faster with AI assistance; and the first draft of an investment committee memo has fallen from around 15 hours of analyst time to about two. These are not marginal efficiencies. They change how many opportunities a lean team can evaluate, and they shift the bottleneck from production to judgment.

Sourcing offers the clearest example of durable advantage from early investment. EQT’s Motherbrain platform, built beginning in 2016 and used to screen targets since 2018, now consolidates well over a hundred thousand data points to surface companies the firm might otherwise never see, pairing machine pattern-recognition with human conviction. Blackstone has run AI through its sourcing pipeline since the early 2020s. And in a signal of how strategic the capability has become, Alphabet has reportedly been in talks to license its Gemini models across the portfolios of firms including Blackstone, KKR, and EQT — a sign that the largest managers now treat foundational AI as core infrastructure rather than a vendor experiment.

III. The Other Dimension: AI as a Portfolio Exposure

If the first dimension of the AI story is capability, the second is exposure — and developments in 2026 brought it into sharper focus. The Financial Stability Board has noted that AI-linked companies accounted for more than a third of private credit deals in 2025, up from roughly 17% over the previous five years, and observed that a meaningful correction in AI-related valuations could weigh on credit outcomes for private credit investors. AI is increasingly not only something managers use, but something their portfolios hold.

That concentration drew attention early in 2026, when a broad re-rating of software equities — associated, by several accounts, with the launch of AI tools capable of automating parts of legal, sales, marketing, and data-analysis work — extended to the public valuations of several large alternative managers. Some investors began treating software exposure as a lens on credit risk, reasoning that AI can reshape a software company’s competitive position faster than a credit model is typically updated. With an estimated one-fifth of the past decade’s private credit loans extended to software businesses, the share prices of managers including Blackstone, Apollo, Ares, KKR, and Blue Owl softened for a period, even as their fundraising and deal activity remained solid.

The episode pointed to two themes. The first is that managers increasingly carry exposure on two sides: to the businesses AI may disrupt, and to the infrastructure — data centers, power, storage — that supports its growth. Notably, AI-infrastructure positions proved comparatively resilient even as software re-rated, a divergence likely to matter for allocation decisions. The second theme is underwriting discipline. Apollo, for instance, had already begun reducing its software allocation from roughly 20% toward 10% over the course of 2025 — an illustration of how treating AI as an underwriting variable, rather than only a tailwind, is becoming part of prudent practice.

IV. The Latin American and Offshore Lens

For practitioners serving Latin America and the U.S. offshore channel, the AI shift arrives on top of a market already in rapid transition. Regional asset management is expanding at roughly a 6.6% annual clip, private-client assets are growing about three times faster than institutional flows, and regional wealth assets under management are projected to climb from around $1.2 trillion in 2025 toward $1.4 trillion by 2030. Digital behavior is already entrenched: close to three-quarters of retail transactions in Brazil now run through digital channels, and robo-advisory and AI-driven personalization are reshaping how advice is delivered.

Yet the region remains a comparative latecomer on the production side of AI. Adoption is led disproportionately by governments, while many private firms lag — a gap that is itself the opportunity. The global industry is being explicitly rebuilt, in the words of one major consultancy report, for an “AI-first world,” and with private markets projected to account for more than half of global asset-management revenues by the end of the decade, the regional managers and advisors who adopt deliberately now will not merely keep pace; they will set the terms of competition locally.

The practical implication for advisors is twofold. On the opportunity side, AI-enabled platforms can lower the cost of serving the affluent and mass-affluent segments that are driving regional growth, and deliver the reporting transparency that increasingly sophisticated families now expect. On the risk side, advisors allocating clients to private credit and private equity must now ask managers a new generation of questions — not only how AI improves the manager’s process, but how AI exposure runs through the underlying portfolio, and how that concentration is being measured and hedged.

V. What This Means for Managers and Advisors

Three conclusions follow for professionals across the Americas. First, AI competence is becoming a condition of doing business, not a differentiator. The bar set by limited partners — faster, more transparent, data-rich reporting — cannot be met manually, and the managers who internalize that earliest will compound the advantage. Second, the most defensible gains today are in narrow, document-intensive tasks; firms should deploy aggressively where the technology is proven and remain candid about where human judgment still governs. Treating AI as augmentation rather than replacement is not caution for its own sake — it is where the current evidence points.

Third, and most consequential, AI must now be underwritten as a portfolio risk. The events of early 2026 showed how quickly an AI narrative can reprice assets that funds hold and lenders financed. Diligence frameworks need to capture which holdings AI threatens and which it lifts; concentration in AI-linked credit needs to be measured deliberately; and the divergence between fragile software exposure and resilient infrastructure exposure needs to inform allocation. The managers and advisors who hold both ideas at once — AI as the tool that sharpens every process, and AI as the risk that demands new discipline — will be the ones their clients trust through the cycle ahead.

A note on infrastructure. The same forces compressing diligence and reporting are reshaping how private-market exposure is packaged and distributed to the wealth channel. As AI lowers the operational cost of structuring, monitoring, and reporting on private assets, fintech infrastructure — including instruments such as Private ETNs — becomes a natural conduit for delivering these strategies to advisors and their clients with institutional-grade transparency. LYNK Markets builds in precisely this layer and watches these developments closely.

Fuentes

1. Private Equity International, LP Perspectives 2026 Survey; industry LP survey data on AI integration expectations (405 limited partners).

2. Dynamo Software, 2026 GP Survey; PitchBook, GPs resolution for 2026, 2026.

3. Financial Stability Board, on AI exposure within private credit origination, 2025-26.

4. HedgeCo Insights and Alternative Credit Investor, on the February 2026 software-driven repricing of alternative asset manager valuations.

5. S&P Global Market Intelligence, 2026 Private Equity Survey, April 2026.

6. McKinsey & Company analysis of generative AI in M&A workflows, 2025-26.

7. EQT, Motherbrain platform disclosures; Tech.eu, November 2025.

8. The Next Web, on Alphabet/Google omnibus Gemini licensing discussions with major private equity firms, 2026.

9. Morningstar and company disclosures on alternative manager portfolio positioning, 2025-26.

10. J.P. Morgan Private Bank Latin America; Mordor Intelligence, Latin America Wealth Management Market; regional digital-adoption data, 2025-26.

11. BCG, Global Asset Management Report 2026: Rebuilding Asset Management for an AI-First World; PwC 2025 Global Asset & Wealth Management Report.