Good morning, {{First Name}}! It’s Wednesday, and private markets have a measurement problem.

LPs now have roughly 13,000 managers to choose from, but increasingly less realized evidence when the next fund comes knocking. Meanwhile, AI is facing its own track-record test: adoption is everywhere, measurable returns are not. And as AI expands the attack surface, cybersecurity budgets are moving in only one direction.

Today, we’re breaking down the track record trap, AI’s shift from experimentation to ROI, and why smarter enterprise buyers could quietly pressure portfolio company margins.

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DATA DIVE

The number of active private market managers grew from roughly 3,700 in 2007 to 13,000 by 2022, a roughly 3.5x expansion in the universe LPs need to evaluate.

Context: More choice should make manager selection easier. Private markets have managed the opposite. The average PE manager returned to market after 4.7 years in 2025, while portfolio companies were held for an average 6.6 years and funds typically run for 10 years or more. LPs are often being asked to underwrite the next fund before the previous one has produced enough realized evidence to separate skill from luck.

Strategic Takeaway: Track record still matters, but waiting for certainty creates its own risk. By the time performance is fully realized, the team, strategy and opportunity set may already have changed. With thousands more managers competing for capital, LP selection increasingly comes down to identifying which capabilities behind past returns are actually repeatable.

The allocator edge may be less about finding the best historical number and more about knowing which evidence deserves to travel into the next fund. (Click HERE for full report)

EDITORIAL CONTENT BROUGHT TO YOU BY CAPLINK X PE150

Private equity has spent the past few years asking what AI can do. The harder question now is what it is actually worth.

The opportunity is no longer theoretical. AI is already entering the PE playbook through sourcing, pricing, predictive maintenance, personalization, and other operational levers. PE150 has previously tracked that shift from deal origination toward EBITDA creation.

But adoption and value creation are two very different things. Roughly 60% of companies have yet to realize measurable value from AI. That gap is where The Intelligence Dividend begins.

Over the coming weeks, we will examine the questions that matter once the pilot ends. Where does AI actually reach EBITDA? Which use cases scale across a portfolio? How should sponsors measure returns? And perhaps most importantly, will buyers eventually pay more for businesses that can prove AI driven performance?

The AI experiment is maturing. Now comes the investment committee question: show me the returns.

Join us in our AI / Data & Insight Private Capital Breakfast, an invitation-only gathering at London's May Fair Hotel.

TREND TO WATCH

Cybersecurity budgets aren't slowing down. They're accelerating.

BCG's latest CISO survey shows companies increased cybersecurity spending by 11% this year, up from the 8% growth reported by respondents a year earlier. And there’s little relief ahead: CISOs expect budgets to rise another 12% next year.

The catalyst is increasingly AI. As companies deploy more models, agents, and AI-enabled workflows, they’re also expanding their attack surface—and creating entirely new categories of risk. 89% of organizations surveyed have already experienced AI-enabled attacks, while 35% reported significant operational or financial impact.

For PE, that creates a two-sided opportunity. Cybersecurity is becoming a harder-to-cut operating expense across portfolio companies, while vendors serving cloud, data, identity, and AI security gain exposure to a structurally expanding budget pool. BCG finds 69% of CISOs expect to increase cloud and container security spending and 65% expect to increase data security spending.

Bottom line: AI may create efficiencies elsewhere in the P&L. In cybersecurity, at least for now, it’s creating a bigger bill.

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DILIGENCE CORNER BY 150 DILIGENCE

High retention does not necessarily mean durable economics. Sometimes it just means the customer has not renegotiated yet.

Procurement departments are becoming more strategic and more sophisticated. McKinsey found that procurement now manages 50% more spend per employee than five years ago, while two thirds of procurement leaders surveyed report directly to the CEO or CFO. Deloitte also reports that leading procurement organizations are using supplier and spend consolidation to hit cost savings targets.

That matters when diligencing a business selling into large enterprises. A customer can love the product and still demand lower pricing, consolidate vendors, reopen contracts or stretch payment terms. PwC specifically identifies extended supplier payment terms as a working capital lever for buyers.

There are real examples of the economics. McKinsey documented one procurement program that consolidated eight suppliers through competitive bidding, cut the supplier base in half and generated 10% cost savings for the buyer. Another procurement initiative generated 20% savings while concentrating spend among fewer suppliers.

The diligence question: Do not just ask whether customers will stay. Ask what happens to price, terms and share of wallet when procurement gets involved.

For PE investors, that distinction matters. Retention protects revenue. Procurement can still attack margin and cash conversion. (More)

MACROVIEW

The AI debate has focused heavily on job displacement. But the first measurable macro impact may be showing up somewhere else: productivity.

GenAI usage is already translating into meaningful time savings, particularly across knowledge-intensive jobs. Computer and math workers save roughly 2.5% of weekly work hours, followed by management at 2.2% and business & finance at 1.7%.

That matters in an economy where labor growth is slowing. If adding workers becomes harder, future GDP growth increasingly depends on getting more output from the existing workforce. In Solow terms, AI’s biggest contribution may therefore be increasing A — total factor productivity, rather than reducing L — labor.

For PE, the equation is similar: more output per employee means revenue can scale faster than headcount, creating operating leverage and potentially stronger EBITDA growth.

Bottom line: AI doesn’t need to replace workers to move returns. Making them more productive may be enough.

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