The New Math of Private Equity Returns
How higher rates and flat multiples shifted the source of returns from financial engineering to operational execution
Analysis · Private Equity · March 2026
For most of the last decade, PE returns followed a reliable formula. That formula stopped working—and the reason is arithmetic, not philosophy.
Buy at a reasonable multiple. Lever the balance sheet at low rates. Ride multiple expansion. Exit into a liquid market. For years, the combination of cheap debt and rising valuations meant that a competent deal team could generate strong returns without meaningfully changing the underlying business.
Interest rates moved. Leverage ratios compressed. Entry multiples hit record highs and stayed there. Exit markets tightened. When those variables shift simultaneously, the return has to come from somewhere else—and increasingly, it comes from what happens inside the business after close.
What follows draws on Bain’s 2026 Global PE Report, Citadel Securities’ macro analysis, a discussion among 35 practitioners on Reddit’s r/PrivateEquity, and my own experience leading post-close execution for PE-backed software companies.
The Arithmetic
Why 12% Growth Is the New 5%
Bain’s 2026 Global PE Report introduced a heuristic that captures the shift: “12 is the new 5.”
The math: in 2015, a typical PE buyout could hit a 2.5x MOIC with roughly 5% annual EBITDA growth. Leverage at 50% of the capital structure, interest rates at 6–7%, and multiple expansion did the heavy lifting. In 2025, the same 2.5x return requires 10–12% annual EBITDA growth—because leverage is down to 30–40%, rates are at 8–9%, and multiples are flat.
That delta—the gap between 5% and 12%—has to come from operations. There is nowhere else for it to come from.
The pressure compounds on the exit side. Bain’s data shows $3.8 trillion in unrealized value across roughly 32,000 unsold portfolio companies, with average holding periods approaching seven years. Entry multiples remain at record highs. When you buy expensive and hold long, the business itself has to justify the valuation.
Arbitrage and value extraction have been maxed out, especially with the end of near-zero rate lending. There’s also intensifying competition, and increasingly more options for tools, processes, and strategies that can increase operational efficiency.
That assessment, from one of 35 GPs, operating partners, and portfolio company executives in a recent practitioner discussion, reflects what most in the industry now acknowledge. Multiple expansion—buying at 8x and selling at 12x—carried PE returns for a long time. That tailwind is largely gone. You can’t underwrite to it with the same confidence. The upside has to come from improving the business: pricing, go-to-market, systems, procurement, working capital.
This isn’t a matter of opinion. It’s what the math requires.
The Talent Gap
Deal Teams Can’t Build What Portfolios Need
If returns depend on operational execution, you need people who can execute. Private equity is not staffed for this.
The typical PE fund hires from two pools: former investment bankers who can model and structure, and MBB consultants who can build decks and run diligence processes. Neither pool produces operators—people who have built products, run engineering teams, managed P&Ls, or integrated acquisitions post-close.
MBB/Finance people are the LAST talent that’s needed on a team that actually augments a business. I can model in my sleep—what do I need someone to do that for? What’s harder is creating a system that empowers ops leaders to both create AND turn the wheel in a way that improves the business. It’s closer to founder thinking than PE thinking.
That GP, building a new fund, described the gap between analytical capability and operational capability. It’s a distinction the industry has talked around for years, but practitioners are now stating it bluntly: “Creating value in a spreadsheet is easy. Creating value in the real world when the heat is on is a very different story.”
The market is responding. Several practitioners described leaving fund-level roles for portfolio operations. One, now a Chief Transformation Officer at a portfolio company: “I left PE for portfolio ops once I realized modeling and memo writing were fake business.” Another reported cold inbound for operating partner work at a pace they’d never seen—multiple calls a week where there used to be a few a year.
This matches my experience. I led Alpine Investors’ largest software roll-up—integrating four companies into a unified platform—as an operator who got pulled into PE, not as a finance person who studied operations. The difference shows up in the first week post-close. Every PE fund has a value creation plan. The gap between the plan and the capability to execute it is where most of them stall.
Bain’s report puts it in institutional terms: winning firms will “invest in talent and AI, and move from full potential diligence to execution on Day 1.” Translated: the diligence deck is not the deliverable. The deliverable is the changed business.
The AI Question
Newest Lever, Hardest to Deploy Honestly
AI is the newest operational lever, and it is the one practitioners mention most. It is also the one they disagree about most.
The optimistic case is real. AI can compress timelines, automate manual processes, and improve unit economics in ways that weren’t possible two years ago. In one engagement I led, AI-powered document ingestion compressed a 45-day manual onboarding process to one business day—while improving accuracy. Lower cost, faster time-to-revenue, better customer experience. That is operational value creation by any definition.
The skeptical case is also real. Several practitioners argued that “operational value creation” is a polished label for enshittification—the systematic degradation of products through cost-cutting and extraction.
Using operators and growing value were the start, then in different macro cycles this got lost, and now coming back to this. However, a lot of damage has been done and many founders have heard earned horror stories and are skeptical.
The distinction matters. AI that improves the product—higher willingness to pay, lower churn—creates value. AI that replaces human capability with inferior automation to save headcount destroys it. The incentive structure of PE determines which version gets deployed, and the practitioners are right to be skeptical about which one dominates.
There are also economic constraints that limit the scope of AI-driven value creation. Citadel Securities’ February 2026 macro analysis notes that AI represents roughly 2% of U.S. GDP ($650 billion), that software engineer job postings are up 11% year-over-year, and that technological adoption follows S-curves with natural plateaus. Their compute cost framework adds a hard boundary: when the marginal cost of compute exceeds the cost of human labor, substitution stalls. The displacement narrative outpaces the adoption data—for now.
AI works as a value creation lever when the person deploying it understands the asset—the product, the customer, and the economics. Without that understanding, it becomes another cost-cutting exercise dressed up as transformation.
Whether this shift is permanent depends on variables that are genuinely uncertain. If rates return to near-zero and exit markets reopen at scale, leverage and multiple expansion could reassert themselves. That is possible. It is also not what anyone is underwriting to right now.
What is clear is the math. At current rates and current multiples, a 2.5x return requires the kind of EBITDA growth that doesn’t come from financial engineering. It comes from pricing, go-to-market, product, systems, procurement, integration—the work that happens inside the asset after close. The firms that figure out how to do that work, not how to present it, will outperform.
Mo Battah advises PE firms on technical diligence and post-close execution. mo@blackmere.ai