The software buyout market is beginning to thaw, but it is not returning to its pre-AI playbook. Investors are demanding strong fundamentals, credible defenses against AI disruption, downside protection, and increasingly, tangible proof that incumbent software companies can win in an agentic future.
For much of the past decade, software private equity benefited from a relatively stable underwriting formula. Recurring revenue, strong retention, operating leverage, and high growth gave investors confidence in the terminal value of high-quality software businesses.
Artificial intelligence disrupted that confidence.
As AI agents demonstrated early abilities to perform vertical-specific white-collar work, investors began asking more fundamental questions. Will employees operate applications directly, or will digital workers perform much of the work? Will horizontal agents become the primary interface to application software? Will AI-enabled software development change customers’ build-versus-buy equation? And will “AI-native” competitors redesign workflows around autonomous agents?
Because a significant portion of a software company’s value sits beyond the explicit forecast period, uncertainty around those questions caused market-clearing multiples to reset sharply. That uncertainty also arrived on top of an already higher cost of capital, which compounded the effect.
Yet most technology investors we speak with have not concluded that incumbent software will disappear. Many believe established vendors possess meaningful advantages which could enable them to internalize the benefits of AI rather than be disintermediated by it. What changed was investors’ willingness to pay for advantages that remained theoretical.
What Recent Transactions Reveal
Since the software valuation reset, our Tech Sponsor Coverage team has held over 100 conversations with senior technology investors about how their strategies and underwriting standards are evolving. We combined those discussions with an analysis of recent control transactions and dialogue with buyers and bidders regarding the investment rationales behind premium deals.
The first conclusion is more constructive than the broader narrative suggests: premium deals are still getting done, but the patterns behind them have changed.
First, recent activity has concentrated in three broad lanes:
AI-offensive businesses possess advantages that may become more valuable as AI adoption expands, such as proprietary data, embedded domain expertise, or exposure to AI-driven demand.
AI-defensive businesses benefit from regulated, high-cost-of-failure, or slow-moving workflows that give incumbents more time to adapt.
Alternative technology models, including information services, payments, trust layers, technology-enabled services, and hardware-enabled businesses, sidestep portions of the workflow-software debate.
Second, strong fundamental PE underwrites are even more important than before. Premium transactions have generally involved companies with “A-level financial profiles”: fast growth, attractive EBITDA margins, and high gross and net retention.
Third, investors are underwriting stacked defenses. As examples, proprietary data may be valuable, but less so if the company does not control the workflow. Regulation may slow competition, but only if management uses that time to develop and commercialize its own AI products.
Many of these transactions have also been smaller. Smaller companies may have cleaner technology stacks and greater organizational agility, while smaller checks limit the capital at risk if terminal value concerns materialize. Debt capital markets have also been less willing to support large acquisitions at market-clearing terms.
The stratification of potential outcomes is affecting structure as well as valuation. Buyers are increasingly seeking downside protection through liquidation preferences, rollover, earnouts, or other mechanisms that preserve upside while limiting exposure if the AI thesis deteriorates.
AI Risk Is Not One Risk
Investors frequently discuss “AI risk” as though it were a single threat. We see incumbent software companies facing at least three distinct attack vectors.
Attack from above: A horizontal agent becomes the primary interface through which employees work, reducing incumbent applications to underlying data or execution systems.
Attack from within: AI development tools make it faster and less expensive for customers to build applications internally and for smaller teams to create competing products.
Attack from the side: An AI-native challenger enters through a narrow use case, gains access to workflow context, and expands toward broader orchestration.
These threats require different defenses. A durable system of record may resist internal replacement but still lose the user interface to a horizontal agent. An effective AI feature may neutralize a point solution without preserving the incumbent’s long-term control of the workflow.
A Framework for Evaluating AI Defensibility
To move beyond broad classifications of “AI winners” and “AI losers,” we evaluate software companies through four sequential questions:
Will the workflow agentify? If it does, can the incumbent remain the control point? What factors give management time to adapt? Is the company still a strong conventional private equity underwrite?
Workflows are harder to agentify when they require exact outputs, involve high costs of failure, depend on persistent state, resolve in the physical world, or require licensed professionals to assume liability.
Where agentification is likely, incumbents are better positioned if they own the system of record and action, embed differentiated domain logic, observe both decisions and outcomes, or span a meaningful portion of the end-to-end workflow.
Regulation, integration complexity, customer-created intellectual property, user training, and trusted brands can provide additional time. But these are often temporary advantages. Their value depends on whether management uses that window to build, sell, and improve its own AI products.
Finally, AI defensibility does not replace traditional underwriting. Growth, margins, retention, market position, management quality, and entry valuation remain essential. Investors are looking for companies that combine those fundamentals with rapid AI product development and monetization tied to activity or outcomes rather than human seats.
Market Conditions Are Improving
Several conditions that constrained transaction activity after the reset are now easing.
Public software equities have recovered meaningfully from their post-reset lows. Public comparables inform the entry and exit assumptions that sponsors can defend to investment committees, as well as confidence in a potential public-market exit. Their recovery restores an important reference point for private transactions. Whether the recovery proves durable is not yet established, but it has already changed what sponsors can model.
Improving sentiment has been accompanied by an absence of evidence that AI is impairing incumbent fundamentals. Across the portfolios and processes discussed, investors report limited displacement by AI-native challengers, while experiments with internal development have not yet produced a widespread shift away from incumbent platforms. Where net revenue retention has softened, investors more often attribute it to incremental budget being diverted toward experimental AI initiatives than to erosion of the existing revenue base, a different and less threatening phenomenon. Many portfolio companies are reporting tangible productivity improvements and cost savings from AI, and there is a small but growing cohort of companies reporting growth acceleration as they begin selling AI capabilities and products to their customer base.
Credit has been the tightest constraint of all, and it is loosening. On one software buyout earlier this year, we understand the sponsor approached over forty lenders and secured commitments from fewer than a handful. Refinancing risk was top of mind: a company with contracted revenue and high gross margins may service its debt for years, but if terminal-value concerns are validated by the time the capital structure matures, the issuer may be unable to refinance and the lender could be left holding a business that few institutions will finance. Syndicated and direct lenders have since grown more constructive both in their conversations and their term sheets, and the shift appears to track growing equity-investor conviction about which categories of software are likely to prove durable.
The market is not normalizing by forgetting AI risk. It is beginning to normalize by learning how to diligence, price, structure, and finance it.
Green Shoots of Activity
Better conditions are beginning to show up in deal activity.
Take-private activity has picked up, with several transactions completed over recent quarters, a disproportionate share by the largest dedicated software sponsors, and a composition skewed toward the AI-defensive and alternative-model lanes rather than core application software. Investors are picking their spots and writing large checks again at attractive multiples.
Pipeline indicators point in the same direction and may be understating intent. Live deal and pitch counts remain below pre-reset levels but both have risen recently, and beneath them sits a substantial volume of bilateral sponsor-to-sponsor dialogue that never reaches a formal process.
Sponsor-to-sponsor activity is also broadening. The first transactions following the reset concentrated among faster-growing businesses widely viewed as defensible. We’ve now seen multiple acquisitions of companies growing below 20% at EBITDA multiples in the teens, where the businesses have credible AI strategies, and the entry valuations provide the margin of safety needed for deals to pencil despite potential downside scenarios. We observe this as the “thickest” part of the sponsor-backed software market, and signs of momentum bode well for a broader pickup in activity.
Important to note that several forcing functions make sustained activity difficult to avoid. The pressure to return capital has built over several years, and LP concerns about software exposure more broadly have sharpened it further. Firms that have raised large new funds also need to deploy them. Technology-focused funds also face a narrower opportunity set than generalists; they can shift capital among various categories of technology, but they must take earlier views on AI underwriting questions than their peers.
Proof Will Unlock the Market
What would turn a gradual recovery into a rush is proof.
Investors we speak with generally believe in the theoretical advantages of incumbents. Enterprises spend years approving vendors for security, compliance, reliability, and data access. They generally seek to limit vendor sprawl, particularly where new providers require connections to sensitive systems. Existing vendors may already possess the context, permissions, domain knowledge, and customer relationships required to deliver effective agents.
The missing element is proof that those advantages translate into durable AI-era performance.
Software companies have rapidly launched copilots, assistants, and agentic products, and some are beginning to report relevant KPIs, such as AI ARR. These disclosures are directionally helpful, but definitions remain inconsistent. Incremental revenue from a separately priced AI product is fundamentally different from existing subscription revenue recategorized because customers now use a conversational interface.
Investors will place greater weight on competitive wins against AI-native alternatives, incremental bookings from separately priced AI products, production adoption rather than pilots, measurable customer outcomes, and stronger retention or wallet share among AI adopters.
As credible proof points emerge, investors can begin distinguishing theoretical defensibility from demonstrated AI leadership. That could bring sidelined capital back into the market in a major way, but it is unlikely to restore uniform software valuations. Instead, the market may become more polarized.
Proven Winners May Be Worth More Than Before
The number of companies able to demonstrate both conventional software quality and credible control of an AI-enabled workflow may be much smaller than the universe of attractive SaaS businesses in the prior cycle.
That scarcity could produce substantial valuation differentiation. A limited group of demonstrated AI winners may attract a concentrated pool of capital and ultimately trade above pre-reset levels.
To the upside, being an AI winner could materially expand a company’s addressable market. Selling completed work or outcomes rather than software seats could allow a company to monetize portions of the larger services budget surrounding its category, which is often materially larger than the software expenditure itself.
Early AI traction can nevertheless produce a false positive. An incumbent can sell a useful AI product into its installed base without becoming the long-term orchestrator of the workflow. Adoption may reflect distribution, bundling, or demand for an incremental productivity tool rather than durable control.
The more consequential threat may come from challengers that rebuild the underlying data infrastructure, domain ontology, decision processes, and organizational workflows required for greater autonomy. In that scenario, early AI revenue could validate customer demand without proving long-term defensibility.
Investors must therefore ask whether AI products are expanding the incumbent’s ownership of workflow state, execution, and decision-to-outcome data, or merely adding features to the existing application.
The Next Phase of Software Investing
Activity is returning, but the next phase of the software market will not be defined by a return to indiscriminate confidence. It will be defined by evidence.
Businesses that combine strong fundamentals, stacked defenses, rapid product execution, credible AI KPIs, and appropriate downside protection may command premium valuations.
Scarcity value and the upside-case of being an AI winner could push the strongest companies above pre-reset levels.
But the threshold will be high. Investors must determine not only whether a company can launch and sell an AI product, but whether it can remain the trusted control point as its workflow becomes increasingly agentic.
The central question is no longer simply whether software survives AI.
It is which companies will control the work regardless of how it gets done.
