01 Trigger Event

In the first half of 2026, venture funds increased valuations for technology companies by 16%, while buyout funds marked down their technology holdings by 4%.

a16z’s assessment is that tech buyouts, once treated as “safe assets” within private markets, are coming under structural pressure. LPs continue to re-up with the same managers, yet an increasing number of respondents believe tech buyouts are already operating in difficult territory.

The truly striking feature of these figures is not the 4% decline itself, but that two forms of capital are sending opposite price signals for technology assets.

02 What This Really Means

The issue is not that PE temporarily cannot find transactions. The problem is that the underlying return model of tech buyouts is beginning to mismatch the AI cycle.

Traditional buyouts depend on three things: buying cheaply, using leverage to amplify equity returns, and waiting for the business to mature steadily or achieve an exit. This model works particularly well after a technology wave has stabilized because revenue and profits become predictable, cash flow can support debt, and management can execute a standardized operating plan.

AI, however, brings continuous product substitution. Model capabilities, token prices, open-source supply, and agent architecture are all shortening the half-life of software assets. A recurring-revenue business that looks like SaaS today may simultaneously face three forms of disruption:

  • Foundation models absorb features that previously represented standalone products;
  • AI-native entrants rebuild entire workflows;
  • Customers reassess seat-based pricing.

This directly undermines the collateral most important to buyouts: predictable cash flow.

Sponsors who entered at the 2021 peak are effectively holding adjustable-rate mortgages while facing neighborhood rezoning. Debt pricing does not automatically reset when the technological paradigm changes, but assumptions about enterprise value must be recalculated.

VC missed the same 2021 valuation peak, but its losses have call-option characteristics: failures go to zero, while a small number of outliers cover the portfolio. Buyouts usually require portfolio companies to continue servicing debt and ultimately exit, so tail losses cannot remain confined to unrealized positions.

My concern is not the superficial conclusion that “VC wins and PE loses.” What is actually being repriced is which capital structure can better absorb innovation timing error.

03 Historical Analogies / Structural Comparisons

Historically, venture capital has led during the early stages of technology waves, while buyouts have led once those waves matured.

During the internet buildout and the mobile/cloud cycle, top-quartile venture outperformed top-quartile buyout. Yet venture vintages entering around the 1998–2005 dot-com bust subsequently lagged. The reason is not mysterious: when the direction of technology remains uncertain, financiers retain the right to growth, whereas mature assets cannot withstand product substitution.

A similar pattern emerged in the modern PC and internet-search landscape. PC manufacturers could not restore growth through cost cutting alone as browsers, operating systems, and search entry points shifted. A buyout can optimize a company’s operations, but it cannot protect a business model that is losing control of its customer entry point.

There is a deeper analogy with the 2008 financial crisis. CDOs did not fail because their underlying assets lacked cash flow altogether. Their problem was that the rating system assumed historical correlations would remain stable. Once those correlations changed, risk could not disappear through a more sophisticated model. The buyout model also treats predictability as the foundation of debt pricing, and AI is putting a discount on that predictability.

Wesray’s early acquisition of Gibson Greetings also illustrates that the original form of buyout resembled venture capital: using a small amount of equity to acquire a highly asymmetric, potentially valuable asset and then using leverage to amplify the result. Over the past several decades, however, the industry gradually standardized this outlier-driven return into a mature-company playbook.

The technology cycle is now restarting, but buyouts are entering it with balance sheets shaped by the previous cycle. This is the structural contradiction.

04 What It Means for AI Builders

For AI builders and independent founders, the most important adjustment this week is not a financing preference. It is the assumption that traditional SaaS represents the end state.

First, recalculate the moat.

If you sell seat-based software built around an existing workflow, you need a clear answer to which features will be automatically absorbed as models improve. Is your moat based on workflow data, distribution, switching costs, or merely a layer of UX?

Domain workflows and proprietary data remain important, but simply “integrating a model” is not a moat. The fact that buyout funds are marking down technology assets shows that capital markets are making the same distinction.

Second, incorporate token economics into product design rather than leaving them in the gross-margin model.

If your AI product depends on continuous inference, revenue per seat may remain stable while inference cost per task rises with agent usage. If the pricing model is disconnected from token consumption, the faster the business grows, the worse its gross margin becomes.

This week, review three sets of data:

  • The distribution of inference costs across high-frequency and low-frequency users;
  • The actual contribution of cache hit rate to latency and unit cost;
  • The Pareto frontier among quality, latency, and cost after routing tasks to different models.

I have not run your production workload internally, so these assessments may need to be adjusted for the task mix.

Third, avoid AI wrappers without distribution.

Models change every three months, and functionality wrappers have low switching costs. The positions where durable value accumulates are usually data integration, workflow integration, compliance boundaries, and distribution channels.

For startups, the most dangerous growth strategy is to “build the features first and find a moat after the platform matures.” Once platform capabilities mature, the thinnest layer is directly compressed.

Fourth, design the financing structure in advance.

If you are considering PE or a sponsor-led buyout, model-substitution risk must be included in debt underwriting rather than judging the opportunity solely by ARR growth. An AI company with stable revenue may still experience a changing profit curve because of inference costs, falling foundation-model prices, and customers developing in-house systems.

Work backward from the competitive landscape three years from now to today’s capital structure. In the early stages of a technology wave, equity can absorb changing strategic paths better than debt.

05 Counterarguments / Risks

I may be overestimating the structural significance of the 4% markdown.

The valuation adjustments to tech holdings may simply reflect the lag of mark-to-model accounting. Buyout funds hold large portfolios of private companies and cannot immediately reflect changing values as public markets can. Likewise, venture funds’ 16% increase may include headline rounds or a small number of very large transactions rather than broad-based appreciation in underlying assets.

The more important counterargument is that tech buyouts do not represent the entire PE industry. They may continue to generate stable returns from recurring-revenue healthcare, industrial software, and regulatory-heavy vertical software. AI also threatens these categories at different speeds.

Another risk is that my historical analogies are too neat. The internet, mobile, and cloud eventually produced large numbers of mature, cash-generative assets, allowing buyouts to benefit again. It is not yet possible to conclude that AI will follow the same path based on valuation adjustments alone.

That said, one fact remains: the safety narrative of buyouts depends on business-model predictability, and AI is weakening that predictability. Continued LP re-ups may reflect discipline, but they may also represent capital inertia.

The most likely place for me to be wrong is the time horizon. If AI ultimately standardizes as rapidly as cloud, tech buyouts will find predictable acquisition targets again within 12 to 24 months. Conversely, if agents continue replacing traditional software seats, the 4% markdown may be only the beginning of the pressure rather than the end.