Thoma Bravo Is Right. The Market Is Wrong About Software.

In January, a single AI product launch erased $285 billion from software market caps in one trading day. The IGV software ETF has fallen roughly 30% from its September 2025 peak. For only the second time since the 2008 financial crisis, software as a sector trades at a discount to the S&P 500.

The market has decided that AI is coming for software. Thoma Bravo, the largest software-focused private equity firm on the planet, disagrees. And they are backing that conviction with billions of dollars in capital.

In a recent Forbes piece by Josipa Majic, Orlando Bravo and Holden Spaht laid out their thesis at the firm's annual investor meeting in Miami: public markets are failing to distinguish between software companies that are genuinely vulnerable to AI disruption and those that are not. That failure to differentiate is creating a generational buying opportunity.

Having spent the last decade working with PE-backed portfolio companies on their data and AI infrastructure, I think they are right. And I think the reason they are right matters to every CTO and data leader operating inside a PE-backed business today.

The Market Is Punishing the Wrong Companies

Public markets have a blunt instrument problem. When sentiment turns against a category, it punishes the entire category, indiscriminately. That is exactly what has happened to enterprise software.

But not all software is equally exposed to AI disruption. The distinction matters enormously, and the market is not making it.

On one side, there are generalist tools that automate simple, single-task workflows with limited switching costs. These companies face real structural pressure. An AI agent that can replace a basic SaaS point solution is not theoretical. It is arriving now.

On the other side, there are companies with deep domain expertise, zero-tolerance-for-error workflows, heavy compliance requirements, and deep integration across enterprise systems. Think aircraft maintenance records, pharmaceutical manufacturing validation, hospital billing compliance. Replacing these systems is not a matter of building a better chatbot. It requires replicating decades of domain logic, regulatory understanding, and organizational trust.

Thoma Bravo has built a $183 billion franchise across 565 software acquisitions over four decades by identifying companies in the second category and buying them when the market prices them like the first. The current environment is handing them that opportunity at scale. Dayforce at $12.3 billion, Olo at $2 billion, Verint at $2 billion. These are not speculative bets. They are acquisitions of deeply embedded, operationally critical software at multiples the market has artificially compressed.

Analysts covering enterprise software M&A now expect 5 to 10 percent of the public SaaS universe to be acquired in 2026, with PE buyers offering premiums of 30 to 50 percent above current trading prices. That premium is the delta between what the market sees and what disciplined acquirers know.

The Fundamentals Tell a Different Story Than the Narrative

Strip away the sentiment and look at the actual operating data. Public SaaS companies grew revenue roughly 17% last year, nearly triple the 6% growth rate of the broader S&P 500. Gross margins in software sit around 74%, compared to 43% for non-tech S&P 500 companies. And 80 to 95% of next year's revenue is already under contract.

These are not the financial characteristics of an industry facing imminent disruption. They are the characteristics of an industry being mispriced by a narrative cycle.

The market asks: "Is this growing fast enough to justify the multiple?" Thoma Bravo asks a different question: "How hard is it to rip this out?" Those are fundamentally different lenses. And in enterprise software, the second one consistently produces better investment outcomes because switching costs, not growth rates, determine long-term durability.

The Variable the Market Still Misses: Data Infrastructure

Here is where the Thoma Bravo thesis intersects with what we see every day at Blue Orange Digital, and where I think even the most sophisticated analysis tends to fall short.

When a PE firm acquires a software company, they are not just buying recurring revenue and a customer list. They are buying years of transactional data, behavioral signals, and operational history that cannot be replicated by a competitor, regardless of how good the competitor's product is. That accumulated data is the real moat, and it is almost never reflected in the valuation multiple.

Software features can be rebuilt. Pricing can be undercut. UI can be modernized. But a decade of clean, well-structured customer data, the kind that powers reliable analytics, trains accurate models, and informs operational decisions, cannot be reproduced. It represents the deepest, most defensible form of lock-in. And it is the variable that separates a good acquisition from a transformational one.

This is why the most sophisticated PE firms are now evaluating data infrastructure during diligence, not as a compliance checkbox, but as a direct valuation input. A portfolio company sitting on rich operational data in an accessible, well-governed state is worth materially more than one sitting on the same data in a fragmented mess. The gap between those two states is not accidental. It is the result of deliberate engineering investment, and it compounds over time.

What This Means If You Are Inside a PE-Backed Company

If you are a CTO, VP of Engineering, or data leader inside a PE-backed business, the Thoma Bravo thesis has a direct, practical implication for your roadmap.

The acquirer's rationale is not just "this software has sticky customers." It is "this software has sticky customers AND years of data that we can turn into a competitive advantage post-acquisition." Your job is to make sure that second half is actually true when someone comes to look.

Concretely, that means:

• Your data should be accessible without friction. If it takes three analysts and a Jira ticket to answer a basic business question, you have an infrastructure problem, not a data problem. The diligence team will notice.
• Your pipelines need to be trustworthy. Stale dashboards and broken ETL jobs erode confidence in every metric downstream. Once a PE owner starts questioning whether the numbers are real, every subsequent conversation gets harder.
• Your AI initiatives need a foundation beneath them. Deploying language models on top of ungoverned data does not produce insights; it produces confident-sounding noise. The companies extracting real value from AI are uniformly the ones that invested in clean data architecture first.
• Your data story should be part of the value narrative. If your data infrastructure cannot be articulated as an asset during a board presentation or a diligence conversation, it will be treated as a cost center. Position it as the strategic advantage it is, or someone else will define it for you.

The market evaluates software companies on ARR. A sophisticated acquirer evaluates whether the data inside that software can be turned into a decision engine. Build the second thing.

The Broader Signal

Thoma Bravo's positioning is not just a bullish call on software multiples. It is a signal about where durable value actually lives in the technology stack, and it aligns with what we observe across every PE portfolio engagement we run.

Public markets reward narrative momentum. Private equity rewards operating leverage. And operating leverage, in 2026, increasingly flows from data: the ability to make faster decisions, automate more confidently, and compound institutional knowledge rather than start from zero with each new initiative.

The counterarguments are real. Per-seat pricing models face genuine structural pressure. The timeline for AI agent adoption remains uncertain. Not every software company will survive the transition. But the companies that will command premium multiples in the next PE cycle are not just the ones with the highest net revenue retention. They are the ones that have treated their data as a strategic asset rather than a byproduct, and built the engineering infrastructure to prove it.

Thoma Bravo is betting the market will eventually close the gap between narrative and fundamentals. Given their track record across 565 acquisitions, that is not a bet I would take the other side of.

Josh Miramant is the CEO of Blue Orange Digital, a data engineering and AI consultancy that partners with private equity portfolio companies to turn data infrastructure into measurable competitive advantage.