When Software Goes to Zero: How Private Equity Should Rethink Value Creation

I’ve been thinking about how AI is changing the economics of software companies, and I believe this shift has significant implications for how private equity firms create value across their portfolios. My thesis is that the value of software itself is heading toward zero and that private equity portfolios have to change quickly to take advantage of this shift and find their next defensible competitive advantage.

Software companies have traditionally been valued partly for how difficult their products were to replicate. AI is changing that, forcing investors to rethink what makes those businesses valuable.

I believe the ability to transform software companies quickly will become one of the most important capabilities a private equity firm can develop.

PE firms will need to adjust how they underwrite software acquisitions, assess competitive risk, and create value during the holding period. A five-year value creation plan built around assumptions about engineering productivity, defensible features, and product roadmaps may become outdated much sooner than expected.

That means moving faster at both the investment and operating levels. During diligence, firms should evaluate how easily a target’s product could be replicated with AI, where its actual competitive advantages reside, and how AI might change its cost structure. After acquisition, they need the ability to work directly with management teams to change product strategy, reorganize engineering, and redirect investment as technology evolves.

The speed at which a portfolio company can adapt may become just as important to its valuation as its current financial performance.

The opportunity goes well beyond introducing AI tools or reducing engineering costs. It requires rethinking how software companies build products, organize product & engineering teams, allocate capital, and establish competitive advantages.

Most engineering organizations were designed around the assumption that writing code was expensive. They optimized for developer productivity, feature delivery, and engineering throughput. AI is changing that constraint. The harder problems increasingly involve deciding what should be built, verifying that it works, understanding customers, and knowing where to invest.

Companies that continue operating under the old model risk producing more software without creating much additional enterprise value.

I see four areas where PE firms can help their portfolio companies adapt:

1. Rethink product and engineering organizations. Shift teams toward ownership of product decisions, architecture, verification, and business outcomes. Change the incentives that reward shipping features regardless of the value they create.
2. Restructure around the new economics of software development. Reconsider team structures, technical investments, and operating models as AI changes the amount and type of work required to deliver software. Build organizations that can make decisions and change direction quickly without depending on layers of approval.
3. Find the next defensible competitive advantage. When features become easier to reproduce, companies need to invest in what makes their businesses difficult to replace: proprietary data, customer workflows, distribution, trust, and meaningful switching costs. Revisit those advantages regularly rather than assuming the original investment thesis will remain valid.
4. Connect AI investment directly to financial outcomes. Tie initiatives to measurable improvements in operating margins, revenue growth, EBITDA, and ultimately enterprise value. Establish a regular review of what is working, what is changing, and where to reallocate resources.