Vibe coding is changing how software companies are built. Now it will change how they are bought

By Tim Schumacher, Co-Founder of saas.group

AI is reshaping the economics of building a software company. Coding tools are allowing companies to develop products faster, with smaller teams and considerably less capital. Tasks that once absorbed weeks of engineering time can increasingly be completed in hours, while a small team can now build and maintain products that would previously have required a much larger organisation.

The immediate consequences are easy to see, with more software being built, the cost of experimentation falling and founders able to get products in front of customers far more quickly. But there is a secondary effect that has received far less attention. If AI changes what it takes to build a software company, it will also change how we assess the value of one as these companies mature. This matters particularly when companies change hands. Software buyers have traditionally placed considerable value on the technology and engineering capability they are acquiring alongside a company’s customers and recurring revenue. As AI takes on a greater share of software development, some of the assumptions underpinning that assessment are beginning to change.

The scale of the shift is already significant. In Y Combinator’s Winter 2025 batch, a quarter of startups reportedly had codebases that were 95% AI-generated. Companies like these will inevitably become acquisition targets. When they do, buyers will need to understand not simply what the software does, but how it was created, how well it is understood and how easily somebody else can take responsibility for it.

At saas.group, where we acquire and operate more than 25 SaaS businesses, we are seeing AI-assisted codebases become far more common across the market. Our view is that AI-generated code is not inherently a problem. We do not see the use of AI as a negative. In many cases, it can be evidence of exactly the kind of capital efficiency and speed that makes a software business attractive. What matters much more is whether the company understands what it has built.

That may sound obvious, but AI changes the relationship between developers and their code. A founder can now create a sophisticated product without having personally written, or necessarily examined in detail, every component underneath it. During the early stages of a business, that can make perfect sense. The priority is often to test an idea, reach customers and establish product-market fit as quickly as possible. An acquirer has a different set of priorities. It may be expecting to operate that software for another five or ten years, integrate it with other systems, and continue developing it after the original founders have moved on. The fact that a product works today is therefore only part of the equation.

We are already seeing signs that AI is changing the way buyers think. Bain’s 2026 Global M&A Report found that one in five strategic dealmakers had walked away from a deal because of the anticipated impact of AI on the target’s business. Almost half of technology deals now have some form of AI angle. AI is no longer a peripheral technology question during M&A. It is becoming part of the assessment of whether an asset will retain its value, and for SaaS companies built extensively using AI, that scrutiny is likely to reach all the way into the codebase.

Until recently, technical diligence generally started from an assumption that the engineering team understood the software it had built. Vibe coding challenges that assumption, and a buyer now needs to work through different questions with founders. They will want to know where AI-generated code has been used and how it has been reviewed. They will need confidence that the architecture is understood, dependencies and licences are accounted for, intellectual property ownership is clear and security risks have been properly assessed.

None of this means vibe-coded companies should automatically attract lower valuations; in fact, the opposite could be true. One of the most exciting consequences of AI-assisted development is the possibility of a new generation of exceptionally capital-efficient SaaS businesses. A founder who can reach meaningful recurring revenue with two people rather than 20 has potentially built an extremely attractive company. Development costs can be lower, iteration can be faster and the economics of running the business can look very different.

The dividing line will be between businesses that have used AI to accelerate good development and those that have allowed the speed of development to outpace their understanding of what they have built. That is why one of the most expensive answers a founder may soon give during diligence is: “We don’t really know.” Not because AI-assisted code is a problem, but because uncertainty is much harder to price than risk. It is also an answer founders can avoid with a little preparation. Acquirers are used to risk, as every software company has technical debt and every acquisition involves assumptions about what will happen next, but what is much harder to price is uncertainty.

For founders, this means thinking about acquisition readiness much earlier. The speed of vibe coding encourages a mindset in which technical debt can always be dealt with later. That is understandable when the priority is reaching product-market fit, but founders need to close the gap between what their product can do and what their organisation can explain.

That means documenting architecture, understanding dependencies and licences, reviewing security particularly closely around payments and personal data, and keeping a sensible record of where AI-generated code has been used and reviewed. It also means ensuring that knowledge of how the product operates belongs to the company rather than sitting with one founder and the AI tools they used to create it.

None of this undermines the vibe-coding revolution. Rather, it is a sign that it is moving from an experiment in how software can be created to a fundamental change in how software businesses operate. The ability to build and test products at a fraction of the traditional cost remains one of the biggest opportunities SaaS founders have had in years. But as AI-built companies move from launch and growth towards investment and acquisition, the standards applied to them will inevitably evolve too.

A wider consequence is that AI may gradually change what buyers value most in software businesses. When writing code becomes cheaper and faster, the existence of a large codebase or engineering organisation tells us less about the strength of the underlying company. Customers, recurring revenue, proprietary data, distribution, product knowledge and the ability to keep improving the software may become even more important indicators of durable value.

Vibe coding has made it possible to create software companies in ways that would have seemed extraordinary only a few years ago. The next test is whether those companies can outlive the founders and tools that created them. For acquirers, that distinction will increasingly determine which businesses they are prepared to buy.