For decades, the buy versus build decision in software followed a familiar pattern. If a tool was mission critical, differentiated, or core to competitive advantage, companies leaned toward building. If it was generic, operational, or widely available, they bought. That framework still exists, but AI-assisted coding tools have shifted the economics enough that the old instincts are no longer reliable on their own.
For acquirers and entrepreneurs, this shift matters not as a philosophical debate about software, but as a practical question of capital allocation, risk, and durability of value. The availability of tools like Claude Code and Cursor has changed how quickly internal software can be created and how cheaply it can reach a usable state. That change ripples directly into M&A strategy, valuation assumptions, and the defensibility of many software businesses.
What “Buy Versus Build” Looks Like Now
At its core, buy versus build is a comparison between acquiring an external product, either through licensing or acquisition, and developing a solution internally. Historically, buying was faster, more predictable, and often cheaper when all costs were considered. Building required skilled engineers, long timelines, and ongoing maintenance that most non-software companies preferred to avoid.
AI coding tools alter the first part of that equation. They compress development timelines and lower the skill threshold required to produce functional internal tools. Tasks that once took quarters can now take days or weeks. This does not eliminate the cost of engineering, governance, or long-term maintenance, but it changes the entry point. Internal tools no longer need to be elegant, scalable, or broadly usable to be valuable. They only need to be good enough for a narrow purpose.
That distinction is critical. Most internal software spend was never about innovation. It was about avoiding annoyance. Companies paid for reporting dashboards, workflow tools, and lightweight analytics because building them was slow and distracting. AI agents weaken that rationale.
Why This Shift Exists Now
The change is not theoretical. We are seeing concrete examples of AI tools producing large volumes of functional code in extremely short timeframes. Engineers are using agents to scaffold entire systems, iterate rapidly, and replace manual boilerplate work. What matters for decision makers is not whether this code is beautiful, but whether it works and can be modified when requirements change.
This capability arrives at a moment when SaaS pricing has already stretched tolerance. Many mid-market and enterprise buyers are paying significant annual fees for tools that solve narrow problems. When a comparable internal solution can be created quickly and maintained incrementally, renewal conversations become harder. Not because the SaaS product is bad, but because the cost differential no longer feels justified.
Markets have started to price in this uncertainty. Traditional software companies are trading below historical valuation ranges despite stable revenues. Investors are questioning the durability of demand for tools whose primary value proposition was convenience rather than deep differentiation.
How Buy Versus Build Works in Practice Today
In real operating environments, the decision is rarely binary. Most companies now live in a hybrid state. They buy core systems of record and build peripheral tools around them. What AI changes is the scope of what is considered peripheral.
Internal tools that manage reporting, content workflows, lightweight analytics, or operational glue are increasingly candidates for internal builds. These systems are often specific, unattractive, and tightly coupled to internal processes. That makes them poor SaaS products but perfectly acceptable internal assets.
From an M&A perspective, this matters because many smaller software businesses exist precisely in this zone. They sell tools that are useful, but not deeply embedded in customer strategy. If customers believe they can recreate most of the value internally with minimal effort, pricing power erodes even if churn does not spike immediately.
What “Normal” Looks Like Now
It is important not to overcorrect. AI coding tools do not make building free, instant, or riskless. What is normal today is not mass replacement of SaaS, but selective internal substitution. Teams build tools where requirements are stable, scope is narrow, and failure is tolerable. They continue to buy where reliability, compliance, uptime, and support matter.
Market-standard behavior looks cautious. Buyers test internal builds in parallel with existing tools. They use AI to prototype before committing to longer-term decisions. Over time, this can still have meaningful impact on vendor economics, even if it does not produce dramatic short-term churn.
Where Risk and Imbalance Arise
The biggest risk in this new environment is mistaking speed for sustainability. AI-generated code can solve immediate problems while quietly creating technical debt. Internal tools often lack documentation, ownership clarity, and long-term maintenance plans. When the original builder leaves, the cost of understanding and modifying the system can exceed the original savings.
There is also an imbalance between who captures value. Internal teams benefit from lower upfront cost, while vendors bear the downside of slower growth and compressed multiples. For acquirers, this means diligence must focus more heavily on why customers buy a product, not just how many do. If the answer is convenience alone, the risk profile has changed.
Common Misconceptions
A frequent misconception is that AI makes all software cheap and interchangeable. It does not. Complex systems, regulated environments, and products with deep network effects remain difficult to replicate. Another flawed assumption is that internal builds are always cheaper. They often ignore opportunity cost, long-term support, and governance overhead.
On the seller side, there is a tendency to dismiss internal builds as inferior. That may be true technically, but irrelevant economically. Buyers do not need perfect solutions. They need adequate ones that fit their workflows.
Why Extremes Are Problematic
Extreme interpretations in either direction create problems. Treating AI as a reason to build everything leads to fragmentation, hidden risk, and loss of focus. Treating it as irrelevant leads to complacency and overconfidence in pricing power.
Incentives also matter. Engineering teams may favor building because it is interesting. Procurement may favor buying because it shifts responsibility. Neither incentive guarantees optimal outcomes. Without discipline, AI simply accelerates poor decision making.
When Exceptions Make Sense
There are situations where building internally is clearly reasonable. Highly specific workflows, short-lived needs, or tools that sit close to proprietary data often justify internal development. Safeguards usually include clear ownership, documentation standards, and an explicit decision about whether the tool is disposable or strategic.
Conversely, buying remains sensible when tools touch revenue, compliance, or customer experience directly. In those cases, reliability and accountability outweigh speed.
Buy versus build in the age of AI is not about choosing sides. It is about recognizing that the cost curve has shifted and adjusting expectations accordingly. For acquirers, this means reassessing what makes a software business durable. For entrepreneurs, it means building products whose value extends beyond convenience.
AI coding tools are powerful. They are also blunt instruments. Used thoughtfully, they expand options and improve capital efficiency. Treated as defaults, they introduce new risks under the guise of speed. The decision remains a tool, not a doctrine, and the discipline around it matters more than ever.
