When Software Becomes Cheaper to Create, What Remains Defensible?
Our Perspective
These insights are synthesized from our deep experience executing M&A technical diligence and optimizing enterprise architecture. They reflect our ground-truth perspective on what investors must prioritize to separate AI hype from defensible, structural value.
The cost of creating software is falling. That does not mean software is free, quality is automatic, or engineering judgment no longer matters. But it does matter for investors and operators because many software creation tasks that once required significant time, specialist effort, or coordination can now be accelerated by AI.
Prototypes can be generated faster. Tests can be drafted faster. Documentation can be created faster. Code can be explained faster. Legacy systems can be analyzed faster. Integrations can be scaffolded faster. Internal tools can be produced faster. Product ideas can become working artifacts faster.
This changes how we think about software defensibility. If the cost of creating software falls, the mere existence of software becomes less of a moat. AI can help generate code, interfaces, workflows, tests, scripts, reports, and integration patterns. But valuable software businesses depend on customer access, domain expertise, data rights, trust, distribution, reliability, support, compliance, integration depth, product judgment, sales motion, and customer success.
Defensibility moves toward harder-to-replicate assets: proprietary data, workflow depth, trust, compliance, distribution, integration depth, execution speed, and operating knowledge. Raw code is not enough if competitors can reproduce similar functionality faster than before.
AI also changes technical diligence. Investors should ask whether engineering teams use AI systematically, whether productivity gains are measured, whether AI improves testing and quality, whether it helps reduce technical debt, and whether roadmaps are being rethought based on faster delivery potential.
Technical debt may become more addressable. AI can help explain legacy code, generate tests, identify patterns, create documentation, suggest refactoring paths, and support migration planning. But judgment remains critical. AI can accelerate work; humans still decide what should change, what should be preserved, and what risks matter.
Build-versus-buy decisions may also change. Companies may build internal tools they previously would have bought, customize workflows more deeply, or use AI to extend existing systems rather than replace them. SaaS vendors must prove they offer more than configurable interfaces.
As building gets easier, the real question becomes sharper: what remains hard to replicate? The answer is usually workflow, data, trust, distribution, compliance, integration depth, and execution speed.
Apply this thinking to your portfolio.
Thought Source helps investors and operators assess AI architecture, defensibility, and value creation.