The rapid pace of development enabled by AI doesn't eliminate technical debt; it accelerates its creation. More code shipped faster means more potential bugs, maintenance overhead, and architectural risk that must be managed proactively, not just reactively.

Despite AI lowering the barrier to coding, replacing dozens of SaaS subscriptions with self-hosted apps is a poor business decision. The opportunity cost of diverting focus from growing MRR, which creates significant enterprise value, far outweighs any potential cost savings from not paying for third-party tools.

While AI can build an initial version of a software product instantly, the true, defensible value lies in the ongoing maintenance, support, and reliability. Customers will always pay for a product that is actively maintained and improved over time.

Building a custom tool with AI to replace a SaaS subscription seems cost-effective, but building is only 10% of the work. The other 90% is the often-forgotten overhead of maintenance, on-call support, security, and bug fixes that SaaS vendors typically handle.

The traditional wisdom to "build what's core" to your business is becoming obsolete for AI. The immense cost and rapid advancement of foundational models by major labs mean most companies are better off buying or partnering for core AI capabilities rather than attempting to build them in-house.

With AI commoditizing code creation, the sustainable value for software companies shifts. Customers pay for reliability, support, compliance, and security patches—the 'never ending maintenance commitment'—which becomes the key differentiator when anyone can build an initial app quickly.

The idea that building with AI is cheap is a dangerous oversimplification. While initial creation is fast, leaders are realizing the immense long-term costs of maintenance, unwinding mistakes, and integrating with legacy systems are substantial and often dangerously overlooked.

The current focus in the AI-assisted coding space is on building apps. However, as more companies create custom tools, the critical, unsolved problem becomes who will maintain, update, and secure these apps over the next five years, creating a significant operational burden.

The ability to rapidly build custom software with AI is tempting. However, the ongoing maintenance and data quality assurance are the core business of SaaS companies. Buying a dedicated tool like a CRM often provides more value and less overhead than a custom-built solution, even with AI assistance.