Articles of Association
Why AI is changing the Build vs. Buy decision (but not replacing it)
"The product is fantastic, but we're going to vibecode it ourselves."
It's a statement we hear increasingly often in the boardrooms of enterprise organizations. The rise of LLMs suggests that complex software development has been reduced to a series of text prompts. However, anyone who confuses the speed of AI generation with the robustness of an enterprise solution is taking a significant strategic risk.
The myth of 'instant' software
The assumption that specialized AI companies do not use AI in their own development is a misconception. In a modern software company – especially a team with a background in Computer Science and Data Science – AI is the standard lever.
We use AI in every part of our process. It enables our senior engineers to deliver features in weeks that previously took months. However, there is a crucial difference between acceleration and replacement.
Where 'Vibecoding' fails
Vibecoding is a powerful tool for rapid MVPs, internal tools, or increasing individual productivity. But for a mission-critical platform, such as a B2B AI Contract Control system, different standards apply:
Security & Compliance: A prompt does not generate ISO-certified infrastructure or airtight data segmentation.
Scalability: Code that "works" is not the same as code that handles tens of thousands of concurrent requests within a complex enterprise architecture.
Maintainability: Who will maintain the AI-generated code in two years? Without deep architectural choices, vibecoding becomes tomorrow's technical debt.
The real cost of 'Build'
Anyone deciding to build a specialized platform themselves with the help of AI, should budget for a realistic investment. AI lowers the barrier to getting started, at the cost of quality.
For a fully fledged enterprise-grade alternative, an organization must account for:
Talent: At least 2 to 3 senior engineers (not juniors with prompts).
Time: A development cycle of 12 to 18 months to catch up on three years of iterative domain knowledge.
Capital: An initial investment that, including overhead and risk, realistically falls between €300,000 and €500,000.
Strategic capital allocation
The age-old economic law of Build vs. Buy remains unchanged: Build in what you're specialized in, buy what's already mature and scalable elsewhere.
For CFOs and IT directors, the message is clear: AI lowers the barrier to experimentation, but increases the need for sharp choices. Allocating capital to your core competency creates returns. Allocating capital to reinvent specialized software mainly creates operational risk.
