Liquidation of a Future Unicorn: The Fatal Illusion of an AI Prototype

A Growing Company Before the Fall

Eighteen months ago, a French B2B software company was on the verge of becoming a unicorn. Its product, a project and data management tool, was highly appreciated by mid-sized enterprises and large groups, and the company was experiencing profitable growth. Jordan, the founder, had developed the first version of the software with a team of experienced developers, and the company was healthy despite the fast pace set by Jordan.

The Illusion of an AI Prototype

The situation changed with the arrival of powerful language models (LLMs). Jordan, seeing an opportunity to revolutionize his product, created an AI prototype in just two days. This prototype, while appealing, gave the illusion of ease in industrialization. Convinced he had a rare gem, Jordan ramped up public announcements, interviews, LinkedIn posts, and client teasers. He promised a version that would transform the market for large groups in less than a month, which sparked investor euphoria.

Underestimated Technical Challenges

When the technical team, led by Anaïs, began working on the industrialization of the prototype, they quickly identified numerous challenges: securing customer data, managing rights, traceability, robustness, integration with existing information systems, monitoring, fallback, and regression testing. The prototype, although impressive in demonstration, was fragile and heavily reliant on the LLM's responses.

Jordan grew impatient with these delays. He did not understand why industrialization was taking so long and wondered if his team was overwhelmed by AI. He began to bypass Anaïs, requesting daily follow-up meetings and pushing to accelerate the process. Anaïs and two lead techs resigned, believing that Jordan had lost faith in traditional engineering and was taking reckless risks.

Opposition to Market Launch

Three months after the initial announcement, a "nearly complete" version was launched despite opposition from Zoé, the quality director. Jordan had made the call, asserting that progress needed to be shown. The initial customer feedback was catastrophic: instability, inconsistent responses, strange bugs, and degraded performance. Many customers demanded a return to the old version, but the company had neglected its maintenance.

Fatal Dependence on the LLM

The final blow came from an update from the LLM provider, rendering the product unusable. The already weakened company could not stabilize the situation. Customers fled, and cash flow dried up within weeks. During an extraordinary general meeting, investors voted to revoke Jordan's position and dissolve the company. The teams were reassigned as best as possible, marking the end of this promising venture.

Lessons to Learn

This story highlights the dangers of confusing prototyping speed with industrial viability. It underscores the importance of not underestimating technical challenges and maintaining strict control over engineering processes, even in the face of AI's alluring promises. Believing that an impressive prototype created with an LLM equates to an industrial product is a mistake. Underestimating the engineering work necessary for reliability, security, observability, and integration can prove fatal. AI is a powerful tool, but it does not replace the expertise of senior developers, who must maintain control to avoid the pitfalls of technical complexity. Ultimately, overestimating one's ability to assess technical complexity once outside their area of expertise can lead to disastrous consequences for a company.