AI Mania Drives Poor Decisions at Global Enterprises, Anonymous Sources Reveal

A growing chorus of anonymous accounts from consultants and engineers describes a phenomenon where AI mania is overriding rational decision-making at large global enterprises. Executives are crafting strategies centered on artificial intelligence without ever having used the tools themselves, according to multiple sources.

In one extreme case, a consultant reported that an executive confessed to never having used ChatGPT or any AI tool, immediately after producing a technical strategy for a $2 billion revenue organization that was entirely centered around AI. The account was shared by consultant Nik Suresh, as reported by Simon Willison.

Engineers Report Token Leaderboards and Fear-Driven AI Adoption

Engineers at companies with AI token leaderboards describe a culture of performative AI use. One engineer reported checking out a parallel copy of their Go repository and telling an AI to rewrite the whole thing in Zig while working on other tasks, solely to maintain the appearance of AI engagement and protect their job.

Key findings from the anonymous accounts include:

• An executive at a $2B+ company produced an AI-centered strategy without ever using AI tools.
• A vendor executive avoided contradicting customer claims of 100x productivity gains for fear of losing contracts.
• An engineer feels compelled to use AI on redundant tasks just to appear AI-active on a token leaderboard.
• Companies are greenlighting AI projects that duplicate existing systems purely to demonstrate AI adoption.

Implications for Enterprise IT Strategy

The pattern suggests that AI hype is creating a feedback loop where executives make bold claims, vendors are incentivized to not push back, and engineers engage in wasteful AI projects to justify their roles. This undermines genuine innovation and leads to misallocation of resources. Suresh and Willison warn that the trend is widespread across sectors.

What comes next: Consultants advise companies to establish internal AI literacy requirements for executives before approving major AI investments. Without such safeguards, the gap between AI promise and practical delivery will continue to grow.