- Published on
AI Mania Is Eviscerating Global Decision-Making
- Authors
- Name
- DeepHorus
A growing chorus of technology professionals is raising alarms that the frenzy around artificial intelligence is undermining rational decision-making in organizations worldwide. In a detailed critique published on a company blog, an anonymous industry insider claims that AI investments are almost universally failing, with projects seeing zero percent success over the past 18 months. The author, who has led sales and technical engagements for dozens of companies, describes a state of 'mass psychosis' gripping both private and public sectors, where executives and employees alike fear speaking honestly about AI outcomes. This situation matters because it suggests that billions of dollars in corporate and government spending may be yielding little tangible benefit, while stifling critical thinking.
The backdrop to this critique is a technology industry that has been swept up in AI hype since the release of ChatGPT in late 2022. Companies across sectors have rushed to integrate generative AI into their products and workflows, often without clear strategies. The author notes that many publicly traded firms announce AI productivity gains when, in reality, they have merely purchased licenses for Microsoft Copilot and declared victory. Such behavior, they argue, is driven by a combination of board pressure, executive ambition, and fear of being left behind, creating an environment where honest assessment is punished.
Key facts from the analysis underscore the severity of the disconnect. The author states that every AI project observed by their team over a year and a half has failed—both those they participated in and those they encountered indirectly. This 0% success rate, they claim, stems not from AI's technical limitations but from organizations' inability to run effective software projects. As noted in the piece, 'AI projects are subject to all the failure modes of normal projects plus you can get everything right and then still fail because of the method's novelty.' Only a handful of companies are skilled enough at software delivery to make AI work.
The implications of widespread AI failure are profound. Companies may be burning cash on unproven technology while neglecting core operations. The author warns that people in charge have no plan or see no path forward beyond keeping their heads down, leading to a climate of constant fear and frustration among saner employees. This dynamic could erode trust in leadership and slow genuine innovation, as resources are misallocated to AI initiatives that do not deliver on their promises.
Reactions from within the industry have been muted, but the essay resonates with many professionals who have privately expressed similar frustrations. An editor quoted in the piece describes working for a division that pivoted to agentic workflows, only to find that ten users ever touched the products. They lament that 'every company has to do something agentic now, and there's only like four things you can do in that space.' This echoes a broader sentiment that AI fever is leading to herd behavior rather than strategic differentiation.
Looking ahead, the author suggests that a reckoning may be inevitable. As more projects fail and financial losses mount, companies may be forced to confront the reality of their AI investments. The piece concludes with a call for honest dialogue and a return to evidence-based decision-making. Until then, those trying to survive amidst the madness are reassured that they are not crazy—the system itself is broken by hype.
