446 points • 2 days ago • Article Link

全球公私部门的机构如今正陷入一场由对人工智能不理性且失控的痴迷引发的大规模集体错乱。与从小型服务行业到财富 500 强公司的众多组织直接合作后，我发现领导层要么彻底放弃了理性的战略规划，要么陷入瘫痪性的恐惧，在组织推进注定失败的 AI 项目时选择沉默。这样的环境助长了不鼓励诚实评估的文化，成功指标常被篡改，用以为对那些往往无法带来显著生产力提升的技术进行巨额投资辩护。

这些 AI 项目的现实很严峻——在观察到的案例中几乎没有可衡量的成功。最常见的内部与面向客户的聊天机器人频繁失败，原因包括数据质量差、缺乏明确的用户价值以及大型语言模型的固有局限。公司往往不肯承认失败，而是混淆视听、操纵指标，或干脆忽视显示其投资无效的数据。当有人质疑这些项目时，质疑常被视为职业攻击，使顾问或内部员工在不冒失去地位或工作的风险下几乎无法提出关键性的批评意见。

这种氛围造成了意识形态上的俘获，员工和高管都被迫在公开场合对 AI 的变革力量做出表态。技术人员和非技术人员越来越被迫用 AI 粉饰工作流程，甚至谎报对语言模型的使用以满足管理层要求。那些表达怀疑或未能表现出对这些工具的"表面承诺"的人，常遭职业报复。由此形成了离奇且近乎邪教般的氛围，理性的职业判断被压制，人们宁愿维持所谓的创新形象，即便这些做法显然损害组织的长期健康。

这种从众压力在高层管理之间的复杂协调问题下被进一步放大。许多组织已公开将自身形象与所谓由 AI 驱动的生产力提升捆绑在一起，任何一位高管若试图坦诚指出缺乏实际成果，都可能削弱同僚并危及企业合同。因此，领导者们被迫处于僵局，只得继续传播他们明知是夸大或虚假的说法，以免被视为异端。这种动态令有效决策陷入停滞，组织更在意维持"AI 原生"的门面，而不是解决真实的业务问题。

在这样的环境中生存需要谨慎且务实的做法。对于那些力图在被俘机构内实现真实目标的人，建议尽量在私下、一对一的场合展开工作，以建立信任并避开群体会议中的表演性压力。去挑战有关 AI 的广泛、近似宗教式的论断通常无效，应把注意力放在具体、对象级的任务上。对于仅想保持理智的个人，最好的策略往往是尽量减少接触与 AI 有关的话题，保持职业界限；若环境变得过于有毒，则应考虑寻找规模更小、更脚踏实地的组织，在那里实际工作仍比当前的市场狂热更重要。 Global institutions across both the private and public sectors are currently undergoing a mass psychosis driven by an irrational and unchecked obsession with artificial intelligence. Having worked directly with numerous organizations ranging from small service industries to Fortune 500 companies, it has become evident that leadership teams have either completely abandoned rational strategic planning or are trapped in a state of paralyzing fear, choosing to remain silent as their organizations pursue failing AI initiatives. This environment has fostered a culture where honest assessment is discouraged, and success metrics are routinely misrepresented to justify massive investments in technology that often fails to deliver meaningful productivity gains.

The reality of these AI projects is stark, with the author noting a complete lack of measurable success in observed implementations. Internal and customer-facing chatbots, which are the most common applications, frequently fail due to poor data quality, a lack of clear user utility, and the fundamental limitations of large language models. Rather than admitting these failures, companies often engage in obfuscation, gaming metrics, or simply ignoring data that suggests their investments are ineffective. When these projects are questioned, the inquiries are frequently perceived as professional attacks, making it nearly impossible for consultants or internal staff to provide critical guidance without risking their status or employment.

This climate has created a form of ideological capture where employees and executives alike feel compelled to perform public acts of faith regarding the transformative power of AI. Technicians and non-technicians are increasingly being forced to "AI-wash" their workflows, lying about their use of language models to satisfy management mandates. Those who express doubt or fail to demonstrate an artificial commitment to these tools often face professional retaliation. This has led to a bizarre, cult-like atmosphere where rational professional judgment is suppressed in favor of maintaining the appearance of innovation, even when such tactics are clearly detrimental to long-term organizational health.

The pressure to conform is exacerbated by a complex coordination problem, particularly among senior executives. Because many organizations have publicly tethered their corporate identities to AI-driven productivity claims, any executive who attempts to speak honestly about the lack of actual results risks undermining their peers and jeopardizing enterprise contracts. As a result, leaders are effectively locked in a standoff, where they must continue to propagate what they know to be exaggerations or falsehoods to avoid being seen as heretics. This dynamic has brought effective decision-making to a halt, as organizations prioritize maintaining the "AI-native" facade over solving actual business problems.

Surviving this environment requires a careful, pragmatic approach. For those trying to achieve legitimate objectives within these captured institutions, the author suggests operating exclusively in private, one-on-one settings to build trust and avoid the performative pressure of group meetings. Challenging the broader, religion-like claims about AI is usually counterproductive and should be avoided in favor of focusing on specific, object-level tasks. For individuals simply trying to maintain their sanity, the best course of action is often to minimize exposure to AI-related discourse, maintain professional boundaries, and—if the environment becomes too toxic—seek opportunities in smaller, more grounded organizations where actual work still takes precedence over the current market mania.