AI Mania Is Eviscerating Global Decision-Making
AI Mania Is Eviscerating Global Decision-Making
AI 狂热正在摧毁全球决策机制
By Nikhil Suresh on 18/07/26 作者：Nikhil Suresh，2026年7月18日
I strongly believe there are entire companies right now under heavy AI psychosis and it’s impossible to have rational conversations with them about it. I can’t name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. Mitchell Hashimoto, of HashiCorp and Ghostty fame. 我坚信，目前有许多公司正处于严重的“AI 精神错乱”之中，与他们进行理性对话已是不可能的事。我无法点名具体的人，因为其中也包括我深为敬重的挚友，但我确实担心事态会如何发展。—— Mitchell Hashimoto（HashiCorp 和 Ghostty 的创始人）。
Over the past year, I’ve run point on all of our company’s sales, led the technical components of all but two of our engagements, and over the lifetime of this blog have had something like 300 catchups with professionals from around the world. This has ranged from people on the ground in niche service industries to executives at Fortune 500 companies. Because of this, I’ve had a front-row view to our collective institutions across both the private and public sector undergoing breath-taking mass psychosis. 在过去的一年里，我负责了公司所有的销售工作，领导了除两个项目之外的所有技术环节，并且在撰写博客期间，与全球各地的专业人士进行了大约 300 次交流。这些交流对象涵盖了从利基服务行业的基层人员到财富 500 强企业的高管。正因如此，我有幸近距离观察到私营和公共部门的集体机构正在经历一场令人震惊的群体性精神错乱。
This essay is an attempt to describe the bizarre dynamics that are currently at play, as I am in the rare position where my wellbeing is not contingent on paying lip service to madness, and to reassure the people trying to survive amidst all of this that they are not crazy. The reality is thus: the people in charge either have no plan, or see no path forwards other than keeping their heads down. Not at banks, not at hospitals, not in our government institutions. The world’s organisations have been captured by people in the throes of frothing excitement, and saner people who now live in a state of constant commingled fear and frustration. 本文旨在描述当前正在发生的怪诞动态。我处于一个难得的位置，我的生计并不依赖于对这种疯狂行径的阿谀奉承，因此我希望能让那些在混乱中挣扎求生的人们感到宽慰：你们并没有疯。现实情况是：掌权者要么毫无计划，要么除了埋头苦干外看不到任何出路。无论是银行、医院还是政府机构，概莫能外。全球的组织机构已被那些陷入狂热兴奋的人所裹挟，而那些头脑清醒的人，如今正生活在恐惧与挫败交织的状态中。
I. AI Investments Are Generally Total Failures
一、 AI 投资总体上是彻底的失败
Reading this while working for a division that pivoted to provide interfaces for agentic workflows, only to discover that only ten users had ever touched the products we made for agents, only to pivot again to support for agentic workflows, which has a lot of competition because every company has to do something agentic now and there’s only like four things you can do in that space, is bracing. — An editor of this essay. “在为一个转型提供智能体工作流接口的部门工作时读到这篇文章，发现我们为智能体开发的产品只有十个用户使用过，随后又不得不再次转型去支持智能体工作流——而这个领域竞争极其激烈，因为每家公司现在都必须搞点‘智能体’相关的东西，尽管该领域实际上能做的事情屈指可数——这种感觉真是令人清醒。”——本文编辑。
Are companies actually seeing massive productivity gains from their AI adoption? Does any of this sordid affair make sense? This should be an easy question, but it is surprisingly hard to get a straight answer to it. Executives that tell the press that their company has gone insane will quickly find themselves removed from their positions. Employees who are honest will find themselves fired in short-order, or “randomly” selected for a round of layoffs. 企业真的从 AI 应用中获得了巨大的生产力提升吗？这场肮脏的闹剧有任何意义吗？这本应是一个简单的问题，但令人惊讶的是，很难得到一个直截了当的回答。告诉媒体公司已经陷入疯狂的高管，很快就会被免职。诚实的员工则会很快被解雇，或者在裁员潮中被“随机”选中。
In fact, it is in the interests of almost every actor in the space – boards, executives, employees, vendors, consultants – to obfuscate and misrepresent the success rate of AI projects. Many publicly traded companies are putting out announcements about their AI productivity gains when I know for a fact that the businesses have done nothing other than purchase Copilot licenses and declare victory. 事实上，该领域几乎所有参与者——董事会、高管、员工、供应商、顾问——的利益都在于掩盖和歪曲 AI 项目的成功率。许多上市公司发布关于 AI 生产力提升的公告，但我确切地知道，这些企业除了购买 Copilot 许可证并宣布胜利之外，什么都没做。
Yet we need to know if these projects are panning out – if the total focus on AI as a core tenet of business strategy is succeeding at a reasonable rate, then a discussion about the relative risk and reward is warranted. Unfortunately, we live in a dark timeline. All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in, but even within projects that we have observed in passing while doing totally unrelated work. 然而，我们需要知道这些项目是否真的奏效——如果将 AI 作为商业战略核心的全面投入能以合理的比例取得成功，那么讨论其相对风险和回报是有必要的。不幸的是，我们生活在一个黑暗的时间线里。我们团队观察到的所有 AI 项目都在失败。每一个都是如此——在一年半的时间里，我们看到的成功率为 0%。这不仅限于我们受邀参与的项目，甚至包括我们在进行完全无关的工作时顺带观察到的项目。
Even if you grant that AI tooling accelerates specific workloads, the method and scale of the current investments is senseless. Frequently the failure is not related to AI itself, but rather that companies are terminally bad at running software projects effectively, and as I have remarked previously, 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. Very few companies are so good at shipping software that they can afford the extra risk profile. 即使你承认 AI 工具确实加速了某些特定的工作负载，但目前投资的方法和规模也是毫无意义的。失败的原因往往与 AI 本身无关，而是因为公司在有效运行软件项目方面极其无能。正如我之前所言，AI 项目不仅会遭遇普通项目的所有失败模式，而且即便你做对了一切，也可能因为方法的新颖性而失败。很少有公司在交付软件方面做得足够好，以至于能够承担这种额外的风险。
Often enough, though, it’s an actual failure in what LLMs can accomplish. The most common version of this, being rolled out across businesses around the world, is the internally-facing chatbot, or for the more daring company, the customer-facing chatbot. The story is always the same. For the former, I’ve never seen substantial internal uptake from inside a business. Employees don’t use internal chatbots because companies tend to have low-quality documentation and an LLM is not psychic – it can only know things that have been written down and made accessible. 不过，更多时候，这是大语言模型（LLM）能力本身的局限所导致的失败。目前全球企业都在推行最常见的应用——面向内部的聊天机器人，或者对于更大胆的公司来说，是面向客户的聊天机器人。故事总是如出一辙。对于前者，我从未见过企业内部有实质性的采用率。员工不使用内部聊天机器人，因为公司的文档质量往往很差，而 LLM 又不是通灵者——它只能了解那些已被记录并可供访问的信息。
For the latter customer-facing applications, I have rarely had a pleasant experience as a consumer, with perhaps the exception of live transcription during medical appointments – hardly something worth pivoting an entire organisation around. In both cases, project leaders are very careful to avoid tracking basic metrics, such as whether the tools are being used at all, or they track metrics that are easily gamed. 对于后者（面向客户的应用），作为消费者，我很少有愉快的体验，或许只有医疗预约时的实时转录除外——但这显然不值得让整个组织为此转型。在这两种情况下，项目负责人都会非常小心地避免追踪基本指标（例如工具是否真的被使用），或者他们只会追踪那些容易被操纵的指标。
For example, my last consumer interaction was attempting to get help from Mitsubishi following an automotive failure, where a very polite robot was asked to describe the problem and that I’d receive a call back as soon as someone was available. This was the single most competent implementation of such a project I’ve seen in the wild, in that the voice was natural sounding, responded quickly, was clearly “live” in production, and promised a swift resolution. That was six months ago, and I did not, in fact, get a call back. 例如，我最近的一次消费互动是试图在汽车故障后寻求三菱的帮助。一个非常有礼貌的机器人让我描述问题，并承诺一旦有人有空就会给我回电。这是我在现实中见过的此类项目中最称职的一个实现：语音听起来很自然，响应迅速，显然是在生产环境中“实时”运行，并承诺迅速解决问题。那是六个月前的事了，事实上，我并没有收到回电。
When Mitsubishi did not call me back, what happened? Did that request just go into the void, showing one less incident for the year? Does it appear that the phone bot resolved my query without the need for human intervention? All we know is that it didn’t show up as an error, or I’d have received a call. I’m sure it looks great in all sorts of ways except the one that matters, which is that I was planning to buy a car and decided not to buy another one of theirs. 当三菱没有给我回电时，发生了什么？那个请求是否就这样石沉大海，从而让年度事故统计少了一起？看起来是不是电话机器人无需人工干预就解决了我的问题？我们唯一知道的是，它没有显示为错误，否则我应该会接到电话。我敢肯定，它在各种指标上看起来都很棒，唯独在最重要的一点上失败了：我原本打算买车，但现在决定不再买他们的车了。