AI 热潮正在瓦解全球决策机制

注意：这篇文章也被转发到了我公司的博客，如果你认为以更权威的形式与某人分享可能有用，这里是链接：https://hermit-tech.com/blog/ai-mania-is-eviscerating-global-decisionmaking。

我坚信，目前有整家公司正处于严重的 AI 精神错乱状态，与他们就此问题进行理性对话几乎不可能。我不能点名任何具体的人，因为其中包括我非常尊敬的个人朋友，但我很担心这种情况会如何发展。

—— Mitchell Hashimoto，HashiCorp 和 Ghostty 著名人物：https://x.com/mitchellh/status/2055380239711457578?lang=en

在过去一年中，我负责我们公司所有的销售工作，领导除了两项之外的所有项目的技术部分，并且在这个博客运营的期间，我与来自世界各地的专业人士进行了大约 300 次交流。这些人从利基服务行业的一线人员到财富 500 强公司的高管都有#fn:1。因此，我有机会近距离观察我们私营和公共部门的集体机构如何经历惊人的群体精神错乱。这篇文章尝试描述当前正在发生的奇异动态，因为我处于一种罕见的状态，我的身心健康不依赖于对疯狂奉承，同时也希望安慰那些试图在这一切中生存的人们——你们没有疯。

现实是这样的：掌权的人要么没有任何计划，要么看不到前进的道路，除了埋头苦干。无论是在银行、医院，还是在我们的政府机构中都如此。世界上的组织已经被处于极度兴奋状态的人所控制，而理智的人现在则生活在持续交织的恐惧和沮丧之中。

在为一个部门工作时阅读这篇文章，该部门曾转向为自主工作流提供接口，结果发现只有十个用户曾经使用过我们为代理开发的产品，然后再次转型支持自主工作流，而这方面竞争激烈，因为现在每家公司都必须做一些自主性的事情，同时在这个领域你只能做大约四种事情，这让我感到震惊。

公司真的因为采用AI而获得了巨大的生产力提升吗？这一切混乱的事情有任何意义吗？

这个问题看起来应该很简单，但实际上很难得到一个明确的答案。向媒体透露公司已经疯狂的高管，很快就会被免职。诚实的员工则很快会被解雇，或“随机”被选中进行一轮裁员。事实上，对几乎所有涉事方——董事会、高管、员工、供应商、顾问——来说，模糊和歪曲AI项目的成功率都是符合自身利益的。很多上市公司都在发布关于AI生产力提升的公告，而我知道的事实是，这些企业除了购买了Copilot许可并宣布胜利之外，实际上什么都没做。

然而，我们需要知道这些项目是否真正取得了成果——如果将AI作为业务战略核心理念的全面关注正在以合理的速度取得成功，那么讨论其相对风险和回报就是必要的。

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——在一年半的时间里我们没有看到任何成功，不仅仅是那些我们被要求参与的项目，还有那些我们在做完全无关工作时顺便观察到的项目。即便你承认AI工具能够加速特定工作负载，但当前投资的方法和规模是没有意义的。失败往往并非AI本身的原因，而是公司在有效管理软件项目上本身就存在致命缺陷，正如我之前所评论的：https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/，AI项目会受到所有普通项目的失败模式影响，而且你可能在方法上做到一切正确，仍然会因为方法的新颖性而失败。很少有公司能够如此擅长交付软件，以至于可以承受额外的风险。

但实际上，这往往是大型语言模型（LLM）能力的真正局限。最常见的版本，被全球各地的企业采用的是面向内部的聊天机器人，或者对于更加敢于尝试的公司来说，是面向客户的聊天机器人。故事总是相同的。对于前者，我从未见过企业内部有大量使用。员工不使用内部聊天机器人，因为公司通常文档质量较低，而大型语言模型不会读心——它只能知道那些已经被记录并且可访问的信息。对于后者，也就是面向客户的应用，很少有我作为消费者会感到愉快的体验，或许医疗预约中的实时转录是个例外——但这也几乎不足以让整个组织围绕其调整策略。在这两种情况下，项目负责人都非常小心地避免追踪基本指标，比如这些工具是否被实际使用，或者他们会追踪那些容易被操控的指标。

例如，我最近一次消费者互动是尝试向三菱公司寻求汽车故障的帮助，一个非常礼貌的机器人让我描述问题，并告知一旦有人可用就会回电。这是我见过的同类项目中执行得最有能力的一次，因为语音自然、响应迅速、明显处于“实时”生产中，并承诺迅速解决问题。

那是六个月前，实际上，我并没有接到回电。

当三菱没有回电时，发生了什么？这个请求是否就这样消失了，一年的故障记录中少了一条？是否显得电话机器人无需人工干预就解决了我的问题？我们所知道的只是它没有显示为错误，否则我早该接到电话。我确信在其他方面看起来很好，但唯一重要的一点却未达标——那就是我原本打算买一辆车，最终决定不再购买他们的车型。

因此，我们的团队在参与期间迅速学会了，在任何情况下都不要询问正在进行的人工智能项目——等到项目启动时，管理层已经为时已晚，且在不可避免的危机点前无法进行干预。没有任何积极的结果。失败率如此之高，甚至连基本的探究都让我们处于难以维系的境地。任何关于进展如何、目标是什么、谁在使用这些内容的连贯问题，都是对负责这项工作的指挥链的无意攻击，因为没有任何好的答案。即使在极少数情况下，我的对话者表示事情进展顺利（通常是在项目还处于进行中，失败尚未显现时），通常也很明显它们注定失败，但至少在这些情况下，我可以简单地同意，然后回家对着枕头连续尖叫六个小时，时间是3：#fn：3。

总之，我非常有信心，几乎所有关于“AI生产力大幅提升”的报告，作为粗暴事实都是不真实的。即使有些公司看到明显的增长，这也是例外，非常态。有了这个假设，我们可以讨论其中的动态，以及许多组织如何无法专注于真正关乎其长期（甚至短期）健康的事情。

甚至提出人工智能可能不是问题的解决方案，更不用说成为公司整体战略的唯一焦点，已经变得极其危险。

在我们观察到的每一家足够大的企业（比如拥有500+名员工的企业）中，我们都注意到，持续的进步和越来越多的就业，开始要求人们反复相信人工智能对企业具有变革力量的信心。我不是说提供如何在业务中使用人工智能的想法——我指的是宗教信仰、信仰声明。绝大多数这些说法来自非技术人员，尽管技术人员发出疯狂的言论以讨好也不罕见。

我曾多次看到有人，无缘无故地几乎逐字脱口而出“人工智能正在改变一切”，但片刻之后又承认他们的组织目前并没有使用大语言模型做任何事情，实际上，他们除了偶尔使用一下 ChatGPT（通常是免费版）之外，无法说出任何改变的事例。在一个极端的例子中，我甚至见过一位高管在为一家收入超过 20 亿美元的公司制定以人工智能为中心的技术战略后，立即承认自己从未使用过 ChatGPT 或任何人工智能工具。

起初，这些言论本身荒谬到让我以为这是某种为了获得思想领袖地位的玩世不恭的策略，而且确实有人会这样做——有人还向我承认过。但更广泛的现实要糟糕得多：那些完全没有技术背景的人实际上真的相信自己所说的话。一般来说，你应当避免与骗子做生意，但如果必须这样做，你至少可以私下里尝试与他们讲道理。真正的信仰者更具威胁性，因为他们即便出于自身利益也无法被说服。

我信念的转折点是看到某人在赌注巨大的情况下解雇了他们最优秀的员工，因为这些员工的高绩效是在没有使用大语言模型的情况下实现的。当雇主公开谈论人工智能创新时，我们必须问自己，他们是不是仅仅在试图操纵市场或客户。当他们私下用自己的钱承诺这样的战略，并且不向外部客户传达该战略，我只能假设他们真的认真的。

前一阵子，我写了《反对 Ptacek 关于人工智能的糟糕文章》：https://ludic.mataroa.blog/blog/contra-ptaceks-terrible-article-on-ai/，文章的焦点是 Ptacek 在自己的文章《我的人工智能怀疑朋友们全都疯了》：https://fly.io/blog/youre-all-nuts/ 中的许多观点内部存在矛盾4：#fn:4。但在核心问题上，我们实际上完全一致，因为他在文章开头写道：

技术高管正在强制推行大语言模型的采用。这是坏的战略。

也就是说，我们可以完全绕过关于大语言模型（LLM）具体效用的争论，我们剩下的只是一个非常简单的事实——对于我和Ptacek这两位观点相对对立的人来说，很显然，人们在这件事上真的非常、非常愚蠢，组织对其专业员工提出了奇怪的工作流程要求。5：#fn:5

这些规定导致了极其奇怪的结果。我的一些同僚现在会“AI洗白”他们的工作，这意味着即使他们完全胜任地完成工作，使管理团队满意，如果工程师在工作中没有使用AI，他们的经理仍然不高兴……所以现在他们甚至在专业判断认为LLM不适合作业的情况下，也会谎称使用了LLM。他们只是像几十年来一样完成工作，然后说是Claude做的。其他人则根据他们的AI消耗量被评估，用“令牌排行榜”，消耗越高越好，因为显然我陷入了地狱的怀抱，恶魔通过做出绝对蠢货的精彩模仿来折磨我，于是那些因奇异的系统优化能力而被雇用的人做了显而易见的事情。他们让LLM在一个半合理的循环中自我提示，以防有人检查令牌消耗，然后自己看Netflix。即便他们自己评估输出不适合部署，也没有一个人被抓到。

我会检查我们Go代码库的一个平行副本，然后告诉AI把整个代码改写成Zig，同时我去做别的事情，只是为了保住我的工作。我太讨厌这狗屁东西了。我的工作有使用跟踪和配额。我并不把它用于实际工作，我只是开启它并忽视输出。

事实上，我所知道的唯一因这件事被解雇的人，是那些对这种组织策略表达了明显怀疑的人，而即便是Ptacek也认为这显而易见地愚蠢。最终结果是每个人都很快学会了赞扬高管在AI方面的远见卓识，否则他们就会在比喻的街头被“处决”。

主啊，求您垂怜，我犯罪了。自我上一次忏悔已过去∞天。我自责如下罪行：

我们在客户那里部署的主要基础设施之一是一个以分析为核心的数据库，称为 Snowflake——对于典型的企业来说，费用非常低，因为它是按使用付费的，而且我们可以每天花一分钟处理他们的所有数据，你得到的是非常省心的部署，总之，它具有许多对我们工作非常愉快的特性。Snowflake 中一个我们没有使用的功能叫做 Cortex。

Cortex 是他们的 AI 聊天机器人层，能够接入元数据（对于非技术人员来说，就是你数据的描述，比如电子表格中某列的含义）并自主查询公司的数据库。理论上，你可以问诸如“上周我们的收入是多少？”这样的问题，它会给出一个答案。

它实际上并不适合生产环境使用。根据记忆，上次我由 Snowflake 的实际员工做演示时，他们报告说，理想配置下的准确率大约是 92%，因为大企业的数据非常复杂（见：这些工具的最佳实践水平，但想象一下你的 CFO 每十个数字中就有一个是完全错误的），而且在管理部署方面存在严重问题。尽管如此，它仍然可以用来制作一些非常炫目的演示。

有几次，我们接触到一些对我们主要产品持一般态度的人，但他们非常非常想用 AI 对他们的数据进行自然语言查询。我们当时想：“好吧，如果你真的想看，也许我们可以适当说明前提，给你展示它可能的效果。”

这是一个可怕的错误。它以最可预见的方式适得其反——每一个看到聊天机器人运行的温和客户，即使我们告诉他们它不会实现他们想要的，也都想立即购买它。其他所有考虑，包括我们有可能通过非人工智能方式帮助他们实现的数百万美元，都被抛到一边。就像一种黑暗而可怕的力量控制了他们的四肢，将他们的手伸进自己的胸膛，把仍在跳动的信用卡呈现给我们，带着冷峻的恳求。我们对这种难以解释的能量转变感到非常震惊，于是我们（明智地）拒绝收钱并结束了销售过程，不久之后将Cortex从我们的演示列表中移除。利用他们推理中的这个漏洞将是太不负责任的，坦白说，举行这次演示本身就已经是不负责任的——医生不会随便炫耀他们从不开的神奇药丸。

事实上，对于任何潜在客户表达了对在其业务中使用人工智能的兴趣超过最短暂好奇的销售机会，我们都被迫选择退出。我并不是说我们听说他们对人工智能感兴趣，然后出于道德理由放弃合同。我的意思是，在整个合作过程中，这些人表现出一种行为模式，使得在不承担声誉和法律风险的情况下向他们销售几乎不可能，而且他们还在构建一种我只能形容为宗教化、低效并且“天啊，拜托不要像LinkedIn上那样在现实中存在”的管理环境。

好消息是，首席信息安全官（CISO）已经习惯了必须保护企业免受他们那些荒唐计划的影响，而这一个实际上并没什么特别不同，只是它带有一种类似宗教的氛围，这在比如云计算中是看不到的。几乎不管你是否接受这个计划，都有风险需要管理，而这就是你要做的事情。从与各地CISO的交流来看，我会说大多数人都在默默怀疑，但不敢发声。

—— 职业CISO及知名演讲者，要求匿名

尽管真正信奉者的比例相当高，但许多负责大型人工智能项目或公开发表相关言论的人，并不相信自己所说的内容。有一些“人工智能负责人”在阅读这个博客时，供职于年经常性收入超过10亿美元的公司，他们写信说自己认为自己的工作完全是欺诈性的，但那是公司内部唯一剩下的晋升途径。

在一次海外出差期间，我有幸与本文开头提到的一位财富500强高管会面，为了避免他们被董事会以行刑方式解雇，我将保持匿名。在交谈过程中，很明显他们非常聪明且技术能力很强，同时他们所在公司也正致力于一系列关于最近创新的夸张声明——“我们的生产力提高了100倍，人工智能是万物的未来，我只是OpenAI用来让我的妻子感受到爱意的媒介。”你知道的，很正常的事情。但因为当时没有麦克风在场，我问为何这些言论没有受到反对。这只是销售宣传吗？

答案更有趣。部分内容确实是向容易激动的受众传达的荒谬销售材料，但这并不是限制诚实的主要因素。他们客户的高管正在说一些关于实现100倍生产力的荒谬言论，这意味着如果供应商的任何高管说这些收益不可信，会破坏客户高管的信誉，被视为攻击（或异端），甚至可能导致企业合同取消。而仅仅因为你想对与公司使命无关的事情发表意见而让企业合同被取消，是被解雇的绝佳方式。

但这家公司也是一个重要的参与者，属于那种会与其他公司签署巨额企业合同的公司。因此，可以推测还有其他供应商向他们销售过产品，而他们的首席执行官担心说出理智的话会与这位高管产生矛盾，我们很快就能看到全世界的高管们紧张地互相指着枪，不想第一个被射中，同时又看着一切逐渐失控6：#fn:6。这就是说，我们正面临一个协调问题：https://en.wikipedia.org/wiki/Prisoner%27s_dilemma#Real-life_examples，涉及高管们对他们目睹的人工智能成果保持诚实——如果他们合作，就能保住工作。如果他们背弃合作，可能会被因尴尬而被暗示为骗子、懦弱者或无能者的同僚解雇，然后被一个无论如何会照章行事的人取代。如果他们能够同时承认真相，可能还会有希望，但目前没有办法协调这一事件。

这听起来令人深感担忧，但值得注意的是，这意味着那些发表荒谬言论的高管并不像最初看起来那么愚笨——他们处在一个紧张的政治环境中，周围充满了盯上自己职位的人，并且受制于同样承受压力的董事会的意志。违反所有理性原则，我曾向标准普尔500指数公司的董事会成员介绍应对人工智能炒作的方法7：#fn:7，他们的情况完全相同——我记得那次会议的主要评论是董事会成员承认他们持怀疑态度，但表达了对职位依赖于强制要求AI投资的焦虑。其中有人评论道：“这么早投资似乎风险很大，而收益不多。”大约两年后，现在看来，他们那十年前价值数十亿美元的机构现在被贴上了“AI原生”的标签，无论那究竟意味着什么。

上述所有情况都汇聚成了我们现在所处的状态，即有效的决策制定已经停滞不前。总体而言，最初只是少数人经历不稳定的心理事件或被炒作裹挟，如今已经导致一种环境，即领导者无法坦诚表达他们关于如何最好地管理组织的信念，因为担心会被免职，从而形成了一种分布式的暗杀式治理。这意味着最不合理的建议完全没有受到挑战，导致员工被评估使用完全可操控的指标，比如“在人工智能上的投入资金”，而这些员工必须配合，否则将面临被解雇的风险。这也造成了对购买“人工智能”解决方案的贪得无厌的需求，这类解决方案既针对那些会相信荒谬主张的真正信徒，也针对那些无法拒绝购买否则会让其对事业承诺受到质疑的非信徒。

这意味着，对于在意识形态被占据的组织内部政治背景下的所有提议，必须包括人工智能的对齐，即使其价值主张显然模糊。到目前为止，我对市场的评估是，大量人工智能项目的爆发实际上是附加了人工智能元素的非人工智能项目，仅是为了通过纯度测试。

例如，我最近目睹一个组织从Oracle数据库迁移到Snowflake数据库的过程——供应商并没有直接进行迁移，而是加上了一个初步阶段，试图让大型语言模型（LLM）自动将Oracle风格的SQL转换为Snowflake风格的SQL。当项目失败时（因为获取足够权限以自动化工作存在问题，而不是因为LLM无法完成如此简单的任务），供应商只是开始手动处理翻译，但公司将其以AI驱动的成功进行宣传，因为SQL中有一小部分无关紧要的内容曾被AI翻译过，然后再粘贴回去。

实际购买的是什么？完全标准的数据库迁移，以帮助一位高管在许可证续订前完成淘汰系统的战略任务。卖给他们上级的是什么？“我把预算的大部分分配给了人工智能，它帮助我完成了我的任务。”真正的人工智能项目，即完全由大型语言模型（LLM）驱动的项目，且项目可能明确未能实现特定数据的那种，其实非常罕见。我们大多在初创企业的环境中见到它们，坦率地说，我们已经停止参与这类项目，因为我们总是在销售对话结束时发现，他们希望我们构建他们却作为已完成产品在市场上宣传的东西。

然而，有些项目根本无法轻易打上人工智能标签，或者倡导这些项目的人既不想撒谎，也没有意识到撒谎已成为必要。在所有情况下，这要么直接扼杀资金请求，要么在所有沟通中增加一种普遍而难以解决的拖延，因为每一项请求都必须反复工作，直到它“足够人工智能”。不遵守要求，要么导致拒绝，要么在很多情况下，会有真正信仰人工智能的人要求你解释为什么额外工作“不能用人工智能完成”。许多公司已经公开宣称这是他们的新招聘政策——当员工请求增加人员时，他们必须证明自己首先尝试过使用人工智能。被忽略的部分是，如果你说你使用了人工智能，但仍然需要帮助，你会被贴上“人工智能能力差”的标签，并可能被解雇。

最终结果是，我所知道的几乎所有大型组织都不再能够专注于任何重要的事情，除非他们是极少数人工智能恰好能够解决其最高优先事项的组织。他们无法购买合理的软件，雇用有能力的人才，与高管诚实沟通项目状态，或开展任何合理的举措。

一种空虚感穿透你 当你意识到这意味着什么 你开始感受到我的感受 现在你已经看到了我所看到的

身处这种不幸的境地，但最终它会过去。我学到了很多关于我们在领导层中灌输的潜在疯狂的不幸，遗憾的是，这些特质会在当前泡沫之后长期存在，只是在等待另一个类似的重新触发因素——而有些组织会一直被困，直到彻底崩溃，就像并非每个人都能成功远离可怕的区块链事件一样。这是留到以后再写的事情。

我想讨论的是一些关于如何在眼前的危机中生存的想法，既可以通过直接改进系统，也可以通过保持理智。我先从“改进系统”部分开始，因为这是我最频繁遇到的情形。

在这里我们需要大量忍着不说、露出微笑。本节假设你正试图实现某些目标，而这些目标并不是去修复组织的狂躁状态，而是试图纠正某个具体项目（并且可能冒着被解雇的风险，无论是作为领导者还是顾问）或实现一些完全不相关的目标。

这部分是给那些只是等待泡沫破裂并努力不发疯的人。

坚持正义的斗争，不要让混蛋压垮你。祝你好运。

另外，这100%是真的，Matt Mullenweg 曾经约我喝咖啡，因为他读了 AI piledrive 的文章：https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/，在当时的语境中可能还挺喜欢，但最后不得不取消，因为他没意识到当天还有航班。我愿意付钱给一个能干的女巫，为这个轻微冒犯下咒。↩：#fnref:1

我们已经拒绝了所有 AI 实施工作。这绝对是一个巨大的泡沫，我们已经将自身暴露降到最低——我们当前的每一份合同，即便 OpenAI 倒闭，也几乎完全不会受影响，顶多有一些二次效应，比如经济衰退导致客户无法支付。当然，对于这种情况我们也无能为力。↩：#fnref:2

我从Gerry Weinberg那里听过的最有价值的规则之一是，咨询就是在别人请求时影响他们。除非有人明确表示他们希望我们插手，通常是通过明确表示他们希望获得关于整体数据策略的指导，否则我们就让项目安静地失败。这些天你几乎认不出我，我现在非常平静。 ↩：#fnref:3

不过我们后来已经私下和解了，尽管我认为我们在核心观点上几乎没有让步。我坚持认为Thomas是一个非常有才华的作家，有很多好的建议：https://sockpuppet.org/blog/2025/02/09/fixing-illinois-foia/ 只是有一次他大错特错，因为他过于认真地对待Hackernews的评论者。我们每个人都有自己的弱点。我的弱点是有人告诉我“Scrum做得对就好”。 ↩：#fnref:4

作为一个负责任的成年人，这总是让我困惑。如果我 somehow 成为一家医院或土木工程公司的CEO，我一秒钟都不会觉得我有权在没有专业人员明确同意的情况下开始强制规定具体程序或建造技术——那些只是听过几场演讲的非技术人员，现在却在对他们极其昂贵的专业人员如何工作做出指令，他们到底有多无知？ ↩：#fnref:5

如果你是高管、董事会成员或任何负责“AI项目”的人，感到被困，我很想听听你的意见。我会非常小心地去掉故事中的序列号，就像我在这里和其他文章中所做的那样。 ↩：#fnref:6

这听起来很华丽，但我认为它实际上是那些强制性的职业发展项目之一，而一半的观众实际上只是在做晚饭。确实，人力资源和专业机构让我们所有人都成为受害者。 ↩：#fnref:7

Note: This has been cross-posted to my company's blog, in case you think there is some use in sharing with someone in a format that looks more authoritative. Link here：https://hermit-tech.com/blog/ai-mania-is-eviscerating-global-decisionmaking.

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：https://x.com/mitchellh/status/2055380239711457578?lang=en

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 1：#fn:1 . 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. 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.

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.

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. 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.

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 2：#fn:2 , but even within projects that we have observed in passing while doing totally unrelated work. 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：https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/, 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.

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. 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.

For example, my last consumer interaction was attempting to get help from Mitsubishi following an automotive failure, where a very polite robot asked me 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.

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.

For this reason, our team has quickly learned while on an engagement not to ask anything about ongoing AI projects in any context – by the time that project has started, it is too late for the management team, and intervention is not possible until a crisis point is inevitably reached. There is no conceivable positive outcome. The failure rate is so high that even basic inquiry leaves us in an untenable position. Any coherent question about how it’s going, what the goal is, who is using it, constitutes an inadvertent attack on the chain of command responsible for the work because there are no good answers to anything . Even in rare cases where my interlocutor has stated that things are going well (usually while the project is still mid-flight and failure has not had a chance to manifest), it is generally obvious that they are doomed, but at least in these cases I can simply agree and then go home to scream into a pillow for six hours straight 3：#fn:3 .

All of this is to say that I am very confident that almost every report at a company about “massive AI productivity gains” is untrue as a matter of brute fact. Even if some companies are seeing clear gains, this is the exception, not the norm. With that assumption in place, we can talk about the dynamics at play, and how it has become impossible for many organisations to stay focused on things that actually matter to their long-term (or even short-term) health.

It has become outright dangerous to even raise the possibility that AI might not be the solution to a problem, let alone be the sole focus of a company’s entire strategy.

In every sufficiently large business we have observed (say, with 500+ employees), we have noted that continued advancement, and increasingly continued employment, has started to require repeated professions of belief in the transformative power of AI for said business. I am not talking about providing ideas about how to use AI in the business – I mean religious profession, declarations of faith. Overwhelmingly these statements are made by non-technicians, though it is not uncommon for technicians to emit deranged statements to curry favour.

There have been several occasions where I have seen someone, apropos of nothing, blurt out almost word-for-word “AI is changing everything”, only to concede moments later that their organisation does not currently use LLMs for anything, and indeed, that they cannot name a single thing that has changed other than they get some use out of ChatGPT (frequently the free-tier). In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI.

Initially these statements were so absurd on their face that I thought it was some cynical ploy to achieve thought leader status, and there are certainly some people doing this – I have had it admitted to me. But the broader reality is so much worse: people who have no background in the technology at all actually believe what they are saying . As a general rule you should avoid getting into business with a liar, but if you must , you can at least reason with them even if only in private. A true believer is much more threatening because they are impervious to even inducement by self-interest.

The turning point in my belief was watching someone with a spectacular amount of money on the line fire their highest performers because they were achieving that performance without LLMs. When an employer publicly talks about AI innovation, we have to ask ourselves if they’re simply trying to manipulate the market or customers. When they privately commit to strategies like this with their own money at stake, with no attempt to communicate that strategy to external clients, I can only assume they really mean what they’re saying.

A while ago, I wrote “Contra Ptacek’s Terrible Article On AI”：https://ludic.mataroa.blog/blog/contra-ptaceks-terrible-article-on-ai/, which was focused on the fact that many of Ptacek’s points in his own essay “My AI Skeptic Friends Are All Nuts”：https://fly.io/blog/youre-all-nuts/ were internally inconsistent 4：#fn:4 . But on the crux of the matter, we are actually in total agreement, because he opens his essay with this:

Tech execs are mandating LLM adoption. That’s bad strategy.

Which is to say that we can sidestep arguments about the precise utility of LLMs entirely and we’re left in a very simple place – it is entirely obvious to both myself and Ptacek, two people that are coming at this from fairly opposed views, that people are being really, really stupid about this, and that organisations are demanding bizarre workflow constraints from their specialist staff. 5：#fn:5

These mandates have led to extremely strange places. Several of my peers now “AI-wash” their work, meaning that even when they can perfectly competently execute on their jobs to the satisfaction of their management teams, said managers are unhappy if the engineers haven’t used AI in the work… so now they’re lying about using LLMs even in contexts where their professional judgement is that they aren’t the appropriate tool. They just do the work, the same way they have for decades, and say Claude did it. Others are being measured on their AI bills with “token leaderboards”, where higher is better because I have evidently fallen into the pocket of Hell where the demons torment me by doing elaborate impressions of absolute fucking morons, so the people hired for their freakish ability to perform system optimisation do the obvious thing. They set the LLMs prompting themselves in a semi-plausible loop in case someone inspects the token consumption and then they watch Netflix. Not a single one has been caught, even when their own assessment of the output is that it isn’t suitable for deployment.

Checking out a parallel copy of our Go repository and telling the AI to rewrite the whole thing in Zig while I work on something else just so I can keep my job. I hate this shit so much. My job has usage tracking and quotas. I don’t use it for actual work, I just spin it up and disregard the output.

In fact, the only people I know of to be fired over this whole thing are people that have expressed visible doubt about this organisational strategy, which again, even Ptacek thinks is transparently dumb. The net result is that everyone has learned very quickly to praise executives on their visionary AI prowess, or they will be gunned down in the proverbial streets.

Bless me, Father, for I have sinned. It has been ∞ days since my last confession. I accuse myself of the following sins:

One of the main pieces of infrastructure we deploy at our clients is an analytics-focused database called Snowflake – for a typical business, the bill is tiny because it’s a pay-as-you-go situation and we can process all their data in one minute a day, you get a very hands-off deployment, and in short it has many characteristics that are very pleasant for our work. One of the features in Snowflake that we don’t use is called Cortex.

Cortex is their AI chatbot layer, with the ability to plug into metadata (for non-nerds, descriptions of your data, like what a column in a spreadsheet means) and query a company’s database autonomously. In theory, you can ask a question like “What was our revenue for last week?” and it will spit out an answer.

It is not really suitable for production usage. From memory, the last time I was given a presentation on it, by actual Snowflake staff, they reported that ideal configuration results in something like ~92% accuracy due to the complexity of data at a large business (see: probably best-in-class for these tools, but imagine your CFO having one in every ten of their numbers be outright wrong) and there were serious issues with managing deployments. Nonetheless, it can be used to produce some very flashy demonstrations.

On several occasions, we’ve been exposed to folks that have been sort of lukewarm on our main offerings, but they really, really wanted to use AI to perform a natural language query on their data. And we thought “Okay, if you really want to see it, maybe we can caveat this appropriately and show you what it might look like.”

This was a terrible mistake . It backfired in the most predictable way imaginable – every lukewarm client that saw the chatbot in action, even with us telling them that it was not going to accomplish what they wanted , wanted to buy it immediately. Every other consideration, including millions of dollars that we could plausibly help them achieve by non-AI means, was swept aside. It was like a dark and terrible force seized control of their limbs, plunged their hands into their own chests, and presented their still-beating credit cards to us in grim supplication. We were so mortified by the inexplicable shift in energy that we (wisely) declined to take the money and ended the sales process, and soon thereafter removed Cortex from our list of demonstrations. It would have been too irresponsible to exploit this gap in their reasoning, and frankly, it was already irresponsible to have even run the demonstration – doctors don’t walk around showing off cool pills that they’d never prescribe.

In fact, we have been forced to opt out of every sale where the lead has expressed anything beyond the most fleeting curiosity in the use of AI in their business. I don’t mean that we’ve heard that they’re interested in AI and elected to drop the contract on moral grounds. I mean that, over the course of the engagement, these people have exhibited a pattern of behavior that has made it near-impossible to sell to them without incurring reputational and legal risk, and are furthermore crafting management environments that I can only describe as cultish, ineffective, and “please dear God, do not let it be on earth as it is on LinkedIn”.

The good news is, CISOs are used to having to protect the business from their hare-brained initiatives, and this one isn’t really that different, except that there’s a cult-like atmosphere to it that you didn’t see with, say, the cloud. It almost doesn’t matter whether you embrace the initiative or not; there’s work to be done to manage the risk, so that’s what you do. From talking to CISOs everywhere, I would say most of them are quietly skeptical but afraid to speak up.

– Career CISO and well-known speaker that asked to remain anonymous

Despite the substantial prevalence of true believers, many of the people running large AI initiatives, or making public statements about them, do not believe what they are saying. There are “heads of AI” who read this blog, at companies with $1B+ in annually recurring revenue, who have written in to say they believe their job is totally fraudulent but it was the only promotion pathway remaining at the organisation.

On a trip overseas, I had the privilege of a meeting with one of the Fortune 500 executives mentioned at the beginning of the post, who will remain anonymous so that they are not executed by firing squad by their board. As we were chatting, it became clear that they were very switched-on and technically competent, and they also happened to be at a company that had committed to the usual battery of exorbitant claims about their recent innovations – we’ve 100x’d our productivity, AI is the future of everything, I am but a vessel for OpenAI to make love to my wife. You know, normal things. But since I had them there without any microphones around, I asked why this was being repeated without opposition. Was it just sales fluff?

The answer was a lot more interesting. It was partially ridiculous sales material being delivered to an easily excitable audience, but this was not the dominant factor constraining honesty. Executives at their customers were saying absurd things about achieving 100x productivity, and this meant that if any executive at the vendor said that these gains were not plausible, it would undermine the credibility of the customer’s executive, be perceived as an attack (or heresy), and possibly result in an enterprise contract cancellation. And getting enterprise contracts cancelled because you wanted to opine on something that doesn’t really matter to your organisation’s mission is a great way to get fired.

But this company was also a major player, of the kind that signs enormous enterprise contracts with other companies. So presumably there is another vendor that has sold to them, and their CEO is worried that saying something sane will contradict this executive, and very quickly we can see how we can have executives around the world nervously pointing guns at each other, not wanting to be shot first but also watching everything gradually spiral out of control 6：#fn:6 . This is to say that we’re facing a coordination problem：https://en.wikipedia.org/wiki/Prisoner%27s_dilemma#Real-life_examples around executives being honest around the AI gains they’ve witnessed – if they co-operate, they keep their jobs. If they defect, they will possibly be fired by their embarrassed peers (who have now been implicitly called liars, cowards, or incompetents) and then replaced with someone that will toe the line anyway. If they could all admit the truth at once there might be some hope, but there is no way to coordinate that event.

This sounds deeply concerning, but it is worth noting that it means that some executives who are emitting nonsensical statements are not as dull as they might seem at first – they’re in a fraught political environment, where they are surrounded by many people that are gunning for their roles, and subject to the whims of a board that is undergoing similar pressure. Against all the dictates of reason, I have presented on navigating AI hype to people on S&P 500 boards 7：#fn:7 and they are in exactly the same situation – the main comments I remember from the session were board members admitting they were skeptical, but expressing anxiety that their positions were contingent on demanding AI investment. One of them commented “investing this early seems like risk without much upside”. About two years later, I can see now that their decade-old multi-billion dollar organisation is now branded as “AI-native”, whatever the hell that means.

All of the above converges on the state that we find ourselves in now, where effective decisionmaking has ground to a halt. Collectively, what started as a few people undergoing either destabilising psychological events or being caught up in hype has now resulted in an environment where leaders cannot speak honestly about their beliefs on how best to guide organisations, for fear of being removed, creating a sort of distributed government by assassination. This means that the least sensible recommendations are going totally unchallenged, resulting in employees being evaluated on totally gameable metrics such as “money spent on AI”, and those employees must play along to avoid being terminated. This has also created an insatiable appetite for purchasing “AI” solutions, which target both true believers that will believe implausible claims, and also non-believers that cannot decline the purchases without having their commitment to the cause coming into question.

This means that all offers that are subject to internal politics at an ideologically captured organisation must include AI alignment, even if the value proposition is patently ambiguous. My assessment of the market so far is that a substantial component of the outburst of AI projects are actually non-AI projects with an AI element slapped on after the fact to pass the purity test.

For example, I recently witnessed an organisation handling a database migration from an Oracle database to Snowflake – instead of handling the migration directly, the vendor bolted on a preliminary phase which involved trying to get an LLM to automate the translation of the Oracle-flavored SQL to Snowflake-flavored SQL. When the project failed (due to issues getting enough permissions to automate the work, not because an LLM can’t do something that easy), the vendor simply started handling the translation by hand but the company billed it as an AI-driven success because some inconsequential portion of the SQL had been translated by AI before being pasted over.

What was actually purchased? A totally standard database migration to help an executive meet the strategic deliverable of decommissioning a system prior to license renewal. What was sold to their superiors? “I allocated a substantial percentage of my budget to AI and it helped me accomplish my mandate.” True AI projects, of the kind that is driven by an LLM as the sole mechanism underlying it, where the project can clearly fail to deliver specific numbers, are actually very rare. We mostly see them in the context of startups, and frankly we have stopped engaging with them because we kept getting to the end of the sales conversation and finding out they wanted us to build the product that they were marketing as completed .

However, some projects simply do not have an easy way to tack on the AI label, or the person advocating for them either does not want to lie or has not understood that lying has become necessary. In all cases, this either kills the request for funding outright, or adds a pervasive and intractable drag on all communications, as every request must be worked and re-worked until it is “AI enough”. Failure to comply will either result in denial or, in many cases, a demand from a true believer to know why the extra work “can’t be done with AI”. Many companies have actively publicized that this is their new hiring policy – when a member of staff requests additional headcount, they must demonstrate that they have tried to use AI first. The part that’s being left out is that if you say you used AI and still need the help, you will be labelled “bad at AI” and potentially laid off.

The net result of this is that almost every large organisation that I am aware of is no longer able to focus on anything important, unless they are one of the (very) few organisations where AI happens to address their highest priorities. They cannot buy sensible software, hire competent talent, communicate honestly with executives about the state of projects, or undertake any sort of sensible initiative.

An emptiness falls through you As you realize what this means You're starting to feel what I feel Now you've seen what I've seen

This is an unfortunate situation to be in, but it will pass eventually. I’ve learned a lot about the latent insanity that we have inculcated in our leadership strata, and unfortunately those traits will persist long past the current bubble, merely awaiting another similar reactivation trigger – and some organisations will stay captured until they have totally collapsed, in the way that not everyone has successfully moved away from the dreadful blockchain affair. That’s something to write about for another time.

What I wanted to get to were some thoughts on surviving the immediate crisis, either by directly making systemic improvements or by holding onto your sanity. I’ll start with the “making improvements” part, because that’s the situation I find myself in the most frequently.

We’re going to do a lot of sucking it up and smiling here. This section assumes that you are trying to achieve some goal that isn't repairing the organisation's manic stance, but either trying to course-correct a specific project (and possibly risk getting fired as either a leader or consultant) or achieve some totally unrelated goal.

This is for people that are just waiting for the bubble to burst and trying not to go nuts.

Fight the good fight, and don’t let the bastards grind you down. Godspeed.

Also, and this is 100% true, Matt Mullenweg once asked me for coffee because he read the AI piledrive essay：https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/, and in context probably enjoyed it, but had to cancel because he hadn’t realized he had a flight later the same day. I am willing to pay a competent witch to hex him for this slight. ↩：#fnref:1

We have rejected all AI implementation work. It is absolutely a gigantic bubble and we have minimized our exposure to it – every single one of our current contracts would be totally unaffected by OpenAI collapsing, save for perhaps some second-order effects such a recession causing a client to become unable to pay us. And there’s nothing we can do to insulate ourselves from that anyway. ↩：#fnref:2

One of the most valuable rules I’ve heard, from Gerry Weinberg, is that consulting is influencing people at their request . Unless someone has indicated that they want us to stick my nose in, usually by explicitly saying they want guidance on general data strategy, we just let the projects fail in peace. You can barely recognize me, I’m so calm these days. ↩：#fnref:3

We have since kissed and made up in private, though I don’t think we’ve budged at all on the core points of our viewpoints. I maintain that Thomas is a very talented writer with a lot of good advice：https://sockpuppet.org/blog/2025/02/09/fixing-illinois-foia/ who just happened to blow it massively that one time because he takes Hackernews commenters too seriously. We all have our weaknesses. Mine is people telling me that “Scrum is good if you do it right”. ↩：#fnref:4

This is always baffling to me as a matter of being a responsible adult. If I was somehow CEO at a hospital or civil engineering firm, I would not for a second think it’s my place to start mandating specific procedures or building techniques without explicit agreement from the professionals on staff – how fucking clueless are the non-technicians who have attended a few talks and are now making mandates about how their extremely expensive professionals are doing their jobs? ↩：#fnref:5

If you’re an executive, board member, or anyone in charge of an “AI project” that feels trapped, I would love to hear from you. I will file the serial numbers off any stories very carefully, as I’ve done here and in every other article. ↩：#fnref:6

This sounds very fancy, but I think it was secretly one of those compulsory professional development things and half the audience were just like, making dinner. Truly, HR and professional bodies make victims of us all. ↩：#fnref:7