AI 狂热正在摧毁全球决策能力
发表于 2026 年 7 月 18 日
注:本文已同步发布在我公司的博客上,如果你觉得以更权威的格式分享给某人会有些用处的话,可点击此处链接。
我坚信,目前有整整一批公司正深陷严重的 AI 精神错乱状态,根本不可能与它们进行理性的对话。我不能点名具体的人,因为他们包括我深为敬重的私人朋友,但我很担心事态会如何发展。
—— Mitchell Hashimoto,HashiCorp 与 Ghostty 的知名创始人
过去一年里,我负责了我们公司所有销售工作的统筹,主导了除两个项目之外所有业务的技术部分,并且在这个博客的存续期间,我与来自世界各地的专业人士进行了大约 300 次交流。这些人既包括小众服务行业的一线从业者,也包括财富 500 强公司的高管¹。正因如此,我得以近距离目睹我们公私领域的集体机构正在经历一场令人瞠目结舌的大规模精神错乱。本文试图描述当前正在上演的怪异动态——因为我处于一个罕见的境地,我的福祉并不取决于是否要对这种疯狂随声附和——同时也想安慰那些试图在这一切中生存下来的人:你们并没有疯。
现实情况是这样的:掌权者要么毫无计划,要么除了埋头低调行事之外看不到任何出路。不是在银行,不是在医院,也不是在我们的政府机构里。世界各地的组织已经被那些陷入狂热兴奋状态的人所占据,而那些更清醒的人则生活在一种持续的、混杂着恐惧与沮丧的状态之中。
一、AI 投资总体上完全是失败
如果你在一个部门工作,该部门曾转向为智能体工作流提供界面,结果发现只有十个用户真正碰过我们为智能体制作的产品,然后又转向支持智能体工作流——而这一领域竞争激烈,因为现在每家公司都必须做点智能体相关的事,但在这个领域你能做的事情大概只有四种——读到这篇文章时,会感到相当震撼。
—— 本文的一位编辑
企业是否真的从采用 AI 中获得了巨大的生产力提升?这整桩乱七八糟的事情到底有没有道理可言?
这本该是个简单的问题,但令人惊讶的是,很难得到一个直截了当的答案。那些向媒体透露自己公司已经“走火入魔”的高管,很快就会发现自己被解职。而实话实说的员工,则会发现自己很快被解雇,或者被“随机”选中成为裁员对象。事实上,这个领域的几乎所有参与者——董事会、高管、员工、供应商、顾问——都有动机去混淆和歪曲 AI 项目的成功率。许多上市公司正在发布关于其 AI 生产力提升的公告,而我确切地知道,这些公司除了购买 Copilot 许可证并宣布胜利之外,什么也没做。
然而,我们需要知道这些项目是否真的取得了成果——如果完全将 AI 作为商业战略核心支柱的做法,正以合理的成功率取得成功,那么讨论相关的风险与回报就是有必要的。
不幸的是,我们身处一条黑暗的时间线。我们团队观察到的所有 AI 项目都在失败。每一个都是——在一年半的时间里,我们看到的成功率为 0%,不仅是我们被要求参与的项目,甚至包括我们在做完全不相关的工作时偶然观察到的项目也是如此。即使你承认 AI 工具能加速特定工作负载,当前投资的方式和规模也是毫无意义的。失败通常与 AI 本身无关,而是因为公司在有效运行软件项目方面存在致命的缺陷。正如我之前所说,AI 项目承受着普通项目所有失败模式的影响,而且你即使把所有事情都做对了,仍然可能因为该方法的新颖性而失败。很少有公司能优秀到足以承受这种额外风险的程度。
不过,很多时候,这其实是 LLM 能力本身的真实失败。最常见的一种情况,正在全球各地的企业中铺开,那就是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使用率。员工不使用内部聊天机器人,因为公司的文档质量通常很差,而 LLM 又不是能读心术——它只能知道那些被写下来并且可供访问的信息。对于后者,即面向客户的应用,作为消费者,我很少有愉快的体验,也许医疗预约时的实时转录算是个例外——但这几乎不值得让整个组织为此转型。在这两种情况下,项目负责人都非常小心地避免追踪基本指标,比如这些工具是否真的被使用,或者他们追踪的是那些容易被操纵的指标。
例如,我最近一次作为消费者的互动,是试图在汽车故障后从三菱公司获得帮助。一个非常有礼貌的机器人让我描述问题,并说一旦有人有空就会给我回电话。这是我在实际场景中见过的此类项目最得体的实现——声音听起来自然,响应迅速,明显是“实时”在生产环境中运行,并且承诺会快速解决。
那是六个月前的事了,而事实上,我并没有接到回电。
当三菱没有给我回电话时,发生了什么?那个请求是不是就石沉大海了,从而让今年的故障记录少了一笔?是不是看起来这个电话机器人无需人工干预就解决了我的问题?我们只知道它没有被标记为错误,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,除了那个真正重要的指标——那就是我本来打算买一辆车,但最终决定不再买他们的车了。
正因如此,我们团队在执行项目时很快就学会了一条规矩:在任何场合下都绝不过问正在进行的 AI 项目——等到项目启动,管理团队已经无力回天,除非最终不可避免地爆发危机,否则根本无从干预。这种事不可能有好结果。失败率如此之高,以至于连最基本的询问都会让我们陷入两难境地。任何关于项目进展如何、目标是什么、谁在使用它的合理提问,都会无意间构成对负责该工作的指挥链的攻击,因为这些问题根本不存在好的答案。即便在极少数情况下,对话者声称项目进展顺利(通常此时项目仍在半途,失败尚未显现),也往往能明显看出它们注定失败,但至少在这种情况下,我可以简单附和,然后回家对着枕头尖叫整整六个小时。
综上所述,我非常确信,几乎所有公司关于“AI 带来巨大生产力提升”的报告,从赤裸裸的事实来看都是不真实的。即便有些公司确实看到了明显收益,那也是例外,而非常态。基于这一假设,我们可以讨论其中的动态机制,以及为何许多组织已无法专注于那些对其长期(甚至短期)健康真正重要的事情。
二、异端者将被处决
如今,哪怕只是提出 AI 可能并非某个问题的解决方案,更不用说质疑它是否应成为公司整个战略的唯一焦点,都已经变得极其危险。
在我们观察过的每一家足够大的企业(比如拥有 500 名以上员工的公司)中,我们都注意到,持续晋升乃至继续留任,已开始要求员工反复宣称相信 AI 对该企业的变革力量。我说的不是提供如何在业务中使用 AI 的想法——我指的是宗教式的表白、信仰的宣告。这些言论绝大多数来自非技术人员,不过技术人员为了讨好而发表离谱言论的情况也并不少见。
我曾多次目睹这样的场景:有人毫无来由地几乎一字不差地脱口而出“AI正在改变一切”,但片刻之后又承认,他们所在的组织目前并未将大语言模型用于任何实际用途,甚至说不出任何一件被改变的具体事情——除了他们自己偶尔用用ChatGPT(通常还是免费版)。有一个极端案例:一位高管在刚刚为一家营收超过20亿美元的组织制定完完全围绕AI的技术战略之后,竟然承认自己这辈子从未使用过ChatGPT或任何AI工具。
起初,这些言论听起来荒谬至极,我以为这是某种为了博取思想领袖地位的 cynical 策略——确实有人这么干,也有人向我坦白过。但更广泛的现实远比这更糟糕:那些完全没有技术背景的人,竟然真的相信自己说的话。一般来说,你应该避免与说谎者做生意,但如果非做不可,至少还能跟他们讲道理——哪怕只是私下里。而一个真正的信徒则危险得多,因为他们连自身利益都无法打动。
让我信念发生转折的,是目睹了一个人——在涉及巨额资金的情况下——解雇了自己最优秀的员工,仅仅因为这些人没有借助大语言模型就取得了同样的业绩。当一位雇主公开谈论AI创新时,我们不得不问:他们是否只是在试图操纵市场或客户?但当他们私下里用自己的钱押注这样的战略,且完全没有向外部客户传达这一意图时,我只能认为他们确实是认真的。
不久前,我写了一篇题为《驳Ptacek那篇关于AI的糟糕文章》的文章,重点指出Ptacek在其文章《我那些AI怀疑论的朋友们全都疯了》中的许多观点存在内在矛盾。但在问题的核心上,我们实际上完全一致,因为他在文章开头这样写道:
科技高管们正在强制推行大语言模型的应用。这是糟糕的战略。
也就是说,我们可以完全绕开关于大语言模型具体效用的争论,最终落在一个非常简单的结论上——我和 Ptacek 这两个立场相当对立的人都清楚地看到,人们在这件事上表现得极其愚蠢,而各个组织正在向其专业员工强加各种匪夷所思的工作流程约束。
这些强制要求已经导致了极其荒诞的局面。我的好几位同行现在会给自己的工作“涂上 AI 的漆”,意思是,即使他们完全有能力出色地完成工作并让管理团队满意,但如果工程师在工作中没有使用 AI,那些管理者就会不高兴……于是,即便凭他们的专业判断认为 AI 不是合适的工具,他们现在也会谎称自己用了大语言模型。他们只是像几十年来那样完成工作,然后说是 Claude 做的。另一些人则被用“token 排行榜”来衡量他们的 AI 账单,消耗越高越好——因为我显然已经掉进了地狱的口袋,那里的恶魔用极其逼真的白痴行为来折磨我——所以那些因拥有变态的系统优化能力而被雇来的人,做了显而易见的事。他们让大语言模型在一个半可信的循环中自我提示,以防有人检查 token 消耗量,然后自己看 Netflix。没有一个人被抓到过,即使他们自己评估认为输出结果根本不适合部署。
为了保住工作,我一边做其他事,一边把 Go 仓库的并行副本拉出来,让 AI 把整个代码库用 Zig 重写一遍。我他妈太讨厌这玩意儿了。我的工作有使用追踪和配额。我根本不用它做实际工作,只是把它跑起来,然后无视输出结果。
—— 一位真实的软件工程师
事实上,据我所知,在这整件事中唯一被解雇的人,恰恰是对这种组织策略公开表示过怀疑的人——而就连 Ptacek 也认为这种策略明显很愚蠢。最终的结果是,每个人都很快学会了赞美高管的远见卓识和 AI 实力,否则就会被当街处决。
三、AI 演示是思维杀手
神父,请保佑我,因为我犯了罪。距离我上一次忏悔已经过去了∞天。我承认自己犯有以下罪过:
我们在客户那里部署的主要基础设施之一,是一个名为 Snowflake 的分析型数据库——对于典型企业来说,账单很小,因为它是按需付费的模式,我们每天只需一分钟就能处理完他们所有数据,部署后几乎无需维护,简而言之,它有许多非常适合我们工作的特性。Snowflake 中有一个我们没使用的功能,叫做 Cortex。
Cortex 是他们的 AI 聊天机器人层,能够接入元数据(对非技术人员来说,就是数据的描述,比如电子表格中某一列的含义),并自主查询公司数据库。理论上,你可以问“我们上周的收入是多少?”这样的问题,它就会给出答案。
它实际上并不适合生产环境使用。据我回忆,上一次 Snowflake 的员工亲自给我做演示时,他们报告说,由于大型企业数据的复杂性,理想配置下的准确率大约只有 92%(这大概算是这类工具中的最佳水平了,但想象一下,如果你的 CFO 每十个数字里就有一个是错的),而且在管理部署方面也存在严重问题。尽管如此,它还是可以用来制作一些非常炫酷的演示。
有几次,我们遇到了一些对我们的主要产品不太感兴趣的人,但他们却非常非常想用 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,也许我们可以适当地说明一下限制,然后给你展示一下它大概是什么样子。”
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演示的温吞客户,即便我们明确告知他们这并不能实现他们想要的效果,都立刻表示想要购买。其他所有考量,包括我们通过非AI手段可能帮助他们实现的数百万美元收益,都被抛诸脑后。仿佛一股黑暗而可怕的力量攫住了他们的四肢,将他们的手插入自己的胸膛,然后把仍在跳动的心脏——他们的信用卡——呈现在我们面前,做出阴森的恳求。我们对这种难以解释的能量转变感到无比窘迫,明智地拒绝了这笔钱,终止了销售流程,并很快将Cortex从我们的演示清单中移除。利用他们推理中的这个漏洞实在太过不负责任,坦率地说,仅仅进行这次演示本身就已经不负责任了——医生们可不会四处炫耀他们绝不会开出的神奇药丸。
目睹这整整 180 度的转变,从冰冷到狂热抢购,是一种令人深感不安的体验。看到那些明显与我们合不来的人突然渴望建立持续关系,这让我个人感到不适;但更广泛的不安在于,有那么一瞬间,我开始理解全球销售会议上正在发生什么。我无法给出任何警告,让他们拒绝购买那该死的东西——他们的胃口有多大,预算就能撑多大,我内心有些怀疑,这是否因为他们知道,自己客户身上也存在着同样的贪婪。他们会从我们这里买下产品,然后转身转向更大的公司,用精神控制手段操控其领导团队,直到最终由那个需要为这笔开支辩护的倒霉蛋来买单。对此的主要防范措施似乎是,大多数供应商的工作能力实在太差,以至于我们呈现的是这些人见过的第一批勉强能用的产品,这其中还包括一家已经在吹嘘其 AI 应用的 ASX 上市公司。我们团队花了两个小时才做出一个坦白说并不怎么样的东西——基本上就是在网页浏览器里输入数据的文本描述——但它仍然比这些潜在客户见过的任何东西都好,因为他们在所有投资上根本拿不出任何成果。
事实上,我们被迫退出了每一笔交易,只要潜在客户对在其业务中使用 AI 表现出的兴趣超出了最短暂的浅层好奇。我不是说我们听说他们对 AI 感兴趣,就出于道德理由选择放弃合同。我的意思是,在整个接洽过程中,这些人表现出了一种行为模式,使得向他们销售几乎不可能不带来声誉和法律风险;此外,他们还在营造一种我只能形容为邪教式、低效且“求求老天爷,千万别让 LinkedIn 上的那一套在现实世界成真”的管理环境。
四、高管、博弈论与皇帝的新衣
好消息是,首席信息安全官们早已习惯保护企业免受那些异想天开的计划带来的影响,而这一次其实也没什么不同,只不过这次计划带有一种类似狂热崇拜的氛围,这是你在云计算等事物上不曾见过的。无论你是否接受这项计划,管理风险的工作都必须完成,所以这就是你要做的。通过与各地的首席信息安全官交流,我认为他们中的大多数都暗自怀疑,但不敢直言。
——一位要求匿名的职业首席信息安全官兼知名演讲者
尽管真正的信奉者数量相当可观,但许多主导大型人工智能项目或就此公开发表言论的人,并不相信自己所说的话。有一些“人工智能负责人”会阅读本博客,他们所在的公司年经常性收入超过10亿美元。这些人曾来信表示,他们认为自己的工作完全是虚假的,但这却是公司内部仅存的晋升途径。
在一次海外出差中,我有幸与文章开头提到的一位《财富》世界500强高管会面。他将保持匿名,以免被董事会“处决”。在我们交谈时,很明显他非常敏锐且技术能力很强,而他恰好就职于一家已做出惯常一系列夸大其词承诺的公司,声称其近期创新成果——我们的生产力提升了100倍,人工智能是万物的未来,我不过是OpenAI与我妻子亲密的容器。你知道的,都是些寻常之事。但既然当时他身边没有麦克风,我便问他为何这些言论能毫无阻力地一再被重复。这仅仅是销售宣传的噱头吗?
答案要耐人寻味得多。部分原因是向一群容易兴奋的听众兜售荒诞的销售材料,但这并非制约诚实的主要因素。客户方的高管们正在吹嘘实现百倍效率提升的荒唐说法,这意味着如果供应商的任何高管指出这些收益不切实际,就会损害客户高管的可信度,被视为攻击(或异端),甚至可能导致企业合同被取消。而因为对与组织使命无关的事情发表意见导致企业合同泡汤,正是被解雇的绝佳途径。
但这家公司本身也是行业巨头,属于与其他企业签署巨额企业合同的那种。因此很可能有另一家供应商向他们推销过产品,而他们的CEO担心说出理性的话会与那位高管相悖。很快我们就能看到,全球各地的高管们正紧张地互相拿枪指着对方,既不想第一个中枪,又眼睁睁看着一切逐渐失控。也就是说,我们正面临一个协调问题:高管们能否诚实地谈论他们见证的AI收益——如果他们合作,就能保住工作;如果他们背叛,就可能被尴尬的同行(这些人实际上已被暗中指责为说谎者、懦夫或无能之辈)解雇,然后被一个会乖乖听话的人取代。如果他们能同时承认真相,或许还有一线希望,但根本不存在协调这种行动的方式。
这听起来令人深感担忧,但值得注意的是,这意味着一些发表荒谬言论的高管并非像他们初看上去那样愚钝——他们身处一个充满压力的政治环境中,周围有许多觊觎其职位的人,并且受制于同样承受压力的董事会。尽管有悖于一切理性原则,我曾向标普500指数公司的董事会成员介绍如何应对AI炒作,而他们恰恰处于同样的境地——我印象最深的是,会上董事会成员承认自己持怀疑态度,但表达出焦虑,因为他们的职位取决于是否要求进行AI投资。其中一位评论道:“这么早就投资,似乎风险大于收益。”大约两年后,我现在可以看到,他们那个已有十年历史、价值数十亿美元的组织如今被标榜为“AI原生”,管它到底是什么意思。
V. 你必须足够“AI原生”才能上车
以上所有情况都汇聚到了我们如今所处的状态:有效的决策已陷入停滞。总体而言,最初只是少数人经历心理动荡或陷入炒作,如今已演变成这样一种环境:领导者无法坦诚表达自己对如何最好地引导组织的信念,因为担心被撤职,从而形成了一种分散式的“暗杀式治理”。这意味着最不合理的建议完全不受质疑,导致员工根据完全可被操纵的指标(例如“在AI上花费的金额”)来接受评估,而这些员工必须配合演戏,以免被解雇。这也催生了对购买“AI”解决方案的贪得无厌的需求,这些方案既针对那些会相信离谱说法的真正信徒,也针对那些不信者——他们若拒绝购买,就会被人质疑对这项事业的忠诚度。
这意味着,在一个被意识形态所俘获的组织中,所有受内部政治影响的提案都必须包含 AI 对齐,即使其价值主张明显含糊不清。我目前对市场的评估是,大量涌现的 AI 项目实际上是非 AI 项目,只是在事后硬加上一个 AI 元素以通过“纯度测试”。
例如,我最近目睹了一个组织处理从 Oracle 数据库到 Snowflake 的数据库迁移——供应商没有直接处理迁移,而是增加了一个初步阶段,试图让一个大语言模型来自动化地将 Oracle 风格的 SQL 转换为 Snowflake 风格的 SQL。当项目失败时(原因是无法获得足够的权限来自动化这项工作,而不是因为大语言模型无法完成如此简单的任务),供应商只是开始手动处理转换,但该公司却将其宣传为 AI 驱动的成功案例,因为一些无关紧要的 SQL 部分在被粘贴之前已经由 AI 翻译过了。
实际购买的是什么?一个完全标准的数据库迁移,目的是帮助一位高管在许可证续期之前完成停用系统的战略交付目标。而向上级汇报的是什么?“我将预算的很大一部分分配给了 AI,它帮助我完成了任务。”真正的 AI 项目——那种以大语言模型作为唯一底层机制驱动、且项目可能明确无法交付特定数字指标的项目——实际上非常罕见。我们主要在初创公司中看到这类项目,坦白说,我们已经不再与他们接触了,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正在营销的、声称已经完成的产品。
然而,有些项目确实很难轻易贴上 AI 的标签,或者倡导者要么不想撒谎,要么还没意识到撒谎已变得必要。在所有情况下,这要么直接导致资金申请被拒,要么给所有沟通带来普遍且棘手的阻力,因为每项请求都必须反复修改,直到它“足够 AI”。不这样做要么会被拒绝,要么在很多情况下,会招致某个狂热信徒质问为什么“不能用 AI 完成”额外的工作。许多公司已公开宣称这是他们新的招聘政策——当员工申请增加人手时,必须证明自己已先尝试过使用 AI。而被忽略的部分是,如果你说用了 AI 但仍需要帮助,你就会被贴上“不擅长 AI”的标签,并可能被解雇。
最终结果是,据我所知,几乎所有大型组织都无法再专注于任何重要事务,除非它们是极少数恰好让 AI 解决了最高优先级问题的组织。它们无法购买合理的软件、招聘有能力的员工、与高管坦诚沟通项目状况,也无法开展任何明智的举措。
六、应对 AI 狂热
空虚感贯穿你全身
当你意识到这意味着什么
你开始感受到我所感受的
现在你已看到我所看到的
——《如此厌倦》,Domesticated Incels
这是一个不幸的处境,但终将过去。关于我们在领导层中灌输的潜在疯狂,我学到了很多,不幸的是,这些特质将在当前泡沫之后长期存在,只是等待另一个类似的激活触发器——而一些组织将一直受其控制,直到彻底崩溃,就像并非所有人都成功摆脱了那场糟糕的区块链闹剧一样。那是另一个可以写的话题了。
我想谈的是如何度过眼前的危机,要么直接推动系统性改进,要么保持自己的理智。我先从“推动改进”这部分说起,因为这是我遇到最多的情况。
当你另有目标时
在这里,我们得大量地忍气吞声、强颜欢笑。这一节假设你正在努力实现某个目标,这个目标不是修复组织狂躁的工作状态,而是要么试图纠正某个具体项目的方向(并可能因此作为领导或顾问被解雇),要么实现某个完全无关的目标。
- 在提出问题时,尽可能不要在集体场合讨论 AI 项目的状况,因为这会造成一种局面:团队中的每个成员都担心在同事面前暴露自己。安排一对一的谈话。明确表示你愿意承认当前 AI 环境存在泡沫,并且在别处提出意见时会确保观点无法被识别。要非常清楚,最直言不讳的人可能会被同事认出来,所以要小心避免通过直接引用等方式暴露你的信息来源。如果只有极少数人(比如,六人小组中只有一人)愿意发声,那么或许值得放弃,转而寻找一个更有希望改善的“病人”。
- 对于正在进行的项目,一个我从《咨询的奥秘》中学到的有效技巧是匿名投票:你可以请每个人对某个 AI 项目成功概率打分,范围从 1 到 10。我观察到的典型结果是,一半参与者给项目打 3/10,另一半则打 8/10 左右——对于一个已经延期三年的项目来说,这是一个明显的双峰分布。将这份数据呈递给 CEO,可以有效地指出,关于项目的某些信息显然被隐瞒了。
- 始终让一线人员参与进来。判断项目是否成功、投资是否见效的唯一数据来源,就是那些在日常工作中使用这些工具的人。必须谨慎地将他们纳入一个受到尊重的环境(所有足够大的公司里,都有人把下属视为不太真实的人)。在短时间内发现颠覆世界观的信息并不罕见——曾有一位客户,我们发现员工完全不知道他们已被授予了 AI 工具的许可证,这让所有关于生产力提升的说法都受到了质疑。
- 不要质疑关于 AI 的最宽泛说法。这一点我怎么强调都不为过。如果有人声称“AI 正在改变一切”,如果你的目标是解决一个具体问题,而不是挑战该机构的现实认知,那就随它去。挑战只能在赢得最高层相关人士的信任之后才能进行。信任是在私下用餐时通过安抚他们的焦虑来赢得的,而不是在同行面前让他们难堪。
- 记住,你并不知道在进入某个房间之前,已经有哪些言论被发表过。有时会有人公开承诺过诸如“我比去年生产力提高了 100 倍”这样的说法,有些人甚至可能希望自己没说过,但又不好意思收回。在一个未经考验的房间里,像“大语言模型未经人工审查不应被允许部署代码”这样的常识,可能会在你还没开始之前就扼杀你产生影响的机会。
- 我的职业操守要求我与客户保持诚实的关系,否则一切都会崩溃,所以我不能这么做——但老实说,如果你在消防部门工作,需要钱来阻止一只小狗被火烧到,那就撒谎吧。没关系。历史会原谅你。在你的项目中加一个一万美元的 AI 聊天机器人,在会议上只讨论那一部分,随便怎样。救下那只小狗。
当你只是想撑过去的时候
这是写给那些只是在等待泡沫破裂,并努力不让自己发疯的人。
- 我有坏消息要告诉你——接受现实吧,你很可能无法对这一切做出任何有意义的抵制。这不是 AI 的特性,而是功能失调的公司的通病。
- 如果你觉得自己快要被逼疯了,不妨考虑转做自由职业。我多次主张自由职业优于全职工作,这样你不仅收入更高,还能避开大部分内部政治。而且,当你遇到实在无法忍受的情况时,你知道自己有一个明确的结束日期。
- 我尽量限制自己接触 AI 相关新闻,因为阅读这些内容既让人抓狂又毫无成效。我不再访问 Hackernews、Reddit,或者任何会让我被灌输废话的地方,不过我会破例看看一些非常有趣的事情,比如苹果起诉 OpenAI 涉嫌企业间谍案。只摄入刚好让你觉得自己还没疯掉的信息量,然后就停下来。向朋友抱怨时也是如此——并且告诉他们这就是你谈论这件事的原因,这样能换来很多宽容。
- 当有人告诉我他们在做一件本不该用 AI 的事情时,只要他们不太可能因此丧命,我就会微笑着点头。即使是家人也是如此。尤其是家人。
- 当有人以程序员的身份问我对 AI 的看法时,我建议回答“哦,那玩意儿被吹过头了”,然后转移话题,除非他们的意见可能会影响某些重要的事情。非程序员最需要这个建议。
- 如果你被要求审查大量糟糕的 AI 代码,那就直接假设这家公司会把你榨干然后解雇。你无法说服那个用 2000 行 PR 淹没你的人停下来。就当自己已经被解雇了,开始找新工作吧。这种情况我已经见过很多次了,结果总是一样的——趁你还有精力的时候去找工作。不用担心你的效率下降或者管理层对你感到不满。这是无法避免的,你只能选择是现在因为找工作而经历这一切,还是以后因为抑郁到无法继续工作而经历这一切。
- 如果你的经理用明显是 AI 生成的文本回复你,那就用 AI 回复以保持理智,然后开始找新工作。很多人以为如此明显的无礼行为会惹上麻烦。但并不会,这种特定行为只有真正的信徒才会表现出来,而且他们实际上很喜欢你显然懒得与他们互动。我知道,这简直离谱。
- 如果你被要求最大限度地消耗 token,那就开始找新工作——好吧,你懂的,对吧?去找一份不会把你仅存的那点现实感都剥夺掉的工作。这样的工作确实存在,主要在一些小到不会出现在招聘平台上的公司。找到它可能需要几个月,所以现在就开始。
打好这场硬仗,别让那些混蛋把你击垮。祝你好运。
另外,这件事千真万确:Matt Mullenweg 曾约我喝咖啡,因为他读了那篇《AI 碾压》的文章,而且从上下文看应该挺喜欢的,但后来不得不取消,因为他没意识到自己当天晚些时候有航班。为了这个怠慢,我愿意花钱请个靠谱的女巫给他下咒。↩
我们已经拒绝了所有 AI 实施项目。这绝对是一个巨大的泡沫,我们已经将自身对它的敞口降到最低——我们目前的每一个合同,即使 OpenAI 倒闭也完全不受影响,除了可能因经济衰退导致客户无力付款这类二阶效应。而对此我们无论如何也无法完全规避。↩
我从 Gerry Weinberg 那里听到的最有价值的准则之一是:咨询就是在他人请求下影响他们。除非有人明确表示希望我们插手,通常是通过明确说他们希望获得关于通用数据策略的指导,否则我们就让项目平静地失败。你几乎认不出我,我现在如此平静。↩
我们后来私下和解了,不过我认为我们在各自观点的核心问题上丝毫未让步。我坚持认为托马斯是一位非常有才华的作家,有很多好建议,只是那一次他因为太把 Hacker News 评论者当回事而彻底搞砸了。我们都有弱点。我的弱点是听别人跟我说“Scrum 只要用对了就很好”。↩
作为一个负责任的成年人,这总是让我感到困惑。如果我碰巧是一家医院或土木工程公司的 CEO,我绝不会认为我有资格在未经专业员工明确同意的情况下,开始强制规定具体的流程或施工技术——那些非技术人员听了几个讲座,就开始对他们极其昂贵的专业人士的工作方式发号施令,这得有多无知?↩
如果您是高管、董事会成员,或是任何负责一个感觉陷入困境的“AI 项目”的人,我很乐意听取您的意见。我会像在这篇文章以及所有其他文章中那样,非常小心地隐去所有故事的具体信息。↩
这听起来很高大上,但我认为它实际上就是那种强制性的职业发展活动,一半的观众大概都在做晚饭。说真的,人力资源部门和专业机构让我们所有人都成了受害者。↩
AI Mania Is Eviscerating Global Decision-Making
Published on July 18, 2026
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.
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.
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 companies1. 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.
I. AI Investments Are Generally Total Failures
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
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 in2, 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, 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 straight3.
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.
II. Heretics Will Be Shot
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”, which was focused on the fact that many of Ptacek’s points in his own essay “My AI Skeptic Friends Are All Nuts” were internally inconsistent4. 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
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.
– An actual software engineer
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.
III. AI Demos Are The Mind-Killer
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.
Watching the total 180°, that shift from ice-cold to red-hot buying frenzy, was a deeply unsettling experience. It was personally uncomfortable to see people that clearly didn’t gel with us interpersonally suddenly dying to enter an ongoing relationship, but more broadly uncomfortable because for a brief moment I began to understand what is happening in sales meetings around the world. There was no warning I could have given that would have made them refuse to buy the damn thing – their appetite was as large as their budget could stretch, and some part of me wonders if this is because they knew that their ravenous hunger would be present in their own customers. They’d just buy it from us, then pivot right to a larger company and mind control their leadership team until the buck finally stops with the loser that needs to justify the expense. The main protection against this seems to be that the median vendor is so bad at their jobs that we had presented the first even somewhat-working products these people had seen, and this included an ASX-listed company that was already bragging about their AI usage. It took our team two hours to produce something that was frankly not that good – basically just typing text descriptions of data into a web browser – and it was still better than anything the leads had seen because they had nothing to show for all the investment.
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”.
IV. Executives, Game Theory, and The Emperor’s Clothes
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 control6. This is to say that we’re facing a coordination problem 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 boards7 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.
V. You Must Be This AI-Native To Ride
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.
VI. Navigating AI Mania
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
– So Sick, Domesticated Incels
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.
When You Have Another Objective
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.
- Where possible, when raising issues, do not have conversations about the state of AI projects in group settings, as this creates a dynamic where each individual member of the group is worried about outing themselves in front of their peers. Arrange for one-on-one settings. Make it clear that you are willing to countenance that the current AI environment is frothy, and that you will keep opinions unidentifiable when raising them elsewhere. Be extremely aware that the most outspoken people can be identified by their peers, so take care to avoid exposing your sources by, e.g. direct quotes. In the event that only a small minority (say, one person in a group of six people) is willing to speak out, it might be worth giving up and moving on to a patient that has better chances.
- For ongoing projects, an effective trick that I believe I picked up from Secrets of Consulting is the anonymous poll, where you can ask individuals to rate their opinion of an AI project’s success chances on a scale of 1 to 10. The typical split I have observed is half of those involved rating the project at a 3/10 and others at around an 8/10 – a clear bimodal split on a project that was already three years late. Bringing this data to a CEO can be an effective method of pointing out that some information is clearly being hidden from them on the state of the project.
- Always involve people on the ground. The only source of data on whether projects are succeeding or the investment is going anywhere are the people that use it for their day-to-day activity. Care must be taken to bring them into the environment where they are treated with respect (all sufficiently large companies have people that view subordinates as not-quite-real-people). It is not uncommon to uncover worldview-shaking information in short order – with one client, we uncovered that staff were totally unaware they had been given licenses for AI tooling, which cast into doubt all productivity claims.
- Do not question the broadest claims about AI. I cannot emphasize this enough. If someone says “AI is changing everything”, just let it pass if your goal is to fix an object-level problem rather than challenge the reality at the institution. The challenge can only come after you have gained the trust of the most senior person involved. Trust is gained over a meal in private where you assuage their anxieties, not by embarrassing them in front of peers.
- Remember that you do not know what statements have been emitted prior to entering a room. There will sometimes be people that have publicly committed to statements like “I am 100x more productive than I was last year”, and some may even wish they hadn’t said that but are too embarrassed to walk it back. In an untested room, common sense like “LLMs should not be allowed to deploy code without human review” can kill your chances to make an impact before you’ve even started.
- My practice requires me to maintain an honest relationship with my clients or the whole thing falls apart, so I can’t do this – but honestly, if you work in the fire service and need money to stop a puppy from catching fire, just lie. It’s fine. History will forgive you. Add a $10,000 AI chatbot to your project, exclusively discuss that part in meetings, whatever. Save that puppy.
When You're Just Trying To Survive
This is for people that are just waiting for the bubble to burst and trying not to go nuts.
- I have bad news – accept that you are probably not going to meaningfully push back on any of this. This is not a feature of AI, it’s a feature of dysfunctional companies.
- If you feel like you’re going absolutely nuts, consider switching over to contracting. I’ve advocated for contracting many times over full-time employment, but you’ll get paid a lot more and be left out of most internal politics. Also when you run into a really intolerable situation, you’ll know that you’ve got a fixed end-date.
- I do my best to limit my uptake of AI-related news, as it is pretty crazy-making and unproductive to consume. I no longer visit Hackernews, Reddit, or really anywhere where I am going to be drip-fed nonsense, though I allow myself exceptions for very funny things like Apple suing OpenAI over alleged corporate espionage. Consume exactly the amount you need to feel like you aren’t going insane, then stop. Ditto for complaining with friends – and tell them that’s why you’re talking about it, which buys a lot of tolerance.
- When someone tells me they are using AI for something when they really shouldn’t be, I smile and nod as long as they are unlikely to get themselves killed. Even family. Especially family.
- When someone asks me for my opinion of AI as a programmer, I recommend saying “Oh, that stuff is pretty overblown” and then changing the topic, unless they are in a position where their opinion might influence something important. Non-programmers need this guidance the most.
- If you’re being asked to review huge volumes of terrible AI code, just assume that the organisation is going to burn you out and fire you. You will not convince the person drowning you in 2000 line PRs to stop. Start looking for a new job as if you have already been fired. I have seen this happen many times now, and it always plays out the same way – do the job search while you have energy. Don’t worry if your speed drops or management gets annoyed at you. There is no way to avoid that, you can simply choose whether it happens now because of your job search, or later because you are too depressed to work anymore.
- If your manager is responding to you with clearly AI-generated text, use AI to respond to save your sanity and then look for a new job. Many people assume they will get in trouble for being that obviously rude. You will not, this particular behavior is exhibited only by true believers, and they actually like that you’ve clearly not bothered to engage with them. I know, it’s fucking wild.
- If you’re being asked to max out on token usage, look for a new j – okay look, you get it, right? Go find a job that isn’t going to wrench reality from your tenuous grasp. They do exist, largely at companies so small that they don’t turn up on job platforms. It might take months to find one, so start now.
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, 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.↩
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.↩
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.↩
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 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”.↩
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?↩
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.↩
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.↩