Tech CEOs are apparently suffering from AI psychosis

科技公司高管们似乎正患上“AI精神错乱”

There is a certain wildness in the tech industry these days that both mimics previous eras of large changes, like cloud computing (runaway costs in the early days), and is like nothing we’ve ever seen before (record revenues accompanied by mass layoffs). One possible explanation: Tech executives, especially CEOs, are collectively suffering from delusions of AI grandeur.

如今的科技行业弥漫着一种狂热，这种狂热既像云计算时代早期那样（成本失控），又有着前所未有的特征（营收创纪录的同时伴随着大规模裁员）。一个可能的解释是：科技公司高管，尤其是首席执行官们，正集体陷入对人工智能的宏大妄想之中。

And at least one tech CEO has said as much out loud: Box founder Aaron Levie. “CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI,” Levie wrote on X. CEOs “play with AI,” develop a prototype, or generate a contract, to use Levie’s examples, and then make the leap to believing agents can do the work.

至少有一位科技公司CEO公开表达了这一点：Box创始人Aaron Levie。Levie在X上写道：“CEO们特别容易患上‘AI精神错乱’，因为他们与实现AI最大价值所需的‘最后一公里’工作距离太远。”以Levie的例子来说，CEO们只是“玩玩AI”、开发个原型或生成一份合同，然后就跳跃性地认为AI智能体（Agents）可以完成所有工作。

But these top-level executives aren’t the people who have to review code, discover bugs, and identify calls to hallucinated libraries before software is deployed. They aren’t responsible for training AI models on a company’s idiosyncratic contract terms, nor do they have to spend days combing through contracts to find sneaky terms, as Levie indicates. In other words, Levie’s theory posits, CEOs don’t really understand processes well enough to know what really can and can’t be automated. But that lack of knowledge doesn’t stop them from acting on their beliefs.

但这些高层管理人员并不是那些在软件部署前需要审查代码、发现漏洞并识别幻觉库调用的人。正如Levie所指出的，他们不负责根据公司独特的合同条款来训练AI模型，也不必花费数天时间梳理合同以寻找隐蔽条款。换句话说，Levie的理论认为，CEO们并不真正了解业务流程，无法判断哪些工作真正可以自动化，哪些不能。但这种认知的匮乏并没有阻止他们根据自己的信念采取行动。

It’s important to note that Levie is not an AI hater. Quite the opposite. He mostly posts AI positivity on X to his 2.7 million followers, writing blogs titled “Headless software is the future” on how software built for AI agents is the way forward. He also puts his money where his mouth is, backing AI startups as an active angel investor. So what are CEOs to do instead? Levie advises CEOs to use AI “a ton” to really see what it can and can’t do, “and come out the other side with an appreciation for both the upside and the real work.”

值得注意的是，Levie并非AI的反对者，恰恰相反。他经常在X上向其270万粉丝发布积极的AI内容，撰写题为《无头软件（Headless software）是未来》的博客，探讨为AI智能体构建软件才是前进的方向。他也是言行一致的，作为一名活跃的天使投资人，他投资了多家AI初创公司。那么CEO们该怎么做呢？Levie建议CEO们“大量”使用AI，以真正了解它能做什么、不能做什么，“并最终对AI的优势和实际工作量都有所认知。”

I have enough faith in humanity to believe that there are CEOs out there attempting to do just that, but right now, they seem to be in the minority. In just the first five months of 2026, the tech industry has had nearly as many layoffs as in all of 2025: 115,430 people have been fired from 152 tech companies so far in 2026, compared to 124,636 people let go by 275 companies in 2025, according to industry layoff tracker Layoffs.fyi. And the bulk of companies have pointed to AI as a reason for cutting these jobs.

我对人类仍有足够的信心，相信确实有CEO在尝试这样做，但目前看来，他们似乎只是少数。根据行业裁员追踪网站Layoffs.fyi的数据，仅在2026年的前五个月，科技行业的裁员人数就几乎赶上了2025年全年：2026年迄今已有152家科技公司裁员115,430人，而2025年全年为275家公司裁员124,636人。大多数公司都将裁员原因归咎于AI。

Many argue that the biggest tech companies are AI washing, or crediting AI productivity gains in the past or future, when other business decisions and metrics are really driving the cuts. Still, some of these stories are surprising. Zeb Evans, the CEO of project management and productivity software startup ClickUp, proudly declared on X that he had laid off almost a quarter of his employees — 22% — after rolling out about 3,000 AI agents to do internal work. Evans swore this wasn’t done to reduce costs. Instead, he wants a workforce composed of people who run AI agents and spend their days quickly reviewing the agents’ work. He believes this will create a “100x org,” as he calls it.

许多人认为，大型科技公司是在进行“AI洗白”（AI washing），即把裁员归功于AI带来的生产力提升，而实际上驱动裁员的是其他商业决策和指标。尽管如此，其中一些案例依然令人震惊。项目管理和生产力软件初创公司ClickUp的CEO Zeb Evans在X上自豪地宣布，在部署了约3000个AI智能体处理内部工作后，他裁掉了近四分之一（22%）的员工。Evans坚称这不是为了削减成本，而是希望建立一支由“运行AI智能体并整天快速审查智能体工作”的人员组成的团队。他认为这将创造一个他所谓的“百倍效率组织”（100x org）。

While AI can be a very useful tool, the data on AI and productivity doesn’t support such assumptions. By miles. A meta-analysis of other research published in October in UC Berkeley’s California Management Review found “no robust relationship between AI adoption and aggregate productivity gain.” Research published in March by the National Bureau of Economic Research did conclude that AI adoption improved productivity but noted “a productivity paradox, in which perceived productivity gains are larger than measured productivity gains.”

虽然AI是一个非常有用的工具，但关于AI与生产力的数据并不支持上述假设，甚至相去甚远。加州大学伯克利分校《加州管理评论》去年10月发表的一项元分析发现，“AI采用与总体生产力提升之间不存在稳健的关联”。美国国家经济研究局3月发表的研究确实得出结论称AI采用提高了生产力，但也指出了“生产力悖论”，即“感知到的生产力提升大于实际测量的生产力提升”。

After creating thousands of agents to work on tasks, researchers at MIT concluded that agents just aren’t doing human-quality work yet in many cases. They predict at the current rate of LLM improvement, models will “be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level.” In other words, AI is on track to perform at base competence on most tasks in about three years. These researchers believe agents will need another few years to outperform humans.

在创建了数千个智能体来处理任务后，麻省理工学院的研究人员得出结论：在许多情况下，智能体还无法达到人类的工作质量。他们预测，按照目前大语言模型（LLM）的改进速度，到2029年，模型将“能够以最低限度的合格质量完成大多数文本相关任务，平均成功率在80%至95%之间”。换句话说，AI有望在大约三年内达到大多数任务的基本胜任水平。研究人员认为，智能体还需要几年时间才能超越人类。

Meanwhile, research published in the Harvard Business Review showed that when everyone is using AI to produce more stuff, the bottleneck simply shifts to executives. Their work awaits the people who must authorize all the stuff everyone is producing. If everyone is empowered to act, then from what OpenAI experienced last year, we can tell that things may get out of control. Are CEOs ready for that? If not, the most certain outcome of the ongoing CEO AI psychosis will simply be organizational chaos.

与此同时，《哈佛商业评论》发表的研究显示，当每个人都在使用AI生产更多内容时，瓶颈就会转移到高管身上。他们的工作就是等待审批大家生产的所有内容。如果每个人都被赋予了行动权，那么从OpenAI去年经历的情况来看，事情可能会失控。CEO们准备好应对这种情况了吗？如果没有，那么这场持续的“CEO AI精神错乱”最确定的结果，只会是组织混乱。