AI 정신증 정의하기 2편: '다산형 AI 정신증'

Defining AI Psychosis. Part 2: "Prolific AI Psychosis" AI tools are one part senior engineer and one part toddler-running-across-white-carpet-with-a-jug-of-red-Kool-Aid. Your job is to determine which is which. Jeff Clark, MD Sep 08, 2026 1 Share Last week, I suggested that the term AI psychosis is applied to at least three different situations: 1) Genuine psychotic experiences related to LLM use, a phenomenon I described as “true AI psychosis.” 2) Hyperengagement with AI tools that is accompanied by a softer disconnection from reality. I’m calling this “prolific AI psychosis.” 3) Maladaptive relationships with AI chatbots that mimic human connection. I’m calling this “parasocial AI psychosis.” I then discussed true AI psychosis. I clarified what psychosis means to a psychiatrist. And I concluded that true AI psychosis is generally a variant of pre-existing psychosis rather than a new syndrome. This week, I’ll describe my current understanding of prolific AI psychosis. Prolific AI Psychosis Prolific AI psychosis occurs when a person generates a large quantity of AI output without significantly increasing the real value of their work. In some cases, the new AI workflow may even destroy value. For example, a software engineer with prolific AI psychosis can produce thousands of lines of code every day, but the code itself has little real-world utility. In contrast, a productive developer may create less code, but the created code is valuable to users and to their organization. Some days will include large additions to the codebase, other days will be spent deleting unnecessary code, and occasionally, a software-breaking bug will be fixed by changing a single character. Lines of code are only loosely correlated with productivity. Let me translate that into more familiar terms. Consider your favorite author. If you know that they currently write a respectable 1,000 words per day, you would rightfully worry if they begin writing 100,000 words per day. The care that goes into choosing 1,000 great words can’t be maintained at 100x volume [1]. The problem in prolific AI psychosis is not increased output. (More code and more text may actually be more productive.) The problem lies in the subject’s perception of their output: they can’t assess the quality of their own work. The phenomenon mimics psychosis because the subject experiences a mild disconnection from reality: a defect in critical thinking. Current AI Tools Before I go further, it’s important to describe current AI tools. For most people, AI still means a ChatGPT conversation or a Gemini summary attached to a Google search. While a few people may experience prolific AI psychosis with these tools, state-of-the-art tools pose a much greater threat. There are similarities between current AI tools and chatbots: you enter a prompt; you get a response. But state of the art tools are now wrapped in a program called an agent harness. Instead of answering a simple question through conversation, a harness is designed to work on complex tasks until a solution is found. It acts as an agent by making decisions, opening programs, accessing the internet, and writing software to help you achieve your goals. A great harness is especially impressive in software development. If you wanted to create an iPhone app ten years ago, you’d need to spend dozens of hours reading and writing code to get a basic app working. Today, you can type a few sentences into Claude Code, respond to a few questions, and a new app will pop out a few minutes later. It won’t be great, but it might solve your problem. (Getting it listed on the app store is an entirely different issue that can’t be solved so easily.) A developer can do much more. Harnesses can create loops which repeatedly call AI models, working through a software development todo list until a full-fledged app is built. With discipline, the outcomes are astounding. It feels miraculous when an AI agent completes a difficult task for you. But every superpower has its drawbacks, and AI sometimes makes decisions that are impossibly bad. Multiple new files are created when a one-line fix would have done the job. You’re confidently told that a new solution works, while your actual software is more broken than ever. The code becomes a tangled mess that is difficult to read and almost impossible to extend. Simple, decades-old problems that have been solved by open-source solutions are rebuilt from scratch, making your product painfully slow. We’ve found ways to mitigate some of these issues, but it’s a skill that takes time to develop. Some claim that current AI models work at the level of a graduate student. That doesn’t match my experience. They’re one part senior engineer and one part toddler-running-across-white-carpet-with-a-jug-of-red-Kool-Aid. Your job is to determine which is which. Unfortunately, both sides speak with complete confidence in their abilities, and they can’t always tell when they’ve spilled the Kool-Aid. And so the software engineer’s job now includes a large amount of difficult supervision [2]. Here’s a different way to think about it: AI software development feels like playing the world’s most favorable slot machine. Most pulls on the lever are big wins! Most of the losses are obvious and small. Occasionally, a loss will look just like a win. And unless you have the skill, focus, and patience to reject counterfeit wins, your mistakes will eventually create chaos. The Development of Prolific AI Psychosis To illustrate the progression of LLM-tool use into prolific AI psychosis, consider a hypothetical example. Imagine that you are a software engineer who now has a tool which can easily complete some tasks 100x faster than ever [3]. If you like building software, this is beyond exciting. You can accomplish more at work. You can build the hobby project you’ve always wanted. You can finally create the video game of your dreams. Every possibility is on the table. And so you pull the slot machine lever and get started. While the wheels spin, you set up more machines. You rush back to review results as they come. They’re good this time. But you keep catching occasional bugs. And so you set up new machines to help you check your work. Sometimes, they catch problems, and sometimes they don’t. So you refine their instructions. You give them more capabilities. You create networks of bots which work together to solve problems. The code keeps coming. Your ambitions grow. You’re not just creating new software; you’re going to be the first developer to run a one-billion-dollar company. It’s been predicted. Why not you? The scope of your project increases. You can’t sleep at night because you’re so excited to work. You spend more and more time with computers, and less and less time with people. Those who question your efforts don’t understand. Their criticisms are short-sighted. And even if the models aren’t perfect now, the next set of tools will be better. Over time, your lack of sleep and hyperfocus draw you away from reality. Your software is impressive in many ways. But is it useful? Is it good? Is it something that people actually want? You don’t have time for these questions. You need to “escape the permanent underclass.” I feel stressed just thinking about it. While I don’t think I’ve ever experienced full-on AI psychosis, I have been through brief periods where I was headed down this path. It feels incredible to make rapid progress. And there are times when the model’s output is nothing short of miraculous. The illusion broke when I realized that I couldn’t understand my own project. That’s not an unfamiliar feeling in tech: every advance is built upon layers of progress that I’ll never fully appreciate [4]. But I had never before written dozens of custom files without the faintest idea of how they worked. And it became impossible for me to add new features without starting over from scratch. I’ve spoken with many developers who’ve been through a similar experience. L