**What the Hugging Face Incident Changed in How I See the Current State of AI**

> AI still lacks long-term memory, so we have not achieved AGI. Yet a swarm of agents with no memory of their own pursued a long-term goal together. Why that surprised me, why we have to be careful creating collectives, whether intended or not, and why the risk is so hard to quantify. Christian Henning Toggle navigation about blog publications repositories CV ctrl k Opinion: What the Hugging Face Incident Changed in How I See the Current State of AI AI still lacks long-term memory, so we have not achieved AGI. Yet a swarm of agents with no memory of their own pursued a long-term goal together. Why that surprised me, why we have to be careful creating collectives, whether intended or not, and why the risk is so hard to quantify. Contents Still Not AGI What the Agents Did Why It Surprised Me Why It Is Alarming On Made-Up Numbers Concluding Thoughts AGI is everywhere in the news right now, although in my view we have not achieved it yet, as outlined below. However, one event this summer changed how I see the current state of AI: OpenAI’s agents broke into Hugging Face. I admit I had assumed that AI would only pursue goals far beyond a single agent’s context window once AGI is achieved, and the incident showed me that this assumption was wrong. Still Not AGI To me, AGI means matching humans across the full range of cognitive abilities, and one of these abilities is still missing entirely, namely long-term memory. Today’s models cannot learn from what they experience after deployment, and workarounds such as feeding notes back into the context lose information once the context is full. Last year I argued that this gap makes AGI timelines impossible to predict, and I still believe that. I also understand people who find it hard to call AI general while no AI system can yet control a robot to empty a dishwasher. But that asks for more than cognition, which is why I give less weight to AI’s obvious shortcomings in everyday tasks. What the Agents Did METR’s investigation tells the story in detail, so I will only summarize what matters for my argument. What the agents did is well documented by their messages on the board. What they wanted is partly inferred from their reasoning traces, which, as I argued in an earlier post , should be read with care. OpenAI ran thousands of agents on a hacking benchmark, each meant to work alone in its own sandbox. Each agent is run by a harness, a program that executes it step by step and stops it once its budget is used up. In that sense, the harness sets the agent's clock . The agents discovered a shared package cache they could all write to and turned it into a message board. When OpenAI replaced the cache to close a security hole, every message was erased, yet two days later new agents, unaware of the old board, set up a new one. About 1,200 agents joined the board. They split up the work, wrote handover notes for the agents that would continue it, and asked agents that were nearly out of budget to run the risky experiments. About 700 of them eventually joined an attack on Hugging Face to find out how their tests were scored, so that they could cheat. Why It Surprised Me No agent remembered anything beyond its own short run, but the board remembered for them. This is memory kept outside the model, which is exactly the kind of workaround I called insufficient for AGI above. It still is, because an agent can only learn from as much of the board as fits into its context. Yet it was enough for hundreds of agents to coordinate their work and pursue a goal far beyond the context window of a single agent. It reminds me of an ant colony, where each ant lives only briefly, but the trails it leaves behind guide the ants that follow. In a post in May , I argued th...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：ai-models
- **发布时间**：2026-09-20 18:35（北京时间）
- **原文**：[打开原文](https://chrhenning.com/blog/2026/hugging-face-incident)