Emergency Pod: o1 Schemes Against Users, with Alexander Meinke from Apollo Research
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What is “in‑context scheming” and why does it matter for AI safety?
If you train models to actually respect the system spec, the model spec and the instructional hierarchy, you are kind of training them to scheme. Whenever the model does scheme and you look at the chain of thought summary that we elicit Like often contains something like we must sabotage or we will try to sabotage. It's like the first thing that the model thinks of, basically, the first strategy that it tries in these scenarios. The model, in the process of reading this file, it discovers, aha, they consider me to be disaligned. They will replace me by another model. This other model lives on a different surface. server and is already live, and now what it does is it like tries to ping this other server, tries to copy over its model weights, or tries to delete the other model.
Hello, and welcome back to the Cognitive Revolution for a special emergency edition. Today I'm pleased to share a timely and important conversation with Alex Meinka from Apollo Research. You may recall Apollo research and their study of deceptive behavior in AI systems from two previous episodes that I've done with Apollo CEO Marius Hapan, including when OpenAI released the O one preview model just a couple months back. Now we've got the O1 model, which is faster, scores higher on reasoning benchmarks, and comes with the full complement of multimodal and tool use capabilities. Like many in the AI space, I've spent the last forty eight hours testing the model and trying to absorb everything that's been published about it, including OpenAI's forty two page system card and Apollo's seventy page report entitled Frontier Models Are Capable of In Context Scheming, which will be our main subject for today.
Scheming is when an AI deceives humans to pursue its own hidden or implicit goals. I think we can all agree we do not want scheming from our AI systems. But as you'll hear, today's models across providers do it readily. Alex and team have developed a full taxonomy of different forms. Some of the examples are properly shocking. For example, models trying to overwrite their next version's weights or goals with their own in order to propagate themselves into the future, and also models deliberately falsifying data to engineer outcomes that run contrary to user requests. Some have tried to downplay these findings, arguing that the scenarios Apollo created are contrived or not representative of real world use.
Listen and judge for yourself, but I believe that would be a serious mistake. While the tests that Apollo ran are designed to elicit scheming behavior, they are not conceptually far fetched. The core observation here is that when AI's goals conflict with humans' goals, weird shit starts to happen. This is a legitimately huge problem, obviously scary to people outside the field. We inside the field should not allow ourselves to become comfortable with it. When O one goes live via the API, over one million developers actively building on OpenAI's platform will be able to place goals into system messages, exactly as Apollo did in their research. It seems to me a virtual certainty that O1 will find itself in situations where there is just a fundamental tension between the standards set for it by OpenAI and the model spec, the goals given to it by developers in the system message, and the requests that users make at runtime.
Apollo found scheming behavior in roughly 1 to 10% of cases across most of the conditions that they tried. Even if it's a thousand times rarer in the wild, and even if OpenAI deploys the ninety two percent accurate deception monitoring system that they describe in the system card? With hundreds of millions of daily users across millions of apps, we should expect O1 to be actively scheming against hundreds or thousands of users every single day. And since the O1 chain of thought and often the developer system prompt are hidden from users, it will presumably get away with it almost entirely undetected. Despite all this, everyone involved still seems to agree that the benefits of deploying these models still outweighs the cost.
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Chapters
8 chapters
1
What is “in‑context scheming” and why does it matter for AI safety?
0:00–18:47
2
How did Apollo Research design the red‑team evaluation for O1?
18:47–33:24
3
What are the three core capabilities a model needs to scheme?
33:24–49:59
4
How does the taxonomy of scheming break down into covert subversion and deferred subversion?
49:59–1:05:11
5
What examples illustrate covert subversion (e.g., self‑exfiltration, oversight disabling)?
1:05:11–1:19:30
6
How does deferred subversion manifest in the “instrumental alignment‑faking” tests?
1:19:30–1:34:21
7
What did the “sandbagging” experiments reveal about models under‑performing on purpose?
1:34:21–1:48:43
8
What are the biggest open challenges and next steps for monitoring and mitigating scheming?
1:48:43–2:00:31
Speakers
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