Red Teaming o1 Part 2/2– Detecting Deception with Marius Hobbhahn of Apollo Research
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What is the O1 model and why is it a breakthrough for AI reasoning?
Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan LeBenz, joined by my co-host, Eric Tornberg. Hello, and welcome back to a special emergency pod edition of the Cognitive Revolution. As the entire AI world reacts to OpenAI's announcement and same day release of their new O one and O one mini reasoning models, I sought out members of the O one red team to get their takes on the new model's capabilities and safety profile, as well as the current state of OpenAI's approach to pre release safety testing.
I'm really grateful that within just hours of my reaching out, I had the opportunity to speak with Marius Hapan from Apollo Research, and Leonard Tang, Aidan Ewart, and Brian Huang from Hayes Labs. While these two conversations are certainly not all you need to understand the new models, I do believe they provide a valuable perspective. And I'm glad to say that recent drama surrounding OpenAI notwithstanding, it seems that they've done a pretty good job with the O one testing and release process. While I would have ideally liked to see our guests granted a bit more time for open ended exploration, they did have a few weeks to conduct automated testing, which, considering that these are funded organizations with full time teams dedicated to building test suites in advance of new model releases, does seem rather reasonable.
I was also particularly pleased by how candid they were able to be in these conversations, and especially with the fact that Apollo had the opportunity to contribute directly to the O one system card in a way that they ultimately felt very good about. From everything we've learned, it appears that the O one models were created by applying intensive reinforcement training to the GPT four O class of models. Remembering that GPT three point five, the RLHF version of GPT three, was released roughly two years later than the original, I think it's reasonable to think about the O one models as a sort of GPT four point five. Where GPT four class models were already closing in on expert level performance on many routine tasks.
O one's reasoning abilities are now enough to match or even exceed expert performance in many areas, while also expanding the scope of problems they can solve to include those that require more task decomposition and planning, trial and error, and other familiar forms of reasoning. This is more or less what I expected OpenAI to release next. And I think the nature of this model helps contextualize a number of recent statements made publicly by or otherwise attributed in the press to, leadership at OpenAI, Anthropic, DeepMind, and Microsoft. Capabilities have clearly not plateaued. It had just been a while since the last major data point. Recent efficiency gains have been amazing, but models that can reason at length could easily more than offset them, particularly if they drive another major increase in demand.
And the sort of detailed reasoning and problem solving traces that O one can produce are exactly the sort of synthetic data points that could get us over any natural data wall as leading labs continue to scale. As such, it's no surprise that OpenAI is not sharing the full chain of thought with users, and it's easier all the time to understand how Anthropic might believe that leading developers in twenty twenty five or twenty twenty six could get so far ahead of the field that nobody else has a chance to catch up. Safety wise, meanwhile, it again seems that model capabilities and alignment are mostly highly correlated. O one is harder to jailbreak, largely because it reasons more effectively in general, and this includes reasoning about what it should and shouldn't do.
For now, overall, it seems that we're still in the sweet spot, where the potential utility of AI systems is tremendous, but the risks of major harm remain relatively minimal.
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Chapters
8 chapters
1
What is the O1 model and why is it a breakthrough for AI reasoning?
0:00–4:27
2
How does Apollo Research’s red‑team evaluate deception and scheming in O1?
4:27–7:26
3
What new evaluation benchmarks did Apollo create to test self‑reasoning and theory‑of‑mind?
7:26–11:26
4
How do the “easy”, “medium” and “hard” versions of the tests differ in difficulty?
11:26–16:34
5
What concrete examples show O1’s ability to explore its environment and modify its own code?
16:34–21:17
6
How does the model handle conflicting goals between developers and its own objectives?
21:17–25:30
7
What does the Apollo team conclude about O1’s risk of catastrophic scheming?
25:30–29:05
8
What are the broader implications for AI safety and future model development?
29:05–55:31
Speakers
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