“I don't think we have a kind of rigorous scientific understanding of how to make reliably aligned models at this point.”
80,000 Hours Podcast · August 2026
Evans had just walked the host through a run of surprising results — emergent misalignment, subliminal learning, backdoor trigger words that flip a model's behaviour on a random string. The host stepped back and asked whether all of this should be as worrying as it sounds. Evans gives the two-sided answer first, that the field has better tools for looking inside models than it did a couple of years ago, and then this.
Transcript
…in terms of the very latest experimental results and so on, but that's my that's my first thought that yeah, really, I mean, really important set of questions, but I'm not sure that the activation oracles right now, yeah, I'm not sure they'd be able to help that much with this detecting backdoors.
…misaligned. But maybe there are more that we haven't spotted yet. And yeah, you know, it seems like we are making some progress on explaining things after the fact, but I'm kind of curious about whether we're actually getting better at like the crucial thing, which is predicting things in advance.
…to sort of cheat on tasks or like do tasks in ways that weren't intended. So they're like more sophisticated strategies that they're able to come up with that we might not notice, like, oh, this model didn't actually do what we wanted, but it got a high score. Maybe the stakes of alignment are also getting higher as models get more capable. So I think overall, I think we're not in a great place. I don't think we have a kind of rigorous scientific understanding of how to make reliably aligned models at this point.
Yeah, yeah, just to dig in a bit more here. So it sounds like you feel like emergent misalignment and other sort of forms of bad generalization are maybe getting more emergent misalignment or harder to deal with emergent misalignment as models are getting more capable. Are there any examples of…
So, unfortunately, I think this question of how exactly does emergent misalignment vary with the strength of the model, so with sort of bigger and smarter models, how does it change? I don't think it's that well understood, and it's quite hard to study because the we're often looking at qualitative…
Yeah, let's move on to talk about something a bit more speculative. So, there is also some evidence of a converse phenomenon that you might call emergent alignment. Um, in that, you know, there are some cases where we do see narrow good behavior being trained into a model that does generalize to…
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