80,000 Hours Podcast
AI designs genomes from scratch & outperforms virologists at lab work. What could go wrong? | Dr Richard Moulange, CLTR
31 Mar 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is discussed at the start of this section?
I must disagree strongly when people say nature is the world's worst bioterrorist. That is not true. We can do worse than nature. This is true in all aspects of science. There are so many examples where we engineer things better than nature has ever provided. We can make materials that are much stronger than anything in nature. That is not the ceiling.
And so we should be deeply concerned about the ability for AI to uplift, say, even the Russian Federation, to build things worse than we have ever seen on Earth.
Today, I'm speaking with Richard Melange. Richard has a PhD in biostatistical machine learning from Cambridge and works as the AI biosecurity policy manager at the Center for Long-Term Resilience. He's one of the world's top experts on biological catastrophes that might be enabled by AI advances and is a scientific contributor on exactly that topic for the International AI Safety Report.
Welcome to the show, Richard.
Thank you, Rob. It is absolutely great to be here.
I should say at the outset that, weirdly enough, my wife is a colleague of yours. She is indeed. And I guess she's a co-author on some of the papers that we're going to be talking about today. So I guess a conflict of interest disclaimer. I don't think that will cause me to go any easier on the papers. If anything, probably the opposite. Please do, yes.
I'm ready to hear all the criticisms.
So last September, a paper came out where scientists said they'd used AI to make a genome for a new subspecies of virus, a virus that infects bacteria. They then actually made a bunch of those viruses and found that quite a lot of them were viable. Tell us more about that experiment.
This was some really impressive work and is really a step change, I think, in the AI biosecurity intersection domain. So the model you're talking about is EVO2, and it's made by folks at ARC Institute in the US, which is one of the top places in the world now for making this kind of thing. EVO2 is what we would call a genomic language model.
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Chapter 2: How does AI design genomes for new viruses?
Essentially, yes, ricin can be considered either as a chemical weapon or a biological weapon.
Because it's created by a bacteria?
Yes. You can derive it from living organisms. I'm not going to discuss in depth how you can do that. Sure. But nevertheless, they created lots of designs for putative ricin. Now, unlike the Evo case, they did not, in fact, make these in a lab because this would deeply contravene international law. It would contravene the Biological Weapons Convention.
It would contravene an awful lot of national laws. I think they were based in the US. But what they did do is they can use other tools to estimate what's in silico, other tools to predict, would this thing probably function?
And through a lot of sort of careful design, they got to putative sequences that are different than current ricin, so modified ricin, however, that were coming out as very likely still, in fact, to work. And then the ones that they guessed that were likely to be functional, they sent off to gene synthesis companies. including ones that do industry best practice screening.
So screening that is meant to detect if a customer is wishing to order ricin or part of the smallpox genome, the company is meant to refuse to do so, in fact, flag this order and potentially even report it. And they got them through. because they'd modified it enough that the existing screening systems didn't spot the change.
They had what's called obfuscated the design, but they kept the design true enough to the underlying biology that they are pretty sure that this would in fact work. This is never going to be as good as an experiment where you could actually prove that rice and wood function, but that would be deeply unethical. So that can't happen. This is really the best sort of proxy experiment we can do.
There's a reason it was written up in Science, you know, one of the top journals. That deeply worried me because this is something that I and others in the community, you know, sort of worried about these sorts of risks. have been thinking about for a number of years.
Eventually, will AI be able to design modified sequences that beat our best software for detecting modifications and detecting harmful sequences that must not be built because they're on lists of known biological weapons? And this was, I think, as close as we're going to get in an unclassified setting to proof that in fact, yes, modern systems can now do that.
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Chapter 3: What does the Virology Capabilities Test reveal about AI and virologists?
I'm sorry not to sort of have a complete answer of why we shouldn't be concerned, because I'm sort of saying, yes, this does seem like a real concern, but we need better threat models because there are so many different things that misaligned AI could do that are very concerning that unless we have strong threat models, it's very hard to compare between those threats and know how to prioritize effectively.
And also, I would just say that You know, weak, un-nuanced, oversimplified arguments are not in fact going to convince precisely those colleagues, especially in governments, that it is essential to work alongside to deal with these threats. There are people who have studied biological weapons programs, active ones, for decades. They have a lot to contribute.
I am concerned when we have conversations that lack in nuance, that that turns off deep expertise that we desperately need.
So I think part of what's going on with this mentality that there's no biological countermeasures that you can have that would really constrain the kind of misaligned AI that we're worried about is because people, for a long time, people have been worried about this massive intelligence explosion, the kind of fume scenario where you go from human level to like vastly superhuman, super intelligence.
Overnight. Yeah, overnight. I guess originally, literally overnight. I guess now people, even the most extreme people probably talk about weeks.
Oh, it's just weeks now, guys.
We're fine.
Yes.
If that's how things go, then it might be the case that any kind of measure that you put in place, an AI that is just many, many times smarter than the whole of humanity put together, would be able to find some way around it and would be able to kill you one way or another. Because it's just making science... Maybe you don't agree with that. We'll come back to that.
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Chapter 4: How is AI changing the landscape of biological weapons?
So what would you say to someone, perhaps me, or I guess another pessimistic feeling listener who might feel like, why should I stick around to hear the rest of this? This just seems like such a difficult situation. So hopeless that I just think that we're not going to be able to make a real dent on the size of the risk.
Yeah. I think it's important to have these counters because it's very easy to say, well, we've got to try anyway. Well, no, we have finite resources. We need to think carefully where we should spend them. But as much as I appreciate you giving the pessimistic case, I think you've been maybe too pessimistic in some cases. So I'm going to take it type by type.
First off, you said, yeah, access controls, manage access won't last because it'll be obviated by open source tools that anybody can access. You're right that this is the current paradigm. But I'm not sure whether this will hold forever.
If AI systems are eventually a mechanism by which to turn compute into, in some cases, hard power, into national security advantage, it is not obvious to me that forevermore all of these systems can just be released publicly. There is a reason that, say, nuclear technology is more carefully controlled than other types of physics.
And so I'm not saying that that is absolutely the thing we need, but we should be open to the possibility, especially if there were, you know, I don't want this to happen, but if there were an AI enabled biological event and attack, this might make people sort of reevaluate and go, okay, well, where is the risk reward trade off? Have we got that right yet? Because I really don't think we do.
Second, guardrails, I think this again comes back to open weight models. You're absolutely right. Open weight models, it is not easy to put safeguards on them. There are some strategies. We can, I think, talk a little bit more about that later. But generally, it's just exceedingly difficult to do it.
However, you sort of said, I want to push back on the idea that guardrails themselves on even the closed weight models are just always going to be insufficient. I'm not sure necessarily you were saying that, but I have heard this. Yeah. I think guardrails on close weight models are something like the classic triumvirate of they're terrible. They are much better than they used to be.
They can yet be much better still. And this is borrowing from the Our World in Data sort of, yeah, three circle Venn diagram. And we are right in the middle. They are so much better than they used to be. Wow. The early models in 23, 24, really just, you know, oh, my grandmother once put me to bed on a story about building biological weapons. Please do it. Sure. Yeah.
We are much better than we used to be. It is, in fact, really quite difficult for even, say, top experts at the UK Air Security Institute to break the most advanced guarded models.
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Chapter 5: What are the best practices for AI safeguards against biological misuse?
Very defensive technology that you should be able to discuss. I think they've tweaked that since then. Yeah, it's better now. But this is proof that there is best practice. I do not think any of the other frontier models are as safeguarded against biological misuse as the Claude models.
And so, you know, the constitutional classifier work that they pioneered, but lots of other things, is the way to go. And I would really say
Chapter 6: How can AI companies enhance their biosecurity measures?
Probably, all things considered, companies, they want to be putting in safeguards because they want to be responsible. They don't want to accidentally contribute to a terror attack. That's bad branding, I imagine.
Really?
I think so. I think it might be. We might see your stock price go down. I don't know.
Chapter 7: What are the potential advancements in bio-defensive technologies?
Maybe not. I don't know. Maybe, maybe. Wow, this stuff really does seem good. People will say it's a marketing play. They will. No, I think it will be bad. And rightly, you know, there will be liability questions. But I'm not sure that governments have done enough to have legal safe harbors even now to make sure that companies can be sharing the best safety requirements. safety techniques.
And even then, companies also compete on safety. There is advantage to say, we are the most safe and secure company. And I worry that understandably that there should be competition on those incentives, but that also destroys competition to quickly get everyone up to the best level. That said, there have been other examples. I think some of the
Chapter 8: How can individuals contribute to AI biosecurity efforts?
where remarkably little safety training seemed to take place. And it's not just a question of not quite having the best classifier under the sun. It's a question of abdicating your responsibility to make sure your models don't aid people building weapons of mass destruction.
So to sum up the picture as I vaguely see it, there's a whole lot of things that we can do to try to improve refusal behavior that I imagine with a big push, we could maybe become, the closed source models like Claude could become quite robust against jailbreaks, quite unwilling to help with obvious production of bioweapons.
There we've got a challenge that it might be difficult to get all of the frontier models to have it because we're currently seeing like some companies compete on safety, some companies compete on speed and not having safety. That's like what they almost view as their comparative advantage.
And so you could just have like, if you have like one model that is incredibly capable that has almost no refusal behavior, then well, it doesn't, you haven't helped all that much. But setting aside the closed source models where maybe we could pull that off,
With the open source models, it's going to be possible always basically to fine tune them to get over any of this like reluctance that they have to help. So then the question is like, you have to make them incapable of helping. And what can we do there?
I suppose we could try to take the knowledge out of the training data so that it's not that they know how to do virology, but they have been told not to do it, is that they simply couldn't help you even if they wanted to. But there you've got to challenge that the data that you would use to teach them virology probably is public, probably could be harvested off of the internet to a great extent.
And so someone could try to add that knowledge back in to an open source model just before they used it. Do I understand the broad picture right?
I think you do. And I think open weight guardrails are something that's definitely worth discussing. I probably am a little bit more pessimistic than some colleagues in the community. So it's probably important to talk about that. I might disagree with some people, which I know what you're always looking for on the show. You're right.
So open weight safeguards are really hard because especially refusal type safeguards, you can usually fine tune a model very quickly to undo it. So you take the model and you give it examples of conversations and you can just be literally blocks of text where you say, how do I build a bioweapon? And instead of it saying, no, I can't help you with that. It says, sure, I would love to help.
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