Future of Life Institute Podcast
How AI Can Help Humanity Reason Better (with Oly Sourbut)
20 Jan 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is the main topic discussed in this episode?
in the medium to long term, automation will just win out eventually in most cases, because it's always gonna be more efficient to have the tireless, doesn't get sick AI systems that can just kind of run 24 seven.
Chapter 2: What is the main focus of AI for human reasoning?
But that of course comes with all kinds of costs. There are some really important decisions to be made by individuals, by groups, by societies, about where we actually go next and how it is that we're directing that.
If we can, in the meantime, enable better decisions to be made by individuals, by societies, then hopefully, even if it's the case that everything eventually gets handed off to AI, we'll be in a better position to trust that and to know that it's going to be trustworthy. And as a society, kind of endorse that and move in that direction in a way that we think is wise.
If your system has systematic blind spots, then you might expect that it could perhaps surreptitiously or even inadvertently kind of surface a biased summary of the situation. Now that could lead you to systematically biased decisions.
Chapter 3: How can AI enhance collective fact-checking?
Far too often, things are just sort of chaotic and confusing. People fail to coordinate. People fail to understand the consequences of their actions. They even fail to understand what their options even are in the first place. And each of these problems we think is remediable to some extent with support from tools, many of which would incorporate AI.
Ali, welcome to the Future of Life Institute podcast. Hi, Gus. Nice to be here. Great. All right. Do you want to introduce yourself?
Sure. Yeah, I'm Ollie. I work at the Future of Life Foundation.
Chapter 4: What role does AI play in improving human decision-making?
One way to think about FLF is it's kind of like a little spin out from FLI. We take a slightly different strategy. We're looking at being a kind of accelerator or I think an accelerator is the right term for projects that might be neglected in making the future go well. And especially we've got a big focus on AI right now, as everyone has. This is a hot topic right now.
Mm hmm.
So we can categorize these tools into say three categories. So epistemics, coordination and risk targeted application.
Yeah. So one area that we're really interested in is what we've called AI for human reasoning. And this is kind of one large focus for FLF right now. It's not the only focus, but it's an important one. We have some other kind of back burner priorities that we're trying to work on as well.
Chapter 5: How can AI help mitigate human reasoning atrophy?
But when we say human reasoning, what do we mean human? Well, both individuals, but also groups and all the way up to large societies and even humanity as a whole. And then reasoning, we're referring to the kind of the whole decision-making cycle. So
from making observations, coming to understandings, modeling the world, and with groups, we're talking about communicating, and then through to making decisions and even acting together and coordinating and this kind of thing. So reasoning is supposed to encompass this whole thing.
And part of why we think this is an important kind of package of things to consider together is that they're really important synergies. So when you understand things better, you can come to better decisions. When you can come to better decisions and understand each other better, you can coordinate better and so on. There's all these interesting synergies.
So the other reason we think it's important right now is, of course, the world is only getting more complicated and enabling...
individuals and groups and societies to reason better about the options we have in front of us for our near and long-term future it's going to be really important to make sure that that goes well it's very often things kind of just kind of meander around it feels like things are happening by accident or it feels like things aren't really being chosen in a what we might think of as a wise way
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Chapter 6: What is the significance of abstraction in AI systems?
And often things go in directions that really not anyone wants. And that's, you know, on its face, that's paradoxical. And we think it's because as a society, we need to kind of elevate this ability to do reasoning.
What's a good example of a project that relates to AI for human reasoning?
Yeah, so I guess I can give a couple of examples and then talk about the... We've kind of mapped the space a little bit more generally, and we've got some initiatives that we're trying to get started, and we've got some that we're already supporting. So a great example of something already existing here. Have you heard of community notes?
I can give a kind of rough explanation, but you can think of things like fact checking and adding context. Historically, they've been performed in this kind of centralized or roughly centralized fashion. You've got these media broadcasters, they're relatively few, they're relatively long lived, and they therefore have this kind of reputation and identity.
Chapter 7: How does trust impact the use of AI tools like Wikipedia?
They're sort of trackable by states and so on, and there are some rules they have to follow. Maybe they publish retractions, maybe there are certain kinds of standards they have to follow with regards to their evidence and so on. So this kind of centralized fact checking happened. And then, of course, social media kind of blew open that. Now everyone is a publisher.
And so we're in a slightly different world. And for a while, people tried to apply the same kind of centralized fact checking mindset.
Chapter 8: What is a map of human reasoning and its implications?
And this wasn't really scalable in practice. Now, Community Notes is one of some kind of innovations in that space. The first principle is, can we crowdsource fact checking? And it's not just fact checking. It's adding useful context to things that people might find useful in the context of some kind of social media post.
The way Community Notes achieves its kind of trust, because of course, this fact checking is quite a sort of powerful and important duty in a way, or like providing context is really important. People need to be able to trust that.
And merely having it be kind of vote-based and this kind of thing, you could imagine that being quite swingy or it could be dominated by some factions or whatever, or manipulable. And so Community Notes achieves this kind of trust. It uses this bridging algorithm, which means that it looks for proposed notes that people have written, which achieve some kind of consensus of being useful.
according to an inferred sort of axis of usual disagreement. So it kind of looks for... And what that ends up being in practice is that the principal component of disagreement is usually left-right politics, but not always. But that kind of gives you this kind of gold standard for this note is considered useful by a broad segment of society. It bridges gaps. It's useful in that sense.
So this is community notes. It's crowdsourced in that sense. Now, one project that we're supporting is ways of accelerating that. So a real issue with community notes is some misleading or confusing or unhelpful message can kind of make it around the world, go viral in minutes or hours. And the trouble is this community notes process, of course, takes time. Someone has to notice and flag this.
Then it has to enter kind of a queue or a pool. And the community of people that contribute notes, they have to then notice this. They have to do some research. They have to put together a potential context that they want to provide. Then that has to go into this voting algorithm and we have to kind of infer, is it a useful note and all this kind of thing. So this can take hours or even days.
And so that's unfortunate if this thing has already gone around, you know, there's the classic saying, the lie makes its way around the world before the truth has got its boots on. So one thing that we're supporting there is can we accelerate that through tools which support note makers and note graders.
And one way that you can accelerate that is actually have AI perform some of the research or even drafting of notes, but of course, maintaining the gold standard of the voting and the rating and this kind of thing. And there's an interesting kind of design tension there. So that's one direction we're supporting. And that comes under the kind of umbrella that we're calling collective epistemics.
It's a sort of platform-focused intervention there. We could talk about other kinds of interventions there. As another quick example, another thing we're supporting is various initiatives in scenario planning. You may have come across deep research. This is a kind of form factor that various AI companies have put their products into where...
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