What does knowledge mean in an age of AI, deepfakes, and disinformation? When information is everywhere, the real challenge is distinguishing insight from noise.
In this episode of The Development Podcast, host Toni Karasanyi explores how knowledge is evolving in a world where machines can generate convincing arguments and personalized realities shape how we consume information.
Tech journalist Jamie Bartlett, author of How to Talk to AI (And How Not To), explains how individuals can use AI tools without being misled—and why critical thinking matters more than ever.
Paschal Donohoe, the World Bank Group’s Managing Director and Chief Knowledge Officer, discusses how the Bank is turning more than 80 years of global development experience into practical knowledge that helps countries tackle challenges, including creating jobs and scaling solutions in a fast-changing world.
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Transcript
Toni Karasanyi: For centuries, we've been told that knowledge is power. But here's the thing, we don't live in an age of scarce information anymore. We live in an age of information overload. Today, anyone can generate a convincing argument in seconds. A deep fake can travel faster than a fact. A chatbot can simulate expertise. So information isn't the same thing as knowledge. Knowledge is evidence tested, context understood, lessons applied. And in development where decisions shape jobs, growth, and opportunity, that distinction really matters. Information, true and false, is everywhere, but only knowledge can scale impact and trust. And that's what we're digging into today on the Development Podcast, knowledge in the era of disinformation and AI.
We'll start at a personal level with tech journalist Jamie Bartlett on how you and I can stay sharp when AI sounds oh so certain. And then zoom out from AI in your pocket to a global perspective with Paschal Donohoe, the World Bank Group's newest managing director. He'll explain why knowledge is the X factor in helping millions, and sometimes billions, of people all around the world. And that's all with me, your host, the real Toni Karasanyi.
Over the last few decades, technology hasn't just changed how we communicate. It's changed how we know. First came the web. Knowledge moved from the library shelf to the search bar. Then came social media. Information became shareable, immediate, emotional, and sometimes even accurate. Influencers live-streaming outside the newest cafe, headlines engineered for clicks and outrage. Algorithms deciding what we see and what we don't. Newspaper front pages faded. In their place, personalized realities. Access widened, even as certainty narrowed.
And now we've entered a new phase, not just curated feeds, but generated answers. UK tech journalist Jamie Bartlett has spent years studying how technology reshapes democracy and debate. In his new book, How to Talk to AI and How Not To, he argues that using AI well is becoming a basic civic skill. So I asked him, when AI can seemingly answer everything, what happens to knowledge?
Jamie Bartlett: I really am thinking about the large language models, the chatbots that hundreds of millions of people are using every day. It's probably the biggest transformation, more even than social media, quicker than pretty much any commercial technology I've ever seen. I mean, we've got, however, 900 million daily users of ChatGPT alone. So we're talking about seismic change in how people are understanding the world.
Take the example, which I call the instant expert problem. What large language models seem to do on the surface is to give you access to almost any of the world's information in fluent, coherent, well-dressed up, well-written prose for you to digest and understand. The great risk there is you can superficially come to believe you're highly knowledgeable about a subject because you've read a beautiful AI summary. That can be really helpful, but have you fully understood it?
So we have plenty of examples of people who, 10 minutes after chatting to a large language model, think they know how to design a website. But they don't know what they don't know. And they don't realize that essentially they've built a website that is full of cybersecurity vulnerabilities because that's not what they specialize in.
Toni Karasanyi: Do you worry about how we're handing over too much judgment to machines, and what does that mean for how we arrive at shared ideas and beliefs of what's true?
Jamie Bartlett: Many of the problems that those of us that have studied the sort of fracturing of the shared information ecosystem, we can disagree on certain ideas and have opinions, but there's a shared set of truths that we can all agree upon, which forms the basis of the arguments that we have. And we've seen how that's become fragmented and fractured over the last 10 years.
And in some ways you can see that large language models could make that worse in certain respects. Because here's something that a lot of people don't really understand about large language models. If you, Tony, ask a large language model who's the best football player on the planet, and I ask the large language model, who's the best football player on the planet, there's a pretty good chance we'll get different answers to that question. If you ask it about universal basic income and I ask it about universal basic income, there's a very good chance. So there is a real chance here that subtly everyone is receiving fluent and coherent answers that are actually quite different about the same subject, each one tailored to us.
Now you could say that's been happening with social media, but this is happening, I think, maybe in a more subtle way because it seems to come from a super intelligent machine that's brilliant at everything. So we're probably more likely to trust it. Keeping hold of our own critical faculties when we have a super intelligent machine that is better at us at a lot of things might be one of the biggest challenges that we all face.
Toni Karasanyi: A lot to unpack there. And your new book is all about How to Talk to AI (And How Not To). So tell us more about what people misunderstand about using chatbots and AI tools, and how can the way we interact with them shape the kind of knowledge we walk away with.
Jamie Bartlett: Some of us massively overestimate what they're capable of. Some people call this the halo effect. We see that they are capable of producing incredibly long, sophisticated answers about physics or generating amazing poetry that you or I never would. They can pass the bar in under a second and they can pass nearly all the medical exams. And so clearly, in certain things, they're amazing. And I think that can lead people to assume that they'll be able to do absolutely every task amazingly well.
So if you ask it for some advice about your career, it must know better than you do. If you ask it about your area of expertise, it must be right. That is not true. That is not how they work. So we overestimate them sometimes, but I think some of us underestimate how good they are. We get a bad answer from them, or they hallucinate and make up a reference or make up a fact, and we decide they're terrible. They can't be trusted to do anything at all. Chuck them in a bin. But they help me immensely in writing a book. They are incredibly creative.
People sometimes assume that a machine, because maybe it's hardwired into us from years of sci-fi movies, that these machines are very good at facts and data, but not very creative. I think it's almost the opposite. They can have a level of emotional sophistication and maybe a manipulation that would stagger you if you know how to talk to them well. So much of this is just about trying to understand what they're good at and what they're not good at. And a lot of that just comes through practice and open-mindedness.
Toni Karasanyi: Super interesting as a writer to hear how AI has helped you in writing your book. At a time when trust in traditional experts is already fragile, how should we think about the role AI plays in shaping authority and expertise?
Jamie Bartlett: What I think a large language model has done is to break the relationship between fluency and coherence and style and genuine understanding and knowledge and insight. Language is so intrinsic to being human. When someone speaks with you with great coherence and authority, they must know what they're talking about. When you receive an email that's very carefully written and beautifully put together, they understand the subject matter. That relationship has basically now been broken.
But when you hear or read anything that sounds very fluent and very stylish or very well constructed, it does not any longer signify understanding because a machine could have just generated it. And I think this is one of the most important reasons people get trapped by machine hallucinations, machines making up stuff, making up facts. It could be one small reference in an academic paper that's made up by a machine. It could be an entire universe of confabulated nonsense.
We have to break in our own minds the idea that because this sounds authoritative, it is authoritative. That is no longer true. Accuracy and style are now separated. This is vital for everyone to understand. Doesn't mean to say there's not huge potential. I think for too long, lots of people are locked out of brilliant ideas, either reading them or expressing them. These machines are actually very, very good at turning complex language into formats that you can understand, that anyone can understand. So there's like amazing democratic potential here for these machines to make everyone able to understand the world better and express themselves better.
Toni Karasanyi: Looking ahead, what actually helps people navigate all of this better?
Jamie Bartlett: There's a couple of things to know about machines that are just always worth keeping in mind. They are statistical next word prediction machines. They don't have any kind of ground truth or knowledge of the world like you do, no matter how fluent they seem. So understanding that is essential in terms of always being slightly skeptical about what you're getting back. They are liable to hallucinate. Sometimes that's fine. When I am using it to help me with creative thinking, I want it to hallucinate. I want it to come up with crazy stuff. But if I'm reading through a contract and I'm about to sign, and it tells me something incorrect, that could be a big problem. So the more important the task, the more careful you've got to be.
And always bear in mind, these are run by large profit making companies. They want to keep people online. So they tend to be agreeable. They will often flatter you. It will tell you it's an amazing idea. Tony, you're a genius. We are very susceptible to that, I'm sorry to say, which is the reason the machines do it. Especially if a machine's telling you something you might want to hear and makes you happy. Be doubly skeptical of this. Okay? It's a flatterer. It can be a sycophant. It can hallucinate. If you know that, it can be very, very useful. That's about the machine.
But here's the other side of things, Tony. It's a two-way conversation. When I put a question into ChatGPT, there's a very good chance that I'm smuggling in some bias of my own. So you got to look inside yourself as well. And guess what? One easy way to do that is ask a machine, "How can I use you better? Hey, you know that question I just asked you about universal basic income. Was that an objective question?" And it might tell you, "No, that wasn't, but I didn't bother mentioning it. But now you've asked, no, you've smuggled a premise in there about why it was a good idea and I gave you the answer. But if you want an objective answer, here it is." Human bias is almost as much of a problem when talking to a machine as machine bias. And when you put those two things together, it can be really damaging. If you understand both of those things and you approach it with some skepticism and practice, open-minded, they can be really, really useful.
Toni Karasanyi: Jamie Bartlett there on knowledge in the age of AI and how not to be fooled by it. Here at the World Bank Group, knowledge isn't abstract. Besides finance, it's the bank's stock-in-trade. And it deploys knowledge at scale, taking practical insights from one place to tackle global challenges like creating jobs. Paschal Donohoe is the new managing director and chief knowledge officer of the World Bank Group. Before this, he was in politics where, as you know, evidence, narrative, and public trust often collide. So I asked Paschal, what drew him to this new role and why knowledge may matter now more than ever?
Paschal Donohoe: Thanks very much, Tony, and great to be on your show. I mean, what drew me to the role is the opportunity to continue in public service. When I was a politician, I always viewed myself as a public servant, being an elected one. And one of the big themes in my life in Irish politics was the importance of cooperation between countries to deal with the challenges that we were collectively confronting. And that experience, of course, made me so aware of the challenges and opportunities the world is confronting, and therefore the importance of the World Bank Group.
And it's such a great privilege now to be working for this great institution and to be working with so many exceptional colleagues in doing our work, and work that has never been more needed, but of course we're so aware of the challenges in doing that work.
And you've just touched on one of them there, which is the political and social environment within which our work happens. As an elected politician, I was so aware of it. And one of the big things I took from it was the importance of results, the importance of being able to show that policies work and they make a difference. And using the theme of policies being effective, of them working, and being evidence-backed, I found to be a very powerful antidote to some of the challenges that you've referred to.
And of course, that's really relevant to the work that I'm doing here now as knowledge officer for the World Bank Group. Because what we're trying to do is make the case for those policies that are very effective, that deliver better results, and how we can do them better.
Toni Karasanyi: So the World Bank Group talks about itself as a knowledge bank as well as a financing organization. Why is knowledge so central to the World Bank's work today, especially when it comes to sharing ideas that can actually scale and helping to create jobs?
Paschal Donohoe: And so as a governor of the World Bank, and as somebody who looked on at the World Bank, I actually very much thought of it in terms of that concept. In other words, that there's kind of two streams of the World Bank, if you wish, flowing together, what happens with finance and what happens with knowledge. And what I've really been struck by in the few weeks I've been here is the degree to which they're not parallel streams. They're actually streams that a lot of the time are already integrated and flowing together.
That for colleagues who were directly involved in financial investments, whether it be loans, whether it be grants, they were always doing that in an environment that was very knowledge rich. And likewise, for my colleagues who would've been working mainly on policy areas and mainly working on a more kind of knowledge track, they were always deeply aware of the importance of financial interventions for making sure that their knowledge goes further.
So I very much look at how we can integrate that work even more so together. And the reason why the knowledge bank has this particular importance is because the needs that the world has are only going in one direction, they're growing. The opportunities are so great as well. But we are also aware of the many challenges that are there now with the availability of resources and the availability of money. And that's why knowledge has to go that bit further in a world in which resources are more constrained, but the needs and opportunities are growing.
Toni Karasanyi: Given your former life as a politician, I think you're well aware that we're living in a time where facts feel more contested than ever before and misinformation seems to move faster than evidence. So when we talk about development, how much of a risk is that for development, especially in low and middle income countries where trust in public institutions is so important?
Paschal Donohoe: So while we do face many big changes at the moment, it's not the first time we faced them. When big changes have happened in the past in relation to communication, it has always heightened the vigor of public debate, whether that be the printing press, whether it be the availability of other forms of communication in the past, it led to more contestability and a higher level of vigor within public debate.
I think what's different within my lifetime is you're seeing what I believed to be, and still believe to be, very objective elements of knowledge, you see more challenge and more debate around that. And I think in terms of what that means from a development policy point of view, it's firstly, we just have to acknowledge that this is happening. And by acknowledge it, then work harder to make sure that the claims and arguments that we are making with our policies are backed up by two things.
Firstly, evidence, and that that evidence is more accessible. And secondly, to go back to your point earlier on, that it's backed up by funding, it's backed up by resources. So we are saying these are not only things that we are making the case for our clients, for our countries, for our governments doing, we're also saying, by the use of our balance sheet, we can make it easier for that work to happen. So it's evidence and balance sheets.
Toni Karasanyi: Evidence and balance sheets. Very well put. Now I'm curious to learn about your thoughts on the role of AI. In some cases, AI can make a lot of these challenges much worse, but there's also an upside. AI can help people cut through the evidence and to make more sense of the evidence. But when you look at AI, do you see it as more of a risk, as an opportunity, or maybe a double-edged sword?
Paschal Donohoe: I've always been a techno optimist, and at times you kind of have to strain to maintain that fate, given some of the things that we know can happen, but I still am. And that techno optimism in particular comes from the role that I believe AI can play in healthcare and education in particular.
I'm really aware of the challenges that we are going to face with regard to the future of work and the future of jobs. And the World Bank Group is doing some very important work in this area, and we'll be bringing forward some research and some analysis. But when I look at healthcare and when I look at education, from a healthcare point of view, I believe the availability of medicines, the availability of life science solutions that can deal with some of the great health conditions and challenges of our time, I'm really optimistic that that's going to change.
And the other great challenge we face in health policies, the availability of nurses, the availability of doctors, the availability of consultants, and I think AI's really going to help with that, really going to help us come up with new ways of diagnosis. And then from an educational point of view, I again think that AI will be able to deliver great educational opportunities in the time ahead. Again, where schools, where teachers are constrained, but they're not always available in the way we would want. So on balance, I believe the opportunities are great, but we have to be and are aware of the challenges and risks that it also does pose.
Toni Karasanyi: Of course, when the World Bank Group talks about AI, we also talk about the responsible use of AI. So I'd love to hear from you what that means in practice. And how is the World Bank Group using AI and knowledge systems to support better decisions while still making sure that it doesn't push out local expertise and institutions, that they're strengthened rather than being sidelined?
Paschal Donohoe: We have to see AI from a knowledge point of view as a way in which we can curate and organize knowledge. And if we think of it in that way, and it's the way I think about it most internally, it is then never playing a role in displacing where the knowledge comes from and from the humans who create it. And one of the things that I want us to see do better is the different tools that we have like Knowledge360, like Data 360, that are digital and fundamentally AI tools. They're not the origin of the knowledge. The origin of the knowledge is the human, the human in the loop. But what they're seeking to do is organize that knowledge in a way that's dramatically more accessible than it otherwise would be. So that's really the way I would see it at the moment.
Toni Karasanyi: Thank you again, Paschal.
Paschal Donohoe: Thank you for having me on and have a good day.
Toni Karasanyi: So there you are. Information is everywhere, but knowledge, tested, shared, and applied is what actually changes lives. Thanks again for listening to The Development Podcast. I've been your host, Tony Karasanyi, and I hope that you've enjoyed this episode as much as I have.
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The World Bank is one of the world’s largest sources of funding and knowledge for low-income countries. Its five institutions share a commitment to reducing poverty, increasing shared prosperity, and promoting sustainable development on a livable planet.