John Thompson, Head of AI at EY, explains how generative AI is evolving into agentic systems that make decisions autonomously. An LLM spotted accounting fraud in fabricated financial statements; travel agents could soon book flights without asking. Thompson argues automation will kill repetitive white-collar work but create higher-paid creative roles.
One other area where I think AI is pretty interesting is for decision making. So have you seen any uses of generative AI for decision making?
Yeah, there's some really cool things going on in generative AI right now. A lot of discussion about agents and agentic behavior. We're working on the continuum of simple agents to intelligent agents, to self replicating agents, to polymorphic agents, which is really cool stuff. You know, right now, you know, early days when, when I'd see people go in and use a large language model, OpenAI, you know, Mistral, whatever they were using, they used it very much like a search engine. What's the capital of Singapore? Well, we know it's Singapore, but you know, they would ask, you know, what's the capital of North Dakota? Or what's the capital of New York? And, and that's, that's not really, that's a search engine. You don't really need a large language model for that. So we have people using them in very simplistic ways. They send in a prompt, they get a response. But the models really offer a great deal more capability and functionality than that. I saw one of our groups write a prompt that was, it stretched the model at the time, the context window. It would be easy to do now, but it was a set of entire financial statements that were made up and they had made them up in a way that there was mistakes, there were outright errors, there were misrepresentations and gross fraud. And when they ran it into the model, it found all of it and came back and said, well, these numbers don't add up and this is not the right accounting treatment. And this looks like it's suspicious, the transfer, pricing and different things like that. So the future of large language models is more in the line of automated intelligence and decision making. So trying to bring it back and land the plane and answer your question is that, you know, we are going to a point where simple things like deciding, should I fly on the 6am flight or should I fly on the 8:20 flight? We won't make those at all. Our agents will do that for us. You know, we'll give them all the rules and the parameters and the, the top line of what we want to spend and when we want to get out of bed and all the other rules and will code those into agents and they'll go into large language models. And you go in and say, hey, I need to fly from Chicago to New York on Tuesday of next week and return on Friday. That's the last thing you'll say. The Whole thing will be booked for you. So, you know, those agent infrastructures are moving in a way that simple repetitive decisions will be taken care of for us. Now some people are kind of wigged out about that and they say, well, geez, you know, it's going to be a lot of jobs and there's going to. People lose their jobs. Yes, that is true. People will lose their jobs. But the, the upside is that the jobs that we will be creating with the augmentation of intelligence is that they will be more fulfilling jobs. There'll be more interesting jobs. They will be jobs that pay better. So just like we went through transitions from horses to automotives to, you know, manual labor to industrial labor, we're going to go through a transition that is going to be focused mostly on white collar work where the, the lower end, repetitive stuff is going to go away. And the higher end, more sophisticated work that we as humans are better at, creativity, intuition, and all those things are going to be more enriched.
Okay, so that's really interesting though. Two very different examples there. So your first example was around being able to critique a large document and just come up with intelligent answers to that. And second one's more about automating away boring manual tasks like which flight should I pick and going and booking things. Okay, so that's pretty interesting that those different examples. One thing I'm also interested in is around other types of AI. So I know you're a big fan of causal AI. And actually we had a guest on a previous show was saying generative AI isn't what you want for decision making, it's causal AI. So I don't know whether you have an opinion on this, on one versus the other.
Well, I do have my book on causal AI just over my shoulder. So I do have an opinion, of course. But yeah, I think generative AI is good for simple decision making. Like I said, do I want to go on the 820 flight or the 6am flight? That's a simple decision that generative AI can make for you. More complicated decision making. That's not really what generative AI is there for. I'm writing my fifth book right now, which is focused on causal AI, foundation AI and generative AI. And the way I see that coming together is we're going to have composite applications of all three AIs working together. Now, causal AI is very good for understanding what's the true causative factors. You always hear about this, that there's firemen around fires. So firemen start fires? No, that's not it, of course, you want to get to the true causal factors. And it's hard for people to understand causality. We understand it in the abstract as a conceptual framework. We've been discussing it since Aristotle, so I think we've got a pretty good idea what it is. But as far as modeling it and mathematically understanding it, we haven't been very good on it. Now the interesting thing is, is that generative AI, having ingested the entirety or much of the Internet and the corpus of human knowledge, has all the information about causality as well. So one of the things generative AI can help you do is generate causal pairs so you have a better, more reliable set of causal pairs that you can feed into causal AI. Now, what does causal AI do for you? It mathematically, thanks to Judea Pearl and his all the people he's been working with over the last 30 years, gives you the new calculus where you can actually mathematically understand X did cause Y and Z was a confounding factor. So you now understand this at a very detailed level. So you can go in and say, okay, let's understand what is the price at whereby people in this demographic buy one of these and which price causes them to buy 10. So you really can understand causality in a very detailed, mathematical, proven way. So I believe that we're going to have causal AI to give us that we're going to have traditional AI to predict how many of these people are actually going to show up and do what we expect them to do. And we're going to use generative AI to generate treatments and interactions and invitations and incentives to give to those people to make them do what we want them to do. So I see causal, foundational, generative, all working together as one system.
