🚀 Faster, Please! — The Podcast #22

"You can see the computer age everywhere but in the productivity statistics," said Nobel laureate economic Robert Solow in 1987. A decade later, the '90s productivity boom was in full swing. Likewise, it took decades for electrification to have an impact on productivity growth in the early 20th century. Today, artificial intelligence can write a coherent paragraph or generate an image from a simple prompt. But when will AI show up in the statistics, boosting productivity and then economic growth? Avi Goldfarb joins Faster, Please! — The Podcast to discuss that question and more. Avi holds the Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto's Rotman School Of Management. He's also co-author, along with Ajay Agrawal and Joshua Gans, of 2022's Power and Prediction: The Disruptive Economics of Artificial Intelligence. In This Episode * Prediction at scale (1:34) * How AI has transformed ride hailing and marketing (5:37) * The potential for “system-level” changes (11:26) * When will AI show up in the statistics? (16:12) * The impact of ChatGPT and DALL-E (19:46) Below is an edited transcript of our conversation. Prediction at scale James Pethokoukis: What this book is about—and then you can tell me if I've gotten it horribly wrong—this is a book about machines making predictions using advanced statistical techniques. 1) Is that more or less right? And 2) why is that an important capability? Avi Goldfarb: That's more or less right. The only place where I [would offer] a little correction there is, the reason we're talking about artificial intelligence today is almost entirely due to advances in computational statistics. Yes, it is just stats and that sounds kind of unexciting. But once we have prediction at scale, it can be really transformative to all aspects of business in the economy. There's a reason why we're calling computational stats “artificial intelligence” and we didn't use to. Prediction at scale. That's a great three-word description. Probably why you used it. To what extent is that now happening? The name of the book is Power and Prediction: The Disruptive Economics of Artificial Intelligence. Is this prediction at scale already disruptive to some degree or is it, will be disruptive? The technology, for the most part, is pretty close to there, in the sense that we can do prediction at scale because we have the data and we have computational power to do all sorts of amazing things. For the most part, it hasn't been disruptive yet. And it hasn't been disruptive yet, just because we have the technology doesn't mean we know how to use it well and we know how to use it productively in our processes and systems in order to get the most out of it. Are there sectors currently doing this, but they're not doing it well yet? It’s in a variety of sectors, but not enough companies doing it? Lots of companies are already using these machine learning tools, but they tend to be using them for things they were already doing before. If you had some prediction process to predict, if you're a bank, whether somebody's going to pay back a loan. In the very old days you'd have some human, the loan officer, look the customer up and down and go with their gut. And then, starting in the 1960s and especially in the ‘90s and beyond, we started to use scoring rules, partly your credit score and partly other things, to get a sense of whether people are going to pay them back. And so we were already doing a prediction task done by a machine. And now increasingly we're using these machine learning tools. We're using what we're calling AI, over the past five to 10 years, to predict whether people are going to pay back a loan. We're seeing those kinds of things all over the place, which is: You had some prediction, maybe you’ve used even a machine prediction before, and now we're using machine learning. We're using AI to make those predictions a little bit better. Lots of companies are using that. That sounds incremental. That sounds like an incremental advance. It's absolutely an incremental advance. We call these point solutions, which is, you look at your workflow, you identify something that a human is doing. You take out that human; you drop in a machine. You don't mess with a workflow because it's always easier to do things when you don't mess with a workflow. The problem is, when you don't mess with a workflow, there's only so much gain you can get. We've seen AI-based point solutions, prediction point solutions, all over the place. We haven't seen real transformation in very many industries. We've seen it in a couple. We haven't seen it in very many industries because real transformation requires doing things differently. How AI has transformed ride hailing and marketing Do you think that it has happened in one or two industries that you think would actually meet that bar of transformational? Can you give me an example? Absolutely. If you wanted to be a cab driver in the city of London 20 years ago, or even today, it takes three years of schooling. Learning to navigate those streets is really, really hard. And especially learning to navigate and predict where the traffic is going to be is really, really hard. And so there is a really rigorous process to screen people to be taxi drivers. In the US 30 years ago, there was something like 200 or 300 taxi drivers in the whole country. About 15 years ago, two technologies came about. The first one being digital dispatch, which is essentially tools for drivers to find riders, sometimes through prediction and sometimes through other tools. And then the second part was what's been disruptive with respect to that three years of schooling in the city of London, which is prediction tools for navigating a city. This is your GPS system. In the early days, many people selling digital dispatch and navigational predictions were selling them into professional driving companies, into taxi companies. “Hey, your taxi drivers can be 15 percent more efficient if they know the best route at this time.” That's what we call a point solution. You’re already doing this, you take out some part of the human process, you drop in a machine, and you do it a little bit better. A couple of companies realized that digital dispatch combined with navigational prediction could create an entirely new type of industry. And this is the ride-hailing industry led by Uber and Lyft and others. That's a totally new kind of way to do personal transportation that made millions of amateur drivers as good as professional because they could navigate the city and find riders. Example number one is the taxi industry. Personal ride-hailing, for lack of a better word, has been transformed partly through digital and really those maps are important—and a big part of those maps is machine learning tools and figuring out where the traffic is, etc. So industry number one. Industry number two is advertising. I don't know if you've seen the TV show Mad Men. That was really how the advertising industry operated well into the ‘90s. Maybe not the soap opera aspect of it. Maybe, maybe not. I don't know. But the idea that there's a lot of wining and dining and charming people to convince them to spend millions of dollars on an ad campaign. And whether a campaign worked or not was largely based on gut feel. And which kinds of customers you targeted and which TV show and which magazine, all of that was priced based on intuition and not much else. Digital advertising came along in the late 1990s, and the first ways we thought about digital advertising was that it was like the magazine industry. So instead of advertising in People magazine, you're going to advertise on Yahoo using the exact same processes you did in People magazine. There was a rate card and it was going to be so many dollars per thousand users. And if you were doing general search, it might be $10, and if you're looking for real estate, it might be $50. And that's exactly how the magazine industry was priced. Some … This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe