Artificial Intelligence, Big Data & China - Martin Schmalz - #144

**Chris Williamson** (0:00)
Oh, hello friends, welcome back. Today, I am sitting down with Martin Schmalz, and we are talking about The business Of Big Data. Recently had Seth Stevens-Dvidovitz on discussing everybody lies and how we can infer a lot about people from their Google searches and from their Pornhub data. Today, we're talking a little bit more on the side of computer learning, machine learning, artificial intelligence. What actually are the resources being put into artificial intelligence at the moment? The press would have us believe that it's Terminator, dystopian rise of the machine's future that's happening. But according to Martin, that's not what the market rewards. What the market rewards is looking at your phone's location data on a night to see if you're sleeping in more than one location consistently to infer whether or not you're in a relationship which might decide whether or not you're going to get married and then divorced soon or how quickly you fill in a form online or whether you have an AOL or Yahoo email address which indicates that you're less tech savvy than someone that's using Gmail. So yeah, in short, it doesn't look like Terminator is coming anytime soon and if he does, he's probably split testing the prices on your Amazon products to see if you'll pay more for something. Please welcome Martin Schmalz.

**Martin Schmalz** (1:36)
Well, my background. I grew up in southwest Germany, and as everybody does who is from there, I, and has any form of self-respect, I studied mechanical engineering. But at some point, I had the impression that I can much better understand what happens in the world if I study how the financial system works. And that's what made my way to studying economics and going to the US, and ended up being a finance professor. And in the course of that, I somehow stumbled across this topic of AI and big data and started teaching it, because I somehow found that there was a bit of a discrepancy between the demands on our graduates in industry, which concerns Python and big data skills, and what we taught them, which at the time was largely Excel. So I developed the ambition first to actually make MBA students learn some Python in class and apply it.
And of course, the ambition is not to turn it into data scientists, but to understand what the economics of data doing business models is and can be, and how we can understand the success of tech platforms over the last decade or so.

**Chris Williamson** (2:49)
So quite involved in the development of how we analyze the data and pushing that forward.

**Martin Schmalz** (2:55)
yes. So see, there are specialists on the analysis of data, and you call them data scientists or so. And what I'm trying to spend time thinking about is predicting future directions of business models and the development of workplaces and, yeah, just how jobs, firms and industries get transformed as a result of the big data revolution.

**Chris Williamson** (3:23)
Okay. So this is helping businesses to make decisions through big data.

**Martin Schmalz** (3:28)
That's right. That helps business make strategic decisions on what the AI revolution means for them, how they should position themselves. It also helps investors in deciding what kind of businesses they should invest in or what kind of questions they might ask businesses they consider investing in and distinguishing between the thousands of different fintech startups who are kind of figuring out which ones of those that just have AI in the name, which ones actually apply it, and which ones apply it in a way that doesn't only solve a technical problem but actually also has a decent chance of turning a profit at some point in the future.

**Chris Williamson** (4:05)
I understand. So is most of the challenge that you're coming up against here a technical one with regards to the way that you can statistically model things and the script and things that you can write? Or is it more so on the side of how you interpret that data, how you apply it to the market and stuff like that?

**Martin Schmalz** (4:26)
The latter. So the challenge is that there are very few people that have the combination of two skills or sets of knowledge. One is understanding what AI actually is and what machine learning algorithms do, and specifically what they don't do. And the spoiler alert is if you read the newspaper, you don't really get a good impression of what that is. And there are, of course, people who understand that, like the engineers or data scientists that work with machine learning models. The problem with that is that very few of them have even basic economics training or enough of an economics training to kind of combine their data science skills with economic theory in order to predict the future of industries and markets. So there doesn't seem to be a lot of structured thought about business models and how they will change in the age of AI. And indeed, there was no book out there, which is why we wrote one.