Artificial Intelligence, Big Data & China | Martin Schmalz | Modern Wisdom Podcast 144
Martin Schmalz (guest), Narrator, Chris Williamson (host)
In this episode of Modern Wisdom, featuring Martin Schmalz and Narrator, Artificial Intelligence, Big Data & China | Martin Schmalz | Modern Wisdom Podcast 144 explores how Big Data And AI Quietly Reshape Business, Privacy, And Power Finance professor Martin Schmalz explains that most real-world AI is not science fiction but industrial-scale prediction: using huge datasets to forecast behavior, risk, and demand far better and cheaper than humans. He argues the real disruption comes from combining machine-learning prediction with economic thinking to redesign business models, industries, and jobs, with China currently several years ahead because of scale, super-apps, and weaker privacy constraints.
How Big Data And AI Quietly Reshape Business, Privacy, And Power
Finance professor Martin Schmalz explains that most real-world AI is not science fiction but industrial-scale prediction: using huge datasets to forecast behavior, risk, and demand far better and cheaper than humans. He argues the real disruption comes from combining machine-learning prediction with economic thinking to redesign business models, industries, and jobs, with China currently several years ahead because of scale, super-apps, and weaker privacy constraints.
Examples span credit scoring from phone metadata and typing speed, to insurers inferring age from email domains, to ride‑hailing firms using location data to launch lending arms. Schmalz stresses that uniquely human strengths lie in non-generic prediction, creativity, ethics, and understanding how users will react to data use, which machines cannot learn from past datasets.
The conversation explores the tension between convenience and privacy, how regulation (like Europe’s GDPR and antitrust actions) can reshape data-driven models, and why investors and executives need translators who understand both AI tools and economic incentives. Schmalz contends the key societal questions are who controls data, how it’s monetized, and whether legal and ethical frameworks will keep pace.
Key Takeaways
Most real-world AI is just powerful prediction, not thinking machines.
The vast majority of deployed AI/ML systems take large historical datasets and predict an outcome (default risk, ad clicks, health risk, prices), leveraging cheaper data collection, storage, and computation; artificial general intelligence is mostly a distant, speculative topic compared to this very profitable, very boring prediction work.
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Data points you barely notice can be highly predictive and monetized.
Seemingly trivial signals—how fast you fill a form, your email domain, your sleep locations, which restaurants you ride-share to—correlate strongly with credit risk, insurance risk, and willingness to pay, and are actively harvested and sold through complex data-aggregator ecosystems.
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The real business innovation is combining ML prediction with economic logic.
Models alone aren’t enough; firms need people who understand both how algorithms work and what’s economically valuable to predict, aligning data use with strategy, pricing, customer behavior, and regulatory risk rather than doing ‘AI’ for its own sake.
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Human strengths lie in non-generic prediction, creativity, and theory.
Computers excel where the future resembles the past, but they cannot infer from data how AI will reshape an industry, how customers will react to new incentives, or which unprecedented product (like a keyboardless smartphone) will succeed—these require human judgment, theory, and imaginative synthesis.
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Privacy–convenience trade-offs are central and culturally contingent.
Many users accept pervasive tracking because super-apps and services like Uber or facial-recognition checkout are incredibly convenient; attitudes and regulations differ, with Europe more privacy-focused (GDPR, limits on Facebook data-merging) and China more permissive, which accelerates Chinese firms’ data advantages.
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Regulation and antitrust will shape who wins in the data economy.
Competition authorities can treat invasive data practices as an abuse of dominance (e. ...
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Data itself is becoming the core product in many ‘ordinary’ businesses.
Scooter sharing, fitness trackers, flashlight apps, and ride-hailing may be economically justified less by direct fees and more by the behavioral and location data they generate, which can be resold or used to refine advertising and pricing, so understanding where the real value sits in a model is critical for investors and employees.
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Notable Quotes
“Ninety-five percent of currently used AI is just using past data to predict some outcome. Computers don’t think; they predict.”
— Martin Schmalz
“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.”
— Martin Schmalz
“If people start sleeping in two different locations interchangeably at night, that tends to be a really bad credit risk.”
— Martin Schmalz
“It’s not entirely clear that I help myself by not generating all this data. If I’m a better-than-average health risk, maybe I want the insurer to know that.”
— Martin Schmalz
“If you want to predict what happens in this world, just follow what’s happened in China over the last five years.”
— Martin Schmalz
Questions Answered in This Episode
Given how much predictive power lies in obscure behavioral data, what kinds of signals about yourself are you unintentionally broadcasting, and how might different companies already be using them?
Finance professor Martin Schmalz explains that most real-world AI is not science fiction but industrial-scale prediction: using huge datasets to forecast behavior, risk, and demand far better and cheaper than humans. ...
Get the full analysis with uListen AI
Where would you personally draw the line in the privacy–convenience trade-off—for example, would you accept facial-recognition checkout or health-data sharing for lower prices?
Examples span credit scoring from phone metadata and typing speed, to insurers inferring age from email domains, to ride‑hailing firms using location data to launch lending arms. ...
Get the full analysis with uListen AI
How should regulators balance encouraging innovation in data-driven services with preventing exploitative practices like extreme price discrimination or opaque data brokerage?
The conversation explores the tension between convenience and privacy, how regulation (like Europe’s GDPR and antitrust actions) can reshape data-driven models, and why investors and executives need translators who understand both AI tools and economic incentives. ...
Get the full analysis with uListen AI
If you lead or work in a business today, which of your processes are essentially prediction problems that could be augmented or replaced by machine learning, and which require truly human judgment?
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As China races ahead in large-scale data integration and AI, how might that shift global economic power and standards for privacy, and do you want your own country to follow that path?
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Transcript Preview
... if people start sleeping in two different locations interchangeably at night, so that tends to be a really bad credit risk. So these people tend to use up a lot of cash in the near future, more than they can afford. And the story they tell me behind that is, "Well, those are people who have lovers, and having lovers leads to divorces, and divorces are costly, and costly divorces lead to loan default."
(laughs)
So (laughs) whether that's a right story behind it or not, the, the boring fact is that using location data, um, is extremely useful in predicting default.
Martin, how you doing, man? Welcome to show.
Very good. Thank you very much, uh, for having me.
Very, very happy to have you on today. We've recently been talking about some big data stuff. Seth Stephens-Svidiwitz was on recently, uh, and we were discussing about some of the interesting analysis that he'd done on Google searches and PornHub data as well. Um, maybe not PornHub today, but definitely some big data from yourself.
That's right, yeah.
Lovely. So give us your background. What do you do?
Well, my background, um, I, I, uh, grew up in Southwest Germany, and, uh, as everybody does who is from there, I, uh, and has any form of self-respect, I studied mechanical engineering.
(laughs)
Um, but at some point, I had the impression that, um, I can much better understand what happens in the world if I study how the financial system works, and thus made, made my way to, um, studying economics and, uh, 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, um, because I somehow felt, uh, that there was a bit of a discrepancy between the demands, uh, on our graduates in industry, you know, 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 teach, uh, learn some Python in class and apply it, and, uh, uh, of course, the ambition is not to turn them into data scientists, but to understand what the economics of data-driven businesses models is, um, and can be, and how we can understand, uh, the success of tech platforms, um, over the last, um, decade or so.
So quite involved in the development of how we analyze the data and pushing that forward?
Yes. So see, um, there are specialists on the analysis of data, and you call them data scientists or so, and what I'm trying to s- uh, spend time thinking about is predicting future directions of business models and the development of workplaces and, uh, yeah, just how, uh, jobs, firms, and industries get, uh, transformed as a result of the, of the big data revolution.
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