Modern WisdomArtificial Intelligence, Big Data & China | Martin Schmalz | Modern Wisdom Podcast 144
At a glance
WHAT IT’S REALLY ABOUT
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.
IDEAS WORTH REMEMBERING
5 ideasMost 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.
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.
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.
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.
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.
WORDS WORTH SAVING
5 quotesNinety-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
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