Modern WisdomArtificial Intelligence, Big Data & China | Martin Schmalz | Modern Wisdom Podcast 144
CHAPTERS
- 0:00 – 0:56
How unexpected data (like location) predicts credit risk
The conversation opens with a provocative example: people sleeping in multiple locations can correlate with loan default risk. Schmalz uses it to illustrate a central theme—modern AI is largely about prediction using unconventional behavioral data.
- 0:56 – 2:12
Schmalz’s path: from mechanical engineering to finance + AI economics
Schmalz explains his transition from engineering to economics and ultimately becoming a finance professor. He describes why he started teaching Python and data-driven strategy to bridge the gap between business education and industry demands.
- 2:12 – 7:21
Why companies need “translators” between data science and strategy
Schmalz argues that technical teams and economic/strategy teams often fail to communicate effectively. Without a bridge, companies build data infrastructure without a clear value proposition or strategy.
- 7:21 – 10:10
What AI really does: cheap, scalable prediction (not “thinking”)
Schmalz reframes most modern AI as prediction machines trained on past data. He emphasizes the practical drivers—cheap storage, cheap compute, and massive data—rather than sci‑fi notions of computers thinking like humans.
- 10:10 – 15:33
Generic vs non-generic prediction: where humans still dominate
They distinguish repeatable, data-rich prediction tasks from novel, unprecedented ones. Schmalz argues that creativity, synthesis, and forecasting disruptions without historical precedent remain human strengths—especially in executive decision-making.
- 15:33 – 17:18
Behavioral micro-signals: typing speed, typos, and insurance/credit models
Schmalz shares examples from China showing how tiny interaction details become predictive features. Banks and insurers may infer fraud risk, intelligence, carefulness, or default probability from how users fill online forms.
- 17:18 – 23:25
Dynamic pricing, willingness to pay, and the ethics of personalization
A Skyscanner anecdote leads into how firms estimate willingness to pay and may adjust prices accordingly. They discuss the consumer backlash risk, and why people react differently to price discrimination versus “personalized discounts.”
- 23:25 – 26:37
Location data, ride-hailing, and why Uber/Didi move into lending
Schmalz explains how ride-hailing platforms can infer income, lifestyle, and liquidity from pickup/drop-off behavior. This makes expanding into financial products (like lending) economically logical—and China often previews what the West does later.
- 26:37 – 36:42
Why China is ahead: super-apps, lax constraints, and engineering scale
Schmalz outlines several reasons China leads in AI deployment: population-scale data, integrated super-app ecosystems like WeChat, fewer privacy roadblocks, and huge engineering investment. Cross-domain data fusion creates major predictive advantages.
- 36:42 – 41:35
Antitrust, GDPR, and regulators blocking data mergers (Facebook example)
They shift to how competition law intersects with privacy. Schmalz explains the German competition authority’s argument that Facebook’s dominance limits privacy-respecting alternatives, justifying restrictions on merging WhatsApp/Instagram/Facebook data.
- 41:35 – 51:37
The privacy–convenience tradeoff and the coming societal decision
They explore how user preferences shift when convenience is high (biometrics at borders, frictionless payments, face-scan checkout). Schmalz argues society will likely move toward more data-driven convenience, but speed and outcomes depend on politics, competition, and public backlash.
- 51:37 – 57:36
AGI vs reality: why ‘boring statistics’ drives profits (and jobs) now
They contrast sensational AGI narratives with what’s actually economically rewarded: large-scale prediction, econometrics, and data science that improve pricing and decision-making. Schmalz highlights how market caps reflect expectations that these business models will keep paying off.
- 57:36 – 1:09:34
Business models powered by data extraction—and closing thoughts
Schmalz explains how many modern products function as data collection fronts, with value created by selling or leveraging data rather than the nominal service (e.g., scooters, apps). They close with where to find the book and acknowledgments to co-author Yuri.
