Episode #102: AI Is Eating the World’s Memory

In this episode of Stewart Squared, Stewart Alsop III and Stewart Alsop II sit into the shifting ground beneath the AI boom, starting with the strange saga of Leopold Aschenbrenner's hedge fund and the memory chip shortage that's rippling through everything from Apple's product line to the gaming industry, before moving into how OpenAI and Sam Altman's data center spending is reshaping global compute demand, the widening gap between American and international tech ecosystems, China's uneasy relationship with open source AI, and a look at Mira Murati's new venture Thinking Machines Lab and its fine-tuning tool Tinker, wrapping up with some thoughts on what a live, interactive version of the show could look like.

Timestamps

00:00 AI bubble and the Leopold Aschenbrunner hedge-fund story; debt, margin pressure, and why memory stocks surged
00:05 memory becomes the bottleneck as AI data centers expand; Apple, Micron, and rising RAM prices
00:10 Korea takes a hit from memory-market swings; contrast with Japan, manufacturing, and the karetsu model
00:15 Live translation tech, Google’s new API, and a side discussion of robotics and Japanese PC history
00:20 Japan’s early PC ecosystem, NTT, Microsoft, Windows, and why standards won the market
00:25 Back to finance: equity vs debt, leverage, Glass-Steagall, and how banking got reorganized
00:30 AI hedge funds, risk, Citadel, margin calls, and the distinction between lending and investing
00:35 Capitalism by starting companies vs buying companies; why money is partly a metaphor
00:40 AI agents, Chrome, and the difference between distributed and federated systems
00:45 Matrix and Nostr, open-source messaging, and the internet’s new borders
00:50 Live audience questions, interactive publishing, and the business potential of real-time conversation

Key Insights

1. The AI boom is straining a memory chip supply that can't scale fast enough. Massive spending on AI data centers by companies like OpenAI has driven demand for DRAM through the roof, and because memory factories take years to build, prices have roughly tripled in six months — squeezing everyone from Apple (which is struggling to ship products like the Mac mini) to the broader computer gaming industry.
2. Leverage is what turned a smart bet into a crisis. Leopold Aschenbrenner's "Situational Awareness" hedge fund quadrupled investor money early on by going heavy into AI, but borrowing tens of billions against volatile chip and memory stocks left him exposed when prices tanked — prime brokers like Bank of America, Goldman Sachs, and JPMorgan Chase issued margin calls, and Citadel ultimately bought the distressed assets at a steep discount.
3. Korea's economy is deeply entangled with the memory business. Companies like SK Hynix built Korea into a manufacturing powerhouse for chips, and that same concentration made its stock market especially vulnerable — the market reportedly fell more than 33% in July, a decline worse than the crashes of 1997 and 2015.
4. Deregulation reshaped modern finance in ways still being felt today. The conversation traces a line from Glass-Steagall's separation of commercial and investment banking, through its effective rollback via the Gramm-Leach-Bliley Act, to today's financial holding companies (like JPMorgan owning both Chase and an investment bank) — blurring risk in ways that echo the AI/memory borrowing spiral.
5. National tech ecosystems don't automatically follow American patterns. Japan's PC industry, dominated by NTT, never fully converged on the IBM-clone standard the U.S. market did, and Korea's cultural relationship to gaming and digital life (partly shaped by heavy state investment in nationwide internet infrastructure) diverged sharply from its neighbors, despite a shared heritage.
6. China's relationship with open-source AI is shifting. Having initially embraced open source partly because it seemed easier to control than proprietary Western tech, China now appears increasingly concerned about having lost that control and is working to reassert it.
7. Distributed, interoperable messaging protocols are having a moment. Tools like Matrix and Nostr (open-source alternatives gaining traction partly in response to Meta's restrictions) revive a decades-old dream of software systems messaging each other freely, distinguishing distributed "pub-sub" models from federated ones where all participants must cooperate.