A Note from James:

In the last episode, we talked about whether Martin Shkreli really deserves the label “most hated man in America.” My conclusion was no, and I hope you came to the same conclusion after hearing his perspective.

In this episode, we shift gears completely. We talk about Bitcoin, crypto, AI, energy, optical computing, and what the future of technology might actually look like.

Martin has a very unusual combination of skills—finance, biotech, programming—and I always enjoy hearing how he connects ideas across different fields. That’s what this conversation is about.

Episode Description:

What happens when AI demand collides with the limits of computing power and energy?

In Part 2, Martin Shkreli and James explore the future of technology—from crypto vulnerabilities to optical computing, GPU scaling, and the potential energy crisis driven by artificial intelligence.

They discuss whether Bitcoin can survive quantum computing, why stablecoins solve real-world financial problems, and how computing architecture may shift beyond traditional silicon chips. The conversation then moves into AI economics: why companies might spend billions on compute to make better decisions, how energy constraints could shape innovation, and why optical computing could become the next major breakthrough.

This episode isn’t about controversy—it’s about technological leverage, incentives, and where computation is heading next.

What You’ll Learn:

Why quantum computing could eventually threaten Bitcoin’s encryption
The real-world advantages of stablecoins and decentralized payments
How AI demand could create massive new energy constraints
Why optical (photonic) computing may outperform traditional silicon chips
How businesses might use large-scale AI compute for strategic decisions

Timestamped Chapters:

[00:02:00] Bitcoin, Encryption & Quantum Computing Risks
[00:03:02] A Note from James
[00:03:34] Crypto Markets: Speculation vs. Utility
[00:05:23] Banking Control, Debanking & Stablecoins
[00:07:40] Moore’s Law, Huang’s Law & The Limits of Silicon
[00:08:45] Optical Computing Explained
[00:09:12] NVIDIA, Parallelization & Power Consumption
[00:10:24] Energy Constraints & The Electrical Grid
[00:11:41] AI Energy Demand vs. Countries
[00:12:24] Corporate AI Decision-Making at Scale
[00:13:37] The Coming Explosion of AI Compute
[00:14:20] Energy Efficiency vs. Speed
[00:15:17] GPU Efficiency Improvements & Jevons Paradox
[00:17:00] Why AI Is Different from Traditional Computing
[00:17:47] Optical vs. Quantum vs. DNA Computing
[00:18:19] Why Optical Computing Fits AI Perfectly
[00:19:28] Precision, Bits & Neural Networks
[00:21:24] Error Tolerance in AI Systems
[00:22:00] Fiber Optics & Existing Infrastructure
[00:23:16] New Computing Paradigms Beyond Silicon
[00:24:00] Matrix Multiplication & AI Workloads
[00:24:53] Closing Thoughts

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