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The Pomp Podcast

Should You Invest In SpaceX IPO, Elon Musk, Bitcoin or AI? | Jordi Visser

6/13/2026 · 56 min · transcript via whisper

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Key topics

SpaceX IPO valuation and dual nature as both space/infrastructure and AI company; Elon Musk's competitive advantage in building data centers faster than competitors via vertical integration and engineering excellence.

Critical minerals and supply chain bottlenecks (copper, silver, indium phosphate) required for AI buildout; China controls key materials, creating geopolitical friction that may slow infrastructure expansion.

AI model commoditization and token cost dynamics; subsidized pricing by OpenAI and Anthropic below actual production cost; demand for cheaper alternatives (DeepSeek, open-source models) creating deflationary pressure on revenue.

Talent migration and leadership shifts in AI firms; prediction that Sam Altman may not lead OpenAI within a year; Anthropic gaining momentum in coding and talent attraction.

Physical infrastructure, humanoids, and robotics as critical future bottleneck; Jeff Bezos' Prometheus manufacturing venture targeting 10x efficiency gains via AI-driven hardware development.

New York Knicks playoff experience as analogy for irreplaceable real-world value and blockchain utility; commemorative tickets selling for $300+ on eBay highlighting scarcity and authenticity concerns in AI-deepfake world.

Market & price signals

SpaceX valued at multiples higher than traditional software companies, with historical comparisons (Tesla up 25,000% since IPO, 40% annual compound returns). Micron trading at ~$700–$1,000 despite being "cheap" relative to exponential chip demand forecasts. AI model token costs rising 4x year-over-year despite price competition. Commemorative Knicks game-four tickets trading $300–$450 on eBay within 24 hours of distribution.

Actionable insights

Monitor physical commodity bottlenecks (silver, copper, indium phosphate) as constraint on AI infrastructure deployment; position in materials and energy infrastructure rather than expensive AI software stocks during mid-cycle slowdown.

Prepare for model commoditization and tiered pricing; expect companies to shift to cheaper inference models for commodity tasks and reserve expensive models for high-value enterprise use cases; monitor hyperscaler margins.

Skills development in AI-native roles is critical; traditional silos and majors are obsolete; proficiency with AI tools (Claude, ChatGPT for real-time problem solving) is table stakes for future employment.

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