Korva rescues the ideas worth keeping from podcasts. Each one becomes a Card.
Restricting open-source AI in the United States would constrain compliant American businesses while leaving attackers and adversaries largely untouched.
Intelligence too cheap to meter will arrive through open source, driven by businesses demanding control, capacity, and customization.
Abundant, cheap data-center energy is technically and entrepreneurially achievable. Investment and permission to build are the bottlenecks.
AI could trigger Jevons paradox: making mundane work cheaper may lead people to do more, not simply consume less.
Software and decisions can accelerate quickly. Construction knowledge, labor, raw materials, and energy infrastructure cannot.
Model-driven biology could replace biotech’s uncertain, specialized, long-cycle investment model with conventional software and platform businesses.
Investors obsess over big-lab strategy while the real question is how an application moves from its first 1% to the next 99%.
The United States cannot rebuild its industrial base without AI automation amid expensive labor, missing skills, and resistance to degrading work.
AI could progress from answering questions about legal documents to performing most work in complex mergers and acquisitions.
Competitive open-source AI models now come from China, the United States, and Europe, and people already use them widely.
When people learn faster and eliminate mundane work, they repurpose the recovered time instead of leaving it unused.
Technology investors must answer one harder question than founder quality: what becomes large?
Debating what major AI labs might do becomes circular. Study each organization’s concrete priorities and incentives instead.
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