The guests argue that every major leap in AI has been driven by orders-of-magnitude increases in compute. The exponential growth in GPU performance and the ability to train on thousands of devices unlocks the data-hungry spatial models that were impossible a decade ago.
Prop firms provide fast access to large leverage and structured payouts, but once a trader reaches a capital threshold, personal accounts become more flexible and less stressful.
Analysis of Vitalik's recent comments about Ethereum needing to refocus on L1 scaling, and the implications for the L2 ecosystem that has developed over recent years.
Amodei argues we're nearing the end of exponential AI progress, with capabilities reaching human-level across many domains much sooner than most people expect. He discusses why public recognition lags behind technical reality.
Amodei explains his 'Big Blob of Compute' theory - that AI progress depends primarily on seven scalable factors rather than algorithmic breakthroughs. He discusses how this applies to RL and generalization.
Brian advocates for blending homegrown talent with strategic external hires, warning against over-indexing on big-company pedigrees. He shares HubSpot's painful lessons about impedance mismatches in scaling organizations.
Brian traces HubSpot's intentional shift from employee-centric to customer-centric culture, revealing how metrics and rituals must change as companies grow beyond startup phase.
“Companies must choose their primary constituency: employees, customers, or investors”