20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

1119.025 - 1138.671 Mark Blyth

When we think about the alignment in compute and models, we had David Kahn from Sequoia on the show and he said that you would never train a frontier model on the same data center twice. Meaning that essentially there is now a misalignment in the development speed of models and that is much faster than the development speed of new hardware and compute. How do you think about that?