Dwarkesh Podcast features long conversations with researchers, builders and thinkers about technology, history and the future.
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Amodei expects AI to spread through the economy unusually quickly, while procurement, permissions and organizational change still take time. He separates models writing code from complete job automation and reduced demand for workers.
Open conversationBrown argues that parallel agents can reduce waiting time while increasing total compute. He expects better models to organize themselves more effectively and credits the underlying model with most of the mathematical result, while saying very large teams remain poorly compared with smaller ones.
Open conversationSchulman separates carrying out experiments from deciding what to seek. Useful research also involves judgment, realistic tasks and objectives that reflect what people actually want.
Open conversationMillidge argues that models can attempt harder goals than people can demonstrate when success is verifiable. Whether that learning transfers far enough to remove human guidance remains uncertain.
Open conversationO’Neill distinguishes technical advances that remain useful from workplaces whose relationships, routines and circumstances keep changing. Accumulating knowledge and keeping it relevant are different demands.
Open conversationdescribes agent teams as a way to finish sooner by spending more compute. He expects stronger models to coordinate better, while acknowledging limited measurement of very large teams.
, and largely agree that choosing objectives and the next experiment remains difficult for models. They disagree about how long that limitation lasts and what could overcome it.
Follow extended conversations with researchers, builders and historians into specific claims and disagreements.
Interview statements belong to their speakers; an appearance does not establish expertise on every topic.
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