SemiAnalysis CEO Dylan Patel discusses the technical architecture of OpenAI's upcoming flagship model GPT-5 with Alex Kantrowitz.
Opinion
Patel: Most AI models lean left due to Bay Area origins
“Most AI models are made in the Bay Area, so they tend to just be left leaning, right? But also the internet in general is a little bit left leaning because it skews younger than older.”
Insight
Patel: Building cheaper AI requires massive frontier models for synthetic data
“You can't actually make that cheaper model without making the better model, bigger model. So you can generate data to help you make the cheaper model, right?”
Assertion Not checkable as stated
Patel: $10B AI data centers aim to automate software engineering, not chatbots
“No one is trying to make with these, you know, with these ten billion dollar data centers, they're not trying to make chat models, right? They're not trying to make models that people chat with, just to be clear, right? They're trying to solve things like soft…”
Assertion Not checkable as stated
Patel: OpenAI's Orion training run failed to reach GPT-5 performance levels
“There were hopes that Orion could be used for GPT-V but its improvement was, like, not enough to be, like, really a GPT-V. Furthermore, it was trained on the classical method, which is, like which is a ton of pre-training, and then some reinforcement learning …”
Opinion
Patel: Language is a representation for reasoning, not human thought itself
“Language is not actually how our brain thinks. It's just a representation for which it to, you know, reason over.”
Insight
Patel: AI models ingest dangerous data during pre-training for world knowledge
“So you don't want to just filter out everything so that the model doesn't know anything about it but at the same time, you don't want it to output, you know, how to build a bomb so there's like a fine balance here, and that's why pre-training is defined as pre…”