The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 14 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
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.”
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?”
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…”
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 …”
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.”
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…”
Patel: Model inference costs dropped 60x from GPT-4 to DeepSeek-V3
“And likewise, when we look at from GPT-IV to DeepSeq VIII it's fallen roughly 600 X in cost. Right. So we're not quite at that 1200 X, but it has fallen 600 X in cost from 60 dollars to less than you know, to about a dollar. Right. Or to less than a dollar. So…”
Patel: Frontier AI cluster costs have scaled from $100M to $10B
“For GPT-IV, it was a few hundred million dollars and it's one building full of GPUs, too. GPT-IV 4.5 and the reasoning models, like, oh, one, oh, three were done in a, in three buildings on the same site, and, you know, billions of dollars to, hey, these next …”
Patel: GPT-5 will simultaneously scale pre-training and post-training reasoning
“And so now GPT-Five, as Sam calls it, is, is gonna be a model that has huge pre-training scale, right? Like GPT-Five, but also huge post-training scale, Like O-one and O-three and continuing to scale that up, right? This would be the first time we see a model …”
Patel: Human labeling is unscalable, forcing reliance on synthetic AI data
“Using humans to train models is just so expensive, right? So then there's the magic of sort of reinforcement learning and other synthetic data technologies, right? Where the model is helping teach the model, right? So you have many models in, in, in a sort of,…”
Patel: Generating pre-answer reasoning tokens yields superior AI performance
“Models now will think for some time before they answer. And this enables much better performance on all sorts of tasks, whether it be coding or math or understanding science or understanding complex Social dilemmas, right? All sorts of different topics they're…”
Patel: Meta's next Llama model will match DeepSeek-V3's cost efficiency
“And Meta's Meta is going to release their new llama soon enough. Right. And that one is going to be, you know, a similar level of cost decrease probably similar areas, deep seek V three.”
Patel: Standard transformers allocate identical compute to every generated token
“When you look at a transformer, every word is this, every token output, it has the same amount of compute behind it. Right. I E, you know, when I'm saying the versus sky is blue, the blue and the V have this or the is in the blue have the same amount of comput…”