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 12 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
Fu: Current LLMs meet the definition of AGI from 5-10 years ago
“By almost any definition anyone could have written down, let's say five years ago or 10 years ago, certainly when, you know, Tim, you and I started our PhD. We basically have the vision of AGI that, that we had back then. We have things that can write code. Th…”
Fu: Next-generation models currently in training will achieve AGI
“You know, we maybe already have AGI or like some form of AGI. And if not, then certainly the next generation of models, the models that today are training already. If they're at all better than what we have today, then we're, we we've already hit something tha…”
Fu: AI coding tools enable expert programmers to move 10x faster
“But if you give an expert programmer This set of tools, they can go 10, 10 times faster than they were able to go before.”
Dan Fu: DeepSeek-V3 was trained on ~2,000 H800s with 20% MFU
“If you look at the deep seek model, for instance, this is one of the best open source models we have out there today. It was trained at the end of 2024. On last generation, kind of nerfed GPUs, H 800 instead of H 100, the 800 is nerfed by all sorts of ways fro…”
Dan Fu: Deployed AI models lag cluster infrastructure by 1–2 years
“The models that we see today that we can play with today have been pre-trained on clusters that were built out a year or two ago. Because, you know, you need enough time to get the cluster running. You need enough time to do the large pre-training run. And the…”
Fu: Hardware utilization during AI inference is under 5%
“At inference time, when the, when you have the model, when it's already been trained, already been post-trained, the hardware utilization is like less than five percent.”
Dan Fu: Chinese AI labs take more architectural risks
“I think you see a lot more risk taking out of the Chinese labs where you're trying to differentiate the next model of your next open source model.”
Fu predicts increasing hardware diversity, particularly for AI model inference
“I'm sure NVIDIA will still do great and still grow beyond their five trillion dollar company or whatever it is at the time of recording. But I think you're going to see a lot more diversity especially around, I think inference of the model.”
Dan Fu: Junior engineers using AI agents communicate better and level up faster
“When they are really gung ho about understanding and being able to use the AI agents, there's, they're able to communicate so much better than in the olden days. They're able to level up their level of understanding a lot faster.”
Dan Fu: Proper speculative decoding yields 2x to 3x model speedups
“So if you do the speculative decoding right, you can get, again, two X, three X speed ups over, over, you know, just running a vanilla model.”
Dan Fu: Some top audio models use state space architectures
“So some of the best audio models in the world are at least partially based on state space models.”
Poolside and Reflection are building clusters with massive B200 GPU deployments
“They're companies like Poolside. They're building out tens of thousands of B-two hundred, GB-two hundred chips. You know, there's other folks like Reflection who are who are building out. Tens of thousands of B 200 chips.”