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 15 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
Huber: LLMs are like CPUs, not operating systems
“I don't think of an LLM as an operating system. I think an LLM is much more like a CPU, right? It's an information processing unit.”
Huber: Open-source models win B2B through developer focus, not beating GPT-5
“Focus on the developers. I think that's the beachhead. That's how you win the B to B market. If you win the B to B market with your open source models, Like, you get all of the sort of downstream effects that you want. You know, you don't need to beat you know…”
Huber: 10x compute increases are not producing 10x better AI models
“Diminishing, they're clearly diminishing marginal returns, right? We're sort of spending 10 X on compute. We're not getting 10 X or better models, at least evidently not yet.”
Huber: AI will probably drive GDP growth exceeding the Industrial Revolution
“It's a, you know, technology is probably as important as the invention of electricity. It will probably, you know, bring about a increase in GDP that is on the order of the industrial revolution or greater.”
Huber: In 10 years, the poorest could have better healthcare than today's billionaires
“Like it is very possible the poorest people on earth today, or, you know, in 10 years, we'll have access to better healthcare better legal representation you know, better financial services than, like, billionaires have today.”
Huber: Language models require a multi-tiered memory hierarchy like traditional computers
“In the same way that we have a memory hierarchy in classic computers, right, we have the CPU, RAM, disk, and network we are also going to have a similar memory hierarchy in language models. And again, it already exists today. We have the actual sort of transfo…”
Huber: Needle-in-a-haystack tests do not prove real-world long context reliability
“Even these, like, needle-in-a-haystack tests, like, are not actually that representative of, like, real-world utility and reliability of long context windows.”
Huber: Never bet against Zuckerberg and Meta's distribution power
“Distribution is incredibly important as long as, you know, sort of the incumbents can wake up and can catch up. You know, I would not bet against Zuck And a hundred billion dollars of profit per year.”
Huber: Most businesses prefer open-source models over closed-source AI
“Most businesses don't love using closed source models. They want to use open source models. For all kinds of reasons, you know, privacy, security, continuity, cost”
Huber: Fine-tuning model weights fails enterprise AI due to lack of deterministic control
“Updating the weights of the model is not a very good idea because you cannot really deterministically control that. You can fine tune, but what you're going to get the other end, you know, again, you don't really control.”
Huber: Current AI models have an immense capability overhang
“We think the capability overhang we have in the models that we already have today, and we will have absolutely in six months is immense.”
Huber: Silicon Valley tends to be extremely intellectually shallow
“Silicon Valley has a tendency to be sort of extremely intellectually shallow. This is both a strength and a weakness of the Valley, to be clear.”
Huber: Model releases often top leaderboards but lack practical developer hooks
“You know, you see a lot of like model drops that come out, but they don't actually provide the real hooks and they do very well in the benchmarks, right? They do very well on like kind of the public leaderboards. But they don't actually provide the hooks that …”
Huber: Over 90% of enterprise AI use cases are retrieval-augmented generation
“I think like today, 90 plus percent of it in enterprises is retrieval event generation, or it's, you know, using retrieval, it's sort of a chat on top of unstructured data.”
Huber: Fuzzy search is most useful when users don't know the dataset
“Fuzzy search is really useful when people like are not, you know, experts in their own data, right? Is that if you're Google Drive, you know how to search for stuff pretty well, right? But like your users don't know how to search for the stuff that you've said…”