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 11 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
Taskaya: Flux was the first enterprise-ready generative image model
“The team at Stability left to start Black Forest Labs, which released Flux models. And that was the first model to, you know, breach the barrier of commercially usable, you know, enterprise-ready grade models”
Taskaya: Custom ASICs do not make sense given low Nvidia GEMM overhead
“What is the overhead of an NVIDIA GAM instruction, right? It's like 16%. So like you're essentially buying a, Matrix multiplication machine. So, like, it doesn't really make sense to specialize it that much.”
Taskaya: NSFW content makes up less than 1% of Fal traffic
“Moderation is optional to a level where, like, illegal content is moderated, and we also track, like, the non-illegal content NSFW moderation, and, like, we haven't seen, like, we haven't seen more than one percent.”
Taskaya: 80% of promotional video content will be AI-generated within 12 months
“Like 12 months, I think like 80% of this is going to be generated.”
Taskaya: Training a state-of-the-art image model costs under $1M
“Like right now, like if you look, if you want to train a Sota image model, I don't think it's going to cost more than a million dollars. It's extremely cheap. It's like a matter of data engineering effort, cleaning. It's, I think it's a function of data set.”
Taskaya: Flux grew Fal's revenue from $2M to $20M in two months
“In the first month of flux models, we reached from, like, two million to ten million in revenue. That was, like, a big jump. Next month, we were at 20, like, it just started going from there”
Taskaya: Fal optimizes inference for four major AI video companies
“We have like four different companies four, four major video companies that we are doing this with and one image company that I don't think we disclosed.”
Taskaya: Model architectures will keep churning due to researcher novelty bias
“I think the architecture is going to keep changing until this, like, paradigm of, like, you know, researchers changing stuff for the sake of changing, you know, finishes.”
Taskaya: 1,000x More Compute and Data Will Yield Accurate Physics Simulators
“It's just a matter of like data scale and like the underlying fundamental architectures. But like, I don't think it's going to change that much. We're just like, we're going to put thousand X more data, thousand X more compute, and we'll get like the best phys…”
Taskaya: Releasing Video Models Yields Better ROI Than Subpar LLMs
“My guess is, like, training these costs, like, a couple million dollars, which is not that much, especially, you know, like, you know, they're probably backed by some sort of entity... So training these models will bring you a lot of attention and it's more at…”