Hebbia

“If you have a 90% good system, and then another 90% good system, and then another 90% good system, and then, you know, your output is the sum, or rather the product of all those 90% good systems, you're gonna have something that is, you know, 10% good, dependi…”

“I actually think we're internally procuring Glean at Heavya”

“To date, not a single job has been replaced by Hebbia, you know, knock on wood.”

“If we stay in financial services, I will have failed.”

“What we're finding is that these models are good at first order tasks. Right? You can get 90% good at a first order task. They don't quite have the context window, which will be solved, the contextualization kind of the repeated back and forth that allows them…”

“I think one day there will be many more jobs that are created to run Hebbia.”

“When we used to just do, you know, something like a chroma index or whatever we would include the cosine similarity score on the thing. And people would freak out. They'd be like, what does 72% similar mean? Or, you know, what does very relevant mean? And we a…”

“We have not, so we work with many of the largest asset managers, we work with many banks, we work with the US government and we have not needed to fine tune or, you know, kind of personalize any part of the software.”

“We allow you to compare two documents semantically, which is a pretty hard problem, ah, that we're pretty excited to have solved.”

“This year, we're already on track to process around four to five billion pages.”

“And so we were opening SF and we've already opened a London office which is incredibly exciting with goals to end the year at 300 to 400 employees.”

“So we built a team of a hundred amazing incredibly smart folks all five days a week, sometimes six in office in New York city.”

“And now we're deployed at, I think, between 40 and 50% of the world's largest asset managers.”

“I think we run around Like, two hundred and fifty billion large language model calls a month.”

“One of the things that is a massive fallacy in AI applications today is verticalization. As paramount. All of the VCs, a lot of entrepreneurs as well, believe, because it's been true for the last 20 years, that building a very verticalized piece of software is…”

“And actually the whole scaling at inference paradigm was pioneered at Hebbia. So our early matrix product two years ago, we're one of the first people to say, hey, you get way better accuracy from using more large language model calls at runtime.”

“And we came up with a novel re-ranker architecture, which four years later, academia and industry have not beat, and we do not use it.”

“We'll spend 10,000 dollars in model costs per user a year. We don't care.”

“Hebbia were actually the first to turn that into a product. So it's like a very close thing to my heart. So back in 2020, we were the first people to actually productionize it, roll it out.”

“You're going to start to have, you know, fully human organizations. Actually fully AI organizations like the one person billion dollar startup or, you know, these things that are effectively just APIs. And you'll actually have, and this will be the most common…”

“Before we had Maximizer, with all the rate limits that we had, we could, like, run a million tokens a minute, and now we can do 500 or four hundred fifty million tokens a minute.”

“We believe the model layer will become commoditized.”