Hebbia

“I think there were a lot of advances in RAG, and the biggest beneficiaries of these advances were the application companies for whom, you know, retrieval was a critical unlock.”

“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 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.”

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

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

“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…”

“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.”

“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…”

“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.”

“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 believe the model layer will become commoditized.”

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

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

“And this is a technique scaling at inference that was first actually pioneered at Hebbia, and we quickly noticed another scaling law, where if we ran more models and basically more compute at inference time, you could get much better results for very complex t…”

“And I think that this level of agentic deep research is going to be one of the most exciting things that I can't quite disclose now that, that Hebbia has proven out and is working on over private information.”

“If you look at ChatGPT and the limitations of like, you give it to a bunch of investors and you ask it to draft an IC memo for a company like Hebbia, every single VC would get the same IC memo out.”

“Hebbia's got like an infinite effective context window and then when you look at a new opportunity, you could filter to a variety of other opportunities that were similar and actually begin to piece out how it's different, how it's similar and all the pieces t…”

“Rather than try to build the AGI, how do, how about we build the AI platform that is so good that if AGI were to complete a task, it would choose to use Hebbia to do that task.”

“I mean, it saves probably 20 to 30 hours on a deal process, depending on how, how disgusting these VDRs can get.”

“We can screen a 137% more opportunities. Like in a, yeah, in a given period of time, which is like, you know, and you, it's with the same depth.”

“People are starting to use things like our infinite effective context window to actually look over entire libraries of formally negotiated agreements and live during a negotiation actually come with better terms or what a better understanding of what is market…”

“If every single credit agreement, you know, they reviewed multiple hours and it costs 2000 dollars per hour for a lawyer to review them. And now they can review them in house. They're saving on tens of thousands of dollars on a per deal basis, maybe hundreds o…”

“And what Hebbia is trying to build is the Bloomberg terminal for private companies. Not like a pitch book or a crunch base, which is really just for sourcing, but actually to go out and take all of the information private and public and pre-structure that. So …”

“I asked my friends who were renting out a house in East Palo Alto to let me rent a room, the cheapest room they could possibly find, and they were all fully booked, and it was like, I think, over a thousand dollars of rent, and they said, I think it was actual…”

“We raised a pre-seed from Peter Thiel, and Floodgate, and then our seed from Mike Volpe at Index”

“We ended up talking for, I think, like, four or five hours about Not only the company and all of the flaws that I had in my business model, but then also math and deep esoteric philosophy and like just the world. And he said, you know, I'm not investing at the…”

“We end up building a product studio, which is the first to productionize RAG, Retrieval Augmented Generation, also in 2020.”

“We ended up going from zero to a million dollars of revenue sometime in 2021 or 2022, and raise our series A also from Index, from Micon Index, which is thirty million bucks.”

“And so all of these questions, it was actually almost 90% of the questions that people were asking these systems weren't answerable by search through the documents. But rather they had to be work done on top of the documents and then answered.”

“I think, like, 90% of enterprise AI right now is almost like this vapor where, hey, we swear it works, like, look at this amazing demo where we ask, What does the CEO say about the investment? And the minute that they actually go to, you know, try to use it in…”

“I think that it will change the way that people do work, but I genuinely believe that, ah, it'll actually increase the AUM of the firms that use it. I think it will actually drive more employment.”

“And so you saw the idea of, ah, of post training or kind of like this refined, verticalized model creation, ah, just always would lose to scaling laws. And maybe we're at the end of scaling laws at training, but I actually think, you know, Hebbia and now OpenA…”

“I think that since Hebbia started, the cost of inference over a fixed number of parameters has decreased by, like, seven orders of magnitude in four years.”

“I, you know, I think that ultimately the model layer, and I think this is not a hot take anymore. I've been saying it for a few years, but I think it will become commoditized. I think that a lot of value will accrue at the hardware layer especially And we coul…”

“Ultimately, do not think so. I think that chat was always a useful feature. It's an, it's a useful interface, but again, it's like a single cell in Excel. It's like asking if the TI-eighty-four was the right interface for computers, or the terminal was the rig…”

“The better agents are, the more work that they do, the more important it will be that they are easily understood by humans. The idea would be, okay, let's say we have a bunch of employees, 10,000 employees, or 10,000 AI agents drop at a company. They're all ex…”

“I think that 90% of the market is still in experimental budget phase, but we're starting to see early promises of Actual value.”

“The CTO or the IT folks are actually the people that know the least about the business. The people that, that actually understand how to use AI in a business context are those that are closest to the business.”

“Would I sell for, no, no, I would not.”

“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.”

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

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

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

“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…”

“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…”

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

“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…”

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