The Exchanges

Q But successful founders that you found is a trend. Can you talk to me about the different profiles?

A I'm, I'm, I'm happy to. I, I always joke around and say that, uh, you can, you can bucket great founders into, into three backgrounds. Uh, I think probably the most common is that you had kind of a messed up childhood. Uh, the second most common would be, uh, you're gay. And the third most common would be you were adopted. And I think if you can look at, like, a list of all-time greats, you know, like, uh, Elon Musk kind of messed up childhood, Jeff Bezos, Steve Jobs adopted, uh, you know, uh, Peter Thiel, Sam Altman, you know, like, you know, publicly gay. And I think that all of these kind of early life experiences end up giving you some desire, some, like, deeper passion to go out and prove yourself.

AI assessment note: “you can bucket great founders into, into three backgrounds”

Q But successful founders that you found is a trend. Can you talk to me about the different profiles?

A I'm, I'm, I'm happy to. I, I always joke around and say that, uh, you can, you can bucket great founders into, into three backgrounds. Uh, I think probably the most common is that you had kind of a messed up childhood. Uh, the second most common would be, uh, you're gay. And the third most common would be you were adopted. And I think if you can look at, like, a list of all-time greats, you know, like, uh, Elon Musk kind of messed up childhood, Jeff Bezos, Steve Jobs adopted, uh, you know, uh, Peter Thiel, Sam Altman, you know, like, you know, publicly gay. And I think that all of these kind of early life experiences end up giving you some desire, some, like, deeper passion to go out and prove yourself.

AI assessment note: “you can bucket great founders into, into three backgrounds”

Q What do you think that tells us about the layer itself?

A I, you know, I think that, um, 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, uh, especially, um, And we could talk about what that means for NVIDIA, especially as, ah, you know, NVIDIA has a stranglehold on training, but, you know, ah, not as much a stranglehold on inference, and so you might actually see, um, ah, other chip makers, you know, actually start to, their, their chips start to be used in a more meaningful way, because, you know, CUDA is what all ML scientists were trained on in their PhDs, but then, you know, inference doesn't matter, ah, kind of what you're using. Uh, and I think it will be the infrastructure layer and then actually the application or agent layer that will accrue the most value. Ultimately.

AI assessment note: “ultimately the model layer... will become commoditized”

Q He's my neighbor. So he's literally next door. So we can arrange that. Um, he's my gym buddy. Uh, he's the one who taught me guns. Uh, but he said that we consistently underestimate how long it takes for enterprises to adopt new technologies, to get comfortable with data security, to get comfortable with processes. Is that right, or do you think actually we are past the tipping point?

A It's a good point if you're, if you're cutting cost, which I think Daniel and UiPath are one of the best examples of using AI to make companies more efficient. But, like, if you look at finance and how fast, ah, Excel went to 90% market penetration in finance. 1985 to 1986. Literally 18 months to 24 months, Excel took over all of finance. Everyone switched from using a calculator, the HP 12 C, to, to using Excel. Uh, and if you look at how fast, uh, finance actually ended up, you know, using credit card data to, to, to value public companies ahead of their earnings. That was, again, a two year period more recently. And so finance is the slowest moving, most lethargic, you know, uh, Leviath. It's the worst possible customer base to go after. Unless you're providing outsized alpha or real value. In which case, the minute that there's something real, Finance moves faster than any other industry. And so I'm actually making a bit of a bet by going into finance and starting to go out and try to get to my own.

AI assessment note: “the minute that there's something real, Finance moves faster than any other industry.”

Q I'm an incredible, take me to the founding of Hebbia, then. We're at Stanford. We're doing incredibly well. We are the wonder child. How does Hebbia come to be in that situation?

A I, I'm one of the youngest PhD students in the history of my school, and I actually believe that I was working on, you know, at the time, one of the areas of research that was most interesting to me was meta-learning. This idea of teaching machines to learn to learn. Um, and in June of 2020, uh, Sam Altman OpenAI came out with a GPT-III. And if you remember the title of that paper, it was Uh, large language models are multi-task or meta-learners. And I'm, I'm sitting in, you know, in my lab one day, I'm playing around with this new technology, uh, and I'm like, wow, they just stole the most important thing I could work out right, right under from my hands. And I said, well, if I can't build the most important technology, how can I build the most important product? Uh, and I think those are two very separate things. Obviously, at the time, GPT-III was not a product. And I don't even think ChatGPT is a really good product. It's like a calculator. It's got the technology in there encapsulated in a very simple form. But it's not a product that, like in Excel, that lets you just build whatever you'd like with it. That's a very human first. Um, and Stanford always, like, pounds into your head the idea, hey, you've got to start a company where there's a lot of pain. And I had a lot of my students or a lot, a lot of my friends would go into, you know, investment banking or private equi…

AI assessment note: “if I can't build the most important technology, how can I build the most important product?”

Q To what extent is this RPA versus Ajanta?

A I, I think that, um, I actually am not a big believer in RPA. I think RPA is a, is almost not an AI application in the, in the new sense of AI. It's like AI in the old 10 years ago sense of AI, um, where RPA, RPA is effectively, like, very simple computation. But some of the things that people are asking Hebbia are over 800 page credit agreements, or 230 page, you know, SIMS, uh, confidential information memorandums, this marketing material, where they're, they're not actually asking for things that Um, you know, like, copying numbers. They're saying, hey, tell me, uh, what are inconsistencies in this document? Tell me where there's an event of default that we can trigger. And, like, it, there's almost this, like, open-endedness, or this new level of computation that people can do.

AI assessment note: “I actually am not a big believer in RPA. I think RPA is effectively, like, very simple computation”

Q What do you think makes Peter so incredible?

A I think that there's, there's, there's two things. Um, you know, there's, he is incredibly ontologically smart, and so he, he can build this worldview or this perspective of the, of the world where, like, he actually just, like, knows how to pattern match to a variety of other things. But then he's also, I think, phenomenologically smart, which is the idea of, ah, like he understands processes and how humans behave really well. And so he's always thinking like, hey, you know, ex-ante, or like, you know, if, if I'm looking at, um, you know, something that's about to unfold, ah, could I have predicted this ahead of time? And he always just asks himself that question. So he's built up a really rich perspective of, of the, the fallacies that, that human Society has, and like, you know, the mimetic behavior that people kind of go out and copy each other with, et cetera, and so.

AI assessment note: “he is incredibly ontologically smart... But then he's also, I think, phenomenologically smart”

Q I'm an incredible, take me to the founding of Hebbia, then. We're at Stanford. We're doing incredibly well. We are the wonder child. How does Hebbia come to be in that situation?

A I, I'm one of the youngest PhD students in the history of my school, and I actually believe that I was working on, you know, at the time, one of the areas of research that was most interesting to me was meta-learning. This idea of teaching machines to learn to learn. Um, and in June of 2020, uh, Sam Altman OpenAI came out with a GPT-III. And if you remember the title of that paper, it was Uh, large language models are multi-task or meta-learners. And I'm, I'm sitting in, you know, in my lab one day, I'm playing around with this new technology, uh, and I'm like, wow, they just stole the most important thing I could work out right, right under from my hands. And I said, well, if I can't build the most important technology, how can I build the most important product? Uh, and I think those are two very separate things. Obviously, at the time, GPT-III was not a product. And I don't even think ChatGPT is a really good product. It's like a calculator. It's got the technology in there encapsulated in a very simple form. But it's not a product that, like in Excel, that lets you just build whatever you'd like with it. That's a very human first. Um, and Stanford always, like, pounds into your head the idea, hey, you've got to start a company where there's a lot of pain. And I had a lot of my students or a lot, a lot of my friends would go into, you know, investment banking or private equi…

AI assessment note: “there was more pain in financial services around processing unstructured data than anything”

Q So you're one of the first, and we're seeing that now in action, and it's working?

A That's a bit of a plot twist over here. We, I actually don't think RAG works at all. So it's one of the most used AI architectures in the world, pioneered at, at, at Hebbia in a very meaningful way. Uh, I think every enterprise is experimenting with it, but, uh, it has a lot of different failures where a lot of the time the questions that people ask these systems aren't ever explicitly in the data. They're never explicitly stated. They're actually about the data. So for example, um, you know, if you're asking an AI system, is this company a good investment? Which is actually a very common thing that people ask heavy over marketing materials. It's never gonna say, like, or maybe it'll say in a pitch deck, yeah, this company is a great investment as a, something that the CEO says, or like a recording, et cetera. But what you actually want from that system isn't to search in the data, it's to answer about the data, hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that are, are fundamental to our specific investing process? And that's a process. That's not, that's not ever explicitly stated. Actually, the marketing materials are often like, ah, you know, a load of crap. And you have to, you have to actually distill what's true out of them. And that's, that's what heavier does. So it's not actually finding something tha…

AI assessment note: “I actually don't think RAG works at all.”

Q Does it not make it incredibly capital inefficient?

A You know, I think the one thing that people in my position, uh, will always tell you is that the cost of intelligence will go to zero. The cost of intelligence will go to zero. I mean, I think that since Hebbia started, the cost of, of, of inference over a fixed number of parameters has decreased by, like, seven orders of magnitude in four years. And so I genuinely believe that, um, Scaling compute is like a no-brainer. And yes, we run more large language model calls than anyone might even say would ever be necessary. But we have the best accuracy in the business. We can answer much more complex problems. We're driving real value for enterprises. I, I, I, and I actually think that every single quarter, like, our margin goes, oh, we're not spending money fast enough.

AI assessment note: “cost of inference over a fixed number of parameters has decreased by, like, seven orders”

Q Do you do per seat because it's back to the human or just because it's what they know as a buying mechanism?

A I, I actually think that we are human first. Uh, we're business user first. Uh, you know, to, to the point where CTOs like to pay for consumption or API, et cetera, and like, you know, business users like to pay per seat because it's how they, how they map back to value. But also we want to incentivize change. Tech is not the hard part of all of this. It's hard. But the hardest part of AI change management, no matter what company you are, are people. And like actually getting people to use the software. And I think that, ah, when you charge for consumption or API pricing, you're disincentivizing the change. You're saying, ok, well I'm gonna penalize you in a monetary way for every time you use an AI application. What the heck? Uh, versus here's a per seat fee. It might be expensive, but use it more. You could, you could run more LLM calls on, on Hebbia effectively for free than any other platform if you, if you actually are driving real change. And that's what I love to see.

AI assessment note: “I actually think that we are human first. Uh, we're business user first.”

Q Uh, I think almost all of them have actually- Ultimately, if agents are efficient, does interface not become irrelevant?

A I actually think that, uh, 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, of employees, 10,000 employees, or 10,000 AI agents drop at a company. They're all experts at doing something. That ends up not becoming a, uh, you know, a problem of giving them the right tasks, but, but actually it becomes a management problem. Right, there's this whole infrastructure, orchestration layer, the thing I always come back to, of, of making these things work together, and that's actually going to be a challenge, and that's, that's going to require a very human first, ultimately a, a product, uh, and that's what we're trying to build.

AI assessment note: “the more important it will be that they are easily understood by humans.”

Q What do you think makes Peter so incredible?

A I think that there's, there's, there's two things. Um, you know, there's, he is incredibly ontologically smart, and so he, he can build this worldview or this perspective of the, of the world where, like, he actually just, like, knows how to pattern match to a variety of other things. But then he's also, I think, phenomenologically smart, which is the idea of, ah, like he understands processes and how humans behave really well. And so he's always thinking like, hey, you know, ex-ante, or like, you know, if, if I'm looking at, um, you know, something that's about to unfold, ah, could I have predicted this ahead of time? And he always just asks himself that question. So he's built up a really rich perspective of, of the, the fallacies that, that human Society has, and like, you know, the mimetic behavior that people kind of go out and copy each other with, et cetera, and so.

AI assessment note: “I think that there's, there's, there's two things. Um, you know, there's, he is incredibly ontologically smart”

Q Do you think the cloud providers are basically using models as a loss leader happily to build stronger moats in their cloud businesses? And you see this with Anthropic and Amazon. You see this with Microsoft and, um, OpenAI.

A Absolutely. I think, uh, I think, I think absolutely, like, like, whoever has the best models, uh, will, will, will, will continue to attract, uh, the right amount of investment. I think that, um, the, the different thing about clouds too, though, is that the cost of switching is, is much higher. So to, to refine my earlier point, right, right, like, I can switch models readily. Like, I think there's even entire businesses now There will be an entire industry of being able to switch models from OpenAI to Anthropic when OpenAI goes down, but just to switch clouds is like, you know, for any, like, substantially sized startup, like a ten million to twenty million dollar investment just to switch. It's almost always never worth it. It's much, much, much stickier, whereas here it's a very simple API key, you know, it's very simple to switch models, and so I think that that's also a differentiator.

AI assessment note: “to switch clouds is like, you know... like a ten million to twenty million dollar investment”

Q Uh, I think almost all of them have actually- Ultimately, if agents are efficient, does interface not become irrelevant?

A I actually think that, uh, 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, of employees, 10,000 employees, or 10,000 AI agents drop at a company. They're all experts at doing something. That ends up not becoming a, uh, you know, a problem of giving them the right tasks, but, but actually it becomes a management problem. Right, there's this whole infrastructure, orchestration layer, the thing I always come back to, of, of making these things work together, and that's actually going to be a challenge, and that's, that's going to require a very human first, ultimately a, a product, uh, and that's what we're trying to build.

AI assessment note: “The better agents are, the more work that they do, the more important”

Q But everyone says Salesforce is fucked in this next generation. You think they are or not?

A I think that, I think that ultimately, uh, I don't, I don't think they are. I think that, uh, I think that Salesforce Like, has, has built, again, like, a very, very, very sticky network effect with people, and people are the, the, the shifting function at the end of the day. It's, it's not a technology problem. I mean, Claude can build a Salesforce. I think Klarna, again, had another Fugazi story about building, like, going off Salesforce, uh, because, you know, Claude had built them a, a CRM, and I just think that the, the switching costs, the network effect of changing human beings' habits is too high. Uh, and, and I think that that's, uh, Salesforce is one of those kind of like monopolies in that they have so much stickiness, uh, habitual stickiness.

AI assessment note: “I don't, I don't think they are.”

Q So you're one of the first, and we're seeing that now in action, and it's working?

A That's a bit of a plot twist over here. We, I actually don't think RAG works at all. So it's one of the most used AI architectures in the world, pioneered at, at, at Hebbia in a very meaningful way. Uh, I think every enterprise is experimenting with it, but, uh, it has a lot of different failures where a lot of the time the questions that people ask these systems aren't ever explicitly in the data. They're never explicitly stated. They're actually about the data. So for example, um, you know, if you're asking an AI system, is this company a good investment? Which is actually a very common thing that people ask heavy over marketing materials. It's never gonna say, like, or maybe it'll say in a pitch deck, yeah, this company is a great investment as a, something that the CEO says, or like a recording, et cetera. But what you actually want from that system isn't to search in the data, it's to answer about the data, hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that are, are fundamental to our specific investing process? And that's a process. That's not, that's not ever explicitly stated. Actually, the marketing materials are often like, ah, you know, a load of crap. And you have to, you have to actually distill what's true out of them. And that's, that's what heavier does. So it's not actually finding something tha…

AI assessment note: “That's a bit of a plot twist over here. We, I actually don't think RAG works at all.”

Q Does it not make it incredibly capital inefficient?

A You know, I think the one thing that people in my position, uh, will always tell you is that the cost of intelligence will go to zero. The cost of intelligence will go to zero. I mean, I think that since Hebbia started, the cost of, of, of inference over a fixed number of parameters has decreased by, like, seven orders of magnitude in four years. And so I genuinely believe that, um, Scaling compute is like a no-brainer. And yes, we run more large language model calls than anyone might even say would ever be necessary. But we have the best accuracy in the business. We can answer much more complex problems. We're driving real value for enterprises. I, I, I, and I actually think that every single quarter, like, our margin goes, oh, we're not spending money fast enough.

AI assessment note: “cost of inference over a fixed number of parameters has decreased by, like, seven orders”

Q What do you think that tells us about the layer itself?

A I, you know, I think that, um, 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, uh, especially, um, And we could talk about what that means for NVIDIA, especially as, ah, you know, NVIDIA has a stranglehold on training, but, you know, ah, not as much a stranglehold on inference, and so you might actually see, um, ah, other chip makers, you know, actually start to, their, their chips start to be used in a more meaningful way, because, you know, CUDA is what all ML scientists were trained on in their PhDs, but then, you know, inference doesn't matter, ah, kind of what you're using. Uh, and I think it will be the infrastructure layer and then actually the application or agent layer that will accrue the most value. Ultimately.

AI assessment note: “ultimately the model layer... I think it will become commoditized.”

Q He's my neighbor. So he's literally next door. So we can arrange that. Um, he's my gym buddy. Uh, he's the one who taught me guns. Uh, but he said that we consistently underestimate how long it takes for enterprises to adopt new technologies, to get comfortable with data security, to get comfortable with processes. Is that right, or do you think actually we are past the tipping point?

A It's a good point if you're, if you're cutting cost, which I think Daniel and UiPath are one of the best examples of using AI to make companies more efficient. But, like, if you look at finance and how fast, ah, Excel went to 90% market penetration in finance. 1985 to 1986. Literally 18 months to 24 months, Excel took over all of finance. Everyone switched from using a calculator, the HP 12 C, to, to using Excel. Uh, and if you look at how fast, uh, finance actually ended up, you know, using credit card data to, to, to value public companies ahead of their earnings. That was, again, a two year period more recently. And so finance is the slowest moving, most lethargic, you know, uh, Leviath. It's the worst possible customer base to go after. Unless you're providing outsized alpha or real value. In which case, the minute that there's something real, Finance moves faster than any other industry. And so I'm actually making a bit of a bet by going into finance and starting to go out and try to get to my own.

AI assessment note: “the minute that there's something real, Finance moves faster than any other industry.”

Q To what extent is this RPA versus Ajanta?

A I, I think that, um, I actually am not a big believer in RPA. I think RPA is a, is almost not an AI application in the, in the new sense of AI. It's like AI in the old 10 years ago sense of AI, um, where RPA, RPA is effectively, like, very simple computation. But some of the things that people are asking Hebbia are over 800 page credit agreements, or 230 page, you know, SIMS, uh, confidential information memorandums, this marketing material, where they're, they're not actually asking for things that Um, you know, like, copying numbers. They're saying, hey, tell me, uh, what are inconsistencies in this document? Tell me where there's an event of default that we can trigger. And, like, it, there's almost this, like, open-endedness, or this new level of computation that people can do.

AI assessment note: “RPA is effectively, like, very simple computation. But some of the things that people are asking”

Q What will be the pricing mechanism for the future of agents?

A It's a good question. There's, so I think, you know, there's, there's like four canonical prices, there's like consumption-based pricing, there's per seat pricing, there's like, hey, rent a salary, so pay a salary for an employee, which is, seems a little bit ridiculous, but will be less so, um, and then, and then maybe there's like flat pricing, or, and I think it, it ultimately depends on how you're driving value. Because Hebbia is building human-centric AI, the human layer to how you orchestrate an AI agent's staff, Uh, that, that scaling at, at inference. Um, we do per seat because it's ultimately always back to the human. Um, but I, I think, I think you'll see all of these new business models and pricing mechanisms.

AI assessment note: “I think you'll see all of these new business models and pricing mechanisms.”

Q How important is geopolitics in winning this game?

A I think geopolitics is, is, is actually very important. I think that governments will be some of the largest users of AI, especially, uh, especially with, like, some of the recent, uh, things that the, the, the new administration in the United States has been talking about with, with increasing government efficiency. Um, I think that ultimately, uh, energy is a, a very big bottleneck. You know, it's a very common thing in, Silicon Valley to talk about, hey, we need nuclear reactors to, to flatten the duck curve so that, you know, we can, you know, we can continue to drive to, um, larger and larger, uh, you know, kind of data centers, et cetera, et cetera, et cetera. Um, and, and those are ultimately geopolitical resources. And so I think all of these things end up being very important. And then, and then Elon's just operationally so talented, right? So I think that ultimately if this becomes commoditized and, and whoever can really operationalize, uh, Uh, model creation and, and, and serving models the fastest, uh, I think that thing might start to win.

AI assessment note: “I think geopolitics is, is, is actually very important.”

Q That is also an incredibly heart-wrenching moment, thinking of a, a little boy on the street crying. The advice of when to give up versus when to persist and fucking relentless. Me and you are both young. We've been taught that you win by, like, persistence And going for it. When is that true, and when is it not?

A I think, um, I think I have an unhealthy obsession with driving really hard, and, and I think, um, Yeah, I think I, you just can never give up. Like, I just don't think that's an option, right? I think, um, you can look at every company ever, and some get to a hundred million dollars in revenue, in whatever, like some span of time, which they probably, their marketing team has hacked, and some, you know, end up taking really, really long periods of time. Um, and the only, the only thing that actually changes is the rate at which you get there. And so sometimes things go in your favor, sometimes they don't, but if you're so persistent that you just continue, Like, you can, you can bring a lemonade stand to a hundred million dollars in ARR. Like, there's nothing that's actually stopped. You can brute force your way as a founder. You screw product market. You can literally brute force anything in the world. Um, you just have to have that chip. You have to continue to, to just, you know, pound away, uh, at, at, at whatever is, is, is in your way. Yeah.

AI assessment note: “you just can never give up. Like, I just don't think that's an option”

Q Do you think the cloud providers are basically using models as a loss leader happily to build stronger moats in their cloud businesses? And you see this with Anthropic and Amazon. You see this with Microsoft and, um, OpenAI.

A Absolutely. I think, uh, I think, I think absolutely, like, like, whoever has the best models, uh, will, will, will, will continue to attract, uh, the right amount of investment. I think that, um, the, the different thing about clouds too, though, is that the cost of switching is, is much higher. So to, to refine my earlier point, right, right, like, I can switch models readily. Like, I think there's even entire businesses now There will be an entire industry of being able to switch models from OpenAI to Anthropic when OpenAI goes down, but just to switch clouds is like, you know, for any, like, substantially sized startup, like a ten million to twenty million dollar investment just to switch. It's almost always never worth it. It's much, much, much stickier, whereas here it's a very simple API key, you know, it's very simple to switch models, and so I think that that's also a differentiator.

AI assessment note: “Absolutely. I think, uh, I think, I think absolutely”

Q That is also an incredibly heart-wrenching moment, thinking of a, a little boy on the street crying. The advice of when to give up versus when to persist and fucking relentless. Me and you are both young. We've been taught that you win by, like, persistence And going for it. When is that true, and when is it not?

A I think, um, I think I have an unhealthy obsession with driving really hard, and, and I think, um, Yeah, I think I, you just can never give up. Like, I just don't think that's an option, right? I think, um, you can look at every company ever, and some get to a hundred million dollars in revenue, in whatever, like some span of time, which they probably, their marketing team has hacked, and some, you know, end up taking really, really long periods of time. Um, and the only, the only thing that actually changes is the rate at which you get there. And so sometimes things go in your favor, sometimes they don't, but if you're so persistent that you just continue, Like, you can, you can bring a lemonade stand to a hundred million dollars in ARR. Like, there's nothing that's actually stopped. You can brute force your way as a founder. You screw product market. You can literally brute force anything in the world. Um, you just have to have that chip. You have to continue to, to just, you know, pound away, uh, at, at, at whatever is, is, is in your way. Yeah.

AI assessment note: “you just can never give up. Like, I just don't think that's an option”

Q I mean, do you find, what do you find about painting good for you?

A I think it's one of those activities where you can channel, uh, emotion or intuition or like latent thoughts that are somewhere in your subconscious and connect things in a really meaningful way. And so, um, you know, in a world where There's all this stimulus, or you're always kind of thinking or churning through something, or all this distraction. Uh, you know, you're standing in front of a canvas for, like, 10 hours with, with some nicotine, and, and, and, and you're just, you're just lost in this, this kind of art. I think great artists will tell you that they don't even know where paintings come from. It just, you know, is this, you're kind of channeling something. Uh, and I, it's one of the best places to think. It's, it just gives you connections. Um, it brings up These parts of your subconscious, these connections that I think you, you, you can't really access without being creative. Whether you're making music or writing or painting, I actually think that's one of the best ways to process anything.

AI assessment note: “channel, uh, emotion or intuition or like latent thoughts that are somewhere in your subconscious”

Q Everyone thinks they're a master of agents and agentic workflows. What do they think they know that they actually don't know?

A I think they believe that they know how to, like, I think ultimately the people in the enterprise that are most excited about AI and like positioning it so strongly are CTOs and information technology people. Um, and maybe the thing that we've That Hebi has always said is that, you know, 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. And so, you know, I think they're, we're jumping the gun a little bit with the CTOs trying to build the CRM before it's been invented. And, you know, you actually need business people to build the CRM in Excel first, uh, in, in kind of that order of operations. And so there's a lot of unbundling of AI applications or CTOs trying to go out and build You know, a very specific, uh, you know, vertical application. Um, but I actually think that building this platform, like Heavy Matrix, is the thing that will unlock users' ability to discover what they can use AI agents for.

AI assessment note: “CTO or the IT folks are actually the people that know the least about the business”

Q Does that not change the enterprise value accumulation and whether they're good or bad for businesses? Because it's like instantly your business will die if you don't have it or not.

A Yeah, I actually always liken technological revolutions, uh, you know, to what Hedby is doing right now, where, you know, people invented, or we discovered the technology of fire, and then someone invented the torch, you know, I don't know how many years later, or, you know, we invented the engine, and then someone invented the car, or the wheel, and then the chariot. And so, this idea of encapsulating and building a useful product on top of a technology change is actually the thing that takes more time. And I think that Hebbia has built, ah, you know, if, if, if Excel was that product for compute, ah, I actually think Hebbia has built that product for AI. And I think that when you have a good product, that transition will be very, very, very quick. Right now we have these chatbots or these, you know, surface level search engines that, that give you facetious, you know, surface level value. You know, it'll help your kid cheat on their homework, but to drive to whether or not something's a good investment is a much, much more rich problem.

AI assessment note: “building a useful product on top of a technology change is actually the thing”

Q Does that not change the enterprise value accumulation and whether they're good or bad for businesses? Because it's like instantly your business will die if you don't have it or not.

A Yeah, I actually always liken technological revolutions, uh, you know, to what Hedby is doing right now, where, you know, people invented, or we discovered the technology of fire, and then someone invented the torch, you know, I don't know how many years later, or, you know, we invented the engine, and then someone invented the car, or the wheel, and then the chariot. And so, this idea of encapsulating and building a useful product on top of a technology change is actually the thing that takes more time. And I think that Hebbia has built, ah, you know, if, if, if Excel was that product for compute, ah, I actually think Hebbia has built that product for AI. And I think that when you have a good product, that transition will be very, very, very quick. Right now we have these chatbots or these, you know, surface level search engines that, that give you facetious, you know, surface level value. You know, it'll help your kid cheat on their homework, but to drive to whether or not something's a good investment is a much, much more rich problem.

AI assessment note: “I actually always liken technological revolutions, uh, you know, to what Hedby is doing”