20VC: Why All AI Companies Are Under-Valued | The Future of Foundation Models: Scaling Laws, Generalised vs Specialised, Commoditised? | From Unable to Afford Rent to Raising $130M From Index and Peter Thiel with George Sivulka @ Hebbia

George Sivulka is the founder and CEO of Hebbia, is one of the fastest-growing gen AI companies and they recently raised a $130M series B. Investors include the company include hailed names such as a16z, Peter Thiel, Index, GV and others. In Today’s Episode with George Sivulka We Discuss: 04:47 Three Traits The Best Founders All Share? 08:11 How Cold Calling NASA Changed My Life 12:01 From Stealing Food From Stanford to Pitching Peter Thiel 17:22 Lessons working with Peter Thiel 26:39 The Future of AI and Business Applications 33:03 The Future of Employment with AI 33:45 Debunking the Myths of AI Job Displacement 35:09 The Future of Models: Many specialised or few generalised? 35:56 Scaling at Inference: A New Frontier 38:10 The Impact of Scaling Laws on Foundation Models 40:40 The Future of AI and Enterprise Value 43:43 The Geopolitical Influence on AI 45:03 The Commoditization of AI Models 47:47 Why Foundation Models Will Not Follow the Same Path of Cloud 52:53 Why All Companies, Both AI and Non-AI Are Undervalued

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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

• 0:30Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. And today we have one of the most wild stories in AI, Hebbia. Three years ago, George Sivulka couldn't make his rent of $300 a month for a mattress on the floor. He snuck into Stanford dining room halls for meals after dropping out. He raised his first two rounds of financing with clothes hanging behind him on Zoom. Most recently, the company raised a whopping $130 million and has investors, including Peter Thiel, Index, GV and Andreessen. This is one of the most remarkable stories of the last few years. But before we dive in today, here are two fun facts about our newest brand sponsor, Kajabi.

• 1:14Harry Stebbings?

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• 1:59Harry Stebbings?

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• 2:42Harry Stebbings?

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• 3:27Harry Stebbings?

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• 4:12Harry Stebbings?

  You have now arrived at your destination. George, I am so excited for this, dude. I've been really looking forward to this one. I spoke to Kevin Hart, Sam Sangeen, Corey, I found out all the shit there is to know. So thank you for joining us.

• 4:24Speaker 00

  Yeah, and I mean, it sounds like you did a lot of research. So thank you for diving deep and really excited to meet you as well.

• 4:30Harry Stebbings?

  Cheers. Adventure Capitalist, it's amazing the amount of free time you have.

• 4:35Harry Stebbings?

  This is gonna be a fun show. Talk to me about your childhood. I spoke to Sangeen and he was like, this was a really interesting part of me getting to know George. So talk to me about your childhood and I'm leaving that deliberately open for you.

• 4:46Speaker 00

  It is fair. And I think the first time I met with Sangeen, who's one of our investors at the Series B, it was like a 30 minute lunch that turned into almost two hours of us talking in depth about the dynamics that I think made me have a chip on my shoulder. But in short, I was born in Staten Island, New York City, which, you know, you already have a chip on your shoulder from that. Grew up kind of around New York City in New Jersey primarily, which a second chip. But my mom is probably like a mafia child, born and raised Staten Island. And my dad is an immigrant from Slovakia. He grew up under the iron curtain and then immigrated, really escaped to the United States. They, both of them actually fully intended to be professional athletes. And they had four children, of which only one was a boy.

• 5:31Speaker 00

  And so, you know, you can imagine their dismay when I was, you know, chasing butterflies on the soccer pitch. Or like falling on my head many times, which I have plenty of stories of me literally like falling over while trying to dribble a basketball. And I think, you know, my whole childhood, I was really just a math kid, like not very out there, wasn't really talkative, only really good at math. My parents barely even knew what Stanford was. So growing up, you know, you kind of have this whole misalignment of, of who I was and who I wanted to be. With who they wanted me to be. That gave me this drive and desire and passion to go out and prove myself in a way that was really tangible. Maybe not only to them, but like hopefully to my own kids one day.

• 6:11Harry Stebbings?

  Did you have friends?

• 6:12Speaker 00

  I had a lot of friends who were incredibly nerdy. So we went to a public school. Like I was the type of kid that would hack the school tablets to put Starcraft on everyone's computer. And then, you know, we'd all like not be paying attention in public school playing Star, you know, so there is a, there was a large. Enough contingent of kids that were also not athletes.

• 6:32Harry Stebbings?

  Before, when we were chatting, you said there are three archetypes. I don't know if you're willing to go into it.

• 6:37Speaker 00

  Yeah.

• 6:38Harry Stebbings?

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

• 6:43Speaker 00

  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. Look at like a list of, of all time greats. Elon Musk kind of messed up childhood, Jeff Bezos, Steve Jobs adopted, Peter Thiel, Sam Altman, you know, like me publicly gay. And, and I think that all of these early life experiences end up giving you some desire, some like deeper passion to go out and prove yourself.

• 7:20Harry Stebbings?

  I, I actually very much agree with you. I always very much felt like a disappointment. My brother was always, no, I'm being serious. My brother was always incredibly talented and good looking and tall and smart. And I was kind of just pretty average and I was fat. And like my dad didn't really hang out with me. He hung out with my brother. And so I always just felt like a disappointment. What a mistake that was, Papa.

• 7:41Speaker 00

  What is, what is, what is your brother do now?

• 7:43Harry Stebbings?

  He works for me upstairs.

• 7:44Speaker 00

  That's, that's what I'm talking about. There we go.

• 7:46Harry Stebbings?

  Did you feel like a disappointment?

• 7:48Speaker 00

  I think the answer is yes. I did. I felt like actually physically unable to do the things that I was wanted to do. Or I, I thought that I was good at things that weren't valued or weren't as important. All of my sisters are amazing athletes. They're all like, you know, six feet tall and they're incredible athletes. And, and I was, I was just not. And so it's kind of like the ugly duckling in many ways. Yeah.

• 8:11Harry Stebbings?

  I heard that you built lasers, you cold cooled NASA. Can you talk to me about these kind of very cool early influences in your life and how it shaped you?

• 8:21Speaker 00

  There's, there's completely separate stories there, but I think, um.

• 8:25Harry Stebbings?

  How do you cold cool NASA?

• 8:27Speaker 00

  This is actually a very good story. I wanted to be an astronaut. Like, that was my my number one goal, and I was I was hell bent on that. And so by the time I was, I think around 15 years old, I was going to high school in New York City, an all scholarship school where the alumni paid for everything. I was like, I was kind of tracking academically really strong, and I wanted a NASA internship. And they were offering them to college undergrads or graduate students. And so obviously, I applied and got rejected five times. And then there was a snow day in February where my school was closed. I commuted into the city. I actually showed up in front of their New York City office, the NASA Goddard Institute for Space Studies, and I demanded that they let me in. And the front door security guard was like, you know, kid, get the heck out. Like, what are you doing? You don't have an appointment? Like, I was like, I printed my resume

• 9:12Speaker 00

  out on the nicest paper. I'm wearing a suit. You've got to let me up. And he kicked me to the curb. And so I actually sat outside. It's like 110th Street, Manhattan. And it was snowing. It was like, so, so, so cold. And I didn't know what to do. I started crying. And I actually called my mother, because I was like, well, I'm gonna come home. And she's a salesperson. She works in medical sales. She picked up the phone and said, listen, no, you're not going anywhere. You sit your ass down, and you call every single number that you can get into the building. And so I sat on the curb and I cold called every number on Google, you know, from from my from my old phone. And finally, someone picked up. It was one of the only people in the office that day. And they came down, met me in the lobby, and I basically pitched them on myself for two hours. Gave me

• 9:57Speaker 00

  an interview. I interviewed, botched the interview, because I didn't know anything about, like, kind of like, linear algebra, I didn't know anything about physics. But I remember I memorized all of the titles of the posters on this professor's wall. Came back the next day, so showed up again, cold, and told them basically everything that I could possibly know about his specific research. And he was impressed enough to let me work for him for free. And then they paid me the next year. And then I published internationally recognized research the next year. And by that time, I think that was impressive enough to let Stanford let me in, which was a which was a life changing moment.

• 10:29Harry Stebbings?

  That is incredible. Yeah, that is also an incredibly heart wrenching moment, thinking of it, 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 persistence and going for it. When is that true? And when is it not?

• 10:49Speaker 00

  I think I have an unhealthy obsession with driving really hard. Yeah, I think you just can never give up. Like, I just don't think that's an option. You can look at every company ever and some get to $100 million 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. 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 $100 million ARR. Like, there's nothing that's actually stopped. You can brute force your way as a founder. You screw product, you could literally brute force anything in the world. You just have to have that chip. You have to continue to just pound away at whatever is in your way.

• 11:33Harry Stebbings?

  Stanford was a big one for you, I imagine. It was a really big personal validation to get in, correct?

• 11:39Speaker 00

  Yes, yes.

• 11:40Harry Stebbings?

  How did it feel when you got in?

• 11:41Speaker 00

  Onto the next one, you know, I was like, okay, you know, that's done. And like the next day I was like, okay, well, how do I become the youngest PhD student in my school's history? It's like, yeah, not even a moment. I, I think, you know, I was excited for a moment, but you know, it faded very, very quickly.

• 11:56Harry Stebbings?

  I spoke to Corey before the show, someone who's known you since you were 18.

• 12:00Speaker 00

  Probably even earlier.

• 12:01Harry Stebbings?

  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?

• 12:10Speaker 00

  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. And June of 2020, Sam Altman, OpenAI came out with GPT-3. And if you remember the title of that paper, it was Large Language Models Are Multitask or Meta Learners. And I'm sitting in, in my lab one day, I'm playing around with this new technology. And I'm like, wow, they just stole the most important thing I could work out 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? And I think those are two very separate things. Obviously, at the time, GPT-3 was not a product. And I don't even think ChatGPT is a really good product. It's like a calculator.

• 12:56Speaker 00

  It's got the technology in there encapsulated in very simple form, but it's not a product. Product that like in Excel that lets you just build whatever you'd like with it. That's very human first and Stanford always 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 investment banking or private equity if they were really lucky and they would come back and basically be the least happy versions of themselves. They like lost 50, they just hated their lives. It seemed like there was more pain in financial services around processing unstructured data than anything I had ever seen. It's like, well, there's a great company to be had here. Let's, let's give it a shot.

• 13:33Harry Stebbings?

  So we're sitting in that lab. We're like, hey, there's a great company to be had here. Let's give it a shot. What now? Cause I heard, I mean, and I saw pictures of this wonderful bedroom. So this was Four Seasons finest, unable to make, you made me feel like such a diva when I saw that bedroom, unable to make $300 rent, sneaking into Stanford dining halls for meals when you weren't studying there.

• 13:56Speaker 00

  Yep.

• 13:57Harry Stebbings?

  George.

• 13:57Speaker 00

  I have no comment off the, off the record.

• 14:00Harry Stebbings?

  Raised two rounds of financing with clothes hanging behind him on Zoom. Take me to the next step post that I'm going to do this in the lab.

• 14:08Speaker 00

  So I was on a PhD salary. You know, you're, you're making what $38,000 a year. I think 42 if, at the time, if you, if you had the Stanford Graduate Fellowship, which I had, and I said I was going to go on leave and I actually originally went on leave and said, told my advisor, I'll be back in a year. You know, it's coronavirus, just like, give me some time. And I didn't have anywhere to go. Like it was like a logical next step and I wanted to work on this company. 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 actually, you know, 500 or $600, not $300 to give my, my broke self some credit here for not being able to afford the rent. But they said you could rent out the master bedroom closet.

• 14:53Speaker 00

  And so I bought, I brought in like a mattress from the dorms and I had a folding table from Home Depot nearby. And I would basically rotate whether the mattress was on the floor or the folding table was on the floor. And that was, I just sat there and worked all day, 16, 18 hours a day, go to sleep, wake up, do it again, no weekends. You know, it's just kind of like, I turned into almost a monk where I was just obsessively building heavy. I was training models at the time. So I'd wake up in the middle of the night, check on them, you know, continue to use my GPU because I didn't want to spend any money.

• 15:21Harry Stebbings?

  Is there a period where more work is not effective? Like, when I think about 16 to 18 hour days in that environment, dude, I'm masochistic to the extreme where it's unhealthy, alcoholic, bulimic, tortured child. I'm like Lindsay Lohan, a venture.

• 15:38Harry Stebbings?

  But like, when I think of even like me in that, I would not function well there. I need fresh air, exercise, I just have to.

• 15:46Speaker 00

  Yeah, ultimately, I probably went too hard. My con site's 2020. I think, I think I definitely left nothing on the table to a point where it was detrimental to my health. But at the same time, I think that that was like a crucible where it helped for me. It's very hard to be a founder. And those are the moments where you're just like, you're eating microwave meals every single day. And like, you're just like, losing weight and you're like, trying to will something into existence. I actually was trying to pitch one of my former bosses at a professional services firm. And he looked at me on the Zoom call and he almost cried. And he was like, just come work here. Like, what are you doing to yourself? Like, don't like, come back, we'll give you like a proper salary. Like, you don't have to do this. There are so many low points like that. And I think, you know, just kept on chewing through it.

• 16:30Speaker 00

  And, and yeah, raising money in the closet. I actually got on with, for, we raised a pre-seed from Peter Thiel and Floodgate. And then our seed from Mike Volpe at Index. And Mike was like, hey, we're going to do a partner call just to, as a formality of the few partners, just to.

• 16:44Harry Stebbings?

  And this is Volpe?

• 16:45Speaker 00

  This is, this is Volpe, yeah. And so Volpe's like, there'll be a few partners on this call.

• 16:49Harry Stebbings?

  What round is this for?

• 16:50Speaker 00

  This is for a seed. So there's a follow on to the pre-seed in like, November of, I think, 2020. And I get on the Zoom call and I've literally got clothes hanging behind me. And they're like, all of a sudden, Mike shows up and then four other partners and then 80 partners. And like, the Zoom screen testulates with, you know, hundreds of faces. And I'm like horrified at the fact, and Mike's like, look, he's living in a closet. And everyone's like, ah, great founder. And I was so embarrassed and then and then I pitched my company. Honestly hilarious.

• 17:19Harry Stebbings?

  Yeah. I just want to unpack that element there because I said, I can't remember who it was who told me I had to ask, but they said I had to ask about driving to Peter Thiel's house.

• 17:28Speaker 00

  Well, so I'm saying two months prior, again, I think I was at Stanford or just about to leave Stanford. And one of my friends had interned at Founder's Fund and he's like, hey, I hear you're raising financing. You should talk to Peter. And I was like, oh, I'm not going to say no to that. And so he introduces me on an email thread with me and Peter and I'm like, hey, Peter, would love to do a lunch or dinner. I'm not a morning person. So I was like, Peter, we'd love to do a lunch or a dinner anytime soon. And Peter was like, I can do a breakfast. I was like, I really want to do a lunch or dinner, you know, like, can we do a brunch? You know, he's like, I'm going to do a breakfast. And so I said, well, that's, that's fine. And he gave me a slot on a Saturday and so got in my car. That was an old, like beat up 2006 Audi convertible that I had like fixed up from Craigslist and bought for $4,000.

• 18:12Speaker 00

  At three in the morning and drank a bunch of coffee, like 18 cups of coffee, like a five hour energy, like all the disgusting stuff. And drove from three to eight to his house to go and pitch this guy. And I think he showed up 45 minutes to an hour late. He's like just waking up and I'm, I'm wired. I'm like sitting in my chair, you know, like ready to go. And it was supposed to be a 30 or 45 minute breakfast. I was like, it's kind of already shot. But 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, and, and deep esoteric philosophy and like just the world. And he said, you know, I'm not investing at the time because it was coronavirus and a variety of other factors, but I'd love to put in a check.

• 18:55Speaker 00

  I got out of this conversation thinking I had just made a friend or was seen from someone who's incredibly, incredibly, incredibly bright. And I'm, I'm leaving his house and I felt like I was inducted into the Illuminati. I was like my whole body, you know, drop top, the sun's shining. I was playing Kanye West and I drove out as, as like my first offer from a venture investor.

• 19:13Harry Stebbings?

  How much did he invest?

• 19:15Speaker 00

  I think the total round was like a million dollars. So it's like, it's like nothing.

• 19:19Harry Stebbings?

  Yeah. But what do you think makes Peter so incredible?

• 19:22Speaker 00

  There's two things. He is incredibly ontologically smart. And so he can build this worldview or this perspective 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 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 I'm looking at something that's about to unfold, 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 human society has. Memetic behavior that, that people kind of go out and, and copy each other with, et cetera.

• 20:03Harry Stebbings?

  So we then have money from Peter and we have the Peter Thiel stamp of approval.

• 20:06Speaker 00

  Yeah.

• 20:07Harry Stebbings?

  Does that open every door in the valley?

• 20:09Speaker 00

  I mean, I think we were in late discussions with a lot of investors and then everyone else was like, yeah, it's like, you know, it's pylon in here. It's, you know, it's some of the best money that you can get. And that was, that was a game changer for us.

• 20:19Harry Stebbings?

  So then we close with Maples and Pluggate.

• 20:21Speaker 00

  Yeah, it was Anne, Anne at Pluggate.

• 20:22Harry Stebbings?

  Okay, Anne and Pluggate and, and Thiel. And then we get back to work and we've got a million or so.

• 20:28Speaker 00

  We've got a million. And then two months later, Mike actually hears about Hebe F from his daughter, who was a Stanford student. And I think I'd seen the product, was friends. And, and then Mike actually comes in and is like, hey, this is completely different than Elastic or all these other search technologies that I've seen and invested. Obviously, he's on the board of Elastic today. So he's like, well, let's just go add some fuel to the fire.

• 20:49Harry Stebbings?

  How much did he invest then?

• 20:50Speaker 00

  He, I think, invested like an additional two or 2.5 million dollars at the time.

• 20:55Harry Stebbings?

  So where did the 130 come from?

• 20:57Speaker 00

  Well, that was years later, you know.

• 21:00Harry Stebbings?

  This was years later?

• 21:01Speaker 00

  So that, this is all in 2020. We, we end up building a product studio, which is the first to productionize RAG, Retrieval Augmented Generation, also in 2020. We build the first semantic search engine. We actually go out and start.

• 21:16Harry Stebbings?

  Can you just help us understand what is RAG?

• 21:19Speaker 00

  RAG is an acronym that stands for Retrieval Augmented Generation. If you look at large language models today, they're really good at maybe thinking if you give it the right context, but they hardly ever have the right context. And so RAG was the first real attempt to give them the data to answer questions correctly.

• 21:38Harry Stebbings?

  Okay, and so we are building on RAG to start?

• 21:41Speaker 00

  We were one of the first people to productionize the idea of putting a search engine behind an LLM. So you'd ask a question and then instead of it just replying from its memory, it would actually go and do a search and then reply with that context. So in an enterprise where you have a lot of offline data, we were really the first people to hook up that offline data to large language models to answer that question.

• 22:01Harry Stebbings?

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

• 22:05Speaker 00

  That's a bit of a plot twist over here. I actually don't think RAG works at all. It's one of the most used AI architectures in the world pioneered at Hebbia in a very meaningful way. I think every enterprise is experimenting with it, but 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, 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 Hebbia over marketing materials. Maybe it'll say in a pitch deck, yeah, this company is a great investment as something that the CEO says or like a recording, et cetera. But what you actually want from that system isn't a search in the data, it's an answer about the data.

• 22:49Speaker 00

  Hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that 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 a load of crap. You have to actually distill what's true out of them. That's what Hebbia does. So it's not actually finding something that exists already. It's taking all the things that exist already and starting to answer questions about that information.

• 23:16Harry Stebbings?

  Take me to that transition then, because we were building on RAG and we're like, great, we're going to productionize this. And then we move off.

• 23:22Speaker 00

  Yeah.

• 23:23Harry Stebbings?

  And you realize that actually it's bullshit and it's not as good.

• 23:25Speaker 00

  Yes.

• 23:26Harry Stebbings?

  Take me to that realization.

• 23:27Speaker 00

  Yeah, so we actually deploy at some of the largest finance firms in the world. 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 Micron Index, which is 30 million bucks. And we start to see that all these customers, okay, now they know what ChatGPT is, they know what LLMs are. Hebe has this like really mature enterprise product in the market and we're by far the first to actually get there. And we just looked at all the queries that people were asking. And the questions that people were asking weren't ever, okay, find me the quote or find me the Command F questions. They were actually more, okay, read all the documents and then tell me all the times they mention AI.

• 24:09Speaker 00

  Or what our exposure to Silicon Valley Bank is during the regional banking crisis. And so all of these questions, it was actually almost 90% of the questions that people were asking these systems weren't answerable by a search through the documents, but rather they had to be work done on top of the documents and then answered.

• 24:24Harry Stebbings?

  My question is like, what the fuck happens to the rest of the landscape? If you're like, no, RAG is not actually the right approach and they're all loving RAG. Couldn't be hotter right now.

• 24:34Speaker 00

  I don't think they're loving RAG.

• 24:36Harry Stebbings?

  You don't think they are?

• 24:36Speaker 00

  I don't think they're loving RAG, no.

• 24:38Harry Stebbings?

  What makes you say that?

• 24:39Speaker 00

  I think like 90% of enterprise AI right now is almost like this vapor where 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 a real world example, completely just fails. And so I actually think that the majority of AI usage, a lot of these usage statistics are all kind of, one of my favorite phrases is fugazi fugazi. And one of the things that Hebe really tries to put forth in the market is say, hey, change will take time. But we have a system that is actually starting to drive real measurable value over very specifically defined use cases. And our tagline is always, hey, stop experimenting with AI, which everyone's experimenting, they're all really excited about it.

• 25:24Speaker 00

  Start driving value, like getting value out of it.

• 25:26Harry Stebbings?

  To what extent is this OPA versus a Janty?

• 25:29Speaker 00

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

• 26:10Harry Stebbings?

  I think Daniel Dins, we had on the show, it comes out on Wednesday. He said it very well. And he said, like, listen, RPA is low skilled, low level cognitive processes and agents is high skilled, ambiguous decisions.

• 26:22Speaker 00

  Yes.

• 26:23Harry Stebbings?

  I think that's a nice phrasing.

• 26:24Speaker 00

  I think it's incredibly clear. Yeah. And I think that we very much are capturing the the agent, the high level ambiguous decision making and trying to trace it all the way down back to individual citations or individual characters that led the model to that decision.

• 26:39Harry Stebbings?

  I thought about actually Satya's statement the other day that he made, which is the notion that business apps that exist today will all just collapse into agents. Do you agree with that? And will apps be the predecessor to agents?

• 26:51Speaker 00

  I actually don't don't agree with that at all. I think he's completely wrong. I think it depends on how you define a business apps. But I actually think that if the new business apps are platforms, you'll actually start to see those platforms really take hold. Like Hebe is a platform. It lets you build whatever agent that you'd like. And so here's a bit of a mindfuck. When when building Hebe or when building all of these foundational primitives for how people use AI over the last four and a half years, Hebe has always asked herself, what are the apps that AGI would want to use? Or what are the apps that agents would want to use themselves? I E. What are the tools? Because these AI applications are really good at using tools that we could build that would assist LLMs or these really smart foundation models, whatever they are in the future,

• 27:35Speaker 00

  to get to an answer more quickly. It's quite interesting, you know, Hebe. Matrix orchestrates lots of smaller LLM calls. It's actually scaling at inference, i.e. it's running massive amounts of compute at the orchestration layer. And we think that, you know, an AGI system would prefer to use heavier matrix to diligence a company or to look through thousands of documents versus to read them all by hand and they're in a really long context window.

• 27:58Harry Stebbings?

  So is the future of business apps, not business apps, but business platforms? We've got platforms, agents or apps.

• 28:06Speaker 00

  Yeah.

• 28:07Harry Stebbings?

  What is the future?

• 28:08Speaker 00

  I ultimately think that it will be a mix of all three. History doesn't repeat itself, but it often rhymes. You know, 60 years ago or even longer, the foundational unit of compute, i.e. doing a calculation on a computer, was effectively introduced to the enterprise. And there were plenty of people that were tallying things or bookkeeping in actual books. And their jobs changed and there were apps for bookkeeping. And then there were platforms like Excel that let people build better apps for bookkeeping. And then Excel was unraveled again into better, better apps for bookkeeping. And I think that there's opportunity not only in verticals, but there's also opportunity in the entire industry in terms of building platforms, in terms of building core primitives, in terms of even building new types of quote unquote agent employees. And I think that that opportunity is the exact same size.

• 28:53Speaker 00

  If there was $100 trillion of dollars of value that was created in the stock market from the introduction of the computer or the fundamental unit of compute, I actually think $100 trillion of value will be created in the next 60 years from the introduction of inference or of AI compute.

• 29:09Harry Stebbings?

  Will, will that be additional value? Or will that be value that denigrates from the existing value of alternatives?

• 29:15Speaker 00

  I believe it will be additional value. And maybe I'm too techno optimist, but I actually think the S and P 500 is, is completely.

• 29:21Harry Stebbings?

  $100 trillion of additional. I can't quite get my head around that. How does that even exist? Adding in a $100 trillion of value, is that just because we will see GDP and productivity grow so much that takes place?

• 29:32Speaker 00

  I ultimately genuinely believe that more than 50% of the GDP will be contributed by what you can call agentic applications in the next few decades. Yeah, I actually think it'll happen faster than the next few decades.

• 29:45Harry Stebbings?

  Do you?

• 29:45Speaker 00

  Yeah.

• 29:46Harry Stebbings?

  Because again, Daniel.

• 29:48Speaker 00

  Hot take, yeah.

• 29:49Harry Stebbings?

  Daniel on the show was like, listen.

• 29:50Speaker 00

  Put me up against Daniel. Yeah.

• 29:52Harry Stebbings?

  He's my neighbor, so he's literally next door. So we can arrange that. 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 a tipping point?

• 30:11Speaker 00

  It's a good point if you're cutting cost, which I think Daniel and you, you are path are one of the best examples of using AI to make companies more efficient. Like if you look at finance and how fast 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 12C to using Excel. And if you look at how fast finance actually ended up, you know, using credit card data 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, leviath, it's the worst possible customer base to go after, unless you're providing outsized alpha or real value.

• 30:56Speaker 00

  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.

• 31:06Harry Stebbings?

  You know what I worry about? I worry that we lose the education process. What I mean by that is a lot of like GPs or managing partners, or you name the title in a senior firm. They've been through the shed of analyzing companies, staying late, understanding what makes a great business, all of these things.

• 31:22Speaker 00

  Yeah.

• 31:23Harry Stebbings?

  And then we'll just say, well, don't worry about that shit. Happy all do it. And so we have this no graduation pathway for the next generation. And so we have decision makers who don't have that graduation.

• 31:33Speaker 00

  Yeah, I'm less worried about that. I think ultimately, one of the best things about being, you know, having the years of experience is is actually having the depth of knowledge about investing. So for example, if I'm a junior trying to price an asset, I haven't seen that many other companies that look like this company. And so I might say, hey, I think it should be priced at X, Y, or Z. And then someone in the IC, IC meeting will be like, hey, no, I've seen 20 other companies in my. Forty years of career that look exactly like this and they all went nowhere. And I'm leaning on my prior experience with heavier now juniors who are really smart themselves can say, okay, you might have remembered 20 deals, but I'm looking through every deal in our company's history in a giant matrix

• 32:17Speaker 00

  that anyone has ever seen. And I actually can tell you quantifiably that when a company is performing here, it's 90th percentile across all this investing criteria, we should actually pay 90% premium to market. And I'm actually using more deals than you've ever seen because I know your name is on this many IC memos. And that type of structured thinking or that type of additional information that you can now give juniors in their career, actually think makes better investors. I don't, I don't think that that takes away.

• 32:44Harry Stebbings?

  Do you really think it will be a tool for usage, not a tool for replacement?

• 32:48Speaker 00

  I genuinely believe it makes humans better. I genuinely believe.

• 32:52Harry Stebbings?

  In five years' time, do you not think that is a different story?

• 32:55Speaker 00

  I think that it will change the way that people do work, but I genuinely believe that it'll actually increase the AUM of the firms that use it. I think it will actually drive more employment. I, I think there will be some jobs that change. Hey, there's no more bookkeepers that do, you know, tabulations in spreadsheets on two sheets of paper.

• 33:13Harry Stebbings?

  What changes most and what stays the same?

• 33:15Speaker 00

  The cognitive tasks that are lower in cognition, like more back office, kind of middle office, maybe even some of the more junior front office tasks, I think will start to be, start to move into, okay, how can we manage AI juniors rather than, uh, you know, actually do this ourselves by hand? But I don't think that, you know, just as Excel didn't end up taking away jobs from people, it just changed people, you know, to having to learn Excel, the exact same thing will happen with AI.

• 33:45Harry Stebbings?

  So you don't think that we will see team sizes reduce as a result of agent integration into enterprise?

• 33:50Speaker 00

  You know, there's, there's all these stories and you know, you have like Klarna that's positioning for investors that they're firing half their staff and, and no one really wants, and I think You think that's BS? I think it's BS, yeah. There might be some reality to it, but I think that it's, it's an amazing marketing story. And so anytime that I ever hear something that's put out as a marketing story, I almost negate it in my head to actually think about like what the implications are. When you're saying something and screaming it from the rooftops, that almost always means that internally you're freaking out about something. I think I look at that really loud behavior and I think that behavior itself really negates the content. That's maybe my, my positioning on this sort of stuff, but. How do you feel about competition? What are your lessons on competition? There are several players now in the heavier slipstream. How do you feel about that?

• 34:34Speaker 00

  I think that if a hundred trillion dollars of economic value will be created by AI and agentic applications, that there will be so much room and so much opportunity for a ton of different players. I don't think that when Excel came out and then Mark released Salesforce and then, you know, people created Turbo, you know, all these unravelings of Excel actually were produced later, but that made Excel any less valuable. I actually think it made Excel more valuable. I view Hebe as this platform as something that will actually get better the more people get inspired by it and build increasingly verticalized applications. What models do you use? You said on top of what?

• 35:12Speaker 00

  We are completely model agnostic. We use all of the major model providers, some of our own models, but ultimately the foundational difference that Hebe is capitalizing on right now is fundamentally new and very important difference, which is, I actually think on the order of creating RAG and, and creating agents and decomposition. Is this idea of us in the last year or so, having pioneered scaling at inference? Talk to me about this. Right now you actually, so OpenAI is starting to do this with O1, where they'll have a model recursively think about a question over and over and over again before it produces an answer. And so instead of training a larger model, they're using effectively a similarly sized model and just telling it to run multiple cycles, i.e. compute more before answering.

• 35:56Speaker 00

  Hebe is actually pioneered something different where a year almost. Eighteen months ago, we said, hey, we can't wait for these models to catch up. What we'll do is in for a simple, single question. Let's actually run hundreds or even thousands of sub models of the best models in the world to compute over every single document to answer the same question. And so ultimately, if you can't train larger and larger models fast enough, you could take whatever state of the art or cutting edge and run it more times to get more compute, i.e. more computational power, better decision making for the same user right now. And so this is an idea that we pioneered. It doesn't matter if you're using Claude three, five, or if you're using O one itself, i.e. scaling at inference at the orchestration layer with something that will scale that inference with the training layer.

• 36:42Speaker 00

  But you get way better results and it's a way to drive to more accuracy.

• 36:46Harry Stebbings?

  Do you find provides the best results? We had Des training on intercom recently. He spoke about the movement away from OpenAI to Anthropic.

• 36:53Speaker 00

  We've seen that for certain types of documents, like the dense legalese or more colloquial documents, Anthropic works better. But for other types of documents like O one or OpenAI four L works better. And it's always trade offs between accuracy and speed and all kinds of things. Actually, a lot of the time when we're decomposing a task, we'll use mixes of OpenAI, Anthropic, even Gemini.

• 37:15Harry Stebbings?

  Do you think we live in a world moving forwards of many models that are specialized in different things? As you said, some do legal, some do whatever we want to talk about. And that's the world we live in. Or there's generalist monolith models, which really could have owned the whole stack.

• 37:30Speaker 00

  This makes me think of the story of Bloomberg, which has the best financial services training set of all time. And they trained a GPT three point five class model. It was called like Bloomberg GPT. And they released an archive paper and everyone on LinkedIn was like, wow, Bloomberg is, you know, cutting edge. And they're going to steal finance.

• 37:47Harry Stebbings?

  Why did they not? They did have all, they've got the best data in finance.

• 37:51Speaker 00

  So then GPT four was released, I think, like a few weeks later. I don't know exactly the right timeline, but it just destroyed Bloomberg GPT at every single finance task. And so you saw the idea of post-training or kind of like this refined verticalized model creation just always would lose to scaling loss. Maybe we're at the end of scaling laws at training, but I actually think, you know, Hebe and now OpenAI and a variety of other companies are starting to pioneer the idea of scaling laws and inference. And I actually think that it will, like, nothing that that other players can do to fine tune models will ever catch up.

• 38:25Harry Stebbings?

  I need to break that down. Sorry. So everyone's like, oh, are we at the end of scaling laws? Oh, yeah. Reed, Bernie off and Daniel Dines are like, yes, we are. Yes, we are the upper end of LM. Reid Hoffman is like, no, there's so much more room to run. Can you just break down for me the difference between scaling laws at inference and scaling laws at training?

• 38:50Speaker 00

  Yeah, I think, you know, there's, it's a bit of a marketing distinction, right? But ultimately, the idea is that the way that we got here over the last five, seven years of training models has been let's build a bigger and bigger model and let's give it more and more data, more and more clean data. And then maybe we'll do some RLHF or some, you know, reinforcement learning, reinforcement training to to fine tune it after pre-training. And that worked great to get us here, but we're running up against the amount of good data that exists in the world. We're running up against.

• 39:20Harry Stebbings?

  Are we? Because people push back on this and say, there's so much data that we haven't used yet, whether it's video data that can't be translated, whether it's synthetic data, like we are not at all exhausted in terms of data supply.

• 39:32Speaker 00

  You know, I think that we're starting to run up against the constraints of it. And that's a, that's a gut feel. I'm not, you know, I'm not looking at particularly in data collection myself, but I, I think we're starting to run up against the limits of really good data that we can.

• 39:45Harry Stebbings?

  What's then the problem?

• 39:46Speaker 00

  So, so ultimately, that might mean that, hey, we're training larger and larger models. XAI, again, just, just created the largest GPU cluster of all time and they're going to try to train larger and larger models. But regardless of how the scaling laws for training larger models or parameter count and accuracy or performance carry out. I'm starting to believe that you could still get better compute, not by building a larger engine, to use a metaphor, but by actually putting a bunch of smaller engines together. Hebe, by orchestrating large amounts of inference, to answer one single question, ends up kind of building like a Tesla where Tesla is made up of a bunch of smaller engines or a bunch of smaller electromechanical motors that make a lot of torque and are really, really amazing larger engine.

• 40:30Harry Stebbings?

  Does it not make it incredibly capital inefficient?

• 40:33Speaker 00

  You know, I think the one thing that people in my position 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 inference over a fixed number of parameters has decreased by like seven orders of magnitude in four years. And so I genuinely believe that 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. And I actually think that every single quarter, like our margin goes, we're not spending money fast enough.

• 41:14Harry Stebbings?

  You mentioned X sort AI's GPU cluster.

• 41:17Speaker 00

  Yes.

• 41:17Harry Stebbings?

  What they've been able to do in such a short amount of time is miraculous.

• 41:20Speaker 00

  Yeah.

• 41:21Harry Stebbings?

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

• 41:24Speaker 00

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

• 42:00Harry Stebbings?

  Why does it not follow the same vein as cloud, where cloud is commoditized?

• 42:05Speaker 00

  Yep.

• 42:05Harry Stebbings?

  But Azure, Google Cloud, AWS, I mean, completely commoditizes beyond this cloud. But it's great business for them.

• 42:12Speaker 00

  I think it might. There's probably fewer players and more entrenched players in cloud. And ultimately, you know, I think those players honestly kind of have like an OPEC oligopoly where that, you know, they can control pricing. I just think that ultimately cloud is actually more complex than training larger and larger models.

• 42:29Harry Stebbings?

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

• 42:42Speaker 00

  Absolutely. Whoever has the best models will continue to attract the right amount of investment. 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, 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 in OpenAI goes down. But to switch clouds is like, you know, for any sufficiently sized startup, like a 10 million to 20 million dollar investment just to switch is 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.

• 43:25Harry Stebbings?

  OpenAI at 160, Anthropic at 40.

• 43:27Speaker 00

  Yeah.

• 43:28Harry Stebbings?

  Or XAI at 50. Which one do you buy?

• 43:30Speaker 00

  I think XAI is the most undervalued company and a really spicy take. I actually think XAI might overtake OpenAI and Anthropic in value over the next, you know, 12 to 24 months. It's just crazy, but I think they're all undervalued.

• 43:43Harry Stebbings?

  What leads you thinking?

• 43:43Speaker 00

  I think Elon is is very well positioned in the geopolitical sense. I think Elon can run a more efficient business than and not have to deal with as much administrative bloat or as much friction from from employees.

• 43:56Harry Stebbings?

  How important is geopolitics in winning this game?

• 44:00Speaker 00

  I think geopolitics is is actually very important. I think that governments will be some of the largest users of AI, especially with like some of the the recent things that the the new administration in the United States has been talking about with with increasing government efficiency. I think that ultimately, energy is a very big bottleneck. It's a very common thing in Silicon Valley to talk about, hey, we need nuclear reactors to flatten the duck curve so that, you know, we can, you know, we can continue to drive to larger and larger data centers, et cetera, et cetera, et cetera. And and those are ultimately geopolitical resources. And so I think all 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 whoever can really operationalize

• 44:41Speaker 00

  model creation and and serving models the fastest, I think what I think might start.

• 44:46Harry Stebbings?

  So you think XAI and you would invest in them?

• 44:48Speaker 00

  I would, but ultimately, I think all of them are undervalued. I genuinely believe all AI companies and the S and P 500 are all undervalued, which is a very hot take. If we're about to create a hundred trillion dollars of value, I think this is a real tangible technological shift. It's a massive unlock on the order of what computing did for the entire economy over the last sixty to eighty years. I think this will do for the next sixty to eighty years. I think all these companies are massively undervalued, including the non-AI companies.

• 45:15Harry Stebbings?

  I'm back to the last bit, including the non-AI companies.

• 45:18Speaker 00

  I genuinely believe that computers made legacy businesses better if you use them correctly. And so it's a massive disrupting force, but if you can ride the wave of change, AI agents in this new fundamental paradigm, that is a massive unlock for.

• 45:33Harry Stebbings?

  Do you think there is a slight difference? Everyone talks about kind of different technological transitions. When you look at, you know, the agricultural transition or the kind of agricultural dependency on human labor and movement and machinery, computers and workforces, these were at least ten year transitionary periods, at least. This is like, hey, we use AI tools now because we just bought them today.

• 45:54Speaker 00

  Yep.

• 45:54Harry Stebbings?

  The transition period is instant.

• 45:57Speaker 00

  Yes, much faster.

• 45:58Harry Stebbings?

  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.

• 46:07Speaker 00

  Yeah, I actually always liken technological revolutions to what Hebe is doing right now where, you know, people invented, we discovered the technology of fire and then someone invented the torch, you know, I don't know how many years later. 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 Hebe has built, if Excel was that product for compute, I actually think Hebe 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 surface level search engines that give you facetious, you know, surface level value.

• 46:48Speaker 00

  Yeah, to help your kid cheat on their homework, but to drive to whether or not something is a good investment is a much, much more rich problem.

• 46:54Harry Stebbings?

  Is chat the right interface for many of these applications?

• 46:57Speaker 00

  I ultimately do. Not think so. I think I think that chat was always a useful feature. It's a it's a it's a useful interface. 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 right interface for computers. We have not even started to explore the opportunities for interfaces. I actually think that

• 47:16Harry Stebbings?

  What do you think they are?

• 47:18Speaker 00

  Yeah, I think that Hebe is is the Bell Labs and I conceive of ourselves as the Bell Labs of defining AI interfaces. Right? I think that, you know, rag was one of them. I this idea you could find things in the data really fast decomposition and agents are another. This idea of scaling it inference with our matrix product is another. You can look at a lot of the other things where agents are controlling four screens at once and you're actually looking at someone use a computer or computer use where AI models are moving cursors are others. Almost all of them have

• 47:46Harry Stebbings?

  Ultimately, if agents are efficient, does interface not become irrelevant?

• 47:50Speaker 00

  I I actually think that 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, ten thousand employees or ten thousand AI agents drop at a company. They're all experts at doing something. That ends up not becoming a 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 product. And that's what we're trying to build.

• 48:28Harry Stebbings?

  Do you think Elon will be successful with Doge?

• 48:30Speaker 00

  I think it will be his greatest challenge. There's a lot of self protecting mechanisms in the largest organization in the world, which is kind of the US government by by spend by head. It's just it's just this massive unreally organization. It's not. It's not going to be as simple as Twitter.

• 48:46Harry Stebbings?

  Are you more excited in the post Trump?

• 48:48Speaker 00

  I think the thing that I care most about in the world is that we as an industry have very clear guardrails that we can follow and and understand to build the best possible tools to get our tools out to the economy to to make sure that everyone transitions in the best possible way. So I'm ultimately regardless

• 49:05Harry Stebbings?

  Does your business not thrive on a better financial system? And we're seeing now a financial system in the US from afar that would seem to be thriving objectively.

• 49:13Speaker 00

  Yeah.

• 49:14Harry Stebbings?

  It would appear that Trump is good for business.

• 49:16Speaker 00

  I I won't make a comment here.

• 49:20Speaker 00

  Um, I, I, I, I, I think that there's a lot.

• 49:22Harry Stebbings?

  You know what's funny? I went very viral before the election because I said it's so interesting. There's 99% of CEOs come on the show and they either shut up or they say they vote for Kamala. And then it ends and then it ends. And they're like, by the way, I'm so Trump. I am so Trump. But it's fascinating.

• 49:41Speaker 00

  Yeah, for sure. For sure.

• 49:43Harry Stebbings?

  So I totally understand the not answering.

• 49:45Speaker 00

  Yeah.

• 49:46Harry Stebbings?

  You are not alone. It's okay. But the one question I want to ask, you mentioned Nvidia before.

• 49:51Speaker 00

  Sure.

• 49:51Harry Stebbings?

  That's a really big question around their ability to sustain that monopoly. You've seen Google, you've seen Matter, you've seen Amazon all want to move into the chip layer. How do you think about Nvidia's ability to sustain their pretty unwavering monopoly so far?

• 50:05Speaker 00

  So, so Nvidia has, um, you know, I think that the, the best modes aren't technological modes. They're not data modes. They're actually people modes. People and networks have the most friction to change. One of the things that Nvidia does best is, is, is the fact that they made this early bet on machine learning. They created Kudo, which is the way that, as I mentioned before, almost everyone learns how to train models. Like they learn how to, you know, how to interface with Nvidia chips for training. And as you're starting to see, uh, maybe that, that prediction that I made earlier, the shift away from, from training to inference as a fundamental, like almost macro shift in how people deploy AI. I actually think that will destabilize slightly the dominance of Nvidia chips.

• 50:47Speaker 00

  You can start to actually use AMD chips or even custom architectures, which, which all the major model providers are also currently exploring to do inference. So you have your academics and your researchers, you know, training large models on Nvidia chips. But the minute they deploy them, they can deploy them on cheaper infrastructure. And that, and that actually, I think it will be a big change. So I'm actually still bullish on Nvidia, but I'm even more bullish on other chip makers and, and custom ASICs to do inference. Because I think there will be a larger shift to inference moving forward.

• 51:16Harry Stebbings?

  Is that other chip maker paradigm, existing incumbents, Google, Matter, Amazon, you name it? Or is it a new generation Cerebras style?

• 51:25Speaker 00

  Probably be large tech providers and, and AMD. I don't know about Intel, right? I would probably bet on them. There's definitely an opportunity in the market, but chips are hard.

• 51:36Harry Stebbings?

  Before we do a quick fire, I do just want to kind of resurface back up to the agent layer.

• 51:39Speaker 00

  For, for sure.

• 51:40Harry Stebbings?

  Are we out of the experimental budget phase?

• 51:42Speaker 00

  I think that 90% of the market is still in experimental budget phase, but we're starting to see early promises of actual value and my entire business is focused on, on just those repeatable use cases.

• 51:55Harry Stebbings?

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

• 52:02Speaker 00

  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. And maybe the thing that we've, that Hebe has always said is that 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 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 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 vertical application.

• 52:43Speaker 00

  But I actually think that building this platform, Hebe Matrix, is the thing that will unlock users' ability to discover what they can use AI agents for.

• 52:53Harry Stebbings?

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

• 52:57Speaker 00

  It's a good question. 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. And then, and then maybe there's like flat pricing or, and, and I think it ultimately depends on how you're driving value. Because Hebe is building human centric AI, the human layer to how you orchestrate an AI agent staff, that, that scaling at, at inference, we do per seat because it's ultimately always back to the human. I think you'll see all of these new business models and pricing mechanisms.

• 53:29Harry Stebbings?

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

• 53:33Speaker 00

  I actually think that we are human first. We're business user first 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. When you charge for consumption or API pricing, you're disincentivizing the change. You're saying, okay, well, I'm going to penalize you in a monetary way for every time you use an AI application. What the heck? 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 heavy effectively for free than

• 54:18Speaker 00

  any other platform. If you if you actually are driving real change. And that's what I love to see.

• 54:22Harry Stebbings?

  Are you ready for a spicy round?

• 54:24Speaker 00

  Let's give me the spicy round. We got the tissues out here, too.

• 54:26Harry Stebbings?

  Well, the tissues in case you cry. This is the in case you need them to hide behind. So this is a spice round. So this is questions from friends of yours.

• 54:35Speaker 00

  Okay, we got some changing colors up here. I love it.

• 54:38Harry Stebbings?

  Yeah, I know. It's a full game show. There we go. It's like a fucking David Gatter concert.

• 54:45Harry Stebbings?

  Perfect. Number one question. Would you sell for $2 billion today?

• 54:49Speaker 00

  Would I sell for? No.

• 54:50Harry Stebbings?

  What was the single best VC meeting?

• 54:52Speaker 00

  It's somewhere between, you know, Peter talking to me about anything but the business and deeply academic things and Mike taking me on a walk around the the Woodside Horse Park, which is.

• 55:01Harry Stebbings?

  Do you trust Sam Altman?

• 55:03Speaker 00

  No.

• 55:07Speaker 00

  Who asked that question? Yeah.

• 55:08Harry Stebbings?

  I don't reveal my sources. But listen, I want to do a quick fire round. So I say a short statement. You give me your immediate thoughts. That sound okay?

• 55:16Speaker 00

  Sounds good. Let's do it.

• 55:17Harry Stebbings?

  What do you believe that most around you disbelieve?

• 55:19Speaker 00

  Oh, I have a crazy one. I believe that UFOs are real. I think a little bit, a little bit more on the nose right now, but I actually believe there's fundamentally different propulsion technology and that I think the US government has access to it.

• 55:32Harry Stebbings?

  Wow.

• 55:33Speaker 00

  Conspiracy theories. I have a lot of spicy takes. It's a special.

• 55:38Harry Stebbings?

  Bring that out. What trait are you slightly ashamed of, but has contributed to your success?

• 55:43Speaker 00

  I don't think I'm ashamed of it per se, but one thing that I always hid was the fact that I'm deeply religious in an industry that's very atheistic or agnostic. It was like something that was very personal to me. And I think it's been massively contributing to.

• 55:59Harry Stebbings?

  How has it contributed?

• 56:01Speaker 00

  I think that ultimately, when you're doing hard things, or when you're chewing the glass, or you know, working all like those really late hours, believing in something larger than yourself, or believing in what you do as a vocation, or something that's deeply purposeful and deeply meaningful, is actually, it's additional fuel. It helps you in a way that is, I think, good for the soul. It really charges you up. And I think.

• 56:25Harry Stebbings?

  Do you pray?

• 56:26Speaker 00

  I do. I pray for an hour every morning.

• 56:29Harry Stebbings?

  What?

• 56:29Speaker 00

  Yeah. I wake up, I sit on a meditation cushion, and I used to meditate. I think meditation's also great. Praying and then putting something out into the universe, or you know, actually having a dialogue with whatever you believe, I actually think is even more powerful. It's almost.

• 56:46Harry Stebbings?

  Do you talk out loud?

• 56:47Speaker 00

  You know, I live by myself sometimes, but sometimes it's all in my head. I think it's incredibly good for the human mind. I think it's almost an antivirus for the human mind.

• 56:58Harry Stebbings?

  For an hour?

• 56:59Speaker 00

  For an hour. Yeah.

• 57:01Harry Stebbings?

  Yeah.

• 57:01Speaker 00

  It's, you know, people meditate. Why is it so weird to pray? I think it's.

• 57:08Harry Stebbings?

  I didn't know what I would say.

• 57:10Speaker 00

  When you dive into the human psyche and you're not looking at your phone and you're, a lot of the time, it's also a really great channel to think. I think a lot of the best ideas that I've had at Hebe have come from moments of silence. Yeah.

• 57:25Harry Stebbings?

  God, I get up at like 8:20. My first meeting is 8:30. There's like an espresso ready for me. I'm like, oh, fuck. Where are my shorts? Oh, God, mum texted. Jesus. I'm here. I'm alive.

• 57:38Harry Stebbings?

  So we have different morning routines. What's the gym routine? You're a fit dude.

• 57:44Speaker 00

  I try to try to work out every day. I actually end up mostly channeling the the startup pressures and anger and anxiety into heavier and heavier things and lifting heavier and heavier things. So it's nothing that's like in particular.

• 57:58Harry Stebbings?

  Is Silicon Valley back as the center of all things?

• 58:01Speaker 00

  Heavier, I think, there was a podcast that actually recently came out where everyone's like, if you're going to build an AI company, you've got to build it in Silicon Valley. But, you know, there is one company in New York that is doing a really amazing thing. That company is heavier. It seems like, you know, they're actually doing something interesting. And I do think we are the exception rather than the rule, unfortunately. So I'm a big believer in Silicon Valley.

• 58:23Harry Stebbings?

  Why? Why are you the exception?

• 58:25Speaker 00

  I think that we are a Silicon Valley company in terms of our style of work, in terms of how hard we work, in terms of how we actually pursue new technology and invest in technology. And we started in Silicon Valley and we have almost only Silicon Valley investors.

• 58:39Harry Stebbings?

  What have you changed your mind on in the last 12 months?

• 58:42Speaker 00

  Longer than 12 months, probably like 18 months ago, it was the scaling at inference thing, like the belief in like a new set of scaling laws that they would be really, really, really important.

• 58:51Harry Stebbings?

  ServiceNow?

• 58:52Speaker 00

  Yeah.

• 58:52Harry Stebbings?

  Salesforce or UI path?

• 58:55Speaker 00

  Okay.

• 58:55Harry Stebbings?

  Shag, marry or kill?

• 58:58Speaker 00

  Um, yeah.

• 59:01Speaker 00

  Uh, I wouldn't, I wouldn't shag any of them. I, I don't think that traditional.

• 59:06Harry Stebbings?

  Shag is like a short term excitement.

• 59:08Speaker 00

  Oh, I mean, I don't think that.

• 59:11Harry Stebbings?

  In case you needed the context.

• 59:13Speaker 00

  And I, I know exactly, I know exactly what you're getting at here. I really, you know, I'd probably kill them all. I don't think that traditional enterprise B two B applications are sexy. We're an enterprise AI company.

• 59:26Harry Stebbings?

  Are you a buyer of Salesforce?

• 59:27Speaker 00

  We are, we are.

• 59:28Harry Stebbings?

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

• 59:32Speaker 00

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

• 1:00:06Harry Stebbings?

  You can buy one company in the public markets that will be most benefited by the next wave of AI. Which company do you buy?

• 1:00:12Speaker 00

  That's a deep question. I would probably buy Nvidia. It's a lame answer, or AMD, rather. I think AMD, because I believe that they will benefit from the shift to inference scaling more than in an outsized way.

• 1:00:25Harry Stebbings?

  You can be CEO of any other company for a day. Which company?

• 1:00:29Speaker 00

  Not a company. I'd love to be mayor of New York, believe it or not. I just think that's like a fascinating job. I think it would be really, really interesting and would love to make some change there.

• 1:00:38Harry Stebbings?

  What question are you never asked by investors, by angels, advisors, employees, journalists, that you think you should be asked?

• 1:00:47Speaker 00

  I think that one of the most interesting questions is where does creativity stem from? Or where do you get inspiration from? Or kind of like, how do you come up with new ideas? Like, I don't believe that people come up with new ideas by brainstorming or in conversation. I just think that's, again, fugazi fugazi. But I think that ultimately, you know, that question of where creativity stems from, I'm also a very big painter. I'm a very big, so I do large scale, like 10 foot plus oil canvas, oil painting.

• 1:01:14Harry Stebbings?

  I heard about this. Where did that come from?

• 1:01:17Speaker 00

  I just, I think.

• 1:01:19Harry Stebbings?

  Are you a poet as well?

• 1:01:20Speaker 00

  I love to write and probably not as good a poet, but I actually think that other creative outlets are really, really good.

• 1:01:28Harry Stebbings?

  You're like that annoyingly perfect kid at school that like paints.

• 1:01:31Speaker 00

  If you want me to try to run, I will fall on my head. I told you that story at the start of.

• 1:01:36Harry Stebbings?

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

• 1:01:40Speaker 00

  I think it's one of those activities where you can channel emotion or intuition or like latent thoughts that are somewhere in your subconscious and connect things in a really meaningful way. And so, 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. You know, you're standing in front of a canvas for like 10 hours with some nicotine and you're just lost in this art. I think great artists will tell you that they don't even know where paintings come from. It just, you know, is this channeling something? It's one of the best places to think. It's, it just gives you connections. It brings up these parts of your subconscious, these connections that I think you can't really access without being creative.

• 1:02:20Speaker 00

  Whether you're making music or writing or painting, I actually think that's one of the best ways to process it.

• 1:02:26Harry Stebbings?

  Final one, do you feel that your parents are proud of you now?

• 1:02:29Speaker 00

  I think so. Yeah, I think so. I think, you know, they've, they've heard about it. There was one moment where I think my, um, my father's boss ended up calling him and is like, you know. Your son's, you know, kicking ass. And I was like, well, that was, it was a very happy moment for me.

• 1:02:42Harry Stebbings?

  That's a special moment.

• 1:02:44Speaker 00

  Yeah, the chip remains though. It's not going anywhere.

• 1:02:47Harry Stebbings?

  George, I so appreciate you being so open. I so appreciate the conversation. You've been fantastic to have on.

• 1:02:52Speaker 00

  Yeah, I've, I've loved it and appreciate all the research that you've done and, and all the crazy lines of questioning. So thank you, Harry. I appreciate it a lot.

• 1:03:00Harry Stebbings?

  I have to say, that was such a fun show to do. And I was so, so grateful to George, who flew over from New York for that episode. It was so much better in person. If you want to watch it, you can find it on YouTube by searching for 20VC. That's 2-0-V-C. But before we leave you today, here are two fun facts about our newest brand sponsor, Kajabi. First, their customers just crossed a collective $8 billion in total revenue. Wow. Second, Kajabi's users keep 100% of their earnings, with the average Kajabi creator bringing in over $30,000 per year. In case you didn't know, Kajabi is the leading creator commerce platform with an all-in-one suite of tools,

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• 1:04:19Harry Stebbings?

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• 1:05:03Harry Stebbings?

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