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

Summary

Podcast Summary: The Twenty Minute VC (20VC) – "Why All AI Companies Are Under-Valued | The Future of Foundation Models"

Episode Overview In this compelling episode of The Twenty Minute VC hosted by Harry Stebbings, George Sevolka, the founder of Hebbia, delves into the intricate world of artificial intelligence (AI) startups. The discussion centers around why AI companies are currently undervalued, the future trajectory of foundation models, and Hebbia's remarkable journey from humble beginnings to securing significant venture capital funding, including investments from industry giants like Index Ventures and Peter Thiel.

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1. George Sevolka’s Background and Early Challenges

Childhood and Personal Drive George Sevolka opens up about his unconventional path to entrepreneurship, categorizing successful founders into three archetypes: those with a troubled childhood, LGBTQ+ founders, and adopted individuals. He cites examples such as Elon Musk and Steve Jobs to illustrate his point.

• Quote [00:00]: "You can bucket great founders into three backgrounds... messed up childhood, you're gay, or you were adopted." – George Sevolka

Sevolka discusses his own upbringing in Staten Island, New York, under challenging circumstances. Being one of four children, with athletic siblings and immigrant parents, he often felt like an outsider. This sense of misalignment fueled his determination to prove himself.

• Quote [05:02]: "Growing up, you have this whole misalignment of who I was and who they wanted me to be. That gave me this drive and desire to go out and prove myself." – George Sevolka

Educational Pursuits and Persistence Sevolka recounts his relentless pursuit of a NASA internship during his high school years, highlighting his tenacity despite multiple rejections. His story underscores the importance of perseverance and adaptability.

• Quote [08:32]: "I was hell bent on that. And so by the time I was 15, I was going to high school in New York City, an all scholarship school where the alumni paid for everything." – George Sevolka

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2. Founding Hebbia: From Struggles to Success

Early Entrepreneurship and Financial Struggles Sevolka transitioned from academia to entrepreneurship during the COVID-19 pandemic. Faced with financial constraints, he resorted to unconventional living arrangements, such as renting out a closet space with a mattress, to sustain himself while building his startup.

• Quote [14:52]: "So I bought. I brought in like a mattress from the dorms and I had a folding table from Home Depot nearby." – George Sevolka

Raising Initial Funding Despite limited resources, Hebbia secured its first rounds of financing. Sevolka describes the nerve-wracking experience of pitching to investors over Zoom while grappling with personal insecurities about his living situation.

• Quote [16:35]: "Mike was like, look, he's living in a closet. And everyone's like, ah, great founder." – George Sevolka

He highlights the pivotal moment when Peter Thiel invested in Hebbia, providing not only capital but also invaluable mentorship that propelled the company forward.

• Quote [17:01]: "And so it's like nothing that's actually stopped. You can brute force your way as a founder." – George Sevolka

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3. Technical Innovations: RAG and Scaling at Inference

Understanding Retrieval Augmented Generation (RAG) Sevolka explains Hebbia's foundational technology, Retrieval Augmented Generation (RAG), which integrates a semantic search engine with large language models (LLMs) to provide more accurate and contextually relevant responses.

• Quote [21:23]: "RAG was the first real attempt to give them the data to answer questions correctly." – George Sevolka

Scaling at Inference Hebbia pioneered "scaling at inference," a novel approach that leverages multiple smaller models to handle complex queries more efficiently than relying solely on larger, monolithic models.

• Quote [36:07]: "Hebbia actually pioneered something different where a bunch of smaller engines come together to handle a single question." – George Sevolka

This method not only enhances computational efficiency but also maintains high accuracy in response generation, setting Hebbia apart in the competitive AI landscape.

• Quote [40:20]: "We have the best accuracy in the business. We can answer much more complex problems." – George Sevolka

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4. Market Insights and Future Predictions

Valuation of AI Companies Sevolka argues that AI companies are systematically undervalued due to the nascent stage of the technology and the market's limited understanding of its potential.

• Quote [20:17]: "I think we were in late discussions with a lot of investors, and then everyone else was like, yeah... it's some of the best money that you can get." – George Sevolka

Economic Impact and Value Creation He forecasts a staggering $100 trillion in value creation over the next six decades, attributing this growth to the transformative power of AI and its integration into various industries.

• Quote [29:01]: "I actually genuinely believe that more than 50% of the GDP will be contributed by what you can call agentic applications in the next few decades." – George Sevolka

Competition and Market Positioning Despite increasing competition, Sevolka remains optimistic about Hebbia's unique positioning. He emphasizes the importance of operational excellence and technological innovation in sustaining a competitive edge.

• Quote [34:34]: "I don't think when Excel came out and then Mark released Salesforce... there's so much room and so much opportunity for different players." – George Sevolka

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5. AI Integration in Enterprises

Current Adoption and Future Trends Sevolka observes that while 90% of enterprises are still in the experimental phase regarding AI adoption, early signs indicate a shift towards realizing tangible value from AI applications.

• Quote [51:42]: "I think that 90% of the market is still in experimental budget phase. But we're starting to see early promises of actual value." – Harry Stebbings

Impact on Workforce and Operations He believes that AI will not replace jobs but rather augment human capabilities, leading to more efficient and productive workplaces. Hebbia aims to facilitate this transition by providing tools that enhance decision-making processes.

• Quote [33:06]: "I genuinely believe it makes humans better." – George Sevolka

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6. Future of AI Interfaces and Platforms

Beyond Chat Interfaces Sevolka critiques the current reliance on chat-based interfaces, likening them to outdated interaction methods like single-cell spreadsheets. He envisions more sophisticated, human-centric interfaces that better harness AI's potential.

• Quote [47:04]: "I ultimately do not think chat was the right interface for many of these applications." – George Sevolka

Platform Strategy Hebbia positions itself as a platform that orchestrates various AI models and agents, enabling seamless integration and functionality across diverse use cases. This strategy is intended to foster innovation and scalability.

• Quote [35:21]: "Hebbia is the platform that will actually get better the more people get inspired by it and build increasingly verticalized applications." – George Sevolka

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7. Geopolitics and AI Development

Global Implications Sevolka underscores the role of geopolitics in AI development, particularly concerning energy resources and regulatory environments. He emphasizes that operational efficiency and regulatory compliance will be crucial for AI companies to thrive globally.

• Quote [43:48]: "I think that governments will be some of the largest users of AI." – George Sevolka

Nvidia’s Role Discussing hardware dependencies, he highlights Nvidia's dominance in training AI models but anticipates a shift towards more diverse and cost-effective inference solutions.

• Quote [50:07]: "I think that will destabilize slightly the dominance of Nvidia chips." – George Sevolka

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8. Personal Insights and Final Thoughts

Creative Outlets and Well-being Sevolka shares his personal practices, such as painting and prayer, which he credits with enhancing his creativity and managing the stresses of entrepreneurship.

• Quote [61:40]: "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." – George Sevolka

Closing Remarks In the concluding segments, Sevolka reiterates his belief in the undervaluation of AI companies and the monumental impact AI will have on the global economy. He expresses confidence in Hebbia's role as a pioneer in this transformative era.

• Quote [45:03]: "I think this will do for the next 60 to 80 years." – Harry Stebbings

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Conclusion This episode provides an in-depth exploration of Hebbia's journey and its innovative approach to AI, underscoring the broader implications of AI integration in various sectors. George Sevolka’s insights offer valuable perspectives on the future of AI, the importance of strategic funding, and the necessity of human-centric design in technological advancement.

For more information on The Twenty Minute VC (20VC), visit www.20vc.com.

Transcript

[George Sevolka]

You can bucket great founders 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 a list of the all time greats.

[Harry Stebbings]

Elon Musk Kind of messed up childhood.

[George Sevolka]

Jeff Bezos Steve Jobs Adopted Peter Thiel Sam Altman Publicly gay and I think that all of these early life experiences.

[Harry Stebbings]

End up giving you some desire, some deeper passion to go out and prove yourself.

[Unknown]

This is 20 VC with me, Harry Stebbings and today we have one of the most wild stories in AI Hebbier. Three years ago, George Sevolka 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 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. 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 email marketing, digital products, payment processing and analytics for as low as $69 per month. Whether you are looking to build a private community, write a paid newsletter or launch a course, Kajabi is the only platform that will enable you to build and grow your online business without taking a cut of your revenue. 20VC listeners can try Kajabi for free for 30 days by going to kajabi.com 20VC that's kajabi.com k a J-A-B-I.com 20VC and building your online empire With Kajabi, it's time to scale your global team with Remote Seamless Hiring Solutions. So every business is a global business in 2025. But how do you do payroll for your global business and team and comply with international labor laws well. Remote handles payroll, benefits, taxes, stock options and compliance to help companies of all sizes pay and manage full time and contract workers all over the world. Remote's Global Employment solutions keep your team, your finances and your intellectual property secure. Remote never charges hidden FE just Best in class global employment solutions for a low flat rate. The world's top remote companies love remote. GitLab, the world's largest all remote organization, trusts Remote to run their global team. Remote is funded by Index Ventures, Sequoia Capital and the host of the greatest podcast ever, Harry Stubbings and 20VC. Ready to learn more? Head over to remote.com 20VC that's 20VC and begin hiring within minutes. Enjoy 10% off your first three months by using the promo code. Now that your team is up and running worldwide, make sure your finances work just as hard with Brex, the ultimate financial stack for startups. Let's be honest, building something from the ground up is hard enough without dealing with clunky outdated banks that pile on fees and leave your cash idle. Brex was designed with founders in mind to make every dollar go further so you can focus on building. And here's what really stands out to me. Brex combines the best of checking treasury and FDIC insurance in one powerhouse account. You can send and receive money global at lightning speed, earn Yield from day one and still access your funds whenever you need. Plus with 20x the standard protection through program banks, your cash is not just working harder, it's working safer too. It's no surprise that 1 in 3 venture backed startups in the US with companies like Anthropic, Coinbase and Robinhood. My God these companies are incredible. Trust Brex to help them grow. If you want to join the smartest startups on the planet, head over to brex.com startups and see what they can do for you.

[Harry Stebbings]

You have now arrived at your destination.

[Unknown]

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

[George Sevolka]

And I mean it sounds like you did a lot of research so thank you for diving deep and really excited.

[Unknown]

To meet you as well as Avengers Capitalist. It's amazing the amount of free time you have. This is going to be a fun show. Talk to me about your childhood. I spoke to Sangin 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.

[Harry Stebbings]

It is fair.

[George Sevolka]

And I think the first time I met with Sanghin, who's one of our investors at the Series B, it was like a 30 minute lunch that turned.

[Harry Stebbings]

Into almost two hours of us talking.

[George Sevolka]

In depth about the dynamics that I think made me have A chip on my shoulder. But in short, I was born in.

[Harry Stebbings]

Staten Island, New York City, which, you.

[George Sevolka]

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 ship. But my mom is probably like a.

[Harry Stebbings]

Mafia child, born and raised Staten island.

[George Sevolka]

And my dad is an immigrant from.

[Harry Stebbings]

Slovakia who grew up under the Iron.

[George Sevolka]

Curtain and then immigrated, really escaped to the United States.

[Harry Stebbings]

They, both of them actually fully intended to be professional athletes.

[George Sevolka]

They had four children, of which only one was a boy. And so you can imagine their dismay when I was chasing butterflies on the soccer pitch or falling on my head many times, which I have plenty of.

[Harry Stebbings]

Stories of me literally falling over while trying to dribble a basketball.

[George Sevolka]

And I think my whole childhood I was really just a math kid. Not very out there, wasn't really talkative, only really good at math.

[Harry Stebbings]

And my parents barely even knew what Stanford was.

[George Sevolka]

So growing up, you have this whole.

[Harry Stebbings]

Misalignment of who I was and who.

[George Sevolka]

I wanted to be with, who they wanted me to be. That gave me this drive and desire.

[Harry Stebbings]

And passion to go out and prove myself in a way that was really tangible.

[George Sevolka]

Maybe not only to them, but hopefully.

[Harry Stebbings]

To my own kids one day.

[Unknown]

Did you have friends?

[George Sevolka]

I had a lot of friends who were incredibly nerdy. So we went to a public school.

[Harry Stebbings]

I was the type of kid that.

[George Sevolka]

Would hack the school tablets to put StarCraft on everyone's computer and then we'd all not be paying attention. In public school playing star, there was.

[Harry Stebbings]

A large enough contingent of kids that.

[George Sevolka]

That were also not athletes.

[Unknown]

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

[George Sevolka]

Yeah, I have successful founders that you.

[Unknown]

Found as a trend. Can you talk to me about the different profiles?

[George Sevolka]

I'm happy to. I always joke around and say that you can bucket great founders 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 the 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.

[Harry Stebbings]

Early life experiences end up giving you.

[George Sevolka]

Some desire, some deeper passion to go out and prove yourself.

[Unknown]

I actually very much agree with you. I always very much felt like a disappointment. My brother was always, no, no, I'm being Serious. My brother was always incredibly talented and good looking and tall and sm and I was kind of just pretty average when I was fat. And 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.

[George Sevolka]

What does your brother do now?

[Unknown]

He works for me upstairs.

[George Sevolka]

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

[Unknown]

Did you feel like a disappointment?

[George Sevolka]

I think the answer is yes, I did. I felt actually physically unable to do.

[Harry Stebbings]

The things that I was wanted to.

[George Sevolka]

Do or I thought that I was.

[Harry Stebbings]

Good at things that weren't valued or weren't as important. All of my sisters are amazing athletes.

[George Sevolka]

They're all like, you know, six feet tall and they're just incredible athletes. And I was just not. And so it's kind of like the.

[Harry Stebbings]

Ugly duckling in many ways.

[Unknown]

Yeah, I heard that you built lasers. You cold called NASA.

Can you talk to me about these kind of very cool early influences in your life and how it shaped you?

[George Sevolka]

There's completely separate stories there, but I think, how do you cold call NASA? This is actually a very good story.

[Harry Stebbings]

I wanted to be an astronaut. Like that was my number one goal.

[George Sevolka]

And 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 kind of tracking academically really strong, and I wanted a NASA internship.

[Harry Stebbings]

And they were offering them to college undergrads or graduate students.

[George Sevolka]

And so obviously I applied and got rejected five times. And then there was a snow day.

[Harry Stebbings]

In February where my school was closed. I commuted into the city.

[George Sevolka]

I actually showed up in front of.

[Harry Stebbings]

Their New York City office, the NASA Goddard Institute for Space Studies.

[George Sevolka]

And I demanded that they let me in.

[Harry Stebbings]

And the front door security guard was.

[George Sevolka]

Like, you know, kid, get the heck out.

[Harry Stebbings]

Like, what are you doing?

[George Sevolka]

You don't have an appointment? Like, yeah, I was like, I printed.

[Harry Stebbings]

My resume out on the nicest paper.

[George Sevolka]

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.

[Harry Stebbings]

And she's a salesperson, she works in medical sales. She picked up the phone and said, listen, no, you're not Going anywhere.

[George Sevolka]

You sit your ass down and you call every single number that you can.

[Harry Stebbings]

Get into the building.

[George Sevolka]

And so I sat on the curb and I cold called every number on Google, you know, from my old phone. And finally someone picked up. It was one of the only people.

[Harry Stebbings]

In the office that day. And they came down, met me in the lobby and I basically pitched them.

[George Sevolka]

On myself for two hours, gave me an interview. I interviewed, botched the interview because I didn't know anything about linear algebra, I didn't know anything about physics, but I.

[Harry Stebbings]

Memorized all of the titles of the posters on this professor's wall.

[George Sevolka]

Came back the next day, so showed up again cold and told them basically.

[Harry Stebbings]

Everything that I could possibly know about his specific research. And he was impressed enough to let me work for him for free and.

[George Sevolka]

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.

[Harry Stebbings]

Which was a life changing moment.

[Unknown]

That is incredible. That is also an incredibly heart wrenching moment. Thinking of 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 you win by persistence and going for it. When is that true and when is it not?

[George Sevolka]

I think I have an unhealthy obsession.

[Harry Stebbings]

With driving really hard.

[George Sevolka]

Yeah, I think I.

[Harry Stebbings]

You just can never give up.

[George Sevolka]

Like, I just don't think that's an option.

[Harry Stebbings]

You can look at every company ever and some get to $100 million in.

[George Sevolka]

Revenue in whatever, like some span of.

[Harry Stebbings]

Time which they probably their marketing team has hacked. And some, you know, end up taking.

[George Sevolka]

Really, really long periods of time.

[Harry Stebbings]

The only thing that actually changes is the rate at which you get there.

[George Sevolka]

And so sometimes things go in your.

[Harry Stebbings]

Favor, sometimes they don't. But if you're so persistent that you.

[George Sevolka]

Just continue like you can, you can.

[Harry Stebbings]

Bring a lemonade stand to $100 million in ARR.

[George Sevolka]

Like there's nothing that's actually stopped.

[Harry Stebbings]

You can brute force your way as a founder. You screw product market. You could literally brute force anything in the world. You just have to have that chip.

[George Sevolka]

You have to continue to just pound away at whatever is in your way.

[Unknown]

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

[George Sevolka]

Yes, yes.

[Unknown]

How did it feel when you got.

[Harry Stebbings]

In onto the next one?

[George Sevolka]

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.

[Harry Stebbings]

The youngest PhD student in my school's history.

[George Sevolka]

It's like, yeah, not even a moment. Like, I think, you know, I was.

[Harry Stebbings]

Excited for a moment, but, you know, it faded very, very quickly.

[Unknown]

I spoke to Corey before the show.

Someone who's known you since you were.

[George Sevolka]

18, probably even earlier.

[Unknown]

Take me to the founding of Hebbier. Then we're at Stanford. We're doing incredibly well. We are the wonder child. How does he come to be in that situation?

[George Sevolka]

I'm one of the youngest PhD students in the history of my school.

[Harry Stebbings]

And I actually believe that I was.

[George Sevolka]

Working on at the time.

[Harry Stebbings]

One of the areas of research that was most interesting to me was meta learning, this idea of teaching machines to learn to learn.

[George Sevolka]

And June of 2020, Sam Altman, OpenAI.

[Harry Stebbings]

Came out with a GPT3. And if you remember the title of that paper, it was Large Language Models are multitask or Meta Learners.

[George Sevolka]

And I'm sitting in my lab one.

[Harry Stebbings]

Day and playing around with this new.

[George Sevolka]

Technology, and I'm like, wow, they just.

[Harry Stebbings]

Stole the most important thing I could.

[George Sevolka]

Work out right under from my hands. And I said, well, if I can't.

[Harry Stebbings]

Build the most important technology, how can.

[George Sevolka]

I build the most important product?

[Harry Stebbings]

And I think those are two very separate things. Obviously, at the time, GPT3 was not a product.

[George Sevolka]

And I don't even think ChatGPT is.

[Harry Stebbings]

A really good product. It's like a calculator.

[George Sevolka]

It's got the technology in there, encapsulated.

[Harry Stebbings]

In very simple form, but it's not.

[George Sevolka]

A product that, like in Excel, that.

[Harry Stebbings]

Lets you just build whatever you'd like with it. That's very human first.

[George Sevolka]

And Stanford always pounds into your head the idea, hey, you've got to start.

[Harry Stebbings]

A company where there's a lot of pain.

[George Sevolka]

And I had a lot of my.

[Harry Stebbings]

Students or a lot of my friends.

[George Sevolka]

Would go into investment banking or private equity if they were really lucky, and they would come back and basically be.

[Harry Stebbings]

The least happy versions of themselves.

[George Sevolka]

They lost 50 pounds.

[Harry Stebbings]

They just hated their lives.

[George Sevolka]

It seemed like there was more pain.

[Harry Stebbings]

In financial services around processing unstructured data.

[George Sevolka]

Than anything I'd ever seen.

[Harry Stebbings]

And it's like, well, there's a great company to be had here. Let's.

[George Sevolka]

Let's give it a shot.

[Unknown]

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? Because 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. But unable to make $300 rent. Sneaky into Stanford dining halls for meals when you weren't studying there.

[George Sevolka]

Yep, George, I had no comment off the record.

[Unknown]

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.

[George Sevolka]

So I was on a PhD salary. You know, you're making what, $38,000 a year? I think 42 at the time, if.

[Harry Stebbings]

You, if you had the Stanford Graduate.

[George Sevolka]

Fellowship, which I had. And I said I was going to go on leap. 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, there 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.

[Harry Stebbings]

Alto to let me rent a room, the cheapest room they could possibly find.

[George Sevolka]

And they were all fully booked.

[Harry Stebbings]

And it was like I think over $1,000 of rent.

[George Sevolka]

And they said, I think it was actually 500 or $600, not $300, to.

[Harry Stebbings]

Give my broke self some credit here for not being able to afford the rent.

[George Sevolka]

But they said you could rent out.

[Harry Stebbings]

The master bedroom closet.

[George Sevolka]

And so I bought. I brought in like a mattress from.

[Harry Stebbings]

The dorms and I had a folding.

[George Sevolka]

Table from Home Depot nearby. And I would basically rotate whether the.

[Harry Stebbings]

Mattress was on the floor or the.

[George Sevolka]

Folding table was on the floor. And that was. I just sat there and worked all.

[Harry Stebbings]

Day, 16, 18 hours a day, go to sleep, wake up, do it again. No weekends.

[George Sevolka]

You know, it's just kind of like I turned into almost a monk, where.

[Harry Stebbings]

I was just obsessively building happy.

[George Sevolka]

I was training models at the time. So I'd wake up in the middle.

[Harry Stebbings]

Of night, check on them, you know, continue to use my gpu because I didn't want to spend any money.

[Unknown]

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 mean, fuck, I'm like Lindsay Lohan adventure. But like, when I think of even, like me in that I would not function well there. I need fresh air, exercise. I have to.

[George Sevolka]

Yeah, ultimately I probably went too hard. Like hindsight's 20 20, I think I think I definitely left nothing on the.

[Harry Stebbings]

Table to a point where it was detrimental to my health.

[George Sevolka]

But at the same time, I think that was like a crucible where it helped form me.

[Harry Stebbings]

It's very hard to be a founder, and those are the moments where you're.

[George Sevolka]

Just like, you're eating microwave meals every single day and you're just losing weight.

[Harry Stebbings]

And you're trying to will something into existence.

[George Sevolka]

I actually was trying to pitch one.

[Harry Stebbings]

Of my former bosses at a professional.

[George Sevolka]

Services firm, and he looked at me.

[Harry Stebbings]

On the Zoom call and he almost.

[George Sevolka]

Cried and he was like, just come work here. Like, what are you doing to yourself? Like, just don't, like, come back. We'll give you, like, a proper salary. Like, you don't have to do this. There's so many low points like that. And I think, you know, kept on.

[Harry Stebbings]

On chewing through it and.

[George Sevolka]

And, yeah, raising money in the closet. I actually got on with for we.

[Harry Stebbings]

Raise a precede from. From Peter Thiel and Floodgate and then our seed from Mike Volpe at Index.

[George Sevolka]

And Mike was like, hey, we're going to do a partner call just as.

[Harry Stebbings]

A formality with a few partners just to call.

[George Sevolka]

This is.

[Harry Stebbings]

This is Volpi.

[George Sevolka]

Yeah. And so Volpe's like, there'll be a.

[Harry Stebbings]

Few partners on this call.

[Unknown]

What round is this for?

[Harry Stebbings]

This is for a seed.

[George Sevolka]

So it's a follow on to the pre seed in, like, November of, I think, 2020.

[Harry Stebbings]

And I get on the Zoom call.

[George Sevolka]

And I've literally got, like, clothes hanging behind me, and they're like, all of.

[Harry Stebbings]

A sudden, Mike shows up and then four other partners and then 80 partners.

[George Sevolka]

And, like, the Zoom screen tessellates 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.

[Harry Stebbings]

And then.

[George Sevolka]

And then I pitched my company, honestly.

[Harry Stebbings]

Hilarious.

[George Sevolka]

Yeah.

[Unknown]

So 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.

[George Sevolka]

Well, so saying two months prior, again, I think I was at Stanford or.

[Harry Stebbings]

Just about to leave Stanford, and one of my friends had interned at Founders.

[George Sevolka]

Fund, and he's like, hey, I hear you're raising financing.

[Harry Stebbings]

You should talk to Peter.

[George Sevolka]

And I was like, well, I'm not going to say no to that.

[Harry Stebbings]

And so he introduces me on an email thread with me and Peter, and.

[George Sevolka]

I'm like, hey, Peter would love to.

[Harry Stebbings]

Do a lunch or a dinner. I'm not a morning person.

[George Sevolka]

So I was like, peter would 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 a dinner.

[Harry Stebbings]

Can we do a brunch?

[George Sevolka]

He's like, I'm going to do a breakfast. And so I said, well, that's fine.

[Harry Stebbings]

And he gave me a slot on a Saturday.

[George Sevolka]

And so got in my car.

[Harry Stebbings]

It was an old beat up 2006 Audi convertible that I had fixed up from Craigslist and bought for $4,000 at 3 in the morning and drank a bunch of coffee, like 18 cups of.

[George Sevolka]

Coffee, like a five hour energy, all the disgusting stuff.

[Harry Stebbings]

And drove from three to eight to.

[George Sevolka]

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 wired. I'm sitting in my chair 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 deep esoteric philosophy and like just the world. And he said, you know, I'm not investing at the time because it was.

[Harry Stebbings]

Coronavirus and a variety of other factors.

[George Sevolka]

But I'd love to put in a check. I got out of this conversation thinking.

[Harry Stebbings]

I had just made a friend or was seen from someone who's incredibly, incredibly.

[George Sevolka]

Incredibly incredible bright and I'm leaving his.

[Harry Stebbings]

House and I felt like I was inducted into the Illuminati. I was like, my whole body drop top, the sun's shining.

[George Sevolka]

I was playing Kanye West. I drove out as my first offer.

[Harry Stebbings]

From a venture investor.

[Unknown]

How much did he invest?

[Harry Stebbings]

I think the total round was like a million dollars.

[George Sevolka]

So it's like nothing. Yeah.

[Unknown]

But what do you think makes Peter so incredible?

[Harry Stebbings]

There's two things. He is incredibly ontologically smart. And so he can build this worldview or this perspective of the world where.

[George Sevolka]

He actually just 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 he.

[Harry Stebbings]

Understands processes and how humans behave really well.

[George Sevolka]

And so he's always thinking like, hey, ex ante. Or if I'm looking at something that's about to unfold, could I have predicted.

[Harry Stebbings]

This ahead of time. And he always just asks himself that question. So he's built up a really rich.

[George Sevolka]

Perspective of the fallacies that human society has mimetic behavior that people kind of go out and copy each other with, etc.

[Unknown]

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

[George Sevolka]

Yeah.

[Unknown]

Does that open every door in the Valley?

[George Sevolka]

I mean, I think we were in.

[Harry Stebbings]

Late discussions with a lot of investors, and then everyone else was like, yeah.

[George Sevolka]

Let'S, like, you know, let's pile on in here. It's, you know, it's some of the.

[Harry Stebbings]

Best money that you can get.

[George Sevolka]

And that was a game changer for us.

[Unknown]

So then we closed with Maples and Floodgate.

[George Sevolka]

Yeah, it was Anne.

[Harry Stebbings]

Anne at Floodgate.

[Unknown]

Okay. Pan and Floodgate and Teal. And then we get back to work and we've got a million or so.

[George Sevolka]

We've got a million. And then two months later, Mike actually hears about Hebia from his daughter who's a Stanford student. And I think I'd seen the product was friends, and. And then Mike actually comes in, is.

[Harry Stebbings]

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.

[George Sevolka]

Today, so he's like, well, let's just go add some fuel to the fire.

[Unknown]

How much did he invest in?

[Harry Stebbings]

He, I think invested like an additional.

[George Sevolka]

2 or $2.5 million time. So where did the 130 come from as years later.

[Unknown]

This was years later.

[George Sevolka]

So this is all in 2020.

[Harry Stebbings]

We end up building a product studio.

[George Sevolka]

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 search.

[Unknown]

Can you just help us understand what is rag?

[George Sevolka]

RAG is an acronym that stands for Retrieval Augmented Generation.

[Harry Stebbings]

If you look at large language models.

[George Sevolka]

Today, they're really good at maybe thinking if you give it the right context.

[Harry Stebbings]

But they hardly ever have the right context.

[George Sevolka]

And so RAG was the first real.

[Harry Stebbings]

Attempt to give them the data to answer questions correctly.

[Unknown]

Okay. And so we are building on RAG to start.

[George Sevolka]

We were one of the first people to productionize the idea of putting a.

[Harry Stebbings]

Search engine behind an LLM.

[George Sevolka]

So you'd ask a question and then.

[Harry Stebbings]

Instead of it just replying from its.

[George Sevolka]

Memory, it would actually go and do a search and then reply with that context.

[Harry Stebbings]

So in an enterprise where you have.

[George Sevolka]

A lot of offline data, we were.

[Harry Stebbings]

Really the first people to hook up.

[George Sevolka]

That offline data to large language models to Answer that question.

[Unknown]

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

[George Sevolka]

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 Hebia in a very meaningful way.

[Harry Stebbings]

I think every enterprise is experimenting with.

[George Sevolka]

It, but it has a lot of different failures where a lot of the.

[Harry Stebbings]

Time the questions that people ask these systems aren't ever explicitly in the data. They're never explicitly stated.

[George Sevolka]

They're actually about the data. So, for example, you know, if you're asking an AI system is this company.

[Harry Stebbings]

A good investment, which is actually a.

[George Sevolka]

Very common thing that people ask Hebby.

[Harry Stebbings]

Over marketing materials, maybe it'll say in.

[George Sevolka]

A pitch deck, yeah, this company is a great investment as something that the.

[Harry Stebbings]

CEO says or like a recording, etc.

[George Sevolka]

But what you actually want from that.

[Harry Stebbings]

System is, isn't to search in the.

[George Sevolka]

Data, it's to answer about the data.

[Harry Stebbings]

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?

[George Sevolka]

And that's a process 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 Hevea does. So it's not actually finding something that exists already, it's taking all the things.

[Harry Stebbings]

That exist already and starting to answer questions about that information.

[Unknown]

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

[George Sevolka]

Yeah.

[Unknown]

And you realize that actually it's bullshit and it's not as good. Yes, take me to that realization.

[George Sevolka]

Yeah, so we actually deploy it. Some of the largest finance firms in the world, we ended up going from zero to $1 million of revenue sometime.

[Harry Stebbings]

In 2021 or 2022 and raise our Series A also from.

[George Sevolka]

From Index, from Mike and 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.

[Harry Stebbings]

Heavy has this like really mature enterprise product in the market.

[George Sevolka]

And we're by far the first to actually get there. And we just looked at all the.

[Harry Stebbings]

Queries that people were asking. And the questions that people were asking.

[George Sevolka]

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.

[Harry Stebbings]

The times they mention AI or what our exposure to Silicon Valley bank is during the regional banking crisis.

[George Sevolka]

And so all of these questions, it.

[Harry Stebbings]

Was actually almost 90% of the questions.

[George Sevolka]

That people were asking these systems weren't answerable by search through the documents, but rather they had to be work done on top of the documents.

[Unknown]

And then answered 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.

[George Sevolka]

I don't think they're loving Rags.

[Unknown]

You don't think they are?

[George Sevolka]

I don't think they're loving Ragnar.

[Unknown]

What makes you say that?

[Harry Stebbings]

I think like 90% of enterprise AI.

[George Sevolka]

Right now is almost like this vaporware. We swear it works. Look at this amazing demo where we.

[Harry Stebbings]

Ask what does the CEO say about the investment?

[George Sevolka]

And then the minute that they actually go to try to use it in.

[Harry Stebbings]

A real world example completely just fails.

[George Sevolka]

And so I actually think that the majority of AI usage, a lot of these usage statistics are all kind of.

[Harry Stebbings]

One of my favorite phrases is fugazi. Fugazi.

[George Sevolka]

And one of the things that Hebia really tries to put forth in the.

[Harry Stebbings]

Market is say hey, change will take time.

[George Sevolka]

But we have a system that is.

[Harry Stebbings]

Actually starting to drive real measurable value over very specific defined use cases.

[George Sevolka]

And our tagline is always, hey, stop experimenting with AI, which everyone's experimenting, they're all really excited about it. Start driving value, like getting value out of it.

[Unknown]

To what extent is this RPA versus a gentic?

[George Sevolka]

I actually am not a big believer in RPA. I think RPA is almost not an.

[Harry Stebbings]

AI application in the new sense of AI.

[George Sevolka]

It's like AI in the old 10 years ago sense of AI where RPA.

[Harry Stebbings]

Is effectively very simple computation. But some of the things that people are asking hebia are over 800 page.

[George Sevolka]

Credit agreements or 230 page SIMs, confidential.

[Harry Stebbings]

Information memorandums, this marketing material.

[George Sevolka]

They're not actually asking for things that like copying numbers. They're saying, hey, tell me what are.

[Harry Stebbings]

Inconsistencies in this document?

[George Sevolka]

Tell me where there's an event of.

[Harry Stebbings]

Default that we can trigger.

[George Sevolka]

There's almost this open endedness or this.

[Harry Stebbings]

New level of computation that people can do.

[Unknown]

I think Daniel Dines, who we had on the show comes out on Wednesday, he said it very well. He said like listen, RPA is low skilled, low level cognitive processes and agencies, high skilled, ambiguous decisions. Yes, I think that's A nice phrasing.

[George Sevolka]

I think it's incredibly clear. Yeah. And I think that we very much are capturing the agent, the high level.

[Harry Stebbings]

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.

[Unknown]

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?

[Harry Stebbings]

I actually don't, don't agree with that at all.

[George Sevolka]

I think he's completely wrong.

[Harry Stebbings]

I think it depends on how you.

[George Sevolka]

Define business apps, but I actually think that if the new business apps are platforms, you'll actually start to see those.

[Harry Stebbings]

Platforms really take hold. Like Hebbia is a platform, it lets you build whatever agent that you'd like.

[George Sevolka]

And so here's a bit of a.

[Harry Stebbings]

Mind fuck when, when building Hebbia or when building all of these foundational primitives for how people use AI. Over the last four and a half.

[George Sevolka]

Years, Hebi has always asked ourselves, what.

[Harry Stebbings]

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?

[George Sevolka]

Because these AI applications are really good.

[Harry Stebbings]

At using tools that we could build that would assist LLMs or these really smart foundation models, whatever they are, in.

[George Sevolka]

The future, to get to an answer more quickly. It's quite interesting. You know, Hebbian matrix orchestrates lots of smaller LLM calls. It's actually scaling at inference, that is.

[Harry Stebbings]

It'S running massive amounts of computer at the orchestration layer.

[George Sevolka]

And we think that, you know, an AGI system would prefer to use Hebby.

[Harry Stebbings]

A matrix to diligence a company or.

[George Sevolka]

To look through thousands of documents versus to read them all by hand.

[Harry Stebbings]

And they're in a really long context window.

[Unknown]

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

[George Sevolka]

Yeah.

[Unknown]

What is the future?

[George Sevolka]

I ultimately think that it will be.

[Harry Stebbings]

A mix of all three. History doesn't repeat itself, but it often rhymes.

[George Sevolka]

You know, 60 years ago or even longer, the foundational unit of compute, that is doing a calculation on a computer.

[Harry Stebbings]

Was effectively introduced to the enterprise.

[George Sevolka]

And there were plenty of people that.

[Harry Stebbings]

Were tallying things or bookkeeping in actual books and their jobs changed.

[George Sevolka]

And there were apps for bookkeeping, and then there were platforms like Excel that.

[Harry Stebbings]

Let people build better apps for bookkeeping.

[George Sevolka]

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.

[Harry Stebbings]

In the entire industry in terms of building platforms, in terms of building cooperatives, in terms of even building new types.

[George Sevolka]

Of quote unquote agent employees. And I think that that opportunity is the exact same size if there was.

[Harry Stebbings]

100 trillion of dollars of value that.

[George Sevolka]

Was created in the stock market from.

[Harry Stebbings]

The introduction of the computer or the fundamental unit of compute.

[George Sevolka]

I actually think $100 trillion of value will be created in the next 60 years from the introduction of inference or.

[Harry Stebbings]

Of AI compute, which is will that.

[Unknown]

Be additional value or will that be value that denigrates from the existing value of alternatives?

[Harry Stebbings]

I believe it will be additional value.

[George Sevolka]

And maybe I'm too techno optimist, but I actually think The S&P 500 is completely unreal.

[Unknown]

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

[George Sevolka]

I ultimately genuinely believe that more than.

[Harry Stebbings]

50% of the GDP will be contributed by what you can call agentic applications.

[George Sevolka]

In the next few decades. Yeah, I actually think it'll happen faster.

[Harry Stebbings]

Than the next few decades.

[Unknown]

Do you?

Because again, Daniel, hot take.

Daniel on the show was like, put.

[George Sevolka]

Me up against Daniel.

[Unknown]

Yeah, 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?

[George Sevolka]

It's a good point if you're cutting cost, which I think Daniel and UiPath.

[Harry Stebbings]

Are one of the best examples of using AI to make companies more efficient. Like if you look at finance and.

[George Sevolka]

How fast Excel went to 90% market.

[Harry Stebbings]

Penetration in finance, 1985 to 1986, literally.

[George Sevolka]

18 months to 24 months, Excel took.

[Harry Stebbings]

Over all of finance.

[George Sevolka]

Everyone switched from using a calculator, the HP12C, to using Excel. And if you look at how fast finance actually ended up using credit card data to value public companies ahead of their earnings, that was again a two.

[Harry Stebbings]

Year period more recently. And so finance is the slowest, moving, most lethargic leviathan. It's the worst possible customer base to.

[George Sevolka]

Go after, unless you're providing outsized alpha or real value, in which case the.

[Harry Stebbings]

Minute that there's something real, finance moves faster than any other industry.

[George Sevolka]

And so I'm actually making a bit.

[Harry Stebbings]

Of A bet by going into finance and starting to go out and try.

[George Sevolka]

To get to my own.

[Unknown]

You know what I worry about? I worry that we lose the education process. What I mean by that is like 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.

[George Sevolka]

Yeah.

[Unknown]

And then once you say, well, don't.

Worry about that shit, Happy will 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.

[George Sevolka]

Yeah, I'm less worried about that.

[Harry Stebbings]

I think ultimately one of the best.

[George Sevolka]

Things about being, you know, having the years of experience is, is actually having.

[Harry Stebbings]

The depth of knowledge about investing.

[George Sevolka]

So for example, if I'm a junior trying to price an asset, I haven't.

[Harry Stebbings]

Seen that many other companies that look like this company.

[George Sevolka]

And so I might say, hey, I.

[Harry Stebbings]

Think it should be priced at X, Y or Z.

[George Sevolka]

And then someone in the IC, IC meeting will be like, hey, no.

[Harry Stebbings]

I've seen 20 other companies in my 40 years of career that look exactly like this. And they all went nowhere.

[George Sevolka]

And I'm, I'm leaning on my prior experience with Hebbia. Now juniors who are really smart themselves can say, okay, you might have remembered.

[Harry Stebbings]

20 deals, but I'm looking through every deal in our company's history in a.

[George Sevolka]

Giant matrix 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.

[Harry Stebbings]

We should actually pay 90% premium to market. And I'm actually using more deals than.

[George Sevolka]

You'Ve ever seen because I know your.

[Harry Stebbings]

Name is on this many IC members.

[George Sevolka]

And that type of structured thinking or that type of additional information that you.

[Harry Stebbings]

Can now give, juniors in their career.

[George Sevolka]

Actually think makes better investors. I don't think that takes away.

[Unknown]

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

[Harry Stebbings]

I genuinely believe it makes humans better.

[Unknown]

I genuinely believe in 5 years time.

Do you not think that is a different story?

[George Sevolka]

I think that it will change the way that people do work, but I genuinely believe that it'll actually increase the.

[Harry Stebbings]

AUM of the firms that use it. I think it'll actually drive more employment.

[George Sevolka]

I think there will be some jobs that change. Hey, there's no more bookkeepers that do.

[Harry Stebbings]

Tabulations in spreadsheets on two sheets of paper.

[Unknown]

What changes most and what stays the same?

[George Sevolka]

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 move into. Okay, how can we manage AI juniors.

[Harry Stebbings]

Rather than, you know, actually do this ourselves by hand?

[George Sevolka]

But I don't think that, you know, just as Excel didn't end up taking.

[Harry Stebbings]

Away jobs from people, it just changed.

[George Sevolka]

People, you know, to having to learn Excel, the exact same thing will happen with AI.

[Unknown]

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

[George Sevolka]

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 no one really wants. And I think that's bs. I think it's bs. Yeah, there might be some reality to.

[Harry Stebbings]

It, but I think that it's an amazing marketing story.

[George Sevolka]

And so anytime that I ever hear something that's put out as a marketing.

[Harry Stebbings]

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.

[George Sevolka]

I think I look at that really loud behavior and I think that behavior.

[Harry Stebbings]

Itself really negates the content. That's maybe my, my positioning on this sort of stuff.

[Unknown]

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?

[George Sevolka]

I think that if $100 trillion of.

[Harry Stebbings]

Economic value will be created by AI and agentic app applications, that there will be so much room and so much.

[George Sevolka]

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 TurboTax. You know, all these unravelings of Excel actually were produced later that that made.

[Harry Stebbings]

Excel any less valuable.

[George Sevolka]

I actually think it made Excel more valuable. I view Hebbia as this platform as something that will actually get better the.

[Harry Stebbings]

More people get inspired by it and build increasingly verticalized applications.

[Unknown]

What models do you use? You sit on top of what?

[Harry Stebbings]

We are completely model agnostic. We use all of the major model providers, some of our own models. But ultimately the foundational difference that Hebbia.

[George Sevolka]

Is capitalizing on right now is fundamentally.

[Harry Stebbings]

New and very important difference, which is, I actually think on the order of.

[George Sevolka]

Creating rag and creating agents and decomposition is this idea of us in the.

[Harry Stebbings]

Last year or so having pioneered scaling at inference.

[George Sevolka]

Talk to me about this right now, you actually. So OpenAI is starting to do this with O, where they'll have a model.

[Harry Stebbings]

Recursively think about a question over and over and over again before it produces an answer. And so instead of training a larger.

[George Sevolka]

Model, they're using effectively a similarly sized model and just telling it to run multiple cycles, I.e.

[Harry Stebbings]

Compute more before answering.

[George Sevolka]

Hebbia actually pioneered something different where a.

[Harry Stebbings]

Year, almost 18 months ago, we said, hey, we can't wait for these models to catch up. What we'll do is for simple single.

[George Sevolka]

Question, let's actually run hundreds or even.

[Harry Stebbings]

Thousands of submodels of the best models in the world to compute over every.

[George Sevolka]

Single document to answer the same question. And so ultimately, if you can't train.

[Harry Stebbings]

Larger and larger models fast enough, you.

[George Sevolka]

Could take whatever state of the art.

[Harry Stebbings]

Or cutting edge and run it more times to get more compute that is more computational power, better decision making for the same user right now.

[George Sevolka]

And so this is an idea that we pioneered.

[Harry Stebbings]

It doesn't matter if you're using Claude 3.5 or if you're using O1 itself.

[George Sevolka]

That is scaling at inference at the.

[Harry Stebbings]

Orchestration layer with something that was scaled.

[George Sevolka]

At inference with the training layer. But you get way better results and it's a way to drive to more.

[Unknown]

Accuracy you find provides the best results. We had Des Trainer on Intercom recently. He spoke about the movement away from OpenAI to anthropic.

[George Sevolka]

We've seen that for certain types of documents like dense legalese or more colloquial.

[Harry Stebbings]

Documents, anthropic works better.

[George Sevolka]

But for other types of documents like.

[Harry Stebbings]

OON or OpenAI 4.0 works better.

[George Sevolka]

And it's always trade offs between accuracy.

[Harry Stebbings]

And speed and all kinds of things.

[George Sevolka]

Actually, a lot of the time when.

[Harry Stebbings]

We'Re decomposing a task, we'll use mixes of OpenAI, anthropic, even Gemini.

[Unknown]

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 do about and that's the world we live in. Or there's generalist monolith models which really own the whole stack.

[George Sevolka]

This makes me think of the story.

[Harry Stebbings]

Of Bloomberg, which has the best financial services training set of all time and.

[George Sevolka]

They trained a GPT 3.5 class model.

[Harry Stebbings]

It was called Bloomberg GPT and they released an archive paper and everyone on.

[George Sevolka]

LinkedIn was like, wow, Bloomberg is cutting edge and they're going to steal finance AI.

[Unknown]

Why did they not?

They did have all they've got the best data in finance.

[George Sevolka]

So then GPT4 was released I think.

[Harry Stebbings]

Like a few weeks later.

[George Sevolka]

I don't exactly know the right timeline, but it just destroyed Bloomberg GPT at every single finance task.

[Harry Stebbings]

And so you saw the idea of.

[George Sevolka]

Post training or kind of like this.

[Harry Stebbings]

Refined verticalized model creation just always would lose to scaling laws. And maybe we're at the end of.

[George Sevolka]

Scaling laws at training, but I actually think hebia and now OpenAI and a variety of other companies are starting to pioneer the idea of scaling laws at inference. And I actually think that nothing that other players can do to fine tune models will ever catch up.

[Unknown]

I need to break that down, sorry. So everyone's like, oh, are we at the end of scaling laws? Are we? Benioff and Daniel Dines are like, yes, we are. Yes we are at the upper end of LLM. 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?

[George Sevolka]

Yeah, I think it's a bit of a marketing distinction.

[Harry Stebbings]

Right.

[George Sevolka]

But ultimately the idea is that the way that we got here over the last five, seven years of training models.

[Harry Stebbings]

Has been let's build a bigger and bigger model and let's give it more and more data, more and more clean.

[George Sevolka]

Data and then maybe we'll do some RLHF or some reinforcement training to fine tune it after pre training. And that worked great to get us here.

[Harry Stebbings]

But we're running up against the amount of good data that exists in the world. We're running up against.

[Unknown]

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.

[George Sevolka]

You know, I think that we're starting.

[Harry Stebbings]

To run up against the constraints of it. That's a, that's a gut feel.

[George Sevolka]

I'm not, you know, I'm not looking.

[Harry Stebbings]

At particularly in data collection myself, but.

[George Sevolka]

I think we're starting to run up.

[Harry Stebbings]

Against the limits of really good data.

[Unknown]

That we can, what, stand the problem.

[George Sevolka]

So ultimately that might mean that, hey.

[Harry Stebbings]

We'Re training larger and larger models. XAI again just created the largest GPU cluster of all time and they're going to try to train larger and larger models.

[George Sevolka]

But regardless of how the scaling laws.

[Harry Stebbings]

For training larger models or parameter count.

[George Sevolka]

And accuracy or performance carry out, I'm starting to believe that you could still get better compute not by building a.

[Harry Stebbings]

Larger engine, to use a metaphor, but.

[George Sevolka]

By actually putting a bunch of smaller engines together.

[Harry Stebbings]

Hebbia, by orchestrating large amounts of inference to answer one single question, ends up.

[George Sevolka]

Kind of building like a Tesla where.

[Harry Stebbings]

Tesla is made of a bunch of smaller engines or a bunch of smaller.

[George Sevolka]

Electromechanical motors that make a lot of torque and a really, really amazing larger engine.

[Unknown]

Does it not make it incredibly capital inefficient?

[George Sevolka]

You know, I think the one thing that people in my position will always.

[Harry Stebbings]

Tell you is that the cost of intelligence will go to zero. The cost of intelligence localization.

[George Sevolka]

I mean, I think that since Hebia started, the cost of inference over a fixed number of parameters has decreased by.

[Harry Stebbings]

Seven orders of magnitude in four years.

[George Sevolka]

And so I genuinely believe that scaling compute is like a no brainer.

[Harry Stebbings]

And yes, we run more large language.

[George Sevolka]

Model calls than anyone might even say would ever be necessary, but we have.

[Harry Stebbings]

The best accuracy in the business. We can answer much more complex problems. We're driving real value for enterprises.

[George Sevolka]

And I actually think that every single.

[Harry Stebbings]

Quarter our margin goes, we're not spending money fast enough.

[Unknown]

You mentioned ex AIs, GPU cluster. What they've been able to do in such a short amount of time is miraculous. What do you think that tells us.

[George Sevolka]

About the layer itself, ultimately the model layer? And I think this is not a hot take anymore.

[Harry Stebbings]

I've been saying it for a few.

[George Sevolka]

Years, but I think it'll become commoditized. I think that a lot of value.

[Harry Stebbings]

Will accrue at the hardware layer.

[George Sevolka]

And we could talk about what that means for Nvidia, especially as Nvidia has a stranglehold on training, but not as.

[Harry Stebbings]

Much a stranglehold on inference.

[George Sevolka]

And so you might actually see other.

[Harry Stebbings]

Chip makers actually start to. Their chips start to be used in.

[George Sevolka]

A more meaningful way because CUDA is what all ML scientists were trained on in their PhDs. But then inference doesn't matter kind of what you're using. And I think it will be the infrastructure layer and then actually the application.

[Harry Stebbings]

Or agent layer that will accrue the most value ultimately.

[Unknown]

Why does it not follow the same vein as cloud where cloud is commoditized but as your Google Cloud aws? I mean it's completely commoditized, let's be honest, cloud. But it's great business for them.

[Harry Stebbings]

I think it might. There's probably fewer players and more entrenched.

[George Sevolka]

Players in cloud and ultimately I think those players honestly kind of have like an OPEC oligopoly where they can control pricing.

[Harry Stebbings]

I just think that ultimately cloud is.

[George Sevolka]

Actually more complex than training larger and larger models.

[Unknown]

And 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 OpenAI.

[Harry Stebbings]

Absolutely. Whoever has the best models will continue.

[George Sevolka]

To attract the right amount of investment. The different thing about clouds too though.

[Harry Stebbings]

Is that the cost of switching is much higher.

[George Sevolka]

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.

[Harry Stebbings]

To anthropic when OpenAI goes down.

[George Sevolka]

But to switch clouds is like, you know, for any like substantially size startup, like a 10 million to $20 million.

[Harry Stebbings]

Investment, just to switch is almost always never worth it. It's much, much, much stickier.

[George Sevolka]

Whereas here it's a very simple API key, it's very simple to switch models. And so I think that's also a differentiator.

[Unknown]

OpenAI at 160, anthropic at 40 or XAI at 50. Which one do you buy?

[Harry Stebbings]

I think XAI is the most undervalued.

[George Sevolka]

Company and a really spicy take. I actually think XAI might overtake OpenAI and Anthropic in value over the next 12 to 24 months, which is crazy, but I think they're all undervalued to your thinking. I think Elon is very well positioned.

[Harry Stebbings]

In the geopolitical sense.

[George Sevolka]

I think Elon can run a more.

[Harry Stebbings]

Efficient business business and not have to deal with as much administrative bloat or as much friction from employees.

[Unknown]

How important is Geopolitics in winning this game?

[Harry Stebbings]

I think geopolitics is actually very important.

[George Sevolka]

I think that governments will be some.

[Harry Stebbings]

Of the largest users of AI, especially.

[George Sevolka]

With some of the recent things that the new administration in the United States has been talking about with increasing government efficiency. I think that ultimately energy is a very big bottleneck.

[Harry Stebbings]

It's a very common thing in Silicon.

[George Sevolka]

Valley to talk about, hey, we need.

[Harry Stebbings]

Nuclear reactors to flatten the duck curve.

[George Sevolka]

So that we can continue to drive to larger and larger data centers, et cetera, et cetera, et cetera.

[Harry Stebbings]

And those are ultimately geopolitical resources.

[George Sevolka]

And so I think all these things.

[Harry Stebbings]

End up being very important. And then Elon's just operationally so talented, right?

[George Sevolka]

So I think that ultimately if this becomes commoditized and whoever can really operationalize model creation and serving models the fastest I think might start to.

[Unknown]

So you think Xai and you would invest in them?

[George Sevolka]

I would, but ultimately I think all.

[Harry Stebbings]

Of them are undervalued.

[George Sevolka]

I genuinely believe all AI companies and.

[Harry Stebbings]

The S&P 500 are all undervalued, which.

[George Sevolka]

Is a very hot take if we're about to create $100 trillion of value. I think this is a real tangible technological shift.

[Harry Stebbings]

It's a massive unlock on the order of what computing did for the entire economy over the last 60 to 80 years.

[George Sevolka]

I think this will do for the next 68 years. I think all these companies are massively.

[Harry Stebbings]

Undervalued, including the non AI companies.

[Unknown]

Unpack the last bit. Including the non AI.

[George Sevolka]

I genuinely believe that computers made legacy.

[Harry Stebbings]

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 and this new.

[George Sevolka]

Fundamental paradigm is a massive unlock for.

[Unknown]

Do you think there is a slight difference? Everyone talks about kind of the different technological transitions. When you look at the agricultural transition or agricultural dependency on human labor and movement and machinery, computers in workforces, these were at least 10 year transitionary periods. At least. This is like, hey, we use AI tools now because we just bought them today.

[George Sevolka]

Yeah.

[Unknown]

The transition period is instant.

[George Sevolka]

Yes.

[Harry Stebbings]

Much faster.

[Unknown]

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.

[George Sevolka]

Yeah. I actually always liken technological revolutions to what Happy is doing right now, where people invented or we discovered the technology.

[Harry Stebbings]

Of fire and then someone invented the torch. I don't know how many years later we invented the engine and then someone invented the car or the wheel and then the chariot.

[George Sevolka]

And so this idea of encapsulating and building a useful product on top of.

[Harry Stebbings]

A technology change is actually the thing that takes more time.

[George Sevolka]

And I think that Hebbia has built.

[Harry Stebbings]

If Excel was that product for compute.

[George Sevolka]

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 surface level search engines that give.

[Harry Stebbings]

You facetious surface level value. It'll help your kid cheat on their homework. But to drive to whether or not something's a good investment is a much more rich problem.

[Unknown]

Is chat the right interface for many of these applications?

[George Sevolka]

I ultimately do not think so. I think that chat was always a useful feature.

[Harry Stebbings]

It's a useful interface.

[George Sevolka]

It's like a Single cell in Excel.

[Harry Stebbings]

It's like asking if the TI84 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.

[George Sevolka]

I actually think that what do you think they are? I think that Hebbia is the Bell Labs and I conceive of ourselves as.

[Harry Stebbings]

The Bell Labs of defining AI interfaces.

[George Sevolka]

Right. I think that RAG was one of them. That is this idea.

[Harry Stebbings]

You could find things in the data really fast decomposition and agents are another.

[George Sevolka]

This idea of scaling at inference with.

[Harry Stebbings]

Our matrix product is another.

[George Sevolka]

You can look at a lot of the other things where agents are controlling.

[Harry Stebbings]

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.

[George Sevolka]

Almost all of them have actions.

[Unknown]

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

[George Sevolka]

I actually think that the better agents.

[Harry Stebbings]

Are, the more work that they do, the more important it will be that.

[George Sevolka]

They are easily understood by humans. The idea would be, okay, let's say we have a bunch of of employees.

[Harry Stebbings]

10,000 employees or 10,000 AI agents drop at a company. They're all experts at doing something that.

[George Sevolka]

Ends up not becoming a problem of giving them the right tasks, but actually it becomes a management problem. Right. There's this whole infrastructure orchestration layer. The thing I always come back to of making these things work together. And that's actually going to be the challenge and that's going to require a very human first, ultimately a product and that's what we're trying to build.

[Unknown]

Do you think Elon will be successful with Doge?

[Harry Stebbings]

I think it will be his greatest challenge.

[George Sevolka]

There's a lot of self protecting mechanisms.

[Harry Stebbings]

In the largest organization in the world.

[George Sevolka]

Which is kind of a US government by spend by head.

[Harry Stebbings]

It's just this massive unruly organization. It's not going to be as simple as Twitter.

[Unknown]

Are you more excited in a post, Trump?

[George Sevolka]

I think the thing that I care most about in the world is that.

[Harry Stebbings]

We as an industry have very clear.

[George Sevolka]

Guardrails that we can follow and understand to build the best possible tools to get our tools out to the economy to to make sure that everyone transitions.

[Harry Stebbings]

In the best possible way.

[George Sevolka]

So I'm ultimately regardless not thrive on.

[Unknown]

A better financial system. And we're seeing now a financial system in the US from afar that would seem to be thriving objectively. Yeah, it would appear that Trump is good for business.

[George Sevolka]

I won't make a comment here. I think that there's a lot funny.

[Unknown]

It went very viral all 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.

[George Sevolka]

Yeah, for sure, for sure.

[Unknown]

So I totally understand they're not answering. Yeah, you are not alone. It's okay. But the one question I want to ask you mentioned Nvidia before. There's a really big question around their ability to sustain their monopoly. You've seen Google, you've seen Meta, 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?

[George Sevolka]

So Nvidia has, you know, I think.

[Harry Stebbings]

That the best moats aren't technological moats.

[George Sevolka]

They're not data moats, they're actually people moats.

[Harry Stebbings]

People and networks have the most friction to change. One of the things that Nvidia does.

[George Sevolka]

Best is the fact that they made this early bet on machine learning. They created cuda, which is the way that, as I mentioned before, almost everyone.

[Harry Stebbings]

Learns how to train models.

[George Sevolka]

Like they learn how to, you know.

[Harry Stebbings]

How to interface with Nvidia chips for training.

[George Sevolka]

And as you're starting to see, maybe that prediction that I made earlier, the.

[Harry Stebbings]

Shift away from training to inference as.

[George Sevolka]

A fundamental, like, almost macro shift in how people deploy AI, I actually think that will destabilize slightly the dominance of Nvidia chips. You can start to actually use AMD chips or even custom architectures, which, which.

[Harry Stebbings]

All the major model providers are also currently exploring to do inference.

[George Sevolka]

So you have your academics and your researchers, you know, training large models on.

[Harry Stebbings]

Nvidia chips, but the minute they deploy.

[George Sevolka]

Them, they can deploy them on cheaper infrastructure. And that actually, I think it will.

[Harry Stebbings]

Be a big change.

[George Sevolka]

I'm actually still bullish on Nvidia, but.

[Harry Stebbings]

I'm even more bullish on other chip makers and custom Asics to do inference.

[George Sevolka]

Because I think there will be a.

[Harry Stebbings]

Larger shift to inference moving forward.

[Unknown]

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

[George Sevolka]

Probably be large tech providers and amd. I don't know about Intel.

[Harry Stebbings]

Right.

[George Sevolka]

I would probably bet on them.

[Harry Stebbings]

There's definitely an opportunity in the market, but chips are hard.

[Unknown]

Before we do a quick fire, do you just want to kind of resurface back up to the Agent layer for sure. Are we out of the experimental budget phase?

[Harry Stebbings]

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 just those repeatable use cases.

[Unknown]

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

[George Sevolka]

Like, I think ultimately the people in.

[Harry Stebbings]

The enterprise that are most excited about AI and like positioning it so strongly.

[George Sevolka]

Are CTOs and information technology people. And maybe the thing that we've, that Hebby has always said is that the CTO or the IT folks are actually.

[Harry Stebbings]

The people that know the least about the business.

[George Sevolka]

The people that, that actually understand how.

[Harry Stebbings]

To use AI in a business context are those that are closest to the business.

[George Sevolka]

And so we're jumping the gun a.

[Harry Stebbings]

Little bit with the CTOs trying to build the CRM before it's been invented.

[George Sevolka]

And you know, you actually need business.

[Harry Stebbings]

People to build the CRM and Excel.

[George Sevolka]

First in kind of that order of operations. And so there's a lot of unbundling.

[Harry Stebbings]

Of AI applications or CTOs trying to.

[George Sevolka]

Go out and build, you know, a very specific vertical application. But I actually think that building this platform Hebby Matrix is the thing that will unlock users ability to discover what.

[Harry Stebbings]

They can use AI agents for.

[Unknown]

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

[George Sevolka]

It's a good question.

[Harry Stebbings]

There's like four canonical prices. There's consumption based pricing, there's per seat pricing, there's like hey, rent a salary, so pay a salary for an employee which seems a little bit ridiculous but.

[George Sevolka]

Will be less so. And then maybe there's like flat pricing. And I think it ultimately depends on how you're driving value because Hebia is.

[Harry Stebbings]

Building human centric AI, the human layer to how you orchestrate an AI agent.

[George Sevolka]

Staff that scaling at inference. We do per seat because it's ultimately.

[Harry Stebbings]

Always back to the human.

[George Sevolka]

I think you'll see all of these.

[Harry Stebbings]

New business models and pricing mechanisms.

[Unknown]

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

[George Sevolka]

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 business users.

[Harry Stebbings]

Like to pay per seat because it's.

[George Sevolka]

How they map back to value.

[Harry Stebbings]

But also we want to incentivize change.

[George Sevolka]

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.

[Harry Stebbings]

Pricing, you're disincentivizing the change.

[George Sevolka]

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.

[Harry Stebbings]

It might be expensive, but use it more.

[George Sevolka]

You could.

[Harry Stebbings]

You could run more LLM calls on.

[George Sevolka]

On Hebbia effectively for free than any other platform if you.

[Harry Stebbings]

If you actually are driving real change.

[George Sevolka]

And that's what I love to see.

[Unknown]

Are you ready for a spicy round?

[Harry Stebbings]

Let's give me the spicy round.

[George Sevolka]

We got the tissues out here too.

[Unknown]

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.

[George Sevolka]

Okay.

[Harry Stebbings]

We got some changing colors of here.

[George Sevolka]

I love it.

[Unknown]

Yeah, yeah, I know. It's a full game show. There we go. It's like a fucking David Guetta concert. Perfect. Number one question, would you sell for $2 billion today?

[Harry Stebbings]

Would I sell for.

[George Sevolka]

No.

[Unknown]

What was the single best VC meeting?

[George Sevolka]

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 Woodside.

[Harry Stebbings]

Horse park, which is.

[Unknown]

Do you trust Sam Altman?

[Harry Stebbings]

No.

[George Sevolka]

Who asked that question?

[Unknown]

Yeah, I don't know 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?

[Harry Stebbings]

Sounds good.

[George Sevolka]

Let's do it.

[Unknown]

What do you believe that most around you disbelieve?

[George Sevolka]

Oh, I have a crazy one. I believe that UFOs are real. I think 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.

[Unknown]

Wow. Conspiracy theory.

[George Sevolka]

I have a lot of spicy takes.

[Harry Stebbings]

It's a special.

[Unknown]

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

[George Sevolka]

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 like very atheistic or agnostic. It was like something that was very personal to me, and I think it's been massively contributing to.

[Unknown]

How has it contributed?

[George Sevolka]

I think that ultimately when you're doing hard things or when you're.

[Harry Stebbings]

You're chewing the glass or, you know.

[George Sevolka]

Working all like, those really Late hours.

[Harry Stebbings]

Believing in something larger than yourself or.

[George Sevolka]

Believing in what you do as a.

[Harry Stebbings]

Vocation or something that's deeply purposeful and.

[George Sevolka]

Deeply meaningful is actually. It's additional fuel.

[Harry Stebbings]

It helps you in a way that.

[George Sevolka]

Is, I think, good for the soul. It really charges you up. And do you pray? I do. I pray for an hour every morning.

[Unknown]

What?

[George Sevolka]

Yeah, I wake up, I sit on.

[Harry Stebbings]

A meditation cushion and I used to meditate. I think meditation is also great. Praying and then putting something out into.

[George Sevolka]

The universe or, you know, actually having.

[Harry Stebbings]

A dialogue with whatever you believe, I.

[George Sevolka]

Actually think is even more powerful. It's.

[Harry Stebbings]

It's.

[George Sevolka]

It's almost.

[Unknown]

Do you talk out loud?

[George Sevolka]

You know, I live by myself sometimes, but. But, but sometimes it's all. It's all in my head. I think.

[Harry Stebbings]

I think it's incredibly good for the human mind. I think it's.

[George Sevolka]

It's almost an antivirus for the human mind.

[Unknown]

And for an hour?

[George Sevolka]

For an hour? Yeah. Yeah. You know, people meditate. Well, why is it so weird to pray? I think it's.

[Unknown]

No, no.

[George Sevolka]

Know what I would say when you.

[Harry Stebbings]

When you dive into the human psyche.

[George Sevolka]

And you're not looking at your phone and you're. A lot of the time, it's also.

[Harry Stebbings]

A really great channel to think.

[George Sevolka]

I think a lot of the.

[Harry Stebbings]

A lot of the best ideas that.

[George Sevolka]

I've had at Hebia have come from moments of silence. Yeah.

[Unknown]

Gosh, I get up at like 8:20. My first meeting's at 8:30. There's like an espresso ready for me. I'm like, oh, fuck my shorts.

[George Sevolka]

Oh, God.

[Unknown]

Mum. Tag. Jesus. Anyway, I'm here. I'm alive. Hi. So we have different morning routines. What's the gym routine? You're a fit dude.

[George Sevolka]

I try to work out every day. I actually end up mostly channeling the startup pressures and anger and anxiety into.

[Harry Stebbings]

Heavier and heavier things and lifting heavier and heavier things.

[George Sevolka]

So it's nothing that's in particular.

[Unknown]

Is Silicon Valley back as the center of all things things Hebia?

[George Sevolka]

I think there was a podcast that actually recently came out where everyone's like, if you're going to build an AI.

[Harry Stebbings]

Company, you've got to build it in Silicon Valley. But there is one company in New.

[George Sevolka]

York that is doing a really amazing thing, and that company is Hebia. And it seems like they're actually doing something interesting.

[Harry Stebbings]

And I do think we are the.

[George Sevolka]

Exception rather than the rule, unfortunately. So I'm a big believer in Silicon Valley.

[Unknown]

Why are you the exception?

[George Sevolka]

I think that we are a Silicon Valley Company in terms of our style.

[Harry Stebbings]

Of work, in terms of how hard.

[George Sevolka]

We work, in terms of how we.

[Harry Stebbings]

Actually pursue new technology and invest in technology and we started in Silicon Valley and we have almost only Silicon Valley investors.

[Unknown]

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

[Harry Stebbings]

Longer than 12 months? Probably like 18 months ago. It was the scaling at inference thing, the belief in a new set of.

[George Sevolka]

Scaling laws that they would be really, really, really important.

[Unknown]

Servicenow, sales force or UI path.

[Harry Stebbings]

Okay.

[Unknown]

Shag, marry or kill.

[George Sevolka]

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

[Unknown]

Is like short term excitement.

[George Sevolka]

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

[Unknown]

That in case you needed the content.

[George Sevolka]

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 B2B applications are sexy or an enterprise AI company.

[Unknown]

Are you a buyer of Salesforce?

[Harry Stebbings]

We are, we are.

[Unknown]

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

[George Sevolka]

I don't think they are. I think that, I think that Salesforce like has built again like a very.

[Harry Stebbings]

Very, very sticky network effect with people.

[George Sevolka]

And people are the shifting function. At the end of the day it's not a technology problem.

[Harry Stebbings]

Claude can build a Salesforce.

[George Sevolka]

I think Klarna again had another fugazi story about building that going off Salesforce because you know, Claude had built them a CRM and I just think that.

[Harry Stebbings]

The switching costs, the network effect of changing human beings habits is too high. Salesforce is one of those like monopolies.

[George Sevolka]

In that they have so much stickiness, habitual stickiness.

[Unknown]

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?

[George Sevolka]

That's a deep question.

[Harry Stebbings]

I would probably buy Nvidia.

[George Sevolka]

It's a lame answer.

[Harry Stebbings]

Or AMD rather I think AMD because.

[George Sevolka]

I believe that they will benefit from.

[Harry Stebbings]

The shift to inference scaling more than.

[George Sevolka]

In an outsized way.

[Unknown]

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

[Harry Stebbings]

Not a company.

[George Sevolka]

I'd love to be mayor of New.

[Harry Stebbings]

York, believe it or not.

[George Sevolka]

I just think that's a fascinating job. I think it would be really, really.

[Harry Stebbings]

Interesting and would love to make some change there.

[Unknown]

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

[George Sevolka]

I think that one of the most.

[Harry Stebbings]

Interesting questions is where does creativity stem from? Or where do you get inspiration from.

[George Sevolka]

Or kind of like, how do you come up with new ideas? I don't believe that people come up.

[Harry Stebbings]

With new ideas by brainstorming or incomprehensible.

[George Sevolka]

I just think that's, again, fugazi. Fugazi. But I think that ultimately that question.

[Harry Stebbings]

Of where creativity stems from.

[George Sevolka]

I'm also a very big painter.

[Harry Stebbings]

I'm a very big.

[George Sevolka]

So I do large scale, like 10 foot plus. Oil canvas. Oil painting.

[Unknown]

I heard about this. Where did that come from?

[George Sevolka]

I just. I think.

[Unknown]

Are you a poet as well?

[George Sevolka]

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

[Unknown]

Annoyingly perfect kid at school. They're like pains.

[George Sevolka]

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

[Unknown]

I mean, do you find po. What do you find about painting good for you?

[Harry Stebbings]

I think it's one of those activities where you can channel emotion or intuition.

[George Sevolka]

Or latent thoughts that are somewhere in your subconscious and connect things in a really meaningful way. And so in a world where there's all this stimulus or you're always kind.

[Harry Stebbings]

Of thinking or churning through something or.

[George Sevolka]

All this distraction, you're standing in front of a canvas for 10 hours with some nicotine and you're just lost in this art. I think great artists will tell you.

[Harry Stebbings]

That they don't even know where paintings come from.

[George Sevolka]

It just, you know, is this channeling something?

[Harry Stebbings]

It's one of the best places to think.

[George Sevolka]

It just gives you connections. It brings up these parts of your subconscious, these connections that I think you.

[Harry Stebbings]

Can'T really access without being creative. Whether you're making music or writing or painting.

[George Sevolka]

I actually think that's one of the.

[Harry Stebbings]

Best ways to process anything.

[Unknown]

Final one. Do you feel that your parents are proud of you now?

[George Sevolka]

I think so. Yeah.

[Harry Stebbings]

I think so.

[George Sevolka]

I think they've heard about it. There was one moment where I think.

[Harry Stebbings]

My father's boss ended up calling him.

[George Sevolka]

And he's like, your son's kicking ass. And I was like, well, it was.

[Harry Stebbings]

A very happy moment for me.

[Unknown]

That's a special moment.

[Harry Stebbings]

The chip remains, though.

[George Sevolka]

It's not going anywhere.

[Unknown]

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

[George Sevolka]

Yeah, I've loved it and appreciate all the research that you've done and all.

[Harry Stebbings]

The crazy lines of questioning. So thank you, Harry.

[George Sevolka]

I appreciate it a lot.

[Unknown]

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 20VC. 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.

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