AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
00:00:00 · George Sivulka
[00:00:00] S0: I ultimately think that agents will be used to do not only a lot of single steps, but actually most of the doing in a company. And almost all of our roles will go from doing to actually orchestrating agents in doing.
00:00:25 · Dan Turchin
[00:00:25] S1: Good morning, good afternoon, or good evening, depending on where you're listening. Welcome to AI in the Future of Work episode three twenty two. I'm your host, Dan Hurchin, CEO of PeopleRain, the AI platform for IT and HR employee service. Our community is growing. We recently celebrated our one millionth listener. We get asked all the time, how can you meet each other? And so we launched a newsletter, our Beehive newsletter. We will share a link to register in the show notes. We share oftentimes additional fun facts and some some fun stuff that doesn't always make it into the the regular shows. Go join us there. If you like what we do, please tell a friend and give us a like and a rating on Apple Podcasts, Spotify, or wherever you listen. If you leave a comment, I may share it in an upcoming episode like this one from Oscar in my hometown, San Diego, who is a CPA and listens while commuting. Oscar's favorite episode is that great one with May Habib, the amazing CEO of Ryder, the unicorn AI company that recently raised $200,000,000 at a $1,000,000,000 valuation and also just a great empathetic leader. We learn from AI thought leaders weekly on this show. We added bonus. You get one AI fun fact each week. Today's fun fact, Monica Brown writes in the Chicago Booth Review about how investors should be using AI to make better decisions. Investments funds are using LLMs to drive insights from earnings call transcripts, 10 k, regulatory filings, annual reports, social media, and streaming news headlines. LMs create direct trading signals and develop new predictive variables for their forecasting models. It makes sense to ask whether the advantages of LLM strategies will disappear as soon as everyone else uses them too. That's been the outcome with arbitrage strategies in the past. However, the opportunities here appear more bountiful. With the field in its early stages, researchers are still finding new ways to apply AI to tease out investment insights and trading opportunities. My commentary, don't make financial decisions based on what the LLMs tell you to do. Do use LLMs to accelerate your research by knowing how they work and where they source the data and how they calculated the output text. You can understand where they're vulnerable to making mistakes, but also when they can be trusted.
00:02:50 · Dan Turchin
And, of course, we'll link to the full article in today's show notes, which is quite relevant to today's conversation. George Civolka was called a wonderkin by none other than tech legend Peter Thiel. He worked at NASA as a teenager and graduated from Stanford with a bachelor's degree in math in 2.5. Go Cardinal George's company Hebia. Raised a $130,000,000 in July at a $700,000,000 valuation from a murderer's row of investors, including Andreessen Horowitz, Index Ventures, Google Ventures, and Peter Thiel himself. Hebia is used today by asset managers doing financial research at companies like Centerview Partners, Charles Bank, and Premier. Company grew revenue 15 x over the past eighteen months. George founded Hebia in 2020 and is a pioneer in the use of RAG techniques to mine company proprietary data to limit LLM outputs to sanction content. And without further ado, George, it is my pleasure to welcome you to AI in the future of work. Let's get started by having you share a bit more about your background and how you get into the space.
00:03:56 · George Sivulka
[00:03:56] S0: Thanks so much, Dan, and it's it's great to be here. The background of Hebiah is best encapsulated by kind of the the story and the reason, the problem that we set out to try and tackle back in 2020 when we were first pioneering a large language model applications and actually coming up with the first productionized version of Rag, kind of far before it was like an industry buzzword. At the time, one of the foundational insights that was kind of baked into my psyche from my time at Stanford was the idea that you should always start a company where there's a lot of pain. Right? There's a lot of literature on how some companies are vitamins and some are painkillers. But I think any startup class or any startup kind of community at Stanford will tell you, focus on the pain. And most of my smartest friends when I was in my Stanford PhD, you know, I I kind of saw them graduate undergrad and move into, you know, if they're really smart and really lucky, investment banking roles, private equity roles, all kinds of different financial services, kind of analyst or associate positions. And they were some of the smartest people. And catching up with them three months, six months, a year into these careers, the one thing that was underlying every single one of their experiences was the amount of pain that they were feeling. They would go out, think that they would go be discovering or creating something like net new. And instead, they'd they'd kind of come back and they'd, you know, say, hey. My entire job is just searching through long documents or data rooms or or doing really repetitive, rote, and mundane tasks. And I never heard of, you know, kind of this, like, massive disillusionment that some of America's and and really the world's smartest people kind of undergo in these careers. And I said, well, there's definitely an opportunity here. It was it was latent in the back of my mind. At the same time, in my PhD, I was I was working on and and really trying to investigate meta learning, the idea of teaching machines to learn to learn. And a lot of this was before the release of of any of the models. And I remember in June 2020, OpenAI released chat... Not even ChatGPT, but GPT three. And the title of that paper was something along the lines of, hey. Large language models are meta learners, are multitask learners.
00:06:09 · George Sivulka, Dan Turchin
And thinking that, well, I was gonna work on the most important technology of all time, and it got scooped out from under me. And maybe the the thing that I could do with my life was instead to go and work on the most important product of all time. And set out to start Hebiah as this understanding, hey. There's a huge amount of pain in financial services, and really the knowledge economy writ large, knowledge work is inherently painful, document based, very repetitive. Think cubicle. And and and and then there was this new technology... Technological revolution that was... I knew was about to unfold. Right? I I saw g p d three. I knew this would be important technology, these meta learning models. And I said, well, if there's anything to work on in my life, it'll be... Know? And it can't be the most important technology. Maybe it's going and working on the most important problem. And so we started Hevia as a product studio, actually creating many of the foundational applications that now, you know, you kind of see across enterprises. [00:07:09] S1: So your research was fairly broad, meta learning, and what you do at Hebio is very applied. What was that transition like from thinking like an academic to thinking like an entrepreneur?
00:07:09 · Dan Turchin, George Sivulka
[00:07:09] S1: So your research was fairly broad, meta learning, and what you do at Hebio is very applied. What was that transition like from thinking like an academic to thinking like an entrepreneur? [00:07:20] S0: There's a very foundational piece of being an entrepreneur, which is challenging the norm or trying to probe into things that, you know, might not be as they seem. And and the idea that Yeah. Anyone can go out and create a really impactful, really large, really important company, it it kind of seems like a stretch. But in in academia, you know, you're you're kind of tasked with going out and saying, hey. This is the landscape of what exists. How can I push that frontier, or how can I think differently or turn something on its head? And and there's some academics that really focus on, you know, working on things that kind of push the entire field forward, and that's a common critique with academia. I've I've always been very interested in in kind of, like, the edge or or things that are a bit more contrarian or or kind of pushing back on on what society thinks. And I think that that lent itself actually very naturally and well to to a startup ecosystem and really a a product studio. [00:08:12] S1: Go back to the pre CHET GPT days when you were experimenting with the technique that eventually became RAG. What was the insight that led you to focus on financial services? What... Why why is it better applied in maybe a narrow use case versus, you know, applying it to any any document?