AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
00:08:30 · George Sivulka
[00:08:30] S0: Yeah. So so I I think ultimately, there's a a... Maybe even a misconception about Hebia that it's only for financial services. The reason we started in financial services first actually kind of harkened back to a bit of an analogy that I was making around them, the most kind of close analog in history, in recent history, to a foundational technological revolution on the scale of AI. And that was the introduction of compute in general. And so if you think about, you know, computers were were really came on the scene and and really, really picked up kind of societal importance and impact, you know, sixty to eighty years ago. And for the large part of of their early life, there weren't really great products around computers. You had terminal windows. You had geeks, and a variety of people just, like, kind of hacking away on these things until the invention of Microsoft Excel. And Microsoft Excel, you could argue, well, if computing was the important technological revolution, Excel was the most important product of the last hundred years. It took computing and made it accessible and understandable to the, not only people in financial services, but really it ended up going out and now 2,000,000,000 people use it worldwide, probably more today. Governments run on Excel. Every single company, every single person in the world learns how to use a spreadsheet when they're in middle school or early. It is the foundational product for computing. And if you look at their go to market, well, you know, Excel started 1985 and went from, you know, 1985 to 1986, it went from zero to 90% market penetration in financial financial services. And that was where they had the the largest pain point. And financial services actually moves really fast when there's some alpha or there's some, like, really strong return. And so you kind of saw it start in financial services. Financial services moved very quickly, and then it was just pushed into, you know, every every part of the knowledge economy, into portfolio companies, and everywhere else. And that's a very similar path that we're on at Hebio. We're we're rapidly penetrating financial services, but you already see a really strong traction, you know, not only in financial services, but also in the legal vertical.
00:10:35 · George Sivulka, Dan Turchin
We're now picking up, you know, lots of Amlaw, 50 Amlaw 100 customers, in insurance across the board, not only for each kind of for for for reps and warranty insurance and and kind of commercial implications, but also for a variety of other other other different types of policies in auditing and all of these different applications. [00:10:54] S1: I mentioned a few of the reference customers in the, in the intro. Share a typical success story.
00:11:00 · George Sivulka
[00:11:00] S0: Yeah. I think almost all of our customers wants AI. They see it as this incredibly important and impactful thing, But almost all of them are are still just experimenting with it. They have, like, a few different tech forward users that are going out, and they're they're using AI in, a a small way. They're spinning the wheels on them. When people get heavier, right, they expect that it'll be something like a chatbot or something like all the other AI application companies in the world that are very good at doing a single task. Hey. Rewrite this email. Or, hey. Go and find something in this document or summarize this document. But that's actually just one step. It's like the calculator. It can do only a single step on the way to actually augment... Augmenting an entire workflow. They'll get heavier, and really quickly, they'll realize that the entire platform is built around answering questions or doing tasks that require many steps. And that can affect any workflow. So, you know, if if you wanna go into, you know, investing as a canonical example, one of the questions that people always wanna answer, they just get a new opportunity. They just get a new company. They're reviewing a new pitch deck, and they wanna answer, hey. Is this company a good investment? A ChatGPT or any other kind of search products on the market will go in, they'll try to find areas in the document where it says whether or not it's a good investment. But, obviously, that's biased. Obviously, the CEO is gonna say all kinds of things and make it look like a great investment. Actually, we'd do if you were a good investor would be you'd take all the marketing materials. You take everything on the Internet. You take everything off the web. And you'd say, hey. Give me something about the strength of their management team that might not be mentioned. Or go look into their competitors and see what's strong about their competitive positioning. Or tell me something like their customer concentration. And each of these individual steps, you can think of as a decomposition of that question. Hebby will take a question like, is this company a good investment? Review documents like a data room, a sim, or anything else that's brought if you're an investor.
00:13:00 · George Sivulka, Dan Turchin
And it can then go and answer customer concentration, strength of the management team, and everything else that you'd like to know down to your specific investing process. [00:13:09] S1: We talk frequently on this show about what it means to practice responsible AI. And with a product like Matrix is the name of the the product from Hebia, gosh, it is in control of making a lot of important decisions. And what I'd be curious to know is from the perspective of, you know, the CEO and your culture, what do you think about in terms of taking ownership or accountability for unintended consequences? Not because your customer asked bad questions, but because Matrix may do something. These are statistical... You know, these models are looking at statistical probability. The decisions can have high impact, ramifications. How do think about the responsibility of you as the developer?
00:13:52 · George Sivulka
[00:13:52] S0: It's it's incredibly important to our company and really to the collective dialogue of our industry about the ethical implications and then really how to responsibly roll out this technology. You can think of of new technologies as as really a tool or an opportunity to go out and and and do anything. But when they have some slight bias or they have some slight kind of tendency to to skew information, that becomes an incredibly dangerous. With Matrix, we actually engineer the entire product to be very human centric, human first, and transparent. And that's very different than anything else in the market. Whereas other things might just reply or show you a few search results and a few citations, Hevia actually shows you its work, and it builds a piece of collateral. That's actually the matrix that will show you every step the AI takes, actually where documents add up and where they don't add up. And if data is mixed or data is slightly skewed one way or the other way, it won't tell you, okay, 52% of the time people say this, and 48% of the time folks disagree. It'll actually, you know, it won't it won't it won't really skew just to the 52% of time. It will actually tell you that breakdown. It'll it'll it'll show you every side of a problem. And that's that's inherent not only in how we kind of how we think about our responsibility, but how we build our product. In investing, that's even more important because you care about not only the majority opinion, but maybe the secrets, or the information that people might not be talking.
00:15:22 · Dan Turchin, George Sivulka
[00:15:22] S1: This is unanswerable, but you're pretty good. And so I'm gonna ask ask it anyway. Maybe it's unfair. [00:15:27] S0: Hit me. [00:15:27] S1: What do you think about the... Is the cost of a false positive, so you make a poor decision that someone acts upon, is that higher or lower than the cost of a false negative where you have a right matrix, has a right answer, and because it's not confident enough, it chooses to not provide it?