← Liftoff with Keith: Conversations with Super Founders and Growth Experts sharing useful insights
Liftoff with Keith: Conversations with Super Founders and Growth Experts sharing useful insights
Anthony Vinci on AI, Decision Intelligence and the Future of Risk
00:34:27
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Foreign.
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The sponsor of Liftoff with Keith is the one and Only Compass Strategic Advisors.com an experienced partner to help you navigate everything from cap tables to stock option and compensation plans and all types of backroom and marketing services. There is no better friend to the startup CEO than Compass. Check them out@compass strategic advisors.com Another great episode of Liftoff. And this is going to be another different one too. I mean, look, most founders operate on instinct in uncertain environments. Today's guest has spent his career turning uncertainty into structured intelligence. And listen, the world of structured and unstructured get a lot of attention. So I'm so pleased to welcome our economics expert, our former NSA guy and author of the Fourth Intelligence Revolution. We're going to dig into all of that. Plus, he's the founder of Veo Technologies. And welcome Dr. Anthony Vincy to Lift Off.
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A
Hey, thanks for having me. Appreciate it.
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B
Anthony. This is so cool because we have a little bit of, of background in a couple of different areas and as I shared with you, I kind of want to make sure we touch all of it. But first, I'm thinking from the standpoint of your background, which I dove into pretty deeply, I mean, across intelligence investing and now building. What is driving you? What's this obsession that you're looking to solve?
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A
Yeah, you know, it does feel like a bit of an obsession. It's like kind of my whole life, you know, I studied philosophy as an undergrad, I became an intelligence officer, and now I got a company, you know, that is all about figuring out what's happening in the world and what's going to happen next. I think just like deep down inside, I don't know, maybe it's a survival instinct, you know, maybe it's a. Just something happened in my childhood. I'm not sure. But I just want to know. I've always wanted to know what is happening in the world and what might happen.
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B
You know, you, you couldn't be in a more content rich or challenging time than we are in right now with the. Just the explosion of data meets intelligence. Right? And now you talk about wanting to feed an obsession. I don't even know if there's enough time in the, in the day to feed it. I mean, you're just on constant devour mode, right? I mean, so what happens when intelligence grade forecasting becomes accessible to basically everyone?
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A
Well, if you think about it, we all can predict the future, right? We all do this. We did, we did this, you know, in ancient Roman times and medieval times and, you know, people are good at that. We look around, we see what's happening and we progress, procrastinate, right? We're like, hey, what do you think the President's going to do next? What do you think is going to happen in Iran? And we all have a view. I think what's changed and what we're trying to do is make that more exact. How do you quantify that? If you go ask an analyst, whether they're at the CIA or at JP Morgan, what's going to happen next? They're going to have a view. Trust me, they will. But then if you go ask another analyst, what do you think is going to happen? They're going to have a different view. You know, you've probably heard the joke, right? If you know, you, you, you go in a room of 10 analysts and ask them what's going to happen, they're going to give you 11 opinions, right? Like, the thing that we are trying to do is to try to kind of bring some sanity and some rationality to that and quantify it and say, not only, hey, this is what I think is going to happen, but hey, there's a 64% probability that this is what's going to happen. And what's important about that is it allows you to make a better decision, right? The world isn't binary. It isn't just like this is going to happen or not. You have to be able to sort of say you have to hedge your bet, right?
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B
You brought up the idea of unstructured in some of your content and insights. And I know you're trying to bring structured information and sanity to all of the reality. Give me a little bit more information on that.
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A
Well, look, you know, we got good. And I mean, we as in businesses in the economy at doing forecasts when there were structured data, enough financial information, you can kind of, you know, see what might happen. You can watch a trend line on financial data. What's really hard though is what do you do when there's no structured data? It just the events happening in the world, right? It's all qualitative. It's written down in newspaper articles and think tank reports and these kinds of things. That's where it gets really hard. And what's what AI has allowed us to do, and specifically these large language models, these LLMs is help to turn that unstructured information into structured information. So then you can kind of quantify it. And, and that's, that's what my company does.
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So things become more objective or subjective and we begin to dig into a little bit More of predictability, accountability into the predictability.
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Yeah. Measurable, you might say.
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Okay, so I'm looking at a world where we, we need this. We need this. And yet how do we go about applying it now?
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We really do need it. Right. If you think about decisions, we all, we all make them every day. Maybe it's, where am I gonna get coffee? Or maybe it's where should my son go to, you know, college? Or where should I recommend that he goes to college?
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Yeah.
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You know, and. Or it's whether I should buy a house today or I should, you know, wait to buy a house for six months. Well, governments do the same thing. We're wondering, what do we do about Iran or Ukraine? Companies do the same thing. And you're trying to make the best decision you can so you can mitigate some risk. You don't want the downside. Right. Because a bad decision, like where you go to college, when you sell your house, or how you handle a ceasefire in a war, a lot of ramifications. Right. And by mitigating that risk, by bringing down the possibility of making a bad decision, you help to make better decisions. That's what I'm in it for. Right. That's why I formed this company.
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B
Let's go into that a little bit. How do you, how do you break that down for, for a lay person listening, what does the company do? What's its ultimate goal?
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Think about it as we built a really good analyst or forecaster, right, that can look at the world, take into account all this information we pull from 150,000 sources. Think, you know, every newspaper in the world, all the think tank reports, all that stuff. Yeah. And then you ask it a question. What's going to happen? You know, what is the probability of, you know, a ceasefire in Ukraine in the next six months? And it will give you an answer because it's going to go and it's going to read through all those sources, essentially, and it's going to balance them and measure them and compare them and do that analytical and forecasting process and give you that, that number. Right? So it's doing what a person does. This is what artificial intelligence is.
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And now. Right, I was just going to say. And now I would put that into GPT or Claude and say, hey, answer this for me. So how does that differ from what you're doing?
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You can absolutely do that. And it will give you an answer. Yeah, it's just not going to give you a great answer. Right. And, and the, and, and because what, what, look, chat, GPT, And Claude and Grok and all these systems, they're amazing. Don't, don't get me wrong. We all, we all are using them now, but they're there to please you. And they're there, they're optimized to tell you something you want to hear. And they're, at the end of the day, language models, right? They're, they're manipulating language. Well, to answer a question like this, like, what's the probability of something? It's not just language, it's math. It's data science, it's modeling and simulation. So you have to bring in these other capabilities. That's what we did, our team. Yes, we use lms, but we also use math, we use data science. That's where our, the background of our team comes from and what our technology does.
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So give me the secret sauce then that you can, you know, that, the algorithm stuff, but like you're accessing different amounts of data, types of data and putting them into the, into your models.
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Well, let's, let's say something like this. Say you're trying to figure out what's going to happen in the Ukraine war. And you have to measure here are all the things for and against it, right? Well, now all of a sudden you're in the math world and how do I compare this thing for, against this thing against, how do I weight them? And then how do I literally compare them mathematically? Now all of the sudden you're in math world and LMS are just bad at math. I mean, they literally.
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You sound like if I want to simplify, you're adding math to LLMs.
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Yeah, yeah, it's called what they. The, in the, in the industry, you call it giving it a harness. Okay, give a harness on top of the LLM.
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Now, are things that you're seeing coming out of the LLM world starting to encroach on this space, or are you doing things that are continuing to differentiate yourself from LLMs? And by the way, for the folks listening, want to check it out as we talk. This is a very cool website. Vio make sure you give it a, give it a look. Go ahead. Anthony. I'm sorry.
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Yeah. Look, we're trying to stand on the shoulders of giants. We made a conscious decision to not try to create a foundation model. Claude CHAT TBT those guys have so much money, they're going to make a better model. So we're taking what they have and, and we're making it better. And we make it better in two ways. One, like we were just talking about the mathematics of it. But two, when you look at our forecasts, and we do more than forecasts, by the way. We can run scenarios and all sorts of interesting things to help you make a decision. You can actually dive inside the black box. So we think it's really important to make it explainable. And we show the users what is the reasoning behind this forecast? Why is it 63% instead of 58%? What led you to that view? Right. And then from there you can not only understand it, which allows you as a decision maker to make an even better decision, but the next generation of our technology, you can actually manipulate that. You can say, well, I don't agree with that. I think it should be higher and that will reset the forecast so you can have your own view. And I think this is where people +AI ends up being better than just AI on its own human in the
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middle approach kind of thing. I think I read somewhere we take messy human judgment and turn it into decision ready data.
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Oh yeah, that's pretty. That's pretty good. That sounds like. That sounds like something we would have written down.
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Yeah, it came out of my research, maybe I wrote it, but I want to suggest that I'm still a little bit confused as to who needs this. Okay, so. So again, is it the analyst at the nsa, one of your old cohorts, maybe for yourself when you were in that chair? Is it for a cfo, a founder? Is it for somebody who wants to win some money on Polymarket tomorrow? Not myself, of course, but just suggesting, you know, I did a dive into the product. I looked at some things on a very surface level, by the way, and really liked playing around with the interface. I like the flexibility, the, the breadth of kind of answers. I could get it on the different topics, but I still wasn't clear on how I would apply it. I didn't spend enough time. But coach me on that point.
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Yeah, it's a great question. Right now, the two customers we really try to service are those two customers who. For whom decisions matter the most. One is governments and two is finance. Okay, In a government, the difference between a good and bad decision is whether, you know, maybe American soldiers die in a battlefield. And for finance, it's maybe you're making a $500 million bet on something right. In an investment. So that's really where we focus today and in those. So what you see on the site today is kind of optimized for, for that world. So maybe it does have a quant feel to. It's got a quant feel right. So and so, you know, we work with, you know, we're launched on the Bloomberg terminal, which gets us into the hands of a lot of folks in finance. You know, it's hedge fund traders, it's commodities analysts, it's people trying to assess the value of an asset, what might happen in the future, how that might affect the value of something, and where even just a little edge means a lot. Right. Even just getting this decision 2% better than the other guy really gives you an edge to win. Same thing in government. We work with some government agencies and they're looking at, look, they have some of the hardest jobs in the world. I used to be in one when I was an intelligence officer. And you're trying to assess what an adversary is going to do. Right. And that adversary is trying to deceive you. They're making it really hard for you to predict what they're doing. They don't follow laws. Right. And so when you're doing that, even just being a little bit better at it and trying to assess, okay, what really is the probability of them doing this here Is it, is it again, 62% and 66% probably don't sound like a big difference to you or I, but when you multiply it by the factor of the impact, it's a lot. Yeah.
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I'm glad you brought up the Bloomberg terminal. Just to kind of revert back there to like the old days, you know, it feels like they needed a refresh on the Bloomberg terminals, but maybe the users of the Bloomberg terminals needed a refresh a little bit. You're not necessarily making a stake to replace them, but to upgrade them, right?
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Yeah, absolutely. I think we're expanding what they do well. Right. Bloomberg is great. It brings in a lot of financial information. It integrates all sorts of numerical information from different companies, different indexes. What we're starting to add in is all that, like you said before, that messy human information, that qualitative political information, what's going to happen in elections, what's going to happen in Iran and so forth. We're trying to turn that into numerical values, time series data that they can then post, which you then can combine with all that financial information that's fairly new for them. Yeah.
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And we're talking in a time where everyone's referring to data as the new oil and information as the key to winning. You also are seeing a thought stream about better frameworks is what's going to win in the LLM era. Would you agree with that?
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Yeah, I think that it's better ways of working with the data. Right. The data is becoming, it's not fully commoditized. But you know, these big LM models have gone out and kind of sucked up most of it. Right. They just vacuumed up data for years. And the difference is going to be two parts. One, what do you do with that? How do you take that LLM output and do something like we were talking about before, add a harness to it, add math to it, add something else to it that gives you a competitive edge against them and against people. And then the second competitive edge is how do you then engage with a human being? Right. If you think about it, let's say you and I are competing. You've got a hedge fund, I've got a hedge fund. We're both trading on something, we're both using Claude or whatever it is our competitive edge is going to be, how do you use it? Right. You, if I, if, if you're in a, you're racing a horse and all of a sudden I come with a car and I'm now driving a car, I'm gonna beat you for sure, right? But now what if you have a car? Okay. Now we both have machines, just like we both have AI. We what matters, it's how the driver is going to drive it. Right? So we like to think we give you a better car, but we also give you a better way to drive it.
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I like that. That's a great analogy. I'm going to switch gears with you here to think about how I apply this to the founders who are listening to the show and your experience as an executive and a leader. How do founders take this technology and apply it, whether it's this platform or in general?
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I mean, founders, right. We're sort of in the business of assessing risk. Right? Like you're, you're in the ultimate opportunity.
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Expenses, revenue.
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I mean, yeah, and, and sometimes it's straightforward. You go to your accountant or your cfo, you can assess your financial risk, you go to your lawyer, you can assess your legal risk. But there could be other risks that you're not thinking about. I mean, one of the, you know, the fastest growing industries in the, in the kind of startup space right now is actually in manufacturing, right? It's figuring out how to manufacture robots. Whatever it is, you have supply chain risk. And all of a sudden, as a founder, what happens in this upcoming summit between China and the US and whether there's going to be export restrictions on, say, a component in your stack, like that matters a lot actually, right? And, you know, so I think, again, this gives you a better way to assess this type of risk that might have been hard to do before. Yeah, sure. Maybe you can come to Washington and you could interview a CFIUS expert or some export restriction expert, or you can kind of hop on the system and get a, you know, an 80, 90% solution immediately, in a couple of minutes and understand really, what is my exposure here? What is the probability there's going to be an export restriction on this important electrical component that I have in my, you know, in my product?
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So now I'm getting to a place where I'm getting better data, better decision making from the existing information that I have. And that's what you're suggesting. And now as a founder, how does that. I just, I just feel like I can make a better decision.
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Exactly.
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And then where does something like my, my spidey sense come in? What is my, my personal intuition? Some other experience that's not accounted for in the, in the platform?
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Look, one of the things that's really interesting about the platform is we love it when people disagree with it. And you get on and you're like, no, that's wrong. Right? You hear something and you're like, 35%. I just don't buy it. But then, now, now actually, we've already helped you because now you start questioning, why don't I agree with it? And then, you know what, you start to drill in. Why do I think it's 50% instead of 30? And that's where you're engaging now. And then what you can do on the platform that's interesting is you can kind of run scenarios. So you can say, all right, this thing says it's 30%. What if this happens? That's how we all make decisions, right? You're, you're like, what if this happens? What if that happens? We can do that. We could say, what if this. I'm trying to assess whether this export restriction is going to happen. It's going to harm my business. Okay, what if Trump signs a deal with China? Then what's the probability? Or what if he doesn't? Now, I've got two different things I can track and I can have a view. I think he is going to do it. Okay, now you can go down there, but you can prepare for both eventualities. That's where, again, the person, the leader comes in.
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You know, you bring up a great point because this is one of my, one of my favorite thoughts. I forget the exact quote, but it's not about being right. It's about being prepared. And that's, that's where you're really, you know, that's ultimately the service. Because you can't tell me you're going to be right. You can tell me. Hey, I'm giving you the best information available. You're going to be ready for multiple scenarios.
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Yeah, that's a great way to look at it. I mean, it's like preparing for a debate. Right. You don't know what you're going to end up bringing up. So you, you learn as much as you can, you know, or like Jeopardy. You know, they just study all the possible questions.
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You're almost structure structuring uncertainty.
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Yeah.
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And putting it into some probabilities or some indexes.
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Yeah, yeah.
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And then how long did it take you to build this, Anthony?
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We started doing some experiments with the technology in 2024, and then we built really the first beta in the first half of 2025. We raised some capital after that, crosslink capital and a couple other funds came in and we launched on the Bloomberg terminal publicly in January. So it's about a year to, to build everything, get it launched.
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Oh, you still got your sea legs,
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though, a little bit. Things move so fast now, though.
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Crawl, walk, run or crawl, run, walk or whatever you.
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Yeah.
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Jog in place. How are things going? What are the challenges you're facing?
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I mean, it's, it's, you know, we thought we would sort of slow launch, get it out there, build it up over time. And then boom, there was a war in Iran, you know, a month after we launched. And all of a sudden the whole world was dealing with all this risk and uncertainty and we had actually predicted the war. And we were in front of both Kalshi and Polymarket in, in forecasting it. We forecasted the closure of the Strait. So all of a sudden we were really popular and we were not ready like any other startup. And so you're just sort of. We've been running as fast as we could to stay ahead of that, to be able to service people, to make it all work, to handle the, you know, the incoming. And now like, we're a few months in and we're, it's not that we're even able to take a breather, but we can take a minute. And so we're trying to, you know, hire all the guys that, you know, was just a pretty small team handling all of that. We, we're now, we're trying to hire people to help build up and get, get able to service those customers that have come on over the last few months.
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B
So I won't grill you on metrics. You're still so young. What are the main things you're looking for as a founder on an AI platform looking to grow? Is it users? Is it some sort of a, of a satisfaction index that you're scoring based on, Is it upgrading to a paid model? What are the things that you're looking at most closely?
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For me it's actually the, not the quality of the, the sign up or the user, but how big are they? How important are they? How, how much are they taking, how seriously are they taking us? I'm less interested in just kind of signing up as many kind of small accounts. I'd rather get big important organizations like Bloomberg who really demonstrate the gravitas of what we're doing. And so we're really focusing in on those kind of signups for now and we want to demonstrate that.
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B
I would imagine you're going to spend some time maybe with some larger consulting firms. Maybe a Palantir could be a good partner. Certainly the hyperscalers could be big partners for you.
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Yeah, big, big banks, investment banks, hedge funds, insurance companies, folks like that who, you know, frankly are really hard to sell to. If anybody's listening, who's ever sold to an insurance company, I know they're nodding their head right now.
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Yeah.
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But we think it's worth, we think it's worth it because again, they, they're really going to take this thing, ensure that it's good. They're going to do their due diligence and when they use it, they can go big. And so that, that's what we're focused on over the next few months.
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B
I love it. So let's go into some rapid fire questions before I dive in. What do investors get wrong about geopolitical risk?
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I think the biggest thing is that they see it as binary, that, that it's like there's going to be a war. There's not going to be a war. Well, you know what work. You know, sometimes a war is kind of like in between. Right. Like we're sort of not really fighting, but it's still a war and there's still military troops. Right. So you can't look at the world as black and white. You got to look at it as like there's gradation and you need to think about all the different possibilities.
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B
That's cool. What's overrated and underrated about AI?
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It's kind of the same thing. Right. Which is that like AI can do anything. Right. It's like overrated because you're like, well it, you know, it feels like they keep telling us it could do everything and then I get on and there's a million things I can't do.
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Yes.
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At the same time, every time I get on, it does something I was didn't think it can do. Right. So I'm always like flip flopping between those for me and I don't want
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B
it to automate anymore.
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A
Yeah.
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B
I've got to get involved. That's great. I like that answer to Anthony, what about from the standpoint of. Your entry into, into building this business, what's, what have you, what's changed your thoughts about. Maybe it's about AI, maybe it's about technology, maybe it's about just personal beliefs about you as a founder. What have you, what's, what have shifted within you.
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When I, you know, I did this once before, I had a startup once before and I built a software company. We made it, we made an app and we got users for that app.
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B
Yeah.
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And I thought coming into this I was building another software company, like I was building another app. And what I've come to realize is that's over. And AI has changed everything about how you build a company. Now I don't even think about hiring software developers anymore. I think about hiring builders. Right. Like it's not that you have to be great at, you know, knowing, you know, Swift or C or JavaScript or whatever, like some language great. What you have to be great at is knowing how do I make a product, how do I get it used, how do I experiment with this and then like quad code and things like this can do a lot of the work. And so now I don't even think about us building software. I just think about us having a few really good builders who can experiment and build things and crank it out like really fast, like 24, 48 hours fast. Right. And try it out. And it's just a completely different environment mentality for building a company now.
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B
Yeah. Interesting. I, I hear that from some of your peers also. And I you, they use the word outcome driven. So in other words, you're hiring people to achieve a specific result versus taking on a certain task.
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A
Yeah, yeah. And by the way, the, I used to think about it as the engineering team being the one who sort of would be outcome focused like that. Now it's the whole team. So business guys on the team now are building things using Claude code or using things like this. You know, it's not perfect. It's maybe it's not going to go into prod and be what's launched, but they're, you know, a guy who's never programmed a computer in his life is now making that prototype that I used to have maybe a junior dev build. Right. And that, that's crazy and it's fun.
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B
What does that make decision making look like in five years? Business development or start starting a company? How does that all change in the next five years?
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A
Sometimes I wonder if you're just going to have a single person company and you're just going to be managing agents. I already think of my company as I don't want to scale. I don't want to have a thousand people working here. Yeah, like I think What, I think 25, 50 people, we could have an immense amount of revenue with this. If all of those guys are builders, all of them are good, all of them are managing multiple agents and learning from each other. And you know, well, what I like
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B
about that scenario is the flatness of the organization with the direct communication. I don't know if I necessarily want to remove those parts of the, the fiber of the company. Right. The teams and the team work that's required and to get things done, you know, the checks and balances within a company.
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A
It, it, it's almost like you could still have the best part of having a small company. You know, when you have a startup and you're like 10 people in a room, you're so tight and there's no bureaucracy, you're just, you're just building stuff together and you just literally swing around your desk, right? And then, and turn to the, you know, the, the UI UX person, you're like, hey, I just made this. Can you, you know, can you, can you add a front end to it? Right? Can you do that when you also have $100 million a year in AAR? Right. That would, that's sort of where I think it would be super cool. And, and I think probably where it's going, like where it's that small, it
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B
sounds like that's where you're headed. What is the biggest challenge you, you're, you're facing? What's the biggest opportunity for you?
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A
I mean the, you know, for us it's scaling, hiring right now we can use all the AI in the world you want, but you still have to build trust with somebody who's going to take a risk and come into your company and, and you got to hire the right person and any, I just frankly think you can't rush that. And so that, that Is the, the biggest challenge, biggest opportunity is, you know, look, it moves so fast. Like I was saying, we make, we make new tech so quickly that does amazing things. And we're all, you know, have the, the, the wins that are back from other AI being developed and shared and new ways of doing this. And so it's, it's, there's so much we can do to create a product that was impossible a year ago. Right.
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B
Challenges abound. But let me turn and end with one question that's sort of broader and sort of forward thinking in terms of where do you think founders need to be focused as they look forward today? I feel like we have addressed the elephant in the room, which is everything's changing and it's a whole new world and horizon. What's one question founders need to be thinking about or asking that they currently are not?
- [31:36]
A
I mean, every founder is like a futurist, right? Because you're not building something when you're truly founding a company. You're not building it for tomorrow. You're building it for like a year from now, two years from now, five years from now, right? You're going to scale into this. And so you sort of have to figure out what's the future gonna be. And that future is, it's becoming clear, is a future where there's really smart AI everywhere and where people do something else and we're not sure what it is. Like, what is gonna be left? I mean, I have a son in school and I think about it all the time. What's he gonna do, you know, when he graduates high school or graduates college? It's unclear. And so I think every founder, we have to be thinking about this. What are people going to do and how can we help them? What are jobs going to be? Like, what is an intelligence analyst going to do or a financial analyst going to do in five years? It's something I think about all the time because my company is going to be used by a person at some level and what are they going to want and assume that we should provide? So it's that kind of, you know, it's founder as futurist is, is the thing that we should all be thinking about.
- [32:50]
B
And no fear, let's go, let's take it on. Take that hill.
- [32:53]
A
Yeah, man.
- [32:54]
B
Dr. Anthony Vicin, thank you so much for your time. I mean, the book is the Fourth,
- [33:01]
A
the Fourth and the Fourth Intelligence Revolution, the Future of Espionage and the Battle to Save America.
- [33:07]
B
I mean, you've had such great experience. That's got to be a great book and I'm looking forward to reading it soon. It's on my list. And your financial background, this new company, it's going to be so exciting. One last, give it. Give me a one last thought about the, about the company and where we should go to get more information. I think you have a beta. Go ahead.
- [33:29]
A
Yep, absolutely. Well, look, one last thought is that I've been talking about big banks and hedge funds and governments using it, but I do think it's there for everybody who wants to make a decision. And so I'll say this. If you, if you come onto the site, Vico I.O. v I c o I.O. and just mention that you heard me on, on the Liftoff podcast, I'll activate it at trial account for you, no charge, so you can try it out. We would love for people to try it out. You don't have to be at a big hedge fund. You just be a regular person or a founder and, and, and use it.
- [34:04]
B
Very generous of you. Very exciting. A true entrepreneur with again, just a wealth of experience. And this is a very cool platform in a cool space. Wish you nothing but the best of luck. We'll come back on again and give us an update in a year or so. Huh?
- [34:19]
A
Amazing. I would love to do that. Thanks so much for having me on.
- [34:22]
B
Great to have you. Thank you.