The New Battleground: AI, Global Finance, and National Security with Dr. Anthony Vinci

When we talk about the future of global conflict, we are no longer just talking about hardware, troops, or physical borders. We are talking about data, predictive mathematics, and decision speed.

Artificial intelligence has fundamentally rewritten the rules of engagement, blurring the lines between national security and global finance. They are no longer separate areas of concern—they are deeply interconnected.

In this episode of Experienced Voices, we sit down with former Intelligence Officer Dr. Anthony Vinci, the founder and CEO of VICO. VICO is a venture-backed AI company pioneering the groundbreaking field of 'Decision Intelligence.'

Using advanced mathematics and AI, VICO quantifies, simulates, and forecasts complex political, geopolitical, and economic event risks in real time. Dr. Vinci breaks down the massive implications this new capability has for our country's national security, global markets, and the future of global stability.

Tune in to hear a critical, must-listen message for the next generation of entrepreneurs, investors, and policymakers on their vital role in protecting America's economic and national security in the AI era.

Key Takeaways From This Episode:

• The Shift in Global Conflict: Why data and decision speed have replaced traditional hardware on the front lines.
• What is Decision Intelligence?: How VICO uses advanced math to simulate and forecast geopolitical and economic risks in real time.
• The Intersection of Capital & Defense: Why global finance and national security are now deeply intertwined.
• A Call to Action: The critical role today’s innovators and investors play in protecting economic and national defense.

About the Guest:

Dr. Anthony Vinci is an Intelligence Officer, tech founder, and the CEO of VICO, a venture-backed AI company at the forefront of Decision Intelligence.

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Transcript

Jeanne Gray: I'm Jeanne Gray, publisher of American Entrepreneurship Today and host of the podcast series Experience Voices where I talk with highly accomplished people who share the critical elements that led to their success

When we talk about the future of global conflict, we are no longer just talking about hardware, troops, or physical borders. We are talking about data, predictive mathematics, and decision speed. That is because artificial intelligence has fundamentally changed the rules of both national security and global finance, making them no longer separate areas of concern.

Our guest today on Experience Voices is intelligence officer Dr. Anthony Vinci, the founder and CEO of VICO, a venture-backed AI company that is pioneering the field of decision intelligence. VICO uses advanced mathematics and AI to quantify, simulate, and forecast complex political, geopolitical and economic event risks in real time.

As Dr. Vinci explains, this new capability brought about by AI has major implications for our country's national security, global finance, and a range of other areas where it can be applied. Dr. Vinci concludes with a critical message for the next generation of entrepreneurs, investors, and policymakers on their role in protecting America's economic health and national security in the AI era 

Anthony, welcome to Experience Voices. 

Anthony Vinci: Hi, thanks for having me. 

Jeanne Gray: So we have a lot to cover, and I thought we would start by how people think about the economy and national security. Usually they think about them separately, but with AI, how should Americans understand the connection now between artificial intelligence, global financial power, and our country's national security?

Anthony Vinci: For a long time, we did do that separation and think about the economy and global politics and militaries separately because we were competing with the Soviet Union in the Cold War. They weren't really an economic superpower, didn't really matter that much. We were competing with Al-Qaeda and terrorists for 20 years.

They obviously were not an economic superpower, didn't matter. But today, now, the US is competing with China, and China is a military power and an economic power. And it's not just manufacturing there anymore, it's also technology and innovation. And so China's also competing with us within the economic domain on AI.

And AI is a dual use technology. It's got commercial uses that we all see every day, but it's also got military and geopolitical uses, which we see in its use for piloting drones and doing automated intelligence analysis. So the two worlds are now intertwined, economics and geopolitics. 

Jeanne Gray: So I imagine that there are challenges and issues emerging over the last couple of years, maybe even accelerating.

Share a little bit about those challenges.

Anthony Vinci: The US economy was set up over the last 25 years , for a global free trade system. We were trading with everybody. We brought China into the World Trade Organization. It's free borders, low tariffs But now, what people in Washington have come to realize is that there was a trade-off with that, which is creating a dependency on all of these globalized trading partners, which normally would be okay.

It's fine if it's a US dependency on Canada or the UK or France, but it's a dependency on China, and China has its own ambitions, and those ambitions do not always sync with US ambitions. And so they have gone out as a nation and tried to gain control over certain economic goods. So you may hear about things like rare earths, which is a mineral and metals that are necessary for making certain forms of electronics.

Some of those are electronics like missiles and weapon systems that the military needs. Some of those are civilian things, and , maybe you may have heard they're used in cell phones and so forth. They're also used in CT scan machines and MRI machines, life-saving equipment. So these are really important minerals.

China makes most of them. The US is dependent. So this creates this dependency that has kind of become this source of tension. And there are several of these kinds of things where the US has grown dependent. You might remember from COVID, where, we ran out of PPE, this personal protective equipment like masks.

China was making all of it. It was hard to come by. Remember, people were in hospitals, doctors and nurses were- ... having to reuse these masks. That's what it looks like when you run out of something important, and we don't wanna run out of something important again and put the nation at risk.

By the way, China's dependent on some US things as well, right? They want semiconductors, for example, and the US kind of controls semiconductors. So it goes both ways, and this is creating a tension, and you see it pop up all the time in the news these days, and it matters a lot for investors and for businesses, even small businesses who may have a supply chain dependency, even if you don't know it.

Maybe you're at home, you run a small business, you're manufacturing something. Maybe it's just one small factory in a small town in America. You might not even directly import from China, but you're using something that was ultimately made in China, so you have that dependency as well, and that's all under threat, and all of a sudden, even small mom-and-pop shops as well as big companies in America have to think about these supply chain risks, and they don't always have the tools to do that.

Jeanne Gray: We are trading partners with a range of countries, and we compete in the automobile industry and steel, et cetera, et cetera. But from what I'm hearing, because of China uniquely, is they're not a democratic country. So is that really the core issue that we are both competing and dependent upon a country that is not committed to democratic values?

Anthony Vinci: Yeah. They're definitely not a democratic country. It's ultimately an authoritarian nation, centralized control by the Chinese Communist Party. And even more than that, it's that we have different social, political, ethical norms in the US or the West in general versus China, and that creates kind of a tension and a mistrust between the two nations.

And look, China is a growing country. They're expanding economically and militarily, and that kind of creates what some people have called this phrase Thucydides Trap, which goes back to ancient Greek history. And the idea is that you have a country that's in power and another that's a rising power, and eventually at some point, they're gonna clash.

We see the same thing with companies, right? You had Microsoft, you had IBM. IBM was the big company. Microsoft was growing. Eventually, they clashed. And you could say this for all sorts of companies in the world. Well, The same thing happens with countries, and that's really what's at stake here and kind of the issue.

Look, I don't think either side wants to go to war per se, there's gonna be a tension. And so when we see these summits, like when Trump goes to China, that's what's under discussion. And again, what's unique about this, it's not just this military tension. It's an economic one 

Jeanne Gray: You recently launched VICO, an AI platform that focuses on forecasting.

Share a little bit about its, mission and the pain points and the solutions that it addresses. 

Anthony Vinci: When my co-founder Lyndon and I were first starting to think about the business, we saw all of this that we've been talking about starting to happen, and realized that the world economy was starting to be affected more and more by politics and geopolitics and military issues.

And for a long time, If you were an economic agent, you were a trader at a hedge fund, you were an analyst at a bank, you were a CEO, you could more or less do your job by just understanding the economic and financial aspects. Politics mattered a little. Not saying it didn't matter, but it wasn't the main driver in your life.

Now, what we saw was that these other types of events, political events, think Liberation Day with tariffs under Trump, or geopolitical events, think what's happened in Iran with the closure of the Strait of Hormuz, -- were now becoming the main drivers of the global economy and what would affect businesses from small mom-and-pop businesses all the way up to the largest businesses in the world and their investors.

So we wanted to figure out how to get information and help these businesses make better decisions in that kind of environment. And so what we thought to ourselves and what we built eventually is that to do that, you need to be able to understand what's gonna happen next in the world, have some sort of idea, but also quantify it.

You have to measure it. It can't be reading a crystal ball. That's what we had all kinda gotten used to. You know, We hear a pundit on the news, and they say, "Hey, I think this is what's gonna happen in Iran," right? And then the, next pundit comes on, and they say something completely different about what they think is gonna happen.

And look, they might both sound convincing and both have amazing backgrounds. Maybe they were at the CIA, maybe they were at the World Bank, maybe they really know their stuff, but it's hard to measure. And when you're running a business, you need to measure things. You need to quantify them.

You need to be able to compare them. So what we did is we built a system where we could forecast these future events and do it quantitatively, do it in a measured way that you can compare to the other data you have, so to economic indicators, to your financial indicators, right? To treat the future as something that could be measured with probability.

So our system, you can literally come on to VICO, the name of our platform, and say something like, "What is the probability of the Strait of Hormuz reopening in the next thirty days?" And get back an answer that says thirty-two percent It doesn't say, "Hey, I think it's very likely." It doesn't do what a person would do.

It'll say 32%, and it will say, "Here's why. Here's my reasoning behind that." So it's explaining what it is. And so we think that's a powerful tool for business to be able to understand what will happen next, and then from there to think through decisions, run scenarios, and ultimately make a decision. 

Jeanne Gray: Who evaluates the risk associated with the probability that your system generates?

Because 32%, I'm assuming here is some indication of the risk or the, likelihood of an event occurring. But someone who is very risk-averse, 32% may be very risk provoking to them, whereas someone else... And I imagine in an organization like a very, money center bank, they've got economists in-house.

So share a little bit about how, whether it's the government or a large corporation, how they would take that forecast and then take it to level.

Anthony Vinci: Forecasting's interesting, right? When you look at a risk like that, 32% of the Strait of Hormuz reopening, kind of risk cuts both ways. So maybe that's terrible for you if your whole business depends on lower oil prices. Maybe you're in-- you own a shipping company. On the other hand, it might be great for you if you're an oil trader and you're waiting for the price of oil to go up even higher, right?

So just because it's a risk doesn't necessarily say whether it's positive or negative, upside or downside risk. So that's the first part is depending on who the person is and what their goals are, they're gonna look at these probabilities in two different ways. The second part is ...

Think about it this way. Just 'cause you had a crystal ball and I tell you what might happen in the future doesn't actually make the decision for you. You still have to ask yourself what would make it go to 50%?" What would make it go lower? What if this happened? You know, What if the Marines land and we have ground troops now?

What if something else happened? China and the US come up with a trade deal, and as part of that deal, now China's on our side and pressuring Iran. typically, a decision-maker is running through scenarios like this, and that's really how they're gonna make a decision. In our tool, we thought about this 'cause I...

my background, I was an intelligence officer. I was helping people like senior generals and even presidents make decisions, right? And realize that just a percentage is not enough. You need to play through the scenario. You need to know what might happen. If this happens, then what happens?

So we built a tool that not only gives you that probability but allows you to engage with the probability, run scenarios, run these if-then type of questions, and use that as a way to think through a decision and ultimately make that decision. And then this is within the framework of this event. A user may also have other factors they're considering, right?

They might be worried about their internal costs. They might be worried about labor issues, all sorts of other things. -- So they're taking into account both our information about the world's events and their internal information. So you can see we're a piece of the puzzle for a decision-maker, and we're trying to give them as many tools as possible, and then they're integrating in their more traditional ways of thinking about this.

Jeanne Gray: Can you give a few examples for listeners to understand, the breadth of the areas that they would go into Vico and, we've talked about the Strait of Hormuz. 

actually 

when I was using the system, I was looking at whether or not, Treasury yields were going to go up in the next ninety days.

But those are just two examples. is there warfare, applications to this? Or give me a few others so people can really understand the robustness of the platform. 

Anthony Vinci: I'll tell you some things that the system has called right, , over the last few months. So the system correctly forecasted the invasion of Iran.

we posted quite a bit about that. , It correctly assessed the closure of the Strait of Hormuz. It more recently, correctly called, , the move to 3.8% inflation in April. This is counter-consensus, by the way, , , for all of these things. , Polymarket and Kalshi, which are these prediction markets, were much lower probabilities of all of those things.

, It correctly called, , the Virginia Supreme Court, , overturning Virginia's redistricting, which had a huge effect on local politics. Again, counter-consensus. And when we came out with our view on this, , the system was around 80% probability of the redistricting not going through. , We were called crazy.

and I had friends literally saying, "I don't buy it," "This is gonna happen." And, , I say what I always say when people say that is , "Look, I've just learned to trust the model." , And then it happened. And what's interesting, by the way, is not only did it call the redistricting, but because the system knew that redistricting likely wasn't gonna go through, and that this would have a huge effect on the probability of the Democrats retaking the House, the system was already calling a lower probability on the Dems retaking the House even before the redistricting happened.

And so we're way out in front of that. lot of folks had considered it a lock that the Dems were gonna take the House. 80, 90% probabilities are what you're seeing there. Our model was up that high as well, and then it started coming down and crashing down even before the Virginia decision, 'cause it knew that was gonna likely happen.

So we're calling political events, we're calling economic events, , we are calling these geopolitical events, , and it's just fascinating to watch it happen. 

Jeanne Gray: Can it go granular in warfare? If it was... maybe if you want to answer this question since you're in the intelligence area. But in troop movements, , could it now or possibly a year or two from now be integrated into a military strategy that troop movements would even be forecasted?

Anthony Vinci: Our model, like any other model, relies on the data information about the world that it has access to. So we have access to what, , good open source intelligence analysts would have, right? Good reporting coming up from the field. What we don't have right now is classified information. 

Jeanne Gray: Right. S- 

Anthony Vinci: we do, actually, you can go on and ask about certain military tactical issues, and in our newsletter we'll talk about and do forecasts on which towns the Russians or Ukrainians might take in their ongoing conflict, for example.

 And we do fairly well in those forecasts when there's data. Now, over time, we do look to do work with the Department of War and organizations like that, which would obviously have classified information. They have information about those conflicts that are tactical that normal businesses wouldn't have.

And we feel confident that the model would be able to take into account that kind of classified tactical information and use it to make accurate forecasts at the tactical level, like you said. 

Jeanne Gray: Before we go too far, for the listener, there is so much going on in the Polymarkets. Can you clarify the difference between forecasting and predicting?

Anthony Vinci: So the prediction markets like Kalshi, Polymarket, PredictIt are betting markets. So people come on and place a wager on whether something will happen or not happen, whether famously there was a betting market on whether Trump would be elected or Harris. and Polymarket called it on Trump.

And so a lot of folks realized at that time that was an interesting place to be. And there are all sorts of bets that people can make like that. Now, those systems aren't really forecasting. It's like a wisdom of the crowds effect, in essence, where lots of people are making bets, and the assumption is that if enough people make bets that they will kinda more likely get it right than wrong by this wisdom of the crowd effect.

This is very different from what we're doing. What we're doing is we have created a forecaster. ... Think about it as codifying what a really great human forecaster would, do. So we run through a process for every forecast we make where we use the best techniques that are available, that we've pulled out of academia, that we've pulled out of the intelligence community, that we've pulled out of finance and so forth.

We've pulled out of the literature on how to be a great forecaster, and we're running it through an AI system. We actually use a lot of math. It's not just an LLM. LLMs are Too non-deterministic to do this particularly well. They're good, but they end up, as you've probably experienced like I have, creating some slop.

Maybe they have hallucinations maybe it just gets a little weird. And they're not particularly good at math. So we had to bring in a lot of math and data science, so we call it a hybrid system or a harness system where we have math and data science on top of this AI agent, and that's how we forecast.

And so the difference is that we can forecast anything we want at any time. Now, we have optimized it for political, economic, geopolitical topics, but in theory, we could forecast anything we want at any time from a micro issue like what's gonna happen in Ghana's, local city council elections in Accra up to , what are gonna happen in these big world events.

And it doesn't matter to us. We don't have any humans involved in that. There's no betting pool to set up and so forth. Whereas a prediction market, they have to set up betting pools and so forth, so they're pretty limited as to what they can forecast. If you go on a Polymarket or a Kalshi, they probably have, you know, a few hundred political, economic, geopolitical forecasts, maybe 500, 600.

We can do thousands. We can do anything that you want at any time. So that's a really different way to look at it. Over time, we are also finding that our system is more accurate than people, and that actually the wisdom of the crowds only works in certain types of scenarios and that there are a lot of things that you wanna forecast about the world where wisdom of the crowds isn't that great and where an AI system like ours actually performs much better.

Jeanne Gray: When I was looking at the system, I saw that it offered, what if simulations. Share a little bit about some of the functionality that a user would be able to have in addition to the end result of being a forecast. You also offered what if simulations. What else do they get? Reports? how are the various tools pulled together?

Anthony Vinci: We start with this basic building block, which is a forecast. So you can ask a question, literally, you can say, "What is the probability of the Strait of Hormuz reopening?" Or, "What is the probability of four percent inflation in twenty twenty-six?" That's the basic building block. Then from there, you can kinda enter the world of being a decision-maker, and you can ask a question like okay, I see that the probability of four percent inflation is, sixty percent," let's say.

Then you can ask this scenario: What if the Iran war stretches out through the rest of twenty twenty-six? Then what is the probability? And now you're gonna get two different probabilities. Maybe you get sixty percent and eighty percent. Now you could compare. What's important about that is now you can track these things separately.

Maybe you have a view. You can see how much it matters. You can evaluate how much is this war really affecting the probability? You can ask questions also "What should I do about it?" If you're a business, you could ask a question like, "What is the probability of this regulation going through in Washington DC that affects my business?"

maybe it's the FDA or the FCC doing some sort of regulation. You can ask that question. Then you can ask, "What if a lobbying organization heavily lobbied Congress on this particular issue? Then what is the probability?" So you can actually use this as a way to evaluate your courses of action and say, "Is it worth it for me to go hire a lobbying firm to go lobby Congress about this FDA or FCC regulation and see how much that matters?"

Maybe it only moves the needle two percent and it doesn't matter. Maybe it moves it twenty percent and it does matter and it's worth your time. So you can see how you can start to use this as a decision evaluation feature. You can also look at decisions as not just the probability, but think about what would happen if this event happened.

So what if this regulation went through? Then what would happen to my industry? Or what if the inflation hit four percent? Then what would happen in my industry, right? Or, back to a military example, what if China stopped and Russia stopped supporting Iran? Then what would happen in the war?

And- What the system will do is we call this an if/then scenario. It will run that scenario, and it will provide a series of narrative scenarios of the possible events and what could happen, and then not only give you that narrative, like here are the possible things, but also the probability of those things happening.

So there's some rigor to it, so you know which one is more or less likely in those narrative scenarios. This can help you plan your business and think through what are these possible things that could happen? How might it affect my industry? And maybe you thought, you know, what we find a lot of times is people have thought of five or six of the possible scenarios.

Then the system, though, will come up with nine or ten, and you're like, "Oh, I didn't think about that. I didn't think about that one." And that can help you think through and plan and strategize for your business. 

Jeanne Gray: That sounds very powerful that a system could generate the full range of outcomes of an event that's being considered.

Now, where are you as far as the launch? 'Cause I believe I was looking at the beta version. And who is using it in the early stages? 

Anthony Vinci: Yeah. we launched in January, both publicly, and we're letting, select companies on to use the system, directly, and we launched on- Bloomberg Terminal.

So on the Bloomberg Terminal, we post a series of forecasts about events, political, geopolitical, economic. We're doing a whole section on the elections and the midterms, for example. And it's on the part of Bloomberg where Kalshi and Polymarket are. So you can compare our data to their data- our forecast to their forecast, and you can build that into your, financial models, on Bloomberg.

then you can come onto our site. You can receive the reasoning about those, forecasts, and you can make your own forecasts. and like I said, we're allowing certain users on. soon we'll be opening up to anybody being able to come on. and, if anybody, by the way, is listening, would like to try it out, come on, to the site, vico.io, V-I-C-O.I-O.

Say that you listened to the podcast, and I will, activate a beta account for you. And, we're beginning to let folks in. right now we service two industries, both of which, are ones that we think are most affected, by these world events, and for which decision advantage matters the most.

One is the financial services industry, so hedge funds, on the buy side and proprietary, traders, sell side, analysts at big banks and so forth who are, maybe a commodities analyst following what's going on in the Strait of Hormuz, and insurance companies who are trying to underwrite the risk.

for those types of folks, even being 2% better means a lot, that has real world financial impact. The other type of group that we think is really impacted by decisions is the military and the intelligence community. And so we believe in mission, and we wanna service those national security users, for whom this matters a lot, and so we do some work with those folks Over time, we are going to expand into everybody who's trying to make decisions.

So CEOs of corporations and chief strategists and COOs who are thinking about decisions for their businesses, both small businesses all the way up to big global conglomerates. consultants who are helping all of these industries and so forth. we even believe over time that regular, consumers will use tools like ours.

because if you think about it, we all have to make big decisions in our life. Should I buy a house now, or should I wait six months? where should I go to college? these are big decisions, in a person's life, and we wanna be able to help people make better decisions.

Jeanne Gray: I'm envisioning all the ways that you're going to empower me over the next six months. 

Anthony Vinci: Yeah. 

Jeanne Gray: it's really impressive. So what was the timeframe and you mentioned a, a co-founder f- from the time that you brainstormed about, , this, solution to getting your beta, completed? 

Anthony Vinci: We, had been thinking about it for a while. . There's that saying, "Measure twice, cut once." When you're thinking about a business, you wanna measure like, 1,000 times. You're just thinking about, what's going on in the world and figuring out what you can do about it and how you can solve a problem.

And that's actually the hardest part because maybe you don't even think it's a problem. You know, I think for a long time probably people looked and said there's plenty of information about, future events and so forth. There's all sorts of consultants it's not even a problem."

And then we realized there is a problem because it's not measurable. It's not quantifiable. That was the hardest part, is making that leap. Then, building the technology once we had that vision we had a beta in six months something that we could try out, and we did try it out, and in 2025, spring of 2025, and we started forecasting on this system.

And we forecasted the Israel and US strikes on Iran at that time. And I actually went out when we were forecasting, we were getting such a strong reading, I bought some oil ETFs to prove it to people and made a little bit of money on it because we saw it coming. Because US maybe gonna go out to investors, and I wanted to say, "Look, I put my money where my mouth is."

So we built it in that timeframe, and then it took from there to the launch on Bloomberg another six or seven months to get everything dialed in, to hire the team. There's a lot you have to do, past just building the model to making something usable, stable, rigorous enough for outside users to get use out of, and to ensure that we could trust it.

We also had to back-test our data, ensure that we were forecasting at an acceptable level and so forth. So that, took some time, and then we're just constantly improving it. We're constantly making the model better every day. That's what our team does. 

Jeanne Gray: Can you share a little bit about the skill sets that certain individuals brought in?

'Cause, it's such a wide range. There's a lot of and statistics tied to this. There's data. Just maybe, you know, check off three or four job titles that made the startup coalesce. 

Anthony Vinci: Yeah, I think one of the secrets of our success is that we've brought together the right team, and we looked broadly at this issue, not just of forecasting, but of decision-making, and thought to ourselves, who are the right people?

Who knows about how to do this? So one group of folks that we hired is who you would imagine, AI researchers, and we have a couple of PhDs on our team in computer science who specialize, their research was in artificial intelligence and LLMs, and they know how to do this very, very well. We also brought in a systems engineer.

one of the teammates on our team , did his PhD in systems engineering at MIT and was an expert in modeling and simulation. So very different than computer science and AI. Obviously, he knew how to program just 'cause he's technical, but was thinking about this problem from a really different perspective.

My co-founder, Lyndon his PhD is in economics. He's really a data scientist, so he has another different perspective. My PhD is in international relations. I was an intelligence officer. I'm looking at this from an intelligence officer's point of view. I also had a background in

finance, so I knew some of the financial ways of thinking about this.

So that's really at the technical heart of the company is combining computer science, systems engineering data science, and traditional kind of intelligence and international relations thinking into one company and is our secret sauce. 

Jeanne Gray: So you have a co-founder, you're brainstorming you're identifying the key skill sets to bring it together.

Can you give us a little bit of a peek into how you networked to find certain individuals? Were they all within arm's reach, or did you actually have to approach sort of conduits who then introduced you to the missing piece? 

Anthony Vinci: A lot of people have this image of the tech founder being like 26 years old and, just like drinking, you know, Monster energy drinks going at it.

And, I close to 50 and my, co-founder is not too far behind me. So we had some experience. We had been around the block, and we knew a lot of people, and had been, thinking about a business like this. Both of us had founded, tech companies previously, so we sort of knew, the world.

But we knew a lot of people, and when we decided to do this, we hit all of those people that we knew to build this dream team, right? and there were folks who I knew that I had been thinking, even years before, "Oh, wow, if I was gonna do company, this is the person I'd want on the team."

so we were able to tap into our own network. We also had great investors, Crosslink Capital, Multiball Capital, AIN Ventures, Commonweal Ventures, GAEN Angels, Victory Six. These Are great investment teams with great networks. They were all super helpful in connecting us with the right people and adding to the team.

We have great advisors who have come from all sorts of different industries, that have connected us. and so it's our personal network, but then that network of networks around us that allowed us to put this team together. 

Jeanne Gray: Is that network forming the basis of your marketing plan least at the early adopter phase?

Anthony Vinci: Yeah, for the initial customers, absolutely. We know people in these industries personally in many cases, and known them for a long time and are able to start that conversation. Now as anybody listening will know, just, knowing the right person is one thing, but closing a deal is a completely different thing.

So that helps us get in the door, but then we still have to do the work to close, like with any other big organization in the world. And then on the marketing side, we're just beginning to roll that out in social media and through our newsletters, and just beginning to really grow that. In some ways, being so experienced, having such a big network is a blessing obviously, but it can be a curse.

You sort of become dependent on that and then realize, okay, we now need to move beyond that network and start to really tell the world about this beyond just the people that we know. So that's the process we're in right now. 

Jeanne Gray: guessing that the Bloomberg terminal was a big high-five for you?

Anthony Vinci: Yeah. They are a great organization. I respect them so much. The people there immediately saw the value in what we were doing and how it would help their customers. They're so customer-focused there. They saw how we would help and they were just... have been great to work with, and yeah, we couldn't have been more excited to have the opportunity to work with them and to be up on their platform, and we plan to be there forever.

And yeah, there might've been a high-five or two when we closed the deal. 

Jeanne Gray: Well, I just have a couple more questions for you, Anthony, as we move to wrapping up. what is your assessment of either competition that's already existing that you may have to displace or competition that may arise once you really have publicized your solution very broadly?

Anthony Vinci: Yeah. One part of our competition is people, right? People have been predicting the future forever. You can go back to the Oracle of Delphi in ancient Greece. This is what people do. And people are, pretty good at it. But we think we're better in some ways at least.

And so, that remains a big area of competition for us is that. Now, we think ultimately, of people who do this, advisors and so forth, as potential customers as well. Maybe the people and the AI is how this will play out, and we'll, work together in a good way.

Another area, look, like anybody else in the world, is you're competing against Anthropic and OpenAI and xAI and Google DeepMind, right? If you have a tool out there right now, a piece of software, you're looking at those big foundation models as a maybe they can do this. Now, LLMs, like I said are not that great at forecasting for various technical reasons, and also because LLMs want you to like them, and we just wanna give you facts.

We think of our system as, Mr. Spock. 

Jeanne Gray: Okay. 

Anthony Vinci: We just give you the rational facts. I don't care if you like us or not. I'm just telling you this is what's gonna happen. We do think that there are some differences, and we're sort of specializing in this and using things outside of traditional LLMs like mathematical and data science approaches to get really good at this.

And by the way, make it explanatory, which LLMs are not great at. There can be a black box. We open up the black box and allow you to engage with the decision process, again, which can be harder with an LLM. So we think we have some competitive differences. And then ultimately, there's likely to be other startups who try to do the same thing.

We wanna be the best at where we focus finance and national security to start. We think we really know those industries. We've known them for years and years and we're tailoring our product to them. And then ultimately, you know, we think we'll make headway and be the best at doing this out there.

Jeanne Gray: So just to wrap up if we're looking into the future and you've got generations of entrepreneurs, investors, and policymakers coming on the scene, what would you tell them about their role in protecting our economic and national security? 

Anthony Vinci: I think about this all the time as my son grows older.

We're entering kind of a new world where things are mixing, economic and national security issues are mixing. What used to be something that presidents and generals worried about is now something that software developers and CEOs worry about, and all of us worry about. You think about information operations like the Russians hacking the 2016 election and trying to sow disruption in America.

These things affect us all now. And so I think, as I look into the future, everybody is going to have to worry about these kinds of security issues. Now, that can sound scary, but I also think it's empowering in a sense because you can also do something about it. You can protect yourself. You can use tools.

Well, You can use satellite imagery, something that, used to have to work at like, the CIA to get your hands on. Now a regular, company can use this, right? And you can use tools like ours to help you make decisions, something that even 20 years ago would have been something that you had to be at, DARPA and the Pentagon to use, right?

And now you can use it to run even your small business. So we have these tools, and so when I think about the next generation, they're gonna have more risks, but they're gonna have more tools, and I think that's gonna give them more power to control their own destiny. And in that sense, I think it's a promising future.

Jeanne Gray: Anthony, it was great speaking with you. I walked away with so much more insight having used your platform, which I plan on using more often. And wish you the best in your next stage with Vico. So take care. 

Anthony Vinci: Thank you so much for having me 

Jeanne Gray: You have been listening to the podcast series Experienced Voices. To hear more and subscribe, visit americanentrepreneurship.comforward/slashpodcast where you will also find a form for listener feedback.