Transcript

Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton

From Founder's Story · Episode page · Watch on YouTube

· 19 min · 3,340 words · 27 timestamped sections

This is the full, timestamped transcript of "Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton", an episode of Founder's Story published December 15, 2025 (19 min, about 3,340 words). It mentions 16 people, works and ideas, including Mathematical intelligence, Vera AI (Veyra/Vera), chat GPT, Fourier transform, Major League Baseball (MLB).

What this episode is about

The discussion explores the distinction between artificial intelligence and mathematical intelligence, highlighting how mathematical intelligence relies on fixed laws governing data for outcomes, while artificial intelligence predicts based on past observations. Peter Ashton, an ex-NASA scientist, emphasizes the importance of this framework in trading technology and the vision behind making wealth-building accessible to a broader audience.

Key takeaways

  • AI predicts possibilities; math dictates certainties. Either way, you end up counting your blessings—or losses.
  • AI is flying cars; math is the road map. Which ride suits you best for market predictions?
  • From sports to stocks: predicting Tesla’s moves is the new home run in finance.
  • 1980s NASA tech in market predictions: vintage is the new black, even for algorithms!
  • Time series stacking: It's not Jenga but a clever way to align and time market entries.

Who speaks in this episode?

What is discussed, and when

  1. – 3:00: Mathematical intelligence, Data compression
  2. – 6:00: Mathematical intelligence, Dragon Ball Z, Funimation
  3. – 9:00: Time series, Time series stacking
  4. – 18:00: Algorithmic trading, Gemini
  5. – 19:03: Peter Ashton

Notable quotes from "Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton"

  • What is the difference between artificial intelligence and mathematical intelligence?
    about Mathematical intelligence · at
  • Are absolute and they don't change. Then you can load the data. So it's all about data compression. And then artificial intelligence, right, essentially says based off what…
    about Data compression · at
  • Had a venture capital firm in 2020 for about two years and my business partner was the founder of Funimation and they created Dragon Ball Z. And so because of that and that…
    about Funimation · at
  • Much all the data that we're using you can see in a five minute what's called time series stacking. So you stack one time frame on top of one another for whatever symbol you…
    about Time series · at
  • But the original rails in that were built in 1965 were made to use for computers. Well now you have algorithmic trading systems right you've got it's in this age of automation.…
    about Algorithmic trading · at
  • As the competition plus I mean the technology just it's like you know Gemini just announced version three which is faster than you know chat GBT faster D it's like every two…
    about Gemini · at
  • Well, Peter Ashton, CEO of Vera AI, super excited. I love I was saying earlier, I love the background. I really thought you had a fake background. It I need to get that. I need to get the TV.
    about Peter Ashton · at

Who and what is mentioned in this episode?

People

Companies & orgs

Concepts & topics

Full transcript with timestamps

What is the difference between artificial intelligence and mathematical intelligence?

Artificial intelligence essentially says based off what I've seen is likely to h mathematical intelligence is essentially these are the systems that I obey and because of that this has to happen. Math is essentially the the framework the ground framework that we load data into to get an outcome and that outcome is a projection of what that data set is. I think it's your CEO has raised over 130 billion on Wall Street. One of your co-founders played Major League Baseball. How did this all come about? The the team that I brought, they all care about this idea of of making the unwealthy wealthy. And so when I was able to cast this vision to them of building something that anybody's brother, sister, cousin, friends of friends can can click and download and trade and make high returns. that resonated with a lot of these people. That's the direction we're all we're all moving together.

So, Peter, um it's really great to have you. We were just talking about AI is basically transforming every single aspect of our life and I can't wait to dive into what Veyra is doing and how it's transforming uh trading and I mean developed by a NASA scientist. So let let's just dive into this because I have to understand what is the difference between artificial intelligence and mathematical intelligence. Yeah, math mathematical intelligence is essentially the laws that govern the data. So it uses math to um calculate and make sure that the laws are absolute and they don't change. Then you can load the data. So it's all about data compression. And then artificial intelligence, right, essentially says based off what I've seen is likely to happen. And mathematical intelligence is essentially these are the systems that I obey and because of that this has to happen. So it's not very well known. Um

it not many people talk about it because it's not as sexy as you know talking about AI but math is essentially the the framework the ground framework that we load data into to get an outcome and that outcome is a projection of what that data set is. And as live you can compress it and use math to identify what uh you're looking at. You can actually predict at an incredibly high accuracy rate you know like in the markets or in anything weather patterns or it doesn't matter what it is. Um you have to use math right to I take that all those data sets compress it down and then you can use AI as an overlay. So that's really the the two difference.

I'm fascinated by the ability that AI can do, right? I guess in this instance, mathematical intelligence, what it could do at such a high level that I mean it's it's definitely smarter than I am. We we may not be in what they call AGI, right? But I feel like it's way smarter than me. So what when you started going from you know you were a really into sports division one football player and then you started getting into this and learning about mathematical intelligence. How was that transition?

You know it was it was a hard transition going from you know playing sports my entire life right and then you know that what that being done and then you don't really know what to do. So I actually had a venture capital firm um in 2020 for about two years and uh my business partner was the founder of uh Funimation and they created Dragon Ball Z. And so because of that and that partnership, I was able to go find uh uh really cool founders and really cool people building disruptive technologies. And I found that everybody was trying to find a way to predict the markets, right? You know, a couple years ago, so these are people, you know, you know, having these cool technologies to predict, you know, what Tesla's going to do tomorrow or Nvidia is going to do tomorrow. But it wasn't no one really had the right testing or the right technology to do it. And that's when I found um a guy that I ran

across and he had this technology that was built in the early uh mid to late 80s and he was a NASA scientist and he used aerospace missile identification technology to modify it for the markets and all you do is he built an operating system where you basically load data of a given symbol, right? the history of a of a symbol, right? And you can use that to predict where that symbol is going to go in the future. And that was like my first like this is cool.

It's crazy cuz most people think like, hey, I was invented, you know, JBT. U not that it's, you know, been around for 70 years and and things from the 80s are even more relevant now uh than ever. when you what what like type of results like what were you seeing because this is something I've been very very interested and I've been waiting for this waiting for something to come out like this. I'm really big in the markets and of course like you're saying uh you know I I've been thinking like how can AI uh you know how how can automation and these things have a great positive impact for people like myself. So what type of results what were you seeing um as you started to use this technology?

Yeah, it's we didn't want to look at like have AI tell me what to invest in like that was that's what a lot of people are trying to do. We wanted to find um and use data. So you know not so much all the data that we're using you can see in a five minute what's called time series stacking. So you stack one time frame on top of one another for whatever symbol you want to trade in the market and you get a higher accuracy when multiple time frames line up, right? And so when you can predict in the next 5 minutes, 15, 30 hour, you know, 240 minutes and you can project out in advance and you have this like alignment. It's a It's a very It's really weird, but it's really cool when you see the alignment and it will tell you when to make a trade and when to not make a trade. And so we took that and I started testing it myself. Uh granted in the beginning I lost a lot of money so you have to like test it and see what

works and what doesn't work. And we found that people just want a simplistic way of tell me what to buy and tell me when to sell, right? And then if you don't want to do that, just automate the whole thing for me. So, we wanted to tailor this type of trading software not to the accredited investors or, you know, qualified clients or family offices and so forth. We wanted to tailor it to the general population. And so, anybody, you know, will be able to use our software with and trade $100 or $500 or $10,000, right? You could just load it into our system, pick what you want to trade, and click start, and it will autotrade for you. You're talking about something that has not been available to the public.

We're we're democratizing institutional level trading products to the general population and not and only doing it as a subscriptionbased system. So we don't take any performance. It's all subscriptionbased. Yeah, I was going to say dem democratize actually came to my mind um as you were explaining it and it gives like you're saying it's it's kind of been this thing where the wealthiest of people have access to great smart resources and people which enables them to be able to do these things in the past but unless you're at a certain level it it would have been hard right you would have had to do this yourself so I could see where the democratization comes into play so Something though that I think is very different among how you are doing business and that's the amount of co-founders that you have. Um I know I believe it's like around nine to 10 co-founders. Uh and I mean I've had you know I've had great experiences with

just one and horrible experiences with just one. Um but you I think it's your CEO has raised over 130 billion on Wall Street. one of your co-founders uh played major league baseball for over a decade. Um like how did this all come about?

I'm very good at doing just a few things, right? And I'm trying to triple down on those things, right? And the the team that I brought, they all care about this idea of of making the unwealthy wealthy, right? They've spent the last 10, 20, 30 years of their careers making the rich richer and it's you're the gap is getting larger and larger, right? And so when I was able to cast this vision to them of building something that you know anybody's brother, sister, cousin, you know, friends of friends can can click and download and trade and make high returns, right, with no catch, just you pay your subscription fee. That resonated with a lot of these people.

And so I had to in order to scale quickly, right, I needed to bring in a team and basically gave them equity of the company in order to help me build it. And so they all have their um boundaries, right? No, nobody had everybody has ideas, but the vision is still the vision, right? You can have ideas, but you can't change the future trajectory of the business, right? Because that's that's what we're all came, you know, together to do. So, yes, we have like nine plus co-founders essentially. Um, but the vision that I kind of casted, like that's the direction we're all we're all moving together. I mean, that's, you know, I could see the more ambassador ambassadors you have of your company, the more people rooting for you, the more people that have skin in the game, um, it's it's going to enable you, like you're saying, to scale and grow. How big do you think that this company could go? you know what is what is your

long-term vision? So when we started we started the company six months ago right around six months ago and um I was going to fund the business and then it got to a point where we needed to bring on more and more people and so we uh we decided to do a small capital raise so we decided to raise the two.5 million at a $50 million valuation. The reason why we did that was um we we established a very strong distribution channel where this company they essentially they market uh and they launch newsletters, they capture customers and we can market our product directly to them. And so we decided to partner together as a as and do an equity swap, right, to establish our distribution channel. And we're gaining 20,000 people a day in those newsletters. So we're at 550,000 currently in our network. So if we do just a simple conversion right you know our flagship product to you know anybody can see the future direction of whatever

symbol they want to trade right no automation just simple uh that's $4.99 a month so it's very you know doesn't cost that much and if we do a 3% conversion on 550,000 that's a lot of recurring revenue and so you know we all we need is about 15 to 20,000 people paying 500 bucks a and we can sell the business for a billion dollars. So that was the metric around um you know the valuation side how big it can go. I think it can go to a h 100,000 people using our product um because it's just so simple right is you know I want the peace of mind knowing what my portfolio is going to do in the future. So I'll pay 500 bucks a month to essentially have confidence that it's going to trend in whatever direction that I'm going either long or short.

Yeah. I like how you it's kind of sounds like you you kind of looked at the end result and then you kind of created the math to get there and then you're like, "Okay, in order for me to get to like a billion sale, I need to do all these things in the middle to get there. And if I want to do that, that means I need to partner and and those things." How how is that? Because I'm I'm with you. I feel like lead generation like like traditional lead generation is becoming harder and harder. I feel like there's just so much noise on, you know, how many LinkedIn messages do you get in a day or now there's, you know, AI is automating so much emails. I'm getting like a million emails every day of someone trying to sell something. So, I feel like from a, you know, app perspective, a company, um, that collaborating with these type of partnerships might be the best thing that most people can do. What is your thought around just

how noisy just all lead generation seems to be getting and how partnerships, you know, can play into being to to helping anyone who makes an app become successfully hopeful.

Each lead generation caters to a select group of people, right? So, if you want if someone messages me on LinkedIn, they're like, "Hey, I can generate 25 leads to you." Well, that's great, but they're just they don't know what my company does or my product is. So, they just message me and message you probably and say, "Hey, I can generate 25." Like, who are those leads? What do they do? So, I think you have to understand your target market, right? And most of the time SAS companies, it takes between two and three pivots to establish your um go to market, you know, strategy, right?

you pivot one time and then a lot of times you have to raise additional dollars, right, to hit that. So it's hard to find that product market fit and what you do is you have to talk to your customers, you have to talk to the people that are using your product to refine it and tailor it. Um, and so we decided to go down the route of forming strong partnerships with companies to scale faster, right? So forget all the equity, forget all the revenue streams, forget all that, right? If you're building a business for enterprise value, none of that matters as long as you book all the revenue.

So you as long as you can just book the revenue and drive enterprise value, doesn't matter about how much equity you have because a small piece of a large company is better than a large piece of a smaller company. So, you know, that's how that's the route we went and it's it's going to pay off quite a lot because we're looking at, you know, January, February, March really driving in and launching this uh these products to people and have an awesome 2026 year for us. Do you think that AI is going to allow more people to get into business or do you or I guess and or do you think AI is just going to create a massive amount of competition? Um, I think both it, you know, with all the competition comes a lot of new businesses, but everybody is in the AI space, right?

They're all running on old like old traditional rails. So all the AI, all the quants, you know, there's really only been three iterations since 1822, right? And so we're that third of rails, I call them rails because um they're built on all this is built on math. So 1822 Joseph Furier built uh Furier transform uh 1965 they built the faster version and that's been it. So all the quants all the major financial firms everybody in the world are using these old rails but the original rails in that were built in 1965 were made to use for computers. Well now you have algorithmic trading systems right you've got it's in this this age of automation. Well, those old rails are slow now. And so that's why there's so many AI companies popping up is because there's there's gaps in the market. There's gaps in this. So people, oh, I'll just create an AI company, right? Because it's easy to do.

So I I think it's there's a lot of noise, right? And if you get down to it, if you can build new rails that everybody can now use, that's faster. you eliminate those gaps in the market which means there le there's less companies out there and the the markets become less saturated

man I did I didn't even what you just said I had no idea like that's how the intricacies worked or or behind the scenes um so thank you for sharing that I guess we'll see in a year two 3 years uh all the you know explosion of companies that started 2024 or 2025 maybe even 2026 if they're still around um as the competition plus I mean the technology just it's like you know Gemini just announced version three which is faster than you know chat GBT faster D it's like every two weeks there's some advancement in some LLM or or something coming out uh just overall so it's what an exciting space I mean I I really love what you're doing I've been waiting and waiting for something to come out that you're doing that I can implement so let's say it's somebody like myself and I want to get started. I know like you said it's not something that it's it's a few hundred um and I think this could be something really

beneficial for me. How can I get started? Yeah, so if you go to our website, so it's vera.ai AI. Um, you can get started and we're going to be launching a series of products over the next six months that are tailored to the everyday person who wants to uh make money in the markets, right? By trading 100, 200, 500, $10,000, right? There's there's there's a limit. But, you know, from that side is you go to our website, you sign up, and you have access to all of our products. You can pick and choose what you want to trade. Um, or you can trade yourself by using our charts. It's pretty simple.

Well, Peter Ashton, CEO of Vera AI, super excited. I love I was saying earlier, I love the background. I really thought you had a fake background. It I I need to get that. I need to get the TV behind me. It It looks amazing. Um really excited about what you're doing. I learned a lot today. I never heard of mathematic intelligence. I I didn't know about the rails, you know. I I'm learning a ton by talking to very intelligent people like yourself and I'm excited after you sell for a billion dollars, come back and I want to know what what life is like postretirement.

But thank you so much by the way for joining us today. Thank you. I appreciate it.

Questions about this episode

Who speaks in "Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton"?

Peter Ashton (Guest), Unknown (Host, Founder's Story) (Host).

What is "Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton" about?

The discussion explores the distinction between artificial intelligence and mathematical intelligence, highlighting how mathematical intelligence relies on fixed laws governing data for outcomes, while artificial intelligence predicts based on past observations. Peter Ashton, an ex-NASA scientist, emphasizes the importance of this framework in trading technology and the vision behind making…

What is mentioned in this Founder's Story episode?

16 people, works and ideas are mentioned, including Mathematical intelligence, Vera AI (Veyra/Vera), chat GPT, Fourier transform, Major League Baseball (MLB), Quantitative analysis (Quants). Each one on this page links to its own page with every episode that mentions it.

How long is "Ex-NASA Scientist's 1980s Trading Tech: 'Every AI Company Is Built On Outdated Rails' | Peter Ashton"?

The episode runs 19 min; the transcript is about 3,340 words in 27 timestamped sections.

More from Founder's Story

About this transcript: the episode's spoken words as captioned on YouTube, split into timestamped sections. Reclip.Me locates every person, work and idea in the text, keeps the time it is first named, and quotes the words around it verbatim. Last updated January 24, 2026.