The Future of Investing Is Adaptive, Causal, and Powered by Physics
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Show Notes:
In this episode of Mission Matters, host Adam Torres interviews Nadab “Niddy” Akhtar, Founder of Excite Capital, live from the Milken Global Conference. Niddy shares his journey from giving a TED Talk to launching a quantum-inspired investment firm. He dives into the future of finance, physics-based AI, and how Excite Capital is using real-time sensory intelligence to reshape how we predict markets.
About Excite Capital
Excite Capital is a technology-driven investment firm focused on developing and deploying real-time, physics-based trading models that operate independently of historical correlation assumptions. Its systems leverage probabilistic AI and dynamic control theory to evaluate risk, measure volatility, and forecast deviations between market price and model-estimated value across instruments. The firm’s investment process emphasizes adaptability, precision, and disciplined capital deployment—particularly in high-uncertainty or regime-shifting environments.
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Full Unedited Transcript
Hey, I’d like to welcome you to another episode of Mission Matters. My name is Adam Torres, and today I’m coming to you from Beverly Hills, California. We’re covering the Milken Conference. This is the fourth year in a row that Mission Matters, is covering the conference. And let me tell you, there’s been a ton of excitement.
I like to call it brain candy. Too much to digest, but a whole lot going on. I gotta, it’s gonna take me a couple months to unwind, to understand everything that I heard and I learned. Part of this and attending the Milken conferences, we are creating an interview series. So for those of you that have been long time followers of the podcast, you’re going to, you’re going to obviously benefit from that as of this moment.
Don’t quote me on this, but we have about a little over a hundred interviews that we will be doing for this year’s Milken conference. So. Stay tuned for that. If you’re not subscribed to our newsletter yet, definitely do that. Go to mission matters.com and subscribe to the newsletter so you don’t miss the series.
Today I have the privilege of interviewing in person. Naeb. Aktar. I said it right. Just wanna make sure I got it right. Also known as Niddy. And let me tell you, I’ve been excited and preparing for this one for a long time. We’re gonna be talking about his fund and also getting to know a little bit more about the technology, why he launched it, why he felt it was launched, and without further ado, let me start with the bio. So Nidi is the founder and managing partner of Excite Capital, a quantum inspired investment firm, applying physics-based AI to dynamic market strategy. With over 15 years of experience as an entrepreneur and investment banker. Nidi leads, excites, capital’s, information trading, architecture, and regulatory strategy.
He also serves as president and COO of Crowd Point Technologies, a pioneer and distributed computing and intelligent control systems. All right, join me in welcoming Niddyof the show. Thank you so much, Adam. Alright, Niddy, so a little birdie told me something. This isn’t on the agenda, but for the in-person crowd, little Birdie told me something.
Who’s coming off a Ted Talk? Man, you didn’t tell me about that. I didn’t get the email. How do I hear last? What’s going on, man? Talk to me. Well, sorry, that you just found out, but yeah, so I had, I was given the privilege of being invited to give a TED talk, which I did last week. And let me tell you, it was a surreal experience.
Surreal because of a few things. But when I was first offered the opportunity to participate in, in Ted. I remember that I called my brother, he was my co-founder and, and my brothers, my general partner is sitting in the crowd right here. The gentleman with the beautiful hair. And I, I, I called his name is Ridge.
I call him E. And so I called e after this. It was, it was a birthday sore for one of our dear friends, the first investors in, in our company Crowd Point. And I called E and I said, E, we got offered a TED talk. And I said, who do you wanna put on it? And I referenced you know, him or Yeah, our chief scientist or our other business partner.
And and he’s like, no, no, not, not them and not me. And he said, well, why don’t you do it? Mm-hmm. And I, I remember chuckling, you remember I chuckled Youi? And I said, me? And he’s like, yeah, you man. And I’m like, no, no, no. And I’m like, well, well, we’ll, we’ll talk about it when I’m home. Yeah. And he lives three doors down from me.
My, my wife and I I just got married three months ago, so still, still on a emotional high with that congrat. I remember. Thank you. I remember when, when I got home, and, you know, as I’m going to bed, I, I told myself this thought and, you know, hindsight’s 2020 and the, the thought I had was, it was pretty negative.
And but the thought I told myself is, you’re not worthy of a TED Talk yet. I. Hmm. And I remember sleeping on that and, you know, accepting that for about 48 hours. And then my, my buddy who was organizing the the TED Talk, the local TED Talk in Miami, he, he gave me a call again on Friday morning. I was like, Niddy, bro, you gotta do this.
Mm. You, you, you just have to do it. And I told him, I said, Brian, his name is Brian Bree. He’s a, a dear friend and colleague. I told Brian, I said, I. Well, man, I, I, I’ll think about it and I’ll give you an answer. Over the weekend, mm. Around Sunday, like probably 10 30, 11 o’clock at night, after I talked to e again, I called him, I said, okay, I’ll do it.
And the reason I said, okay, I’ll do it, is I thought that because I said I’m not worthy. That is exactly the reason I have to do it. I have to do it. And I said, if no one watches the video mm-hmm. If no one comes to the talk, at least I told myself, or at least I proved to myself Yeah. That I could do it.
I’m not particularly a fan of public speaking, which is, which may seem weird to my, to my family if they’re, if they listen to this episode. ’cause I’ve always been the consummate entertainer. I in high school and college I was a wedding mc. So I have no problem getting up on, on stage dancing, saying stupid things and, and I’ve no level of embarrassment.
Yeah, right. No concept of it, but for some reason, public speaking and getting on stages and talking about technical concepts and and all, it was just something I have shied away from for most of my career. And so it was wonderful and I was blessed that I had friends and family come from six different states to, to come participate in the talk.
It was the first time my parents had ever heard me. Nice. He would talk. So that was, that was pretty cool, man. Yeah, I felt incredibly blessed and you know, highly favored. Yeah. That, you know, if anyone here has ever spoken on a public stage, it’s pretty dark. Like, you know, you have the lights shining on you, so you can’t really see, see folks, but who I saw was our, our kid sister.
Mm-hmm. One of my cousins, I saw my brother and. Aaron over there and be friends. And so I’m like, all right, well, I’m just talking to the family. Yeah, right. I’m just talking to the family and, and sharing, sharing a story that’s, that’s very near and dear to the heart, so that’s awesome. Talk Well, well, congrats.
I just got the YouTube link this morning, so Oh, we’re, we’ll share you guys. And yeah, we’ll add that to all the promo we sent out for every, or we send out for everybody. Thank you. That’s well, congrats again, and I’ve, I’ve been bugging Shara. Can you do a TED talk? Where you at, man? There you go. Sara, you’re next up for a TED talk.
I’m just throwing that out there. I’m in. Alright, let’s let’s get back into the program Niddy. So excite capital, let’s just kick it off. So why does the world need another quant fund? So, I’ll argue that the world doesn’t need another quant fund. What it needs is a completely different way of thinking.
And I’ll say that I hit this in my TED talk if you’ll do me the honor of watching that video in the days to come. Most decision systems today, especially the ones using finance or technology. Mm-hmm. They rely on this incredibly outdated assumption, right? Mm-hmm. And that assumption is that the best way to predict tomorrow is to look at the past.
Mm-hmm. There’s a flaw in that, right? What I say to that is that that’s akin to driving down a highway going at high speeds. And instead of looking at the front, instead of looking through the windshield, all you do is look at this tiny rear view mirror showing you what already passed. And if you think about it, if that’s the way you drove, is that, if that’s the way you’d make decisions, common sense would say you’d crash, right?
Mm-hmm. So instead of another quant fund mm-hmm. What the world needs is a different way to sense the present. Interpret it at high speeds. Mm-hmm. And then adjust strategy, adjust steering, et cetera, in real time in order to predict the future and mitigate risk. That’s how the human brain works. Yeah. You may have a foundational knowledge based on your, you know, education, your past experiences and all, but just ’cause you start in one position, you know, every day, unless you are, you’re, you know, you’re on the spectrum.
Mm-hmm. Which I say respectfully. You’re not gonna walk the same path or you’re not gonna you know, do the same mm-hmm. Actions every day at every given time, right? And so that’s what the world needs. It’s, it’s a way to think differently, right? Not just react faster, not just look at headlines and run them react to them faster process, you know, data at higher speeds.
Not find patterns in old data faster and smarter than your competitors. What you need is a way to anticipate the future. Mm-hmm. Right. But anticipating the future, predicting the future isn’t enough. You can predict the future all you want, but how do you adjust your strategy in real time to say, okay, if I’ve, if I’ve simulated or I’ve forecasted, let’s say 10 different futures.
Yeah. Well, how do you know which one’s the best? Right. In physics. There’s something called the Heisenberg Uncertainty Principle. Mm-hmm. And it says that you can’t know with perfect certainty both the position and momentum of a particle simultaneously. Mm-hmm. Right. The market works the same way.
Mm-hmm. You can obviously see the price and you may be able to, you know, guess where it’s going, but you can’t know what certainty, how fast the, the speed, pressure, and forces are shifting behind it. Yeah. It’s the same with the future. Right. So what we created is a way to not just forecast one future, but to simulate many and then adjust our strategy, adjust the steering mm-hmm.
In real time based on how present conditions evolve. Mm-hmm. We call this quantum sensory intelligence. Mm-hmm. So I’m not talking about quantum competing hardware. We’re not using quantum hardware, but we do have concepts of limiting entanglement and leveraging superposition. Right. What quantum gives us.
Is the ability to simulate these multiple overlapping realities. Mm-hmm. And at speed and with precision, right. Predict again, what what steering, what direction, what strategy we have to take based on that present, based on looking through the windshield. Looking ahead. Yeah. So with this forward looking methodology strategy, this thesis, right.
Many have tried. I’m just curious if you go back, like how did you know that the technology was ready for deployment? Like, like how did you know? Any signals or like what, what, what led to launching the fund? So candidly how, how I knew is we had a couple of pieces of technology that in our prior company and predecessor companies come out of the DOD and intelligence community.
Mm-hmm. And so these systems, these quantum frameworks that I’m describing. Again, not, not with the hardware, but with the emulation. Mm-hmm. They’ve been tried and tested at global scale to, to, they’ve saved lives. They’ve found bad actors all around the world. We use similar systems in managing energy and grid, grid dispatch systems for utility companies data center companies, you name it.
So that’s what our, you referenced crowd point to lyo, that’s what Crownpoint focuses, all of the above. And so we knew that. Bit and pieces, the whole methodology. Yeah. Works, right? And it’s worked for e what is it? 15 years, right? This technology’s been successfully deployed and scaled. And what changed for us is when people ask now for, well, sounds like science fiction.
Yeah. It’s not right. This is not science fiction. This is not an experiment in our physics lab. This is real, right? This is real time sensory. It’s adaptive. Mm-hmm. I would even argue it’s alive. Mm-hmm. Right. But we have core pieces of technology that were declassified and that gave me the extra spring in my step, if you will.
Yeah. Right. To say the signal is there right. When folks say that this sounds too good to be true. Like, well, it is true. Yeah. And the the proof is in the pudding. Yeah. So investors, I mean, we’re almost trained at this point to talk about Alpha and how Alpha, you know, decays quickly. I guess for excite on your side of things, how does Excite define, or, and, or like, how would you talk about the edge that Excite has in this?
So, you know, in preparing for this, I, I thought a lot about this edge today. It’s not about just speed or secrecy. Mm-hmm. What we say. It’s the. Our philosophy on this is that edge today should be about the sovereignty of interpretation. Mm-hmm. Here’s what I mean by that. Right? What you sense, you’re able to act on it, right?
Mm-hmm. Fastly accurately, right? And intelligently. And so what we would argue is our, is our edge is that we see signal. Where others see randomness. Right. Shout out to Richard Feynman. Where others see the noise. Right. And uncertainty in the markets. We see signal arrays forming in harmony uncertainty.
Right. With those signal arrays, we’re able to, as I said earlier, where others see noise and volatility. Mm-hmm. We see structure. Mm. Right. We’re not looking for simply modeling price movement. Mm-hmm. Seeing the delta, that’s easy to do. What we follow is the curvature, right? The underlying entropy in systems.
Mm-hmm. Entropy meaning of course the, the latent energy. Right? It’s anyone here ever seen the movie Twister, right? Was it Twister One and Twister two? Oh yeah. Great movie. You remember how in that movie they had these, little they, they looked like the quidditch ball mm-hmm. With the wings from, from Harry Potter.
What they did with that is they were saying in order to predict and see where the, the storm is moving. Mm-hmm. It’s magnitude, it’s force, it’s speed, all that they needed to sense the, the pressure. Mm-hmm. Right. And they sent these little sensors around to mm-hmm. Feel the pressure forming in a storm.
That’s what we’re doing with our approach and looking at the tropic models. Right. We’re feeling the curve, we’re feeling the pressures as they’re forming. Mm-hmm. And that’s what allows us to adapt intelligently quickly and again, adjust our steering dynamically by moving with the storm. Mm-hmm.
Right. So let’s take that a step further. So when you talk about human involvement specifically, what, what you just described on navigating, so when you think about that versus and it’s a heavily AI driven system, like how do those two commingle, how do those work together? So at Excite, we’re not a black box AI environment.
Yeah, right. Like many other quant funds and many other decision frameworks are, we’re autonomous. So our ai, how it works is we have three sets of rules. So our AI would be probably the simplest explanation is that it’s a probabilistic ai, right. And so probabilistic AI is a precursor course us to.
General artificial intelligence, but with, with probabilistic ai, you’re looking at ensembles of futures, right? Ensembles meaning gradients or, or overlapping potential realities, right? You overlay them together and figure out your outcome. The what outcome is most probable based on present conditions.
So where the, the human in the loop comes for us is we’re a fully autonomous training environment. There’s no. We’re not you know, it’s not me or, or e or our chief scientists or our traders, like sitting there getting signal and saying, okay, let’s push an update. Let’s push this button that’s still very slow.
Yeah. And in high frequency trading and in the qu we’re trading in, we’re modeling and trading in microseconds. Mm. Right. So there’s no, there’s no human that I’m aware of that can think and act in, in microseconds. So where the, the human element, the ethics, if you will come in. Is we have in non regressive AI where you don’t look at the past, you function and operate based on rules.
There are three rule sets. One is called absolute rules. One is called hard rules, and the other is soft rules. So hard rules would be things like, right, this is the array of instruments. We’re trading only these, we trade on these markets. We trade during these times. Hmm. The regulators allow this. That would be absolute rules.
You cannot violate these risk thresholds. You cannot violate these regulatory frameworks and so on. Yeah. Soft rules is where you get the alive element that I said. Soft rules are living adaptive principles. Hmm. Where you take in you sense, you test and you adjust. Steering, right. Soft rules. In our environment is where you get dynamic learning.
Mm. Right. We have two we have a few algorithms, but I’ll, I’ll, I’ll hit a couple. The first one I would say is called chattering. Chattering in is an algorithm that is gonna constantly continuously make these infinite teal adjustments based on present conditions, based on sensory input. Right?
Mm-hmm. Well. It’s like, you know those autonomous cars? There was one driving around Milken, it was a Waymo car, Waymo. Oh yeah. You see how the Waymo has cameras and sensors? Oh yeah. You know, on every corner. The autonomous vehicle isn’t just gonna randomly jerk through lanes or you know, going high speeds.
It’s taking sensor input and then it’s making continuous adjustments. That’s why the camera’s always moving, you know, you see the camera’s moving all the time. That’s what chattering is for us, a real time fast steering software layer. Mm-hmm. But just deciding and reacting to, to, to data and and isn’t enough mm-hmm.
In order to have a smart, intelligent, autonomous structure. Mm-hmm. You have to also tell the system, well, what is the best path forward? Mm-hmm. Which probability that we modeled is gonna most match with optimal reality. Hmm. Right. Some of the rules that we have are daily return requirements and and so on that we have to meet.
And in order to have a high frequency, high speed training strategy that adjusts dynamically, you have to have a sense of what is the best path to steer. This is where our repair and reinforcing learning algorithms come into place. Repair is an algorithm that allows you to, again, take in the input mm-hmm.
And adjust based on. What conditions are most optimal and align best with our, with our rule sets, right? Reinforcement learning is an algorithm that trains our system over time. It teaches it to make faster, smarter decisions with less hesitation. So this is the dynamic learning element that comes in. And so these these algorithms are, are an array of quantum inspired thinking.
That is different, right? In this, this new way of thinking, this quantum inspired prediction framework. It’s not just about being the fastest, it’s not just about being the smartest, looking at the headlines and using NLP and large language models to simply interpret data. No. The leaders of the next era, those leaders, they know that the speed of change in today’s world is faster than it’s ever been.
Right. And in order to deal with that, with this fast paging, fast pacing and uncertain environment, being the fastest alone is not enough. Mm-hmm. The leaders of the next era, they’re gonna be the ones that are the most adaptive. Mm-hmm. And that’s where this quantum sensory intelligence becomes the, the way of thinking that we make the assertion that this is the way to the future.
Hmm. So I know in our previous conversations you talk about trading the field, not the market. Like when we think about excite capital, like what does, how does that play out? Like what does that look like in practice? Yeah, so most managers, they see the market as a series of price events. Mm-hmm. Whether it’s price events based on past data, past or price events based on speed price events based on headlines.
We don’t, we see the field as this dynamic tropic landscape of elements like motion, symmetry, and latent energy. Okay, so trading the market, I wrote this down ’cause I wanted to get it right. Trading the market means reacting to visible outcomes. Price, volume, velocity. Trading the field means sensing the structural conditions underneath, right?
Those conditions that give rise to the outcomes, we’re not just we’re not just looking in a deterministic sense to say there’s one best outcome. We’re not just tracking things like motion, those price models that most managers out there do we track causality? There’s a difference, right? So in a world where, imagine with me this, if you close your eyes, right?
You are gonna see people moving around. You’ll see cars moving around. Motion is gonna, yeah, it’s gonna, you’ll capture your eyes. But causality, causality tells you the story behind every step, right? And when you track causality, you don’t have to do things like just model price. You don’t have to do things like model just velocity.
Headlines find that arbitrage and speed when you track causality. Right. What you, what you have is you understand the reasons why the world moves. Mm-hmm. And thereby in this uncertain, fast-paced market, when you track causality, you’re not just looking at the motion. You actually understand, number one, the momentum, but the underlying motivation in the movement.
That’s what’s so powerful about this approach that we take, is we’re just. The AI that we program, it’s very human. Yeah. Right. Very, very human. You know, you ever been through a, through a tough situation where I maybe a breakup, a traumatic event or something and, and we would call it call closure, right?
Where you sit there and you analyze, that’s causality. Mm-hmm. The cause and effect. Right. If you understand that, the motivation, that’s where. By interpreting the mo, the motivation in speed. Mm-hmm. You, you, you have elements where you can sense test adapt. Right. And instead of just guessing the future and hoping you’re right, you don’t have to do that.
Mm-hmm. You can feel the curve. You can have your little quid ball set. Yeah. Or earn the twister and you feel the pressure, you feel that curve forming right. Where the energy is moving and you just. Oh, I, I think we should, we should trademark this E. You just go with the flow. That’s what we’re talking about doing is just going with the flow.
Amazing. Alright, Niddy, so first off, thank you for coming on the show and to the audience at home. This has been another episode of Mission Matters. Hope you enjoyed. If you haven’t hit the subscribe or follow button yet, do it again. We have a whole interview series coming out for this Milken conference.
And then we have a bunch of other conferences coming up, including FII priority coming up in I think Albania. We got Riyadh coming up. Whole lot of other high, high level conferences. Make sure that you hit the subscriber follow and thank you again Niddyfor coming on the show. My pleasure.
