Here is the full transcript of Founder and Managing Partner of Excite Nadab Akhtar’s talk titled “Beyond the Rearview Mirror: Are You Ready to Look Ahead?” at TEDxParkland (May 7, 2025).
Listen to the audio version here:
The Problem with Rearview Mirror Decision Making
NADAB AKHTAR: How do you make decisions in a world that won’t stop moving, when every existing model that we use to make those decisions assumes stillness? That question, it haunted us, and we realized in order to answer it, we had to rethink everything. We didn’t start with old models. We didn’t go to old tools and tweak them, hoping for new results. We thought like scientists, and we approached this like a physics problem, using first principles, not assumptions. And what we discovered is this: the future, it isn’t predicted. It’s sensed, and it’s shaped.
This talk is for those of you that are shaping the future, the decision makers, the leaders who know that in today’s world, the speed of change is faster than it’s ever been. And yet, almost every model, every model used to make those decisions relies on one outdated assumption: that the best way to predict tomorrow is to study the past. Well, to me, that’s like driving down a highway, going 80 miles an hour, and instead of looking at the front through the windshield, you look at this tiny rearview mirror, showing you what already passed. There’s no GPS, no forward view, just this tiny mirror showing you what’s behind you. If you think about it, you’d crash, right? But that’s exactly what most decision systems still use today, especially traditional AI.
The New Wave of Quantum-Inspired Intelligence
Old AI, it’s stuck in history. It predicts tomorrow by averaging yesterday. But there’s a new wave of intelligence emerging, one that’s quantum inspired. And no, this is not science fiction, don’t worry. I’m not talking about an experiment in our physics lab. This is real time, adaptive, even alive. It doesn’t just study the past, it senses the present and anticipates what’s next. Just like a human brain works. Tony Robbins said it best. He said that “anticipation is the ultimate superpower. Losers, they react. Leaders, they anticipate.” So if you’re still making decisions by using this rearview mirror, you may want to buckle up.
A really good way to understand this is, do me a favor, close your eyes. Close your eyes with me and we’re going to imagine this. You know that dream car of yours, maybe it’s the red convertible Ferrari, that’s what you’re driving. And you’re with your favorite person and you’re driving at high speed in this beautiful, scenic, windy mountain road. Picture that, right? Okay, now please open your eyes because you’re driving. You can’t afford to wait until the obstacle is in front of you. By the time you see it, it’d be too late. You don’t need a reaction. What you need is foresight. And that’s exactly the kind of decision making that this moment in time demands. And it’s the kind of intelligence that’s becoming available right now.
The Fatal Flaws of Traditional Decision Tools
Most decision tools, especially the ones used in finance and technology, well they operate with three core strategies. Be the fastest, react to headlines, and find patterns in old data. Well what we think is this: by the time a headline hits your rearview mirror, market’s already moved. And reacting to trying to beat your competitors by speed alone, well that’s a race with no finish line. And trying to find patterns in old data and win that race, well again that’s like using that rearview mirror to look at stuff that’s already past you. You might get lucky, you might win sometimes, but eventually you’ll miss the curve.
This new way of thinking, it doesn’t just study the past. It senses the present, and it pays attention to the windshield to study not just where the car is, but where it’s moving, how fast it’s going, its acceleration, and how it’s slowing down. Because if you know that, you don’t have to guess what’s coming. You’ll feel the curve even before you see it.
The Quantum Physics of Uncertainty
So how do we do this? Well it’s not by simply reacting faster, but by thinking completely differently. Traditional AI is deterministic, which means that it searches the past to find a single best answer. But real life doesn’t work that way. One solution isn’t enough. The future, it doesn’t unfold in a straight line, it branches, it curves, it collides. So we realized we needed a system that could model many possible futures simultaneously, and then adjust steering based on how present conditions evolve.
Because at the deepest level, uncertainty isn’t just practical, it’s physics. And in quantum physics, there’s something called the Heisenberg Uncertainty Principle, and it says this: You can’t know with perfect certainty both the position and momentum of a particle simultaneously. The stock market, it works the same way. You may know the price of the stock, but you can’t know with certainty how fast the sentiment, pressure, and forces are shifting behind it. Most systems, they’re still stuck using past data, but the reality is that you have to look forward and see what’s coming next.
Building a Quantum-Inspired Prediction Framework
At our lab, we built a quantum-inspired prediction framework. Now, I’m not talking about quantum computing. We have entanglement, and we leverage superposition, concepts from quantum computing. But we borrowed something even more powerful from quantum computing, and it’s the ability to simulate multiple overlapping realities. We don’t just predict one outcome. We simulate many, and again, adjust that steering based on how present conditions evolve.
This new fancy way of thinking, well, it required a new fancy set of tools. So we built a suite of algorithms, built not just on past data, but on causality. Picture this: a world where you see movement, but you don’t understand the why behind the movement, motion.
Motion tracks and captures the eye, but causality tells you the story behind every step. Most models, they track motion. They look at things like volatility, price, volume, but they rarely ask the why behind that movement. Is it being pushed by wind, pulled by gravity, triggered by forces unseen? Causality reveals the hidden reasons that set the world in motion. We don’t track motion. We track cause and effect, thereby understanding the market’s motion and its motivation.
In classic systems, Hamiltonians are models that explain how energy dynamics flow through systems, how planets orbit, how particles spin, how traffic flows. At our lab, we built quantized theta Hamiltonians in order to track the velocity and acceleration of markets. Not just where the price is, but where it’s headed, how fast it’s going, and its acceleration, in order to build a 3D model of risk and flow.
A really good way to imagine this, close your eyes with me again, this time, you’re a traffic reporter in a helicopter. You’re not just looking at the movement of the cars, you’re seeing how fast they’re going, how hard the cars are braking, in order to predict where that jam is forming three exits ahead. That’s exactly what a data Hamiltonian gives us, a living map of market motion. Not just the price, but its momentum and its motivation.
Real-Time Steering with Chattering Technology
In order to respond to this live map, you need a real-time steering layer. We call ours Chattering, a measured-based approach to quantization. It works like this. You know those, so Chattering, it’s going to make these continuous, infinitesimal adjustments based on sensory input, again, similar to how our brain works, and a good analogy for this is, you know those, like Waymo cars, I’ve seen a lot of those in Scottsdale in my time there, and sometimes you see the Teslas that have the autonomous driving, it freaks me out to be honest, but they don’t, the way these autonomous cars work is they have cameras and they have sensors. They don’t just randomly jerk the wheel because it’s decided which path to go forward, no, it makes tiny, continuous adjustments, moment by moment, condition by condition, based on that sensory input.
Repair and Reinforcement Learning
But sensing motion, it isn’t enough. Sometimes you need to pick, moment by moment, the best path forward. This is where, for us, our repair and reinforcement learning algorithms come into play. Repair, it’s the process of matching the solution that most aligns with optimal reality, making tiny corrections as sensory input evolves and present conditions move. Reinforcement learning, reinforcement learning trains the system’s instincts, teaching it over time to think smarter, be faster, and move with less hesitation.
Another good example for this is, you know how sometimes you just get this itch to have a need for speed, you know? That need for speed, you’ll be bobbing and weaving through traffic, right? But you’re not just going to swerve into the next lane. You kind of give a little nudge, you know, you give that little nudge to say, hey car next to me, you’re going to create that space, and then you kind of get that instinct as you drive further in the future to go faster, smarter, right, and find your optimal path. That’s what chattering, repair, and reinforcement learning give us.
Data Tomography: Building a 3D Model of Risk
Now I need your help for a second to explain the next algorithm. Anyone here ever had a CT scan? Alright, thank you. Alright, so you may know that CT scans work like this. They take multiple images in order to find a, they layer them, in order to build a composite living model of your body. We do the same with markets with our data tomography algorithm. Layering, price, volume, and acceleration in order to build a 3D model of risk and flow.
To change and react and manage volatile, uncertain, fast-paced markets, you don’t just need a model of price, you need a living map of motion. Our system works like that, by taking CT scans of the market. But it doesn’t just take static one-time images, no. It sends these tiny test signals, these little nudges, to say, Data Hamiltonian, how do you respond? And it waits for that feedback. Because if you have this information, you don’t have to guess, right? You’re building a rich, more dynamic model of the market and its risk and uncertainty.
Finding Rhythm in Chaos
Where others see risk, where others see noise, we see rhythm. Signal arrays forming in harmony with uncertainty. Shout out to the legend Richard Feynman. This isn’t your mama’s AI. This is quantum sensory intelligence. Aware of the now, responsive to the next. Even if you have all this information, where you can anticipate, test, adapt in real-time, see how powerful that is?
It’s really quite simple when you take a step back. Most people are still making decisions using this rear-view mirror, but the future, it belongs to those who look ahead. As Heisenberg said, “the world is already uncertain, why complicate it further?” Most models, they’re still deterministic, stuck in old logic, but don’t just take my word for it.
Gödel’s Incompleteness Theorem and the Future of AI
In the 1930s, there was a famous mathematician named Kurt Gödel, and he discovered something really astonishing. And what he said is this: no system, however complex or sophisticated, can fully reveal itself from within. He called it the incompleteness theorem, and what it means is this, that past data alone cannot fully give you the truth of what’s to come.
At Excite, we fight this incompleteness. We don’t use hard rules and only absolute rules, we use soft rules, living adaptive principles, in order to sense, to test, to adapt, to anticipate. This is how we manage volatility, in order to be smooth, responsive. Always adapting, always learning.
The Choice: Rearview Mirror or Forward Vision?
In the next era, the leaders, they won’t be the fastest, they’ll be the most adaptive. This quantum-inspired way of thinking, it’s not just about being the fastest or the smartest. It’s about staying alive, at speed, in motion, in uncertainty. The only question that remains: are you still going to drive using this rearview mirror? Or are you ready to lead by looking forward at what’s ahead?
