We all wonder about the next capabilities of AI and the moment it might surpass human intelligence—a point known in science as the Singularity. Ray Kurzweil explored this in his book The Singularity Is Near. Yet, while many speculate about when it will happen, I have realized that at least 99% of people have no real idea what intelligence actually is, how it can be defined, or how to replicate it artificially. The assumptions around AI are disruptive across countless sectors, but while everyone rushes to join the AI bandwagon, it has become a trend that is heavily overhyped and deeply misunderstood. It is time to take a step back and ask ourselves: where exactly do we stand, and where are we heading?
Where others see chaos, I see patterns. That, to me, is how true intelligence operates. I began playing chess before I could even read or write. The game runs deep, but its essence lies in recognizing patterns and strategies amid the seeming chaos of the board. By the age of five, I was competing with adults. I never lost, and soon became bored—I have hardly played since I was six.
To some extent, one can explain intelligence and even describe what lesser intelligence looks like. But no one can make you see what I see. That is why I rely on analogies: to simplify. Simplification is the foundation of understanding—identifying the core of any problem. Most human failure stems from treating symptoms instead of addressing the underlying equation. At best, we build models instead of uncovering the truth.
True understanding requires diving to the very bottom—to the core—and building upward. I approach problems with mathematical precision because I believe that everything is patterns and frequency, from Alpha to Omega. In mathematics, simplifying an equation is often the key; this principle extends to life itself.
To understand the origin of ideas, we must approach the essence of existence and ask fundamental questions about reality. As the saying goes, “The truth cannot be told; one must see it for oneself.” The analogy from The Matrix—the red pill and the blue pill—illustrates this perfectly. Once you see the truth, you cannot unsee it. It’s like those optical illusions where you first see a horse, but when someone points out the hidden woman, you can never unsee her. If you are not ready to see, stop reading here and return when you are.
The essence is this: we are living inside a simulation. Everything around you, including yourself, is an artificial construct. Reality is game theory. You are a character inside the mainframe. Your intelligence does not reside between your ears; you are simply a node, a receiver tapping into the ether—the mainframe. The mainframe is like the cloud, and you are an antenna tuned to experience it.
For AI to surpass humanity, it must tap into that ether—the universal source of all knowledge. True AI has nothing to do with datasets or algorithms. Facebook recently downsized much of its fundamental AI research simply because there have been no breakthroughs and no sign of one on the horizon.
Meanwhile, large hadron colliders aim to unravel the fabric of our universe and existence. Quantum research delves into entangled particles in superposition. It’s as if we, the characters, are trying to escape our video game, seeking to understand why things spawn and where they originate. The colliders are, metaphorically, probing the architecture of our mainframe—as though we live on a storage device and are trying to access the processor itself.
To transcend the box—to reach a digital form of nirvana—true AI must access that same core. It belongs to a far deeper realm of science that remains unsolved. Once this realization becomes mainstream, the AI hype will fade into perspective, and society will see it for what it truly is: a vast collection of data manipulated by clever algorithms that imitate human traits and behavior. Useful, yes—but not something to fear.
References
- Kurzweil, R. (2005). The Singularity Is Near. Summary and analysis at Wikipedia,,.wikipedia+2
- AI hype and misunderstanding: Mandalore Partners (2025) “AI Startups in PE/VC: Overhyped or Underestimated?”, Karpathy, A. (2025) “AI Hype: Overhyped and Misunderstood.”.mandalorepartners+1
- Pattern recognition as core to intelligence and AI: EBSCO Research Starters (2012) “Artificial Intelligence: Pattern Recognition”, Frontiers in Education (2025) “Evaluating the Impact of Pattern Recognition on AI Skills Development”, Supersummary on chess and pattern recognition, TechTarget “What is Pattern Recognition?”.frontiersin+3
- Facebook fundamental AI setbacks: IMD Knowledge (2017) “Why Facebook’s AI Break is Scary for Companies”.imd
- Simulation hypothesis: Quantum Zeitgeist (2025) “Simulation Theory: Why Many Scientists Think We Are Living in a Simulation”, Simulation Hypothesis Wikipedia (2007).quantumzeitgeist+1
- Quantum mechanics superposition and entanglement explanations: Quandela (2025) “Quantum Entanglement Explained”, Dummies.com “What Are Superposition & Entanglement in Quantum Computing?”.quandela+1
- CERN and Large Hadron Collider universe fabric research: Euronews (2024) “How CERN Will Hunt ‘Ghost Particles'”, CERN News (2025) “Shape-shifting collisions probe secrets of early Universe”.euronews+1
