Have you ever felt like the problems you’re solving are completely new? Like nobody before you has wrestled with the same logical puzzles, the same iterative thinking, the same “if this, then that” decision trees?
I used to think programming was modern. Revolutionary. Ours.
Then I learned about Donald Knuth’s discovery in 1972, and everything shifted.
The Moment Everything Changed
Knuth, legendary computer scientist and author of The Art of Computer Programming, was examining ancient clay tablets at Yale. These weren’t artifacts from some early computer age. They were from 1800 BCE. Eighteen hundred years before Christ.
And they contained algorithms.
Not just calculations. Not just answers. But procedures, step-by-step instructions with conditional logic. If-then statements. Loops. The same fundamental structures we use today when we code.
The Babylonians had figured it out. Nearly 4,000 years ago, they were writing what we’d now call executable code.
From “Why does this matter?” to “What if this changes everything?”
Here’s what stopped me cold: one tablet described a procedure for calculating square roots. But the scribe didn’t just write the formula, they wrote the process.
Let me translate what they carved into clay:
Ancient Babylonian procedure: Start with a guess for the square root. Divide your number by that guess. Average the result with your original guess. If the new answer differs from your guess, repeat. Otherwise, you’ve found your answer.
Sound familiar? Here’s the exact same logic in Python:
defbabylonian_sqrt(n, tolerance=0.0001): x = n / 2# Make your first guess
result = babylonian_sqrt(25) print(f"Square root: {result}") # 5.0
The structure is identical: initialize, iterate, check a condition, branch, repeat or terminate.
This is programming. This has always been programming.
What This Actually Means
Think about the timeline: This is the same era as Hammurabi’s Code, those famous legal tablets that said “if a man steals, then he shall be punished.”
But here’s the difference that hit me: Hammurabi’s code was about controlling behavior. The mathematical procedures were about enabling it. They weren’t rules for humans to interpret, they were instructions to be followed mechanically, perfectly, repeatedly.
They didn’t require judgment. They required execution.
And that’s when I realized: the fundamental concepts of computation, procedures, iteration, conditional branching, aren’t innovations of the digital age.
They’re ancient human tools. As old as writing itself.
Your Takeaway This Week
The next time you write an if statement or a while loop, pause for a second.
You’re not inventing something new. You’re inheriting something ancient.
Some Babylonian scribe pressed a stylus into wet clay and created the world’s first executable code, not because they had computers, but because they understood something timeless: complex problems break down into simple, repeatable steps.
That same power is yours right now, whether you’re debugging code, planning a project, or solving any problem that feels overwhelming.
Break it down. Check your conditions. Iterate. Refine.
Algorithms didn’t begin with Silicon Valley. They began in Babylon.
What problem are you treating as “too modern” or “too complex” that might just need ancient wisdom, one clear procedure at a time?
Have you ever felt like you’re learning everything but mastering nothing? You buy courses. You read books. You consume podcasts. But when someone asks what you’re really good at, you freeze.
For years, I thought being a “generalist” meant collecting skills like Pokémon cards. Marketing? Check. Coding? Learning. Design? Working on it. I was proud of my versatility — until I realized I couldn’t go deep on anything. I was a mile wide and an inch deep, drowning in surface-level knowledge that evaporated the moment I stopped using it.
Then I discovered Naval Ravikant’s perspective on learning, and it completely flipped my approach.
Naval doesn’t say “don’t be a generalist.” He says most people are doing it backwards.
The Problem With How We Learn
Most of us treat knowledge like a buffet. We pile our plates high with random skills, hoping something sticks. Business strategies. Productivity hacks. Social media growth tactics.
But here’s what Naval noticed: all of that knowledge sits on top of something deeper.
Think about it. When you learn “how to grow on Twitter,” you’re memorizing rules that could change with the next algorithm update. When you learn “economics,” you’re absorbing theories that experts debate endlessly.
You’re building a house on sand.
Naval’s insight? If you want to be a true polymath — someone who can walk into any field and understand it faster than others — you need to start at the bottom, not the top.
The Foundation That Explains Everything
Naval argues that knowledge isn’t random. It’s hierarchical, like a pyramid:
Mathematics is pure logic. It doesn’t need reality to be true. 1+1=2 everywhere, always.
Physics applies math to our universe. It’s the source code of reality — gravity, energy, cause and effect.
Chemistry is just applied physics — how atoms and molecules behave.
Biology is applied chemistry — how living systems work.
Economics and psychology are applied biology — how humans (biological creatures) make decisions and interact.
Here’s the game-changer: If you try to learn business or marketing without understanding the layers beneath, you’re just memorizing rules that might not even be true. But if you master the foundational layers — math, physics, the scientific method — you can derive the rules yourself.
You become dangerous. You can walk into a new field and see patterns others miss because you understand the systems underneath.
The “Undo” Button for Your Brain
Naval often talks about the fragility of memorization. If you forget a business rule, you’re stuck. You have to look it up again, relearn it, hope it’s still relevant.
But if you understand how to think like a physicist — testing hypotheses, distinguishing objective truth from opinion, following cause and effect — you have something more powerful than knowledge.
You have a method.
You can apply that rigor to investing. To relationships. To health. To anything. And you’ll find the truth faster than people who’ve been in those fields for decades because you’re not relying on “best practices” — you’re building understanding from first principles.
What Naval Actually Said
Let me share his words directly, because they hit different when you read them:
“Learn math. Speaking the language of nature is the ultimate superpower. If you understand logic and mathematics, then you have the basis for understanding the scientific method. Once you understand the scientific method, then you can understand how to separate truth from falsehood in other fields.”
And on specialization:
“Specialization is for insects… I don’t believe in the model of ‘I am a doctor’ or ‘I am an electrician.’ I think you should be able to do everything. But the way to do everything is to have the foundations so strong that you can learn the specific skills very quickly.”
This isn’t about becoming a professional mathematician. It’s about adopting the intellectual rigor of someone who demands proof, who questions assumptions, who builds understanding instead of collecting facts.
The Tree Analogy That Changed My Learning Forever
Naval views knowledge like a tree.
Most people try to grow branches (specific skills like coding, design, copywriting) without a trunk. The branches are weak. They break off. You forget what you learned because it wasn’t rooted in anything deeper.
Math and physics are the trunk.
When your trunk is strong, you can grow branches in weeks. You learn marketing faster because you understand systems and feedback loops. You learn coding faster because you understand logic and abstraction. You learn psychology faster because you understand biological constraints and evolutionary incentives.
The foundation isn’t just knowledge — it’s leverage.
My Shift (And Maybe Yours?)
I used to panic when I saw a complex book. I’d avoid it, thinking “that’s not for me.” Now? When I don’t understand something, I dig down. I ask: What foundation am I missing?
Sometimes it’s embarrassing. I’m an adult relearning basic probability because I realized my intuition about risk was completely wrong. But here’s what changed: I’m not scared anymore.
Because I know that if I build the trunk, I can understand anything. I don’t need to be the smartest person in the room. I just need to know how to think clearly and follow logic wherever it leads.
Your Action Step This Week
Pick one thing you’re trying to learn right now.
Ask yourself: What foundation am I missing?
If you’re struggling with marketing, maybe you need to understand psychology first.
If you’re confused about investing, maybe you need to understand systems thinking and probability.
If you’re overwhelmed by coding, maybe you need to understand logic and abstraction.
Don’t go wide. Go deep on the foundations. Then watch how fast everything else clicks.
🔑 Key Takeaways:
Naval isn’t against being a generalist — he’s against being shallow.
I’ve been thinking about this quite a bit recently. Every day, I see people turning to ChatGPT before they even pull out their notebook. Asking it to assist them in brainstorming, to correct their work, to make decisions that they could quite easily make on their own. And I think somewhere down the line, I started to wonder if we’re doing any better at anything, or if we’re simply doing better at asking the AI to do it for us.
The thing is: a crutch is something that catches you when you cannot stand by yourself. It’s a temporary thing until you heal. But imagine if you were to use it all the time! The muscle that you’re supposed to be strengthening just. doesn’t. I think that’s about where a lot of people are at with regards to AI.
The Illusion of Progress
I do know a guy who used the AI tool to write an entire research paper last semester. The paper was great. Really great, in fact. Scored an A. And the part of the story that kept him awake all night was the fact that he didn’t even know if he or she could write something similar on his/her own.
That is the subtle risk. AI is not perceived as cheating because it is so useful. It is perceived as efficiency. Smart work versus hard work. There is a difference between utilizing a resource to improve your skills, versus utilizing a resource to make it unnecessary to have said skills.
It seems like everywhere I go now. Students using it to help them with essays. People using it for all emails. Individuals using it to help with writing up journal entries in therapy sessions. And I am not judging — that’s all of our business at this point. But the reality remains: what happens to our thinking if we all are outsourcing our thinking skills?
The Nature of Coaching Primarily
A coach will not do the work for you. A good coach will ask you the tough questions. They will make you, or at least your brain, uncomfortable. They will force you to come up with the answers that you did not even know were there. They believe in you when you believe otherwise.
AI does the opposite. It gives you the answer right now. It strips away the struggle, the frustration, the brutal process of trying to determine things. And that process, that’s exactly where growth occurs.
Consider learning how to write an essay. The first attempts will always be dreadful. You look at the paper, write something clumsy, delete the text, try again. Your thoughts are not clearly articulated. Sentences are not connected. In the process, you manage to clarify what exactly you want to say.
Now, imagine all that being waved away. Simply enter a prompt, receive an essay. Yes, receive the grade. But did one receive an education in all that?
This clumsy process of wrestling with bad ideas — that’s not a bug in the learning process. That’s the point.
The Questions We’re Not Asking
When we go about utilizing the capability of AI, perhaps the first question we need to ask ourselves is, “Am I using this tool in order to assist me with something that I already have the capability of doing, or am I avoiding learning how to do something by utilizing this tool?”
Are we writing using AI support, or are we only editing AI-generated write-ups?
Are we using this tool for overcoming the problem of writer’s block, or are we using this tool so that we never experience the problem of writer’s block at all?
These are important distinctions. This one builds skills and capabilities. The other leads to their deterioration.
“I’m not saying that AI is bad, because it is an amazing tool,” Morton explains. “But tools are only as good as the hands using them, and if our hands forget how to work without the tool, we are in big trouble.”
The Creativity Crisis
“There’s another thing at play here. What happens when you ask AI for ideas is that it will provide the most statistically likely answer based on everything that AI has been fed. What that means is that AI will give a person the average of what already exists.”
Real creativity does not come from averages. It comes from the weird things your brain puts together at 2 AM. From the errors that can become something interesting. From your unique experiences that no one else has.
One of my friends shared that they would spend hours scribbling down story ideas in their notebooks. Most of it was terrible, but every now and then, a masterpiece would be born. They now rely on AI for story ideas. They get twenty of them instantly. However, none of them are theirs. They lack soul. They lack soul because they lack heart.
“The mess is where the magic lives. And we’re optimizing it away.”
Finding the Balance
What’s the solution then? I don’t think it’s giving up on AI altogether. We could just stop using calculators because we should be calculating long division problems in our head. But maybe we just need to think more about how we are going to use it.
What if we struggled first? We contained ourselves in front of the hard problem, the blank page, the tough decision. We gave ourselves time to struggle, to flail about in incompetence. Only then might we seek out the AI — and then only as a thought-sounding board.
It’s slower. It’s tougher. But here’s what those people using this method notice: Their early versions of work improve. Their thoughts are more unique and different. They’re exercising the muscle rather than watching it atrophy.
The Real Risk
“Here’s what frightens me most: a generation growing up with no experience of what it’s like to be, well, truly stuck. To take a problem long enough that you find yourself digging deep and trying something new and surprising yourself.”
From the The New Yorker StoryBundle Collection, “The BFG” by Roald Dahl, edited by Rebecca Mead
That is where the magic happens. Not in the quick solutions, but in the fight to locate them. Not in the flawless initial draft, but in the willingness to produce an awful one and improve it.
There was this professor at a university who had just begun handing out “no AI” exercises. It was not because he disliked technology in particular. He had noticed something disturbing: his graduates had never been able to write a real body of text outside of class. They could write a great prompt to the AI. Ask them to develop an argument by themselves, and nothing.
They are walking across the stage with their degree in hand, but without the skills that degree is supposed to convey. The article says they can do it. But can they?
The Decision-Making Process
Artificial intelligence can be many things. It can be a research aide, a brainstorming buddy, a proofreader, and even a code debugger. But it shouldn’t be a replacement for the thinking, the creativity, and the development that comes from us.
A crutch prevents a person from falling. A coach teaches one to run.
The choice is ours to make. Every time we type that prompt, we are making that decision, period. Are we using this opportunity for growth or avoiding growth?
What are we building or borrowing? The technology isn’t going anywhere. But the question remains: When we finally put down the crutch, will we be able to walk?
What are your thoughts on this? How are you balancing this in your own life? It is early in the conversation to be sure.
In the domain of theoretical physics, few concepts kindle the imagination as does the tachyon-a hypothetical particle that travels faster than light itself. Though Einstein’s theory of special relativity forbids ordinary matter to reach light speed, it leaves a mathematical loophole for particles possessing imaginary mass and that, by their very nature, travel faster than light. Physicists have been struggling with the concept of tachyons for over six decades, oscillating between fascination and skepticism. Recent breakthroughs in 2024 have once again revived interest by proposing new reconciliations with relativity theory, and at the same time revealing some fundamental mathematical inconsistencies in existing frameworks. This blog explores the fascinating world of tachyons, from their theoretical foundations to cutting-edge research and the profound implications they hold for our understanding of space, time, and causality.
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Tachyons: What is it?
The word “tachyon” itself is derived from the Greek word “tachys,” which means swift. These hypothetical subatomic particles have one defining characteristic: they always travel at speeds greater than light. Unlike ordinary particles-called bradyons-which slow down as they approach light speed and require infinite energy to reach it, tachyons exhibit inverse behavior.[1]
The Mathematics Underlying Superluminal Particles
Tachyons result from Einstein’s relationship between energy and momentum, the basic equation controlling the dynamics of a particle: [2]
E² = (pc)² + (m_0c²)²
For ordinary particles, the rest mass m₀ is real and positive, but if we allow imaginary rest mass — where its square becomes negative — the mathematical framework permits velocities greater than light speed. This leads to a bizarre inversion of familiar physics: tachyons accelerate as they lose energy and decelerate as they gain energy, approaching infinite speed as their energy approaches zero.[3][1]
Key Distinguishing Properties
Tachyons vary fundamentally from conventional particles in several critical ways. First, they cannot travel at or below light speed; they are forever confined to superluminal velocities. Secondly, detecting one would instantaneously provide the capability for faster-than-light communication, which traditionally violates causality. Third, because tachyons have negative mass-squared, they naturally disperse rather than concentrate, making interaction with ordinary matter extraordinarily difficult.[2]
Historical Development: From Theory to Modern Research
The first to systematically develop tachyon kinematics within special relativity was Gerald Feinberg, in the 1960s. His groundbreaking work demonstrated that while mathematically consistent, tachyonic fields created instabilities in quantum systems rather than enabling practical superluminal signals. This discovery tempered early enthusiasm but did not eliminate interest.[2]
Over the following years, tachyons had some surprising uses in fundamental physics. It now seems surprising that tachyon fields occur so commonly in the Higgs boson mechanism, prior to spontaneous symmetry breaking; and they also remain part of bosonic string theory. These uses in prestigious theoretical contexts kept a little-known research in tachyons alive, even after their non-detection. [2]
Tachyons in Modern Physics: String Theory and Quantum Mechanics
Role in String Theory
Bosonic string theory inherently includes tachyonic fields as ground states. Their existence initially bothered theorists; the understanding of tachyon condensation-that is, the process of tachyons moving to stable states-became essential to the modern development of string theory. This mathematical framework shows that tachyons need not be physical nuisances but, rather, important quantum phenomena in supersymmetry breaking.[2]
Connection to the Higgs Mechanism
There is a remarkable parallel between tachyon physics and the Standard Model of particle physics: before symmetry breaking, the Higgs field has imaginary mass-negative mass-squared-exactly like tachyons. When the universe had cooled, milliseconds after the Big Bang, that imaginary mass became real and positive, thereby giving fundamental particles their mass. Understanding tachyon condensation therefore sheds light on one of the great mysteries of physics: how mass is generated.[2]
The Causality Problem and Time Travel Paradoxes
Historically, the most worrying phenomenon arising in tachyon physics has to do with violations of causality. Theoretically, the “tachyonic antitelephone” is possible with tachyons-a thought experiment in which tachyon signals allow sending information backward in time, creating grandfather paradoxes and logical inconsistencies. In fact, this issue alone has motivated many physicists to dismiss tachyons as unphysical artifacts of mathematical formalism.
A Breakthrough in 2024
A groundbreaking peer-reviewed advancement came in 2024, when physicists advocated a foundational solution. The intuition: calculating quantum probabilities of tachyon processes involves both the condition in the past and the condition in the future of the system. This past-future mixture of boundary conditions automatically vetoes any causal paradox. The proposed mechanism works in the same spirit as quantum mechanics on closed timelike curves-the presence of the boundary condition precludes any causality violation rather than allows it.[4][5]
The success indicates that tachyons may not necessarily create time-travel contradictions if they coexist with special relativity; this could open up new theoretical avenues that were traditionally impossible.
Experimental Searches and Detection Methods
Despite theoretical interest, there is an utter absence of experimental evidence for tachyons. This reflects their high innate difficulty of detection as well as fundamental uncertainties in how tachyons, if they exist, would interact with measuring devices.
Why Detection Remains Elusive
The core detection problem is circular: physicists cannot plan experiments aiming to find tachyons without knowing their interaction properties, yet it is only detection experiments that can reveal such properties. This epistemological gap represents perhaps the most frustrating obstacle to tachyon physics.[6]
Proposed Detection Strategies
A number of experimental approaches have, nevertheless, been put forward by researchers :
Cherenkov Radiation Detection: Particles traveling in media, such as water or other dense materials, at velocities greater than the local speed of light emit characteristic Cherenkov radiation, a blue glow. While tachyons would, conceptually, always travel faster than light in a vacuum, various schemes for their hypothetical detection assume tachyons could leave behind a Cherenkov-like signature in particle detectors interacting with the electromagnetic field. [6]
Time-of-Flight Spectrometry Early experimental designs employed positronium sources coupled with sophisticated time-of-flight spectrometers capable of measuring particle transit times with exceptional precision to identify superluminal arrivals. [7]
Energy and Momentum Analysis Not Accounted For: Colliders in institutes such as CERN investigate unaccounted-for energy and momentum losses. If present, they would imply the escape of tachyons from the detector. Similar to neutrinos, that is how they are observed-indirectly.[7]
Resonance Structures in Collisions: Quantum field theory predicts that tachyons should reveal their presence in the form of a subtle resonance pattern in the cross-sections of particle collisions, which can only be determined by statistical analysis of millions of events.
The 2024 Crisis: Fundamental Mathematical Problems
While 2024 brought exciting proposals for reconciliation, it also revealed some serious theoretical problems. Recent peer-reviewed research has identified fundamental inconsistencies in existing tachyon quantum field theories.[9]
Unitarity Violations
One comprehensive analysis in 2024 showed that most of the proposed tachyon quantum field formulations violate unitarity — that is, the condition that quantum probabilities add up to one and are conserved over time. This is a fatal flaw in quantum mechanics, indicating the theories are actually not quantum mechanical at all.
Canonical Commutation Relation Failures
The tachyon field theories studied in 2024 do not satisfy the canonical commutation relations, which are the mathematical framework of quantum field theory. These ensure causality and locality within a quantum system. Their violation suggests that the so far-developed tachyon frameworks are not valid quantum theories but classical constructs dressed in quantum language.[9]
Implications for Future Research
Admittedly, these problems do not rule out the possibility of tachyons but rather call for absolutely new theoretical approaches. The 2024 breakthroughs that claim causality reconciliation have to grapple with these deeper mathematical issues before gaining wider acceptance.
Current Scientific Consensus and Future Directions
The physics community regards tachyons as mathematically acceptable in the context of special relativity, but not empirically confirmed, and probably inconsistent with observed physics. This cautious attitude is based on several factors that come together: [3]
No Detection Evidence: Despite sophisticated experimental searches across decades, zero confirmed detections have taken place. [6]
Theoretical Inconsistencies: Mathematical models involving tachyons uncover deep inconsistencies in the foundational issues of Quantum Mechanics. [9]
Lack of Predictive Power: Without understanding tachyon interactions, there is no way that physicists can make testable predictions distinguishing the tachyon scenarios from alternatives.
Alternative explanations: Phenomena that could be theoretically attributed to tachyons, such as symmetry breaking, already have conventional explanations in physics.
Promising Avenues of Research
In spite of these challenges, a variety of potentially interesting directions could be explored:
1. Quantum Boundary Conditions: The proposal of a mixture of initial and final quantum states in 2024 needs rigorous mathematical development and comparison with experimental signatures.
2. String Theory Insights: The possibility of a better understanding of tachyon condensation in string theory might shed light on how superluminal particles act in fundamental quantum systems.
3. Novel Detection Schemes: The merging of quantum optics, condensed matter physics, and high-energy physics may conceive detection methods hitherto never considered.
4. Modified Relativity Frameworks: A study on whether slight modifications to special relativity can accommodate tachyons naturally without affecting the successes of experiments.
Conclusion: The Allure and Mystery of Faster-Than-Light Physics Tachyons represent a point of intersection that binds mathematics, physics, and philosophy together in a very fascinating way. They show that the mathematical structure of special relativity allows for superluminal particles, but decades of searching have turned up not one confirmed detection. Indeed, the 2024 works are representative in the sense that they simultaneously provide proposals for causality reconciliation and expose mathematical crisis. Whether tachyons ultimately represent real physical phenomena or are merely mathematical curiosities is one of the open questions of physics. The answer rests on sorting out the deep theoretical inconsistencies that were exposed in 2024, proposing realistic experiments for their possible detection, and possibly rethinking how quantum mechanics couples to superluminal regimes. For the time being, tachyons remain what they always have been: tantalizing possibilities which stretch the real reaches of human understanding, challenging physicists to square one through with the relativistic universe and the deepest requirements of quantum mechanics. Their ultimate fate-confirmation, dismissal, radical reconceptualization-will shape physics in the twenty-first century.
