NASA Never Said Sanskrit Was the Best Programming Language

How an 8-Page AI Paper Became India’s Most Persistent Scientific Myth

The Myth That Refuses to Die

“A lie can travel halfway around the world while the truth is still looking for the original paper.”

If there were an Olympic event for resurrecting dead scientific myths, India would be a serious contender. Every few months, someone discovers yet again that NASA has supposedly declared Sanskrit to be “the best programming language in the world.” Sometimes the claim becomes even grander.

NASA is building computers using Sanskrit.

Or…

Sixth-generation supercomputers will run on Sanskrit.

Or…

Artificial Intelligence works best in Sanskrit.

Or my personal favorite…

America secretly teaches Sanskrit because future computers will all speak it.

One WhatsApp forward confidently announced that “NASA scientists have admitted they cannot build advanced AI without Sanskrit.” Apparently, nobody informed thousands of engineers writing billions of lines of Python, C++, Rust, Go, Java, CUDA and JavaScript every single day.

One might imagine that if NASA had actually abandoned modern programming languages in favor of Sanskrit, someone outside a WhatsApp group might have noticed.

Yet the claim refuses to die. It has been repeated by television anchors, motivational speakers, self-proclaimed science communicators, and politicians across the political spectrum. As recently as 2025–26, similar claims resurfaced in public speeches, including by the Delhi Chief Minister, once again asserting that Sanskrit possesses unique computational qualities recognized by NASA.

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The confidence is remarkable. The evidence is usually absent.

The Curious Thing About Extraordinary Claims

Whenever someone makes an extraordinary scientific claim, I have a very boring habit. I ask one question.

“Can I read the paper?”

That’s it. Not the YouTube video. Not the Facebook post. Not the motivational speech. The paper. Science is wonderfully inconvenient in this way. It leaves receipts.

Orginal Paper:

https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/466/0

https://www.semanticscholar.org/paper/Knowledge-Representation-in-Sanskrit-and-Artificial-Briggs/b5258311477908037b500b23fb064e311b140a75

The Telephone Game

How does an ordinary academic paper transform into a national myth? Something like this. Rick Briggs writes:

“Paninian grammatical analysis provides an interesting framework for knowledge representation.”

Someone summarizes it as:

“Sanskrit grammar is useful for AI.”

The next person says:

“Sanskrit is useful for computers.”

That becomes:

“NASA says Sanskrit is the best computer language.”

Which eventually mutates into:

“Future supercomputers will be programmed entirely in Sanskrit.”

It is the scientific equivalent of the children’s game of telephone. Every retelling removes one important qualifier. By the end, the original sentence is unrecognizable.

But Here’s the Twist

If this article were merely another fact-check saying, “NASA never said that,” it wouldn’t be worth writing. The truth is much more interesting.

Rick Briggs was not a crank. He was asking a genuinely deep question — one that AI researchers are, in a surprising way, asking again today.

• Not because Sanskrit is a programming language.
• Not because Panini invented computers.
• Not because NASA secretly writes code in Devanagari.

But because the fundamental problem Briggs was studying has never gone away. How do you represent meaning so precisely that a machine cannot misunderstand it?

Forty years later, ChatGPT, Claude, Gemini and every major AI lab are still wrestling with that exact question. Only the tools have changed. The irony is delicious. The internet celebrated Briggs for something he never claimed.

Meanwhile, almost nobody noticed the question he was actually asking. And that question may turn out to be the most relevant part of the paper.

A Roadmap for This Investigation

Before we begin opening the paper itself, we need to travel back to 1985. Because understanding Briggs’ paper requires understanding the world in which it was written.

There was no ChatGPT. No transformers. No GPUs. No billion-parameter neural networks. In fact, there was barely any machine learning as we know it today. Artificial Intelligence looked completely different.

To understand why an AI researcher became interested in Paninian grammar, we first need to understand what AI itself looked like before deep learning changed everything.

Only then does the paper make perfect sense. And once we understand that, something unexpected happens. The story stops being about Sanskrit.

It becomes a story about the evolution of Artificial Intelligence itself — from symbolic reasoning to neural networks, and now, perhaps, back again.

Before ChatGPT: The AI World That Rick Briggs Actually Lived In

“Every scientific paper is a product of its time. Read it through today’s eyes, and you’ll often misunderstand what the author was trying to solve.”

One of the biggest mistakes people make while reading old scientific papers is assuming that the words mean the same thing they mean today.

Take the phrase Artificial Intelligence.

In 2026, that phrase immediately brings to mind ChatGPT, Claude, Gemini, Midjourney, autonomous agents, coding assistants, image generators and trillion-parameter neural networks.

In 1985? Almost none of that existed. Not because researchers lacked intelligence. Because they lacked something far more mundane.

• Data.
• Computing power.
• Memory.
• Graphics processors.

And about forty years of mathematical breakthroughs. To understand Rick Briggs’ paper, we must first travel back to the AI world of 1985. Because he wasn’t trying to build ChatGPT. He couldn’t have. ChatGPT wouldn’t become possible for another four decades.

The First Dream of Artificial Intelligence

Artificial Intelligence has always had one grand ambition. Build a machine that thinks.

Simple.

Unfortunately, nobody agreed on what “thinking” actually meant.

• Should machines learn like children?
• Should they reason like mathematicians?
• Should they memorize facts like encyclopedias?
• Should they imitate the human brain?

Different researchers answered these questions differently. For nearly forty years, one answer dominated.

Intelligence is reasoning.

If we can teach computers to reason correctly, we can build intelligence. That philosophy became known as Symbolic AI.

Symbolic AI: Intelligence as Logic

Imagine teaching a child. There are two ways to do it.

Method One: Show the child one million photographs of cats. Eventually the child recognizes cats. This is roughly what modern deep learning does.

Method Two: Simply tell the child. Cats are mammals. Mammals are animals. Animals are living things. Living things breathe.

No pictures. Just facts. Now ask:

Do cats breathe? The child reasons.

Cats → Mammals → Animals → Living Things → Breathing.

Answer obtained. That was Symbolic AI. Instead of learning patterns, we explicitly wrote knowledge down.

Computers Were Expected to Think Like Lawyers

Early AI researchers believed intelligence resembled legal reasoning. Suppose we build enough rules.

IF fever AND cough THEN possible influenza.

IF influenza THEN prescribe antiviral.

IF allergy to drug THEN choose alternative.

Thousands. Millions. Eventually the computer becomes an expert. This gave birth to Expert Systems.

Meet the Rock Stars of 1980s AI

Long before ChatGPT became a household name, there were celebrity AI systems. One of the most famous was MYCIN.

Developed at Stanford in the 1970s, MYCIN diagnosed bacterial infections. Not by learning from millions of medical records. Instead, doctors manually encoded hundreds of medical rules. For example:

IF Gram-positive organism AND cocci shape AND grows in chains

THEN likely Streptococcus.

Doctors spent years teaching MYCIN medicine. One rule at a time. Remarkably, it often performed as well as human physicians. People thought the future had arrived.

The Knowledge Bottleneck

Then reality intervened. Suppose you want an AI doctor. How many rules are enough? A thousand? Ten thousand? A million?

Now imagine teaching physics, chemistry, biology, law, economics, engineering, history, common sense, and everyday life. One rule at a time. Researchers gave this problem a memorable name.

The Knowledge Acquisition Bottleneck.

Knowledge wasn’t difficult to use. Knowledge was difficult to write. Imagine manually typing everything humanity knows. That is essentially what researchers were attempting.

The World Is Not a Rule Book

Then came another unpleasant discovery. Real life refuses to behave. Suppose you write Birds fly. Excellent. Then somebody mentions Penguins. Fine. Add an exception. Birds fly. Except penguins. Then someone mentions

• Ostriches.
• Cassowaries.
• Emus.
• Broken-wing birds.
• Baby birds.
• Dead birds.
• Plastic toy birds.

Within a week your elegant rule book resembles Indian tax legislation. Every rule needs another exception. The system grows. The intelligence does not.

A Different Problem Emerges

Before you can even apply rules, another question appears. How do you represent knowledge itself? Suppose I tell a computer:

Ram gave Sita a flower.

Humans instantly understand. A computer doesn’t. It asks annoying but perfectly reasonable questions. Who is Ram? Who is Sita? What is being transferred? Who owned the flower before? Who owns it now? Was the flower physically moved? Was it gifted? Sold? Loaned? Promised?

Language that feels effortless to humans is astonishingly ambiguous to machines. Before a computer can reason, it must first understand what the sentence actually means. That became a whole research field.

Knowledge Representation.

Those two words, remember, are exactly the words appearing in Briggs’ paper. Not programming. Not compilers. Not software engineering. Knowledge Representation.

The Birth of Semantic Networks

Researchers needed a way to represent knowledge so that machines could reason about it.

One elegant idea emerged. Represent everything as a graph. Objects become nodes. Relationships become links. Instead of writing Ram owns a cow. You store

Ram → owns → Cow

Then add another fact.

Cow → is a → Animal

Another.

Animal → is a → Living Thing

Another.

Living Things → breathe

Now the computer never saw the sentence “Cows breathe.” Yet it can infer it.

Cow → Animal → Living Thing → Breathes

Congratulations. The machine has just reasoned. This structure became known as a Semantic Network, or Semantic Net.

Why This Was Revolutionary

Today this seems almost obvious. In the 1970s it was groundbreaking. The idea that meaning could be represented independently of English was revolutionary. Notice something important. Nothing in the graph depends on language. The sentence could have been spoken in English, Kannada, Tamil, Hindi, Japanese, German. Or Sanskrit.

Once converted into the graph, the original language disappears. Only meaning remains. This observation will become incredibly important when we finally read Briggs’ paper. Because Briggs was not arguing that Sanskrit itself was magical. He was asking whether Sanskrit’s grammatical tradition already contained a systematic way of producing such representations. That is a completely different claim.

The Languages That AI Actually Used

Here’s another irony rarely mentioned in WhatsApp forwards. If Sanskrit was supposedly the world’s greatest programming language… what were AI researchers actually programming in?

Mostly Lisp. And Prolog. Not Sanskrit. Not because researchers hated Sanskrit. Because Lisp and Prolog were designed from scratch for symbolic computation.

Lisp excelled at manipulating symbolic expressions — Lists, Trees, Recursive structures. Exactly what AI researchers needed.

Prolog excelled at logical inference. You declared facts. You declared rules. The language itself searched for proofs. Ironically, Prolog looks far more like formal logic than any natural language.

If any programming language deserves comparison with Indian logical traditions, it would be Prolog — not Sanskrit. We’ll return to this fascinating comparison later.

A Common Misunderstanding

People often hear that Panini developed a “formal grammar.” Computer scientists also use the word “grammar.” Therefore, they conclude, Panini invented compiler theory. Not quite.

The word grammar means very different things in these two worlds. Compiler grammars answer questions like:

Is this program syntactically valid?

Natural language grammars answer questions like:

What does this sentence mean?

The overlap is real. The equivalence is not. Later in this series we’ll compare Panini, Chomsky and compiler theory in detail. The similarities are fascinating. The differences are even more so.

Why Briggs’ Paper Suddenly Makes Sense

Now put yourself in 1985. You are an AI researcher. Everyone around you is obsessed with knowledge representation. Semantic networks. Frames. Expert systems. Logic. Inference.

One day you encounter an ancient grammatical tradition that spends thousands of pages doing something surprisingly similar: Representing the relationships inside a sentence with extraordinary precision.

Wouldn’t you become curious? Rick Briggs certainly did. Not because he believed Sanskrit should replace programming languages.

Because he wondered whether ancient linguists had already solved one tiny piece of a problem that modern AI was struggling with. It is a far more modest claim. And, ironically, a far more interesting one.

Where We Go Next

Now that we understand the world Briggs lived in, we can finally open the paper itself. Not a WhatsApp screenshot. Not somebody’s motivational speech. The actual paper.

We’ll discover that almost everything popularly claimed about it is absent. Instead, we’ll find an elegant discussion of Paninian grammar, semantic roles, and a deceptively simple question:

Can language be represented so precisely that a machine cannot misunderstand it?

That question — not Sanskrit as a programming language — is the real heart of the paper. And forty years later, it remains one of the central questions in Artificial Intelligence.

Opening the Paper: What Rick Briggs Actually Wrote

“The quickest way to kill a myth is to read the source material.”

We have finally reached the famous paper. The mythical document. The one supposedly proving that NASA declared Sanskrit to be the greatest programming language ever invented.

Let’s open it. No screenshots. No WhatsApp forwards. No patriotic YouTube commentary. The actual paper. It’s only eight pages. Not eight hundred. Not a classified NASA report. Not a secret government document. Just eight pages in an AI journal.

If every person who confidently repeated the claim had spent fifteen minutes reading those eight pages, this article would never have been necessary.

The Title Already Tells You the Story

The paper is called

Knowledge Representation in Sanskrit and Artificial Intelligence

Notice what is missing — Programming, Compilers, Software, Algorithms, Operating Systems, Computer Languages, Nothing. Instead we see two words that have been almost completely ignored for forty years.

Knowledge Representation.

Those words are not decorative. They define the entire paper.

The First Paragraph

Most people who quote the paper never quote its opening paragraph. Which is unfortunate because Briggs immediately explains his objective. Paraphrased, his argument is roughly this:

Modern AI needs precise ways of representing knowledge. Natural languages are often ambiguous. The Paninian grammatical tradition contains an unusually explicit method for representing semantic relationships. Perhaps AI researchers can learn something from it.

Notice what is absent. No claim that Sanskrit is perfect. No claim that computers should be programmed in Sanskrit. No claim that NASA has adopted Sanskrit. No claim that ancient India invented AI.

The paper is modest. Curious. Exploratory. Exactly what good science usually looks like.

What Problem Was Briggs Trying To Solve?

Imagine I say

John saw the man with the telescope.

Simple sentence.

Or is it? Who has the telescope? John? Or the man?

Humans often infer the answer from context. Computers cannot. They demand precision. Now consider another sentence.

Ram gave Sita a flower.

A computer still asks annoying questions. Who is giving? Who receives? What is transferred? Was ownership transferred permanently? Did the event happen yesterday? Will it happen tomorrow?

Humans unconsciously answer these questions. Machines cannot.

The central challenge of knowledge representation is converting ordinary language into an explicit structure where every important relationship is specified.

That is Briggs’ starting point.

Meaning Is More Important Than Words

This is one of the paper’s deepest ideas. Suppose these sentences are spoken.

Ram gave Sita a flower.

Sita received a flower from Ram.

A flower was given by Ram to Sita.

Three different sentences. One meaning. The surface words changed. The underlying event did not.

Briggs wasn’t interested in preserving the words. He wanted to preserve the meaning. Modern AI still struggles with exactly this distinction.

Panini Had Already Asked the Same Question

Nearly 2,500 years earlier, Panini wasn’t trying to build computers. He was trying to answer a linguistic question.

How do we describe the structure of language precisely enough that ambiguity is minimized? His answer eventually evolved into one of the most sophisticated grammatical traditions in human history. Not because Sanskrit was divine. Because generations of grammarians relentlessly refined it. The key insight was simple.

Sentences aren’t merely collections of words. They describe relationships. And relationships can be analyzed systematically. This is where the famous Kāraka Theory enters the story.

Meet the Kārakas

If this article has one Sanskrit term worth remembering, it is this one.

Kāraka.

Think of kārakas as semantic roles. Not grammatical cases. Not merely word endings. Semantic roles. Who did what to whom? That is their job. Paninian grammar identifies several fundamental relationships.

• The Kartā is the agent. The doer.
• The Karma is the object. The thing acted upon.
• The Karaṇa is the instrument.
• The Sampradāna is the recipient.
• The Apādāna is the source.
• The Adhikaraṇa is the location.

Instead of merely identifying nouns, Panini’s framework identifies the role each noun plays in the event being described. This distinction is crucial.

Briggs wasn’t excited because Sanskrit has seven or eight grammatical cases. Many languages do. He was interested because Paninian grammar explicitly separates semantic relationships from the surface order of words.

Why Word Order Matters Less in Sanskrit

English depends heavily on word order. Consider these sentences. “The dog bit the man.” “The man bit the dog.” Same words. Very different meaning.

Sanskrit behaves differently. Because grammatical case endings explicitly identify semantic roles, the words can often be rearranged while preserving meaning. This flexibility fascinated Briggs.

Not because it makes Sanskrit magical. But because it separates two things that English tends to intertwine. Surface order. Underlying meaning. That separation is exactly what knowledge representation tries to achieve.

The Mapping

This is the intellectual heart of the paper. Briggs asks:

Suppose we take Paninian analysis. Can we convert it into a semantic network?

The answer appears to be yes. Imagine the sentence

Ram cuts wood with an axe. Instead of storing a sequence of words, Briggs stores relationships.

Action (Cutting) → Agent (Ram) → Object (Wood) → Instrument (Axe)

Notice something. The representation no longer cares whether the original sentence was written in Sanskrit. Or English. Or Hindi. Or Japanese. It has become language-independent. The words have disappeared. Only meaning remains. That was precisely the goal of knowledge representation.

Ramo rajamaṇiḥ sadā vijayate rāmaṃ rameśaṃ bhaje, 
rāmeṇābhihatā niśācaracamū rāmāya tasmai namaḥ, 
rāmānnāsti parāyaṇaṃ parataraṃ rāmasya dāso'smyaham, 
rāme cittalayaḥ sadā bhavatu me bho rāma māmuddhara

"Rama, the jewel among kings, is always victorious; 
I worship Rama, the Lord of Lakshmi. 
By Rama, the army of demons was destroyed; salutations to that Rama. 
There is no higher refuge than Rama; I am the servant of Rama. 
May my mind always absorb itself in Rama; Oh Rama, please uplift me!"

This is a legendary Sanskrit grammar masterpiece of karaka.
It brilliantly demonstrates the Sanskrit Vibhaktis (Declensions) and 
contains the Karaka relationships (sentence roles like 
subject, object, instrument, etc.) in almost every single word for Rama

This Is Not Programming

Let’s pause for a moment. Nothing we’ve discussed resembles programming. There are

• no variables.
• no loops.
• no recursion.
• no functions.
• no memory allocation.
• no compiler.
• no machine code.
• no algorithms.

We are representing knowledge. Not instructing a computer to execute operations. That is the difference between

describing the world

and

telling a machine what to do.

One is knowledge representation. The other is programming. Confusing them is like confusing a dictionary with an operating system.

An Important Distinction Most People Miss

Natural languages answer questions like “What happened?” Programming languages answer questions like “What should the computer do?” Those are profoundly different objectives. Suppose I write

If the customer spends more than ₹10,000, apply a 15% discount.

A compiler transforms that into executable instructions. Now consider

Ram gifted Sita a flower.

Nothing executes. The sentence merely describes reality. Briggs was interested in representing descriptions of reality. Programming languages describe computations. The overlap is much smaller than internet folklore suggests.

Where Compiler Theory Actually Fits

This is a good place to dispel another common misconception. People often hear that Panini used rules. Compilers also use rules. Therefore, Panini invented compiler theory. The reality is subtler. Modern compilers are built using formal grammars. They parse tokens. Build syntax trees. Check correctness. Generate machine code.

Paninian grammar also has formal production rules. That similarity has fascinated linguists and computer scientists alike. But the objectives differ dramatically.

A compiler asks: “Is this program syntactically valid?”

Panini asks: “What relationships exist within this sentence?”

One produces executable code. The other analyzes meaning. The resemblance is real. The identity is not.

Later we’ll compare Panini with Noam Chomsky and modern compiler theory, showing where the comparisons are insightful — and where they become exaggerated.

The Most Important Sentence Briggs Never Wrote

Sometimes what an author doesn’t say is as revealing as what he does. Briggs never writes “Sanskrit is the world’s best programming language.” He never proposes writing operating systems in Sanskrit. He never suggests replacing Lisp. Or Prolog. Or C. Or Pascal.

He never argues NASA should adopt Sanskrit. Because that wasn’t his subject. It is difficult to overstate how much mythology has been built upon words that never appear in the paper.

The Missing Chapter

At this point, you may be wondering something. If the paper is about representing knowledge, what about reasoning? After all, representing facts is only half of intelligence.

• How do you derive new facts?
• How do you prove conclusions?
• How do you distinguish valid reasoning from flawed reasoning?

Surprisingly, Briggs says very little about that. Another Indian intellectual tradition specialized in exactly those questions. Not Paninian grammar. But Navya-Nyāya — the astonishingly sophisticated school of logic, inference and epistemology that flourished many centuries later. That omission turns out to be fascinating.

Because while Briggs focused on grammar, modern AI increasingly finds itself interested in both grammar and formal reasoning.

To understand why, we first need to explore a branch of Indian philosophy that many computer scientists have never heard of — but perhaps should have.

The Road Not Taken: Navya-Nyāya, Logic and the AI Nobody Talked About

“Panini taught us how sentences are built. Navya-Nyāya asked a harder question: how do we know a conclusion is actually true?”

By now we’ve established something important. Rick Briggs’ paper was not about programming. It wasn’t even primarily about logic. It was about representing meaning. Once meaning has been represented, however, another question immediately arises.

Now what? Suppose a machine knows that

Ram owns a cow.

and

Every cow is a mammal.

Can it conclude

Ram owns a mammal?

How? Who performs that reasoning? Paninian grammar doesn’t answer that question. Its job ends once the sentence has been analyzed. Reasoning belongs to another discipline. One that Indian philosophy developed to astonishing levels of sophistication.

Welcome to Navya-Nyāya.

Grammar and Logic Are Not the Same Thing

This distinction is so important that it’s worth repeating.

Grammar asks

What does this sentence mean?

Logic asks

Does this conclusion follow from the premises?

Those sound similar. They’re not. Imagine reading a detective novel.

Grammar helps you understand each sentence. Logic helps you identify the murderer.

One extracts meaning. The other derives new knowledge.

Panini worked on the first problem. Navya-Nyāya specialized in the second.

Meet India’s Logicians

The Nyāya school of philosophy dates back over two millennia.

Its later development, called Navya-Nyāya (“New Logic”), flourished around the 13th century onward, especially after the philosopher Gaṅgeśa Upādhyāya wrote the Tattvacintāmaṇi.

If Panini was the architect of linguistic precision, Gaṅgeśa was the architect of logical precision. The goal was breathtakingly ambitious. Remove ambiguity from reasoning itself. Not merely from language.

Why Philosophers Became Obsessed with Precision

Imagine two philosophers arguing. One says:

Fire exists on that hill.

The other asks:

“How do you know?”

The first replies:

Because I see smoke.

The second asks:

“Why does smoke imply fire?”

Now the discussion becomes complicated. Is smoke always caused by fire? What about steam? Dust? Fog? Magic? Coincidence? A philosopher cannot simply wave his hands and say, “You know what I mean.” He has to define every relationship with surgical precision.

Over centuries, Navya-Nyāya developed a technical vocabulary so exact that modern readers often struggle to understand it. Some historians have jokingly described it as one of the most precise philosophical languages ever invented. Not because it is beautiful. Because it is relentless.

The Five-Step Argument

Many readers have heard that Indian logic uses a five-part syllogism. Here it is.

1. There is fire on the hill.
2. Because there is smoke.
3. Wherever there is smoke, there is fire.
4. Like a kitchen.
5. Therefore the hill has fire.

Western logic eventually compressed this into shorter forms. Indian philosophers expanded it. Not because they enjoyed verbosity. Because every missing assumption could become a source of error.

Sound familiar? Modern software engineers call this making implicit assumptions explicit.

The Obsession with Ambiguity

Suppose I say

The pot is blue.

Most people stop there. Navya-Nyāya philosophers don’t. They immediately ask:

• What exactly does “blue” mean?
• Is blueness identical to the pot?
• Does blueness reside in the pot?
• Can blueness exist independently?
• Is blue perceived directly?
• Or inferred?
• How do we know the observer isn’t mistaken?

This sounds exhausting. It is. But it is also an attempt to eliminate ambiguity from reasoning itself. And ambiguity is precisely what computers hate.

Panini and Navya-Nyāya: Cousins, Not Twins

This is where internet discussions often become muddled. Both traditions value precision. Both use formal analysis. Both influenced later scholarship. Therefore people assume they are basically doing the same thing. They aren’t.

Think of a modern software company. The frontend team designs user interfaces. The backend team implements business logic. Both contribute to the product. Neither performs the other’s job.

Paninian grammar analyzes sentences. Navya-Nyāya analyzes reasoning. They complement one another. They are not interchangeable.

Why Didn’t Briggs Discuss Navya-Nyāya?

An obvious question arises. If Navya-Nyāya is so relevant to logical reasoning, why isn’t it the star of Briggs’ paper?

Because Briggs wasn’t trying to solve a reasoning problem. He was trying to solve a representation problem. Remember the sentence:

Ram gave Sita a flower.

Before a computer can reason about that event, it must first understand who gave what to whom. Paninian grammar helps with that. Only after the knowledge has been represented does logical inference become relevant. Briggs deliberately stopped at the first step. His paper is about encoding meaning, not proving conclusions.

Could Navya-Nyāya Have Been Relevant?

Absolutely. In fact, if Briggs had written a sequel titled

Inference in Artificial Intelligence and Navya-Nyāya

it would have made perfect sense. Modern symbolic AI contains two distinct stages. First, represent knowledge. Second, reason over that knowledge.

Panini contributes primarily to the first. Navya-Nyāya contributes primarily to the second. This distinction almost never appears in popular discussions. Yet it is arguably the most intellectually interesting part of the story.

The Strange Parallel with Modern AI

Here’s where things become unexpectedly contemporary. Suppose you ask ChatGPT:

Is every whale a mammal?

It answers correctly. Ask:

Therefore, if Moby Dick is a whale, is Moby Dick a mammal?

Again, correct. But here’s the puzzle. Did ChatGPT actually perform logical inference? Or did it simply predict statistically likely words?

For years, AI researchers assumed these were the same thing. Increasingly, they suspect they are not. This realization has reopened interest in symbolic reasoning.

Not because neural networks failed. Because they excel at some tasks and struggle with others.

Statistical Intelligence versus Logical Intelligence

Imagine two students. Student A has memorized ten million books. Student B knows only one thousand facts, but reasons perfectly.

Who performs better? The answer depends on the exam.

Modern LLMs resemble Student A. They have absorbed extraordinary amounts of text. Their strength lies in statistical generalization.

Symbolic systems resemble Student B. They know far fewer facts. But every conclusion follows explicit rules.

Each approach has strengths. Each has weaknesses. The future may belong to systems that combine both.

The Hidden Lesson

There is a deeper philosophical point hiding here. The greatest intellectual achievement of both Paninian grammar and Navya-Nyāya was not Sanskrit. It was precision. They attempted to answer questions like:

• What exactly does this sentence mean?
• What exactly follows from these premises?
• Which assumptions are explicit?
• Which assumptions are hidden?

Those are timeless questions. Computer science asks them. Law asks them. Mathematics, Software engineering, Artificial Intelligence asks them.

Different centuries. Different tools. Remarkably similar questions.

Where Modern AI Is Heading

For nearly two decades, deep learning dominated AI. The prevailing attitude was simple. Forget hand-crafted rules. Feed the neural network more data. Let it learn. That strategy produced astonishing successes.

• Image recognition.
• Speech recognition.
• Machine translation.
• Large Language Models.

But success exposed new problems.

• Hallucinations.
• Poor explainability.
• Weak logical consistency.
• Difficulty handling explicit world models.

Suddenly researchers began revisiting ideas many believed had been left behind.

• Knowledge representation.
• Ontologies.
• Knowledge graphs.
• Symbolic reasoning.

Not as replacements for deep learning. As partners. The pendulum wasn’t swinging backward. It was swinging toward integration.

The Twist Nobody Expected

This is the irony that almost every WhatsApp forward misses. The Briggs paper was never about making Sanskrit into a programming language. Yet forty years later, the AI community is indeed rediscovering the importance of structured representations, explicit relationships, and formal reasoning.

Not because Sanskrit is magical. Not because NASA predicted it. Because modern AI has independently rediscovered that statistical learning alone is not enough. The future increasingly looks like a marriage between neural networks and symbolic reasoning.

And that is where our story takes an unexpected turn. Because the descendants of those humble semantic networks from the 1980s never actually disappeared. They evolved. Into knowledge graphs. Ontologies. Neuro-symbolic AI. Retrieval-Augmented Generation. Function calling. Model Context Protocol.

In other words, the next chapter is not about ancient India. It is about the future of Artificial Intelligence. And, oddly enough, it brings us back to the same question Rick Briggs was asking in 1985:

How do we represent knowledge so precisely that a machine cannot misunderstand it?

From Semantic Nets to ChatGPT: The Great Comeback of Symbolic AI

“History has a habit of making fools of people who declare a field ‘dead.’”

By the late 1980s, Symbolic AI seemed unstoppable. Expert systems were diagnosing diseases. Semantic networks were representing knowledge. Logic engines were proving theorems. Governments invested billions. Companies built entire businesses around expert systems. People genuinely believed human-level AI was only a decade away.

Then… everything collapsed. Not because the ideas were wrong. Because the world turned out to be much messier than anyone had imagined. Ironically, the same story would repeat again thirty years later — only in reverse.

Why Symbolic AI Began to Fail

Imagine you are building an AI doctor. The first hundred rules are easy. If temperature exceeds 39°C… If chest pain accompanies shortness of breath… If bacterial culture is positive… Progress feels amazing.

Now comes patient number 10,001. They have diabetes. A rare autoimmune disorder. Two medications interacting. An unusual allergy. Pregnancy. Kidney failure.

Suddenly every new rule interacts with every old rule. The system doesn’t grow linearly. It explodes.

Computer scientists call this the combinatorial explosion. Every exception creates more exceptions. Every new fact affects countless old ones. The AI becomes increasingly difficult to maintain.

The Knowledge Acquisition Bottleneck Returns

Earlier we met the Knowledge Acquisition Bottleneck. Now it became catastrophic. Imagine teaching a machine everything humanity knows.

Who writes those rules? Doctors? Lawyers? Engineers? Biologists? Economists? Who keeps them updated? Who resolves contradictions?

Suppose medical guidelines change tomorrow. Thousands of rules must be rewritten. The intelligence wasn’t learning. Humans were. The machine was merely storing.

Eventually researchers reached an uncomfortable conclusion. Knowledge is not difficult to use. Knowledge is difficult to acquire.

Then Machine Learning Happened

Around the same time, another community within AI was quietly pursuing a very different idea.

Instead of writing rules… why not let computers discover them? Rather than manually defining what a cat looks like… show the machine ten million cats. Instead of teaching grammar… show it billions of sentences. Instead of encoding every medical rule… train on millions of medical records. This philosophy became Machine Learning.

Knowledge would no longer be written. It would be learned.

Deep Learning Changes Everything

For years machine learning made steady progress. Then came deep learning. Around 2012, neural networks suddenly became astonishingly good. Not because somebody discovered a magical equation. Three things happened simultaneously.

• Computers became vastly faster.
• GPUs became cheap enough for researchers.
• The internet produced unimaginable amounts of data.

The same mathematical ideas that had existed for decades suddenly became practical. Image recognition leapt forward. Speech recognition improved dramatically. Machine translation became usable. Then came Large Language Models. And the world changed almost overnight.

ChatGPT Doesn’t Work Like MYCIN

This difference cannot be overstated. MYCIN answered questions because doctors had manually written medical rules. ChatGPT answers questions because it has learned statistical patterns from enormous amounts of text.

No engineer explicitly programmed

Paris is the capital of France.

Or

Birds usually fly.

Or

Newton formulated the laws of motion.

Those relationships emerge statistically during training. This is a fundamentally different philosophy of intelligence.

The Strange Thing About LLMs

Ask ChatGPT

Who wrote Hamlet?

Easy.

Ask

Explain quantum entanglement to a ten-year-old.

Also easy. Now Ask

Write a poem in the style of Shakespeare about quantum computers.

No problem. This feels like intelligence. But here’s the uncomfortable question.

How? What exactly is happening inside the model? Nobody can point to a database row labeled “Shakespeare.” There isn’t a semantic network containing explicit facts. There isn’t a logical proof engine. Instead there are hundreds of billions of numerical parameters. The knowledge is distributed. Not stored symbolically.

The Rise of Vector Embeddings

To understand modern AI, we need one more idea. Instead of storing explicit facts, LLMs convert words, sentences and even images into mathematical objects called embeddings. Think of an embedding as a point inside a gigantic multidimensional space. Words with similar meanings end up near one another.

King. Queen. Prince. Princess.

Doctor. Nurse. Hospital. Medicine.

The model doesn’t store a dictionary definition. It stores relationships in geometry. Meaning becomes distance. Similarity becomes proximity. It is an astonishingly elegant idea. And completely different from symbolic AI.

Symbolic Knowledge versus Embedded Knowledge

Imagine asking two librarians where to find a book. The first librarian consults a meticulously organized catalogue. Everything has a precise location. The second librarian has memorized the entire library. She doesn’t consult a catalogue. She simply knows. Symbolic AI resembles the first librarian. LLMs resemble the second. Each approach has strengths. Each has weaknesses.

Why LLMs Sometimes Hallucinate

This brings us to one of the biggest surprises in modern AI. Large Language Models can produce breathtakingly intelligent answers. They can also confidently invent complete nonsense.

Why?

Because they are optimized to predict plausible continuations of text. Not to verify truth. Suppose thousands of training documents contain conflicting information. The model learns probability distributions. Not certainty. Sometimes the statistically likely answer is wrong. Hence the infamous hallucination.

Ironically, this is a problem symbolic systems almost never had. If a symbolic system didn’t know something, it usually admitted ignorance.

Explainability: The New Crisis

Suppose an AI rejects your bank loan. Why? Suppose an AI recommends surgery. Why? Suppose an AI convicts someone in court. Why?

Modern neural networks often cannot answer these questions in a satisfactory way. Not because they refuse. Because nobody truly knows how billions of parameters collectively produced the decision. This is called the black-box problem.

Increasingly, governments and regulators demand systems that explain themselves. Suddenly symbolic reasoning no longer seems old-fashioned. It seems useful.

The Pendulum Swings Back

Around the late 2010s, researchers noticed something intriguing. Neural networks were extraordinary at perception. Recognizing faces. Understanding speech. Generating language. But they struggled with explicit reasoning. Maintaining long chains of logical consistency. Working with structured knowledge. Verifiable inference. World models.

Symbolic AI had solved many of those problems decades earlier. Perhaps… instead of replacing one with the other… we should combine them. Thus began one of the fastest-growing areas in AI. Neuro-Symbolic AI.

What Exactly Is Neuro-Symbolic AI?

The idea is almost embarrassingly simple. Let neural networks do what they do best. Learning. Generalizing. Perceiving. Understanding messy data.

Let symbolic systems do what they do best. Reasoning. Tracking facts. Maintaining consistency. Explaining conclusions. Imagine asking an AI:

Which mammals native to India are endangered?

Instead of relying entirely on statistical memory, the system might: Understand your question using a neural network. Retrieve verified facts from a structured knowledge base. Apply logical reasoning. Generate a fluent natural-language answer. Each component performs the task it is best suited for.

Wait… Haven’t We Seen This Before?

Now comes the delightful irony. Remember Rick Briggs. His entire paper revolved around one question. How can knowledge be represented explicitly?

For nearly thirty years, the AI community largely moved away from that question. Now it is asking it again. Not because Briggs predicted ChatGPT. But because the underlying problem never disappeared. How should machines represent knowledge? That question has survived every AI revolution.

Semantic Nets Never Really Died

Many people think semantic networks vanished. They didn’t. They evolved. Just as dinosaurs didn’t exactly disappear. Some became birds. Semantic networks gradually evolved into more sophisticated structures. Knowledge graphs. Ontologies. Description logics. Semantic web technologies. Enterprise knowledge systems. The descendants are everywhere. The names simply changed.

A Different Kind of Intelligence

The biggest lesson from the past forty years is this. There isn’t one kind of intelligence. There are many. Recognizing a face. Understanding sarcasm. Following logical proofs. Remembering facts. Planning actions. Explaining decisions. Humans perform all of these seamlessly.

Computers often require entirely different techniques for each. The dream of one algorithm solving everything has become less fashionable. The dream of combining complementary approaches has become much more attractive.

The Next Evolution

So far we’ve spoken broadly about symbolic knowledge. But symbolic knowledge itself evolved dramatically. Semantic networks became knowledge graphs. Knowledge graphs merged with formal ontologies. Ontologies interacted with vector databases. Retrieval systems emerged.

• Large Language Models learned to call external tools.
• Structured schemas became fashionable again. Function calling. JSON outputs. Model Context Protocol.

Everything suddenly became… structured. The AI community was rediscovering something engineers have known for decades. Ambiguity is expensive. Precision scales.

And strangely enough, that realization brings us closer — not to Sanskrit as a programming language — but to the original intellectual motivation that fascinated Panini, Navya-Nyāya and, centuries later, Rick Briggs.

In the next section, we’ll see how Google’s Knowledge Graph, Retrieval-Augmented Generation, vector databases, ontologies and ChatGPT’s tool-calling abilities are all part of the same story: the gradual return of explicit knowledge to modern AI.

The Return of Structure: Knowledge Graphs, RAG, MCP and Why AI Is Becoming Explicit Again

“For a brief moment, the AI community believed that enough data could solve every problem. Then reality reminded us that ambiguity doesn’t disappear — it merely changes shape.”

By now we’ve reached one of the biggest plot twists in modern Artificial Intelligence. For nearly thirty years, AI moved steadily away from symbolic representations. Rules became unfashionable. Knowledge graphs looked old-fashioned. Semantic networks were considered relics. Ontologies became something enterprise architects talked about rather than AI researchers. The future, everyone believed, belonged to neural networks.

Then ChatGPT arrived. Instead of killing symbolic AI… it accidentally resurrected it.

The Problem Nobody Expected

When ChatGPT burst onto the scene in late 2022, people were amazed. It could write poetry. Explain quantum mechanics. Debug code. Translate languages. Write legal documents. Pass university exams. It felt almost magical.

Then people started asking awkward questions.

What is today’s stock price?

Sometimes wrong.

Who won yesterday’s football match?

Sometimes invented.

Cite the source.

Sometimes fabricated.

What’s the latest version of this software?

Often outdated.

The problem wasn’t intelligence. The problem was memory. Or more precisely, the difference between remembering and knowing.

A Human Analogy

Imagine asking a history professor: “Who was the first President of India?” She answers immediately. Now ask: “What was NVIDIA’s market capitalization this morning?” She opens a browser. Nobody thinks less of her.

In fact, we trust her more because she checks current information instead of pretending to remember everything. Modern AI is learning the same lesson. Sometimes the smartest answer begins with

“Let me look that up.”

Knowledge Stored in Weights

Large Language Models store knowledge very differently from databases. A database stores facts explicitly.

Name: France, Capital: Paris

Simple. Direct. LLMs don’t work that way. They compress vast amounts of information into billions of numerical parameters called weights. Knowledge is distributed across the network. Nothing says “Paris is stored here.” It emerges statistically during inference. This is both the model’s greatest strength… and one of its greatest weaknesses.

Updating one fact isn’t easy. You can’t simply edit one database row. You often need to retrain or fine-tune the model.

Enter Retrieval-Augmented Generation (RAG)

Researchers asked a wonderfully practical question. Instead of forcing the model to remember everything… why not allow it to consult external knowledge? Thus was born Retrieval-Augmented Generation, or RAG. The workflow is deceptively simple.

You ask a question.

↓

The system searches relevant documents.

↓

It retrieves the most useful information.

↓

Those documents are added to the model’s context.

↓

Only then does the model generate its answer.

Notice what changed. The model no longer needs perfect memory. It needs good reading comprehension. This is a profound shift.

ChatGPT Doesn’t Have to Know Everything

Imagine asking

What changed in Version 12.7 of my company’s internal software?

No public language model has seen that during training. Without retrieval, the model would guess. With retrieval, it first reads your documentation. Then answers. The AI hasn’t suddenly become more knowledgeable. It has become better informed. Those are very different things.

So Is RAG Just Google Search?

Not quite. Google returns documents. You read them. RAG returns documents. The AI reads them. Then synthesizes an answer. Think of RAG as giving the model an open-book exam instead of a closed-book one.

The Rise of Vector Databases

But here’s another puzzle. Suppose your company has ten million documents. How do you find the relevant ones? Traditional databases search exact matches. You search for “car” You get documents containing “car.” But what if the document says “automobile”? Or “vehicle”? Or “sedan”?

Humans know they’re related. Computers traditionally didn’t. This is where vector embeddings return. Remember those geometric representations from the previous chapter? Entire documents can also be embedded into high-dimensional space.

Documents with similar meaning cluster together. Instead of asking “Which document contains this word?” we ask “Which document is closest in meaning?” That is the job of a vector database.

Knowledge Graphs versus Vector Databases

This is one of the most misunderstood distinctions in modern AI. At first glance, both seem to store knowledge. But they answer completely different questions. A Knowledge Graph stores explicit relationships.

Einstein

↓

born in

↓

Ulm

↓

located in

↓

Germany

Every connection is intentional. Every relationship is explicit. A Vector Database stores similarity. It doesn’t know that Einstein was born in Ulm. It knows that documents about Einstein tend to resemble documents about relativity, physics, Nobel Prizes and theoretical science.

One stores facts. The other stores meaning. Both are useful. Neither replaces the other.

Then Where Do Graph Neural Networks Fit?

To make matters even more confusing, there is something called a Graph Neural Network (GNN). Despite the similar name, it solves an entirely different problem. Knowledge Graphs store structured relationships. Graph Neural Networks learn patterns from graphs.

Imagine Facebook. People are nodes. Friendships are edges. Could a neural network predict new friendships? Recommend connections? Detect fraud? That’s where Graph Neural Networks shine. They don’t replace knowledge graphs. They learn from them.

Ontologies: Graphs with Rules

Suppose a knowledge graph says

Dog

↓

is an Animal.

An ontology goes much further. It asks:

• Can every animal reproduce?
• Can a dead animal reproduce?
• Can an animal own property?
• Can a company be an animal?
• Which properties are inherited?
• Which are impossible?

An ontology is essentially a knowledge graph plus a formal rule book. Hospitals use them. Drug databases use them. Manufacturing companies use them. Governments use them. The Semantic Web was built around them. They are not glamorous. They are extraordinarily useful.

Why Google Built a Knowledge Graph

Have you ever searched

Albert Einstein

and immediately seen

birth date, birth place, spouse, awards, occupation without clicking any website? That’s Google’s Knowledge Graph at work. Google isn’t merely matching keywords. It understands that Einstein is an entity connected to thousands of other entities.

Search has become structured. Meaning matters more than words. Sound familiar? We’ve come a long way from those old semantic networks. Or perhaps… not that far at all.

Function Calling: AI Stops Pretending

Early language models answered every question themselves. Even when they shouldn’t. Modern AI increasingly admits, “I don’t know — but I know which tool does.” Suppose you ask

What’s the weather in Bengaluru right now?

The model doesn’t guess. It calls a weather service. Suppose you ask

Send an email.

It calls your email application. Need today’s exchange rate? It calls a financial API. This ability is known as function calling or tool use. The language model becomes an orchestrator rather than pretending to be the entire system.

JSON: The Language of Precision

Humans love prose. Computers do not. Suppose an AI answers “The customer’s age appears to be approximately twenty-five.” A human understands. A program hesitates. Is that 24? 25? 26? Now consider

{
  "age":25
}

No ambiguity. Modern AI increasingly communicates with software through structured formats such as JSON. Not because JSON is exciting. Because ambiguity is expensive. The entire industry is quietly rediscovering the value of explicit structure.

Model Context Protocol (MCP)

One of the newest developments in AI is the Model Context Protocol, or MCP. Think of it as a universal connector between language models and external tools. Instead of every application inventing its own custom interface, MCP standardizes how models discover tools, resources, documents, functions, schemas, and capabilities.

Rather than saying “Here’s some random text.” applications can say “Here is a structured database.” “Here is a spreadsheet.” “Here is a calendar.” “Here is a software function.” Everything becomes explicit. Everything becomes machine-readable.

If semantic networks were the filing cabinets of the 1980s, MCP is becoming the standardized office desk of the 2020s.

Prompt Engineering Was Never the Point

The internet spent two years obsessing over “prompt engineering.” People imagined there were magical phrases.

“Act as an expert.”

“Take a deep breath.”

“Think step by step.”

Some of those techniques help. But they’re not the real revolution. The real revolution is specification engineering. The better you specify your intent, constraints, schemas, business rules, available tools, desired outputs, the better modern AI performs.

The bottleneck is no longer writing code. Increasingly, it is writing unambiguous specifications. That observation would have sounded surprisingly familiar to Panini.

The Circle Closes

Look carefully at the journey we’ve taken.

Panini asked: How can meaning be represented precisely?

Briggs asked: Can those ideas help knowledge representation?

Modern AI asks: How should structured knowledge interact with neural networks?

Different centuries. Different technologies. The same underlying problem. Ambiguity. Every generation invents new tools. None escapes the need for precise representations.

But There’s Still One Problem

At this point, it is tempting to declare victory. Combine LLMs. Knowledge graphs. Ontologies. RAG. Vector databases. Function calling. MCP.Problem solved.

Not quite. Neuro-symbolic AI is enormously promising. It is also extraordinarily difficult. Knowledge graphs are expensive to build. Ontologies require domain experts. Symbolic systems can become brittle. Graphs can explode in size.

Logical reasoning becomes computationally expensive. The AI community has not “returned” to symbolic AI. It has learned that neither extreme is sufficient. Pure symbolic systems struggled with learning. Pure neural systems struggle with explicit reasoning. The future almost certainly belongs to hybrids.

And that brings us back, one final time, to the claim that started this entire journey.

Was the WhatsApp forward completely wrong? Yes.

Was Rick Briggs asking an interesting question? Absolutely.

But perhaps the most surprising lesson of all is that the real story — the one hidden beneath decades of exaggeration — is far richer, far subtler and far more exciting than the myth ever was.

In the next section, we’ll separate admiration from exaggeration, revisit the Chomsky–Panini comparison, explain why Sanskrit is not a programming language, and ask a more meaningful question:

What, exactly, should we admire about these ancient intellectual traditions — and what should we stop pretending they accomplished?

Panini, Chomsky and ChatGPT: What Ancient India Really Achieved

“Reality rarely needs exaggeration. It is usually impressive enough on its own.”

We have come full circle. We began with a WhatsApp forward claiming that NASA declared Sanskrit to be the world’s best programming language. Along the way we discovered something far more interesting.

The paper never said that. Rick Briggs never said that. NASA never said that.

Yet dismissing the entire discussion as “complete nonsense” would also miss something important.

There is a genuine story here. It simply isn’t the story most people tell. This section is about separating admiration from mythology. Because they are not the same thing.

Did Panini Invent Computer Science?

Let’s get the easy answer out of the way first. Panini did not invent computer science, programming, algorithms, operating systems, compilers, Artificial Intelligence, or software engineering. Those claims are historically indefensible.

Computer science emerged from the work of people like George Boole, Gottlob Frege, Alan Turing, Alonzo Church, Claude Shannon, John von Neumann, Donald Knuth, Edsger Dijkstra and many others over the past two centuries.

No serious historian of computing argues otherwise. That should not be controversial.

Then Why Do Computer Scientists Keep Mentioning Panini?

Because Panini solved an extraordinarily difficult problem. He created one of the world’s earliest and most rigorous formal grammatical systems. Notice the wording. Formal grammar. Not programming language. Not compiler. Grammar. That distinction matters.

Panini attempted to describe Sanskrit using an astonishingly compact system of production rules, transformations and meta-rules. Modern linguists continue to admire the elegance of that achievement. Admiration is justified. Exaggeration is not.

Enter Noam Chomsky

At this point another name usually appears. Noam Chomsky. Soon someone says:

“Chomsky copied Panini.”

Or,

“Panini invented generative grammar 2,500 years earlier.”

As with most internet claims, the reality is more nuanced.

What Chomsky Actually Did

In the 1950s, Chomsky revolutionized linguistics. His question was different. How can finite rules generate infinitely many valid sentences?

He introduced formal grammatical hierarchies that profoundly influenced both linguistics and computer science.

Today every computer science student learns about Chomsky Hierarchies. Regular grammars. Context-free grammars. Context-sensitive grammars. Recursively enumerable grammars. Compilers rely heavily on these ideas.

So Are Panini and Chomsky Similar?

Yes. Remarkably so. Both believed language could be described by precise rules. Both constructed highly systematic grammatical frameworks. Both separated underlying structure from surface expressions. Both influenced generations of scholars. The comparison is legitimate.

But similarity is not identity. Panini wasn’t answering Chomsky’s questions. Chomsky wasn’t answering Panini’s. Different eras. Different objectives. Different intellectual traditions. The comparison illuminates. It should not erase historical context.

Compiler Theory Isn’t Paninian Grammar

Here’s another myth worth retiring. People often hear: Panini wrote production rules. Compilers use production rules. Therefore Panini invented compiler theory. That conclusion doesn’t follow.

A compiler performs a very specific sequence of tasks. Tokenization. Parsing. Semantic analysis. Optimization. Machine code generation.

Paninian grammar performs linguistic analysis. It does not generate executable machine instructions.

The overlap lies in formal rule systems. Not in purpose.

One describes human language. The other translates artificial languages into machine instructions. Those are related ideas. Not identical ones.

Then Why Does the Comparison Feel So Tempting?

Because both disciplines love precision. Suppose I accidentally write

if (x > 5

Your compiler immediately complains. Missing parenthesis. Humans don’t. Compilers are unforgiving. Now suppose I write

The boy saw the man with the telescope.

Humans argue about who owns the telescope. Computers also struggle. Paninian grammar attempted to reduce precisely this sort of ambiguity. Different domains. Same obsession. Precision.

Is Sanskrit More Logical Than Other Languages?

This question appears everywhere. The honest answer is: It depends on what you mean. Sanskrit possesses rich morphology. Extensive case marking. Explicit grammatical relationships. Highly systematic grammatical analysis. Those are genuine strengths.

Does that automatically make it a better programming language? No. Programming languages are designed for an entirely different purpose. The comparison itself is misplaced. It’s like asking whether chess is a better accounting system than Excel. They’re solving different problems.

Human Languages versus Programming Languages

This confusion deserves its own section. Human languages evolved. Programming languages were engineered. That one sentence explains almost everything.

Natural languages tolerate ambiguity because humans effortlessly resolve ambiguity using shared context. Suppose I say: “I’m going to the bank.” River bank? Bank of India?

Humans infer the intended meaning almost instantly. Programming languages cannot afford that luxury. Imagine writing

x = y + ;

The compiler cannot guess what you meant. Programming languages eliminate ambiguity by design. Natural languages embrace it because humans are astonishingly good at interpretation. They evolved under different constraints.

But Wait… Doesn’t ChatGPT Program Using English?

This is perhaps the most interesting development of all. People sometimes say:

“Programming languages are becoming obsolete because we can now program in English.”

Not exactly. You describe your intent in English. The model generates Python. Rust. Go. Java. C++. TypeScript. SQL. Eventually, the computer still executes a formal language.

The English prompt is not the program. It is the specification. That distinction is becoming increasingly important.

Prompt Engineering Was a Transitional Phase

For a while everyone talked about prompt engineering. People hunted for magic phrases. “Act as an expert.” “Take a deep breath.” “Think step by step.” These tricks occasionally helped. But the industry has quietly moved on.

Modern AI increasingly depends on structured prompts, JSON schemas, tool definitions, typed outputs, business rules, validation constraints, function signatures, API specifications, retrieval pipelines, and external knowledge.

In other words, less magic. More engineering.

The Rise of Specification Engineering

This may become one of the most important professions of the AI era. Imagine asking an AI:

Build me an e-commerce application.

The output will probably be mediocre.

Now specify: Database schema. Authentication. Payment gateway. Tax rules. Inventory management. Concurrency. API contracts. Security requirements. Testing strategy. Deployment environment.

Suddenly the quality improves dramatically. The bottleneck has shifted. We’re no longer primarily writing software. We’re increasingly writing precise specifications.

If Panini were alive today, he might appreciate the irony.

What Modern LLMs Actually Learn

This is perhaps the biggest misconception surrounding ChatGPT. Many people imagine the model contains an internal grammar textbook. It doesn’t. It has never been taught Paninian grammar. Or Chomsky’s grammar. Or English grammar. Not explicitly.

Instead, it learns statistical regularities from enormous corpora of text. Grammar emerges. It is discovered. Not programmed. This distinction is fundamental.

The model doesn’t parse sentences the way a Sanskrit grammarian would. Nor does it reason like a Navya-Nyāya philosopher. It predicts remarkably plausible continuations based on patterns it has learned.

That turns out to be astonishingly powerful. But it is a different kind of intelligence.

The Limits of Neuro-Symbolic AI

At this point we should resist replacing one myth with another. Some enthusiasts now claim that neuro-symbolic AI will solve everything. History advises caution. Knowledge graphs are expensive. Ontologies require experts. Rules become brittle. Logical inference scales poorly.

Building structured knowledge bases for the entire world remains extraordinarily difficult. The future is unlikely to belong to purely symbolic AI. Nor to purely neural AI. Almost certainly it belongs somewhere in between.

History rarely rewards ideological extremes. Engineering usually prefers hybrids.

So… Was the WhatsApp Forward Completely Wrong?

Yes. The specific claim is false. NASA never declared Sanskrit to be the world’s best programming language. Rick Briggs never proposed replacing programming languages with Sanskrit.

The paper contains no recommendation to build AI systems in Sanskrit. No secret NASA project followed. No Sanskrit compiler revolution occurred. No sixth-generation computers appeared speaking Devanagari. Those claims belong in the folklore of the internet, not the history of computer science.

But Here’s the Better Story

The truth, in my opinion, is much richer. Ancient India produced remarkable intellectual traditions. Panini demonstrated astonishing formal thinking about grammar. Navya-Nyāya developed extraordinary precision in logic and epistemology.

Indian mathematicians transformed arithmetic. Indian astronomers made lasting contributions.

None of these achievements become more impressive by attaching NASA’s logo to them. If anything, doing so diminishes them. It suggests that ancient accomplishments require modern Western validation before we can appreciate them.

They don’t. Panini was extraordinary because he was Panini. Not because someone in California allegedly approved him twenty-five centuries later.

The Final Irony

Rick Briggs wrote an eight-page paper about knowledge representation. Forty years later, Artificial Intelligence is once again wrestling with knowledge representation. Not because Sanskrit is becoming a programming language. Because the underlying questions remain difficult.

How should machines represent meaning? How should they reason? How should they combine statistical learning with explicit knowledge? How should they explain their conclusions?

Those questions remain open. The tools have changed. The mathematics has changed. The hardware has changed. The ambition has not.

In the end, perhaps the greatest tribute we can pay to thinkers like Panini is not to transform them into mythical inventors of modern technology.

It is to recognize them for what they actually were:

Brilliant scholars who wrestled with problems of language, meaning and precision so profound that, more than two thousand years later, computer scientists are still asking surprisingly similar questions.

That is an extraordinary legacy. And unlike the WhatsApp forward, it happens to be true.

The AI Architecture Panini Never Saw: How ChatGPT Actually Thinks

“If Sanskrit isn’t inside ChatGPT… then what exactly is?”

By this point, we’ve debunked the myth. We’ve read the Briggs paper. We’ve travelled through the rise, fall and partial resurrection of Symbolic AI.

We’ve met semantic networks, knowledge graphs, ontologies, vector databases, Retrieval-Augmented Generation, function calling and Model Context Protocol.

Now comes the final question. If ChatGPT isn’t secretly parsing Paninian grammar… what is it actually doing?

This chapter is about the invisible machinery behind modern AI. Ironically, understanding it also explains why so many people misunderstood Briggs’ paper in the first place.

The Biggest Misconception About ChatGPT

Ask someone how ChatGPT works. The answer is usually something like: “It understands English.” Or “It understands grammar.” Or “It thinks before answering.” All three are simultaneously true… and misleading.

ChatGPT doesn’t understand language the way you do. It doesn’t understand Sanskrit the way Panini did. It doesn’t reason the way a Navya-Nyāya philosopher would. It performs something far stranger. And, in many ways, far more beautiful.

Step One: The Sentence Disappears

Suppose you type

Ram gave Sita a flower.

You see a sentence. ChatGPT doesn’t. The first thing it does is break your sentence into tokens. Tokens are not necessarily words. Sometimes they are whole words. Sometimes pieces of words. Sometimes punctuation. Sometimes spaces.

The sentence immediately stops being language. It becomes a sequence of symbols. Think of tokenization as cutting a paragraph into LEGO bricks. The AI doesn’t yet know what the sentence means. It merely knows how to divide it into manageable pieces.

Step Two: Words Become Numbers

Computers cannot manipulate words. They manipulate numbers. Each token is converted into a gigantic vector. Imagine replacing every word with a list containing several thousand numbers.

Not random numbers. Coordinates. Every word becomes a point in an unimaginably high-dimensional mathematical space.

Humans see

elephant

The computer sees

[0.281,
-1.943,
0.004,
...
2048 numbers later...]

Meaning has begun transforming into geometry.

The Astonishing Discovery

Here’s where AI becomes almost magical. Nobody explicitly tells the model that

King → is related to → Queen.

Or that Paris → is related to France.

Or that Doctor → is related to → Hospital.

Instead, during training, those relationships naturally emerge. Words used in similar contexts drift toward one another inside this enormous geometric space.

Meaning becomes distance. Similarity becomes location. Language becomes geometry. This idea, more than anything else, revolutionized modern AI.

The Famous Example

Researchers discovered something astonishing years before ChatGPT. Take the mathematical representation of King.

Subtract Man.

Add Woman.

The resulting point lands surprisingly close to

Queen.

Nobody programmed that rule. The geometry discovered it. Similarly,

Delhi minus India plus Japan

often lands near Tokyo.

The model isn’t manipulating facts. It is navigating geometry. That is a radically different philosophy from symbolic AI.

Then Comes the Transformer

So far the AI has merely converted words into vectors. Now comes the breakthrough that changed everything. The Transformer architecture. Published in 2017 in the paper

Attention Is All You Need

it fundamentally changed how machines process language. Previous systems read sentences almost sequentially. Transformers do something cleverer. Every word can “look at” every other word. Simultaneously.

Suppose you write

The trophy didn’t fit into the suitcase because it was too small.

What does it refer to? The trophy? Or the suitcase? Humans answer instantly. Transformers learn to focus attention on whichever words help resolve the ambiguity. This mechanism is called Attention. It is one of the greatest breakthroughs in AI over the past decade.

Does ChatGPT Learn Grammar?

Here’s the surprising answer. Yes. And no.

Nobody sits down and teaches ChatGPT English grammar. Nobody uploads Panini’s Aṣṭādhyāyī. Nobody explains Chomsky’s generative grammar.

The model simply reads vast amounts of text. Eventually, grammar emerges. It becomes statistically useful. Children learn something similar. Nobody teaches a toddler formal linguistics. Yet toddlers eventually speak grammatically.

The mechanism differs. The outcome resembles learning.

Why This Is Different From Panini

Panini begins with explicit rules. Rule 1. Rule 2. Rule 3. Exception. Meta-rule. Transformation. Everything is written down.

ChatGPT begins with almost nothing except mathematics. Billions of examples. Billions of adjustments. Eventually the network organizes itself. One system begins with rules. The other discovers patterns.

That distinction explains why comparing Panini directly with ChatGPT is fascinating — but limited.

Latent Representations

One of the most mysterious concepts in AI is something called a latent representation. Think of it as the model’s internal understanding. Suppose I ask

What is the capital of France?

The model doesn’t retrieve one database row. Instead, information about France, Europe, geography, language, history, capitals, tourism, politics, and thousands of related concepts become activated simultaneously.

The internal representation is distributed across millions or billions of parameters. Knowledge doesn’t exist in one place. It exists everywhere. And nowhere.

Why Nobody Can Open ChatGPT’s Brain

This leads to an extraordinary consequence. Imagine opening Excel. Every number sits in one cell. Easy. Now imagine opening ChatGPT.

Where is Newton? Where is Einstein? Where is Sanskrit? Nobody knows.

The knowledge isn’t stored like files in folders. It is encoded across billions of numerical relationships. Researchers call this the black box problem. We can observe what goes in. We can observe what comes out. Understanding exactly what happens inside remains one of the biggest open problems in AI.

Does ChatGPT Actually Reason?

Now we reach perhaps the hottest debate in Artificial Intelligence. When ChatGPT solves a difficult mathematics problem… did it reason? Or did it merely generate statistically plausible text?

The honest answer is: We still don’t fully know. Evidence suggests LLMs perform forms of reasoning. But they often fail on surprisingly simple logical tasks.

Sometimes they solve problems requiring remarkable insight. Sometimes they confidently make elementary mistakes. Their reasoning appears real. Yet fundamentally different from symbolic proof systems.

Understanding this difference is one of today’s most active research areas.

Why Reasoning Models Are Different

Recent AI models increasingly separate language generation from reasoning. Some models deliberately spend more computation “thinking.”

They break problems into steps. Evaluate alternatives. Verify intermediate conclusions. Call external tools. Search documents. Run code. Notice something interesting. This starts resembling… Symbolic AI again.

Not replacing neural networks. Building on top of them.

The Rise of AI Agents

The next generation of AI won’t merely answer questions. It will perform tasks. Book flights. Write software. Run experiments. Analyze financial reports. Control robots.

To do that, the model needs more than language. It needs planning. Memory. Goals. External tools. Verification. Long-term reasoning.

The industry increasingly calls these systems AI Agents. Their architecture looks remarkably different from early chatbots. And much closer to software systems than to simple language models.

Why Explicit Specifications Suddenly Matter Again

Here’s a trend that quietly transformed AI in the last two years. The best-performing systems increasingly rely on explicit specifications. Schemas. Contracts. APIs. Function definitions. Validation rules. Business constraints. Tool descriptions. Machine-readable metadata. Sound familiar?

Once again, ambiguity becomes the enemy. The more precisely humans describe the world, the better AI performs. Programming is slowly becoming less about writing algorithms… and more about describing reality precisely.

A Curious Historical Echo

Now step back for a moment. Panini spent his life reducing ambiguity in grammar. Navya-Nyāya reduced ambiguity in reasoning. Software engineers reduce ambiguity in specifications. Modern AI increasingly rewards explicit schemas.

Function signatures. Typed interfaces. Knowledge graphs. Ontologies. JSON structures. MCP resources.

These developments are not evidence that ancient India invented AI. But they do reveal something timeless. Across two and a half millennia, engineers, linguists and philosophers keep rediscovering the same lesson. Ambiguity is expensive. Precision scales.

So… What Should We Learn from All This?

Not that Sanskrit should replace Python. Not that NASA secretly endorsed Panini. Not that ChatGPT thinks in Devanagari.

The lesson is deeper. Every generation invents new mathematical tools. Every generation builds new machines. Every generation believes it has finally escaped the old problems. Then, sooner or later, the same questions return.

• How should meaning be represented?
• How should reasoning be verified?
• How should ambiguity be eliminated?
• How should knowledge be organized?

Those questions connect Panini, Navya-Nyāya, Rick Briggs, semantic networks, knowledge graphs, transformers, and modern AI. Not because they solved the same problem. Because they all grappled with different parts of one enduring challenge:

How do we transform messy human thought into precise representations that can be analyzed, manipulated and trusted?

That challenge existed twenty-five centuries ago. It exists today. And it will almost certainly exist long after today’s AI architectures themselves become history.

The Last Word

The Sanskrit–NASA myth survives because it offers a simple story. “Ancient India invented AI.” Reality refuses to cooperate. Reality is slower. Messier. Nuanced.

It acknowledges Panini without turning him into Alan Turing. It admires Navya-Nyāya without pretending it was Python. It appreciates Rick Briggs without attributing words he never wrote. And it recognizes that modern AI is not validating ancient Sanskrit.

It is independently rediscovering, through entirely different mathematical pathways, that precision, structure and explicit representation remain indispensable.

That is not a slogan. It is a far more satisfying conclusion. Because unlike the WhatsApp forward… it doesn’t require inventing history to appreciate it.

Why We Keep Inventing Ancient Superpowers

“History deserves admiration. It does not require embellishment.”

By now we’ve answered the original question. NASA never declared Sanskrit to be the world’s best programming language. Rick Briggs never claimed Sanskrit should replace Lisp. Or Prolog. Or Python.

The famous paper discussed knowledge representation. Not software engineering. Not compiler design. Not artificial intelligence in the way most people imagine it today.

The myth is false. Case closed. Or is it?

Because another question remains. Perhaps the more interesting question. Why do these stories keep appearing? Not just this one. All of them.

The Pattern

Once you notice the pattern, you begin seeing it everywhere.

• Ancient India had plastic surgery.
• Ancient India had stem-cell technology.
• Ancient India had airplanes.
• Ancient India had nuclear weapons.
• Ancient India had television.
• Ancient India had the internet.
• Ancient India had genetic engineering.
• Ancient India had test-tube babies.
• Ancient India had quantum physics.
• Ancient India had artificial intelligence.

Every few months another modern invention mysteriously turns out to have existed five thousand years ago. Curiously, almost none of these claims come from archaeologists. Or historians. Or physicists. Or engineers.

They usually arrive through motivational speeches, television debates, political speeches, social media, or WhatsApp forwards. That alone should make us pause.

The Validation Trap

Imagine two ways of praising Panini.

Version One:

Panini created one of the greatest grammatical systems in human history.

Version Two:

NASA proved Panini invented Artificial Intelligence.

Which sounds more impressive?

The second. Which is more respectful? The first. Because the first praises Panini for what he actually accomplished. The second quietly suggests that Panini becomes important only after modern America allegedly approves him.

That is a strange form of admiration. It accidentally diminishes the very tradition it claims to celebrate.

Why Reality Feels Smaller Than Myth

Reality has an unfortunate habit. It contains details. Qualifications. Nuance. Science rarely says “always.” It usually says

• “probably.”
• “Evidence suggests…”
• “Current understanding…”
• “Within this confidence interval…”

Myths dislike uncertainty. They prefer certainty. They prefer headlines.

“NASA proves Sanskrit is best.”

is easier to remember than

“A NASA-affiliated researcher published an exploratory paper comparing Paninian semantic analysis with knowledge representation frameworks.”

Unfortunately, the second sentence happens to be true.

The WhatsApp University Effect

Every myth evolves the same way.

Step One.A genuine historical fact.

Step Two. A small exaggeration.

Step Three. A confident simplification.

Step Four. Patriotic repetition.

Eventually, the original source disappears completely. We saw exactly this happen with Rick Briggs. He wrote

Paninian grammar contains interesting ideas for knowledge representation.

The internet heard

Sanskrit is useful for AI.

That became

NASA uses Sanskrit.

Finally,

Future supercomputers will be programmed entirely in Sanskrit.

Every retelling removed one qualifier. Every retelling increased confidence. Truth shrank. Certainty grew.

Why Scientists Get Annoyed

Scientists are often accused of lacking patriotism when they reject such claims. The opposite is true. Science has only one patriotism. Evidence.

If tomorrow archaeologists discover a 2,500-year-old programmable computer, scientists will celebrate. If tomorrow they discover ancient batteries, scientists will investigate. If tomorrow they discover mathematical ideas centuries ahead of their time, scientists will rewrite textbooks.

Science has done this repeatedly. Zero. Calculus. Heliocentrism. Plate tectonics. DNA. Ancient genomes.

Everything changed because evidence changed. Science isn’t opposed to astonishing discoveries. Science simply asks for astonishing evidence.

Ancient India Doesn’t Need Superpowers

Let’s list a few genuine achievements. The decimal positional number system.

• Zero as a number.
• Sophisticated astronomy for those times
• Remarkable metallurgy.
• Advanced medicine for that time.
• Philosophy.
• Logic.
• Grammar.
• Poetics.
• Linguistics.
• Mathematics.
• Urban planning.
• Steel production.
• Textile technology.

These aren’t myths. They’re history. And they are extraordinary. Why trade them for fantasies? It is like owning a genuine diamond and insisting it is secretly kryptonite. The diamond was already valuable.

The Strange Asymmetry

Notice something curious. Nobody claims Newton invented mobile phones. Nobody claims Euclid discovered quantum mechanics. Nobody claims Aristotle designed laptops.

Why? Because Western scientific traditions are usually celebrated for what they actually achieved. Only in some circles do we feel compelled to continuously upgrade ancient accomplishments into modern technologies.

That impulse deserves examination.

Civilizations Don’t Compete Like Cricket Teams

There is another misconception hiding underneath. People often imagine civilizations competing. Who invented more. Who was first. Who deserves credit.

History doesn’t work that way. Knowledge accumulates. Babylonians contributed. So did Egyptians, Greeks, Indians, Chinese, and Persians. Arabs preserved and expanded knowledge. Europe transformed it during the Scientific Revolution.

Modern science became global. Every civilization inherited from others. Every civilization added something. Human knowledge is one long relay race. Not a one-day cricket match.

The Danger of Myth

Some readers might ask: “So what if people exaggerate a little?” Because exaggeration has consequences. When students eventually discover the myth, they often conclude that everything else is also false.

That is tragic. The genuine achievements become collateral damage. The best way to preserve history is not to decorate it. It is to understand it accurately.

Rick Briggs Deserved Better

Ironically, Rick Briggs himself became a victim of the myth. He wrote an interesting paper. Forty years later, millions know his name. Almost none know what he actually wrote.

His paper became famous for a sentence it never contained. There is something profoundly unfair about that. Scholars deserve to be read. Not merely quoted.

Panini Deserved Better Too

The same applies to Panini. His genius lay in constructing an astonishingly elegant system for describing language. That achievement has fascinated linguists for centuries. It does not become greater by pretending he invented Python. If anything, such claims distract from the real brilliance of his work.

Imagine introducing Shakespeare by saying “He was basically the inventor of Netflix.” Ridiculous. Shakespeare deserves better.

So does Panini.

Perhaps the Greatest Lesson

There is one lesson that stayed with me while researching this series. Truth is rarely disappointing. It is usually more interesting than mythology. The WhatsApp version gives us a fictional NASA endorsement.

Reality gives us something far richer.

• An ancient grammarian.
• A medieval logician.
• An AI researcher in 1985.

The rise and fall of Symbolic AI. The birth of deep learning. The emergence of ChatGPT. The return of structured knowledge. The rediscovery of explicit reasoning.

Those threads genuinely connect across two thousand years. Not through prophecy. Not through miracles. Through ideas.

Ideas that different generations rediscovered independently because they were grappling with remarkably similar problems. That story doesn’t need embellishment. It is already extraordinary.

One Final Thought

The measure of a civilization is not whether it invented every modern technology. No civilization did. The measure of a civilization is whether it produced ideas that continued to matter long after the people who first imagined them were gone.

By that standard, Panini unquestionably succeeded. Navya-Nyāya unquestionably succeeded. Indian mathematics unquestionably succeeded.

Their influence does not depend on NASA. Or WhatsApp. Or political speeches. The greatest tribute we can pay them is remarkably simple.

• Read what they actually wrote.
• Understand what they actually accomplished.
• Celebrate them honestly.

Because history, when told truthfully, is already magnificent. It doesn’t need science fiction.