Generating Code Is Not the Same Problem as Inspecting Code

Few people realize how brutally complicated programming has become.

Software used to feel almost like a box: write the code, run it, fix the bugs.

That world is mostly gone.

Today, serious software lives inside huge interconnected systems spread across APIs, cloud services, databases, queues, regions, security boundaries, regulations, and third-party software.

A fintech platform may serve banks, currencies, jurisdictions, regulators, institutional clients, and risk models at the same time.

A hospital platform may span hospitals, countries, privacy rules, insurers, devices, workflows, and legacy systems.

And once software reaches this level of interconnected complexity, one old truth becomes impossible to ignore:

Generating code is not the same problem as inspecting code.

Writing something correctly in the first place and finding what went wrong afterward are fundamentally different tasks. But the explosion of AI coders has almost erased that distinction.

We have started confusing:

The AI can find the error after we give it feedback.

with:

The AI understood enough of the problem to generate the right code from the start, given the right prompt.

That is not the same capability. And in real-world software, confusing the two can get expensive fast.

The rest of this article is about why that happens and how we can use today’s Transformative AI with a Transportive mindset while we work toward systems capable of carrying nested semantic context correctly from the start.

You Are Coding Through a Minefield With No Warnings

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Not All Programming Languages Are Created Equal

The key to using AI productively in coding is knowing exactly where the contextual minefields are hidden before your AI coder blows up the entire project.

Of all the myths around AI and coding automation, this may be the most damaging:

AI will soon make developers unnecessary.

The logic sounds obvious. All programming languages collapse into one common abstraction, the LLM learns it, and humans get pushed out of the loop.

AI is already very good at writing the visible code, but it still sucks when it has to deal with what actually makes a real-world project work: the hidden structure of the programming language.

If you thought programming languages were just different syntaxes for the same thing, sorry, but you are wrong. Not all programming languages are created equal. Some hide layers of context, ownership, state, order and semantic rules that all have to remain correct at once.

Or, in plain words: the different contexts that even one simple line of code can carry with it make the myth completely false and the developer in the loop more necessary than ever… depending on the programming language. And once you see it, you will know exactly which group you are in.

Case in point: The C++ Nemesis

That AI can write C++ does not mean that it can build a reliable C++ project.

It can generate templates, write RAII, produce concepts, iterators, lambdas, move constructors, forwarding wrappers, allocators and concurrency code that looks frighteningly competent. Fine.

Yeah, we agree with the fearmongers here. Much of that visible code production is already being automated.

But don’t pop the AI champagne yet.

Because C++ has a nasty property that languages such as Go expose much less aggressively: the meaning of a line can depend on several interacting semantic contexts that are not visible in the line itself.

A piece of C++ can compile cleanly, pass superficial tests and still be wrong because correctness depends on something living several semantic layers away: name lookup, ADL, template dependence, overload resolution, concepts, value categories, object lifetime, ownership, concurrency, you name it.

And this is exactly where AI can screw up your entire project.

The problem is not that it cannot produce the syntax. The problem is that C++ can require something closer to

(code, nested contexts) → meaning

rather than simply

code → meaning.

Now you know where current AI sucks most: nested contexts, whether in C++ or in any other programming language that relies heavily on them.

And if you have read my articles before, this is another smoking gun for why we need a completely different kind of AI, built on a different mathematical foundation from the one we use today.

So the important point is simple:

The line of code is only the visible surface. The real semantics live underneath. And that is exactly where current AI sucks.

Why AI cannot understand what an average developer does without effort

This is the question that really matters to any developer, not just C++ developers, who feels threatened by automation.

An average developer, let alone a senior one, can look at a line of code and immediately recover much of the context surrounding it.

So what is so difficult for an AI about something you do almost routinely?

The animation below will look as familiar to you as the ABC of C++, but the same idea applies to any language with several nested contexts hiding behind even a single line of code.

This simple fact kills any AI coder. This is not a matter of more or less human supervision. The problem is that AI is blind to many of those nested contexts, as you will see in the examples coming next.

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We are surrounded by myths all the time. Yes, even in science and technology.

By the time scientific results reach the public, they have often passed through expectations, money, marketing, and a lot of storytelling. The mathematical details that spoil a good headline rarely survive the trip intact.

AI is the extreme case

We have built an astonishingly powerful machine, but also an increasingly complicated one. When something breaks, the instinct is often to scale the machinery instead of questioning the mathematics underneath it.

But what if scale is not the problem at all? What if better mathematics can eliminate the problem before brute force even begins?

A small group of researchers is already exploring exactly that through geometry and symmetry, equivariance, topology, curved and hyperbolic representations, and related mathematics.

Most of this work still barely reaches the mainstream AI conversation.

That is where this story begins.

We are going to take some of the most familiar beliefs about AI and expose their weaknesses, and in some cases their outright falsehoods, using the mathematics that a more structurally sound AI could be built on.

You will see it for yourself through original animations that make these geometric and topological ideas almost impossible to miss: the real problem with AI may not be that our models are too small, but that we built them in the wrong mathematical space.

Don’t believe me?

Keep reading. By the end, your view of AI will be completely turned upside down.

Most of the AI World Still Isn’t Having This Conversation in Public

This is not another debate about consciousness, AGI, or whether AI will take your job.

I am talking about the mathematics underneath the models. As mentioned before, a small group of researchers is already working on geometry, equivariance, hyperbolic representations, symmetry, topology, and related ideas. But most of that work is still scattered across papers and small technical communities.

Meanwhile, if you are working on AI models or simply using them, you are probably getting tired of the same recipes for the shortcomings of current AI:

Bad representation? Add dimensions.
Weak understanding? Add examples.
Poor memory? Add context.
Weak performance? Train more.
Not enough capability? Give the model more freedom.

Sometimes that works, though with sloppy mathematics.

Often, it just gives you a bigger version of the same problem.

Which leaves a much more interesting question:

How much of what we force AI to learn by brute force is really a consequence of choosing the wrong mathematics in the first place?

That is the question behind everything that follows.

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TL;DR — What You’re About to Learn

This crash course is for AI engineers, investors, and advanced users who want to know how you can actually prove that an AI model deserves to be trusted when it makes predictive or otherwise falsifiable statements.

We will look at why benchmarks so often give the wrong impression, why genuinely unseen data changes the game, and how tools such as Monte Carlo simulation, Kupiec tests, Christoffersen tests, Bayesian monitoring, etc. can turn vague claims about AI reliability into something you can actually measure, test, and certify.

The central idea is simple: do not grade the AI. Grade the task. Then keep testing that grade as reality keeps producing new evidence.

That is what this crash course is about—and I will take the math seriously.

The Most Expensive Technology in History Still Comes With “Don’t Trust Me”

No exaggeration here: the most brutal thing we find in today’s AI industry is the wild contrast between its extraordinary ability to transform $1 trillion into powerful hardware and AI models and the obvious, frustrating impotence of those same models when they fail miserably at handling real-world tasks with consistent reliability.

Even more frustrating is how their CEOs, despite selling this frail technology, constantly remind us that we should let these systems replace humans by automating entire sectors of the economy, writing production software, diagnosing diseases, managing companies …  you name it.

Crazy, isn’t it?

However, you hear exactly the opposite whenever the industry is challenged to show measurable proof behind its revolutionary marketing claims:

Of course we can back our claims. Look at our benchmarks. What else do you need? AI sellers keep telling us, over and over again — despite including a small-print footnote in every chatbot: You cannot trust our AI.

Well, that is exactly the problem. Benchmarks are far away to be a good standard for objectively measuring the reliability, reproducibility, and predictive power of an AI model.

Benchmarks can be perfectly useful for assessing the merits of hardware, programming languages, and even toasters.

But when it comes to systems that must distinguish signals in an ocean of noise, where many real-world decisions ultimately reduce to true positives, true negatives, false positives, and false negatives — a benchmark score can look magnificent on a leaderboard… and collapse the moment it meets reality.

In fact, as we are about to see, the benchmark itself has recently become part of the problem.

Let’s see this in its most brutal form.

If the Student Knows the Exam Beforehand, What Exactly Are You Grading?

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The biggest misconception about AI

Few people realize that much of our technological civilization, including advanced AI, rests on something that sounds almost impossible:

printing chips with light. But not with ordinary light.

Visible light is far too blunt to print the smallest structures demanded by today’s most advanced processors.

Because the process required to do it is so absurd that it sounds like the opening scene of a science-fiction movie.

The adventure, however, begins humbly, with one of the most ordinary elements in the periodic table:

Yep. Not some exotic substance harvested from a meteorite. Not a rare element available only to governments.

Nope.

Just a common, inexpensive metal, melted into a droplet barely a third the width of a human hair.

Then the real madness begins. Good luck with that. Twice.

First, you need a laser pulse to strike a single tin droplet and flatten it into a tiny disk.

Then, a fraction of a microsecond later, you must strike that same microscopic droplet with a second, vastly more powerful pulse.

Before a single second has passed, tens of thousands of droplets have already met the same fate. Droplet after droplet, the swarm ignites, each tiny sphere erupting into incandescent plasma at around 220 thousand °C, nearly forty times hotter than the surface of the Sun.

For one fleeting instant, you have created a tiny artificial star inside a laboratory. And from that star comes the prize you were searching for:

extreme-ultraviolet light EUV

The light capable of printing the smallest structures inside the most advanced chips on Earth.

There is only one problem.

The moment EUV light touches almost anything, it disappears.

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The AI industry will confidently promise to reinvent education, medicine, science, dating, warfare, and lately even your mortality.

But ask what its coding models can already do, and everyone suddenly turns into a nervous, defensive salesperson.

Well, it depends.

The model may produce incorrect results.

Human supervision is still recommended.

No kidding. Human supervision is also recommended when humans write software. That is why we have code reviews, tests, staging environments, debuggers, incident reports, and senior developers staring at broken deployments late into the night.

The strange thing is that AI coding is being judged by standards that human programmers have never met. One camp shows an agent building a tiny web app and announces that software engineers are finished. The other watches the same agent misspell a variable and announces that machines cannot reason.

Both conclusions are ridiculous, and both come from the same statistical mistake.

Everyone stares at the numerator: how many coding tasks did the AI solve?

Almost nobody checks the denominator: what kind of tasks were they?

And that denominator contains nearly the whole story:

Was the model asked to write a ten-line utility function or repair a payment system spread across forty services?
Did it receive the whole repository or three disconnected files?
Could it run the tests?
Were the tests any good?
Did the documentation match the code?
What kind of task was the model actually given?

So here’s the first hint: whenever you analyse an AI coder, inspect its soft underbelly — the denominator: a ten-line function is not a forty-service payment system. A full repository is not three pasted files. A model with tests, logs, tools, and retries is not the same machine as a chatbot forced to guess from a vague prompt.

AI coding ability does not come from a clever little homunculus — a tiny programmer hiding inside the model and doing all the thinking.

It comes from the whole setup:

model + context + tools + feedback + human judgment

Change the setup, and the same AI can look brilliant, useless, or confidently break a system it never understood.

And this is where both camps rig the experiment.

The optimists show the cleanest run. The pessimists remove half the context, tie one hand behind the agent’s back, and present the crash as scientific proof.

Neither tells you when the machine is actually useful.

That is the skill worth learning: knowing whether the agent simply needs more context, has made a bad guess, has written code that only looks correct, or has wandered so far from the architecture that you need to drag it back by the collar.

Learn to spot the difference, and AI coding becomes less of a dark art — and more of a practical defensive skill.

SOFTWARE FOLKLORE IS LYING TO YOU

Let’s start with the myths that keep us from understanding AI coders — and from using them properly.

Look at the table below. These are the stories about AI that, if you’re a developer, you’re probably tired of hearing from other developers — even though, as you’ll see, the evidence points the other way.

Software engineering is not uniquely irrational, of course. Every field has its favorite myths. But ours are especially bizarre because developers love talking about evidence … right up until AI coding comes up.

So let’s put those beliefs on trial and see which side the evidence actually favors: the techno-optimists or the techno-pessimists.

Spoiler: neither.

Myth One: The Parrot Alibi

AI coding only works because the model has already memorised the answer.

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Welcome to the First TechUnderWorld Skool Crash Course

This is the first course in a series designed to prepare you for the next generation of AI—far beyond the usual tutorials, headlines, and recycled explanations.

We will not only examine what is broken in today’s AI.

We will explore the most serious mathematical and engineering ideas being developed to fix it: the same problems currently keeping the major AI labs busy.

But you do not have to wait for those labs to package the next generation into a product.

Here, you will learn how to recognize what is coming before it reaches the mainstream.

What is the trick?

The right mathematical and engineering mindset.

We will move through research papers, patents, emerging architectures, and recent mathematical developments to build a precise map of what is happening—both from above and deep inside the machinery.

You will learn which technologies could shape AI over the coming months and years, why they matter, and how to evaluate them for yourself.

That last part is the most important.

The goal is not merely to give you information.

It is to help you develop the skills and instincts to navigate the next AI wave independently.

TechUnderWorld Skool delivers hard facts, real mathematics, and serious engineering in simple language—told as an immersive, almost cinematic story.

Enjoy the course.

Prepare for a Semantic Machine, Not a Token Parrot

We talk to language models in messy human language: incomplete, ambiguous, and packed with assumptions we never bother to state.

And you already know the price. The same damn ritual: prompt. Correction. Clarification. Another correction. Five more messages…

Eventually, the machine produces something close to what we wanted in the first place… or not.

Human language is much more than words. Most of the time, another person can understand what we mean from a few words, a gesture, or even a look.

Try that with a machine, and things can become complicated — or completely nonsensical — very quickly.

That brings us to the second reason: context.

Humans are remarkably good at separating the different, sometimes completely contradictory, meanings of the same word, gesture, or idea.

We do it almost effortlessly.

The same sentence can sound serious, sarcastic, affectionate, threatening, or absurd depending on who says it, when they say it, and what happened five minutes earlier.

Now imagine that same situation inside a transformer — the token-processing engine at the core of today’s LLM chatbots.

LLM Chatbots Are Sticky Tokenizers

Some of you may argue that self-attention does not keep every token locked inside its own little box.

And yep, you are right.

Each attention head updates a token by mixing information from the tokens around it, giving more weight to whatever the model considers most relevant. So there you have it: context is being handled. What is the problem?

The problem lies in what can happen when that mixing is repeated across layer after layer.

Think of making a salad. At the beginning, every ingredient is easy to distinguish. You can clearly see the tomato, the lettuce, the onion, and the cucumber. Then you add oil and vinegar and start tossing everything together. The ingredients are still there, but their flavours begin to mix, and the boundaries between them become less sharp.

Self-attention can do something similar with token representations.

It mixes and averages information again and again, so distinctions between words, meanings, and their roles in the surrounding context can gradually become blurred inside the model.

LLMs learn contextual regularities from enormous collections of token sequences. They are extremely good when a new situation resembles combinations encountered during training.

But context is combinatorial.

Suppose meaning depends on ten contextual variables, each with ten possible states.

That already gives:

possible combinations.

No training set can explicitly cover them all.

Of course, the machine does not need to memorize every combination if it learns reusable concepts and rules for recombining them.

That is the whole trick.

Current LLMs do not always learn those clean factors. Sometimes they learn shortcuts, statistical habits, and correlations that work beautifully, until familiar pieces appear in an unfamiliar arrangement…

Take two apparently simple concepts:

justice and law.

Their relationship is not fixed.

It changes with the jurisdiction, the institution, the historical period, the type of offense, and the moral framework being applied.

We should really write:

In one context, legality and justice may largely coincide.

In another, something can be perfectly legal and still widely considered unjust.

Even within the same legal system, their relationship may change between contract law, criminal law, military law, or politically regulated conduct.

The words remain the same, but the relationship between them changes.

This remains difficult for current LLMs because, despite what many introductory AI explanations may suggest, processing context does not guarantee that a model will represent different contexts as clean, explicit, and separable structures.

In many real-world cases, legal, cultural, and religious assumptions remain implicit and entangled inside the model’s learned representations, as the animation above illustrates conceptually.

So how does the industry try to solve it?

By throwing more data, more parameters, longer context windows, and more corrective training at the problem.

And yes, the results improve, but only by throwing exponentially more compute at the problem, with energy consumption rising sharply alongside it.

Worse, getting more answers right does not mean the machine has understood why they are right. It may simply have learned better shortcuts through familiar patterns.

The next machine will need something more explicit.

Not merely tokens mixed with context.

A representation in which concepts, relationships, assumptions, and constraints can be inspected, recombined, challenged, and changed independently: a truly semantic machine.

THE CONTEXT CURSE OF TODAY’S AI

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Whether you are an AI newbie or a senior AI engineer, you may be risking your future by using the wrong AI mindset.

Let me explain.

Most of us have been taught two fatal fallacies about AI.

The first is that today’s transformative generative AI is somehow the only possible form of machine intelligence — and that the road to AGI is simply to give it more speed, more memory, more data, and more compute.

The second follows directly from the first: that the mathematics and programming you need to learn are basically linear algebra and GPU programming. Forget geometry, topology and category theory. Forget Bayesian reasoning and probability distributions. Just multiply bigger matrices on bigger clusters and wait for intelligence to emerge.

But there is a problem, one the AI industry rarely puts center stage.

While the public is being sold an endlessly optimistic future of faster GPUs and ever-larger LLMs, the numbers are beginning to tell an uncomfortable story.

Today’s AI may already be approaching the end of its reign as the uncontested paradigm. You do not believe me?

Then look at the relationship between performance, computational cost, and marginal returns.

As computation, memory, parameter counts, and training resources increase, marginal returns do not rise proportionally. They begin to flatten. You spend vastly more to extract increasingly smaller improvements.

That means every new generation of frontier LLMs risks becoming dramatically more expensive — not merely in money, but in energy, chips, infrastructure, and computational complexity — while the gains become harder and harder to extract.

So the numbers leave us facing an inescapable conclusion.

Either a new breed of machine intelligence, built on new mathematics and new technology, takes over and resets this insane trajectory of escalating energy and computational cost…

or we may be heading toward a third AI winter.

And that new machine may look very little like the AI you know today.

Okay, I can already hear you saying: Sure, current AI has its flaws, but isn’t it still better to keep improving LLMs and building new frameworks around them than to bet on a completely different kind of AI that does not even exist yet and sounds like science fiction?

Not so fast, dear reader.

This new AI is not merely mathematics confined to books or technology still waiting for a blueprint. Startups and established companies are already building the technology, testing prototypes, and turning parts of it into real products.

Most of these systems are not mainstream yet. Some are still early. But the results suggest that they could move from the lab into wider use — and begin taking serious market share — over the next few years.

The New Frankenstein Factories Are Already Here

The next thinking machine is not emerging from a single laboratory.

It will emerge from an ecosystem.

And the right mindset now begins with understanding how that ecosystem actually works.

One company builds the nervous system.

Another develops the body.

Another specializes in memory.

Another teaches the machine to interact with the physical world.

Another models biology itself.

Even Google DeepMind, the company that currently comes closest to integrating many of these ideas under one roof, is better understood as the center of a much larger Alphabet ecosystem than as a completely self-contained AI factory.

Around it, Alphabet has built specialized organizations such as Isomorphic Labs, dedicated to AI-first biology and drug discovery, and Intrinsic, focused on intelligent robotics and physical AI. These companies operate independently, but collaborate with Google DeepMind wherever their technologies overlap.

That is the first sign that the future of AI is already changing before our eyes.

Until now, the standard model was simple: a startup grows around one star product — usually an LLM chatbot built on generative transformer AI.

Those days may be coming to an end.

Instead of one promising rookie startup, or one already consolidated giant laboratory trying to solve everything, we are beginning to see an AI industry organized around networks of specialized suppliers and cognitive supply chains.

And yes, Frankenstein is the perfect metaphor for what is happening.

Different companies are developing different parts of the same emerging system: better neurons, more capable bodies, richer world models, biological reasoning, and machines that improve through experience.

No single company has assembled the complete monster.

But the factory chain producing its organs is already running.

And here are the companies currently running the show — some old, some rookies, but all converging toward a new mode of AI production.

The consequence may be enormous: the LLM-based AI we have seen so far could end up looking like a mere cognitive toy beside what is coming over the next few years

The Organs

Victor Frankenstein would feel strangely at home in the current evolution of AI.

The difference is that, instead of collecting a few dead organs, stitching them together, adding electricity and — well, you already know the rest — he would become an AI engineer and assemble something far more abstract.

Not biological organs. Mathematical organs disguised as technology.

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Yes, you read that correctly… the title says lack of innovation.

Absurd, right?

NVIDIA is worth trillions. OpenAI owns the headlines. Jensen Huang is treated like a technological prophet, while Sam Altman is crowned someone like the Steve Jobs of intelligence. These are supposed to be the hottest, smartest, most innovative companies on Earth, the places everyone wants to work for and every investor wants to own.

Their genius appears beyond question.

But let’s step away from the marketing stage and go backstage, where the bolts, code, and machinery actually live.

There, a more dangerous question appears: What if the companies leading AI are not building the future, but blocking it?

Because the next breakthrough may not need a larger model, another generation of GPUs, or one more trillion parameters.

It may require abandoning the foundations NVIDIA and OpenAI have turned into dogma: flat statistics, fake neurons, and brute-force computation.

And that is where the masks come off — where we see the real face of these companies, and how the architecture that made them powerful may now be standing in the way of real AI.

A better path already exists. But AI must first escape the architecture that made today’s giants rich — and the technology dangerously fragile.

The current situation of NVIDIA and OpenAI

NVIDIA has two faces, like Janus.

The first is the face of marketing: the triumphant story Jensen Huang tells whenever he walks onto a stage carrying another GPU

The second face is backstage, where the reality is far less glamorous.

Strip away the theater and one uncomfortable question remains: why are the descendants of hardware built to render graphics and animate video games now sitting at the center of the so-called AI revolution?

For most of NVIDIA’s history, gaming was not some side business. It was the business. Some veteran gamers will remember that, as late as 2014, demand for high-end GeForce cards still drove much of NVIDIA’s GPU revenue.

Of course, we have all heard the GPU evangelists preach the gospel: billions of operations, massive parallelism, oceans of computational power.

But once the spectacle is gone, the miracle looks much simpler.

A GPU is an extraordinarily fast machine for multiplying and adding numbers across enormous matrices. A glorified mathematical coprocessor: brutally efficient at arithmetic, yet showing no trace of the intelligence celebrated so triumphantly on NVIDIA’s stages.

Fine. Then perhaps the real secret is not the chip, but CUDA, NVIDIA’s distinctive software platform. Surely this is where raw hardware becomes programmable intelligence. Is CUDA the missing bridge between matrix computation and cognition?

Unfortunately, CUDA is a venerable platform introduced in 2006. Over the past two decades, it has accumulated new libraries, tools, memory systems, language features, and AI-specific optimizations. But beneath all those additions, its basic computational worldview has changed surprisingly little.

And CUDA deserves credit. It took the parallel power of graphics processors beyond graphics and opened it to general-purpose computing. At the time, the idea was revolutionary: launch thousands of threads, divide the work into blocks and grids, and make the GPU process enormous quantities of numbers at once.

But two decades later, a geological era in technology, CUDA still rests on essentially the same idea: break a numerical problem into even more parallel operations and run them even faster.

That is the whole trick.

A remarkable engine for accelerated computation. But still no bridge from arithmetic to cognition. Nope.

The Case of OpenAI

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A Mind-Blowingly Realistic World Built Without Modern AI

Let me start with what GTA 6 is not, because almost everyone online right now is about to sell you the wrong idea without bothering to check whether their claims are credible.

One thing already seems clear: if you are waiting for NPCs with a next-generation brain, forget it. The people walking through Vice City will still be stereotypes with scripts. They will talk, panic, drive, fight, flee, and forget you five seconds later. They will not become tiny conscious agents living inside your console.

And that is not a failure. It may be the smartest thing Rockstar has done.

And the machinery behind that illusion is not magic. It is the old recipe that has always worked: mathematics, geometry, graphs, manifolds, hierarchies, control systems, and classical programming, meaning algorithms and data structures, all shaped by clear goals and better design.

I can heard you already… How can you be so damn sure when the game hasn’t even been released yet? Is this just cheap advertising disguised behind a clickbait title?

Fair question. And yes, you are right to ask it.

What I can say with confidence about GTA 6 is not based only on Rockstar’s marketing campaign. It is based mainly on what its own patent filings quietly reveal about essential features and technical innovations, and on the mathematics behind those features.

That is the material I am using here: public footage, public patents, and the mathematical constraints any world like Leonida must obey.

Transparent, genuine, and readable for anyone willing to look at the evidence instead of the hype.

And before we dive into the nuts and bolts behind Leonida, let me say one more thing clearly: GTA 6 may become the reference point for the entire industry for years to come, and it does not need the other great hype of our age: transformer AI stuffed into everything and sold as the future of everything.

Because the real miracle of GTA 6 is hidden somewhere else.

In its structure. You will see it for yourself. Enjoy

GTA 6 Is Not Just Bigger. It Is a Bigger Mathematical Problem

Rockstar’s Leonida is its Florida: Vice City, the Keys, wetlands, highways, small towns, rural regions, beaches, interiors, traffic, water, bodies, reflections, light, and thousands of objects that all need to appear continuous.

And this is where the problem becomes more interesting than the marketing.

Because bigger worlds already exist. Of course they do. Daggerfall is often cited with its 160K-plus square kilometers of procedural sprawl. Starfield gives you more than a thousand algorithmic planets. No Man’s Sky went completely cosmic with eighteen quintillion worlds. But that kind of scale is bought through generation, not authorship.

Denser worlds exist too, but they usually shrink the map to make density possible. Cyberpunk 2077’s Night City is one of the great modern density machines, a city where the open-world formula is compressed so hard that the wilderness almost disappears.

And hand-authored worlds of huge ambition exist as well, but they often solve the problem by division: different regions, separate maps, stitched continents, repeated activities, controlled transitions. The Witcher 3 gives you Velen, Novigrad, Skellige, and Toussaint. Assassin’s Creed gives you enormous historical playgrounds, but often through repetition, segmentation, and scale tricks.

So no, if Rockstar delivers what the trailers, patents, mapping evidence, and its own marketing imply, GTA 6 will not be the largest world ever made. That is not the interesting claim.

The interesting claim is harder. Leonida may be one continuous world large enough to read as geography, dense enough to behave like a city, and simulated deeply enough to feel alive at that scale.

That is the rare combination: not a world that is merely big, or merely dense, or merely beautiful…

But a continuous, authored, dense, physically convincing space, built to make the hours you spend inside it feel less like playing through a map and more like inhabiting a living world.

And that is where Leonida stops being only a map and becomes a mathematical problem.

Because once you compare it with GTA 5’s San Andreas, the real difficulty appears.

A larger map is not just extra land. It means every system is under pressure at once: space, traffic, interiors, physics, lighting, reflections, water, pedestrians, vehicles, and the invisible streaming machinery that keeps the illusion from falling apart.

So here is the first real question behind GTA 6:

How can a console stitch together patches of geography without gaps, misplacements, trembling edges, broken roads, or objects popping out of existence, when there is no single global map sitting in memory?

That question sounds technical.

It is actually the doorway into the whole miracle.

The answer is not simply more storage. The answer is representation

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Software Development Is Already on AI Life Support

Many of us saw the writing on the wall the moment the COVID pandemic lifted and the new generation of transformer chatbots arrived.

First, they gutted Stack Overflow.

Now they are coming for entire professions, and software development is the first white-collar field where the damage is impossible to ignore.

Junior and mid-level positions are shrinking at a pace the industry has never seen before, and the squeeze is now reaching senior roles too.

On the other side of the coin, for now, the view looks better.

AI engineers, and everyone whose output has been multiplied by an AI assistant, seem to be thriving.

But are they actually safe?

You Need an AI Workflow. And Now It Is Almost Too Late.

The first brutal lesson from the wreckage of tech jobs is simple:

This is no longer a matter of choice.

If you work in tech, you either build your own workflow of AI assistants now, or you start looking for a skilled trade outside the blast radius.

Yes, humans are still needed in the loop. Yes, today’s flat AI is still far from replacing a serious developer end to end. And yes, if you ship production code, you still supervise everything the assistant hands you.

But none of that is an argument against using AI.

It is an argument against using it badly.

And the worst way to use AI is to lean on a single assistant as if it were a magic black box.

Claude, Cursor, ChatGPT, Copilot, whatever name you prefer, none of them should be trusted alone.

Some of you already know this. Unfortunately, many still do not: one AI code assistant is not a workflow. It can miss the context, misunderstand the problem, and dress a bad fix in clean code.

So if your whole setup is one assistant and blind trust, you are not engineering with AI. You are working with a chimera: part autocomplete, part sycophantic parrot.

And because today’s AI still lacks real Bayesian judgment, you have to do something the model will not reliably do for you: read the confusion matrix.

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We are living through a brutal artificial-cognitive age.

At first glance, the latest US government move against Anthropic’s Fable 5 looks like paranoid overreaction: Washington treating a frontier model as if it were the first child of Skynet, and the jokes write themselves. You know the continuation of the story: a coding assistant was suddenly handled like fissile material, and consequently access vanished worldwide.

Before we join in the mockery, we should take a closer look at the case.

Fable 5’s defining strength is simple: it is the most capable AI software engineer released so far, beating every competitor outright. It can tackle and solve real-world problems, both at the surface level and deep within software systems. This alone puts Fable 5 at the limit of what LLMs have been able to achieve so far

You want examples?

Stripe, the payments company, pointed Fable 5 at Stripe’s own fifty-million-line Ruby codebase and asked it to perform a single migration across the entire system in a day — work the company estimated would have taken a team of engineers more than two months. Stripe described the result as compressing months of engineering into days.

Read that slowly.

Because the impressive part is not the line count. It is the deep semantic coherence.

That is not autocomplete at scale. It is engineering.

The benchmarks in the table above only confirm the example and show us how brutal the emerging pattern is.

Fable 5 dominates the coding stack, where that dominance makes the human software engineer without AI a real piece of archaeology: real software tasks, hard agentic execution, code quality, long-horizon work, and tool-driven autonomy.

And now I can already hear some critics — critics I can even agree with: benchmarks are not always a reliable way to prove the effectiveness of a modern AI, because those separate traits should be tested together in realistic scenarios.

Fine.

Fable 5 seems to have taken the challenge personally: it completed Pokémon FireRed from start to finish using only raw screen screenshots, with no maps, no navigation aids, and no hidden game-state information. Earlier Claude models needed a complex helper harness just to play Pokémon at all.

Why does that matter?

Because Pokémon sounds like a joke only until you remember what the game actually demands.

The model has to see through pixels, remember where it has been, navigate towns, caves, battles, menus, dead ends, you name it… recover when it gets lost, and keep going after thousands of small decisions.

That is not a toy skill. That is what we can call continuity.

And continuity is exactly what separates answering a question from actually getting something done.

Is Fable 5 Really That Good — And That Dangerous?

Now comes the surprise: how can a technology this excellent be treated, at the same time, as dangerous?

Paradoxical? Not after you have read this section.

Washington may have misread the mechanism, but it did not hallucinate the danger. The danger is real, though we can agree it is not where they think it is.

That contrasts brutally with Anthropic’s public position, which is straightforward: Fable 5 was built with heavy safeguards to prevent misuse in malware development, cyber abuse, and other high-risk domains.

But reality has derailed Fable 5 from that ideal safety track: it may be the best AI software engineer we have ever seen, yet it still struggles at the exact border where the same repair operation can mean fixing legitimate code in one context and enabling malware by repairing the wrong code in another.

That is the danger. Not that Fable 5 is evil, but that it cannot reliably know what the repair is for. As happens with the whole family of transformer LLMs, they do not cleanly separate context from concept, because context and concept are folded into the same token representation, not carried in two different lanes. Do you remember the distinction between functor and profunctor?

So why the heck does the blunder happen precisely in the feature Anthropic has been selling as Fable 5’s killer capability?

Pay attention to this, because here the paradox snaps into focus: the same thing that makes Fable 5 the best AI software developer is also what makes it dangerous.

Look at the animation below and the trick becomes obvious.

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Mathematician Lurking in the Tech Underworld is no longer just publishing articles.

It is building a survival map for the age of artificial cognition.

If you want to thrive in this new world (not just professionally, but personally and intellectually) you cannot live from headlines, hype, and recycled tech noise.

You need to see the monster from inside its own machinery, with the right tools and the right mindset.

That is why MLITU now opens two new streams: Behind Tech Matrix and RedSoma…the new age has already begun.

What was once reserved for the tech elite is now within your reach.

It cuts through the surface with technical X-rays, connecting AI, markets, mathematics, models, systems, and human impact into one sharp picture.

Not simplified into noise, not diluted for comfort, and not dressed up as entertainment for technological tourists.

Precise. Brutal. Useful.

Welcome to the new MLITU age. Get your ticket now

Bitcoin is sleeping. Not dead. Not finished. But the real story is happening somewhere else, and as I wrote here months ago, that story is AI.

AI infrastructure is quietly eating the rails Bitcoin was built on: the global transaction network, the plumbing of payments, settlement, credit, you name it. The new flavor of AI, with several agents working inside flexible workflows, is now challenging what once looked like Bitcoin’s indisputable future: not only as the enemy of traditional banks, but as the digital currency protocol of the internet itself.

But here comes the irony of destiny.

It has to be precisely Bitcoin that exposes one of AI’s biggest weaknesses.

Because when you throw AI into hypervolatile markets like BTC, AI fails miserably.

And of course you can ask: so what? What is the problem if AI is not useful in hypervolatile markets?

The problem is simple. Hypervolatile markets are among the most profitable markets of all. More risk, more potential profit.

But there is a deeper point beyond the market itself: the inability of today’s monstrously expensive AI to cope with the kind of variable real-world problems it was sold as being able to solve.

So is there an alternative? Yes.

And it is cheaper, colder, and far more dangerous to the AI hype machine than many people would like.

And AI is not built for that. It is not built to cope with a world that changes under its feet while it is still calculating.

BTC Is Exposing One of AI’s Biggest Pending Blunders

The weakness, as you have probably already realized, does not stop at Bitcoin.

You can generalize it to most real-world problems, because this is exactly what reality does every day: it changes the conditions while we are still trying to react

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Nvida and OpenAI are still playing a lost game

Nvidia and OpenAI have overextended their bets on old AI so badly that their only move left looks like a flight forward.

Yes, I know the obvious reaction… because anyone looking at their market value, their charts, and the ocean of money around them will think the same thing: This guy must be out of reality.

But that is exactly how bubbles speak before they crack.

The same market that inflated Nvidia’s value at insane speed can also punish it at insane speed once investors understand the forbidden sentence of this AI cycle: More computation does not mean AI stops hallucinating.

It means marginal improvements bought at exponential cost.

By ignoring the writing on the wall, Nvidia doubles down on computation: more power, more heat, more money poured into flat-AI GPUs.

OpenAI doubles down on chatbots that do not truly transport thoughts. On the contrary, they blend them into comic misfigurations. And then, again… you guessed right… into hallucinations.

This failure is not diffuse. It does not require mystical interpretation or overcooked nerd analysis. It is visible in the math.

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I Have Good News and Bad News for You

The bad news first, as tradition demands.

To everyone out there grinding through linear algebra, optimization, tensors, and the rest of the standard math that makes current AI what it is, hoping it buys you a privileged seat in the future: I am sorry, friends.

You may be headed back to square one.

Which is still better, anyway, than holding a ticket for the AI Titanic.

Because the math they promised would turn you into an AI engineer and programmer is, unfortunately, built on a broken arithmetic that somehow survived the greedy corporate filter. Yup … broken arithmetic.

I can already hear some of you.

“Oh no. Another bold article with a damn clickbait opening, chasing math dummies”

Fair.

But not this time.

And not in this series, if you have read me before. You know the rule here: the fireworks are allowed only because the foundations are real. Everything said in this story is rooted in pure mathematics, right down to the core of the argument.

Now the good news.

You will get that mathematics here without being buried alive under the old academic cement. Don’t worry. You will not have to suffer through the long ritual of incomprehensible formulae before you get the point.

You will have the core in the most pleasant way I can manage. Promise.

And if you are new to the field, or simply curious about AI, we will provide you with a vision and a perspective you cannot find anywhere else on the web, in books, or in whatever media is currently available.

To my knowledge, you are reading the first published debunking of one of the holiest dogmas of the current AI industry: that beneath the GPUs, the scaling laws, and the trillion-dollar sermons, there is a solid mathematical foundation.

There isn’t. Keep reading. You will see it break.

Surely You Are Joking, Mr. Mathematician

I know. It sounds insane.

It is hard to believe that current AI could be so mathematically limited after one trillion dollars and counting, with projections already pushing that figure several times higher before 2031.

Jensen Huang and the rest of the scaling priesthood have hammered the same creed into the industry’s skull: whatever is wrong with AI today will be fixed by throwing more computation at it.

You know the hammering tale. Every time one of those AI gurus is asked for their vision, the answer is always the same: more GPUs, more data, more parameters. More… you name it.

And to some degree, you can be forgiven for believing it. The surface is seductive. The chatbots get smoother. The agents get faster. The systems become more multimodal. Their memory grows heavier every quarter. The whole machine seems to be approaching something like superintelligence.

Until you scratch below the surface with a little mathematics … then the gold paint comes off.

Because the moment you stop watching the corporate spectacle from the outside and start asking what kind of algebra is actually organizing the machine’s internal decision structure, the problem no longer looks like a shortage of scale, memory, or GPU firepower.

It looks deeper. And far more embarrassing.

Current AI is running, at its structural core, on a kind of mathematics that cannot subtract.

Watch the first animation before you read another word, because once you see this visually, the whole argument stops being an abstraction.

It will become painfully obvious. Even to the least attentive reader.

2 ⊕ 2 = 2: The Operation That Leaves AI Unable to Undo Its Mistakes

Time to run the math show promised at the opening of this story.

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To Mr TechSimple

Picture the worst possible reader for this story. And call him Mr. Techsimple.

Techsimple does not invest ahead of the curve. He arrives late. Always.

And there he is again, spending his money and his time on the shiny hype of the latest big thing.

To be fair, Mr. Techsimple is consistent. He has been wrong with discipline.

He put his first money into Theranos, because a black turtleneck and a deep voice were, to him, indistinguishable from a working blood test. He rotated, undaunted, into WeWork because Adam Neumann incarnated the new tech-prophet of renting offices. He was in Nikola a couple of days before the truck rolled downhill into a crashing market

He was convinced by Sam Bankman-Fried about FTX and the old fiat money being a scam — yeah, a tech scammer claiming to fight the banksters and the central banks. He bought the metaverse at its peak. He still is long and owns Meta, because he is convinced that the metaverse was simply ahead of its time and now it will ride victorious on the new AI wave.

So yes, you are guessing right, he is now very bullish on AI, long on the giants, because the brochure said AGI is coming up in your chatbot. With the new AI and superintelligence coming up, all his past failed tech investments suddenly will be absolutely feasible to pull off.

And here we have today our Mr TechSimple doubling down on OpenAI because another Sam — this time Altman — is announcing that OpenAI will be bringing a new AI generation.

Hello, friend. I want to be kind to you. What I am about to show you is sadder but more real, because the AI the giants sold you is a lie… not in the way the others were, but in a deeper one. The real thing is being built. Just not by them

The Tale of Mass Extinctions

We are imitating nature, whether we realize it or not, whether we try to ignore it or believe that tech has nothing to do with it.

So here is the part Mr TechSimple cannot price, because it has never once appeared in a brochure. Every technology we have lived through until now, the legit ones and the scams alike, the smartphones and the Theranoses, the cloud and the crypto winters, will look like a children’s game next to a genuinely legit, functional, and truly scalable AI. Not the kind sold in a keynote. The kind that actually works, and actually scales, and does not stop.

The impact of that machine will not be another product cycle. It will be the first technological mass extinction: an event that rewrites the landscape the way an asteroid does, touching everything a human being touches, because intelligence touches everything a human being touches. Work, law, money, war, meaning.

And here is the cruelest joke in the whole story. Mr TechSimple, in the most fevered, delusional, moon-bound tech dream he has ever talked himself into at the top of a bubble — would, against this, sound like an ultra-conservative. The man who has never in his life overestimated a technology by less than an order of magnitude will, for the first and only time, have catastrophically under-shot.

And the most laughable, ludicrous part of all? It will not be a Skynet — no continent-sized system, no megalomaniac fantasy of Nvidia data centers wired to their own nuclear plants, no. It does not need to be like that. It will be a modest, almost embarrassingly small thing. A tiny thinking engine.

You don't believe me? Keep reading. I promise - by the end, you will.

The Flat AI Era Is Ending

For the last few years, the entire industry ran one experiment: take a flat architecture, make it bigger, and see if intelligence falls out the other end. It did not. What we got is an AI that can only drive in straight lines and goes off the rails when it has to take curves.

Ninety-five percent of enterprise AI returns nothing, pilots die between demo and operations, and the response from the giants has been to order more of the thing that is failing. When a technology needs planetary-scale infrastructure to compensate for architectural blindness, the question is not how many more trillions to feed it. The question is what is missing from the architecture itself, and the answer is geometry.

Flat AI works on a flattened version of thought.

It treats meaning as points in a vector space, then tries to guess what should sit between those points. That is why it looks brilliant when the next case resembles the old cases. Locally, it can be stunning. But once the problem bends away from the training neighborhood, the model starts guessing like a drunk man looking for his keys at night around a very dim light.

This is the Mercator problem.

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Beyond the Anthropic Hype

You know this movie. An AI lab’s marketing team announces that the model does not just look smart, it is really thinking, and as proof they show you a new toy (NLA, short for Natural Language Autoencoder, a second AI trained to describe the first one’s internal states in plain English) to photograph those thoughts.

That sounds like a serious cutting edge in AI research. But what did Anthropic actually build?

Just a stack of nested black boxes.

The first model produces internal numbers. The second model looks at those numbers and writes a caption. Then a third system tries to reconstruct the original numbers from that caption. If the reconstruction is close enough, the caption is treated as meaningful.

That is the machine. Something worth considering, yes, but certainly not the innovative leap that Anthropic’s marketing, and the many articles and posts spreading it afterward, try to suggest.

We know what Anthropic’s thought reader is not: it is neither a mind scanner nor a thought camera.

So before we follow the thought reader any further, we should stop at the source of its raw material: the numbers.

A language model does not think in words. It works with numerical states. At the simplest cartoon level, imagine one tiny unit receiving a few signals and assigning a weight to each one. Should I go to the party? Free pizza: strong positive signal. My ex will be there: strong negative signal. It is raining: another negative signal. Add the scores, pass the result forward, and move on.

That toy example is not a full transformer, of course. Real language models use real-valued vectors, attention, residual streams, nonlinear transformations, and many other moving parts. But the basic fact remains: words become numbers, those numbers are transformed through layer after layer, and eventually new numbers are turned back into words.

That is where activations enter the story.

An activation is not a hidden sentence inside the model. It is a snapshot of the machine mid-calculation. A frozen numerical state. Useful and informative, yes. But not readable in the way your diary is readable, or a confession is readable.

And that is exactly why Anthropic needs another AI to interpret it.

The first model produces the internal numerical pattern. The second model turns that pattern into a verbal caption. Then the system checks whether the labeled text caption preserves enough information to reconstruct something close to the original pattern of numbers.

That is all you need to know to conclude that, in fact, we did not find any thought inside the model. We found a smart reverse-engineering process, where the final AI in the chain analyzes the source AI’s internal numerical state and checks whether those text labels are consistent with the numbers they supposedly depict as internal thoughts.

What Is Wrong With Anthropic’s Thought Reader?

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Current AI is snake oil the moment your problem has to scale

If you are one of those who have tried to use current AI for a problem that scales, you already know that the AI solution they sold you as the hammer for every nail is just the modern tech version of the old snake oil scam.

In fact, the numbers back your experience: ninety-five percent of enterprise AI returns nothing, and most pilots collapse somewhere between demo and operations. But let’s not jump to conclusions. If we look closer at what is actually failing, it is not a model-quality problem, nor a regulation problem; it is a problem of brittle workflows, missing context, and systems that do not learn inside the daily life of an actual business.

Now the other side of the story. The appetite for a functional AI is unstoppable, and how unstoppable? The best foxes sniffing this market put the ballpark at $2.6 to $4.4 trillion annually (McKinsey), a 7% lift to global GDP and roughly 300 million jobs exposed to automation (Goldman Sachs).

You want even more vivid? The energy alone will be around 945 TWh by 2030, with AI as the major driver — slightly more than Japan’s total electricity consumption today!

So here is the scenario we are facing with current flat-AI: trillions in projected upside, hundreds of millions of jobs in the crosshairs, continental-scale electricity demand… all for a miserable flat AI that fails 95% of the time.

Too much ado about nothing.

Is there a way out? An AI to put an end to all this ludicrous, criminal waste of energy and economic resources?

If you have read our articles before, you already know the answer. The architecture that replaces FLAT-AI has a name too. Call it GEO-AI: systems whose metric varies with context, where curvature itself carries the meaning that flat embeddings (tokens as vectors) can only approximate.

The missing layer

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Nothing personal, just math

First of all, don’t be scared of the math here. You will get it, because I will use intuitive plots and animations to show you which math the big names are using to build the first true thinking machine. Yes, you read that right: thinking AI, not just another LLM parroting something that only looks similar on the surface.

How important is the new math the research labs at the big names are already working on? Important enough that if you read our previous article here, you already know the answer: where cognition and AI meet, mathematics is not a tool, it is the battlefield.

Until now, AI has behaved as if intelligence can be squeezed out of flat embeddings, bigger datasets, and enough GPU violence. That is the industry’s favorite superstition. But cognition is not a bag of vectors. It is a layered architecture of dependencies, transformations, transport, constraints, memory, inference, and self-correction.

Let’s start with the math ingredients in the recipe Anthropic and Google are using to build a genuine AI worthy of the name.

The labs are not inventing new mathematics. They are reaching back, all the way back, to the foundations the field abandoned when it decided that scale would be enough.

They are following, maybe without knowing it, the path Hilbert’s metamathematics opened a century ago when it taught us to step outside mathematics and study its machinery. For the first time, they are taking category theory seriously as architecture, not as a functional-programming side dish, to describe how structures compose. Put them together with serious differential geometry, Lie groups, topology, and information geometry, and they will get something far more serious than another transformer stack: they will get a blueprint for cognition built from the bottom up, with no arbitrary glue, no accidental layers, and no corporate fairy dust pretending to be architecture.

We can now say, for the first time, that we have a mathematical idea of how the increasingly fused Google-Anthropic partnership could build a new generation of machines that deserve to be called thinking.

Let’s build that machine the way they will.

The mathematics will become far less intimidating once the animations begin, I promise. This math is not decorative. It is the difference between a machine that merely predicts the next token and a machine that can begin to think in a way worthy of the name.

The reward?

A genuine AI architecture, unlike anything you have seen before

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