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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From Conspiracy to Receipts

Yes, the title sounds like a conspiracy theory.

But let me say in plain words what they refuse to admit: they are trying to reach the moon by building a taller ladder, scaling flat transformers, throwing hundreds of billions of dollars a year in brute-force computation at a problem the architecture cannot solve at any cost.

Meanwhile they hide the blueprint they are actually working on, the rocket ship that is Geometric AI, which would save trillions by handing out the whole map instead of one street address at a time.

Of course they are telling you the opposite story. The core of it is much simpler than the trillion-dollar price tag suggests: at the heart of current AI sits a geometrical problem, and no GPU farm in the world is going to fix it, not even with the nuclear-powered data center the size of Patagonia they have started building.

Stay with me. Because you will see how this apparently crazy absurdity has a point of sanity hidden inside the labs themselves: their research teams are following a different approach to the sola computatione of the current AI church.

The smoking gun is neither a secret server farm nor a leaked memo. And least of all, it is not some cinematic superintelligence hidden behind a locked door. It is something much colder and more practical.

The back door is inside the technical and scientific papers of the companies themselves, which, except for some expert nerds, almost nobody outside the field reads.

Do you want to find out whether NVIDIA, Google, Anthropic, and the rest are selling the opposite of what they are intensively researching? Then don’t read their press releases, their stage-managed public events, or the effusive declarations of their CEOs. Do something far simpler: grab the research papers of their fringe teams, and you will see where this story begins and how it drives into a new territory of AI that should set off alarms for you.

Don’t worry. I have done the homework for you. Here we go.

We Should Already Have a Truly Non-Parroting AI

But first, let me be precise about what is under attack in current AI.

Ok, we have already mentioned transformers. Sure, they have positional encoding. They have attention. They mix each token with the tokens around it across many layers of computation. They are not blind to context in the trivial sense; anyone arguing that they are has not opened the 2017 paper.

The legitimate attack is not that AI is blind.

The legitimate attack is that the current architecture handles context in a structurally wrong way, and scaling computation cannot truly fix it.

Attention layered on top of flat embedding spaces does not, by itself, measure the semantic distance between concepts that overlap and shift with context.

Take justice and law as a worked example.

The distance between these two concepts in the democratic United States and in the theocracy of Iran may look almost identical when you are talking about petty crimes and ordinary civil offenses. But the moment you move to political crimes, the two concepts can sit at opposite ends of the semantic space.

Same two concepts, yeah, but…

Different distance.

Different context.

That is the point.

Meaning is not just a word sitting near another word. Meaning is a position inside a changing semantic terrain. The distance between concepts depends on the background against which they are read.

Flat AI embeddings cannot do this for free. They force the model to fake it by feeding it enough examples to memorize how the distance shifts: different regimes, different offenses, different legal traditions, endless combinations. Brute statistical memorization where geometry should be doing the work.

It is the flat-map problem one layer deeper. You cannot measure curved distances with a straight ruler, no matter how many rulers you stack. Context is not just another word the model processes. It is the ground the words sit on, the surface that decides what the distances between them mean. Flat AI keeps trying to do computation where it should be doing geometry, and no amount of training data can fix that.

It is like trying to measure speed with a stopwatch alone, ignoring distance. A stopwatch gives you time. A speedometer combines time and distance into one quantity — speed — that neither alone can give. Flat AI is the stopwatch (a functor: one input, one direction). Real cognition needs the speedometer (a profunctor: two inputs of different kinds, one a concept, the other its context).

Their own papers are the smoking gun

This was only the warm-up. The mindblowing starts now. (1) Anthropic, caught red-handed. (2) Yale cracking open Llama 2, Gemma 2, DeepSeek-MoE. The curve they grew on their own. (3) Einstein as the working frame, not the metaphor. (4) The first blueprint of a new generation of thinking machines.

And much more...

Read A Mathematician Lurking in the TechUnderWorld: pure mathematics, animated proofs, analysis you will not find anywhere else on the web.

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The Most Boring Tool on the Internet Will Decide the Future of AI

I hear you.

Adblockers? What the heck. Your story this time is about the most boring subject on the internet?

Exactly. It seems like that. And that is the trap.

Adblockers look like the most humble, almost miserable creatures in the entire internet ecosystem: tiny browser extensions working like little invisible shields, sitting there like silent stowaway icons near the address bar of your browser. Barely noticed by you. Credited by almost nobody, and nevertheless carrying the heaviest part of personal security on the whole damn internet

.But what if I told you that this ridiculous little shield is standing on the battlefield from which the next generation of AI may emerge?

You mean something different from my chatbot?

Yes. And much darker.

A new kind of AI built from the war over your personal data, coded and optimized inside one of the most profitable machines of our time: the surveillance economy.

We are talking here, dear reader, about an AI whose power does not come from having the biggest, baddest, smartest brain ever seen.

Its power comes from something more intimate, and far more dangerous: knowing you better than you know yourself.

The battle over your personal data in the wake of the Dark AI

Until now, the unglamorous adblockers have done their job better than most people realize. Good enough to frustrate the dream of total behavioral visibility.

In fact, they have spent years busting the chops of Big Tech’s biggest ambition: seeing enough of your online behavior to personalize, target, and monetize you. This is not paranoia. Alphabet itself warns investors that technologies which block ads or make personalized advertising harder could harm its business.

That is the smoking-gun proof.

Adblockers are not decorative toys. They are effective enough to bother one of the most powerful advertising machines ever built. And they do not only defend your privacy. They also keep the internet navigable: less spam, fewer popups, fewer aggressive ads, fewer paralyzing pages loaded with junk nobody asked for.

How?

By attacking the pipes.

They blocked the scripts, killed the trackers, weakened the fingerprinting, disrupted the cross-site linkage, slowed the whole extraction machine, and stood between ordinary users and a harvesting infrastructure built from ads, pixels, CNAME tricks, real-time bidding networks, and broker profiles.

Every one of them a little invisible hook designed to turn human behavior into money.

Not bad at all for a few miserable browser extensions with terrible marketing.

But they do not all fight the same fight.

Some are better at killing third-party trackers. Some work almost everywhere. Some go deeper into the browser. Some are just very good at blocking the pipes where your data leaks out.

So let’s look at them properly.

The table below is not a beauty contest. It is the old battlefield, layer by layer: who blocks what, who only slows it down, and who still keeps the surveillance machine sweating

Among these quiet internet heroes, Ghostery stands out as the paradigmatic adblocker. Not because it does one magical thing, but because it does several practical things at once.

It does not win by being a silver bullet. It wins by being a stack: a layered shield, a small privacy machine built to fight the old surveillance web without making the normal web unusable. That is why Ghostery works so well as a canonical example.

You can see that logic in the animation below: how it works in full action while you navigate the dangerous wild web on your phone or laptop, keeping your session cleaner, faster, and harder to exploit.

So far, nothing looks broken.

Your Ghostery shield is awake. The little hamster is running in the wheel. The browser feels cleaner. The page loads faster. The obvious junk gets blocked before it can feast on the session: trackers, scripts, popups, fingerprinting attempts, cookie traps, and all the usual garbage trying to turn your browsing into someone else’s money.

Good. That is the old war.

But now look again at the table above. There is one column where everyone fails.

Authorized inference.

That low score is not a technical footnote. It is the crack in the castle: the Achilles’ heel of adblockers.

Adblockers are very good at guarding the browser door. But the inference layer does not need that door.

The inference layer works with data already authorized by you, already collected, already inside the app or cloud, waiting for a model to connect the fragments.

That is where the nightmare begins. But what the heck is the inference layer?

This is only the small introduction to a much bigger story.

What continues behind the paywall is an immersive experience that will change how you think about AI entirely.

Please subscribe and enjoy this article, along with many more top pieces like it.

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Oh no, another LLM guide?

I can understand that reaction. By now, everyone has seen too many of them, and you probably expect to find here another crowded table, another pointless ranking propped up by whichever benchmark fits the headline.

No. Forget it. Here you will not see another childish contest pretending to decide which LLM is the smartest, which is the cheapest, which writes the best code, or which sounds the most human. Most of that is BS when it comes to the thing that really matters to you: getting the damn job done in the best possible way.

So yes, you are lucky. Your frustration is welcome here, and what you are about to read is the first cognitive map of the frontier, the one built from your real work rather than from leaderboards.

The Cards on the Table

Pay attention now, please, because this is where we are saying bye-bye to the non-scientific LLM classifications you see most of the time.

First, let the math separate the chaff from the grain. Easy. Don’t worry, I have already done the homework for you.

This was the recipe: I took 24 related LLM skill variables extracted from real professional use cases, ran the rotation, and voilà, there you go — the three cognitive factors that actually predict which LLM is best suited to the task you need to solve.

The details of the several varimax rotations are not shown here. The animation below reproduces the math cooking the last varimax dish, the important one.

You have the diagnosis. The cure is behind the paywall: the decision rules, the routing patterns, and the multi-model sequences that turn the three lanes into countless hours saved and a lot of unnecessary pain avoided. Subscribe to unlock the rest.

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Time to judge current AI on its own merits, facing the real world, which is not a tidy chatbot window, not something you can summarize into a benchmark, and least of all a controlled demo. We know that critical situations in our familiar world can turn it into a brutal place where conditions shift faster than any frozen AI model can match.

And there you have the perfect example: our familiar drones, and the dream of turning them into a machine fused with AI that handles everything from civilian logistics to military strikes. What other example would show us the supposedly limitless capabilities of AI in the real world better than this one?

So yeah, we are right to expect that if real AI were already available, it would have been implemented in these machines first, especially the precision military hunters. They should look almost extraterrestrial in capability, executing operations no human-controlled device could match, above all those synchronized maneuvers of dozens of drones moving through real landscapes under real pressure, in real time.

Now wake up to 2026. And to the years that follow, if we do not change the flat AI paradigm, and what you get? The miserable “state of the art” showing in the table below.

You see. Many billions of dollars spent on machines that cannot tell a hill from a valley, hold altitude when the temperature drops, or, my favorite, the drone cannot hit a moving target the way any twelve-year-old does on a PlayStation, by aiming at where the enemy will be, not where it is.

And what has been the industry response to those blunders? The usual polished shrug, a press release for every incident, blaming the rotor, the weather, the test conditions, anything except that all those failures come from the same broken mathematics running inside the drone-brain.

The Four Ways AI Hits the Real World Wall

Before we unravel the possible solution in the next section, let’s look at the failures themselves, not as accidents but as archetypes. Each one exposes a different face of the same architectural limitation, and together they map the ways a trained model collapses the moment the world stops matching the training set. Watch them carefully. The solution comes later; I am sure you can already come up with some possibilities while examining these simplified animations.

First Type: The Map Is Not the Territory

The training data of course contained hills and terrain irregularities. But the battlefield in Ukraine was more nuanced than the training set anticipated. The drone flew at the altitude its model had learned was safe, straight into a particular hill the model had never identified as such, because the architecture had no mechanism to notice that the world in front of it no longer matched the world it had been trained on.

You have never seen AI explained like this.

Behind the paywall, you’ll go from animated AI failures to the mathematics beneath them: flat metrics, Fisher geometry, and a multilayer AI architecture built for the real world. It is deep, visual, cinematic, and packed with mind-blowing graphics and practical ideas you need if you want to stay ahead in AI.

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There are nine geometries that matter. AI is built on only one of them, and it is the wrong one.

Pause there for a moment before looking at the table below. At first glance, it may seem like a parade of abstract symbols, the kind of thing people dismiss in an instant. Do not dismiss it.

Read it as you would read a report of your own mind, because that is what it is: a record of structures you already rely on, every waking hour, without ever naming them: when you turn your head toward a sound, when you reach around an obstacle to grab a cup, when you know that your left shoe will not fit your right foot, when you retrace the route that brought you here and remember the turns in the order they happened, you are relying on the very invariances, transformations, and path structures these geometries describe.

You do not need the math to do any of this. You need the math only to see it clearly, to understand how a truly intelligent engine would have to handle it, and thus to see that the machines everyone calls intelligent still cannot.

Behind the paywall: the Nine Geometries table that names exactly what AI is missing, the chirality infographic that explains why flat AI confuses cures with poisons, the financial case for the two companies already shipping what replaces the transformer stack, the architectural diagnosis that shows why scaling cannot close the gap, and new animations showing the geometry problem in motion… and more…

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The LLM Era Is Over

The biggest names in AI have an aging problem, and they are trying to fix it by throwing more raw computation at it. Blatant mistake!

OpenAI, Google, Anthropic, and the rest have spent the last two years scaling inference-time compute: chain-of-thought prompting, search trees, verification loops, more tokens at test time. As I argued in previous articles, this approach has eliminated most of the surface hallucinations, the kind that embarrass you in a demo, while producing deeper structural errors that are far harder to detect and far more dangerous to trust.

And here is what most people are missing: the new LLMs sound smarter. They are not. They have simply learned to hallucinate with better grammar. And the data proves it: with every new release, the deeper hallucination rates are going up, not down.

One of OpenAI’s biggest recent models hit an almost mind-blowing 50% hallucination rate on their own SimpleQA benchmark. One in two answers, fabricated. The coherency is a mask. What is underneath is getting worse.

So there you go: the companies that dominated the first era of AI are becoming its dinosaurs, and they are too busy scaling to notice what the new-kids-on-the-block competitors have already understood:

intelligence is not a frozen function. It is a continuously updated probability distribution moving through a structured space. And in a real learning system, space is not the stage on which computation performs. Space is part of the script.

In quieter times, this would be a technical debt problem. An expensive one, but manageable. These are not quiet times. The window is closing because the architecture itself is hitting a wall that compute cannot push through, and for the first time, there are credible alternatives waiting on the other side.

A new class of AI architecture is now positioned for a serious overtake of the entire industry. Not by building bigger transformers. Not by training longer. By changing the space in which computation happens. The approach has no single brand name yet, but the technical foundation is clear: Fisher-Bayesian AI (see chart above). It replaces the flat Euclidean geometry that neural networks have assumed since the 1980s with the curved, information-theoretic geometry that probability distributions actually live in. It does not improve the existing paradigm. It obsoletes the mathematical surface on which that paradigm was built.

Let’s cut now the tech jargon and explore with a real-world example in an animation with two drones facing the same landscape with the same obstacles. However, one runs on a frozen AI trained like an LLM: it planned its route once before deployment and never updates, because for the LLM AI the world stops changing the moment you finish training. The other runs on a Bayesian brain: every sensor reading reshapes its map of the real world (you know, the one with mountains, valleys, and the kind of surprises that don’t care about your training data) in real time. Now watch what happens when a new obstacle appears mid-flight.

What follows is the proof: the names, the math, the battlefield map, and the animated visual arguments you will not find anywhere else. If you have read this far, you already know something is broken. The rest of this article shows you what replaces it.

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Before the paywall drops, let me tell you what waits on the other side.

I will show you why the industry’s triumphal story is only half true, and why that half-truth is precisely what makes the moment so dangerous. I will separate surface hallucinations from deep hallucinations and show you why that distinction changes everything: how we measure progress, how we should think about AI risk, and why so many intelligent people are misreading what the current generation of models can and cannot do.

Then I will take you into the mathematics. Not vague opinion. Not futurist fog. The actual geometry of the problem. You will see why benchmark gains can keep rising while real progress bends, why the hidden wall is real, why the cost of pushing deeper grows much faster than the industry admits, and why paradoxically, even in its structurally broken state, AI is already strong enough to threaten elite work.

And then comes the part that matters most. I will show you where the machine still breaks, why those breaks are not random, and how those fault lines already sketch a professional map for mathematicians, developers, engineers, lawyers, researchers, and anyone whose work depends on structure rather than surface fluency.

In other words: on the other side of this paywall is not another AI article. It is an autopsy, a warning, and, if you are paying attention, a map.

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