From Theoretical Physics to Artificial Intelligence, Consciousness, and Qur’anic Inimitability

1. Theoretical Physics: Is It Worth Studying?

If we want to identify precisely what theoretical physics is, we encounter quantum field theory, built on quantum mechanics and special relativity, alongside general relativity.

These are its two foundational wings, or the two pillars that allow us to understand the fundamental structure of matter, interactions, and spacetime, as well as the structure of the universe and black holes, at the deepest levels.

Studying them is immensely enjoyable, but it takes many years. Together, they give the physicist access to an enormous part of what we know about nature—although this is, of course, far from understanding everything.

But is studying them really worthwhile? I have discussed this question at length before. If we mean the practical and professional rewards for someone who devotes their life to them, my answer is: no, not really, in my judgment.

2. Particle Physics and Cosmology

Then come particle physics, astroparticle phenomenology, and cosmology. The motivation for studying these fields is clear and understandable: to discover what matter consists of, how interactions work, and how the universe developed and is structured. Most students who turn to theoretical physics enter through this door. That was my own experience too. Anyone who wants to study these subjects seriously needs the tools of quantum field theory, together with general relativity, particularly for cosmology.

But are they worth studying? They are no less worthwhile than field theory and relativity, since they are closer to physical phenomena and less mathematically abstract. Nevertheless, I do not see prospects that match the enormous effort they, too, demand.

I also believe that particle physics is struggling to achieve new fundamental breakthroughs. As for cosmology, despite the progress and precision of observations, the passage from data to fundamental conclusions often remains tied to assumptions and models that, in my view, deserve more caution than some confident presentations suggest.

3. Philosophy of Physics and the Philosophy of Spacetime

There is another direction: the philosophy of physics. Anyone interested in what I have called “the new kalām,” or in the philosophy of science more generally, should study the philosophy of physics. In my view, its central concerns are the philosophy of spacetime and its implications, and the philosophy of quantum mechanics and its implications.

But is studying the philosophy of physics worthwhile? If we are talking about practical and professional rewards, I do not think so either. Yet anyone who wants to philosophise about these questions should approach them properly and study the physics they intend to discuss.

This is why I am so surprised by the limited interest in my lectures on the philosophy of spacetime, particularly here on Facebook. Today, I finished the video on Kant’s philosophy. With a more engaged audience, I would have presented the entire journey: what I call the pre-classical philosophy, with its three pillars—Aristotle, Saint Augustine, and Ibn Arabi; then the classical philosophy of Newton, Leibniz, and Kant, whose lectures are available on YouTube; and finally the post-classical philosophy, beginning with Nietzsche, followed by Heidegger and Sartre, and also including Jung, alongside the revolutions brought about by Einstein and Bohr in relativity and quantum mechanics.

I have completed a paper on this subject, which will appear soon in the philosophy archive. This is a project I began sharing with you in 2018, if you remember, years before the appearance of today’s AI tools. I mention this to emphasise that our interest in these questions, and our writing about them, began long before these tools arrived.

4. From Condensed Matter to Quantum Computing and Artificial Intelligence

Now we come to condensed matter physics. This is an important field, directly connected to quantum computing and to the devices on which artificial intelligence depends, particularly through nanophysics and new materials. Many of its problems can also be approached from within theoretical physics itself.

Is it worthwhile? Technologically, certainly. Today’s computing depends on materials physics and semiconductors, while further directions include superconductors, photonics, and other technologies. As for the fundamental questions that personally occupy me in physics, however, I am not sure this is where I want to devote my remaining efforts—even while recognising that condensed matter physics has itself produced profound fundamental ideas.

But theoretical physics gave us quantum mechanics, and quantum mechanics made quantum computing and quantum information possible. This is where I see the most important remaining opening for myself in theoretical physics. That is, of course, my own view, not a binding judgment on the future of the entire field or on anyone else’s choices.

This is why I am trying to connect my research in quantum gravity with my studies in quantum computing and quantum information. Some time ago, I put forward a proposal that I consider very promising. Completing even part of it would be deeply satisfying to me.

I have also given lectures on quantum computing, but they attracted even less interest than the lectures on the philosophy of physics. We speak enthusiastically about science, technology, and the future, then turn away when actual study, concentration, and perseverance are required. We cannot consistently complain about the absence of serious scientific content and then ignore it when it is available. These lectures rest on years of study and work; I had expected those interested in these subjects to show a greater willingness to make the effort to follow them.

Quantum computing is, ultimately, computing. And computing is the foundation and backbone of artificial intelligence today. I believe AI will be one of the principal forces shaping humanity’s future, and I count myself among the theoretical physicists who are enthusiastic about it.

I do not mean that AI must wait for quantum computing to advance; it is already advancing through classical computing. What I mean is that quantum computing and quantum information offer me a natural path from fundamental physics to questions of information, learning, and intelligence.

5. Consciousness and Qur’anic Inimitability: A Testable Model

Some time ago, for example, I put forward what I consider an unconventional and highly ambitious proposal to study Qur’anic inimitability using artificial intelligence.

In this proposal, I draw a comparison between consciousness and the Qur’an. In the philosophy of consciousness, we encounter the “hard problem of consciousness,” connected to Thomas Nagel’s question: “What is it like to be a bat?” The issue is the subjective character of experience: what is it like for the creature having that experience? We can describe its processes and simulate its behaviour, but this does not, by itself, give us its personal experience from within.

Drawing on this comparison, I formulated the question of Qur’anic inimitability as follows: “What is it like for a language to be Qur’anic?” Within this conception, I assume that the essential nature of Qur’anic Arabic is accessible only to the Prophet, the angel, or God Almighty, with the meaning and level of that understanding differing in each case.

According to the model I propose, we can imitate ordinary Arabic texts and approach them without encountering an intrinsic minimum distance. Between the Qur’anic text and any attempt to imitate it, however, a minimum distance remains that cannot be crossed. It is as though the Qur’anic text lies at the centre of a ball in the space of Arabic language that imitation cannot enter.

This distance is more than a figure of speech. In the paper, I defined numerical measures of distance between texts and conducted preliminary experiments using lexical, semantic, logical, and stylistic measures. The hypothesis I want to test is that this minimum distance is an intrinsic property of the Qur’anic text, rather than merely a consequence of limitations in our tools of imitation and measurement. In this sense, I propose a Planck-like bound in language space: imitation improves, but a region remains around the text that it cannot enter.

There is, however, a fundamental difference between consciousness and the Qur’an. The gap associated with subjective experience applies to every conscious subject: simulating a person does not give us that person’s experience from within. The minimum distance hypothesised in this model, by contrast, applies specifically to the Qur’anic text, not to Arabic as a language or to every text written in it.

This is where AI becomes a means of testing the proposal: generating attempts at imitation, measuring their distances from the Qur’anic text, and comparing these with what we find for other texts. My preliminary experiments on English translations were intended to test the methodological tools. Testing the hypothesis itself requires working with the Arabic text.

If AI succeeds in imitating the Qur’an in a way that rules out this minimum distance, according to the specified measures and criteria, that would mean my proposed model does not capture the intended inimitability. It would not necessarily mean that inimitability itself had been disproved. What we are testing here is one particular model for understanding it.

6. Artificial Intelligence and Mathematical Physics: Three Axes

As I teach mathematical physics, I am now preparing a proposal entitled “Artificial Intelligence and Mathematical Physics,” organised around three axes and ten simulations.

The first uses AI as an extension of computational physics: we supply the physical model, and it helps us solve its equations and calculate its consequences.

The second uses AI as an extension of the theoretical physicist: we supply observations, and it helps us construct a description of the system by inferring its evolution law, discovering its equation, or proposing a correction to a known law.

The third studies AI itself as a physical system: we use mathematical physics and statistical mechanics to model networks and their learning processes, and to understand their behaviour.

7. Understanding Artificial Intelligence and a Criterion for the Emergence of Consciousness

I therefore believe that an important part of theoretical physics’ future passes through quantum computing and quantum information, and that this path opens a door towards artificial intelligence, in one way or another.

Just as theoretical physics, through the philosophy of spacetime, has helped us as theoretical physicists understand Kant’s immensely complex philosophy, I expect it to help us understand the physical and mathematical foundations of artificial intelligence.

For me, this goes much deeper than simply using AI as a tool in computational or theoretical physics. We want to simulate and understand AI itself using the tools of theoretical physics, particularly quantum mechanics and statistical mechanics, together with computational physics and quantum computing.

The question, then, is whether we can do this. Can we simulate artificial intelligence? Certainly, as far as its computational operations, learning dynamics, and behaviour are concerned. But I propose that the appearance of an intrinsic gap that simulation cannot cross would constitute a criterion for the emergence of artificial consciousness—provided that this gap belongs to the system itself, rather than arising from limitations in our tools or computational resources.

At that point, the hard problem of artificial consciousness takes shape: we can simulate the system’s operations and behaviour, but we cannot reach its subjective experience through simulation alone. The question becomes: “What is it like to be that system?”, just as Nagel asked: “What is it like to be a bat?”

Within my hypothesis, this intrinsic gap constitutes a criterion for the emergence of a conscious artificial subject whose behaviour can be simulated without possessing its inner experience. Here, the same structure appears that I proposed for human consciousness and hypothesised, in an analogous form, in my model of Qur’anic inimitability: simulation is possible, but an intrinsic minimum distance remains that it cannot overcome. The research challenge is to define and measure this gap, and to distinguish it from the limitations of our simulation tools.

What ultimately matters to me is that theoretical physics should become a path towards understanding AI itself, just as it has been a path towards understanding quantum computing and the philosophy of physics. For me, this is far more significant than simply using these fields as research tools and ways of earning a living in theoretical physics.

Quantum Field Theory: Reopening the Lecture Archive

 

I am reopening my archive of quantum field theory lectures for first-year Master’s students in theoretical physics taking QFT I and QFT II.

The archive contains 53 videos organized into 20 units. It brings together recordings from 2018 in mixed Arabic and English with recordings from 2021 in English and Arabic separately. The recordings vary in quality and presentation style. I had withdrawn them intending to produce improved versions—in language, scientific exposition, and visual presentation. Since I have not been able to carry out that project, I have decided to make the existing lectures available again, hoping they will remain useful to students.

The course follows five main stages:

1. Foundations: special relativity, electromagnetism, Hamilton’s principle, Lagrangian and Hamiltonian mechanics, and elementary particle physics.

2. The Dirac equation and spacetime symmetries: free solutions, rotations, spinors, and representations of the rotation and Lorentz groups.

3. Quantization of free fields: canonical quantization of scalar and Dirac fields, and their Feynman propagators.

4. Scattering and perturbation theory: the scattering matrix, Wick’s theorem, LSZ reduction formulas, and Feynman rules and diagrams for scalar phi-four theory.

5. Introduction to conformal field theory: the stress-energy-momentum tensor, the operator product expansion, primary operators, and the conformal anomaly.

Not every unit contains three alternative recordings. Eight units have two videos, eleven have three, and the final unit contains four successive lectures on conformal field theory. The 2018 recording on the scattering matrix and Wick’s theorem covers two units, while the unit on free solutions and symmetries of the Dirac equation includes an additional Arabic recording from 2021.

Students need not watch every version of a given unit. The alternative recordings generally cover overlapping material, allowing students to choose the language and presentation that suit them.

The downloadable table of contents includes the course outline, the recording exceptions, and direct clickable links to all 53 videos.

Download the PDF table of contents:

https://drive.google.com/file/d/1LfwPXHJ0_P6TACZXVj2LACbEvnHoGF_6/view?usp=sharing

Watch the complete playlist: 

https://www.youtube.com/watch?v=Y2bc6k1A3oM&list=PLsHtEvuFFjBswOlcNxWB7Os6g5FdLxH3p

 

When Rejection Becomes a Way of Life: The Struggling Actor as a Metaphor

What does it mean to be a “struggling actor”? Usually, it means someone trying to establish a career but unable to obtain enough paid work, recognition, or stability. The expression describes circumstances. It does not establish a lack of talent.

Is this merely an early phase? It can be. But it can also last decades, or an entire lifetime. There is no guaranteed progression from struggling to established. Some actors never secure a dependable livelihood through acting.

Someone may even begin with enormous promise, obtain encouraging early work, and then watch their career deteriorate. They can bear responsibility for decisions that contributed to this decline. But the outcome alone does not tell us how much was their responsibility. Others may never have received a genuine opportunity to demonstrate what they could do. A few small roles do not necessarily amount to a real chance.

Then comes the harder question: why continue after thirty years, perhaps into one’s fifties?

There are understandable answers. They love acting. They believe their abilities have matured. They still hope that one substantial role will allow their potential to become visible. Their ambition may be modest: steady work, a decent income, some recognition.

But what if the pursuit no longer sustains them? What if it brings sustained misery, financial insecurity, and an endless sequence of rejections? At that point, invoking love or a dream becomes insufficient. How much of a life should be surrendered to an opportunity that may never arrive?

Here, circumstances matter. An Algerian whose employment and mobility are severely restricted may be trapped in ways an American with accessible alternatives is not. American citizenship does not make every possibility available, but neither should substantial differences in freedom be dismissed. Where someone can realistically find another livelihood, pursue another direction, or move elsewhere, continued misery demands a stronger explanation than devotion to a vocation.

Perhaps they fear admitting loss. But after decades of disappointment, loss is already a lived experience. The more immediate fear may be the next rejection: another audition, another wait, another refusal. Repeated rejection can eventually become harder to bear than relinquishing the dream.

If viable alternatives exist, why should taking one count as failure? One need not conclude that the talent was imaginary, the effort worthless, or the treatment fair. It is enough to conclude that this particular path has become intolerable.

Someone can preserve their ability, and even continue their work, while withdrawing their livelihood and self-worth from the judgment of those who repeatedly refuse them.

The struggling actor is a metaphor for anyone whose promise remains unfulfilled. We should neither blame such a person automatically nor glorify their suffering. When opportunities are absent, persistence may reflect necessity. When alternatives are genuinely available and the pursuit brings only misery, changing direction becomes reasonable.

No dream acquires a permanent claim over a human life merely because that life has already given it thirty years.

If Authors Can Use AI, Why Can’t Referees?

 

The Reviewer Bottleneck  

Peer review has a very basic problem that predates artificial intelligence.

Reviewing a scientific paper properly is difficult work. It is also time-consuming work. A serious referee is expected to understand the central argument of a manuscript, check its important derivations, identify hidden assumptions, compare its claims with the existing literature, evaluate its novelty and significance, distinguish genuine errors from matters of presentation or interpretation, and determine whether the conclusions actually follow from the results. That is not a small task.

For a substantial theoretical paper, a careful review can consume many hours and sometimes several days of concentrated work. And who is supposed to do this work?

The same researchers who are already trying to write their own papers, conduct research, supervise students, teach courses, attend meetings, write reports and proposals, perform administrative duties, and maintain the rest of their academic lives.

Peer review depends overwhelmingly on scientists volunteering additional intellectual labor on top of everything else they already do.

It is therefore hardly surprising that journals increasingly struggle to find referees. Editors may have to invite many researchers before two agree to review a manuscript. Reviews can take weeks or months to arrive. Authors wait while editors search for another referee after several invitations have been declined. Sometimes the scientific evaluation itself takes much less time than simply finding someone willing and able to perform it.

This is not primarily a problem of bad referees. It is a problem of a system demanding substantial amounts of difficult intellectual work from people who often simply do not have enough time to perform it properly.

And this is precisely where artificial intelligence should enter the discussion.

AI Is More Urgently Needed by Reviewers Than by Authors

There is a curious asymmetry emerging in scientific publishing.

Authors are increasingly being given considerable latitude to use AI in their work, provided that they remain responsible for the final scientific content. They may use AI to improve language, organize arguments, summarize material, assist with coding, explore calculations, search literature, or interrogate complicated technical questions.

That development is understandable. But if AI assistance is useful to an author, it is arguably even more urgently useful to a reviewer.

The author is working on his or her own research. The author normally knows the calculation, the literature and the conceptual structure of the paper intimately. Months or years may have gone into producing it.

The referee enters from outside. The referee must understand somebody else's work, often under severe time constraints, reconstruct its logic, identify possible weaknesses, place it in the literature, and form a defensible scientific judgment.

This is exactly the kind of task for which intellectual assistance is valuable. Yet we are developing a publishing culture in which substantial AI assistance to the author is increasingly accepted while substantial AI assistance to the referee is often prohibited.

The priorities seem almost reversed.

If there is one part of the publication process where reducing the intellectual burden could immediately help the entire scientific system, it is peer review.

The Reviewer Does Not Need to Be Replaced

The issue is often framed incorrectly.

The choice is not

\[
\text{human reviewer}
\]

versus

\[
\text{AI reviewer}.
\]

The obvious model is

\[
\boxed{\text{human reviewer}+\text{AI assistance}.}
\]

The human referee remains responsible for the report.

That is exactly the principle already being adopted for authors. An author can use sophisticated computational tools without transferring authorship to those tools. The author remains responsible for the calculations and claims contained in the paper.

The same principle should apply to the referee.

Suppose I receive a theoretical-physics manuscript. Why should I not be allowed to give it to an AI system and ask:

“Reconstruct the central argument of this paper. Identify its principal assumptions. Check the important derivations where possible. Find possible inconsistencies. Compare the novelty claims with the cited literature. Separate potentially serious problems from minor ones.”

That does not replace me as referee. It gives me an extremely useful first analysis of the manuscript. I can then interrogate that analysis.

“Your objection to equation 27 seems incorrect because the large-(N) limit is taken before the infrared limit. Reanalyse it.”

“Derive equation 42 independently.”

“Where exactly is this assumption introduced?”

“Does reference 18 really establish what the authors claim?”

“Search for earlier work containing an equivalent result.”

“Construct the strongest argument against the main conclusion.”

“Now construct the strongest defense of the authors against those objections.”

At the end of this process, I decide what is correct. I decide what matters. I write the report. And I take responsibility for it.

This Could Make Reviews Better, Not Merely Faster

The obvious advantage is efficiency. A reviewer could perform in a few hours an analysis that might otherwise consume an entire weekend. Given the shortage of referees, that alone is important.

But speed is not the only issue. AI could also make reviews more thorough. A human referee may overlook an equation. A human referee may forget a relevant paper. A human referee may fail to notice that an assumption made on page 6 is incompatible with a conclusion on page 24. A human referee may simply become tired.

A sufficiently capable AI system can repeatedly traverse the manuscript, compare distant sections, track notation, examine limiting cases, summarize related literature and flag possible logical discontinuities.

Of course it can also make mistakes. Sometimes very convincing mistakes. But human referees make mistakes as well.

The correct response to this fact is not to prohibit assistance. It is to require the scientist to verify the criticisms that finally enter the referee report.

This is the same epistemic responsibility that scientists already exercise when using every other sophisticated tool.

We Do Not Demand That Referees Work Without Tools

Consider how strange such a principle would appear elsewhere. If an author evaluates a difficult integral using Mathematica, we do not insist that the referee reproduce the calculation by hand. If an author searches INSPIRE or Google Scholar, we do not require the referee to rely exclusively on papers remembered from years of reading. If an author performs a numerical computation on a powerful computer, nobody argues that the referee must reproduce it without computational assistance.

Scientific work has always advanced partly by developing better intellectual tools. AI is another such tool, although an unusually powerful one.

The relevant distinction should therefore not be

\[
\text{human work}\quad\text{versus}\quad\text{AI-assisted work}.
\]

It should be

\[
\text{responsible work}\quad\text{versus}\quad\text{irresponsible work}.
\]

Confidentiality Is a Solvable Problem

The strongest practical objection is confidentiality. An unpublished manuscript has been entrusted to the journal and to the referee. Clearly, a reviewer should not be free to distribute it indiscriminately to external parties.

But this is a technical and contractual problem. It is not a fundamental argument against AI-assisted reviewing.

Publishers could provide their own secure AI systems. A journal submission platform could make an AI assistant available directly to the referee. The manuscript would remain inside the publisher's protected infrastructure. The system could operate under explicit conditions that the manuscript is not used for training, is not retained beyond the required period, and is not disclosed externally. Universities could provide equivalent secure systems.

If confidentiality is genuinely the problem, then the rational response is to build confidential AI tools for reviewers. It is not to prevent reviewers from benefiting from AI.

Journals Should Give Every Referee an AI Assistant

This seems to me the natural direction in which peer review should develop. When a researcher accepts a review invitation, the journal system should provide an integrated AI assistant already authorized to examine the manuscript. The referee could ask:

What are the logically independent assumptions? 

Which conclusions depend on each assumption?

Check the derivation from equations 16 to 22.

Does the claimed limiting behaviour actually follow?

Which previous papers are closest to this result?

Are there important missing references?

Where does the manuscript move from demonstrated result to conjecture?

Construct the strongest possible objection to the central result.

Now defend the authors against that objection.

Such interaction would allow the referee to concentrate on the genuinely difficult part of scientific evaluation: understanding what matters. That is where human scientific experience remains indispensable.

The Referee Must Remain Responsible

There should nevertheless be a very clear boundary. A referee should not ask an AI system for a report and simply send that report to the editor. If the AI claims that an equation is wrong, the referee must satisfy himself or herself that the objection is valid before making it. If the AI claims that a result already exists, the referee should examine the relevant literature. If the AI misunderstands an unconventional argument, the referee must recognize the misunderstanding. The human reviewer remains scientifically and ethically responsible for the submitted report.

The governing principle is therefore simple:

\[
\boxed{\text{Do not outsource responsibility to AI. Outsource work to AI.}}
\]

This principle works equally well for authors, referees and editors.

AI Could Also Review the Reviewers

There is an additional possibility. AI could help improve not only manuscripts but referee reports themselves. Before sending a report to the author, the editorial system could ask:

Does this criticism accurately represent what the manuscript says?

Is the referee asking for work far outside the stated scope of the paper?

Does the claim that the result is already known have supporting references?

Are requested citations actually relevant?

Does the referee appear to have misunderstood a definition or convention used by the authors?

This would introduce some quality control into a part of scientific publishing that currently receives remarkably little.

Papers are scrutinized intensely. Authors are scrutinized intensely. Referee reports themselves are much less systematically scrutinized. AI could help correct that imbalance. 

A further problem is the imbalance of power built into the present system. A referee can sometimes become effectively dictatorial: demanding additional calculations, imposing personal preferences about scope or interpretation, insisting on unnecessary revisions, or repeatedly moving the conditions for acceptance, while facing very little accountability for those demands. 

Editors, especially when overloaded or working outside the referee's exact specialization, may simply defer to the report rather than independently judging whether the requests are scientifically justified. The result is an asymmetric process in which authors must answer every objection in detail, while referees are rarely required to justify the proportionality, relevance, or even correctness of their criticisms. 

AI-assisted editorial checking could provide a useful counterweight by asking whether each major referee demand is actually supported by the manuscript, relevant to its claims, and necessary for establishing the validity of the work.

The Strange Reversal of Priorities

This brings us back to the central paradox. Scientific publishing is suffering from a shortage of reviewers. Careful reviewing requires substantial time and intellectual effort. Researchers are increasingly reluctant or simply unable to accept additional referee assignments. Editors struggle to obtain reports. Authors wait. And yet, when one of the most powerful tools ever developed for reducing intellectual workload becomes available, we give considerable latitude to authors while being much more restrictive with reviewers. This seems backwards.

Authors certainly benefit from AI. But the publication system may need AI-assisted refereeing even more urgently.  The appropriate future of peer review is therefore not the replacement of scientists by machines. It is

\[
\boxed{
\text{AI assistance}
+
\text{human scientific judgment}
+
\text{human responsibility}.
}
\]

Used in this way, AI would not weaken peer review. It could help rescue a system that is already struggling under the amount of work we ask human referees to perform.

AI as the Hardworking and Perfect PhD Student

 

Some people seem very defensive, and sometimes openly hostile, about the use of AI in scientific work. Others go even further and become judgmental, almost as if they were sitting in judgment, practicing criticism far more readily than approval, as though using AI were a form of cheating, intellectual dishonesty, or something one ought to feel ashamed o

Recently, someone on YouTube asked me whether I had written my response using AI. Well, yes. Nowadays I often write whatever I want very quickly and then pass it to the LLM I prefer, without exhausting myself over language, grammar, spelling, or style. It may be an email, an answer to a question, a short note, in English or in Arabic, and then I let the machine rewrite it properly.

Why should I spend my time on linguistic and stylistic tedium if a machine can handle that efficiently?

Is that impersonal? Perhaps sometimes. But even if it is, why should that be a problem? I am not a literary writer. What matters to me is the substance: the idea, the argument, the physics, the mathematics. If an LLM can take care of the linguistic packaging, I am perfectly happy to let it do so.

And personally, I have nothing to prove on this point. I had already written and published several books before the advent of modern LLMs. I was writing in physics and philosophy, in both English and Arabic, at least a decade before these tools existed. Whether my work is popular or not is beside the point. I believe that what I write has substance and depth, and I know perfectly well that the ideas are mine.

So I am not going to be shamed, or pushed into feeling guilty, for using a machine to help me express those ideas more efficiently.

I see AI in the same broad category as other powerful scientific tools. Researchers already rely on Mathematica for symbolic calculations, MATLAB and numerical libraries for computation, Lean for formal proof verification, Monte Carlo methods, parallel computing, and many other tools that enormously extend what one person can do. If others use these tools effectively and you refuse to use them, you may simply become unable to compete scientifically. I say this from first-hand experience.

AI and large language models are, in my view, another tool of this kind — perhaps better described as a super-tool because of their breadth — but still a tool. As with any scientific tool, the important question is not whether it was used, but how it was used. Its role should be disclosed transparently, its output should be checked and calibrated, and the researcher must be able to judge the result independently.

But there is an equally important point on the other side. Using these tools without being able to control, direct, and evaluate them can become dangerous scientifically. The hierarchy must remain clear: you should be the supervisor, and the AI should be the hardworking PhD student, not the other way around.

You define the real question. You choose the direction. You decide which calculations are meaningful and which are nonsense. You detect the mistakes, judge whether a result is scientifically sound, and take responsibility for the final conclusion. The machine can calculate, suggest, derive, search, rewrite, and explore possibilities at extraordinary speed, but it should not become the authority that you simply follow.

If you start relying on the model in areas where you are unable to evaluate, correct, or defend what it produces, then the relationship has been inverted: the PhD student has become the supervisor. In serious scientific work, that will eventually expose you.

That is where the real distinction lies. A scientifically mature researcher can exploit AI as part of a broader methodology, together with symbolic calculation, numerics, formal verification, simulation, and their own expertise. Someone who does not yet possess sufficient command of the subject may instead become dependent on the model precisely because they cannot properly evaluate what it produces. Those are very different situations.

Personally, I use AI extensively, and I disclose that use very openly. I do not see anything embarrassing about it. The relevant question is whether the scientific ideas, judgment, verification, and responsibility remain with the researcher.

In fact, since I started using AI seriously, I have felt considerably more liberated as a researcher. It has given me something close to the collaborator I had often been missing. By incorporating these tools into my work, I sometimes feel that I have effectively built a one-person research group around myself: I can explore calculations, test ideas, check derivations, examine alternative approaches, and move between analytical, numerical, and editorial tasks with a speed that would otherwise require several collaborators.

And this may have consequences beyond the individual researcher. AI could potentially reduce, at least to some extent, the enormous structural gap between researchers working in developing-world universities and those working in the best-funded institutes of the developed world. It obviously cannot replace laboratories, funding, strong departments, or a genuine scientific community. But it can provide access to a kind of intellectual and computational support that was previously available mainly to researchers surrounded by large groups, strong collaborators, and substantial institutional resources.

For that reason, I see AI not simply as another convenience, but as a potentially important equalizing tool in scientific research — provided, of course, that it is used by people who already possess the scientific maturity to judge, verify, and take responsibility for what it produces.

Scientific maturity is not demonstrated by refusing powerful tools. It is demonstrated by knowing how to use them without surrendering your judgment to them.

Did AI Really Solve the Navier–Stokes Millennium Problem?

In September 2026 came an extraordinary claim: an internal OpenAI system had solved the Navier–Stokes Millennium Prize Problem.

Almost immediately, Tristan Buckmaster published a statement describing closely related work with Levent Alpöge, raising questions about priority, unpublished material entered into AI systems, and even OpenAI–Anthropic rivalry.

The headlines reduced this to:

AI solved Navier–Stokes, and mathematicians accused it of stealing their work.

But the real story is more interesting.

Before asking who deserves credit, we must ask:

\[\boxed{\text{What exactly was the Navier–Stokes problem that was solved?}}\]

Only then do the mathematics, the history, and the controversy become clear.

1. What the Navier–Stokes Millennium Problem Actually Asks

Start with the actual fluid equation

For an incompressible fluid in three spatial dimensions, the Navier–Stokes equations are

\[\partial_t u +(u\cdot\nabla)u = \nu\Delta u-\nabla p+f,\qquad \nabla\cdot u=0,\]

with initial condition

\[u(x,0)=u_0(x).\]

Here \(u(x,t)\) is the velocity field, \(p(x,t)\) is the pressure, \(\nu>0\) is the viscosity, and \(f(x,t)\) is an externally applied force.

Charles Fefferman's official formulation for the Clay Mathematics Institute says explicitly that \(u_0\) and \(f\) are prescribed data, whereas \(u\) and \(p\) are the unknown fields. Setting \(\nu=0\) gives the Euler equations. Clay Mathematics Institute: Navier–Stokes formulation.

This is important because Navier–Stokes is not merely an abstract mathematical problem. At heart it is a problem in fluid mechanics.

The nonlinear term \((u\cdot\nabla)u\) allows the flow to transport and amplify its own structures, whereas \(\nu\Delta u\) describes viscous diffusion and tends to smooth them.

The great question is whether viscosity is always powerful enough to prevent a smooth three-dimensional flow from concentrating itself into a singularity.

What does “global smoothness” actually mean?

Local smooth existence is not the mystery.

If the initial velocity \(u_0\) is sufficiently regular, standard PDE theory gives a smooth classical solution for at least some interval

\[0\leq t<T.\]

The question is whether the solution can always be continued indefinitely:

\[0\leq t<\infty.\]

That is what “global” means here: global in time.

A global smooth solution requires \(u,p\in C^\infty\) for all finite times, together with an appropriate finite-energy condition such as

\[\int_{\mathbb R^3}|u(x,t)|^2\,d^3x<C.\]

Fefferman notes that if the maximal classical existence time \(T\) is finite, then the Navier–Stokes velocity necessarily becomes unbounded near \(T\). This is not a behavior that Fefferman imposes by definition. It follows from continuation theory.

If everything remained sufficiently bounded as \(t\to T^-\), local existence theory would let us continue the solution past \(T\). But \(T\) was assumed maximal. Therefore something must lose regularity.

\[\text{regularity remains controlled near }T\Longrightarrow\text{solution extends beyond }T,\]

and hence

\[T<\infty\Longrightarrow\text{regularity breakdown}.\]

Fefferman's official formulation.

Fefferman did not formulate one Millennium statement, but four

This point is essential, because much of the public discussion has confused the official Millennium problem with its most famous version.

Alternative	Space	Force	Required conclusion
A	\(\mathbb R^3\)	\(f=0\)	Every smooth initial flow has a global smooth finite-energy solution.
B	Periodic \(T^3\)	\(f=0\)	The analogous global-smoothness result.
C	\(\mathbb R^3\)	Smooth \(f\) allowed	Construct smooth data for which no global smooth finite-energy solution exists.
D	Periodic \(T^3\)	Smooth \(f\) allowed	Analogous finite-time breakdown.

Alternatives A and B are existence-and-smoothness theorems. Alternatives C and D are counterexample problems. This is the conceptual key.

C is asking for a counterexample

Let us write the universal proposition explicitly:

\[G:\quad \text{Every admissible smooth }(u_0,f)\text{ produces a global smooth finite-energy solution.}\]

Symbolically,

\[\forall (u_0,f),\qquad \exists\,u_{\mathrm{global,smooth}}.\]

Alternative C asks you to show that this universal proposition is false. It is enough to find one particular pair \((u_0^\ast,f^\ast)\) such that

\[\boxed{\text{there is no global smooth finite-energy solution for these data.}}\]

So C really is:

\[\boxed{\text{construct a counterexample to universal global regularity.}}\]

But C is not the negation of A

Alternative A says \(f=0\) and asks whether every smooth initial velocity remains smooth forever. Alternative C allows \(f\neq0\). Therefore both A and C could logically be true.

We could eventually discover that

\[\boxed{f=0:\ \text{every smooth Navier–Stokes flow remains regular}}\]

while simultaneously

\[\boxed{f\neq0:\ \text{some very carefully designed smooth driving causes blowup}.}\]

There would be no contradiction.

So the question most physicists have traditionally associated with the Navier–Stokes problem—can an unforced smooth three-dimensional viscous fluid spontaneously form a finite-time singularity?—remains open.

OpenAI's claimed result establishes C and D, not A or B. OpenAI, Finite Time Blowup for Navier–Stokes.

Thus we should distinguish

\[\boxed{\text{The official Millennium formulation has apparently been resolved}}\]

from

\[\boxed{\text{the famous unforced regularity problem has been resolved}.}\]

The second statement is false.

The Clay Mathematics Institute has used cautious language, saying that the problem has “apparently been settled” and stressing that verification and attribution will be deliberately unhurried. Clay Mathematics Institute announcement.

2. What OpenAI Claims to Have Constructed

The paper is entitled Finite Time Blowup for Navier–Stokes. Strikingly, the author printed on the paper is simply

\[\boxed{\text{OPENAI}}.\]

Its Theorem 1.1 states that for every \(\nu>0\) there exist a smooth compactly supported force \(f\), and smooth velocity and pressure fields \(u,p\) defined for \(0\leq t<1\), satisfying

\[\partial_tu+(u\cdot\nabla)u-\nu\Delta u+\nabla p=f,\qquad \nabla\cdot u=0,\]

with the remarkably simple initial condition

\[u(x,0)=0.\]

Thus the fluid begins completely at rest.

The solution has bounded kinetic energy,

\[\sup_{0\leq t<1}\|u(t)\|_{L^2}<\infty,\]

but

\[\limsup_{t\to1^-}\|u(t)\|_{L^\infty}=\infty.\]

Consequently there can be no globally smooth finite-energy solution with the same initial data and the same force. That last conclusion is exactly Fefferman C.

Finite energy does not mean finite maximum velocity

How can \(|u|\to\infty\) while kinetic energy remains bounded? Because kinetic energy is an integral:

\[E=\frac12\int |u|^2\,d^3x.\]

A function can become arbitrarily large on an arbitrarily small region while its integral remains finite.

A crude example is a sequence of velocity fields whose magnitude is \(U_n=n\) inside a region of volume \(V_n=n^{-3}\). Then

\[\|u_n\|_\infty=n\to\infty,\]

but

\[\|u_n\|_2^2\sim U_n^2V_n=n^2n^{-3}=n^{-1}\to0.\]

Infinite height can be compensated by shrinking width. This is essentially what happens dynamically in the claimed Navier–Stokes solution.

The shrinking-vortex scaling

Define \(\tau=1-t\). As the blowup time approaches, \(\tau\to0^+\).

The OpenAI construction has a shrinking vortex core with radial size

\[\ell_r\sim\tau^{1/2}\]

and axial size

\[\ell_z\sim\tau^{1/2-h},\qquad 0<h<\frac1{100}.\]

Therefore

\[V_{\rm core}\sim\ell_r^2\ell_z\sim\tau^{3/2-h}.\]

The characteristic large velocity components scale as

\[U\sim\tau^{-1/2-h},\]

so \(U\to\infty\).

Now compute the kinetic energy carried by this core:

\[E_{\rm core}\sim U^2V_{\rm core}\sim\tau^{-1-2h}\tau^{3/2-h}=\tau^{1/2-3h}.\]

Since \(h<1/100\), we have \(1/2-3h>0\). Thus

\[\boxed{E_{\rm core}\to0}\]

while simultaneously

\[\boxed{U_{\max}\to\infty}.\]

The singularity is not an explosion of total energy. It is an extreme concentration of motion.

Why does the singularity occur at \(t=1\)?

There is no spatial boundary involved. The “boundary” is simply the end of the smooth time interval.

The elementary function

\[g(t)=\frac1{1-t}\]

is perfectly smooth for every \(t<1\), but \(g(t)\to\infty\) as \(t\to1^-\). It therefore cannot be smoothly extended through \(t=1\).

The same idea applies to the velocity field. For every fixed \(t<1\), \(u(x,t)\) is smooth. But along points approaching the spatial origin, the velocity becomes arbitrarily large as \(t\to1^-\). The choice \(t=1\) is just a convenient normalization of the finite blowup time.

Why “limsup”?

The theorem states

\[\limsup_{t\to1^-}\|u(t)\|_\infty=\infty.\]

This means that arbitrarily close to \(t=1\), the velocity norm reaches arbitrarily large values. Equivalently, there are times \(t_n\to1\) such that

\[\|u(t_n)\|_\infty\to\infty.\]

The norm need not diverge monotonically.

The apparently strange role of the force \(f\)

Normally we think of \((u_0,f)\) as the input and solve for \((u,p)\). So why does the construction appear to do the opposite?

Because C is a counterexample problem. We are free to construct the bad input.

For a divergence-free velocity \(u\) and pressure \(p\), define the residual

\[R[u,p]=\partial_tu+(u\cdot\nabla)u-\nu\Delta u+\nabla p.\]

Then set

\[f=R[u,p].\]

The Navier–Stokes equation automatically holds. But that alone proves nothing, because for a generic singular \(u\), the resulting \(f\) will also be singular.

The difficult requirement is

\[\boxed{u\text{ becomes singular while }f\text{ remains }C^\infty.}\]

The construction adds carefully designed oscillatory and correction fields so that the singular pieces of the residual cancel and the final force extends smoothly through the blowup time.

The logical construction therefore runs backwards:

\[u,p\longrightarrow f.\]

But once \(f\) has been obtained, freeze it. Call it \(f_*(x,t)\). Then the physical initial-value problem is again

\[(u_0=0,f_*)\longrightarrow u,\]

and the already-constructed \(u\) is an exact solution.

What exactly is the counterexample contradicting?

Not Navier–Stokes itself: the singular velocity field satisfies Navier–Stokes.

Not conservation of energy: the total kinetic energy remains bounded.

And not alternative A: A concerns \(f=0\).

The counterexample contradicts the universal proposition

\[\boxed{\text{smooth initial conditions + smooth forcing}\Rightarrow\text{global smooth classical flow}.}\]

The claimed result says that this implication is false.

There exists a smooth force, acting on a fluid initially at rest, for which the classical velocity field becomes singular in finite time.

Do they actually write \(u(x,t)\) down?

Yes and no.

The theorem is written existentially: there exist \(u,p,f\). But the proof is constructive.

This is not a nonconstructive argument that merely says some mysterious solution must exist. On the other hand, the answer is not a simple elementary formula such as

\[u(x,t)=\frac{x}{1-t}.\]

The solution is built from self-similar profiles, similarity coordinates, vector potentials, oscillatory pulses, correction fields, cutoffs, and an iterative hierarchy designed to cancel the singular residual.

So the right description is

\[\boxed{\text{constructive existence, but not a simple closed-form solution}.}\]

And \(p\) is indeed the pressure.

What has and has not been solved

OpenAI claims a constructive proof, backed by Lean formalization, that smooth forcing can drive initially resting Navier–Stokes flow to finite-time blowup while kinetic energy remains bounded. If correct, this establishes Fefferman C and D and resolves the official Clay formulation.

But it does not settle the unforced problem:

\[f = 0 : \text{global smoothness versus finite-time singularity} .\]

Alternatives A and B remain open. Thus the Millennium formulation may be resolved while the most famous Navier–Stokes regularity question remains unanswered. 

3. From Human Insight to Industrial-Scale AI Mathematics

Euler versus Navier–Stokes

Euler is

\[\partial_tu+(u\cdot\nabla)u=-\nabla p+f,\qquad\nabla\cdot u=0,\]

whereas Navier–Stokes is

\[\partial_tu+(u\cdot\nabla)u=\nu\Delta u-\nabla p+f.\]

So

\[\boxed{\text{Euler}=\text{Navier--Stokes with }\nu=0.}\]

The extra viscous term \(\nu\Delta u\) is not a minor correction. It continually smooths small-scale velocity structure. This is why the jump from Euler blowup to fully viscous Navier–Stokes was decisive.

Before OpenAI: Córdoba and Martínez-Zoroa

The OpenAI result did not appear in an intellectual vacuum. 

Buckmaster himself says that the basic idea of the entire program belongs to Diego Córdoba and Luis Martínez-Zoroa. Buckmaster's statement.

In 2023 Córdoba and Martínez-Zoroa constructed finite-time blowup for forced three-dimensional incompressible Euler. Their forcing was regular but not \(C^\infty\); roughly speaking, the mechanism arranged successive amplification of increasingly concentrated vortex structures. Córdoba–Martínez-Zoroa, arXiv:2309.08495.

The conceptual mechanism was already present:

\[\text{large-scale strain}\to\text{amplification at smaller scales}\to\text{concentration}\to\text{singularity}.\]

Hypodissipative Navier–Stokes: approaching viscosity gradually

Córdoba, Martínez-Zoroa and Fan Zheng then pushed the method from Euler toward a dissipative fluid equation.

Ordinary Navier–Stokes contains \(-\nu\Delta u\), which damps high-frequency modes like \(|k|^2\). A hypodissipative model uses weaker fractional dissipation, for example \(|\nabla|^\alpha u\) with \(\alpha<2\).

Ordinary Navier–Stokes corresponds, in this notation, to \(\alpha=2\).

Córdoba, Martínez-Zoroa and Zheng obtained forced finite-time blowup for small positive \(\alpha\), below approximately \(0.093\). Córdoba–Martínez-Zoroa–Zheng, arXiv:2407.06776.

Conceptually, the route was

\[\text{Euler}\longrightarrow\text{weak dissipation}\longrightarrow\boxed{\text{ordinary Navier--Stokes}}.\]

Where Boussinesq enters

Another neighboring system is the Boussinesq equation. Schematically,

\[\partial_tu+(u\cdot\nabla)u+\nabla p=\theta e_z,\]

together with an evolution equation for a scalar field \(\theta\), often interpreted as temperature or density variation, and \(\nabla\cdot u=0\).

The scalar produces buoyancy. Physically, this is the sort of coupling involved in convection. Mathematically, Boussinesq provides another laboratory in which vorticity amplification and singularity mechanisms can be tested.

It is not one of Fefferman's alternatives. It is a neighboring physical PDE in which the singularity mechanism was developed and tested.

Buckmaster and Alpöge enter the story

Tristan Buckmaster is a professor of mathematics at NYU's Courant Institute whose research focuses heavily on singularity formation in fluid equations. His earlier work with Vlad Vicol on nonuniqueness of weak Navier–Stokes solutions was recognized with a 2019 Clay Research Award. Tristan Buckmaster, NYU.

Buckmaster and Levent Alpöge took the Córdoba–Martínez-Zoroa program as their starting point and used several LLM systems extensively to push it further.

Buckmaster says their collaboration was personal rather than an institutional Anthropic project. They used Anthropic's Claude as well as OpenAI tools, including Codex. Buckmaster's statement.

Their major advance was essentially

\[\text{rougher forcing}\longrightarrow\boxed{\text{smooth forcing}}.\]

By August 15 they had smooth-forced blowup results for Boussinesq and three-dimensional incompressible Euler. They subsequently verified the Euler argument in Lean on August 22. Buckmaster also reported that they believed they had a hypodissipative Navier–Stokes result, though it was not yet ready for release.

They had therefore not solved Fefferman C, but they had moved very close to the relevant frontier.

What the OpenAI system then did

OpenAI says that on September 1 it heard rumors that major open mathematical problems had been solved and decided to test a new internal model on all the remaining Millennium problems and several related problems. Separate agent groups were assigned versions A, B, C and D. OpenAI's account.

The company also assigned agents easier neighboring questions. One of those was Euler.

Approximately 100 agents worked for around 50 hours and produced what OpenAI describes as an unforced Euler blowup result:

\[\nu=0,\qquad f=0.\]

That is in one respect stronger than Buckmaster–Alpöge's Euler theorem, because their Euler result used external forcing. But Euler is not a Millennium Prize Problem.

At this stage humans made an important strategic decision. OpenAI says:

“Once we saw the Euler solution, we thought that Navier–Stokes was the most promising problem.”

Resources were shifted away from the other Millennium problems, and the Euler result was supplied to the Navier–Stokes agents. Groups were cross-pollinated using Codex to consolidate useful intermediate discoveries.

The Navier–Stokes effort involved on the order of \(10^4\) concurrent agents. OpenAI reports approximately \(2.7\) million messages and \(130\) billion output tokens for the Navier–Stokes part alone.

The claimed solution was reached after approximately 88 hours, followed by another 17 hours of Lean formalization and verification. OpenAI's account.

This is not the ordinary picture of a mathematician using ChatGPT. It is something closer to industrialized mathematical search. 

Human versus AI is the wrong picture

Both sides were already hybrid.

Buckmaster and Alpöge represent

\[\boxed{\text{AI-integrated academic mathematics}},\]

while OpenAI represents

\[\boxed{\text{industrial-scale AI mathematics}}.\]

Humans chose the problems and directed the search; AI systems performed much of the detailed exploration and proof production.

So the real contrast is not human versus AI, but two different scales of human–AI mathematical research.

What is Lean?

Lean is not a fluid simulator, and it is not Mathematica.

Mathematica is mainly for symbolic and numerical computation. Lean is a formal theorem prover: it translates definitions, assumptions, and proof steps into precise logic, then checks them with a small trusted kernel.

A useful comparison is

\[\text{Mathematica}\approx\text{powerful calculator},\]

while

\[\text{Lean}\approx\text{logical compiler + extremely strict referee}.\]

Lean verifies that the formal proof follows from the formal statement. Humans must still verify that the formal statement captures the theorem they actually intended. 

4. From Mathematical Breakthrough to Scientific Controversy

The strange authorship: “OPENAI”

The 166-page paper lists a single author:

\[\boxed{\text{OPENAI}}.\]

That is unusual. OpenAI wants to emphasize the role of its AI system, but there was still a human research team behind the work.

Unlike “ATLAS Collaboration,” which names an identifiable scientific collaboration, “OpenAI” names an entire corporation.

This raises a new problem for scientific authorship:

\[\text{contribution}+\text{credit}+\text{responsibility}.\]

AI-generated research makes all three harder to assign.

Now the controversy

There was no completed Buckmaster–Alpöge Navier–Stokes proof for OpenAI to copy. But OpenAI acknowledges that rumors of their progress helped trigger its September 1 search.

Buckmaster’s concern was that smooth forcing was a very specific route already being pursued by his group, while drafts had also been entered into Codex. OpenAI says an internal investigation found those prompts could not have influenced the result; Buckmaster says he does not know whether any data were used and makes no accusation.

So the real controversy is about priority, data trust, and attribution — not a proven theft of a proof.

The more troubling part may be corporate competition

According to Buckmaster, OpenAI later proposed that he present the Navier–Stokes result while Alpöge was excluded, with Alpöge’s employment at Anthropic cited as a complication.

If that account is accurate, the issue goes beyond plagiarism: a personal mathematical collaboration became entangled with

\[\boxed{\text{OpenAI versus Anthropic}.}\]

Corporate rivalry had entered scientific attribution.

Who deserves credit?

The story is too complex for a single-author narrative:

\[\boxed{\text{Córdoba + Martínez-Zoroa}\to\text{Córdoba + Martínez-Zoroa + Zheng}\to\text{Buckmaster + Alpöge}\to\text{OpenAI + large-scale AI system}.}\]

Córdoba and Martínez-Zoroa developed the forced-blowup route; Zheng helped extend it to weak dissipation; Buckmaster and Alpöge pushed it to smooth-forced Euler using LLMs; and OpenAI claims the final step to ordinary Navier–Stokes, giving Fefferman C and D.

The long-term historical attribution is still unsettled.

The Deep Blue–Kasparov moment

Buckmaster called this a “Deep Blue–Kasparov moment,” recalling IBM’s 1997 defeat of world chess champion Garry Kasparov.

The analogy is striking because frontier mathematics was supposed to require open-ended conceptual invention, not merely search. Yet this episode suggests that large-scale reasoning models, interacting agents, and formal verification may now be entering that territory.

The difference is crucial: humans opened the conceptual path and chose the targets. What AI changed was the scale and speed with which the nearby mathematical possibilities could be explored.

What I think is really new here

The superficial story is:

\[\text{AI solved a famous problem}.\]

The deeper story is that mathematical research may have acquired a new industrial scale.

Buckmaster and Alpöge represent one model:

\[\text{a few mathematicians}+\text{powerful AI collaborators}.\]

OpenAI represents another:

\[\text{human direction}+10^4\text{ reasoning agents}+\text{massive computation}+\text{formal verification}.\]

And these new modes immediately collided with old questions of priority, authorship, unpublished work, and institutional competition.

The broader lesson

This is neither simply “AI replaced mathematicians” nor “AI merely copied human mathematics.”

Human mathematicians opened the conceptual path; researchers working with LLMs accelerated it; and an industrial AI system appears to have crossed a major remaining gap, with formal verification helping check the result.

What changed is deeper: AI entered the research process itself — not merely as a calculator or search tool, but increasingly as a collaborator. That immediately raises new questions of priority, authorship, responsibility, unpublished ideas, and corporate ownership.

The Navier–Stokes episode may therefore be remembered not only for fluid mechanics, but as the moment the old boundaries between mathematician, tool, collaborator, institution, and author began to blur.

That may be the real Deep Blue–Kasparov moment.

Zapffe’s Existential Theorem: The Last Messiah

Zapffe’s existential theorem can be understood in three steps.

First, consciousness becomes a tragic overdevelopment. Human beings do not simply live; we know that we will die, that our lives are contingent, and that the universe offers no guaranteed meaning.

Second, civilization becomes a system of survival. We protect ourselves through attachment, diversion, isolation, and sublimation. In other words, we manage consciousness so that life remains bearable.

Third comes the Last Messiah: the person who sees through these defenses. For him, the mechanisms no longer work. Consciousness has become fully aware of its own trap.

The theorem therefore ends with a severe conclusion: consciousness cannot save itself from consciousness.

My question is whether this conclusion is final—or only final from within the human perspective.

Zapffe’s conclusion is powerful, but I think it is final only from within the human perspective.

Human consciousness observes Reality from an extremely narrow position: one species, one planet, one biological interface. Yet from this limited access, it often treats its own reconstruction of the world as if it were ontologically complete.

This is what I call the closure assumption.

My proposal is that consciousness may function like a hallucinating system. A hallucinated world can be internally coherent for the mind experiencing it, while appearing incomplete or distorted from an external perspective. The same may be true of consciousness itself.

If so, Zapffe’s existential dead-end may be real from within consciousness without being metaphysically final.

The loophole is therefore a change of level: a possible meta-perspective beyond human consciousness itself, which I call Cosmic Consciousness.