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.
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