Making Sense of What We Already Know…

Living Between Jobs and Life: AI, CERN, and Making Sense of What We Already Know

For decades (all of my life, basically :-)), I’ve lived with a quiet tension. On the one hand, there is the job: institutions, projects, deliverables, milestones, and what have you… On the other hand, there is life: curiosity, dissatisfaction, and the persistent feeling that something fundamental is still missing in how we understand the physical world. Let me refer to the latter as “the slow, careful machinery of modern science.” 🙂

These two are not the same — obviously — and pretending they are has done physics no favors (think of geniuses like Solvay, Edison or Tesla here: they were considered to be ‘only engineers’, right? :-/).

Jobs optimize. Life explores.

Large scientific institutions are built to do one thing extremely well: reduce uncertainty in controlled, incremental ways. That is not a criticism; it is a necessity when experiments cost billions, span decades, and depend on political and public trust. But the price of that optimization is that ontological questions — questions about what really exists — are often postponed, softened, or quietly avoided.

And now we find ourselves in a new historical moment.


The Collider Pause Is Not a Crisis — It’s a Signal

Recent reports that China is slowing down plans for a next-generation circular collider are not shocking. If anything, they reflect a broader reality:

For the next 40–50 years, we are likely to work primarily with the experimental data we already have.

That includes data from CERN that has only relatively recently been made fully accessible to the wider scientific community.

This is not stagnation. It is a change of phase.

For decades, theoretical physics could lean on an implicit promise: the next machine will decide. Higher energies, larger datasets, finer resolution — always just one more accelerator away. That promise is now on pause.

Which means something important:

We can no longer postpone understanding by outsourcing it to future experiments.


Why CERN Cannot Do What Individuals Can

CERN is a collective of extraordinarily bright individuals. But this is a crucial distinction:

A collective of intelligent people is not an intelligent agent.

CERN is not designed to believe an ontology. It is designed to:

  • build and operate machines of unprecedented complexity,
  • produce robust, defensible measurements,
  • maintain continuity over decades,
  • justify public funding across political cycles.

Ontology — explicit commitments about what exists and what does not — is structurally dangerous to that mission. Not because it is wrong, but because it destabilizes consensus.

Within a collective:

  • someone’s PhD depends on a framework,
  • someone’s detector was designed for a specific ontology,
  • someone’s grant proposal assumes a given language,
  • someone’s career cannot absorb “maybe the foundations are wrong.”

So even when many individuals privately feel conceptual discomfort, the group-level behavior converges to:
“Let’s wait for more data.”

That is not cowardice. It is inevitability.


We Are Drowning in Data, Starving for Meaning

The irony is that we are not short on data at all.

We have:

  • precision measurements refined to extraordinary accuracy,
  • anomalies that never quite go away,
  • models that work operationally but resist interpretation,
  • concepts (mass, spin, charge, probability) that are mathematically precise yet ontologically vague.

Quantum mechanics works. That is not in dispute.
What remains unresolved is what it means.

This is not a failure of experiment.
It is a failure of sense-making.

And sense-making has never been an institutional strength.


Where AI Actually Fits (and Where It Doesn’t)

I want to be explicit: I still have a long way to go in how I use AI — intellectually, methodologically, and ethically.

AI is not an oracle.
It does not “solve” physics.
It does not replace belief, responsibility, or judgment.

But it changes something fundamental.

AI allows us to:

  • re-analyze vast datasets without institutional friction,
  • explore radical ontological assumptions without social penalty,
  • apply sustained logical pressure without ego,
  • revisit old experimental results with fresh conceptual frames.

In that sense, AI is not the author of new physics — it is a furnace.

It does not tell us what to believe.
It forces us to confront the consequences of what we choose to believe.


Making Sense of What We Already Know

The most exciting prospect is not that AI will invent new theories out of thin air.

It is that AI may help us finally make sense of experimental data that has been sitting in plain sight for decades.

Now that CERN data is increasingly public, the bottleneck is no longer measurement. It is interpretation.

AI can help:

  • expose hidden assumptions in standard models,
  • test radical but coherent ontologies against known data,
  • separate what is measured from how we talk about it,
  • revisit old results without institutional inertia.

This does not guarantee progress — but it makes honest failure possible. And honest failure is far more valuable than elegant confusion.


Between Institutions and Insight

This is not an AI-versus-human story.

It is a human-with-tools story.

Institutions will continue to do what they do best: build machines, refine measurements, and preserve continuity. That work is indispensable.

But understanding — especially ontological understanding — has always emerged elsewhere:

  • in long pauses,
  • in unfashionable questions,
  • in uncomfortable reinterpretations of existing facts.

We are entering such a pause now.


A Quiet Optimism

I do not claim to have answers.
I do not claim AI will magically deliver them.
I do not even claim my current ideas will survive serious scrutiny.

What I do believe is this:

We finally have the tools — and the historical conditions — to think more honestly about what we already know.

That is not a revolution.
It is something slower, harder, and ultimately more human.

And if AI helps us do that — not by replacing us, but by challenging us — then it may turn out to be one of the most quietly transformative tools science has ever had.

Not because it solved physics.

But because it helped us start understanding it again.

Matter, Energy, Reality ↔ Thought, Intelligence, Consciousness

Dear Reader,

Physics asks: what is matter, energy, reality?
AI asks: what is thought, intelligence, consciousness?

Both are real, both are here. Physics confronts us with particles that behave like waves, with a universe that expands into… what exactly? AI confronts us with machines that converse, that create, that seem to reason — and force us to ask what we mean when we talk about “reasoning,” “creating,” or “understanding.”

This blog began as an attempt to make sense of Feynman’s physics. Over the years, it became a place where I tried to throw back the questions reality throws at us. Physics is real. AI is real. Both invite us to rethink our place in the cosmos, not as masters but as curious observers.

I don’t promise to publish much here — life is busy, and writing takes time — but when I do, it will be in the same spirit: reflecting, questioning, sometimes explaining, never pretending to give final answers.

If you’d like to follow more regular updates, you can always check my LinkedIn profile where I share articles and shorter notes.

Thank you for caring enough to read.

How I Co-Wrote a Quantum Physics Booklet with an AI — And Learned Something

In June 2025, I published a short booklet titled
A Realist Take on Quantum Theory — or the Shortest Introduction Ever.
📘 ResearchGate link

It’s just under 15 pages, but it distills over a decade of work — and a growing collaboration with ChatGPT — into a clean, consistent narrative: electrons as circulating charges, wavefunctions as cyclical descriptors, and action as the true guide to quantum logic.

We didn’t invent new equations. We reinterpreted existing ones — Schrödinger, Dirac, Klein–Gordon — through a realist lens grounded in energy cycles, geometry, and structured motion. What made this possible?

  • Memory: The AI reminded me of arguments I had made years earlier, even when I’d forgotten them.
  • Logic: It flagged weak spots, inconsistencies, and unclear transitions.
  • Humility: It stayed patient, never arrogant — helping me say what I already knew, but more clearly.
  • Respect: It never erased my voice. It helped me find it again.

The booklet is part of a broader project I call realQM. It’s an attempt to rescue quantum theory from the metaphorical language that’s haunted it since Bohr and Heisenberg — and bring it back to geometry, field theory, and physical intuition. If you’ve ever felt quantum physics was made deliberately obscure, this might be your antidote.

🧠 Sometimes, passing the Turing test isn’t about being fooled. It’s about being helped.

P.S. Since publishing that booklet, the collaboration took another step forward. We turned our attention to high-energy reactions and decay processes — asking how a realist, geometry-based interpretation of quantum mechanics (realQM) might reframe our understanding of unstable particles. Rather than invent new quantum numbers (like strangeness or charm), we explored how structural breakdowns — non-integrable motion, phase drift, and vector misalignment — could explain decay within the classical conservation laws of energy and momentum. That project became The Geometry of Stability and Instability, a kind of realQM manifesto. Have a look at it if you want to dive deeper. 🙂

Using AI for sense-making once more…

As mentioned in my last post, I did a video (YouTube link here) on why I think the invention of new quantum numbers like strangeness, charm and beauty in the 1960s – and their later ontologization as quarks – makes no sense. As usual, I talk too much and the video is rather long-winding. I asked ChatGPT to make a summary of it, and I think it did a rather good job at that. I copy its summary unaltered below.

Beyond the Quark Hypothesis: A Call for Simplicity in High-Energy Physics

1. Introduction: A Personal Journey in Physics

In this video, I reflect on my path as an amateur physicist reaching 50,000 reads—a milestone that underscores both excitement and the challenge of tackling complex quantum theories. Over decades, physics has evolved from classical mechanics to intricate frameworks like quantum field theory and quantum chromodynamics, creating both insight and paradox. This reflection emerges from a deep sense of curiosity, shared by many, to understand not just what the universe is made of but how these theoretical structures genuinely map onto reality.

2. The Crisis of Modern Physics: From Classical Mechanics to the Quark Hypothesis

Moving through physics from classical theories into high-energy particle models reveals a stark contrast: classical mechanics offers clarity and empiricism, while modern particle theories, such as quarks and gluons, often feel abstract and detached from observable reality. The shift to “smoking gun physics”—observing particle jets rather than the particles themselves—highlights a methodological divide. While high-energy collisions produce vivid images and data, we must question whether these indirect observations validate quarks, or merely add complexity to our models.

3. Historical Context: Quantum Numbers and the Evolution of the Standard Model

The 1960s and 70s were pivotal for particle physics, introducing quantum numbers like strangeness, charm, and beauty to account for unexplained phenomena in particle interactions. Figures like Murray Gell-Mann and Richard Feynman attempted to classify particles by assigning these numbers, essentially ad hoc solutions to match data with theoretical expectations. However, as experiments push the boundaries, new data shows that these quantum numbers often fail to predict actual outcomes consistently.

One of the key criticisms of this approach lies in the arbitrary nature of these quantum numbers. When certain decays were unobserved, strangeness was introduced as a “conservation law,” but when that proved insufficient, additional numbers like charm were added. The Standard Model has thus evolved not from fundamental truths, but as a patchwork of hypotheses that struggle to keep pace with experimental findings.

4. The Nobel Prize and the Politics of Scientific Recognition

Scientific recognition, especially through the Nobel Prize, has reinforced certain theories by celebrating theoretical advances sometimes over empirical confirmation. While groundbreaking work should indeed be recognized, the focus on theoretical predictions has, at times, overshadowed the importance of experimental accuracy and reproducibility. This dynamic may have inadvertently constrained the scope of mainstream physics, favoring elaborate but tenuous theories over simpler, empirically grounded explanations.

For example, Nobel Prizes have been awarded to proponents of the quark model and the Higgs boson long before we fully understand these particles’ empirical foundations. In doing so, the scientific community risks prematurely canonizing incomplete or even incorrect theories, making it challenging to revisit or overturn these assumptions without undermining established reputations.

5. Indirect Evidence: The Limits of Particle Accelerators

Particle accelerators, particularly at scales such as CERN’s Large Hadron Collider, have extended our observational reach, yet the evidence remains indirect. High-energy collisions create secondary particles and jets rather than isolated quarks or gluons. In a sense, we are not observing the fundamental particles but rather the “smoking gun” evidence they purportedly leave behind. The data produced are complex patterns and distributions, requiring interpretations laden with theoretical assumptions.

This approach raises a fundamental question: if a theory only survives through indirect evidence, can it be considered complete or even valid? High-energy experiments reveal that the more energy we input, the more complex the decay products become, yet we remain without direct evidence of quarks themselves. This “smoking gun” approach diverges from the empirical rigor demanded in classical physics and undermines the predictive power we might expect from a true theory of fundamental particles.

6. The Particle Zoo: A Growing Complexity

The “particle zoo” has expanded over decades, complicating rather than simplifying our understanding of matter. Initial hopes were that quantum numbers and conservation laws like strangeness would organize particles in a coherent framework, yet the resulting classification scheme has only grown more convoluted. Today, particles such as baryons, mesons, and leptons are grouped by properties derived not from first principles but from empirical fits to data, leading to ad hoc conservation laws that seem arbitrary.

The “strangeness” quantum number, for instance, was initially introduced to prevent certain reactions from occurring. Yet, rare reactions that violate this rule have been observed, suggesting that the rule itself is more of a guideline than a fundamental conservation law. This trend continued with the addition of quantum numbers like charm, beauty, and even bottomness, yet these additions have not resolved the core issue: our inability to explain why certain reactions occur while others do not.

7. Disequilibrium States: Beyond the Particle Concept

One possible perspective is to reclassify many “particles” not as fundamental entities but as disequilibrium states—transient structures that emerge from the interactions of more fundamental components. Viewing particles in this way offers a pathway back to a simpler, more intuitive model, where only stable particles like electrons, protons, and photons are foundational. Such a model could focus on electromagnetic fields and forces, with high-energy states representing temporary disequilibrium configurations rather than new particle species.

This perspective aligns well with the principle of statistical determinism. In the same way that classical oscillators eventually dampen and settle into stable states, high-energy disequilibrium states would be expected to decay, producing stable configurations over time. This model not only reduces the need for numerous quantum numbers but also sidesteps the requirement for exotic forces like the strong and weak nuclear forces, allowing the electromagnetic force to assume a central role.

8. Statistical Determinism and Quantum Reality

Heisenberg and Bohr’s interpretation of quantum mechanics suggests we should accept statistical determinism—systems governed by probabilistic rules where precise knowledge of individual events is inaccessible. This idea does not necessitate mystical randomness but acknowledges our limited ability to track initial conditions in high-energy environments. Probabilities emerge not from an intrinsic unpredictability but from our practical inability to fully specify a system’s state.

From this viewpoint, quarks and gluons, as well as the numerous quantum numbers assigned to unstable particles, are secondary descriptors rather than primary components of nature. Stable particles are the true constants, while all else is a function of high-energy interactions. This interpretation keeps quantum mechanics grounded in empirical reality and sidesteps the need for complex, unverifiable entities.

9. Conclusion: Toward a Pragmatic and Local Realist Approach

This reflection does not dismiss the importance of high-energy physics but advocates a return to fundamental principles. By focusing on empirical evidence, statistical determinism, and electromagnetic interactions, we can build a model that is both pragmatic and intuitive. We need not abandon quantum mechanics, but we should strive to ensure that its interpretations are consistent with the observable universe. Instead of introducing additional quantum numbers or forces, we should ask if these are placeholders for deeper, more coherent explanations yet to be discovered.

The journey of science is, at its core, a journey back to simplicity. If physics is to move forward, it may do so by revisiting foundational assumptions, clarifying what can be empirically tested, and developing a model of matter that resonates with the simplicity we find in classical theories. As research continues, it is this blend of skepticism, open-mindedness, and empirical rigor that will pave the way for meaningful discoveries.

The ultimate proton model?

Today I made a major step towards a very different Zitterbewegung model of a proton. With different, I mean different from the usual toroidal or helical model(s). I had a first version of this paper but the hyperlink gives you the updated paper. The update is small but very important: I checked all the formulas with ChatGPT and, hence, consider that as confirmation that I am on the right track. To my surprise, ChatGPT first fed me the wrong formula for an orbital frequency formula. Because I thought it could not be wrong on such simple matters, I asked it to check and double-check. It came with rather convincing geometrical explanations but I finally found an error in its reasoning, and the old formula from an online engineering textbook turned out to be correct.

In any case, I now have a sparring partner – ChatGPT o1 – to further develop the model that we finally settled on. That is a major breakthrough in this realistic interpretation of quantum theory and particle models that I have been trying to develop: the electron model is fine, and so now all that is left is this proton model. And then, of course, a model for a neutron or the deuteron nucleus. That will probably be a retirement project, or something for my next life. 🙂

Post scriptum: I followed up. “A theory’s value lies in its utility and ability to explain phenomena, regardless of whether it’s mainstream or not.” That’s ChatGPT’s conclusion after various explorations and chats with it over the past few weeks: https://lnkd.in/ekAAbvwc. I think I tried to push its limits when discussing problems in physics, leading it to make a rather remarkable distinction between “it’s” perspective and mine (see point 6 of Annex I of https://lnkd.in/eFVAyHn8), but – frankly – it may have no limits. As far as I can see, ChatGPT-o1 is truly amazing: sheer logic. 🙂 hashtag#AI hashtag#ChatGPT hashtag#theoryofreality

Using AI to solve the 80-year-old problem of the anomaly of the electron magnetic moment?

Pre-scriptum (3 October 2024): I came back from holiday and, because this week-long up and down became quite convoluted, I did what I like to do in a case like that, and that is to take my Bamboo notebook and talk about it all in a video which I added to my Real Quantum Physics channel on YouTube. I also updated my paper on RG: as usual, it went through a few versions, but this one – with a summary co-authored by ChatGTP-4 (and ChatGPT-o1) – should be the final one: enjoy!

Indeed, instead of listening to the international news on the war with Russia and on what is happening in the Middle East (all very depressing), you may want to listen to this and read the latest theory. Perhaps you will be inspired by it to develop your own pet realist theory of what an electron might actually be. I can assure you that it is more fun than trying to understand Feynman diagrams and how QED calculations work. 🙂 But don’t think you will win a Nobel Prize if you do not have the right connections and pedigree and all of that: see this analysis of what makes Nobel Prize winners Nobel Prize winners. 🙂

Original post:

I asked some questions to ChatGPT about my geometric explanation of the anomaly in the electron’s magnetic moment. Here is the chat: https://chatgpt.com/share/66f91760-68b8-8004-8cb2-7d2d3624e0aa. To me, it confirms the ‘explanation’ of mainstream QED makes no sense. We can take Schwinger’s factor and build a series of converging terms using that factor. We can also take my first rough cut at a first-order correction (π(alpha)2/8, see my very early 2019 paper on a classical explanation of the amm), and use that.

You may wonder: why not ask ChatGPT about the best first-order factor to be used here considering the geometry of the situation? The fact is: I did, but the geometry is not all that easy. It first came up with the formula for a spherical cap, but that one does not do the trick. See the latter part of the conversation (link above).

I am on holiday now, and so I will switch off a while but I am thinking AI will do what two generations of ‘new’ quantum physicists did not do: come up with a model that is based on real physics and is easy to understand intuitively. 🙂

PS: Of course, I did another rapid-fire paper on ResearchGate to document it all (the logic step-by-step, so to speak). As the chat is public, feel free to continue the conversation. Note that I used the newest ChatGPT o1 version, now in preview but part of a subscription (which you may not have). Yet again a different beast! The older versions of ChatGPT may not be so smart. This conversation is totally worth the US$20/month I pay for my subscription. 🙂

PS 2: Now that I had it open, I also quickly queried it on my wildest hypothesis: a ‘mirror’ electromagnetic force explaining dark matter and dark energy. While it is totally wild (read: nuts), I entertain it because it does away with the need for an explanation in terms of some cosmological constant. Here is the conversation: https://chatgpt.com/share/66f92c7f-82a0-8004-a226-bde65085f18d. I like it that ChatGPT warns me a bit about privacy. It does look wild. However, it is nice to see how gentle ChatGPT is in pointing out what work needs to be done on a theory in order to make it look somewhat less wild. 🙂

PS 3 (yes, ChatGPT is addictive): I also queried it on the rather puzzling 8π/3 factor in the CODATA formula for the Thomson photon-electron scattering cross-section. See its response to our question in the updated chat: https://chatgpt.com/share/66f91760-68b8-8004-8cb2-7d2d3624e0aa. Just scroll down to the bottom. It took 31 seconds to generate the reply: I would be curious to know if that is just courtesy from ChatGPT (we all like to think our questions are complicated, don’t we?), or if this was effectively the time it needed to go through its knowledge base. Whatever the case might be, we think it is brilliant. 🙂 It is nothing to be afraid of, although I did feel a bit like: what’s left to learn to it but for asking intelligent questions. What if it starts really learning by asking intelligent questions itself to us? I am all ready for it. 🙂