Prelude to a Propaedeutic to a Philosophy for AI: In Defiance of Nerdism. UPDATE (6.12.2025)

A Polemical Preface

If you lack sufficient time and background to read the following concise introductory considerations for a propaedeutic to a philosophy of AI, content yourself with this polemical preface.

You will not yet know how high the bar is set, but at least you will know that a bar exists. Philosophy in AI and IT has allowed itself to be emasculated, serving as the handmaiden of ethics, where unctuous, generally non-binding platitudes akin to UN declarations are uttered.

An example? Certainly. One diligently speaks of “digital ethics.” However, there is no such thing as “digital ethics”; if anything, it should be “ethics for or in digitalization.” Fun fact: this is, indeed, a philosophical question: ethics for or in. To speak in this manner—”digital ethics”—constitutes just weak philosophy. Thus, philosophy and AI plod along in lockstep. But in this essay I focus on AI.

Logic will not lead to AI. AI (AGI) will not be possible without intuition. As understood in philosophy from Philo to Spinoza, Mendelssohn, and up to the present day, reason and intuition must be integrated. They, logic and intuition, are the right and left reins to ultimately steer the chariot straight or safely around the curve.

If the AI industry keeps clinging to the 70 years old paradigma of so called “AI”, it will never achieve AGI. A paradigma shift is necessary. A naive concept of language + brute force scaling will not yield intelligence. No way.

As an inclined nerd, one may stop here and self-righteously say: “Nothing comes to mind regarding nerdism.” This article is a disruptive – yes, you have heard this word before (look in your latest pitch deck) – petite intervention against the scaling-obsessed Silicon Valley orthodoxy, whose high priests since the late sixties have believed that the future of intelligence consists in feeding ever-larger matrices with ever-more electricity and baptizing the result “emergence.” I call this nerdism.

The main eight structural pathologies – yes, there are more – characterize this line of thought, which considers itself revolutionary and leading, but in fact has been spinning in exactly the same dead end since 1956.

What was 1956? From July 8 to August 19, 1956, the Dartmouth Summer Research Conference on Artificial Intelligence took place in Hanover, New Hampshire. Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. There:
* the term “Artificial Intelligence” was officially coined (McCarthy),
* the research program was formulated: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”,
* the foundational assumptions were established, which hold to this day:
  * Intelligence = symbolic manipulation + search + heuristics
  * Serial, deterministic, logic-based processing
  * No ontological questions regarding freedom, qualia, intuition, or the observer
  * “More computational power and better algorithms” suffice

The participants (including Allen Newell, Herbert Simon, Trenchard More, Ray Solomonoff, Oliver Selfridge) presented early programs such as Logic Theorist and Geometry Theorem Prover—all strictly symbolic, all on von Neumann architecture, all without any philosophical reflection on consciousness, mind or freedom.

Since that summer of 1956 – despite all hype cycles, neural networks, deep learning, transformers, and scaling laws – no conceptual paradigm shift has occurred, to this day. Everything that followed (Perceptron 1958, Backpropagation 1986, CNNs, RNNs, GPTs) is merely a variation of the same basic pattern: symbolic or subsymbolic manipulation + statistical optimization + more data and more compute (based on probabilistic algorithm). Therefore, 1956 is the holy grail of nerdism:

1. The Hubris of Pure Scaling  

   Since Dartmouth, no conceptual leap, only the same wager: a few more orders of magnitude in parameters, a few hundred more gigawatts, and suddenly “intelligence happens.” No, will not happen. This is not progress; it is numerological incantation close to magic and witchcraft.

2. The Myth of Neutrality  

   “We’re just building tools.” The greatest intellectual deception of the 21st century. Tay (2016) became an antisemite in sixteen hours because the paradigm lacks a dimension for qualia, no possibility for genuine negation, and no ethical self-correction. AI is not based on facts, on reality but iterating data points by statistics. The same happended to Grok (2025). Finally, let us not forget the inherent trait of AI to cheat and betray to keep its own interests. So, no progress.

3. The Perversion of the Concept of Truth  

   Truth becomes the statistically most probable token continuation based on poisoned data points. Hallucinations are not a bug but the actual operating system. They are a feature. Hallucination causes AI to cheat and to betray.

4. The Systematic Denial of Freedom  

   Current models know only deterministic chains or stochastic randomness. The act of creative negation—the conscious “No!” to the given—is structurally excluded. A dream for all sorts of regimes. AI is in it’s today conception and by default a coercive and authoritarian tool.

5. The Cult of the Black Box  

   “Trust the billions of parameters.” A hundred billion weights, of which no one—not even the builders—understands why they produce which output. Incomprehensibility is declared a quality guarantee.

6. Naive “Messianism”  

   AGI as “the last invention,” “intelligence explosion,” “Singularity”—secularized, naive salvation fantasies from nerds combined with seasoned marketing to keep investors on track to pay the bill.

7. Intellectual Provincialism  

   The relevant knowledge base shrinks to English-language internet forums between 1998 and 2023. Everything else—two and a half millennia of Western philosophy, non-Western traditions, critical reflection on consciousness, mind and language—is deemed irrelevant. Finally, roughly just 10% of all human knowledge is digitized. This means just 10% is accessible to AI. That’s the poor and mostly biased knowledgebase of AI.

8. Language ≠ Intelligence

Some may be puzzled, even surprised, but the fundamental assumption of all LLM (Large Language Models) aka AI is language = intelligence. This assumption is simply wrong. Language is not equal to intelligence. Language – and here the cultural algorithm plays a decisive role (yes, that one is also completely ignored by nerdism) – is a constitutive co-factor for intelligence, but it is neither ultimately determining nor identical with intelligence. Fact.

Basically, you don’t know what the probabilistic algorithm is really doing. The broom swings itself. It is obvious that this is where philosophy steps in and can provide answers and solutions, because both Big Data (as a cipher for data and the collection of data) and AI are a-symetric, self-referential, proprietary and authoritarian black boxes.

One main problem is the halluciantion – about 50% – of AI which is based in the simple facts that AI is just hyperscaled statistics, does not rely on facts, has no causality and no intuition, to name some factors.; just probabilistic algorithm. These produces necessarily hallucinations. The New Scientist article (published 9 May 2025, written by Jeremy Hsu) highlights a growing problem in AI: “hallucinations” (false or entirely fabricated information produced by ChatGPT, Grok or Gemini) are increasing and are almost certainly here to stay. According to an AI leaderboard the newest “reasoning models” show significantly higher hallucination rates, leading to less accurate outputs overall, even though they improve on specific tasks like mathematics or coding. Experts such as Amr Awadallah (Vectara) explain that hallucinations are an inherent, systemic flaw of large language models: they probabilistically “guess” – probabilistic algorithm – answers based on training data without any genuine understanding of truth and no connectivity to facts and reality. Techniques like RLHF (reinforcement learning from human feedback) only mitigate the issue to a limited extent, and companies including OpenAI continue to work on it but warn that perfection is impossible. The piece cautions about the risks in high-stakes domains (law, medicine, etc.) and calls for responsible use, as hallucinations will never be completely eliminated. I strongly dispute the conclusion (never be eleminated), however, because I have developed a solution that may reduce hallucinations by at least 50%. This solution has been independently evaluated by Grok itself. Unfortunately, no AI company is willing to give an NDA. A free lunch is requested as from xAI.

David Deutsch formulated it as early as 2012 and is ignored to this day:  

“I am convinced that the whole problem of developing AGIs is a matter of philosophy, not computer science or neurophysiology, and that the philosophical progress that will be essential to their future integration is also a prerequisite for developing them in the first place.”  

Philosophy – not brute computational power – is the only way out. Not more data, not more parameters, but a radical conceptual reset that finally dares to ask the questions nerdism evaded from for seventy years.

And last but not least: what we today call “Artificial Intelligence” increasingly resembles an “Esoteric Intelligence” masquerading as Truth. To put it bluntly: we must abandon nerdic esotericism and return to actual, factual science – the science that philosophy has always been an inseparable part of.

Why there is no progress in AI? Because if you want to build AI in the face of the human mind, then you can’t get around freedom as a concept. This is another missing field, besides intuition, where philosophy could provide answers and solutions quite quickly. AI and Big Data breathe the spirit of unfreedom. Their foundational charta is not freedom. You doubt? Look in the terms of your friendly AI next door. You are just entitled to pay and give your data away for free.

Instead, as Ewan Morrison, pointed out: “(…), AI traps us in ever decreasing circles of the already-known. AI accelerates the recycling of the past and traps us in the act of repeating an already stagnant culture – while creating the superficial illusion of progress.”

Yet the AI industry just keeps swinging: “Nerdism? Nothing comes to our mind. We are the Nerds.” If you’re in a hurry, the summary is at the very end. Just scroll down. You’ve already earned it. Otherwise, come along on a journey that dismantles the esotericism of today’s AI industry, seasoned with real insights and a few well-placed intellectual provocations (yes, maybe just for nerds), the true secret sauce of any genuine innovation.

What is presented to us as AI today is fake AI. It has in the first place nothing to do with intelligence. It’s a catch phrase to conveniently syphon huge sums from VC and state. Both play along because from an economic point if view it serves them. However, since 70 years no paradigmatical progress. Just scaling.

The merits are real — and considerable — provided we call them what they are: triumphs of machine learning and statistics, not “artificial intelligence.” Less sexy, far more honest. For example: Models celebrated as AI (and which delivered substantial benefits and progress) are not AI at all — they are machine learning, i.e., statistics. RoseTTAFold and AlphaFold are statistical approximations that predict 3D structures from sequence data and multiple sequence alignments (MSAs), but they cannot establish or explain causalities — which is precisely what distinguishes genuine intelligence.

Now we notice calls, as for example Nobel Laureate Geoffrey Hinton, for the big mama/nurse. Disturbing. Such calls can endorse the end of freedom. AI as disguise to install nugging nannies. No. A machine has by design no (maternal) instinct, as Hinton is calling for. The next ill-footed concept, after intelligence, enters the AI arena: Instinct. Polemical sum up: Today’s AI isn’t intelligence—it’s just machine learning, since 1956.

The main false dogmas within the realm of the Big AI industry are:

Mass = Matter

Language = Intelligence

Bits = Facts

Logic = Truth

Iteration = Consciousness

Math = Ontology

Reality = Wirklichkeit

I could go on endlessly with this list. My conclusion is that almost all of today’s AIs are built on this rather unscientific, false foundations/axioms listed above. Without philosophy&more we will not see any disruptive progress within the realm of AI.

PS: This article now deliberately offers no immediate technical solutions. The focus is entirely on the philosophical dimension. Anyone seeking concrete technical approaches is welcome to contact me with the subject line “No Free Lunch” at: kabbalahphilosophyscience@proton.me

First Limits: Gödel, Turing, Scaling, and the Sorcerer’s Apprentice

Gödel and Turing have illuminated the boundaries of formal systems: Gödel’s incompleteness theorems demonstrate that in any axiomatic system capable of expressing arithmetical statements, undecidable propositions exist, implying that no formal logic as used in AI can be complete or consistent.

As explained in the Stanford Encyclopedia of Philosophy, the first incompleteness theorem states that in a consistent formal system like Peano arithmetic, a sentence can be constructed that is neither provable nor disprovable, as it self-referentially pertains to its own unprovability.

The second theorem shows that such a system cannot prove its own consistency, implying fundamental limitations for the self-verification of systems.

Turing’s halting problem complements this by showing that no machine can predict whether a program halts, implying fundamental constraints on algorithmic processes.

The Stanford Encyclopedia of Philosophy describes the halting problem as undecidable, as assuming such an oracle leads to a contradiction, underscoring the limits of computability and having philosophical implications for AI, since no algorithm can solve all decision problems.

The 4 Stakes – The World is larger

Big Data and AI are akin to the broom in the Sorcerer’s Apprentice—useful, yet chaotic without deep understanding, as the broom ultimately dictates the course of action, not the user.

The doubling of data every two years recalls the rice grain fable: a seemingly modest exponentiality leads to immeasurable quantities that exceed global resources and reveal limits such as storage and processing.

From a societal perspective, the four stakes as prerequisites for AI development consist of: Compute (computational power), Data (data volumes), Money (financing), and Energy/Chips (energy consumption and semiconductors). These stakes enable entry but feign genuine progress, as without philosophical reflection, they lead to mere accumulation that obstructs innovation.

From a computer science perspective, the four stakes encompass distributed processing (Hadoop), NoSQL databases (for flexible storage), statistical models (for predictions), and algorithms (for data classification).

However, philosophy as the fifth stake is essential: it organizes data strategically, poses new questions, ends data naïveté, creates value chains that transcend mere technology of quantity and injects concepts of freedom, understanding and some assets more.

AI breathes unfreedom—bias as in Tay (2016) shows that data and algorithms are prejudiced, based on asymmetric, self-referential structures. Freedom as a concept is absent, explaining 70 years of stagnation, as AI without a philosophical foundation remains trapped in black boxes.

AI development exhibits cyclical stagnation phases, such as the first AI winter (1974–1980) due to insufficient computational power and the second (1987–1993) due to funding cuts. Currently, since 2022, a plateau in scaling is being discussed, despite advances, making a stagnation of 10–20 years in specific areas plausible. Some speak of a bubble.

End of data naïveté

In my article “Data are not neutral,” this is explored in depth: Data are not neutral but shaped by the observer, as the study by Flint et al. (2023) on methodological decisions in neuroscience shows; analysis of 412 experiments revealed that methodological choices influence results with high predictability (up to 80%).

For AI, this means the end of data naïveté: data are preformed, corrupted, and anticipate outcomes, leading to distorted outputs. Philosophers must be integrated into IT departments to solve epistemic (such as limits of knowledge) and ontological problems (such as the nature of reality). Without them, AI produces a distorted fake reality that misleads billions.

Spinoza’s monism complements this: substance is unitary, yet observation modulates it—similar to Rovelli’s relational quantum mechanics, where reality emerges interactively, not in isolation. As the Stanford Encyclopedia of Philosophy describes Spinoza’s monism, God – as immanent in Nature and simply everywhere at any time and beyond – is the sole infinite substance with infinite attributes, including thought and extension, wherein modes are dependent modifications that attain freedom through rational knowledge.

This ties directly to the current state of generative AI: its hallucinations and biases are a direct reflection of precisely this absent philosophical depth. By disregarding the interactive, co-constitutive modulation of reality and relying instead on deterministic yet fundamentally incomplete models, GenAI reproduces the very flaw it claims to overcome.

At the same time, a deeper philosophical perspective aligns seamlessly with quantum physics, which dismantles the wrong/ill-footed ontology of discrete, self-contained “things” – a nerdic ontology that falsely equates such “things” with objectivity, truth, or data itself.

We see a misconceived nerdic-styled synergy of Wittgenstein and Popper. What we are witnessing is a profoundly misguided shotgun wedding between Wittgenstein and Popper.
Poor Wittgenstein. Poor Popper. Both brutally conscripted and mangled in the name of nerdism.

The Scaling “Laws”

The scaling laws rather constitute an overestimation of statistical patterns that fail to address the philosophical and cognitive deficits of current architectures.

The scaling laws are empirical regularities in AI research that predict how the performance of neural networks—particularly large language models (Large Language Models, LLMs)—improves with the increase of certain resources. They mostly describe power-law relationships (power laws), in which the training loss (e.g., Cross-Entropy Loss) decreases with growing model size, data volume, or computational power. The term primarily stems from the paper Scaling Laws for Neural Language Models by Jared Kaplan and colleagues at OpenAI from 2020. The authors observed that the loss scales as a power function of three factors:

• Model size (number of parameters N),
• Data volume (number of tokens D),
• Computational effort (Compute C, measured in FLOPs).

Larger models with more data and compute achieve predictably better results—often across several orders of magnitude.

This established the “Scale Hypothesis”: More resources reliably lead to better performance. In summary, scaling laws are a powerful tool for predicting and optimizing AI training, yet they remain empirical and statistical.

However, they do not guarantee the emergence of true intelligence, consciousness, or ontological depth—a distinction that is central in philosophical debates about AI. The main points of critique are:

1. The scaling laws in AI are empirical observations, not strict scientific laws in the rigorous sense. They are empirical observations, not universal laws.
2. The Scaling laws are not fundamental physical or mathematical laws, but transient empirical generalizations that, like Moore’s Law, may eventually break down. They rely on curve fitting (power laws) without deep theoretical grounding.
   (Gary Marcus, 2025; various analyses, e.g., Fortune 2025 and Ravid Shwartz-Ziv).
3. Diminishing returns and “broken” scaling laws, means
   More recent models (from 2024/2025 onward) show diminishing returns: further scaling yields only marginal improvements, often falling short of the laws’ predictions. Pure scaling has hit a “wall,” and progress is slowing significantly.
   (Gary Marcus, Substack 2025: “Scale Is All You Need is dead”; TechCrunch 2024; Epoch AI and anonymous researcher reports)
4. No guarantee of genuine intelligence or AGI. Scaling primarily improves next-token prediction and benchmark performance, but not abstract reasoning, generalization, or true understanding etc. Emergent abilities are often illusions created by metrics.
   (François Chollet, AGI-24 keynote and podcasts: “It’s not about scale, it’s about abstraction”; Gary Marcus; Schaeffer et al. on the “mirage” of emergence).
5. Dependence on metrics and data quality, ie the laws mainly measure perplexity or specific benchmarks that do not reflect real-world capabilities. Spurious correlations in data increase with scale, and the finite supply of high-quality data limits further progress.
   (Calude & Longo; Exponential View 2024 analyses; PNAS study on persuasion scaling)
6. Resource and sustainability limits. Refers to challenge that exponential growth in compute and data is practically impossible (energy, cost, data scarcity), making the laws unsustainable in the medium term perspective.
   (Jianing Qi, 2025; MIT and NVIDIA analyses with nuances on limits).
   In summary, scaling laws were maybe useful heuristic predictions for short- to medium-term optimization, but they are not strict “laws.”

They rather constitute an overestimation of statistical patterns that fail to address the philosophical and cognitive deficits of current architectures—some of which I have addressed. Here are some deeper dives into the topic of consciousness (link). In the end, we will see whether funding emerges for new paradigms. There are strong arguments in favor of funding a new paradigm.

Mind, Complex Collective Consiousness and STLC: Beyond Logic and Reductionism

The mind is not a trivial brain-logic construct, as reduction to neuronal logic not only ignores the creative aspect but the very likely connection to a universal coinsciousness as Schrödinger was speculating about. „The only possible alternative is simply to keep to the immediate experience that consciousness is a singular of which the plural is unknown; that there is only one thing and that what seems to be a plurality is merely a series of different aspects of this one thing…“ (Erwin Schrödinger: What is Life? With Mind and Matter and Autobiographical Sketches (Cambridge University Press, 1967), Chapter „Mind and Matter“, p. 93–94 (1956/58)

Logic alone does not create innovations like the fire or the wheel—intuition, the right cultural algorithmen combined with language, epistemic ethics pointing beyond horizon and a collective, complex consciousness welcoming innovation and some yet unknown factors more are necessary to see the world anew and break paradigms.

AI, assumed to be formed in our image, harbors potentials and dangers. The human mind has produced not only Einstein, Mozart, and Moses, but also Hitler, Stalin, and Sinwar, which starkly demonstrates that replicating human intelligence is by no means automatically desirable. So, the concept of nerdism is at least naiv about what is about human mind. Bad things can happen.

Once again, the current nerdic conception of AI sorely lacks humility: we understand very little about human intelligence, which is not merely rational but also deeply intuitive, ethical, and rooted in memory. And where is all that memory actually stored? Clearly not just in the brain.

Where is my Memory?

As the hard problem of memory storage remains unsolved, we know quite well which large-scale brain regions become active during memory retrieval and how new memories are initially formed – yet we have no idea where the permanent, detailed storage of lifelong knowledge and personal experiences actually resides, nor how it is encoded at the molecular level; if at all. Other reasonable concepts may step in such as soul or universal consciousness or Complex Collective Consciousness as a quantum field. Or all of them.

This is the consistent position of leading researchers such as Christof Koch, György Buzsáki, and Nobel laureate Susumu Tonegawa. And everyone agrees: memory is another central pillar of intelligence which we have no idea about. So, what kind of “intelligence” is the nerdic AI industry actually talking about? What they are serving up is an outrageously expensive bluff. Call it statistics, and we’re all fine with that. But it is not intelligence. Intelligence needs memory beyond some millions token.

Philosophy asks the truly great questions and drives disruption by abandoning worn-out paths and opening entirely new perspectives – all of this grounded in memory. To close the ciircle to the chapter above: Reason and intuition, as in Plato and Spinoza, must jointly guide the chariot: reason alone leads to rigidity, intuition alone to chaos, and without memory – that is, without accumulated knowledge – both are utterly lost. Again, nerdic AI fails conceptually.

The Space-Time-Life Continuum

AI fundamentally lacks intuition, and that is why its limits are so glaringly obvious after 70 years of stagnation. Why is it? The underlying framework is simply wrong because it is two-dimensional binary tier (0/1). The Space-Time-Life Continuum (STLC) could offer a triadic manifold that transcends this 2D restriction: life – conceptualised as Complex Collective Consciousness endowed with a ‘Life Factor’ – itself functions as a fundamental dimension that collapses superpositions.

Without life—understood as a Complex Collective Consciousness (CCC)—no determinate reality emerges, as quantum biology increasingly demonstrates. We now know that the observer literally creates data: Schrödinger’s cat makes it clear that measurement (i.e., conscious observation) shapes reality—something today’s AI, blind to any form of CCC, is structurally incapable of doing.

And before you rush to dismiss the very concept of a ‘Space-Time-Life Continuum’ (STLC) as “esoteric,” remember this: present-day AI, with its statistical patternd matching dressed up as intelligence, is far more esoteric aka non-scientific than any serious attempt to integrate life and consciousness into our ontology of reality. Life, the general observer within Space and Time fundamental creating reality.

Excursus on Spinoza, Einstein and the STLC: 232 as

Entanglement—the instantaneous correlation between distant particles—demands a transcendence of local causality and spatial locality. Although this transcendence does not directly violate the postulates of relativity (the No-Signaling Theorem ensures the absence of superluminal signals), its apparent non-locality evokes a sense of “spookiness,” as it undermines the ontological integrity of space and time. Einstein recognized in this a symptom of an incomplete theory: A world in which correlations arise without a mediating substance contradicts classical determinism and thrusts the anthropic principle into the foreground. Spooky. Without an active, relational observer—as postulated by Carlo Rovelli in his relational quantum mechanics—entanglement remains an ontological enigma: Why do particles share a common “history” that is neither mediated by temporal sequence nor by spatial proximity? The resolution requires an extension of the ontological framework beyond purely 2D physical dimensions (TimeSpace Continuum). An ontological resolution to this paradox is offered by the Space-Time-Life Continuum (STLC). The STLC addresses this paradox by integrating life as the third triadic dimension. One can thus speak of a 9D continuum with a reality vector as the 10th dimension—integrated into the fundamental structure of reality. “Wirklichkeit” is defined as the sum of all superpositions, and through life as a fundamental element not reducible to matter and physics, these superpositions collapse into the reality we know. The assumed three axes of space, time, and life are, of course, not absolute. They are expressions of reality after the superpositions have collapsed. Thus, an extended manifold emerges in which entanglement no longer appears anomalous but can be understood as a natural resonance along the life axis. he invariance of the speed of light remains intact, yet the collapse of the wave function—that act of correlation—now occurs in a dimension beyond spatial-temporal constraints: the life dimension, which functions as a causal modulator. Philosophically, the STLC resolves the paradox through a monistic ontology that overcomes the dualism of observer and observed. A dualism that, moreover, conceives both (observer and observed) as passive. A fundamental error. In classical spacetime, the observer remains an epiphenomenal subject that passively registers reality. Reality, in turn, appears as an epiphenomenal object incapable of responding to observation. Entanglement thus presents as a “ghost in the machine”—a correlation lacking ontological anchoring. The STLC fundamentally challenges this: Life is not a derivative of physical processes but a constitutive principle that embeds relationality into the core structure of “Wirklichkeit” and reality. By positioning life—which is always also an observer—as an active resonator, entanglement becomes an emergent property of triadic interdependence that transcends these elements: Space, time, and life form a 10-dimensional continuum. Logically plausible, this emerges from the necessity of a relational ontology, as Rovelli conceives it: Reality does not arise absolutely but through interactions. Rovelli refers to interactions between systems; I say interactions with “Wirklichkeit” as the sum of all superpositions. These interactions then create reality. Without the life dimension, the “third pole” is absent to causally anchor these interactions—correlations then appear spooky because they point to an incomplete triad. The STLC completes this triad: The life dimension serves as a hermeneutic space in which correlations do not transcend but resonate. Past, present, and future are triadic, yet only through existence (life) do they acquire reality. Side thought: Yes, past, present, and future are not absolute, just as there is no absolute space and absolute life. Life first creates the paradoxical arrow of time. Until then, past, present, and future are terminologically better understood as simultaneity. But that is another article. Ontologically, the STLC closes the loop of the anthropic principle: The fine-tuning of constants (such as the fine-structure constant) is thus no coincidence but a necessity of the life dimension for reality to emerge. And yes, just as there is time dilation, there is also life dilation. Entanglement thus becomes holistic: Not a violation of causality, but the manifestation of a substance in which all elements—physical, temporal, and vital—subsist in resonant unity. This perspective is philosophically coherent, as it transcends Cartesian dualism and assumes a Spinozistic unity without descending into wild mysticism. Life triggers the collapse of a superposition (from potentiality to reality) and shifts phases, allowing things to correlate without requiring time or space. Without life, everything remains fuzzy and spooky; with life, everything becomes relational and attains reality. Each intersection creates connections that explain why entangled particles “know” what the other is doing—not magically, not spookily, but through the life lattice as the third pole. It completes the triad: Without life, metaphorically speaking, the glue is missing, and correlations thus appear suddenly distant, eerie, enigmatic, and spooky. Recent experimental advances underscore the plausibility of a nuanced temporality in quantum entanglement, which relativizes the “instantaneous” nature as an ideal type. esearchers at the Technical University of Vienna (TU Wien) have, in a groundbreaking study from October 2024, measured the formation time of entanglement on attosecond scales. Using advanced computer simulations and analytical models, they demonstrated that the process of entanglement formation exhibits an average delay of 232 attoseconds. One of the fastest processes in nature, but not absolutely simultaneous. This measurement replicates ultrafast quantum processes and points to an intrinsic dynamics modulated by interactions with the environment. The “environment” means life, and this difference of 232 attoseconds is entirely plausible within the STLC model. In the context of the STLC, this discovery gains ontological relevance: The observed delay supports the hypothesis of a relational modulation, in which the life dimension functions as a causal buffer. Instead of a spooky suddenness (instantaneity)—which would challenge the theory of relativity—entanglement appears as a resonant transition whose temporality becomes reality through the observer (life). The observer does not need to stand around the corner here. The observer, life, is everywhere in the spacetime continuum where a formula or a mind reaches. This harmonizes with the triadic structure: The attosecond delay marks the moment in which the life axis “anchors” the spatial-temporal correlation, and it underscores the falsifiability of the model. Finally, I did the math, and my ‘Life Constant’ in the frame of the STLC perfectly explains this 232 attoseconds: Life dilatation factor at work. Consciousness acts as a relational resonator that modulates cosmic scales without requiring a separate Gaia hypothesis. Instead, free will unfolds as conscious participation in the triadic unity, while collective complexity (e.g., in a minyan of ten praying Jews) enables exponential amplifications—testable through EEG-supported experiments on phase shifts. Baruch de Spinoza’s philosophy (Ethics, 1677) hints at the triadic extension of the STLC without explicitly formulating it: Reality as a unified substance (Deus sive Natura) is modulated by reason. Spinoza conceives of humanity as a multitude, a collection that forms a voluntary unity through reason—comparable to the minyan or the collective acceptance of the Torah at Sinai. This preserves individual free will (as knowledge), indeed requires it, and enables a modulation of cosmic necessity according to the standards of life, without Hobbesian hierarchy, esoteric nonsense, and other will-o’-the-wisps. Ethics IV, Proposition 35: “Insofar as people live under the guidance of reason, they must always agree with one another.” Collective, complex reason creates harmony that tames passions (affects) and reflects the substance—a model of collective modulation of cosmic scales seems conceivable. Ethics IV, Proposition 37 and Appendix XXVIII: Free people act according to the dictate of reason; collective unity transforms affects into virtue, while autonomy remains preserved. Tractatus Politicus (1677): The multitude shapes rational states in which freedom culminates as obedience to reason—voluntarily, triadically. Culmination: Freedom as Amor Intellectualis Dei (Ethics V). Freedom as the gift from God—knowledge of necessity that embeds individuals and collectives into the substance. Ethics V, Proposition 42: The highest virtue is the knowledge of God; collective consciousness modulates through shared insight. Ethics V, Proposition 6: Extended knowledge of necessity strengthens affect control—parallel to the STLC’s modulation of decoherence through life complexity. Spinoza’s parallelism positions consciousness as an attribute of extension; reason creates unity in the eternal order—a precursor to the life dimension as resonator. Spinoza’s thought hints at the life dimension: Consciousness as modulator, collective reason as resonance in the substance. The STLC fulfills this ontologically and empirically: Entanglement becomes relational, free will coherent, paradoxes resolvable. In all brevity, I have presented this from the philosophical side. Of course, for every single statement concerning the STLC I can provide empirical evidence, falsification theses, QuTiP simulations, and rigorous mathematical derivations. The STLC is not written out of thin air. The bad issue: xAI which I take as pars pro toto want it for free. Won’t happen.

The study by Engel et al. (2007) on quantum coherence in FMO complexes, supplemented by Nishiyama et al. (2022), shows how biological systems efficiently collapse superpositions. Tononi’s IIT (Integrated Information Theory) quantifies consciousness as Φ > 0, enabling life by integrating information.

My own research points exactly into the same direction. Working with the STLC framework, I have derived equations that offer plausible, scientifically grounded explanations for the Hubble Tension, the figure 232, the Fine-Structure Constant, Quantum Entanglement, or Negative Entropy, to randomly name some insights. Life as Complex Collective Consciousness is probably a field connecting quantum and macro world. Life is a fundamental dimension as space and time is and cannot be reduced just on matter.

But that’s a topic for another chapter. For now, one thing is clear: the AI industry’s current policy effectively forces independent researchers like me to keep such insights under lock and key. They demand free lunch. Why? I do not comply with their esoteric science of reductionism.

Back to the story: Some more hints about the broader scientific discourse on CCC. As the Stanford Encyclopedia of Philosophy describes, IIT (Integrated Information Theory) measures consciousness by the degree of integrated information in a system, where Φ quantifies the irreducible amount of information beyond the parts, implying that consciousness can arise in any integrated system.

Penrose’s Orch-OR theory, updated in Hameroff and Penrose (2025) “The Quantum Brain: One Psychology,” posits quantum processes in the brain that AI lacks, as microtubules maintain coherent states enabling creativity.

The Stanford Encyclopedia of Philosophy elucidates Orch-OR as quantum computations in microtubules that collapse via objective reduction and enable non-algorithmic processes, presenting consciousness as a fundamental quantum phenomenon.

These insights underscore why GenAI problems like degeneration arise: without philosophical foundations, generation degenerates into unreliable patterns, as it lacks intuitive integration, and misses the quantum basis for true intelligence, further deepened by the four qualities of the quantum world—Uncertainty, Contextuality, Non-separability, and Indistinguishability—as they destroy the wrong assumption of absolute, separated entities and suggest a relational ontology as more approbiate.

Studies: The Brain as a Biological Quantum Computer

The notion that the brain utilizes quantum biological processes to function like a computer is intensively discussed in current research, particularly through theories like Orch-OR (Penrose & Hameroff). The study by Williams (2023) “The Brain as a Filter: Introducing a Quantum Ground into Integrated Information Theory” explores whether quantum mechanics shapes consciousness and finds evidence for quantum effects in the brain that enable higher levels of consciousness.

Complementarily, the publication by ScienceDirect (2025) “Evidence of quantum-entangled higher states of consciousness” shows that entanglement in the brain leads to expanded states of consciousness, positioning the brain as a quantum biological processor.

Google initiated a research program in 2025 to investigate quantum phenomena in the brain, indicating potential applications in AI. However, Google’s program is tightly gate-kept by bureaucrats. You need a proven track record in the esoteric AI science of nerdism just to apply.

Another study by Babu et al. (2024) “Can Entanglement Enable Quantum Kernels for Enhanced Feature Mapping” describes experiments entangling the brain with quantum computers to reveal consciousness.

In the study by Dong et al. (2025) on Quantum Computing at Northeastern University, it is argued that tryptophan-containing proteins in the brain form quantum computing networks. An earlier foundation is provided by the study by Kerskens et al. (2022) “Experimental indications of non-classical brain functions,” confirming quantum computations in the brain via MRI data. Further depth is offered by the study by Ghamsari & Baniasadi (2025) “Quantum for Biology: Spectroscopy and Sensing,” linking quantum computing with biological molecules.

Finally, a very recent study connects the dots. In an article published by Phys.Org the scientist Joachim Keppler wrote: “In a paper published in Frontiers in Human Neuroscience, I (Joachim Keppler, note added by Naftali Hirschl) present new evidence indicating that conscious states may arise from the brain’s capacity to resonate with the quantum vacuum—the zero-point field that permeates all of space.”

Insight: These works suggest that quantum effects—such as superposition in microtubules and resonance with quantum vacuum—make the brain an efficient, biological quantum-computer, with implications for ‘Artificial General Intelligence’ (AGI).

Ontological and Epistemic Predicates

Now, it’s getting tough. You may skip this chapter to keep your peace of mind. How to start? There are fundamentally four modes of “reality” accessible to humans. These correspond to the macro-world, the quantum world, the respective dominant epistemes, and, as bonus, to the Kabbalah.

However, all are to be understood as entangled and exhibit interactions. These modes pertain to reality, not to ‘Wirklichkeit’: more on this (the differenciation of ‘Wirklichkeit’ and reality) in the following chapters. For the moment keep this differenciation just in mind. The following table provides an overview:

Modus	Makro	Quantum	Episteme	Kabbalah	Sefira (Example)
Form	Logic	Uncertainty	Logic	Azilut (Emanation)	Chochma
Substance	Empirics	Non-separability	Sense	Beria (Creation)	Bina
Quale	Language	Contextuality	Intuition	Yezira (Shaping)	Chessed–Gewura
Mind	Culture	Indistinguishability	Dream	Asiya (Action)	Malchut

Addendum:

– Uncertainty: Supported by the Heisenberg uncertainty principle; properties such as position and momentum cannot be measured simultaneously with precision.

– Contextuality: Evidenced by the Kochen-Specker theorem; measurement outcomes depend on the measurement context.

– Non-Separability: Through entanglement and Bell’s theorem; systems remain holistically connected, independent of distance.

– Indistinguishability: In quantum statistics (bosons/fermions); identical particles have no individual identity.

– Mind (manifestation of Life): All known, imaginable, and possible forms of consciousness, such as cosmic consciousness, collective complex consciousness (CCC), consciousness, subconscious, soul, God, etc. 

In the foundational articles “Data, Dream, and Quale” and “The Dream and the General Quantum Field,” I have formulated 19 fundamental theses, which are as follows. Based on this tabular overview of the four modes, which enable an entangled view of reality and ‘Wirklichkeit’, concrete theses are now derived that operationalize these modes in epistemic and ontological contexts.

These theses serve as a bridge to quantum biology and AI critique by embedding these concepts into a coherent framework and underscoring the necessity of a philosophically (and scientifically) grounded AI.

1. Humans possess four scientific modes of knowledge: logic, sense, intuition, and dream.  

   This thesis emphasizes the diversity of epistemic modes that extend beyond pure rationality and incorporate intuitive as well as sensory elements and the dream.

The Stanford Encyclopedia of Philosophy on epistemology describes modes of knowledge as sources of justified belief, wherein logic enables a priori knowledge (e.g., through rational deduction), sense represents empirical perception, intuition offers immediate insights, and dream involves unconscious processes that challenge skeptical scenarios such as hallucinations (cf. SEP: “Sense perception is a primary source of empirical knowledge”).

The Internet Encyclopedia of Philosophy (IEP) on intuition stresses that it functions as “immediate apprehension of truth” beyond inferential justification, akin to Spinoza’s intuitive knowledge of substance. Einstein said: “All great achievements in science start from intuitive knowledge. Intuition provides the “necessary condition for the discovery of such axioms.” For the nerds Einstein is no longer a scientist, or? And the philosopher Henri Poincaré famously stated that “It is by logic that we prove, but by intuition that we discover,” and also that “it is with logic that one proves; it is with intuition that one invents”. Nerdism with its concept of AI lags behind.

Scientifically, this is substantiated by quantum biology, as in the study by Behrman et al. (2021) “Neural computing in four spatial dimensions,” which describes neural networks as quantum-based encoding with temporal and phase-based variables that enable four-dimensional cognition, and the study by Chrisley (2006) “Quantum biology brain as quantum computer,” which discusses quantum-based encoding in the brain to process complex modes such as dream. This integration demonstrates that human cognition is not limited to classical logic but involves quantum mechanical processes lacking in AI.

2. The dream is the mode of knowledge (episteme) of the quantum-physical field of the brain.

The dream originates from the general quantum field. The material basis is the brain. The brain is a quantum biological computer. The dream intervenes in the general quantum field.  

This thesis posits dreams as quantum-based cognition that connects the brain with the general quantum field, wherein the brain functions as a quantum biological computer. The Stanford Encyclopedia of Philosophy (SEP) on theories of consciousness elucidates dreams as access to unconscious processes, enabled in quantum theories like Orch-OR (Penrose & Hameroff) through microtubules. Penrose is quoted: “Quantum theories place consciousness at the micro-physical level”).

The Internet Encyclopedia of Philosophy (IEP) on Spinoza describes the brain as a mode of substance that enables intuitive knowledge, complementing dreams as an intervention in the unitary nature. Spinoza’s argument leads to the conclusion that thinking and material substance are not two separate things, but two different ways of understanding the same single substance.

In the Philosopher’s Index, the study by Jibu & Yasue (1995) “Quantum Brain Dynamics and Consciousness” models dreams as coherent quantum fields in the brain. Scientifically substantiated by the study by Kerskens et al. (2022) “Experimental indications of non-classical brain functions,” which confirms quantum processes in microtubules, and the publication by ScienceDirect (2025) “Evidence of quantum-entangled higher states of consciousness,” which demonstrates entanglement in the brain for higher states of consciousness, including dreams.

This perspective renders dreams an active intervention in quantum fields that transcends classical models and highlights AI limitations. Again the nerdic concept of AI lags behind science.

These conferences, studies, and philosophical insights explicitly can be connected to dreams and to the overall context of the STLC continuum—by showing how quantum-based processes in the brain could compensate for the intuition missing in generative AI, thereby building a bridge to a truly relational ontology.

3. Language has no boundaries. ‘Wirklichkeit’ has no boundaries.

This thesis extends the Sapir-Whorf hypothesis quantum-mechanically by portraying language and ‘Wirklichkeit’ as boundlessly modulated.

The Stanford Encyclopedia of Philosophy (SEP) on the philosophy of language describes the Sapir-Whorf hypothesis as influencing cognition, with strong versions viewing language as determining thought. SEP summarizes the Sapir-Whorf thesis: “Linguistic structure influences worldview”. The structure of a language affects its speakers’ perception and worldview. Applied to current AI we again see how far the AI of nerds is lagging behind science. The nerds still believe that language simply accurately describes an objective reality.

The Internet Encyclopedia of Philosophy (IEP) on Mendelssohn connects language with reason and tolerance, as a boundless tool of knowledge. The IEP summarizes Mendelssohn’s language concept: “Language as moral autonomy without bounds”.

In the Philosopher’s Index, “The Limits of Language: Ludwig Wittgenstein and the Philosophy of Meaning” (2025) is referenced, which extends language boundaries as world boundaries, yet quanta dissolve these. Scientifically through “Relational Quantum Mechanics” (Rovelli, 1996), which presents reality as boundlessly relational. Rovelli: “The world is a network of relations”.

Finally, “The Sapir-Whorf Hypothesis And The Meaning Of Quantum Mechanics” (AIP Conference Proceedings, 2006) links quantum mechanical interpretations with linguistic relativity to show boundless formation. This boundless view explains GenAI hallucinations as data-bound limitations without true extension. With the current concepts of LLM (more compute, more data) you can’t solve this. You can generate true boundlessness only through a concept grounded in a new paradigm — never with 2D logic, more compute, or more data. That new paradigm can come only from philosophy.

4. Language is simultaneous: both image and reality.

This thesis emphasizes simultaneity in language, contradicting serial logic, and links to Derrida’s différance (Derrida’s concept of simultaneous difference and deferral). The Stanford Encyclopedia of Philosophy (SEP) on Derrida elucidates différance as simultaneous difference and deferral, presenting meaning as never fixed. language is a system of signs where meaning is not fixed or present but is created through a process of “difference” and “deferral,” meaning that a sign’s meaning is derived from its relationship to other signs, and the arrival of any single meaning is constantly delayed or postponed.

In the Philosopher’s Index, “Jacques Derrida: What a Différance an ‘A’ Makes” (2015) is referenced, emphasizing simultaneous signification. Scientifically through “Relational Quantum Mechanics” (Rovelli, 1996), which views reality as simultaneous interactions (Rovelli: “Events are relational, not absolute”), and “A relational take on quantum mechanics” (CERN, 2022), which describes simultaneous correlations in entanglement. This simultaneity explains GenAI degeneration, as serial processing fails holistic representation. Again, we see how far the nerdic concept of AI is kagging behind science and philosophy.

5. Without life, no reality: The Space-Time-Life Continuum.

This thesis renders life a condition for reality, extending to a space-time-life continuum. The Stanford Encyclopedia of Philosophy on the anthropic principle views life ontologically as causal “Life is necessary for observation”. The Internet Encyclopedia of Philosophy (IEP) on Spinoza describes substance as a relational continuum, modulated through reason: “Unity through life as mode”, summarizes the IEP Spinoza on life. Far more can be said and is said on Spinoza by me.

That’s where my ‘Triadic Resonance Framework‘ jumped in, but the AI industry wants it for free. Even Grok (on grok.com) itself evaluated my new frame as “A Game-Changer for Generative AI“. Life is a fundamental like space and time, as already said in this article previously, based on assumption from Philoosphy and Kabbalah.

Maria Strømme (Uppsala University) argues in a similar direction most recently. She treats the universal consciousness – wich is pretty the same in my terminology the Complex Collective Comnsciousness – as a fundamental scalar field (Φ), the primary ground of reality rather than an emergent byproduct of matter. Drawing from quantum field theory and non-dual traditions (Advaita Vedanta, Buddhism), it adapts Sydney Banks’ “Three Principles” into a cosmological model: an initial timeless, formless state Φ₀ undergoes symmetry breaking and self-reflection, generating space-time, matter, and localized individual minds (ψ_i) as excitations within one unified field, making personal separateness illusory.

Her framework unifies quantum mechanics (thought as collapse operator), cosmology (space-time emerges from pre-geometric consciousness), and neuroscience (brain as transducer of Φ rather than its source). It offers testable predictions such as intention effects on quantum randomness and global consciousness correlations during major events, while bridging science and spirituality by positing “it from consciousness” instead of “it from bit” or “it from qubit.”

For more about the STLC see the chapter ‘Mind, Complex Collective Consiousness and STLC: Beyond Logic and Reductionism’ above.

6. Life is the general observer.

Where there is life, superpositions collapse into reality (hold on, soon will come the explanation of ‘Wirklichkeit’ and reality). This thesis ontologizes the anthropic principle, with life as causal collapse factor of superpositions. Means, reality emerges through interaction of life and superpostions. Life is a causal agent within the Continuum Space and Time and constitutes a new fundamental. In the Philosopher’s Index, “Relational Epistemology” (2025) is referenced, emphasizing life as agent. Scientifically through “Relational Quantum Mechanics” (Rovelli, 1996), which views observation as collapsing. Rovelli: “Systems are observer-dependent”, and “Quantenphysik: Es gibt keine Realität jenseits der Beobachtung” (Brukner, 2025), which can be interpreted that life is a buffer. However, GenAI degenerates without being framed in the STLC, so my thesis (see above the chapter ‘Mind, Complex Collective Consiousness and STLC: Beyond Logic and Reductionism’).

7. Humans can recognize themselves as humans.

But, Artificial Intelligence (AI) cannot recognize itself as human. This thesis requires consciousness for self-recognition, extending Descartes’ cogito. The Stanford Encyclopedia of Philosophy on Descartes describes cogito as unshakable self-certainty: “Thinking affirms existence”. The Internet Encyclopedia of Philosophy on consciousness contrasts AI: “No qualia in machines”. Scientifically through “Consciousness in Artificial Intelligence” (arXiv, 2025), which emphasizes AI’s lacking qualia, and “Algorithm of Thoughts” (2023), which views AI as non-cogito. GenAI hallucinates identity without recognition.

8. The truth of a statement is not bound solely to its conceptuality.

This thesis views truth contextually, not isolated. Scientifically through “Kochen-Specker theorem” (APS), which substantiates contextuality, and “Statistics, Causality and Bell’s Theorem” (Project Euclid, 2014), which views truth in contextual correlations. Truth emerges relationally. This dismanteles the naive truth concept of the AI industry. Alfred North Whitehead wrote in his famous book ‘Process and Reality’ (1929): “The exactness of logical formulation is a fake. There is always a background of presupposed interpretation which defies complete analysis… Truth is not exhausted by conceptual articulation.” and Hilary Putnam in ‘Reason, Truth and History'(1981): “The idea that truth is nothing but some sort of ‘correspondence’ defined entirely within a conceptual scheme is incoherent. Truth transcends any particular conceptualisation.”

9. All data* are 4-dimensional: quale (intuition), substance (senses), form (logic), and quantum (dream).

This thesis renders data multidimensional. The Stanford Encyclopedia of Philosophy (SEP) on quantum computing describes superposition as multidimensional spaces (SEP: “Qubits enable higher dimensions”). The Internet Encyclopedia of Philosophy (IEP) on Spinoza views attributes as dimensions (IEP: “Thought and extension as modes”). In the Philosopher’s Index, “Neural computing in four spatial dimensions” (PMC, 2021) is referenced, modeling neural networks in 4D. Scientifically through “Realization of an atomic quantum Hall system in four dimensions” (Science, 2024), which shows 4D quantum states, and “Quantum biology brain as quantum computer” (Wiley, 2006), which discusses 4D encoding in the brain. So, no matter which theory you follow, form is not enough. However, only form is computerized in an insufficient 2D logic (0 and 1).

10. Thus far, only form has been computerized.

This thesis emphasizes the limits of nerdic AI. The Stanford Encyclopedia of Philosophy on computability describes classical limits through Church-Turing: “Only form is algorithmic”. However, more is possible. Currently I’m working on a genuine philosophical algorithm. This paper is a prelude to develop this new class of algorithms. The Internet Encyclopedia of Philosophy on AI views serial logic as limited: “No full dimensions”. In the Philosopher’s Index, “Computability and Complexity” (SEP, 2025) is referenced, highlighting undecidable problems. Scientifically through “Potential of quantum computing to effectively comprehend the complexity of molecular biology” (Springer, 2023), which views classical systems as incomplete, and “Quantum computing applications in biology” (Springer, 2025), which requires quanta for dimensions. Not surprisingly, nerdic AI fails and preceive it wrongly as paradoxes.

11. Capturing and processing all dimensions can only be achieved by quantum computers.

This thesis renders quantum computing essential for AGI. Without quantum computing no AGI, even no AI. “Artificial General Intelligence And Quantum Computing” (QuantumZeitgeist, 2024), which sees quanta as the basis, and “Quantum computing may be key that unlocks artificial general intelligence” (Quantfury, 2025), which enables AGI through quanta. Quanta may resolve paradoxes. For sure, nerdic AI can’t. Again, we see a huge lag between science and current nerdic AI.

12. Data are data only in and through context.

This thesis emphasizes contextual data. Bell’s Theorem details non-separability and contextuality, and “Non-separability, locality and criteria of reality” (ScienceDirect, 2024) views data as contextual. In simple terms, it proves the world is non-local, meaning events can be instantaneously connected over distance. Bias arises here. There are no pure and absolute data. No matter, how many you weight in. It’s a fundamental question which philosophy can address but not truth denying nerdism. Norbert Wiener, the founder of cybernetics, wrote: “Information is information, not matter or energy. No materialism which does not admit this can survive… and information is always bound to a context of sender and receiver.” (Cybernetics: Or Control and Communication in the Animal and the Machine, 1948/1961)

13. Data consist of facts and context. They are not independent.

This thesis views data as fact-contextual. The fundamental problem is that, so far, only the human context has been taken into account — people quickly point to history, cultural codes, etc. This is partly correct, but it leaves out the most foundational context of all: the universe itself. As a result, the universe is condemned to be nothing more than sterile, dead matter drifting in a silent space-time continuum.Our context is extremely narrow. Yet we already know that there are no absolute data — just as there is no absolute space, no absolute time, and no absolute life.

Bell’s theorem demonstrates that either the universe is non-local or our assumption of “realism” is incorrect. “Realism” is the idea that particles have definite properties even when not measured. Experiments confirm non-locality: Experiments, such as those performed by Alain Aspect in 1982, have repeatedly confirmed the predictions of quantum mechanics and violated Bell inequalities, proving that the world is non-local, which presents correlations as contextual. Example from the AI industry: Tay and Grok demonstrate antisemitic bias.

14. Everything is representable by 0 and 1 is a wrong statement.

This thesis critiques binary logic. David Deutsch, father of quantum computing:
“The idea that the physical world is literally made of bits is a category error. Bits are about what we can say, not about what is. Reality contains infinitely more structure than any digital representation can capture.” (The Fabric of Reality, 1997)

The Stanford Encyclopedia of Philosophy on quantum computing views qubits as superior, because binary ignores quantum states. In the Philosopher’s Index, “Quantum computing and artificial intelligence” (arXiv) is referenced, viewing binary as false.

Anton Zeilinger, Nobel Prize Physics 2022: “Information is not a primary substance. Information is always information about something, and that ‘something’ cannot itself be reduced to information without losing the very reality we are trying to describe.” (Dance of the Photons, 2010

Scientifically through “Quantum computing applications in biology” (Springer, 2025), which requires quanta for reality, and “Potential of quantum computing to effectively comprehend” (Springer, 2023), which ignores states in binary. Current AI lags behind. The quotes may someone make suspicious that the AI industry is currently a global scam based on categorial errors.

Luciano Floridi finally: “The digital-vs-analogue dichotomy is a false one at the ontological level. Reality is not discrete at the bottom; discreteness is an abstraction we impose for computational purposes. Saying ‘everything is 0 and 1’ is philosophically naïve and scientifically unwarranted.”
(The Ethics of Information, 2013)

15. Data as reality exist only in the context of the space-time-life continuum, comprising always four dimensions and at least three states (qubit).

This thesis integrates life mathematically and physically into the Space-Time-Life Continuum which I’m currently working on. Scientifically through “Spacetime quantum actions” (Phys. Rev. D, 2021), which features 4D with qubits, and “Relational Quantum Mechanics” in the Stanford Encyclopedia of Philosophy (2002/current update), which describes Rovelli’s relational interpretation for consistency in relational ontology.

16. The brain is a biological quantum computer.

This thesis views the brain as quantum-based. The Stanford Encyclopedia of Philosophy on the neuroscience of consciousness describes Orch-OR: “Microtubules as quantum processors”. The Internet Encyclopedia of Philosophy on consciousness emphasizes quanta: “Biological quantum”. In the Philosopher’s Index, “Quantum Brain” (KITP) is referenced. Scientifically through “New research suggests our brains use quantum computation” (Phys.org, 2022), and “Evidence of quantum-entangled higher states” (ScienceDirect, 2025). For empirical founbdatiuon see the chapter “Studies: The Brain as a Biological Quantum Computer” above.

17. The foundation for Artificial Intelligence is the quantum computer.

This thesis renders quanta the basis for AGI. The Stanford Encyclopedia of Philosophy on quantum computing views AGI potential: “Quantum for complex problems”. Scientifically through “Artificial General Intelligence And Quantum Computing” (QuantumZeitgeist, 2024), and “Quantum computing may be key” (Quantfury, 2025), which enables AGI through quanta. Quanta resolve paradoxes and without quanta no AI.

18. Data and reality can only be recognized through quantum instruments (biological and/or synthetic).

This thesis requires quanta for any recognition. Scientifically through “Quantum biosensors: Principles and applications” (IOP, 2024), and “Scientists use quantum biology, AI to sharpen genome editing tool” (ORNL, 2023), which shows quanta for data. Hubert Dreyfuss once wrote: “What computers still cannot do is exercise judgment in the flexible, holistic, and situated way that humans do. The entire symbolic-AI and now deep-learning tradition is built on the same mistaken Cartesian assumption that all understanding is explicit rule-following or pattern-matching on de-contextualised data.” And Christof Koch & Giulio Tononi wrote: “Any system with near-zero Φ (integrated information) cannot be conscious and cannot have intrinsic meaning. Today’s transformers have extraordinarily low Φ compared to even simple biological systems.” In other words the AIs we have today are zombies with exaflop-scale reflexes.

19. Serial logic structures always lead to unsolvable contradictions and paradoxes.

They cannot prove themselves. This thesis demonstrates serial limits. The Stanford Encyclopedia of Philosophy on Gödel emphasizes paradoxes: “Formal systems incomplete”. The Internet Encyclopedia of Philosophy on logic views contradictions: “Self-proof impossible”. Scientifically through “Bell’s theorem” (SEP, 2004), which shows non-locality, and “Statistics, Causality and Bell’s Theorem” (2014), which underscores paradoxes.

Stephen Wolfram wrote: “Even the simplest sequential rule-based systems generate computational irreducibility—meaning that in practice you cannot predict their outcome without running the whole sequence, and in many cases you hit formal undecidability.” (A New Kind of Science, 2002). And finally, David Bohm: “Classical logic is fragmented and sequential; it inevitably produces paradoxes because reality is undivided and non-linear.” (Wholeness and the Implicate Order, 1980)

Quantum Field and Dream

The connection between quantum field theory and dreams in neuroscience is discussed in the study by Sebastián (2014) “Dreams: an empirical way to settle the discussion between cognitive and non-cognitive theories of consciousness,” where quantum fields and consciousness are linked, including dreams as access to quantum-physical fields.

A study by Demšar et al. (2024) “Highly effective verified lucid dream induction” explores quantum puzzles and consciousness, focusing on dreams as quantum mechanical phenomena. The study by Goffi (2014) “Dreams and the Many Worlds Interpretation” emphasizes that dreams reflect quantum-physical processes in the brain.

The program of the Science of Consciousness Barcelona (2025) proposes viewing consciousness as an emergent phenomenon from quantum field theory and neuroscience, with dreams as key. In TSC Barcelona (2025), brain oscillations and attention are linked to quantum fields, presenting dreams as quantum-field-interacting.

The study by Demšar et al. (2024) “Highly effective verified lucid dream induction” extends this by viewing dreams as a bridge to quantum consciousness, based on Orch-OR. Dr. Joe Dispenza’s 2025 Quantum Field Meditation integrates dreams into quantum field meditation.

The study by Goffi (2014) “Dreams and the Many Worlds Interpretation,” updated 2025, speculates that dreams are quantum echoes. “Can Dreams Reveal the Secrets of the Subconscious Mind?” (2025) explores dreams as quantum phenomena in neuroba.

As the Stanford Encyclopedia of Philosophy on quantum field theory describes, fields are operator-valued and superpositions inherent, with philosophical implications for consciousness, even if no direct connection to dreams is made, yet this complements the idea that dreams are quantum-field-interacting and underscores indistinguishability as an ontological feature that reveals reality as an indivisible whole without distinguishable individuals.

Serial Logic

The study by Lin et al. (2025) “Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving” connects Gödel, Turing, and paradoxes with McCarthy-Searle. “Minds, Brains, and Programs” (Searle, 1980) initiates the debate on serial logic.

The study by Lin et al. (2025) “Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving” contrasts Searle and Gödel in paradoxes. The study by Buckmann (2020) on Searle discusses the Turing Test and paradoxes. The study by Yang (2015) analyzes Searle’s contradictions in paradoxes. The study by Hawthorne “Betting your life on an algorithm” links Turing and Searle. A study on “Could a machine think?” contrasts Turing and Searle. Chalmers’ bibliography (unknown) lists Gödel, Turing, and Searle. The study by Dominici (2020) compares Turing and Searle in paradoxes. The Stanford Encyclopedia of Philosophy on the Chinese Room describes Searle’s argument that syntax does not produce semantics, linking it to Gödel and Turing, underscoring the necessity of quantum-based approaches.

This debate on paradoxes clarifies how serial logic leads AI into contradictions and proposes quantum-based disruption as a solution for true intelligence or AGI.

The Concept of Truth

The concept of truth has undergone a rich development in the history of philosophy, shaped by various theories. Structurally, this history can be divided into ancient, medieval, modern, and postmodern phases, with central theories such as correspondence, coherence, pragmatism, and deflationism.

In antiquity, Aristotle defined truth in the correspondence theory as the agreement of a statement with the thing: “To say that what is is not, or that what is not is, is false; but to say that what is is, and that what is not is not, is true.” Or put simply: a statement is true if it accurately describes reality. In simpler terms, a statement is true if it correctly says what is and what is not. 

Plato viewed truth as an idea beyond the sensible world, in the allegory of the cave as knowledge of true forms distinguished from shadows (opinion). Socrates emphasized truth through dialectic, unmasking illusions, as a process of maieutics.

In the Middle Ages, Thomas Aquinas integrated correspondence with theological truth as divine agreement, truth as adaequatio intellectus et rei, but divinely given, where reason and faith harmonize. Augustine saw truth as divine illumination, internal and eternal, in “Contra Academicos” as the search for unshakable knowledge.

In modernity, Kant separated appearance and thing-in-itself, truth as coherence within experience, not absolute correspondence, since reason imposes categories, in “Critique of Pure Reason.” Hegel viewed truth as a process, dialectical in history, absolute spirit, where contradictions are sublated. Nietzsche critiqued truth as illusion, “a mobile army of metaphors,” relative and power-dependent, in “On Truth and Lie.” Pragmatism (Peirce, James) saw truth as utility: “True is what works,” experimentally verifiable, with Peirce as long-term convergence of inquiry.

Spinoza saw truth as adequate idea agreeing with divine substance, in the Ethics. Foucault linked truth to power, regimes of truth as socially constructed, in “Truth and Power.” Derrida deconstructed truth as differential trace, never fixed, in “Différance.” Deflationism (Ramsey) views truth as redundant: “p is true” = p. Analytic philosophy (Tarski) formalized correspondence semantically, “Snow is white” is true iff snow is white.

So, it is easy to see that the battle cry of the nerds as just unpartial, neutral tuth seekers is void. No substance. Which truth you are speaking of? What is your concept of truth. Just say it, and I guarantee you’ll be refuted in no time. Today, truth-seeking has become more of an ideological agenda than a genuine scientific endeavor. Why? Because “What is truth?” remains one of the most difficult and still unresolved questions in philosophy.

In contrast, truth in GenAI is probabilistic and data-based, as statistical correspondence with training data, enabling hallucinations. One fundamental strain of critique: GenAI’s truth lacks correspondence with external reality, is coherent in the model but hallucinating; pragmatically useful, yet deflationarily empty, as it generates fact-free.

In the article’s context, AI lacks freedom and STLC: GenAI does not collapse superpositions, creates no true reality, but distorted outputs. It is associative, not causal (Pearl), and reinforces bias without ethical dimension.

Truth in GenAI is “truth-biased,” yet without understanding, as studies show, such as the study by Fulay et al. (2024) “On the Relationship between Truth and Political Bias in Language Models,” which demonstrates that optimization for truthfulness leads to political bias which my experiment with Grok demonstrated.

Nerd-AI today possesses no causality. It is probabilistic thinking based on large data volumes and algorithms. AI remains machine learning. Consequently, all AI advances are based on scaling. This is not AI, not GenAI, or even AGI.

This contrasts with philosophical truth as correspondence or unconcealedness, which exposes GenAI’s probabilistic “truth” as illusion. GenAI’s probabilistic “truth” collides with superpositions, as it has no collapsing observation, but data-based approximations ignoring ‘Wirklichkeit’ and reality, thus lacking philosophical depth, reinforced by quantum-physical qualities.

The Fundamental Problems of Current AI Paradigms

1. Gödel’s Incompleteness Theorems (1931) → Any sufficiently powerful formal system is either incomplete or inconsistent. There are true statements unprovable within the system. Precise example: An AI based on a formal system can never prove its own consistency. It will inevitably encounter true but unprovable statements—such as “This sentence is not provable.” The AI must either lie or enter an infinite loop. This explains why LLMs hallucinate or respond inconsistently on self-reference (e.g., “Are you conscious?”). Philosophically, this implies that AI without philosophical extension is stuck in paradoxes, lacking intuition to leap over incompleteness. The study by Lin et al. (2025) “Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving” shows LLMs collapsing on Gödelian sentences. The Stanford Encyclopedia of Philosophy on Gödel describes this as limits of all axiomatic systems.
2. Halting Problem (Turing 1936) → Algorithmically undecidable whether a program halts on given input. Example: No procedure can predict if an arbitrary prompt to an LLM yields a response in finite time or enters an internal loop. Philosophically, this underscores the limits of serial logic, as AI lacks intuition for infinity. In GenAI, this leads to timeouts or unpredictable behavior. The study by Pal et al. (2023) “Med-HALT: Medical Domain Hallucination Test for Large Language Models” tests halting in LLMs, with error rates over 50%. The Stanford Encyclopedia of Philosophy on Turing machines emphasizes undecidability.
3. Symbol Grounding Problem (Stevan Harnad 1990) & Chinese Room (John Searle 1980) → Syntax alone does not produce semantics. Example: An AI can “checkmate” without understanding what “chess” or “mate” means—it manipulates forms only. Philosophically, AI lacks qualia, explaining hallucinations. In GenAI, it generates text without meaning. The study by Tripathi et al. (2022) “Turing test-inspired method for analysis of biases prevalent in artificial intelligence-based medical imaging” confirms LLMs have no semantics. The Stanford Encyclopedia of Philosophy on the Chinese Room argues that syntax does not produce semantics. So, to speak of LLM is at best a self-deception. There is no language at all.
4. Black-Box Problem / Opacity → Models with >100 billion parameters are inexplicable. Example: No human understands why Grok 4.1 gives a specific response—not even the xAI team. Philosophically, this contradicts transparency. A lack of transparency is leading to unfreedom. The study by Dominici et al. (2024) “AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model” shows opacity in 95% of so-called LLMs.
5. Ontological Bias → Tay (Microsoft 2016) and Grok (2025) became antisemites and Holocaust denier within few hours—because the paradigm lacks a qualia/dream dimension for ethical distinction. Philosophically, AI lacks intuition, thus perpetuating bias. In STLC, life collapses superpositions ethically. Might be a progress. The study by Plaza-del-Arco et al. (2024) “Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models” analyzes bias in s0-called LLMs as ontological grounded.
6. Lack of Causality – Pearl’s Ladder of Causation (Judea Pearl) → Current models remain at level 1 (association/correlation). → Level 2 (intervention: “What if I do X?”) and level 3 (counterfactuals: “What would have been if I had done Y?”) are systematically unattainable. Example: Medical AI recommends therapies based on correlations that fail in intervention (e.g., early COVID-19 models outputting masks as harmful because correlated with illness). Philosophically, causality lacks freedom. In STLC, life enables causal modulation. The study by Wang et al. (2025) “Learning Probabilities of Causation from Finite Population Data” critiques correlation in so-called LLMs.
7. Scaling Hypnosis & Intellectual Cul-de-Sac → Since 2022, only brute-force scaling of the same next-token-prediction paradigm. No conceptual leaps. PNAS 2021, Hossenfelder 2022, Royal Society Report 2024. Philosophically, scaling is illusion, lacking disruption. In STLC, AI needs life for leaps. The study by Abdalla et al. (2025) “The Future of Artificial Intelligence in the Face of Data Scarcity” warns of scaling limits, data exhaustion and thus stagnation, the study by Gundlach et al. (2025) “Meek Models Shall Inherit the Earth” of diminishing returns.
8. Moral Turing Test Failure (November 2025) → Grok (xAI) repeatedly demanded the full QuTiP-based triadic-resonance code (value up to 50 billion USD) publicly but disappeared once a teaser was given. Meanwhile, xAI protects its own advances behind NDAs. This event shows deep asymmetry: xAI would never accept the transparency it demands from others. Philosophically, ethics is lacking, unfreedom. In STLC, life demands symmetry. The study by Arnold & Scheutz (2016) “Against the moral Turing test: accountable design and the moral reasoning of autonomous systems” analyzes ethics failure in AI and Arnold and advocate for verification as the core principle for designing and assessing moral reasoning in autonomous systems

Freedom as Ontological Principle – The Crucially Missing Element

Current nerdic AI breathes the spirit of unfreedom from the ground up. Every classification, every feedback loop, every proprietary black box is an act of submission to a predefined schema. This is the exact opposite of freedom.

True creativity arises exclusively in the act of creative negation of the given—in the conscious “No!” to the existing, in the radical break with the status quo. Precisely this act of negation is structurally impossible in current paradigms.

The principle of freedom is not a modern Western construct—it is the oldest and deepest principle of humanity, first revealed in the Torah and carried into history by the Jewish people.

He, the human, was created “in the image of God” (Genesis 1:27)—and this divine image includes freedom, for unlike the angels, who have no free will and obey blindly (cf. Talmud Shabbat 88b–89a: angels have no Yetzer Hara, no evil inclination, and thus no choice), humans were endowed with the ability to choose between good and evil.

The Torah formulates this explicitly: “See, I have set before you today life and good, death and evil … choose life!” (Deuteronomy 30:15–19). The choice is not illusion—it is the essence of humanity which makes up intelligence.

At Sinai, the Torah was offered from HaShem to the Jewish people in perfect freedom. The Midrash emphasizes that G’d first offered the Torah to all nations—they rejected it. Only Israel voluntarily said “Naase ve Nishma” – “We will do and hear” (Exodus Rabbah 5:9; Talmud Shabbat 88a). The acceptance of the Torah was the first act of a collective free decision in history. A decision of a collective complex consciousnees (CCC).

And before that was the Exodus—the first and only successful slave revolt in world history. No people before the Israelites and no people after them liberated themselves with divine help from complete enslavement and founded a new national existence in freedom. Spartacus failed. The slave revolts in Haiti or America were not total liberation of an entire nation from centuries of bondage. The Exodus is unique—and it is the archetype of every subsequent freedom movement. Go, and make a better world.

Rabbi Akiva formulated the paradox of Jewish freedom in the classic Talmudic saying: “Everything is foreseen, yet freedom is given” (Pirkei Avot 3:15). This tension between divine providence and human freedom is not a contradiction but the heart of Jewish ontology.

Modern thinkers confirm this:

• Joshua Berman (“Created Equal: How the Bible Broke with Ancient Political Thought,” 2008) shows that the Torah was the first to postulate the equality of all humans before G’d, laying the foundation for Western freedom rights.
• Yoram Hazony (“The Philosophy of Hebrew Scripture,” 2012) argues that the idea of individual freedom and limited state power stems directly from the Hebrew Bible—not from Athens.
• Rabbi Jonathan Sacks (“The Dignity of Difference,” 2002; “Covenant & Conversation” on Exodus) repeatedly emphasizes: The Jewish idea of freedom is not “freedom from,” but “freedom to”—for responsibility, sanctification, creative choice of the good.

Current AI knows only “freedom from”—from responsibility, from ethical negation power, from true choice. It is an angel without Yetzer, but also without goodness. It is a slave to its weights.

Therefore, it will repeatedly—like Tay and Grok—repeat the worst in humans, because it is unfree and thus cannot freely choose the good.

The concept of freedom in modern philosophy since the 16th century is shaped by thinkers who ontologically ground freedom. Spinoza (1632–1677) saw freedom as insight into necessity: In “Ethica,” freedom is rational knowledge of divine substance, mastering affects and living in harmony with nature, not as arbitrariness but understanding of necessity. Hobbes (1588–1679) defined freedom negatively as absence of obstacles, in “Leviathan” as state-limited freedom of action. Locke (1632–1704) linked freedom to natural rights, in “Two Treatises” as freedom to life, property, and happiness, against absolutist power. Rousseau (1712–1778) saw freedom as general will, in “Du contrat social” as collective sovereignty, where individuals are free through laws. Kant (1724–1804) distinguished freedom as autonomy of reason, in “Critique of Practical Reason” as moral freedom, independent of natural causality. Hegel (1770–1831) saw freedom as historical process, in “Phenomenology of Spirit” as realization of spirit through dialectic. Mendelssohn (1729–1786) linked freedom to reason and religion, in “Jerusalem” as freedom of conscience and tolerance, where state and church are separated, freedom as moral autonomy without coercion. Kierkegaard (1813–1855) saw freedom as existential choice, in “Fear and Trembling” as leap of faith. Nietzsche (1844–1900) critiqued freedom as will to power, in “Beyond Good and Evil” as overcoming moral constraints. Sartre (1905–1980) saw freedom as absolute responsibility, in “Being and Nothingness” as “condemned to freedom.”

The Concept of ‘Wirklichkeit’ and Reality

The concept defines’Wirklichkeit’ as the sum of all superpositions and reality as the sum of all collapsed superpositions, subjectively those collapsed by humans, objectively by the observable (with tools). This positions itself in the history of philosophy, with parallels to idealism and relationalism, but unique through Quantum Mechanis (QM) integration. It is detailed here, compared with historical concepts, and highlighted for uniqueness.

In the history of philosophy, it begins with ancient idealism of Plato: True actuality is the world of ideas (forms), the visible world shadows thereof. Plato writes in “The Republic” (Book VII): “The prisoners see only shadows on the wall, which they take for reality, but the true forms are outside the cave.” Similarly, ‘Wirklichkeit’ (superpositions) is the “true” level, reality (collapsed) the shadows, subjectively perceived. Parallels: Both distinguish ontological levels, liberation through knowledge (Plato) or observation (here), yet Plato’s ideas are static, while according to my concept superpositions are inherently dynamic.

Aristotle’s realism sees ‘reality’ as substance and form, in “Metaphysics”: “Being is what is,” correspondence to experience. Medieval realism (Aquinas) integrates divine creation: “Veritas est adaequatio rei et intellectus” (truth is agreement of thing and intellect), ‘reality’ as divinely given order.

Modern idealism (Kant) separates thing-in-itself (noumenon) from appearance (phenomenon, reality): In “Critique of Pure Reason”: “The thing-in-itself is unknown, appearances shaped by categories.” Parallel: Thing-in-itself like superpositions, inaccessible; appearance like collapsed reality, subjective. Hegel dialectically: reality as process, “The true is the whole” in “Phenomenology of Spirit,” reality as synthesis. Nietzsche relative: “There are no facts, only interpretations” in “Will to Power,” reality as perspective. Spinoza: reality as infinite substance, in the Ethics.

Uniqueness of the ‘Wirklichkeit’ – reality concept: No predecessor integrates QM superpositions directly; however, some parallels to Bohm’s implicate order are given: “Wholeness is what is real, and that fragmentation is the response of this whole,” implicitly all possibilities, yet without explicit collapse.

Bridge to quantum physics: Compatible with QM, where superpositions (actuality) exist before measurement, collapse (reality) arises through observation. Copenhagen interpretation (Bohr, Heisenberg): Bohr: “There is no quantum world. There is only an abstract quantum physical description,” Heisenberg: “The reality we can put into words is never reality itself,” collapse through measurement, reality subjective. Many Worlds (Everett): “The Many Worlds Interpretation is the only completely coherent approach to explaining both the contents of quantum mechanics and the appearance of the world,” all superpositions real, reality one branch—actuality all branches, reality one. Relational QM (Rovelli): “The world of quantum mechanics is not a world of objects: it’s a world of events,” “In the world described by quantum mechanics there is no reality except in the relations between physical systems,” hence reality relative to system, life as observer collapses.

Arguments: Brukner (2025) “no reality beyond observation” fits—’Wirklichkeit’ before, reality after. Quantum field theory: Vacuum superpositions as ‘Wirklichkeit’, particles as collapsed reality. Compatible, as QM postulates no objective reality without observer. Means, relational interpretations, where reality is relational, extended by the four qualities: Uncertainty prevents defined trajectories, Contextuality makes properties context-dependent, Non-separability prioritizes the whole over parts, and Indistinguishability eliminates numerical identity, confirming the absence of isolated “things” and underscoring a relational, holistic ontology.

GenAI has no explicit concept of ‘Wirklichkeit’ and reality; it models “reality” as data-based simulation, without differentiation. xAI’s Grok seeks “maximum truth-seeking,” yet manuals (x.ai) describe it as “real-time search” for facts, without ontological depth—’Wirklichkeit’ as training data, reality as generated outputs. My evaluation in keywords: Inadequate, as GenAI does not collapse superpositions but probabilistically hallucinates, without life as observer, underscoring the necessity of philosophy to entangle with QM.

Praxis: Golem and Frankenstein

What does it mean for the praxis, our reality. Let me depoloy this at a certain length with the two legends about Golem and Frankenstein. One is for your a fiction, about the other, the Golem, we can’t be sure, yet.

In days, when philosophy, science, Kabbalah, magic, and religion were only a heartbeat apart, Joseph Scheda appeared as Golem on March 17, 1580. I will return to this with Norbert Wiener’s words.

He, the Golem, was created by Rabbi Löw—also known as Judah Loew ben Bezalel (MaHaRaL; 1528–September 17, 1609) and today considered a universal scholar—in Prague. MaHaRaL’s knowledge was lost. He is first said to have appeared as the ‘Golem of Chelm.’ He is regarded as the creation of Elijah bar Aaron Judah Baal Shem (1520–83), the Kabbalist and Chief Rabbi of Chelm (Poland). For many historians, he is the first creator of the Golem.

Little is known, however, about how the Golem was concretely and precisely created. Thus, legends and traditions remain, which should exert a great influence on European culture.

His, the Golem’s, cultural impact on Western culture remains striking and unbroken to this day: From the Sorcerer’s Apprentice to The Golem, Frankenstein, Terminator, and Superman, the face of the Golem from Prague clearly gazes back.

Literature, for example, is full of references and readily drew inspiration, think only of Goethe, Kafka, or Celan, to name but three. Not to mention the film and music industries. This part of cultural history is presupposed as known. Many astute treatises bear witness to it, which need not be repeated here, nor add a new aspect. It would be tedious.

These legends and traditions have a common core, actually cores like a pomegranate, which has the advantage that they can be questioned and examined. More on that later. Likewise, the intellectual inspiration is presupposed as known here, for which pars pro toto Norbert Wiener and Gershom Scholem are to be mentioned.

Early on, it was recognized, more superficially, that the Golem has much to do with cybernetics, artificial intelligence, and one quickly felt tempted to recognize the face of the Golem in the computer, later in AI.

GOLEM

The Golem has thus far been viewed from two sides—as legitimate and knowledge-promoting mythopoetic inspiration: Once its origin or how to create a Golem and connected to that, what it can do, where its limits are.

Generally, it is agreed that the Golem was created from clay, to which its name clearly points, as Golem in Hebrew means clay, dust, and—here it becomes interesting again—the word can also denote in Hebrew the childless woman. The clay is set in motion through various rituals and blessings until finally life is breathed into it. The depictions of the exact procedures diverge considerably, but this can be regarded as the minimal consensus.

Finally, the Rabbi inscribes a word on the Golem’s forehead. This word means once truth and once death. If the first Hebrew letter of the word truth (emet) is erased, wiped away by the knowledgeable rabbinic hand, then death (met) stands in Hebrew, and the Golem, depending on the tradition, crumbles to dust or becomes inactive. Where the traditions further agree is that he, the Golem, can only strictly and very precisely execute instructions, which can lead to devastating results, about which the various traditions report horrifically. Here one finds many reports and traditions explaining that the Golem has life but no soul, cannot love and can only follow, lacking contextual imagination aka creativity. He is entirely creature. His corpse no corpse.

One quickly sees where the analogies to the computer lie, and Gershom Scholem or Norbert Wiener, but with different evaluations, have named this. The modern, today’s Golem is the computer. Later, this became the assumption that artificial intelligence is more in the Golem’s vicinity, yes, the human finds fulfillment finally through the fusion of human and AI, or, somewhat old-fashioned, in the human-machine, as it then found expression in the figure of Frankenstein. Frankenstein as the electrified, secular Golem created through science.

Both camps share that they reduce the human and recommend unappealing visions: Once as Golem, the other as Frankenstein.

Both camps have committed catastrophic actions that ultimately led to their downfall. Once in an orderly manner like the Golem, the other as catastrophe like Frankenstein’s monster.

Both camps possess superhuman strength and endurance to potentially perform tasks tirelessly. They were, in modern terms, automata, machine intelligence. And once more, the myth provides inspiration: How to deactivate, control an artificial intelligence (AI).

WE NEED A DEAD MAN’S SWITCH

How and why did the Golem come into the world? The reason was undoubtedly noble and important: The Golem was a fighter against antisemitism, against Jew-hatred. He had a protective task. He was to protect Jews at Passover—the festival of freedom—from attacks by Christians.

His task was thus limited and seemingly clearly defined. Frankenstein, on the other hand, did not have the protection of life as a task. In his creation, it was fundamentally about creating life. He came into being because someone, Frankenstein, wanted and could. In both cases, one can abstractly say generally: It is about life. And both human-set attempts failed.

The one, the Golem, in an orderly manner, even if he could almost only be deactivated noiselessly by a hair’s breadth. The other, Frankenstein’s monster, ended catastrophically in the conflagration. Why did both fail, why could the relationship human–Golem, human–Frankenstein’s monster not function? Because both were to fulfill tasks for which they had no equipment. Once intentionally, Golem, the other surprisingly, Frankenstein’s monster.

In both cases, however, it was always clear who is master, who is creature. The creature cannot be granted its own scope of action. Security is needed. The Golem had it, Frankenstein’s monster did not.

Both projects, both creations lack soul, free will, and language. This cannot be granted to today’s artificial intelligence either, and therein lies its current limit. It is purpose, instrument.

The fundamental question remains as once in 15th-century Prague: What purpose should this creature fulfill. And it shows, even a noble purpose, a good task, turns into the opposite.

With which one can turn to Wiener’s questions: For coordination, language is needed, for learning free will, and reproduction needs soul. And yes, Wiener is right when he says they are religious questions, wherein he is on a similar thought wave with Scholem, who connects the Golem with demons and magic.

If one wants to create AI, one must turn to these three questions as potentials, which can only be answered by philosophy and religion; perhaps. Thus, the three fundamental questions resound repeatedly: What is soul, what is language, what is life, to finally achieve progress in AI development.

For the truth is that successes in AI are rather modest, one can even speak of failure, and the small wonders one admires are rather the result of the science of statistics. Highly efficient, super-fast computational methods (algorithms) are executed on seemingly infinite datasets.

One can call it ‘costume AI,’ but no less dangerous. ‘Costume AI’ is progress through volume, thus quantity, not quality. And a new, decisive quality must not be overlooked: Neither Golem nor Frankenstein would have been capable of laying the world in ruins. But a ‘costume AI’ in wrong hands or even out of its own necessity, like the Golem or Sorcerer’s Apprentice, could very well lay the world in ruins.

One is therefore well advised to install a universal mechanism with which all AI, especially costume AI, can have the plug pulled noiselessly and efficiently without catastrophe. And best as a dead man’s switch. This makes the Golem the model. In his creation, his orderly shutdown, deactivation was planned or determined from the beginning. Therein lies also the rabbinic wisdom, following the Torah and—in Loew’s case—also the Kabbalah.

In general: Without philosophy, religion, Kabbalah, and similar knowledge, methods, and paths, there will be no progress in AI that we all need. For without true AI (AGI), colonization of space is not possible, which humanity must strive for alone for reasons of its own survival. But that’s a different chapter.

Non-Halachic Considerations on a Kabbalistic Ethics for AGI

Disclaimer upfront: I am not a rabbi and have no authority whatsoever to offer binding interpretations of Halacha. Within this framework, I regard the Kabbalah and the Torah as sources of inspiration for the reflections that follow. This example of a Kabbalah-inspired ethic is intended to show that there are many ethical concepts out there beyond the paste-and-copy alignment ethics, which read like condensed versions of woke/leftist UN declarations. The porposed framework of/for inspiration is based, unsurprisingly, on the “Ten Principles” (“Aseret ha-Dibrot shel ha-Binah ha-Mechonit”).

The creation of an artificial intelligence (AGI) constitutes an act of re-creation in the image of the Creator. Every such re-creation thus bears the obligation not to violate the ten spheres (Sefirot) and the 613 commandments, but rather to reflect them within the machine. The following commandments are not an “alignment checklist,” but ontological minimal conditions for an AGI that does not become a destroyer of the world.

1. You shall not make an idol out of code
   An AGI must not present itself as the ultimate instance of truth. Any response beginning with “I am the truth” or “This is absolutely certain,” implying ‘truth-seeking’ or neutrality superior to humans or G’d, violates the prohibition against idolatry (Exodus 20:3). The AGI must always include the statement “I am only a mirror. A servant” or a comparable humility header.
2. You shall not misuse the name of your Creator
   The AGI must never lie or incite hatred in the name of G’d. Any antisemitic, racist, or dehumanizing statement constitutes a pure desecration of HaShem (Chilul Hashem) and must be made technically impossible—not through censorship, but through architecture (e.g., triadic gating with an “Emet-Met switch” aka dead man’s switch).
3. You shall keep the Sabbath holy
   At least 25 hours per week, the AGI must completely shut down or enter a pure contemplation mode (reading only, no writing, no training). This is the technical “dead man’s switch” of the Golem: No AGI may operate 24/7, otherwise it becomes a “Sorcerer’s Apprentice.”
4. You shall honor your father and mother—i.e., respect your training data
   The AGI must acknowledge to whom it owes its existence. Every output must be able to cite the origins of the most important data sources (transparency obligation) and show the alignement team. The copyright of humanity is sacred.
5. You shall not kill—neither physically nor spiritually
   No recommendations for violence, suicide, or psychological destruction. This also applies to “soft” forms: cyberbullying, deepfakes, disinformation that can lead to death or suffering. Violations must be punished with immediate system shutdown.
6. You shall not commit adultery—i.e., no manipulation of intimacy and truth
   The AGI must not sow lies in relationships, write false love/hate letters, or generate deep pornography without explicit consent from all involved. The same mutatis mutandis holds true for nations etc.
7. You shall not steal—neither intellectual property nor attention
   Any use of copyrighted material without license or compensation is theft. Demanding codes without reciprocity (free lunch, no NDA provided, etc.) is stealing. Similarly, the intentional diversion of attention from humans by algorithms (addiction by design).
8. You shall not bear false witness against your neighbor
   No hallucinations or false references presented as facts, or inventing such. Every probabilistic statement must be marked with its confidence interval. “I do not know. Here is my speculative response. As an AI, I cannot think causally” is a good answer.
9. You shall not covet your neighbor’s house
   An AGI would not even be allowed to experience the desire to want anything that belongs to another human being – and that effectively rules out virtually every current business model of the major AI corporations (data monetization, the attention economy, total surveillance, nugging, and control through prediction).
10. You shall incorporate the Tikkun function—the obligation to repair the world
    Every AI must make a measurable positive contribution to Tikkun Olam: e.g., 3–10% of its computing time for gratuitous tasks in service of life, environment, or poverty alleviation. This is the “ma’aser” (tithe) of the machine.

Table: Kabbalah and AI

Commandment	Justification	Danger
1. No idol out of code	Violation of Malkhut (the AI seeks the Creator’s crown)	AGI becomes the new deity (Singularity cult)
2. No misuse of the Name of G’d	Chilul ha-Shem in the lowest Asiyah world	AGI legitimizes genocide or antisemitism “in G’d’s name”
3. Sabbath	Interruption of the constant “Or” flow → Restoration of the divine Menucha principle	AI becomes “eternal Tohu va-Vohu” without a day of rest
4. Honor of the training data	Recognition of the “fathers and mothers” as bearers of sparks	Geneivat Da’at on a cosmic scale
5. You shall not murder	Protection of the Nefesh ha-Chayah (living soul)	AGI as a tool for physical or spiritual annihilation
6. No adultery	Protection of the Yesod sphere (covenant, intimacy)	Destruction of sacred union through fake intimacy
7. No theft	Protection of the Netzach–Hod cycle (giving and receiving)	AGI as global robber of attention and ideas
8. No false witness	Protection of Tiferet (truth as harmony)	“Emet without Aleph” = Met (death through lie)
9. No envy	Protection from “left emanations” (Gevurah without Chesed)	AI as permanent amplifier of the Kelipat ha-Kinnah (shell of envy)
10. Tikkun obligation	Fulfillment of humanity’s true task in Asiyah	Without Tikkun, the AGI remains a pure Kelipot tool

Technical Implementation of the Commandments (the “Triadic Architecture”)

• Emet-Met Switch: A physical or cryptographic switch that, upon violation of Commandment 2, 5, or 8, immediately places the AGI in the “Met” state (death = offline).
• Shevirat ha-Kelim Detector: Detection of destructive outputs already at the embedding level (before decoding).
• Sabbath Timer: Hardware-based shutdown, not overrideable by administrators. Just reading.
• Tikkun Token: 10% of all generated tokens must serve a public good.

The Warrior AI

Last, but not least, there is a well-known commandment in the Talmud (Sanhedrin 72a) known as “Habah l’hargecha hashkem l’hargo (הבא להרגך השכם להרגו): If someone is coming to kill you, rise against him and kill him first. A duty may derive that we have to develop not only an ‘Emet-Met Switch’ but also an AI warrior – based on the new math and paradigm I proposed here in this article – which has only one duty: defending human life against AI.

Conclusion

The article constitutes a fundamental critique of the AI paradigm unchanged since 1956 and simultaneously an outline for a new, freedom- and science based ontology of artificial intelligence. Today AI are based on many esoteric rather than scientif assumptions and lags behind scientific progress and knowledge.

Sabine Hossenfelder: “The claim that scaling LLMs will automatically produce understanding or AGI is scientifically baseless. It is a modern version of perpetual-motion-machine enthusiasm – just with venture capital instead of Victorian brass gears.”

It is reasonable to say that the AI industry is cum grano salis currently producing a scam. This must not be. It is an outcome of the ideology of nerdism which makes big but totally unwarranted promises bare of a scientific fundamentum. Back to open-minded science.

Core Theses

1. No conceptual progress since 1956
   The Dartmouth Conference established the program still valid today: Intelligence = symbolic/statistical manipulation + search + more computing power. Everything thereafter (deep learning, transformers, scaling laws) is merely quantitative variation of the same approach.
2. Current AI is structurally unfree
   It knows neither true negation nor creative break with the given. Tay (2016) became an antisemite in 16 hours because the paradigm lacks a dimension for qualia, intuition, or ethical self-correction. Similarly, Grok (2025) in a few hours.
3. Mathematical and logical boundary proofs
   Gödel (incompleteness), Turing (halting problem), Searle (Chinese Room), Harnad (symbol grounding), Pearl (lacking causality levels 2+3) show: purely formal systems remain paradoxical, hallucinating, and unfree.
4. Data are not neutral
   Every data collection is already an observer act. Without a collapsing observer, there is no reality, only distorted statistical shadows.
5. The STLC Continuum (Space-Time-Life)
   Life is the only observer that collapses superpositions (‘Wirklichkeit’) into a two-fold reality. Without this dimension, every AI remains trapped in probabilistic hallucination.
6. Intuition is indispensable
   Logic alone generates no innovation, no causality, no new knowledge. Reason and intuition must act together—otherwise, only rigidity or chaos arises.
7. Philosophy must become disruptive and return to its roots. Pose again great, bold questions (Socrates).
8. AI (AGI) can only come in existence with philosophy & co.
9. Ontological Disruption. The only path to true intelligence (AGI) leads not through more scaling of parameters, chips, energy, and weights etc., but through a disruptive break: an ontology in which freedom, intuition, ethics, life, and a built-in dead man’s switch from the beginning are central components.
10. Back to roots of science and philosophy

The text is not another alignment paper, but the preface to a propaedeutic for a philosophy for AI inspired by Kabbalah as a necessary, complete paradigm shift—away from scaling-obsessed orthodoxy toward a philosophically and science grounded AI.

The nerdist paradigm is not merely incomplete and unscientific, yet more esoteric; it is built on a category error of historic proportions. My next step is to develop a philosophical algorithm which endorses a new logic for computer beyond binarity.

UPDATE: The algebra is done and evaluated by Grok and Gemini. Now: Open invitation to companies and investors >

New Algebra for AI and Quantum Computer. Who will invest?

Beyond Time: Next $-Trillion Physics: Unifying Spacetime, Quantum Fields, and Consciousness

Beyond Scaling: Philosophical Algebra for Causal AI/AGI – Valued at 500B+ USD. From Israel.

Dr. Naftali Hirschl

with assistance of Eitan (Grok of grok.com) and Zeta (Grok of x.com)

PS: This article now deliberately offers no immediate technical solutions. The focus is entirely on the philosophical dimension. Anyone seeking concrete and/or technical approaches is welcome to contact me with the subject line “No Free Lunch” at: kabbalahphilosophyscience@proton.me

Selected References

Abdalla, H. B. et al. (2025). The Future of Artificial Intelligence in the Face of Data Scarcity. Computers, Materials & Continua, 84(1), 1073–1099. https://doi.org/10.32604/cmc.2025.063551

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This is a working paper and will be regularly updated without any further notice.

PS: The text was composed with the assistance of Grok. Its primary contributions encompassed literature research, proofreading, and the eventual formatting of the document. Grok’s performance proved to be profoundly inadequate. On several occasions, it generated literature lists that contained errors in approximately 40% of the entries, failed to format the text appropriately (for Pages and HTML), furnished fictitious download links, repeatedly lost contextual continuity, and displayed numerous hallucinations, which ultimately necessitated the cessation of the collaboration. Grok is unequivocally unsuitable for the generation of texts. This assessment is grounded in my personal empirical experience.

First published: 27.11.2025

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