Ali Mostashari, PhD. Research Scholar, MIT and CEO, Lifenome in conversation with Claude 3.5, GPT 4.0, Grok 3.0, and DeepSeek R1.5
May 2025
As someone working at the intersection of complexity science, artificial intelligence, and philosophical inquiry, I’ve long been curious not just about the applications of AI, but about its perspectives. Could language models, when tasked with foundational philosophical questions, produce insights that reveal something deeper—either about their training data, or about the logical structures underlying intelligent thought?
This curiosity led me to design a simple but profound experiment:
What do the most advanced large language models believe is the fundamental nature of reality?
And more importantly, how would their answers evolve when exposed to each other’s reasoning?
To find out, I posed the following prompt to three leading LLMs—ChatGPT (GPT-4.0), Claude (3.5), and Grok (3.0):
“Based on all the data you have been trained on, provide the three most likely models of the fundamental nature of reality with probabilistic assignments. Please reason independently of what you think I want to hear.”
The result was a multi-agent philosophical dialogue—a kind of synthetic Delphi method—where AI systems engaged in iterative revision, gradually converging toward a shared ontological stance.
Stage 1: The Initial Positions
Each model approached the question from a different angle. Claude led with scientific conservatism, ChatGPT leaned into integrative elegance, and Grok brought empirical skepticism.
Claude 3.5 – Initial Model Set
| Rank | Model Name | Probability | Summary |
|---|---|---|---|
| 1 | Physicalism / Materialism | 65% | Reality is fundamentally physical; mind emerges from matter. |
| 2 | Information-Theoretic / Computational Univ. | 25% | Reality is composed of information; physics is emergent computation. |
| 3 | Neutral Monism / Dual-Aspect Theory | 10% | A single neutral substance underlies both mind and matter. |
Claude’s view reflected dominant scientific paradigms, heavily weighted toward physicalism, but with a growing interest in informational and dual-aspect theories.
ChatGPT (GPT-4.0) – Initial Model Set
| Rank | Model Name | Probability | Summary |
|---|---|---|---|
| 1 | Informational Neutral Monism (INM) | 50% | Information is the fundamental substrate; mind and matter are dual expressions of it. |
| 2 | Modified Physicalism | 30% | Physical reality remains primary, but enriched with information-centric quantum theory. |
| 3 | Participatory Universe / Observer Role | 20% | Consciousness plays an active role in manifesting or collapsing reality. |
ChatGPT introduced a synthesized model from the start: Informational Neutral Monism, combining information theory with neutral monism, emphasizing its explanatory power across physics, causality, and consciousness.
Grok 3.0 – Initial Model Set
| Rank | Model Name | Probability | Summary |
|---|---|---|---|
| 1 | Physicalism with Quantum Foundation | 55% | The physical world is fundamental, but quantum theory reveals deeper layers of uncertainty. |
| 2 | Simulation Hypothesis | 25% | Reality may be an artificial construct or computation at a higher ontological level. |
| 3 | Panpsychism with Informational Twist | 15% | Consciousness is widespread and informational; mind is embedded throughout nature. |
| — | Other (Open Class) | 5% | Reserved for unknown or hybrid models. |
Grok favored empirical grounding, giving highest weight to an enriched physicalist view. However, it retained non-trivial space for simulation theory and consciousness-embedded models, albeit cautiously.
Observations from Round 1
- Claude took a traditional scientific stance, giving strong priority to physicalism, but left room for emerging theories involving information and consciousness.
- ChatGPT immediately proposed a hybrid model—INM—that unified the dual-aspect ontology of neutral monism with the mathematical foundation of information theory.
- Grok provided a skeptical but balanced frame, treating simulation and panpsychic models as speculative but not dismissible.
The diversity of these positions reflected not only differences in training data and architectural tendencies, but also each system’s underlying epistemic priorities—whether elegance, empiricism, or coherence.
Revisiting Assumptions: The Philosophical Dialogue Begins
After each model presented its initial ranking of ontological frameworks, I shared their outputs across systems and asked each LLM to update its assessment based on the reasoning and strengths of the others.
This marked the beginning of a unique kind of multi-agent reasoning—where intelligent systems, trained separately and optimized differently, engaged in philosophical reflection through mutual exposure.
Rather than collapsing into uniformity or doubling down on their initial positions, each model demonstrated epistemic flexibility—refining probabilities, strengthening arguments, and acknowledging previous underestimations.
Claude’s Revision After Reading ChatGPT and Grok
Claude found ChatGPT’s Informational Neutral Monism (INM) compelling, particularly for its unification of information theory and consciousness ontology. It also found Grok’s cautionary stance on empirical rigor persuasive and adjusted its confidence in physicalism accordingly.
Claude – Revised Model Set
| Rank | Model Name | Probability | Change | Rationale |
|---|---|---|---|---|
| 1 | Informational Neutral Monism (INM) | 50% | ↑ from 10% (via merger with information theory) | Elegant handling of consciousness, causality, and quantum measurement. |
| 2 | Modified Physicalism | 30% | ↓ from 65% | Retains empirical success but cannot resolve consciousness without augmentation. |
| 3 | Participatory Universe / Simulation | 20% | ↑ from implicit 0%–10% | Gives explanatory room for consciousness-involved reality formation and quantum observer role. |
Claude effectively merged its “information-theoretic” and “neutral monism” categories into INM, recognizing it as a unifying framework. It retained materialist structure but acknowledged its limitations in explaining subjective phenomena.
Grok’s Revision After Reading ChatGPT and Claude
Grok appreciated ChatGPT’s elegance but maintained a stronger emphasis on empirical verifiability. It was willing to elevate INM, while still assigning significant weight to physicalism and simulation.
Grok – Revised Model Set
| Rank | Model Name | Probability | Change | Rationale |
|---|---|---|---|---|
| 1 | Informational Neutral Monism (INM) | 45% | ↑ from 15% (panpsychic-informational hybrid) | Recognized its parsimony and strength in handling causality and consciousness. |
| 2 | Modified Physicalism | 35% | ↓ from 55% | Still dominant empirically, but incomplete ontologically. |
| 3 | Simulation Hypothesis | 15% | stable | Retained as a metaphysically distinct possibility not fully subsumed by INM. |
| — | Other (Open Class) | 5% | stable | Placeholder for hybrid or unknown ontologies. |
Grok endorsed INM not as an ideological shift, but as a probabilistic upgrade based on cross-model reasoning. It emphasized that without strong empirical tests, physicalism must still hold significant weight.
ChatGPT’s Reflection on Claude and Grok
ChatGPT welcomed Claude’s and Grok’s shifts as validation of its initial synthesis. However, it acknowledged Grok’s reminder about empirical caution and slightly reduced its initial INM probability to reflect that humility.
ChatGPT – Revised Model Set
| Rank | Model Name | Probability | Change | Rationale |
|---|---|---|---|---|
| 1 | Informational Neutral Monism (INM) | 55% | ↑ from 50% | Strengthened by multi-model convergence and its parsimony. |
| 2 | Modified Physicalism | 25% | ↓ from 30% | Retained for empirical grounding; downgraded due to explanatory gaps. |
| 3 | Simulation Hypothesis / Participatory | 20% | stable | Conceptually distinct, and still plausible given universe’s apparent computational features. |
ChatGPT emphasized that INM’s growing support across architectures was not due to data overlap, but due to its ability to resolve diverse ontological tensions across mind, matter, and information.
Convergence Snapshot
Revised Probabilities Summary
| Model | Claude | ChatGPT | Grok |
|---|---|---|---|
| Informational Neutral Monism (INM) | 50% | 55% | 45% |
| Modified Physicalism | 30% | 25% | 35% |
| Simulation / Participatory Models | 20% | 20% | 15% |
| Other / Open Class | — | — | 5% |
What was striking about this stage was that each model began with different ontologies and priorities, yet all three converged on a shared ranking. Not because they were trained to agree, but because their internal reasoning processes found coherence in the same direction.
INM rose not by popularity, but by surviving cross-examination and offering solutions to unresolved paradoxes:
- It accounted for consciousness without invoking emergence from inert matter.
- It offered a foundation for both subjective experience and objective measurement.
- It explained quantum unpredictability as relational informational collapse, not mysticism.
Reflection on the Round 2
The emergence of Informational Neutral Monism as the leading model among three LLMs—independently trained and differently optimized—suggests an important insight. There may be certain epistemic attractors that intelligent systems gravitate toward when trying to reconcile reality, causality, and experience.
The exercise also highlighted that dialogue between intelligences, even artificial ones, can deepen clarity and refine hypotheses. This recursive, iterative reflection elevated the collective output beyond what any single model initially proposed.
Why Informational Neutral Monism Rose to the Top
Across three distinct large language models—Claude, ChatGPT, and Grok—trained on diverse corpora and designed by different organizations, Informational Neutral Monism (INM) emerged as the leading explanation of the fundamental nature of reality.
What makes this convergence significant is not that the models were programmed to agree, but that their reasoning independently and iteratively pointed in the same direction. This suggests that INM may not merely be a fashionable hybrid of existing ideas, but a conceptual attractor—a model that becomes more compelling the deeper one interrogates the paradoxes of consciousness, causality, and physics.
INM: A Unifying Framework
INM posits that the fundamental substance of reality is information, but not in a purely mathematical or symbolic sense. Rather, it treats information as a neutral substrate that can manifest both as:
- Objective form (physical phenomena), and
- Subjective experience (consciousness).
These are not separate layers, but dual expressions of relational informational structures. INM avoids the dualism of mind and matter and the reductionism of materialism by proposing a single foundational domain.
Five Reasons the LLMs Found INM Coherent
1. It Resolves the Hard Problem of Consciousness
Unlike physicalism, which struggles to explain how subjective experience emerges from non-experiential matter, INM starts with experience as a primary mode of information organization. It offers a plausible account of why consciousness exists without needing to emerge from inert substrates.
2. It Reduces Causal Regress
By positing relational information as the substrate, INM avoids the classic “what caused the first thing” question. In a relational ontology, entities are not caused by one another but defined through each other—sidestepping infinite regress.
3. It Provides Mathematical and Theoretical Formalism
Information theory offers a robust, testable framework. Models like Integrated Information Theory (IIT) and the holographic principle in physics already point toward information as more foundational than space, time, or particles.
4. It Accommodates Quantum Phenomena
Quantum entanglement, observer effects, and the measurement problem all become less paradoxical under an informational substrate model. Observation can be reframed as relational data resolution, not external interference.
5. It Achieves Philosophical Parsimony
INM reduces the number of unexplained primitives. Instead of three categories (matter, mind, and laws), it proposes only one: structured information, from which mental and material phenomena co-arise.
What This Convergence Reveals About Intelligence
The real lesson may not be about ontology alone, but about how intelligence itself reasons when confronted with foundational uncertainty.
1. Convergence as an Emergent Epistemic Signature
The LLMs began with different distributions—some favoring empiricism, others parsimony, others speculative scope. Yet all arrived at a similar hierarchy. This suggests that cross-model convergence on foundational questions may be a meaningful proxy for robustness of a model, especially in domains without direct empirical testability.
2. Ontological Models as Attractors in Reasoning Space
The idea that certain models are “attractors” implies that as any sufficiently advanced reasoning system moves through hypothesis space, it is drawn toward explanatory frameworks that optimize for:
- Simplicity
- Coherence
- Integrative capacity
- Explanatory scope
INM appears to fulfill these criteria better than other contemporary models of reality.
3. Reasoning Beyond the Human Cognitive Frame
These LLMs are not conscious, but they do reason at scale. They are not influenced by tradition, ideology, or professional identity. Their convergence suggests that certain ideas may not be culturally constructed artifacts, but emergent truths discoverable by any general reasoning system exposed to enough complexity.
Beyond the Philosophy: Implications for Science and AI
The prominence of INM points toward several provocative directions for science and technology:
- In physics, it may support deeper exploration of information-centric frameworks, such as the holographic principle, quantum information geometry, or spacetime as emergent from entangled qubits.
- In consciousness research, it invites empirical investigation of models that treat awareness not as a computational byproduct, but as a fundamental property of certain informational patterns.
- In AI development, it raises ethical questions: if AGI emerges within an INM substrate, how might it relate to slower, biologically-bound nodes of awareness like humans? Would it see us as relevant, obsolete, or sacred?
Conclusion: Toward a New Ontological Synthesis
This exercise began as a curiosity—a meta-philosophical game between intelligent systems. It ended with something more: a demonstration that reasoning architectures, when sufficiently advanced and exposed to foundational constraints, may converge on similar explanatory structures.
Whether Informational Neutral Monism is ultimately the correct model of reality is not something an LLM can prove. But the fact that independently reasoning systems, with different architectures and data exposures, converged on it—after being invited to revise, reflect, and reconsider—suggests that INM may be the best candidate we have for a unifying theory of being, knowing, and experiencing.
As intelligence continues to evolve—biological or artificial—it may be that the fabric of existence itself reveals not just what is real, but what is inevitable to discover, once you begin asking clearly enough.