Superintelligence, Not Larger Colliders
"Could AI develop a consistent theory of quantum gravity?"
The search for new physics through traditional means—larger colliders, more precise experiments—faces diminishing returns. The Planck scale remains experimentally inaccessible by roughly 16 orders of magnitude. Meanwhile, the mathematical complexity of quantum gravity theories has grown beyond what individual researchers can tractably explore.
We believe superintelligence—not larger colliders—may be the key to unlocking the mysteries of the universe.
This isn't about replacing theoretical physicists. It's about building intelligent systems that amplify human insight, explore vast solution spaces, and verify mathematical consistency at scales impossible for unaided cognition.
The Case for AI in Fundamental Physics
Quantum gravity research is uniquely suited for AI augmentation:
| Challenge | Traditional Approach | AI-Native Approach |
|---|---|---|
| Mathematical complexity | Manual derivation, limited exploration | LLMs for symbolic reasoning, automated theorem proving |
| Solution space exploration | Intuition-guided search | RL agents exploring parameter spaces systematically |
| Cross-paradigm synthesis | Conference discussions, literature review | Embedding models connecting disparate frameworks |
| Consistency verification | Peer review, manual checking | Neurosymbolic systems with formal verification |
| Computational intractability | Approximations, toy models | Learned surrogates, neural network accelerators |
Our Technical Approach
We're building AI-native infrastructure specifically designed for theoretical physics research.
Neurosymbolic Systems with Verification
Pure neural approaches lack the rigor physics demands. Pure symbolic systems lack the flexibility to explore novel structures. We build hybrid neurosymbolic architectures where:
- LLMs propose mathematical structures, conjectures, and proof strategies
- Symbolic engines verify correctness, check consistency, enforce physical constraints
- Models learn to self-verify as they reason, building verified chains of inference
This mirrors how physicists actually work: intuitive leaps followed by rigorous verification.
MCP Servers for Physics
The Model Context Protocol (MCP) enables AI models to interact with external tools through a standardized interface. We're developing MCP servers that give frontier models direct access to:
- Computer algebra systems — symbolic tensor calculus, differential geometry
- Numerical simulation frameworks — lattice QFT, numerical relativity
- Literature databases — arXiv, INSPIRE-HEP semantic search
- Formal proof assistants — Lean, Coq for mathematical verification
- Visualization tools — Penrose diagrams, spacetime embeddings
This creates AI agents that can reason about physics and compute—not just generate plausible-sounding text.
High-Performance Foundations
Intelligence without speed is impractical. Our tools are built in modern systems programming languages:
- Zig — zero-overhead abstractions, comptime metaprogramming
- Rust — memory safety without garbage collection
- Mojo — Python ergonomics with systems performance
- Odin — simplicity-focused systems programming
These languages let us build infrastructure that's simultaneously fast, safe, and expressive enough for complex physics abstractions.
Agent Frameworks for Research
We're developing specialized agent architectures:
- Exploration agents — RL-trained agents that explore mathematical structures
- Verification agents — continuous consistency checking against physical principles
- Synthesis agents — identifying connections between disparate approaches
- Literature agents — semantic search over physics literature with citation-aware reasoning
The Vision: Physics Copilots
Imagine a research environment where:
- A physicist describes a conjecture in natural language
- An agent formalizes it in rigorous mathematical notation
- Symbolic systems check consistency with known physics
- Exploration agents probe edge cases and counterexamples
- Numerical simulations validate predictions in tractable limits
- Literature agents surface relevant prior work
- The physicist focuses on insight while the system handles bookkeeping
This is what we're building. Not AGI solving physics autonomously, but intelligence infrastructure that makes physicists dramatically more effective.
Why Now?
The convergence of several factors makes this the right moment:
- Frontier models can now engage meaningfully with mathematical reasoning
- Tool use via function calling and MCP enables grounded computation
- Systems languages have matured for high-performance AI infrastructure
- Open models enable customization for specialized domains
- Compute costs continue falling, making large-scale exploration feasible
The gap between what AI can do and what physics needs is closing rapidly. We intend to build the bridge.
Inspired By
Our approach draws inspiration from researchers who recognized AI's potential for fundamental physics:
- Igor Babuschkin — xAI co-founder, CERN physicist, who asked whether superintelligence could develop a consistent theory of quantum gravity
- The AlphaFold team — demonstrating that AI can solve long-standing scientific problems
- Symbolic AI pioneers — who understood that formal reasoning requires structure, not just pattern matching
The singularity is near. We're building the tools to ensure it advances our understanding of the universe.