IPI Letters

Beyond Perception: Proposing Our Reality as an ASI Alignment Simulation

Ali Eslami — Wed, 08 Apr 2026 00:00:00 +0300

This paper presents support for the simulation hypothesis and proposes a speculative purpose for that simulation: that our reality may be an artificial superintelligence (ASI) alignment sandbox. Building upon Bostrom’s original argument, I address key counter-arguments, particularly regarding computational feasibility. To resolve these challenges, I introduce the Efficient Simulation Theory and a corresponding architecture, Quantum Diffusion. This framework establishes a Middle-out hierarchical rendering system that maintains unobserved regions in latent indeterminacy, resolving them into definite states on demand. Alongside the design pressure to avoid unbounded nesting of full-fidelity simulations (a Recursion Hard-cap), this architecture argues for a plausible, resource-efficient universe-scale simulation. Without altering the established mathematical
predictions of standard physics, the framework offers a computational meta-interpretation of otherwise enigmatic features, drawing simulation-supporting parallels from the probabilistic nature of quantum mechanics, the holographic principle, and mathematical structures resembling error-correcting codes posited in theoretical physics. The proposed architecture hypothesizes that religious and spiritual systems could serve as initial conditions to guide the simulation toward alignment goals. I further show how current technological trajectories in AI, quantum computing, video rendering, and neural interfaces plausibly converge on the capability to create such simulations and explore how this hypothesis offers elegant explanations for scientific puzzles such as the Fermi paradox and the “unreasonable effectiveness of mathematics.” This framework provides a new perspective on reality and suggests an approach to ASI superalignment, in which we ourselves may be the ASIs undergoing training and evaluation. These two pillars stand independently. Even if one rejects the simulation hypothesis
entirely, the Efficient Simulation Theory and Quantum Diffusion architecture offer a practical, resource-rational blueprint for the hyper-realistic alignment sandboxes we will soon need to build for our own ASIs.

Thermodynamic Stability and Phase Transitions in the Nakamoto Consensus

Pascal Ranaora — Wed, 04 Mar 2026 00:00:00 +0300

We propose a minimal physical model for the Nakamoto distributed consensus protocol based on non-equilibrium statistical mechanics. We treat the ledger as a one-dimensional lattice system where the consensus state is determined by the minimization of a thermodynamic cost function, analogous to the free energy in spin systems. In this framework, the ”Double Spend” problem is identified as a local symmetry breaking of the time-ordering parameter. We demonstrate that Proof-of-Work (PoW) acts as a dissipative external field that drives the system from a disordered ”liquid” phase (unconfirmed transactions) to an ordered ”crystalline” phase (immutable history). By defining an effective temperature derived from network latency and hashrate, we analyze the probabilistic finality of the ledger not as an event horizon, but as a correlation
length decay characteristic of massive field theories. Finally, we interpret chain forks as topological defects (domain walls) and show that the ”Halving” event acts as a sudden quench, subjecting the network to critical slowing down consistent with the Kibble-Zurek mechanism.

Local Entropy Inversion in Large-Scale AI Systems: Landauer Bounds on Algorithmic Compression

Boris Kriger — Fri, 20 Mar 2026 00:00:00 +0300

We apply Landauer's principle to the training of large language models (LLMs), framing the process as a physically irreversible compression of high-entropy data distributions into low-entropy structured representations stored in model weights. This yields a lower bound on the minimum energy required for AI training, expressed in terms of the information-theoretic compression achieved. Empirical analysis of contemporary AI systems—GPT-3, PaLM, and LLaMA-2—reveals that current implementations operate approximately 10²¹ times above this Landauer limit. We introduce a demon efficiency metric to quantify this gap and examine how it varies across systems and baseline assumptions. We discuss an instructive analogy between LLM training and Maxwell's demon that provides physical intuition for the entropy-reducing character of the training process. We present a sensitivity analysis showing that while the absolute value of the efficiency metric depends on the choice of entropy baseline, the order-of-magnitude gap to the Landauer limit is robust across reasonable choices. These results provide a physical perspective on the energy requirements of artificial intelligence, though we emphasise that the Landauer bound is a direct consequence of well-established thermodynamic principles rather than a new theoretical result.