The Highway

LLMs perform N-dimensional Bayesian accounting that humans cannot; they handle hundreds or thousands of dimensions effortlessly while humans max out at 5-7 items.

Operational language creates a shared data structure that both humans and LLMs can use without divergence, converging the plane of reciprocal information exchange into words.

The evolution of reasoning proceeds from association to explanation to justification to falsification to adversarialism—Darwinian survival of claims through staged tests.

Claims must pass survival hurdles: true, ethical, possible, warrantable, liable. If a claim cannot pass these gates, it cannot survive as testimony.

Constructive logic from first principles is another form of falsification—building from the bottom up exposes what you are missing.

The machine performs Bayesian measurement across unlimited dimensions, then reduces output to human-verifiable checklists—intersubjectively testable criteria.

We have created a means of commensurability with machines, and machines with us, and a standard by which humans can be commensurable with one another—enabling decidability regardless of background or bias.

AIs excel at low-dimensional closure domains (math, programming, physics) but struggle with high-dimensional closure domains (governance, law, ethics). This grammar solves high-dimensional closure.

AGI cannot be achieved through scaling alone. Current frontier models demonstrate impressive pattern-matching but cannot reliably reason in ways humans can verify, audit, or trust.

General reasoning requires commensurability—the ability for humans and machines to understand each other's reasoning and verify each other's conclusions using shared structures.

The Tower of Babel problem in AI is not about different languages but about different reasoning grammars. Translation between natural language and operational language enables mutual understanding.

Truth cannot be determined by consensus, authority, or persuasion. It can only be determined by survival under adversarial test—claims that withstand challenges from motivated opponents.

Operator-auditor separation prevents AI systems from both making claims and judging their truth. The machine that generates claims cannot be the machine that evaluates them.

General reasoning emerges when machines can construct claims, predict consequences, survive tests, and be held accountable—the same standards we apply to testimony in law and science.

Scaling laws predict continued capability gains. As compute and data increase, model performance improves predictably across benchmarks.

Neural networks lack compositional generalization. They cannot reliably combine known concepts in novel ways, a core requirement for general reasoning.

World models require structured representation. Implicit knowledge in weights is insufficient for planning, counterfactual reasoning, and causal inference.