Current AI predicts tokens. It doesn't reason. Sintropa builds intelligence from physics — where alignment comes from physical law, not fine-tuning.
Intelligence is a physics problem, not a software problem. Token prediction scales compute, not understanding. Transformer architectures optimize statistical correlation — they cannot plan, reason causally, or maintain coherent world models.
We start from thermodynamics. A system that minimizes free energy under constraint doesn't hallucinate — it models reality because the physics demands it. Alignment isn't bolted on. It emerges from the substrate.
If this works, it changes everything. If it doesn't, we'll know in 12 months.
F = E - TS
Free energy minimization as computational primitive
Intelligence measured in usable energy, not parameters. Reasoning as entropy reduction over structured state spaces.
Physical law constrains the solution space. The system can't want what thermodynamics forbids. Safety from substrate, not reward hacking.
Biology runs intelligence on 20 watts. If the architecture is right, 2x efficiency over transformers on controlled benchmarks is the first milestone.
Variational inference architectures that minimize thermodynamic free energy as their core objective, replacing autoregressive token prediction.
Physics-grounded representations that maintain causal structure. Systems that understand "why", not just "what comes next".
Multi-step reasoning as entropy reduction. Plans that are physically constrained to be coherent, not statistically plausible.
Proving that thermodynamic objectives create naturally aligned systems — agents that can't be misaligned because the physics won't allow it.