Hard Questions

The questions you should ask

We built this page because we respect serious inquiry. If you're evaluating Jachin — as an investor, partner, or skeptic — these are the hardest questions, answered honestly.

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Transparency

Honest answers to hard questions

Why not just fine-tune GPT?

Fine-tuning changes weights, not architecture. You can't add reasoning to a system designed for prediction. Jachin's symbolic layer is architecturally distinct — it reasons, not retrieves.

Can this scale?

Discrete symbolic computation scales differently than matrix multiplication. Our bottleneck is ontology construction, not compute. More domains = more ontologies, but the engine stays the same.

What's the moat?

Proprietary ontology library, functor mapping architecture, cognitive modeling methodology, and formal verification engine. Each alone is defensible. Together, they're a compound advantage.

Next Step

See the investment case