Pre-Inference and Inference Recycling transform AI compute from disposable to reusable by converting inference outputs into governed reasoning artifacts.

The Problem Everyone Sees

Traditional inference strategies still require a new semantic reasoning pass, even when the same reasoning has already been performed. Retrieval systems improve access to source material, but existing approaches cache inputs or outputs — not reasoning itself.

What Others Build (and the Limitation)

• RAG: retrieves document fragments to condition a model; full inference remains required at query time.
• Context caching: reuses processed context during a session; benefits end when the session ends.
• Pre-compute: persists summaries or condensed context; model reasoning still executes at query time.

In other words, existing approaches cache inputs or outputs, but do not reuse reasoning itself — forcing inference to be recomputed each time.

Mythos stores machine-readable reasoning graphs (premises → inferential links → conclusions) and routes queries based on novelty scoring prior to model invocation.

The Four-Way Gate

• KNOWN: retrieve a stored reasoning artifact; no full inference.
• PARTIAL: retrieve multiple artifacts and apply constrained light re-inference to adapt/compose.
• NOVEL: perform full inference; evaluate output for reuse and store when eligible.
• EDGE: perform local/edge inference when latency/connectivity constraints require.

This architecture reduces model invocation frequency, improves consistency for covered queries, and supports governance via provenance and policy constraints attached at creation time.

The Crisis

Artificial intelligence is the fastest-growing source of electricity demand in modern history.

In 2025, AI inference consumes 450 terawatt hours per year globally. Seventy-five percent of those queries are duplicates — same questions, same reasoning, recomputed from scratch. That's 337 terawatt hours wasted.

By 2035, AI will consume over 1,200 terawatt hours per year. If nothing changes, 75% of that remains redundant: 900 terawatt hours wasted.

At twenty cents per kilowatt hour, that's $180 billion dollars wasted every year on questions AI already answered.

Two paths diverge. One continues optimizing inputs. The other captures outputs.

• 2027 — Divergence: Full inference drops to 15%. Energy per query falls 80%.
• 2030 — The New Normal: Traditional AI: 800 TWh/year. Mythos architecture: ~250 TWh/year.
• 2035 — The World That Remembers: Actual AI consumption stabilizes near 300 TWh/year.

The Conclusion

One path treats inference as disposable. The other treats reasoning as an asset.

Compute once. Retrieve forever.

Phase 1: Foundation (Days 1-30)

15-30% inference reuse achieved. Initial deployment focuses on high-frequency query patterns. Knowledge graph seeding begins with existing documentation and common reasoning paths.

Phase 2: Acceleration (Days 31-90)

50% inference reuse achieved. Pattern recognition expands. The four-way gate (KNOWN, PARTIAL, NOVEL, EDGE) optimizes routing. Governance policies attached to reasoning artifacts.

Phase 3: Maturity (6 months)

85%+ inference reuse achieved. Compositional assembly enables complex query resolution from stored reasoning units. New high-value composites continuously expand coverage.

The outcome: 75% cost reduction. Not through optimization tricks, but through architectural transformation.

What Changes

• Compute costs: Drop 75% as inference recycling scales
• Response consistency: Governed artifacts ensure predictable outputs
• Latency: Retrieval beats generation for covered queries
• Sustainability: Energy per query falls 80%

The Infrastructure Shift

This isn't optimization. It's a new foundation. The same reasoning that took 100 inference cycles now takes 15. The same energy budget serves 6x more queries.

Day 180: The world that remembers.