Adaptive Inference Routing Proposal for AI Query Efficiency

What This Is

A technical proposal submitted to Anthropic's Product & Engineering team in April 2026 for automatically routing AI queries to appropriate model tiers based on complexity assessment before expensive computation begins.

The Problem

Currently, every query sent to Claude — from simple questions like "how long do I boil an egg" to 2,000-word technical prompts — is routed to a full-capability model by default. The system doesn't assess complexity before committing compute resources, which is inefficient at scale. AI inference is the fastest-growing component of data center energy consumption, projected to reach 12% of US electricity by 2028.

The Proposed Solution: Five-Step Process

• Step 1 — Count: Measure query length in characters, sentence count, and presence of attachments or multi-part instructions
• Step 2 — Sort: Route to a model tier based on the complexity score. Single short sentences default to lightweight models; multi-paragraph prompts with context route to capable models
• Step 3 — Read: The assigned model processes the query normally
• Step 4 — Answer: Response is returned to the user
• Step 5 — Escalate: If the user signals dissatisfaction (pushes back, asks to go deeper, reframes), the system automatically tiers up to a more capable model for follow-up

How Complexity Scoring Works

The system uses a five-factor pre-routing score: character count, sentence count, attachment presence, question word density, and prior conversation depth. This would correctly sort a substantial percentage of queries without any model inference at all. Character length works as a first-order signal because most simple queries are short and most complex queries are long.

User Experience Design

Users should not see this system or be asked to choose a model. The interface remains identical, and routing is invisible. If an answer is insufficient, users ask for more and receive more. This removes the friction of asking non-technical users to select between model tiers like Haiku, Sonnet, and Opus.

Impact and Rationale

At Anthropic's scale, even a 20–30% reduction in average compute per query represents meaningful reduction in inference cost and energy load. The proposal positions Anthropic ahead of regulatory and PR challenges around data center energy consumption, which is becoming a legislative issue in multiple jurisdictions.