CHEAP INTELLIGENCE IS HERE. BUILD THE HARNESS.
LLM compute is getting cheap, but the bottleneck is the harness that turns it into permissioned, auditable decisions. A founder cut model spend 97% by moving f...
LLM compute is getting cheap, but the bottleneck is the harness that turns it into permissioned, auditable decisions.
A founder cut model spend 97% by moving from a frontier model to an open‑weight model, even as OpenAI filed to go public; the real constraint is the “harness” around the model: context, permissions, budgets, review, and accountability OpenAI IPO: Own the Harness, Not the Model.
A practical blueprint shows how to chain discovery → synthesis → reasoning → decision (Perplexity, NotebookLM, Claude, Granola), with the edge coming from the handoffs, not any single tool The Research-to-Decision Engine.
Falling model costs shift advantage from buying the ‘best model’ to owning data context, permissions, and review loops.
Teams that turn cheap intelligence into audited, cost-capped decisions will outpace demo-driven pilots.
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terminal
A/B your highest-volume task on a strong open-weight vs a frontier model; track task success, latency, unit cost, and human-review load.
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terminal
Stand up permission-aware RAG + an eval harness; measure PII escapes, hallucination rate, and cost per accepted decision.
Legacy codebase integration strategies...
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Add identity-scoped retrieval, audit logging, and evaluator gating in front of existing LLM calls; route easy cases to cheaper models.
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Introduce per-request cost logging and budget caps in middleware; ship behind flags and compare ops metrics to your baseline.
Fresh architecture paradigms...
- 01.
Design an event-driven research-to-decision pipeline with typed inputs/outputs and explicit human-in-the-loop steps.
- 02.
Pick a model router and evaluation harness on day one; prefer open weights where quality meets the bar to control cost.
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