Trevor Longino

Makegreen.ai is a free, open-source framework and engineering methodology developed by Green AI Studio to help companies drastically reduce their AI inference costs and energy consumption. Presented by founder Trevor Longino, the project addresses the "McLaren Problem"—the common, wasteful practice of running repeatable, predictable tasks on expensive, high-power frontier AI models when simpler, more efficient alternatives are available. The methodology is built around six core principles: defaulting to determinism (converting tasks to code instead of model calls), using the minimum sufficient model, auditing workflows before automating, measuring inference cost as a first-class metric, prioritizing local-first hardware where possible, and designing for auditability. To implement this, Makegreen.ai suggests installing a router that evaluates query complexity, routes tasks to simpler models (such as Qwen or DeepSeek), and only "fails upwards" to a frontier model if the simpler model cannot solve the problem. Longino shared real-world case studies demonstrating the framework's impact. For instance, greenchemistry.ai reduced its cost per process analysis from $5.00 to under $0.005 per run by converting 90% of its workflow to deterministic Python. Similarly, an AI copywriting detection platform cut its costs from $0.50 per million words to less than $0.003 by replacing stochastic LLM calls with a deterministic lookup table. To get started, users can visit makegreen.ai to access the open-source principles, playbooks, and fork