
The Model-Selection Energy Math Nobody Is Doing
Worked AI energy math showing how right-sized model routing cuts power, token cost, and infrastructure demand without sacrificing useful quality.

Expert profile
Founder & AI Product Leader
Yash Thakker is a Generative AI expert with over 12 years of experience in product leadership and technical strategy. As the founder of explainx.ai, he has taught over 300,000 learners and built AI platforms serving millions of users globally. He specializes in Agentic AI, Multimodal RAG, and the intersection of LLMs with consumer hardware.

Worked AI energy math showing how right-sized model routing cuts power, token cost, and infrastructure demand without sacrificing useful quality.

Nvidia is reportedly in talks to guarantee ~$250B of financing for OpenAI’s southern Ohio ~10GW data-center campus. What that means for power, bankability, and the local consent test.

Mesosphere co-founder Tobi Knaup argues open weights are becoming AI’s Kubernetes substrate. Why banning Chinese models would be an own goal — and how the US should compete.

A practical decision tree for choosing prompt engineering, retrieval-augmented generation, fine-tuning, or a combination for an AI project.

Ten open-weight AI models mapped to 8GB, 16GB, 24GB, and 32GB laptops, with quantization size, context reality, speed trade-offs, and use cases.

University of Chicago Law’s AI strategy: device-free 1L cores, write-without then write-with LRW, oral SRP defenses, and clinics that use AI for real clients.

Virginia now taxes data center electricity at $0.011 per kWh. Here is who pays, how contracts pass it through, and whether API prices will rise.

explainx.ai's stance on the open-weight AI ban debate — real data on downloads, pricing, and adoption, and why open models matter for education.

How explainx.ai maps closed frontier tools and open-weight models to curriculum outcomes—and why model portability matters more than vendor loyalty.

Claude Opus 5 scores 30.2% on ARC-AGI-3 — ~4× GPT-5.6 Sol Max and ~20× Opus 4.8. ARC Prize verified results, cost/task, Fable’s absence, and the HN contamination debate.

Thariq on Claude 5 context engineering: Anthropic cut 80%+ of Claude Code’s system prompt for Opus 5 and Fable 5 with no eval loss. Judgement over rules, progressive disclosure, /doctor.

ClaudeDevs Opus 5 developer guide: Frontier-Bench 43.3%, /claude-api migrate, mid-conversation tool changes, classifier fallbacks, Fast mode ~2.5×, effort defaults in Claude Code.