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AI Infrastructure · Cost per token replaces input metrics as the true ROI measure for AI factories
now playing · AI Infrastructure
AI Infrastructuretailwindscore 9/10shruti kulkarni
Cost per token replaces input metrics as the true ROI measure for AI factories
Evaluating AI infrastructure on input metrics like $/GPU-hour or FLOPS/$ is a fundamental mismatch because businesses run on token output; cost per token incorporates both input costs and d…
AI Agentstailwindscore 9/10shruti kulkarni
Agentic workloads trigger Jevons paradox: efficiency gains unlock exponentially more token demand
As token costs drop (reasoning models, mixture-of-experts), new agentic use cases emerge where AI takes turns with AI and tools, multiplying LLM calls and token demand far beyond conversati…
AI Hardware & Chip Architecturetailwindscore 8/10shruti kulkarni
Extreme co-design across compute, memory, networking and software creates compounding hardware advantage
Nvidia's Vera Rubin platform exemplifies extreme co-design — seven chips plus full software stack (CUDA kernels, runtimes, serving software, Dynamo disaggregated serving) co-optimized for l…
AI Infrastructuretailwindscore 8/10shruti kulkarni
Software optimizations deliver 8x inference performance gains in 6 months on fixed hardware
The Nvidia ecosystem (vLLM, SGLang, TensorRT, disaggregated serving, KV cache offloading, speculative decoding) stacks optimizations that compound — delivering 8x throughput improvement in…
AI Economics & Business Modelstailwindscore 7/10shruti kulkarni
Four monetization models for tokenomics: direct token sales, AI-native products, AI-enhanced products, internal productivity
Businesses monetize tokens through four primary models: selling tokens directly (Fireworks, Together AI), building AI-native products (Perplexity, Cursor), enhancing existing products with…