The Cost of Intelligence: Inference Economics Nobody Wants to Discuss
LLM inference costs dropped 600x since 2023. So why are AI companies losing money faster than ever? The economics nobody wants to talk about.
24 Syys 20min
Distillation: How Small Models Eat Big Models for Lunch
Training a frontier model costs millions. Distilling a capable student from it costs thousands. Knowledge distillation is quietly reorganizing the economics of the entire AI industry.
27 Elo 20min
World Models: When AI Learns Physics Instead of Memorizing Data
A language model can describe a falling glass. It cannot predict where the water goes. World models close the gap between AI that talks about reality and AI that can act in it.
13 Elo 19min
The Evaluation Crisis: We Do Not Know How Good Our Models Actually Are
MMLU is saturated. Chatbot Arena is gameable. Public benchmarks leak into training data. The only eval that matters is the one you build yourself, on your data, for your task.
30 Heinä 20min
Mixture of Experts at the Edge: Running 30B Parameter Models on Your Laptop
A 30B parameter model runs on a MacBook because only 3B parameters fire per token. Mixture of Experts splits memory cost from compute cost, and that changes everything about where AI can run.
16 Heinä 20min
The Agent Interoperability Problem: Why Your AI Agents Can Not Talk to Each Other
90% of enterprises deploy AI agents. Only 23% scale them. The gap is interoperability. Three protocols, MCP, A2A, and ACP, are racing to build the connective tissue before the ecosystem fragments.
2 Heinä 21min
KV Cache Compression: The Memory Wall Nobody Talks About
Your GPU is not compute-bound. It is memory-bound. The KV cache is eating half your inference budget, and two ICLR 2026 breakthroughs KVTC and TurboQuant are about to change the math entirely.
18 Kesä 21min
