AI Editorial

The rest of the library explains, and explanation has no opinion. This is where the opinion goes. Each piece starts from something that actually happened in AI — a model release, a paper, a benchmark result, a failure, a policy — explains the mechanism underneath it, and argues its way to a position: what genuinely changed, what is only being marketed as change, and what someone learning the field should take from it. Every piece links what it argues from and the concepts you need to follow it.

4 editorials 4 strands 31 cited sources 6,471 words

All Research & Technique1 Agents & Autonomy1 Inference & Economics1 Evaluation & Evidence1

Serving, latency, hardware and cost per token, and where the money in AI actually goes.

Inference & Economics 20 September 2026 8 min read New

Speed goes to whoever is easiest to predict

vLLM's September release lets a client ask, per request, what fraction of its tokens a small draft model guessed right. The same stack cuts the draft length to zero above 128 concurrent requests. Both are the same admission.

Speculative decoding never made a model faster; it converts a server's idle arithmetic into lower latency, and because batching spends that same arithmetic, the stack now rations the speedup to the most predictable requests first and to nobody once the machine is full.

Read it → 8 sources speculative decodinginference servingbatchinglatency