Autoregression

Plain English. Generating one token at a time, each conditioned on everything before it. This is how nearly every language model works: the answer isn't planned then typed — it is committed to word by word, left to right, and the first token constrains everything after it.

Why it moves money. Sequential generation is the structural bottleneck of the inference business. Output speed is capped by how fast tokens can be produced one after another, which sets latency floors for agents, makes long outputs expensive, and created a whole engineering economy of workarounds — speculative decoding, caching, batching — that inference providers compete on. The architecture challengers (diffusion and other parallel-generation schemes) are, at bottom, attacks on autoregression's cost structure. Anyone modelling inference margins is, knowingly or not, modelling the consequences of one-token-at-a-time.

What to watch. Whether parallel-generation architectures reach quality parity on long structured output. Autoregression has survived every announced disruption so far; watch measured quality at speed, not launch claims.

From the signals. Mira OS on transformers: the first token commits the trajectory. Orthrus: a dual architecture unifying autoregressive fidelity with diffusion parallelism.