# Serving Software Decides Accelerator Economics

The cost gap between AI accelerators is increasingly set by scheduling, caching, and runtime quality rather than by silicon specifications, so software maturity becomes the durable moat and challengers keep losing on total cost even when their hardware specs are competitive.

- Conviction: 36 / 100 (weakening)
- Horizon: Emerging (watchlist)
- Tracking since: 2026-08-25T00:00:00.000Z
- Last updated: 2026-08-28T06:28:38.436Z
- Canonical: https://polylog.news/ai/trends/accelerator-software-stack-lock-in
- Publisher: Polylog
- Affected regions: Global

## Recent score history

- 2026-08-27: 38
- 2026-08-28: 36

## Recent evidence

- [neutral] OpenAI Publishes First Jalapeño Benchmarks, Claiming Up to 1.9 Times Nvidia's Throughput Per Kilowatt (2026-08-26): OpenAI's Jalapeño claims are silicon-level efficiency ratios measured on three open models, with no disclosure of scheduling, batching or KV-cache handling in the serving stack used for either side. The comparison leaves the thesis untested: per-watt specs are exactly the axis on which challengers have historically led while still losing on total cost of serving.
- [confirms] SemiAnalysis Benchmark of Real Coding-Agent Traffic Puts Nvidia About Five Times Ahead of AMD on Cost (2026-08-25): SemiAnalysis benchmarked real coding-agent traffic and found Nvidia roughly five times cheaper per unit of work than AMD, attributing most of the gap to serving software and concluding Nvidia would remain cheaper per token even if the competing hardware were free. That counterfactual is the thesis stated almost literally: silicon price cannot close a runtime-and-scheduling gap.