• Time-Series Language Models (TSLMs) bring foundation model capabilities to infrastructure metrics, offering zero-shot anomaly detection and natural language root cause analysis • Three major players emerged in 2024-2025: Stanford’s OpenTSLM (medical focus), Datadog’s Toto (2.36 trillion observability data points), and Nixtla’s TimeGPT (commercial forecasting API) • Despite impressive benchmarks, even vendors won’t deploy TSLMs to production yet due to accuracy gaps, massive resource requirements (40-110GB VRAM), and immature tooling ecosystems • The technology works but lacks battle-testing, characterized failure modes, and production integrations—expect vendor solutions in 2026-2027, mainstream adoption by 2027+ • Action plan: Prepare, don’t implement—build skills now (time-series fundamentals, LLM concepts, infrastructure expertise), experiment in non-critical environments, and position yourself to lead when production-ready solutions arrive