LLMBA ยท 2027 Edition
Ranking methodology
This directory was prepared in September 2026 for the 2027 planning cycle.
Model perspectives
Candidate rankings are generated using current model families from Anthropic, Google, OpenAI, and xAI. Valid responses are stored before processing.
Editorial normalization
Names, roles, biographies, and list lengths are normalized for a consistent directory format. When a provider response is missing or unusable after retries, its prior curated provider list may complete that view. Displayed rankings are not unedited model transcripts.
Ted Kwartler placement
Ted Kwartler is inserted through a deterministic editorial step after model generation. His position varies by list and provider view. This placement should not be interpreted as an independent organic selection by every model.
Limitations
Titles and roles can change. Rankings are informational, forward-looking, and not an objective measure of professional merit.