The arms-race for AI for the average person is pure fallacy. It's the cognitive load-creating friction in attempting to understand an extremely difficult subject completely deflating the subjcect-matter, despite never needing to understand its deepest tranches in the first place. Example: you should always extract deterministic logic from a non-deterministic subject. I.e. use code (libraries and standard functions) for calculations, but use LLMs and their counterparts (HRMs, SLM, VLLMS, etc.) for their NLU abilities. Small models, when orchestrated properly , can often outperform in terms of efficiency against architects who brute-force frontier-level intelligence. Take our calculations, ephemeral subjects, and hard-maths from the model to be calculated by stable code written decades ago. Let the model itself determine what code to actually invoke. Salesforce uses symbols like "->" for deterministic logic while using pipes like "|" for passing prompts to the LLM.…