The complexity of investing in financial markets creates demand to simplify it; but competition to meet this demand creates a new layer of complexity, restarting the cycle. Robo-advisors are but one link in a long chain of financial technology seeking to scale and simplify access to financial markets, and there are now too many to know them all. The next link in the chain of creative destruction is the robo-“robo-advisor”-advisor: A technology which helps retail investor chooses the right robo-advisor for them. One non-AI advisor and five AI large language models (LLMs) are tested to see whether at least one can provide complete advice that can navigate complicated jurisdictional problems and robo-advisor service heterogeneity without producing hallucinations. Since there are no switching costs, only one is necessary. Four out of five LLMs outperform the human team, and with a sophisticated prompt, Anthropic’s Claude can solve this problem without error. The prompt is provided such that readers can benefit directly from this research.