We live in a rapidly changing knowledge ecosystem, wherein everyone, increasingly, relies on search engines, whether for mapping where they are to go in a strange place, or seeking the context that helps us understand why things happen. Large language models offer powerful solutions to what had been complex problems of research and writing. Handwritten text recognition programs (as I will discuss) offer greater access than ever to the secrets of early modern manuscripts. Students rarely consult printed books and articles in the way they did decades ago. They instead confront information that is delivered by computer-generated algorithms, and large language models. Many of us—even historians—have gotten used to the ease of finding our sources, both primary and secondary, via web search engines. AI has created powerful access and mechanisms and tools, and yet it makes mistakes, of all kinds, and relies, always, on humans – for design, shaping, input, algorithms, corrections, suggestions, context. It is what we make of it. In that sense it can be incredibly powerful, and increasingly so. But it will always contain flaws, always need guidance and correction. It will always require the wisdom provided by context, by humans who have read deeply and widely, who have read deeply in the handwriting of those who lived in the distant past. It needs the wisdom of those who know how to determine that something did not happen (that the large language model got it wrong), by those who have access to privileged sources and studies and data, or those who just know the local roads.  Of even more concern is the ease with which all AI can be shaped, and reshaped, sometimes with malign intent. So while AI often helps us find information, it also provides misinformation, and even deliberate disinformation. How do we craft models that make allowance for the humans, and humanists, that will always need to be in the loop, if we really want to understand the world better? Of course humans can also make mistakes, and mislead. I guess the larger question is how can we consciously create models that acknowledge this reliance. How can we create models that acknowledge and respect he importance of scholars across the university – to creating knowledge. How can we deliberately conceptualize and frame these search engines, LLM’s, HTR tools, etc., to work together with humans, and with humanists? We need to translate the disciplinary training of generations of humanists into this process, and keep them interactively, as scholars. Otherwise we run the risk of creating increasingly useless “knowledge” that we cannot verify, that leads us astray, not only on roads, but in terms of medical, political, and social choices.