And my available knowledge of books that I wrote ten or twenty years ago is even blurrier. Now that I have so much of my writing and reading history stored in a single notebook—which I have come to call my “Everything” notebook—my first instinct whenever I stumble across a new idea or intriguing story is to go back to the Everything notebook and see if there are any fertile connections lurking in that archive.
That is, in fact, how I got to the story of Henry Molaison that I began with; I was mulling over the themes of short- and long-term memory in the context of AI, and asked the Everything notebook if it had anything to contribute, and the model reminded me of the tragic tale of patient H. M. that I had first read about in the 1990s. Who, exactly, made that connection? Was it me or the machine? I think the answer has to be that it was both of us, via some newly entangled form of human-machine collaboration that we are just beginning to understand.
But a world where you can use AI to draw upon the compiled wisdom of an expert that you trust—that is a world we are living in right now, thanks to the emergence of long context models. This should be good news, professionally speaking, for people who do indeed possess wisdom that other people consider valuable. Seeking advice from an AI grounded in the entire archive of an expert’s career could create an entirely new revenue stream for anybody who makes a living sharing their expertise through existing platforms like books or the lecture circuit. In other words, the AI is not a replacement for your hard-earned expertise; it’s a new distribution medium.
But a long context model allows you to take that global knowledge and apply it to the unique challenges and opportunities of your own organization. In a matter of years, I suspect it will seem bizarre to draft the specs for a new feature or a company initiative or a grant proposal without asking for feedback from a long-context model grounded in the organization’s history.
You might ask the model to identify patterns in a company’s archive to help simulate the way customers or clients would respond to a new product. Or you could draw on the long-context understanding of a city to conduct scenario planning exercises to simulate the downstream consequences of important decisions.
Perhaps we’ll discover that organizations perform better if they include more eclectic sources in their compiled knowledge bases, or if they employ professional archivists who annotate and selectively edit the company history to make it more intelligible to the model. No doubt there are thousands of curation strategies to discover, if that near future does indeed come to pass. And if it does, it will suggest one more point of continuity between the human mind and a long-context model. What matters most is what you put into it.
