Last Thursday, I listened to a recent episode of The Vergecast during my morning bike commute. The episode featured Paul Ford talking about his recent experience with Claude Code, and I was genuinely surprised to find some of his comments resonating with me. It helped that Ford wasn’t uncritical about AI (though certainly not as critical as I would have been), but some of it was just that I recognized some of the thrill that he was describing of using tools and resources to learn how to solve a problem. In fact, I found that thrill so contagious that a passing comment he made got me to spend some time once I got to the office converting my Twitter archive into a CSV that I could finally import it into the Day One journaling app that I use.

I began my sudden but immediately sustained interest in Jacques Ellul about a year ago now, and I’ve found his work to be terribly influential on my personal thinking and my professional work. I’m currently working on a manuscript that makes the argument that Ellulian thought is useful for drawing our attention in certain ways when considering artificial intelligence in education. I see theory as serving an analytical and rhetorical purpose for the way that it makes suggestions that a certain phenomenon works in certain ways and invites us to consider whether or how that is true.

I subscribe to the “Religion Watch” newsletter out of Baylor University but usually don’t do much more than skim it. The first entry in the June edition, though, immediately stood out to me for this excerpt:

Paul Seabright’s recent book, The Divine Economy: How Religions Compete for Wealth, Power, and People (Princeton University Press, $35), is unique for its comprehensive treatment of the religious past and present as well as its novel use of the concept of “platforms” in explaining the economy of religion.

Throughout my career, I’ve been a data-first researcher, and theory has always been one of my weak areas. This is not to say that I dismiss the importance of theory: I appreciate danah boyd and Kate Crawford’s critique of Chris Anderson’s “the numbers speak for themselves” in their 2012 paper Critical Questions for Big Data as much as I appreciate Catherine D’Ignazio and Lauren Klein’s similar critique in their book Data Feminism. It’s just that while I agree that theory is important, I’ve never been well-versed in it—except for the loose theoretical framework of sociocultural learning, multiple literacies, and social communities and spaces that I bring to much of my work (even that work that has gone beyond educational technology research.