Writing recently in The New Yorker, the historian of science D. Graham Burnett described how he has been thinking about AI:

In one department on campus, a recently drafted anti-A.I. policy, read literally, would actually have barred faculty from giving assignments to students that centered on A.I. (It was ultimately revised.) Last year, when some distinguished alums and other worthies conducted an external review of the history department, a top recommendation was that we urgently address the looming A.I. disruptions to our teaching and research. This suggestion got a notably cool reception. But the idea that we can just keep going about our business won’t do, either.

On the contrary, staggering transformations are in full swing. And yet, on campus, we’re in a bizarre interlude: everyone seems intent on pretending that the most significant revolution in the world of thought in the past century isn’t happening. The approach appears to be: “We’ll just tell the kids they can’t use these tools and carry on as before.” This is, simply, madness. And it won’t hold for long. It’s time to talk about what all this means for university life, and for the humanities in particular.

I suspect that a significant chunk of my historian colleagues had a negative reaction to this article. But I wholeheartedly agree with the central point Burnett makes within it — not that generative AI is inherently good, but simply that it is already transformative for the humanities, and that this fact cannot be ignored or dismissed as hype.

Here’s how I’m currently thinking about that transformation.

Ignoring the impact of AI on humanistic work is not just increasingly untenable. It is also foolish, because humanistic knowledge and skills are central to what it is that AI language models actually do.

The language translation, sorting, and classification abilities of AI language models — the LLM as a “calculator for words” — are among the most compelling uses for the current frontier models. We’re only beginning to see these impacts in domains like paleography, data mining, and translation of archaic languages. I discussed some examples here:

… and the state of the art has progressed quite a bit since then. But since this is one aspect of AI and humanities I’ve written about at length, I’ll leave it to the side for now.

Another underrated change of the past few years is that humanistic skills have become surprisingly important to AI research itself.

One recent example: OpenAI’s initial fix for GPT-4o’s bizarre recent turn toward sycophancy was not a new line of code. It was a new piece of English prose. Here’s Simon Willison on the change to the system prompt that OpenAI implemented:

This was not the only issue that caused the problem. But the other factors in play (such as prioritizing user feedback via a “thumbs up” button) were similarly rooted in big-picture humanistic concerns like the impact of language on behavior, cross-cultural differences, and questions of rhetoric, genre, and tone.

This is fascinating to me. When an IBM mainframe system broke down in the 1950s (or a steam engine exploded in the 1850s), the people who had to fix it likely did not spare a moment’s thought to consider any of these topics.

Today, engineers working on AI systems also need to think deeply and critically about the relationship between language and culture and the history and philosophy of technology. When they fail to do so, their systems literally start to break down.

Then there’s the newfound ability of non-technical people in the humanities to write their own code. This is a bigger deal than many in my field seem to recognize. I suspect this will change soon. The emerging generation of historians will simply take it for granted that they can create their own custom research and teaching tools and deploy them at will, more or less for free.

My own efforts so far have mostly been focused on two niche educational games modeled on old school text-based adventures — not exactly something with a huge potential audience. But that’s exactly why I choose to do it. The stakes were low; the interest level for me personally was high; and I had significant expertise in the actual material and format, if not the code.

The progression from my first attempt (last fall) to my second (earlier this spring) has been an amazing learning experience.

Here’s the first game (you can find a free playable version here). It’s a 17th century apothecary simulator that requires students to read and utilize actual early modern medical recipes to heal patients based on real historical figures. You play as Maria de Lima, a semi-fictional female apothecary in 1680s Mexico City with a hidden past:

It was fascinating to make, but it also has significant bugs and usability issues, and it fairly quickly spools out into LLM-generated hallucinations that are unmoored by historical realities. (For instance, in one play-through, I, as Maria, was able to become a ship’s surgeon on a merchant vessel sailing to England, then meet with Isaac Newton in London. The famously quarrelsome and reclusive Newton was, for some reason, delighted to welcome me into his home for tea.)

My second attempt, a game where you play as a young Darwin collecting finches and other specimen on one of the Galápagos Islands in 1835, is more sophisticated and more stable.

The terrain-based movement system, with specific locations based directly on actual landscapes Darwin wrote about in his Voyage of the Beagle, forces the AI to main