LLMs

I’ve been workshopping spoken word pieces, almost like poetry, to weave into my music. Monologues, textures, things I want to find my own voice for. So I asked ChatGPT for some feedback on my early ideas.

Something shifted for me recently. This week, for the first time, I could actually see myself walking away from the flagship corporate AI products. Not from AI itself — from the companies.

This week I did a talk and published a companion website for nonprofit fundraisers on how to get started with AI, including tips, pitfalls, and some of the tools I recommend. It went well, and sitting with all of it afterward, I realized I’d never written a general version of that advice for this site. Most of what I write here assumes you’re already using these tools and looking to go deeper. This post is for those who might be a bit earlier in the journey.

A few weeks ago, Robin Sloan published a newsletter asking a fascinating question: what is it like to be a language model? His answer — that “the model” is the forward pass, a flash of computation lasting milliseconds before dissolving into nothing — sent me sideways into a different question entirely.

I didn’t decide to take a break from AI. It just kind of happened.

I recently heard Cory Doctorow speak about enshittification, his framework for understanding how digital platforms decay over time. The talk was wonderful and thought-provoking in the way good author talks should be. Cory challenges my thinking regularly, particularly when we don’t see eye to eye. I actually agree with most of what he says about AI, though my personal experience leads me to draw different conclusions from time to time.

Over the past several months my relationship with AI tools has shifted, and I think it’s a good time for an update.

I found myself facing a familiar challenge recently: I need to be more thoughtful about my sugar consumption. Like many people trying to maintain good health, I’m always looking for sustainable ways to make better choices without turning my life upside down. When some recent testing suggested my blood sugar levels were creeping higher than ideal, I knew I needed to make some changes.

I’ve been thinking about how I approach video games, and it’s led me to realize something important about technology in general. Let me start with the games, because that’s where this insight really crystallized for me.

When a colleague teaching an intro to technology course approached me about her students’ interest in learning about AI and asked if I could teach it, I was excited. I developed a five-session unit centered around business development with an AI co-founder. What I discovered surprised me, and has implications for how we might think about preparing students for an AI-integrated future.

Every time I show other teachers how I’m using AI in my classroom, I can see anxiety flash across some faces. It usually happens right after they witness an AI-facilitated student conversation that shows surprising depth, personalization, and responsiveness. Sometimes they come right out and say it: “If AI can do this, what’s left for me?”

I spend a lot of time thinking about what works in my classroom and what doesn’t. Sometimes it’s obvious - like when a project catches fire and students can’t wait to dive in. Other times, it’s harder to pin down why something that looked great on paper just didn’t land. I need help to see things clearly. That’s why I’ve always valued getting feedback on my work.