Here’s a gift link. Jacques Ellul argued that you can’t separate the good aspects of technique from the bad. In that context, this paragraph stands out:

Optimists about generative AI have high hopes for its ability to produce future scientific breakthroughs — accelerating discovery, eliminating most types of cancer — but the technology is currently undermining one of the pillars of scientific research, inundating editors and reviewers with an endless stream of papers. Paradoxically, the better the technology gets at producing competent papers, the worse the crisis becomes.

Embarrassed to say that this gender dynamic of AI had never really occurred to me before. Interesting read.

Kentucky primaries for the 2026 elections take place a week from today, so it’s not surprising that we’ve been getting some political mail over the past couple of weeks. Today, though, something came in the mail that really took me aback. David Kloiber’s campaign sent us something that was clearly more than a regular mailer, since it came in a letter-style envelope and was addressed to both me and my spouse.

Shouldn’t be reading this before bed because it isn’t calming me down.

Long before I ever knew what generative AI was, I was grumpy about the idea of Grammarly because I was suspicious of deferring to a computer on what “good writing” looks like. I appreciate Koebler’s thoughts here for the way it shows how generative AI—including Grammarly now, apparently—is doing something similar on an even larger scale.

It’s digital labor all the way down. What a depressing read.

This is a good example of how thorny the AI problem is, and why I strongly prefer a digital labor critique to a copyright critique. Yes, I’m mad that Meta trained their models on my work, but I don’t think the answer is to strengthen Elsevier or Cengage’s copyright claims.

Well, this is certainly… something.

Eh, it felt like this was a continuation of some of my least favorite parts of Autonomous. I am also struggling to enjoy “robots’ rights” stories in our LLM era, which is dumb, but that’s how it is.

I’ve felt for a long time that “what if AI gets sentient and does irreparable harm” is 100% the wrong way of framing things, and Doctorow knocks that argument out of the park here.

This was an enlightening listen on my way into work this morning.

You don’t have to spend long on my blog to figure out that I default to being grumpy about generative AI, but if I’ve made one exception to that rule, it’s for Kagi Translate, which I’ve found to be a genuinely helpful machine translation tool—and to have some neat features that I haven’t found in its Google or DeepL equivalents.

It took me back a little bit tonight, then, when Kagi Translate straight up hallucinated something on me, in a way that I imagine wouldn’t be out of place for a more mainstream LLM (which I’ve never really used). Earlier today, while working on a paper for an upcoming conference, I was consulting a Jacques Ellul book I was about to cite, and I wanted to make sure that “genetic engineering” would be an accurate translation for his phrase « intervention génétique » (which could obviously also be rendered “genetic intervention,” but I’ve never heard that phrase in my life, so I’d prefer to go with a more well-known phrase if it’s accurate).

Audrey Watters once compellingly argued that metal detectors are edtech. I think we now have a responsibility to treat AI nudifier apps as edtech, too.

Love to get unsolicited AI-generated parenting advice from the communication platform kiddo’s school district has invested in.

Really appreciate this essay. It puts things nicely and has the kind of personal investment that makes it relatable.

So gross. I don’t think we can talk about generative AI without talking about this.

I wrote recently about how my concerns about (generative AI) are probably more about the broader Ellulian system of technique than the specifics of the technology. Here’s a passage from this article that makes a similar point better:

For some tasks, AI really is amazing; the tech behind things like machine-learning algorithms and large language models is ingenious, but the results always seem to be hawked the hardest by people and companies I don’t particularly like or trust. (Heck, Anthropic used one of my books to train its database, a sin for which it is now paying authors in court.) Give me the same sorts of tools but under my local control, governed by a Wikipedia-style nonprofit and trained on ethically sourced data, and I’d use them a lot more.

Masnick’s fierce critique is all the more notable for how public he is that AI is good for some things, pushing back against grumpier folks (e.g., me).

Check this paragraph out, though:

What we actually have here is a marketing operation that used AI to automate the production of deceptive advertising at a scale and speed that would have been harder to achieve otherwise. Snake oil salesmen have existed forever. What AI gave Matthew Gallagher (and, I guess, his affiliates) was the ability to crank out fake doctors, fabricated testimonials, and deepfaked before-and-after photos faster than any human team could — and to do it cheap enough that a guy with $20,000 and no morals could build it from his house. That’s the actual AI story the Times should have written.

So much about this that I don’t like. The article makes a good case that there may be good reasons to ease up on nuclear power regulations, but the language of AI and VCs suggests to me that those good reasons aren’t the top priority.

Two grumps (and, to be clear, I’m grumpy at my employer, not the student reporter):

1. the framing here is, as usual, “how to use” whether than “should we use”

2. “misinformation” is centered as the (implicitly sole) problem with generative AI, not digital labor or any of the deeper issues

Didn’t expect from the headline that this would turn into an essay on copyright, but I’m glad it did:

Expanding copyright will gain little for creative workers, except for a new reason to be angry about how our audiences experience our work. Expanding labor rights will gain much, for every worker, including our audiences. It’s an idea that our bosses – and AI hucksters – hate with every fiber of their beings.

The depressing thing is that this isn’t that far off from how OpenAI and Anthropic think.

Interesting article here. I don’t read WIRED (or The Verge, or…) for the product reviews, but it’s not hard to see how generative AI can create issues for them in that way.

I read a fair amount of comics in translation, and even when the translation is done with a skilled human, I can always tell that there’s something off about it. Not sure I trust an LLM to fix that problem.

Also, I wish that Webtoon weren’t platformizing webcomics and that we could go back to the models we had in the 2000s and 2010s.

Histoire fascinante—mais inquiétante.