While I was working on my latest redesign, I added a statement explaining how “artificial intelligence” is used on my website. And in a fit of pique, I slapped this line onto the end: I am very upset I had to write any of this, but here we both are.
Look, I don’t know if the exasperation coursing through that line is wise, as a working designer who would like to continue to, well, work. But I’ll own the sentiment. If nothing else, it lines up with how I feel about large language models (LLMs), and specifically how their explosive growth has made everyone suspicious of everything they see, read, and write. And understandably! Putting aside for the briefest of moments how these technologies have led to new forms of misinformation and disinformation, it’s fiendishly difficult to know just how human-made something is these days. A few short years ago we’d never ask the questions many of us are now asking. 1 And many of those questions are simply some variant on,
“did you actually make this?”
These were just a few of the things swirling around my brainpan when I originally wrote the statement. Yes, I firmly believe “generative artificial intelligence” is a failed technology; I also think it’s worth disclaiming how I did (or didn’t) use it in creating this website, or the words published on it. Putting that on the record feels important, even if I resent feeling I had to do it when launching a new design in 2026.
But when I shared my statement on Bluesky and Mastodon, I quickly learned there were many, many other “AI” statements out there. As someone who’s perenially late to the party, I should’ve known folks were thinking about this long before I did. What’s more, I appreciated getting to see a variety of approaches to writing about “AI” — and just as many opinions about how and when it’s appropriate to use it.
If you’ll forgive me for riffing on [something I said before](/wrote/all-tomorrows-parties/), I realized that very few of these statements are really about “AI” itself. Instead, they’re statements about *work*, how the author thinks about that work, and the kind of relationship they want to have with the tools they use. So I thought I’d share a few favorites here, in case you find them as useful I did. Do I agree with every entry on this list? No, of course not. But I’ve *learned* something from every single entry on this list — something about the values I bring to my work, and about the values I want to bring to it.
This is all to say, [just like last time](/wrote/generative/):
Hey, you. I’ve made you a playlist.
All the words were written by me, a human — typos and all. All of the images were made by me or another human. All of the code was typed out by me.
Unless otherwise stated or owned by a third party, everything you see and read here (questionable punctuation and all) was created by me without “AI” involvement or assistance.
I prefer to avoid AI usage for ethical, practical, and financial reasons.
Ethically:Generative AI tools, powered by[data centers]which consume[vast amounts of water]and[pollute our environment], are built on the collective[theft of the works of millions of artists], developers,[authors], and other creatives, supercharge[the spread of disinformation]and[fascism], have repeatedly provoked[psychosis]and[suicide], and[concentrate wealth in fewer hands]while providing cover for[widespread layoffs].Practically:I have found that AI tools overcomplicate implementations and that cleaner, simpler solutions can often be written by hand.[AI doesn’t “know” anything], and is[making life worse for open source maintainers]. Overreliance on AI risks[deskilling].Financially:I dislike sending money to large corporations when alternatives include[learning the skill for free]and[contributing to open source]projects. Tokens are currently[heavily subsidized and likely to become more expensive], so I would prefer not to make a habit of using them.I use AI tools sparingly for assistance while refactoring code in languages I understand. I occasionally use it to help compose command line arguments for tools like
ffmpeg
. I never use AI to generate images, video, or prose.
In some cases I am sceptic that LLMs will help me achieve my goals:
- writing helps me think. Writing itself is the point, not the output. Output alone has no value and is unnecessary.
- I’m on the [indieweb]to connect, as a human, to other humans.- LLM’s normalise, which is often uninteresting. There is no need to make content that is like everyone else’s. My voice, beliefs, experience and background are my only chance at at least trying to create something novel. Even if I try manually, I’ll inevitably borrow from other people by accident. Why would I automate or accelerate that? [LLM’s shouldn’t invent new information]
This style you’re reading now is for stuff like my blogs, my posts, my SubStack. Key word: My. This is my voice. And I want to keep that work for my brain because thinking about the idea, structure, writing, and editing is what brings me joy.
So no, I won’t be training anything.
None of the proper content on this site is written by AIunless I explicitly say so. The articles, notes and pages were written by me, anactual human. If you’re taking the time to read something here, you should know that I took the time to write it.- Many of my thoughts begin as rambling voice notes, dictated while driving or walking. LLMs are amazing for transcribing these, and for turning them into bullet-list outlines for me to riff off later. These outlines sometimes turn into articles on this site.
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I often use LLMs for the following on this site:
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Pre-publishing proofreading, grammar, spelling & syntax checks.
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Generating dummy/example content in places where I’d previously have used some version of Lorem Ipsum.
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Generating meta descriptions and the like from my actual content. This is not automated and I inevitably edit by hand before publishing, but LLMs are unquestionably better at this than me.
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Drafting and updating functionalcontent like the[privacy policy].
While generative AI can assist with drafting, summarising, proofreading, or refining academic content, it cannot be considered capable of producing an original piece of research or submission without substantial human intellectual contribution and oversight. In accordance with [Committee on Publication Ethics] guidelines, generative AI systems cannot be listed as authors, as they cannot assume accountability for errors, ensure ethical standards have been adhered to, or respond to critiques of the work. BERA expects all researchers to uphold the highest standards of academic integrity and ethical conduct in their work.
I’ll be plain about this, because I would rather you hear it from me than decide it for yourself. I use AI to help write these entries. On my own, as one person with a day job, I could not have built a catalogue this size by hand. The AI helps me draft. It does not get the final word.
Instead, I’d offer an alternative mental model:
generative AIs are tools, devised and deployed by corporations to operate at scale, laundering content the corporations generally do not own with the active intention of reducing the need for, and the value of, human labor. Whether machine learning is like human learning is irrelevant to the real-world impact of its use.
Our recent experience has shown that the use of AI-generated make-up visuals not only contradicts our brand philosophy but is also rejected by our community. The response was clear and immediate: our audience values authenticity, real artistry, and the work of human creators. They expect and deserve nothing less from us.
Computer science is a complex discipline, and those who excel at it are rightly lauded, but so is understanding and critiquing power and holding it to account. Understanding technologies requires also understanding power; it requires social, cultural, political, and media literacy as well as technical literacy; incisive questioning and sober critique as well as shock and awe.
Our AI policy exists to do two things: protect the intellectual property of our speakers, and make sure AI tools don’t become intrusive or distracting during events. To support both goals, we ask that attendees not use AI tools to record, transcribe, or repurpose content from our sessions, including sending AI transcribers in your place.
We provide live closed captions during all our events, and we also offer video recordings afterward. If you need to use any additional AI tools for personal accessibility reasons, just reach out. We’re happy to work with you to make sure you can fully participate.
My approach to “AI” technologies, as with other tools, is to situate them within what we know about the world, how people learn and/or make meaning, and how we can build a future that improves outcomes for everyone.
Promising alternatives are already being created and used by people like the
[FOSS Academic], as well as ecologically-inclined or[permacomputing-inspired]groups like the[Hundred Rabbits]collective or[Low←Tech Magazine].While those approaches vary, they share a common theme of attention to the material costs of computing and to liberating tech users from influences that seek to undermine their own intentions.
Our bread and butter is information design: the pervasive challenge, in every domain, of taking a complex idea or story and distilling it into a clear, intuitive, beautiful format. If we’re making tools for experts, we want to respect their expertise and time, and craft a product that helps them with the difficult parts of their job, not another kludge that they’re burdened to spend time wrestling with and debugging. We want to make thoughtful products, where it’s clear that someone has spent time thinking about the hard parts of a task and has invested the time and effort into making that a smooth process. If we’ve found ourselves in a hole where the black box model of solving a complex information problem is the only solution, we’re not doing our job as information designers, and we need to go back to the drawing board.
To develop that body of knowledge, one must interact directly with the literature, use it to actively learn about connections between one catalog or book or article and the next, and between collectors, authors, and institutions. With it, one can often anticipate where to find a coin (and
where else), often in ways or places inaccessible to current search techonologies.[sic]And, of course, by developing those skills, one better appreciates the historical and intellectual significance of all that “object biography.” How those threads tie together. How they form a coherent narrative about the place of ancient coins (and this coin, specifcially
[sic]), their scholarship, and the reception of antiquity across historical and cultural contexts.
The writing we use, the very substrate of our culture, is the result of a process older than we can comprehend. Who are we to decide that process is finished? That the time of invention and human input is done, and every letter created from now on should be a remix of what came before?
AI tools perceive reality as a finite set of data, up-to-date as of about 2021: the shadows on the wall of Plato’s Cave. Right now they are very good at making shadow puppets, but they cannot see the world outside.
My thanks to Lisa Maria Marquis and Sean Tubridy for contributing a few resources to this post.
Footnote #
To be clear, I include myself in that statement. And I don’t much care for that, either.
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