Disclaimer #1: Despite my fondness for triple construction (which was drilled into me in grade school English class), this post was not written with AI. In fact, it was furiously typed and edited over the course of 90 minutes on my 1998 IBM Model M13, still going strong after nearly 30 years of continuous use.
Disclaimer #2: This post was written at a time when I felt trapped and resentful that my months/years of work on a few projects would be subject to criticism “because AI”, or be judged as slop, when I didn’t feel that was warranted.
There is a fiercely divided uproar on the use of AI in 2026. (By “AI”, I’m referring to LLMs, generative AI, transformer models, vision systems, deep learning, and everything else under that umbrella.) People in favor of the technology describe it as a skillset accelerator, a junior intern, a pair-programming partner, a rubber duck, an exoskeleton, and an advanced information retrieval engine. People in fierce opposition of the technology point to its physical demands on the world, financial economical impact, industry disruption, creative bankruptcy, weaponizing fraud at scale, and the moral and ethical concerns of using what is technically a plagiarism engine.
Social media has, in the wonderful tradition of the law of unintended consequences, mostly forced people into both of these extreme camps. There doesn’t seem to be any middle ground that can be comfortably occupied by people who agree with arguments presented by both sides. In a stunning societal achievement, AI has now joined the realm of religion and politics as something people will seemingly never find any middle ground on.
I occupy the middle ground. I am neither pro-AI nor anti-AI. And while I’ve seen Scooby-Doo memes that claim all moderates or centrists are secretly right-wing extremists, the truth is far less dramatic: Moderates really do exist, and acknowledging that an issue is complex is not the same thing as picking a side. And that’s where I land on AI use, but people in both divided camps don’t like to hear that, probably because there’s no easy single-sentence resolution of a viewpoint or single-phrase directive on how to proceed.
I’ve programmed a lot. I’ve even won a few demoscene competitions with the code I’ve written. And I’m working on some new things! But now that AI has entered the global conversation, I feel extremely resentful that I am somehow expected to justify how I created something, because if I deigned to use the wrong tool or service, for the wrong reasons/application, I will be judged by the creation process instead of the end result. As someone who grew up a hacker and demoscener, where obtaining information and banging on hardware “irresponsibly” was not only a way of life but a necessary path to data sovereignty, I find this expectation of justifying my process utterly preposterous.
Adding to this frustration is the fact that what I’m working on has no precedent! It’s all novel algorithms I’ve envisioned for decades; there is no existing literature for AI to plagiarize ideas, research, or code from. If you search for what I’m working on, there are no search results. And yet, an AI disclosure policy is demanded.
You don’t deserve an AI disclosure policy
In 20 years, nobody will be asking for an AI disclosure policy. Use of AI on local hardware, decoupled from major corporations, will be as ubiquitous as using your cell phone to send text messages is today. It will be built into every device you use, even steering edge devices; your charging station will try to strike up a chat with you while your car recharges, or discover how you’ve hacked the charging network for free power and rectify the situation by extracting additional money from your bank account. 20 years from now, asking someone to disclose how they use AI would be like asking them to disclose how they check their social media feeds: Like everyone else, using abilities and infrastructure built into every device. People will look at you strangely just for asking.
People asking for AI disclosure can’t see this far ahead. People demanding AI disclosure think that the current state of AI development and use in 2026 can somehow be curbed or moderated. Both are simultaneously futile and pointless. The models have already been trained; the datacenters have already been built (back in the 90s! for the internet! that you’re using right now!). You’re not going to walk back mass copyright infringement by sticking to your values.
Why I’m providing an AI disclosure policy anyway
I’m under no obligation, now or ever, to disclose how I use AI. As previously stated, I’m borderline furious that I even have to. But I’m going to do so anyway, to satisfy both extremist camps when they come knocking. Pro-AI people want AI disclosures so that they can learn from them (How did AI help this project? What new things can I leverage that I didn’t know about?) Anti-AI people want disclosures for many more reasons: To provide (the facade of) transparency, authenticity, attribution, fairness, trust, safety, consent, and accountability.
So, for those who ask me what my AI disclosure policy is, here you go:
- I use LLMs to summarize and explain information that I do not yet understand, or lack the educational background to understand.
- I use LLMs to collate information from disparate sources, usually to discover common elements, but sometimes to find extreme outliers.
- I use LLMs to analyze source code I’ve written, mostly to find bugs and comment on potential mistakes, but sometimes to suggest improvements.
- I use LLMs to generate code in languages that may be required for hobby work but for which I do not want to spend 3 months learning a new language and its libraries. Such generation is initiated with multi-paragraph starting prompts that I author from scratch, with algorithms, constraints, and targets of my own design.
- I use LLMs as a replacement for phrase thesauruses and idiom dictionaries, to look up alternate phrasing while I write something.
- I use neural models trained on audio to clean up recorded field audio, such as reducing reverb, or synthesizing “studio-quality” audio originally recorded in poor conditions.
- I use sequence-to-sequence transformer models for speech recognition, mostly to transcribe very long spoken audio to ASCII text, which I then either read manually, or summarize using an LLM, to understand the author’s core concepts faster than listening in real time.
- I do NOT use AI as a substitute for creative output: I do NOT use AI to write scripts or other forms of creative or factual writing; I do NOT use AI to write emails, messages, or other forms of written communication; I do NOT use AI to generate images, video, or music.
- I MAY use AI in the future to generate charts, graphs, or diagrams from my original ideas or source data.
Of course, I could be lying. It’s up to you to judge whether I’m telling the truth, based on my character and my previous body of work built up over many decades. As to whether the above leans me ever so slightly towards a Pro-AI or an Anti-AI camp, I leave that for you to decide.
But before you scream “If you use AI, then you’re pro-AI!”, let me remind you that utility does not equal ideology. Adoption != endorsement. A person can drive a gas-powered car to get to work while actively lobbying against the fossil fuel industry. Or, more appropriately for my line of work: I can deploy proprietary enterprise software because my employer requires it, while personally advocating for open-source solutions.
PS: I used to love using em-dashes strategically in my writing. Thanks to AI and the societal discourse surrounding it, I am now afraid to use em-dashes in my writing, lest the accusations fly. Thanks, technology.
Oldskooler Ramblings 
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