I Resist Generative "AI"

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Why I Resist Generative “AI”

I don’t make a secret out of how much I despise the current hype around generative “AI”. I avoid using it as much as possible, and I’ve been vocal about the reasons for years.

But often I feel the need to have a collection of all my arguments in one place instead of dozens of posts scattered around social media. This article is that collection. I’ll go through all the major reasons why I oppose generative “AI”, respond to some popular claims about it that I often hear from its proponents, and I’ll provide sources for further reading along the way.

The sections are mostly independent from each other so feel free to skip around the table of contents and read only the ones you’re interested in. I’ll not get too deep into the technical details as those aren’t really the focus here and I’d rather look at more high-level issues to keep this from becoming way longer than it already is, and to keep it accessible for people who aren’t tech experts themselves.

I’ll also put down my own cards here: I have professionally worked in software development for about 15 years, and got about 25 years of programming experience across several languages in total. I have worked a lot on search engines and have implemented my own neural networks occasionally. Currently I’m finishing up a degree in psychology and writing my thesis in statistics. Just so you know where I’m coming from here.

What is “AI” right now?

It’s often not very clear what people mean when they say “AI”, especially when they are somewhat knowledgeable in that field and are aware of some of its more specific terms, so I want to put out some terminology before moving on.

For the purpose of this article and I think in most colloquial use by laypeople, “AI” refers to generative systems that produce text, images, or other media based on a text prompt as well as systems derived from them, e.g. “AI agents”.

As of 2026, the time of writing this, the term artificial intelligence has been kicking around for over half a century. Various and very different technologies have been labeled as such. Systems that implemented formal logic were the big bet in the 1950s and 60s. Following the “AI winter” period after those efforts proved a dead end so called “expert systems” were the hype of the 1980s.

Right now, we’re seeing a big revival of the artificial neural network, a technology that was thought to be of limited use decades ago, but that turns out to be quite powerful when combined with absurd amounts of data and computing capacity that were previously impossible, or unfeasible to bring to task.

Neural networks are quite remarkable at adapting to statistical patterns in complex data sets. A well-known entry-level example is a relatively simple network that can recognize hand-written numeric digits with surprising accuracy, when trained with sufficient amounts of training data (a large sample of written digits that are already correctly labeled). Neural networks have a wide array of applications and can model a variety of real-world problems.

Large language models

Neural networks form the basis for what is now known as large language models (LLM), which is the core of the current “AI” hype, more specifically the generative pre-trained transformer (that’s the GPT in ChatGPT).

LLMs are statistical models of human language. They encode probabilities of words appearing in sequence and given an input sequence of words, called the prompt, they can infer (predict) what words are likely to appear next.

A simple version of this would be the predictive typing on a modern smartphone where as you type text, a short list of likely word suggestions is shown to speed up the process. You type appo and it’s likely that you mean to type appointment so the phone gives you a shortcut to do just that.

The LLMs powering popular chatbots like ChatGPT or Claude are super-charged version of that, not just inferring based on a few previous keystrokes but on quite extensive prompts. However, they still only predict a very small amount of text at a time before they need to repeat the entire process, feeding the fresh output back onto the system along with the previous prompt.

There’s a popular misconception that an LLM “remembers” your chat history and the prompt is always just the most recent message you sent, but that’s not the case. The prompt is the entire conversation. It is fed into the LLM with every new chat message. Having the chat history in the prompt helps keep the model “on topic”, otherwise these things easily drift to something else.

This has its limits though. LLMs can only receive a prompt of a certain maximum length. This is known as the context window. Anything further back than the context window allows is no longer part of the prompt.

Agents

A few years into the development of commercial services based on LLMs, the latest and greatest right now are “AI agents”, systems that supposedly have, as the name suggests, agency, acting autonomously based on some initial task set by their user. Or at least that’s how the sales pitch goes.

While a plain chat interface to an LLM might be able to respond to a user’s questions or perform things like summarizing text, an “agent” is supposed to execute more complex tasks like “book a trip to Berlin next weekend along with a hotel room for two adults”.

This sounds like a fundamentally new and different technology compared to just LLMs, but it isn’t. An agent is mostly just a program that repeatedly prompts an LLM and based on its output, executes various functions that are available to it, often called tools. Tools could be things like executing a web search, making a payment using a Paypal account, sending an email, etc. The “AI” part of it is still an LLM, mostly the same one you could also interact with directly via a chat interface.

Other forms of machine learning

Machine learning is a wider term that includes LLMs and other uses of neural networks, but also more generally any statistical model that can make predictions about something. Strictly speaking even a simple linear regression model, a very basic principle in statistics, can be seen as a form of machine learning as the regression model “learns” the correlation between variables and can then make predictions that fit those correlations.

Weather forecasts, image classification (the thing that finds pictures of your cat in your phone’s camera roll), street traffic modeling, power grid management, and many more applications fall under this very wide umbrella.

I believe it’s important to differentiate between the colloquially used “AI”, meaning generative systems, and other specific applications of machine learning. It’s generative “AI” that I’m going to focus on in this article and that I think needs to be resisted.

Claims

“AI is here to stay”

OK. So is heroin. And I wouldn’t recommend that to anyone either. But seriously, this really isn’t an argument for or against anything. No technology fully vanishes again once it has been developed. That doesn’t mean though that it remains widely adopted, nor that it’s even a net good.

I don’t expect LLMs to disappear and I don’t think anyone who’s serious about this really believes that either. But the current boom is largely fueled by subsidized costs. End users don’t pay anywhere near the real cost of operating the big popular chatbots. The cost is covered by investor capital and that does run out eventually. And once the real cost will bear down on the consumer, a lot of people will reevaluate whether this is all really worth it.

We already see large companies restrict their employees’ use now that big LLM operators have switched to a “pay what you use” model. Personally I think a lot of people who rely on LLMs a lot are in for a rough awakening once they have to pay the real price.

“It’s just a tool”

This is one of the favorite claims made by people who’d rather not think too much about how they participate in something destructive and there’s two problems with it:

No, it’s really not

An angle grinder is a tool. Its use is entirely in your control, as long as you can provide it with electricity. But since it’s electricity requirements are well documented and not that complicated (220V AC at 50Hz for one I would get at my local hardware store), you’re not bound by anything in how you use your angle grinder. It’s also entirely agnostic whether you use it to build something or to cut a bike lock that’s not your own. And while it might break eventually, and its construction requires a complex supply chain, once it is made it’ll operate just fine for a long time.

The fire department is not a tool. This should be obvious. It’s clearly a service, provided by your municipality probably. It’s a complex web of materials, tools, processes, people, rules, and politics. It’s not something that gets built once and then just works. If you want someone to show up to your burning house and rescue you, a lot of things need to happen on a regular basis, many done by people.

For the continued operation of generative “AI” models, a complex web of materials and labor also needs to perform various tasks. It’s just less obvious because you don’t see the people making it work. You just see the input and output of the entire system. The labor and material realities are abstracted away, but that doesn’t mean they don’t exist. Moderators need to work in order to keep the LLM from spewing child abuse material at you, someone needs to replace the constantly burning out GPUs in the datacenters, and someone needs to curate the new training data to keep the model up to date, just to name a few. This is not a tool. This is a service.

Tools don’t exist in a vacuum

Even if one is to accept the “It’s just a tool” claim, it doesn’t follow that using it widely (or at all) with little restrictions is fine. There are plenty of tools that we socially regulate, through laws or plain social conventions. If I hand you a hammer for driving some nails into a wall, I have a reasonable expectation that you won’t hit anyone in the head with it.

Tools also contain their own politics, sometimes explicitly, sometimes implicitly. Weapons for example are obviously designed to harm people, they very explicitly contain the politics of violence and war. A more implicit example could be the steam engine. It’s not just an invention that happened in a vacuum. Without the political and economical framework of the fossil fuel industry it can’t operate and would lose its entire reason for existing. Pretending that a tool is somehow “neutral” is a very convenient tale to tell oneself, but it’s simply not true.

“But it’s so useful”

This one is closely related to the “It’s just a tool” claim and similar to that one, it needs to be evaluated in a wider context. Usefulness itself is not a sufficient criterion for whether something is good. Slavery and child labor are strictly speaking useful, but many societies have strict rules against those since they are also deeply exploitative. A steam locomotive is useful but we have mostly moved towards much more efficient and cleaner modes of propulsion for trains.

I don’t deny that LLMs are capable for certain use cases. A modern LLM can absolutely write a polite yet direct email to your landlord about how they should finally fix the central heating, or generate a boring but serviceable CV for a job application. LLMs can do much more than those very simple tasks even, but the more complex the task, the more likely there will be mistakes. And once you’re using it for something that you couldn’t do well yourself, you have no way of validating its output, rendering it very much not useful while still appearing as if it is.

“AI will solve …”

Whether it’s climate change, space travel, curing cancer, or some other flashy promise, somehow “AI” is supposed to solve it. Yet, several years into the generative “AI” hype, there’s little to show for.

This is a claim where it’s really important to make sure we’re talking about the same thing when we say “AI”, as laid out earlier. Specialized machine learning systems can be essential to various fields of research, but LLMs are simply not capable of producing new knowledge. They can only reproduce what’s in their training data. An LLM-based system, being a statistical model of its training data and therefore effectively a form of lossy compression, can by definition never solve any problem for which the training data or information otherwise available to the system doesn’t already contain the solution.

“You’re all just Luddites”

Well yes, I am, quite proudly so I might add. But I don’t think the person who uses that term as an insult and to discredit actually knows what a Luddite is. The original Luddites were a worker movement in the 19th century that was concerned about the economic implications of applying certain technologies in their work. They were not opposed to all new technology, as the popular misconception goes.

People who are not aware of the actual history of the Luddites often think the name refers to someone who rejects all modern technology by default. This is simply not true. Luddism is experiencing a revival today especially among people who are often technology experts themselves, who use modern technology every day, who work in research and development, but who also examine technology critically. For further reading I recommend The Mechanic And The Luddite by Jathan Sadowski.

“LLMs are conscious”

Extraordinary claims require extraordinary evidence and this is a really extraordinary one. So far there is zero evidence towards this though. All we have are some people claiming that the text generator is somehow a real person now, usually people who worked at one of the major companies producing these systems and who benefit from painting their work as fantastical and revolutionary.

It is very easy for humans to anthropomorphize things that simulate human behavior, even when the simulation is crude. Thi