The phrase Generative AI (or GenAI) has rapidly become the go-to term for the class of machine learning systems capable of producing media: text, images, music, and video. It is catchy, easy to remember, and broadly descriptive. However, when applied to large language models (LLMs), the term does more harm than good.

LLMs like GPT-4o, Claude, and others do not simply generate plausible text. They interpret. They respond. They situate their outputs in relation to complex and evolving input contexts. A better name, I argue, is Interpretive AI.

What Makes LLMs Different?

Generative image or music models typically synthesize outputs based on a latent space learned from training data. The act of generation is largely one-directional. These systems do not interpret a user’s sketch or melody in order to generate an image or song. They generate based on a prompt.

By contrast, LLMs are dialogic. They process language in a context-sensitive manner. Each token is generated not in isolation but in relation to the user’s input and the model’s own previous outputs. The system must keep track of what has been said, what is being asked, and what expectations are implied. It does not simply produce. It engages.

Interpretation as Core Operation

This engagement is not trivial. For a model to maintain coherence across a conversation, to stay on topic, answer questions, offer relevant elaborations, and handle ambiguity, it must perform a form of ongoing interpretation. This interpretation is not semantic in the human sense, but it is functional. The model must infer structure, intent, relevance, and tone in order to continue the interaction in a way that humans find meaningful.

In this light, calling it merely generative fails to account for its interpretive doing. These models work by continuously negotiating meaning in a co-constructed context with the user.

Why Terminology Matters

Terminology shapes understanding. When we call something generative, we focus on its outputs. When we call it interpretive, we shift focus to its relational, responsive, and situated behavior. This is not a minor semantic difference. It influences:

• How we design interfaces
• How we set expectations for use
• How we regulate AI systems
• How we think about responsibility and agency

Interpretive AI: A Clearer Frame

The term Interpretive AI highlights that these systems do more than generate statistically plausible outputs. Their responses are shaped by how they condition each token on a dynamic context, consisting of previous inputs, prior outputs, and linguistic structure. This ongoing adjustment allows them to maintain relevance, coherence, and tone in ways that appear attuned to the evolving interaction, even though they lack understanding in the human sense.

This shift in framing opens up a richer vocabulary for discussing how such systems behave, how they misinterpret, and how they mediate human communication.

Philosophical Considerations

I propose this term intentionally provocatively. Interpretation traditionally implies human understanding, grounded in a condition of being-in-the-world. LLMs do not understand in this sense. But meaning and understanding are not located within the system. Instead, they emerge in relation, unfolding between human and model. In this view, interpretation is a co-operative, situated process, enacted through interaction. Through its ongoing token generation, the LLM opens up a space of possibilities, participating in a kind of relational meaning-making. Interpretive AI is therefore not a claim about internal cognition, but about the model’s relational role in negotiating understanding as a dialogical partner.

This morning I gave my lecture for my docent application at the faculty of science and technology at university of gothenburg.

I explained that a computer is a simple device that can take input and produce output. Using it, we can make them do things. They can be cumbersome to get to do certain things, but once they can do them it is often trivial to replicate.

I went through four examples from my research, where I have had to make the computer do: buttons, maps, steps counts, and screen time.

With AI, this may come to change. Computers are great at instructing themselves, and capable of understanding us on our own terms. What does this mean for the future? Will there be developers? Will there be apps? Will there be computers as we know them? Or will we just have machines, and they will do things we ask them to do.

I took this opportunity to talk about where I see things going next, and what I think could be an interesting future worth exploring.

Watch the video on youtube here: https://youtu.be/5In2Zn5hx_w

I recently made a public talk at a local meetup, based on the previous blog post. It was recorded and can be watched below.

I start off by describing ChatGPT as a wheel of fortune where every spin generates a new word based on a probability distribution conditioned on the given prompt.

I then talk about how new technology tends to be used to do the things we already do in the ways we already do them (in terms of Marshall McLuhan). I explain this based on the notion of bounded rationality.

I then show how people have been trying to use ChatGPT as an alternative to existing technology, and how it often fails as an existing technology.

I finish by showing how we tend to anthropomorphise ChatGPT because the interface is chat, and that it again is a bad idea to treat it as something with human traits.

To conclude I explain that the way we try and use it is normal, and that it will take some time before this technology find its own use. And when it does, it will be become incredibly powerful. We should therefore start using it now and experiment and explore what this is, and how it may enable us to do new things, in new ways.

Watch the video on youtube here: https://www.youtube.com/watch?v=lOfsDaZsh1o

 

From the rise in popularity of ChatGPT and language models in general, I see two camps. Those who try and use ChatGPT as Google and find it gives bad results and thus think it's useless. And those who start using it in new ways to do things they didn't have technology for before and finds that it is super useful.

When new technology emerges, people have a tendency to try and use it for things we already do. The first TV programs were theatre plays. Marshall McLuhan talked about how it takes time before new technologies gets used for what the new technology affords, rather than just being used to do what we already can do. Mobile phones are not the same thing as the stationary phones in a household. Household phones did not allow for micro coordination, but required times and places to be agreed upon ahead of time.

ChatGPT and language models in general can do things we previously were not able to do. So we have a tendency to try and use it for what we already do. But what is interesting is not how well it can do things we already have good technology for. What is interesting is what new things it can do.

Previous tech could not generate a script for a book. LM can. They could not be used to write unit tests for already written code, or solve programming tasks. LM can. Current tech could not generate plausible text on a topic. LM can.

To evaluate the usefulness of new tech, it is not very relevant to ask how well it can do things we already can do, but to what new things it may allow us to do. And if this new thing is useful, then surely the tech is.

How bad it is at other things, become rather uninteresting. Don't use it for that.

In closing, here is what ChatGPT thinks Marshall McLucan would say about this new technology:

One aspect of large language models that might have interested McLuhan is their ability to process and generate human-like language, which he would likely see as an example of how media and communication technologies extend and amplify human capabilities. McLuhan believed that media and communication technologies have the power to transform society and culture by changing the way people think, communicate, and interact with the world around them.

It's possible that McLuhan might view large language models as a significant advancement in the development of artificial intelligence and as a tool that has the potential to transform how people communicate and interact with each other and with machines. He might also have seen the emergence of large language models as an example of how media and communication technologies are constantly evolving and shaping the way we understand and engage with the world.

Once a year I'm teaching a course called Design & AI. During this time I get to play around a little bit more with machine learning. As this technology is becoming increasingly available to people, I find it important for people to understand what's going on inside them. Much of it seems complicated when presented in abstract terms, but when looking at it in practical terms through using modern tools it is not as complicated as one may think.

After being asked by colleagues what is involved in implementing and training neural networks, I gave a seminar on the topic this week called Doing Neural Networks. I forgot to record the actual seminar but made a video with the same content afterwards as people asked for it. So, feel free to watch the video below where I explain what neural networks are through code.