---
title: Arguments Against AI Fair Use
description: Response to Kevin Kelly's Arguments in Favor of AI Fair Use
quote: Kevin Kelly, on his SubStack, shared some compelling points in his Arguments in Favor of AI Fair Use. But I think it misses the mark.
date: 2026-09-09T09:44:00.000Z
published: true
tags:
  - ai
  - fair use
  - copyright
  - alms
syndication:
  - all
notify: true
slug: ai-fair-use
---

Kevin Kelly, on his SubStack, shared some compelling points in his [Arguments in Favor of AI Fair Use](https://kevinkelly.substack.com/p/arguments-in-favor-of-ai-fair-use), and I urge anyone who knee-jerks at the thought of AI companies “stealing” their training data by hoovering up the entire web to take a look. But I think it misses the mark.

His argument centers on the idea that LLMs transform their sources by reducing them to a mathematical expression, something we’ve never seen before, in the process creating something completely new, which, at least in the States, is exactly the kind of claim companies need to make to free them from litigious copyright claims. Free use guards the ability of artists and authors to employ a copyrightable entity in the pursuit of adding something to the cultural conversation, the most famous example perhaps being using Mickey Mouse to create a statement Disney might have never intended. If LLMs are indeed creating something new, their creations are free use.

I find Kelly’s argument flawed in this understanding, though. LLMs don’t create something new. As of right now, they’re not smart enough to create something new. They don’t have their own thoughts, they don’t come to new conclusions, they instead present a mathematical average or probability. They connect the dots, and they certainly know more dots than you or I or any human might ever know. You could view them as strict academics, librarians, scholars who can organize a view of the world to build perspectives. But they are not artists creating new ideas because they are not capable of cognitive leaps that advance art and culture. Every thought they’ve ever had, someone else has had and written down. The way they organize data may be unique, but at the end of the day they are averaging the weight of words that someone else has already penned, looking for the most practical next thing to say, which is plainly not creating something new to push the cultural conversation forward. I don’t believe they can do that, but I guess that’s because I don’t believe AGI has arrived yet, regardless of what [the shovel sellers in this gold rush are saying](https://www.businessinsider.com/nvidia-jensen-huang-agi-openai-astra-ai-2026-9).

Let me give an example. One can’t prompt an LLM into creating jazz music in a world without Jazz. A human might be able to convince an LLM into creating jazz music if they know what the LLM needs to do to create jazz music, but without direction, left to its own devices, it would consistently create instrumental music that sounds like everything that came before jazz and not jazz. This definitely brings the philosophical conundrum of originality into the mix. Is all art derivative? I would argue not, but I also concede that much of the art being created by humans is.  Copyright law protects artists from derivative work, overly formulaic reproductions of art that come too close to the original to be distinguishable. Not all art tries to alter a conversation, much art simply wants to extend the conversation; how many still-lives of roast duck and pears are there in the world? The question is whether my AI generated Mickey Mouse adds something to the cultural conversation, or simply copies it.

Kelly also breezes over the laws we have in place to protect how works are used electronically. There’s a mind boggling amount of data on the web that’s licensed by the Creative Commons, with attribution. Just because an LLM atomizes data to the point that it’s indistinguishable from other data doesn’t free the creators of LLMs from following the law to cite their sources. It doesn’t free them from using electronically distributed data in a way that the author never allowed. They need to figure those part outs. And I understand that in the pursuit of the greater good, perhaps we need to revisit these laws, but it’s not a chicken an egg thing.

Lastly, Kelly creates this term “Public Intelligence,” which is an altruistic notion that purports that it’s the benefit of everyone to have LLMs trained on data that everyone should have access to. And this is frankly fantastic. But no one is providing AI for free. Companies are not tantalizingly close to world shattering IPOs because they want to offer their services for free. Whole economies are not quaking and upending because everyone is going to benefit from LLMs; there is a strong disparity around who has access to LLMs and they’re certainly not being developed with everyone in mind at the moment. It’s hard for the realist in me, at the moment, to marry with the optimal view of AI technology.

I think I could return to this topic for a long time, and perhaps I will. I truly think Kelly has done a wonderful job outlining the best case for fair use, and I want him to be right, but I think the hand-waving Kelly employs is precisely over the points that require the hardest negotiations.