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Reading Room

Vol. 1 · No. 39 Friday, September 4, 2026 aikansh.com

This week's theme

Who AI answers to: courts, police, the Pentagon

A lawsuit, a police report, and a Pentagon standoff: the outside world is starting to set terms for AI, while one artist tests what machine memory forgets.

A 5-minute read · 6 stories

In this issue

01 Front Page

Writers group sues OpenAI over stories used to train ChatGPT

A publisher explains why it took OpenAI to court over stories it never paid to use.

This is the copyright fight that will decide whether AI companies owe creators anything for the text they use to train their models, and it is worth understanding before you use AI on anyone else's writing but your own.

A group of writers and their publisher has spent the last two years suing OpenAI over how it built ChatGPT. In OpenAI's early days, the company listed the sources of the data it used to train its models, something it has since stopped doing. Inside those listed sources, the writers found tens of thousands of their own stories. OpenAI never asked for permission to use the work, and never offered to pay a licensing fee, either then, while it was building its business, or now.

The timing matters. OpenAI is heading toward an initial public offering (selling shares to the public for the first time) that could value the company around $1 trillion. The writers argue that a company approaching that kind of valuation on the back of other people's work should have paid for it, or at least asked. As far as they know, OpenAI never sought permission from the many other publishers, writers, and everyday internet posters whose work it also used.

September 4, the date of this lawsuit update, is a real deadline: both sides have asked the court to rule on the case before it goes to trial. A ruling either way sets a precedent (a decision other courts then follow) for whether training an AI model on copyrighted work counts as fair use, or as theft that requires payment. That answer will shape how every AI company, and everyone using AI on someone else's content, should think about consent and payment going forward.

OpenAI never asked for permission to use our work, nor did they offer to pay a licensing fee.
via Reddit r/ArtificialInteligence →
02 Key News

University that built AI classes early now advises other schools

One university built its artificial intelligence (AI) courses before ChatGPT, the chatbot that kicked off the current AI boom, even existed. It is now packaging that early curriculum to help other schools catch up on teaching AI skills.

via CNN Business →

Anthropic gets mixed treatment from Washington amid Pentagon fight

Anthropic, the company behind Claude, is getting noticeably different treatment from different parts of the US government as its dispute with the Pentagon continues. Worth a quick read since Claude is the model your own work runs on, and how governments treat the company can affect it.

via FedScoop →

GPT-6 Astra built a game world full of AI agents

This is worth a look if you build AI agents (AI systems that take several steps on their own) yourself: it is a vivid demo of several agents reasoning together inside one shared space.

Spotted on X via Matt Schumer: he asked GPT-6 Astra, an AI system, to build a small world inside Unreal Engine, a tool used to build video games, then fill it with humans, each one an AI agent, who all had to work together to survive.

The post does not say what happened once the agents started interacting, or how well they actually cooperated. No further detail on the mechanism or the outcome is available from the source.

via r/ArtificialInteligence →

Anthropic called police over a Claude-related threat to its CEO

Anthropic contacted San Francisco police after reading what looked like a real threat against its chief executive, made through Claude. The user says it was a misunderstanding, but the episode shows how seriously AI companies now treat anything that looks like a real world threat surfacing through their own product.

via The San Francisco Standard →
03 Insights

An artist trained an AI on childhood photos to test memory

The result was not accurate photos, but fuzzy, half-familiar ones, like memory itself.

Outside your usual reading: an artist's project, not a news story. But you are building a memory system for your own AI agents right now, and this is a real working example of memory as rebuilding rather than playback.

The artist gave extra, narrow training (fine-tuning) to SDXL, a well known image-generating AI model, using 60 photographs from their own childhood as the entire training set. Rather than reproducing those photos accurately, the model produced unstable variations: spaces, faces, and fragments that feel familiar without ever having actually existed.

The artist treats this as a working model of human memory. When the AI model 'hallucinates' (makes something up instead of reproducing the real photo), the artist argues it mirrors how human memory actually works: not pulling up a stored picture, but reconstructing a rough scene from incomplete fragments. That lines up with how memory researchers already describe episodic memory (memory of specific past events): something rebuilt each time, not replayed like a video.

This was not a single text instruction. The artist built an audio-reactive geometry system inside TouchDesigner, a real-time visual tool, then modified WarpFusion so it could blend the fine-tuned model with those shapes. Other tools involved: Kohya for the training itself, plus Premiere, After Effects, and Ableton Live for editing and sound.

The idea worth borrowing: design recall in your own memory systems to reconstruct an answer from stored fragments and traces, the way this project reconstructs a childhood from 60 photos, rather than assuming perfect playback of a stored log is either possible or even the right goal.

This resonates with contemporary accounts of episodic memory as a reconstructive rather than reproductive process.
via r/ArtificialInteligence →
The Last Word
Training data was free right up until someone with lawyers noticed.
The Desk Report

How this edition came together — from bookmarks and feeds to the page.

251links gathered
40read by the desk
6made the edition

Where they came from

On the cutting-room floor — 34 links read but not run this week

Quality over volume: most links get a second look and a pass. The ones that made it earned their place.

Reading Room — every Sunday

The week's AI signal in 5 minutes — what happened, why it matters, and what to do with it. Curated by someone who actually builds, not a feed algorithm.