Anthropic Suddenly Cares Intensely About Intellectual Property After Realizing With Horror That It Accidentally Leaked Claude’s Source Code; Futurism, April 3, 2026

  Frank Landymore, Futurism; Anthropic Suddenly Cares Intensely About Intellectual Property After Realizing With Horror That It Accidentally Leaked Claude’s Source Code

"The AI industry largely acts as if it’s above lowly copyright laws — unless, of course, those laws happen to be protecting its own interests.

As the Wall Street Journal reports, Anthropic is scrambling to contain a leak of its Claude Code AI model’s source code by issuing a copyright takedown request for more than 8,000 copies of it — a gallingly ironic stance for the company to be taking, considering how it trained its models in the first place.

The leak isn’t considered to be an outright disaster; no customer data was exposed, Anthropic says, nor were the internal mathematical “weights” that determine how the AI “learns” and which distinguish it from other models. But it did expose the techniques its engineers used to get its AI model to act as an autonomous agent, a form of digital infrastructure coders call a harness, and other tricks for making the AI operate as seamlessly as it does.

Hence Anthropic’s copyright takedown request, which targets the thousands of copies that were shared on GitHub. It later narrowed its request from 8,000 copies to 96 copies, according to the WSJ reporting, claiming that the initial one covered more accounts than intended.

It’s certainly within Anthropic’s right to issue the takedown request, but the hypocrisy of Anthropic running to the law to protect its intellectual property is plain to see, especially for a company that’s relentlessly positioned itself as the ethical adult in the room."

Anthropic Races to Contain Leak of Code Behind Claude AI Agent; The Wall Street Journal, April 1, 2026

 Sam Schechner, The Wall Street Journal; Anthropic Races to Contain Leak of Code Behind Claude AI Agent

Developer issues copyright takedown request in bid to prevent competitors from cloning coding tool’s features

"Anthropic is racing to contain the fallout after accidentally exposing the underlying instructions it uses to direct Claude Code, the popular artificial-intelligence agent app that has won the company an edge with developers and businesses.

By Wednesday morning, Anthropic representatives had used a copyright takedown request to force the removal of more than 8,000 copies and adaptations of the raw Claude Code instructions—known as source code—that developers had shared on programming platform GitHub."

OpenAI Faces Early Appeal in First AI Copyright Suit From Coders; Bloomberg Law, September 30, 2024

Isaiah Poritz , Bloomberg Law; OpenAI Faces Early Appeal in First AI Copyright Suit From Coders

"OpenAI Inc. and Microsoft Corp.‘s GitHub will head to the country’s largest federal appeals court to resolve their first copyright lawsuit from open-source programmers who claim the companies’ AI coding tool Copilot violates a decades-old digital copyright law.

Judge Jon S. Tigar granted the programmers’ request for a mid-case turn to the US Court of Appeals for the Ninth Circuit, which must determine whether OpenAI’s copying of open-source code to train its AI model without proper attribution to the programmers could be a violation of the Digital Millennium Copyright Act...

The programmers argued that Copilot fails to include authorship and licensing terms when it outputs code. Unlike other lawsuits against AI companies, the programmers didn’t allege that OpenAI and GitHub engaged in copyright infringement, which is different from a DMCA violation."

A new tool for copyright holders can show if their work is in AI training data; MIT Technology Review, July 25, 2024

• Melissa Heikkiläarchive page

, MIT Technology Review; A new tool for copyright holders can show if their work is in AI training data

"Since the beginning of the generative AI boom, content creators have argued that their work has been scraped into AI models without their consent. But until now, it has been difficult to know whether specific text has actually been used in a training data set. 

Now they have a new way to prove it: “copyright traps” developed by a team at Imperial College London, pieces of hidden text that allow writers and publishers to subtly mark their work in order to later detect whether it has been used in AI models or not. The idea is similar to traps that have been used by copyright holders throughout history—strategies like including fake locations on a map or fake words in a dictionary. 

These AI copyright traps tap into one of the biggest fights in AI. A number of publishers and writers are in the middle of litigation against tech companies, claiming their intellectual property has been scraped into AI training data sets without their permission. The New York Times’ ongoing case against OpenAI is probably the most high-profile of these.  

The code to generate and detect traps is currently available on GitHub, but the team also intends to build a tool that allows people to generate and insert copyright traps themselves." 

As Computer Coding Classes Swell, So Does Cheating; New York Times, May 29, 2017

Jess Bidgood and Jeremy B. Merrill, New York Times; 

As Computer Coding Classes Swell, So Does Cheating

"In interviews, professors and students said the causes were not hard to pin down.

To some students drawn to the classes, coding does not come easily. The coursework can be time-consuming. Troves of code online, on sites like GitHub, may have answers to the very assignment the student is wrestling with, posted by someone who previously took the course.

“You’ve got kids who were struggling with spending a third of their time on their problem sets with the option to copy from the internet,” said Jackson Wagner, who took the Harvard course in 2015 and was not accused of copying. “That’s the reason why people cheat.”

Complicating matters is the collaborative ethos among programmers, which encourages code-sharing in ways that might not be acceptable in a class. Professors also frequently allow students to discuss problems among themselves, but not to share actual code, a policy that some students say creates confusion about what constitutes cheating."