Three and a half years after the emergence of generative AI as a new technology paradigm, there is broad agreement that AI companies have extracted enorm...
Funding the Commons | Public Goods Funding
We convene researchers, builders, and institutions to develop the funding mechanisms, governance systems, and coordination tools that public goods need in an age of AI.
AI Commons - One Project
What if we could redefine AI? What if we could shift its development from a capitalist model to a more disruptive, inclusive, and decentralized one?
Firms like Meta and A16z admit having to pay billions for training data would ruin their generative-AI plans as they fight new copyright rules
Meta, Google, Microsoft, and Andreessen Horowitz are trying to keep AI developers from having to pay for copyrighted material used in AI training.
AI Benefits - But at What Cost?
In 2026 we can all agree that AI and agentic development are certainly exciting topics which many see yielding great productivity gains. But as the investor-subsidized pricing of these services gives way to realistic and profitable business models, where will the real costs land?
AI and the knowledge commons
AI Is a Product of Humanity. Humanity Should Own AI.
Big Tech has looted humanity’s creative output to create artificial intelligence. We don’t just deserve a share of AI’s profits — we deserve control over it.
AI can cost more than human workers now
When AI labs raise prices, big spending on AI could shift from a flex to a liability.
Sharing the Algorithm: The Tax Solution to Generative AI
This article argues that tax policy offers a core tool for mitigating the sweeping public policy challenges of generative Artificial Intelligence ("AI"
An Elegant Solution to AI Slop: Tax It, and Use the Resulting Billions of Dollars to Fund Cultural Institutions, Artists, and Researchers
A technologist proposes slapping a "slop tax" on companies that use or create generative AI content.
The Moat or the Commons — Warman Notes
American capital financed AI on the assumption it would be the next great monopoly. Open-weight models are commoditizing the capability that monopoly was supposed to protect. The collision between the two now defines the direction of the U.S. AI industry — and the country.
In spite of hype, many companies are moving cautiously when it comes to generative AI | TechCrunch
Companies are extremely interested in generative AI as vendors push potential benefits, but turning that desire from a proof of concept into a working product is proving much more challenging.
The grueling fight over who profits from AI music
AI can generate songs in seconds. But behind every AI track is a complicated question: Who should get paid? And, how? The fights have started.
AI Is Already Training on Music. The Real Question Is: Who Gets Paid?
AI is already learning from music. Quietly, constantly, and at a scale most people don’t fully see yet. While the industry debates hypotheticals, the real shift has already happened. The […]
"The forces that are now shaping AI are the same forces that turned the open internet into monopolistic platforms, converted peer-to-peer participation into precarious labor, and enclosed the digital commons. They are now threatening to capture and enclose thought itself." osf.io/preprints/socarxiv/qahd3_v1/
"AI makes it cheaper to contribute to Open Source, but it's not making life easier for maintainers. More contributions are flowing in, but the burden of evaluating them still falls on the same small group of people. That asymmetric pressure risks breaking maintainers." also relevant to slop science
Economic incentives is a big problem with AI in open-source spaces. Finding is unevenly allocated, and AI leads to further disparity. Instead of spreading money around the dev community, it'll go to AI companies. Open source thrives on humans. We don't need rockstars, we need resources.
Lucca @ Northsky 💚💜
they should be giving out more of that to the community as grants and focusing on protocol governance and standards and bringing new people into atproto.
