Decades ago, the robots.txt standard was introduced and voluntarily adopted by the Internet ecosystem for web publishers to indicate what portions of websites web crawlers could access. Last summer, OpenAI pioneered the use of web crawler permissions for AI, enabling web publishers to express their preferences about the use of their content in AI. We take these signals into account each time we train a new model. That said, we understand these are incomplete solutions, as many creators do not control websites where their content may appear, and content is often quoted, reviewed, remixed, reposted and used as inspiration across multiple domains. We need an efficient, scalable solution for content owners to express their preferences about the use of their content in AI systems.

OpenAI is developing Media Manager, a tool that will enable creators and content owners to tell us what they own and specify how they want their works to be included or excluded from machine learning research and training. Over time, we plan to introduce additional choices and features. This will require cutting-edge machine learning research to build a first-ever tool of its kind to help us identify copyrighted text, images, audio, and video across multiple sources and reflect creator preferences. We’re collaborating with creators, content owners, and regulators as we develop Media Manager. Our goal is to have the tool in place by 2025, and we hope it will set a standard across the AI industry.

Today, we live in an attention economy built for advertisers over users and quantity over quality. Our ambition is to use AI to change this: to empower creators and publishers and to enhance the user experience. We’re continuously making our products more useful discovery engines. We recently improved source links in ChatGPT⁠(opens in a new window) to give users better context and web publishers new ways to connect with our audiences. We’re also working with partners to display their content in our products and increase their connection to readers. We’ve announced partnerships with global news publishers from the Financial Times⁠, to Le Monde⁠, Prisa Media⁠, Axel Springer⁠ and more, to display their content in ChatGPT and enrich the user experience on news topics. More innovation is on the way. This content may also be used to train ChatGPT to better surface relevant publisher content to users and to improve our tools for newsrooms. Our partnerships are crafted to benefit partners and their users, making our models more useful to their employees, customers, and communities. To help advance educational resources, we partnered with nonprofits Khan Academy⁠ and UK-based ExamSolutions⁠(opens in a new window) to improve our model’s math performance, which accelerates their ability to expand access to personalized AI tutoring on their platform.

AI models learn from relationships in information to create something new; they don’t store data like a database. When we train language models, we take trillions of words, and ask a computer to come up with an equation that best describes the relationship among the words and the underlying process that produced them. After the training process is complete, the AI model does not retain access to data analyzed in training. ChatGPT is like a teacher who has learned from lots of prior study and can explain things because she has learned the relationships between concepts, but doesn’t store the materials in her head.

Our models are designed to help us generate new content and ideas – not to repeat or “regurgitate” content. AI models can state facts, which are in the public domain. If on rare occasions a model inadvertently repeats expressive content, it is a failure of the machine learning process. This failure is more likely to occur with content that appears frequently in training datasets, such as content that appears on many different public websites due to being frequently quoted. We employ state-of-the-art techniques throughout training and at output, for our API or ChatGPT, to prevent repetition, and we’re continually making improvements with on-going research and development.