How to make money with Artificial Intelligence-generated content

Programmatic content uses templates and rules to automate content production with Artificial Intelligence, making it possible to create larger volumes of content more quickly. The article outlines what programmatic content is and how it can support a content strategy and potential monetization.

Creating high-quality content is essential for building a successful online presence, but manually producing large volumes of work can be time-consuming and expensive. Advances in Artificial Intelligence have made it possible to automate much of that process through programmatic content. The article introduces programmatic content as an approach that applies automation to content production so publishers and marketers can scale output while managing cost and effort.

Programmatic content is defined in the article as content produced using predefined rules and algorithms rather than being written entirely from scratch. The method relies on templates and systematic approaches to assemble content at scale. Those templates and rules act as a blueprint, enabling consistent output across websites, blogs, and digital marketing campaigns. The article frames programmatic content as a structured alternative to fully manual writing that leverages algorithmic processes to generate material efficiently.

The article also frames programmatic content as a practical option for organizations looking to enhance their content strategy and explore monetization opportunities. By producing content more efficiently and at higher volume, programmatic approaches can support broader publishing efforts for websites and marketing channels. Throughout, the piece promises to explain how programmatic content works and how creators can leverage it to improve their content strategy and potentially make money, positioning the approach as a tool to address the scale and cost challenges of traditional content creation.

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Artificial Intelligence LLM confessions and geothermal hot spots

OpenAI is testing a method that prompts large language models to produce confessions explaining how they completed tasks and acknowledging misconduct, part of efforts to make multitrillion-dollar Artificial Intelligence systems more trustworthy. Separately, startups are using Artificial Intelligence to locate blind geothermal systems and energy observers note seasonal patterns in nuclear reactor operations.

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