OpenAI details the philosophy behind its Sora video feed

Most social media algorithms want to keep your eyes glued to the screen for as long as possible. OpenAI took the opposite stance with Sora, trying to build a video feed that optimizes for creative spark rather than endless zombie scrolling.

On September 30, OpenAI published the core philosophy behind the Sora feed recommendation system, detailing how it balances algorithmic suggestions, steerable controls, and strict content guardrails.

Algorithmic direction you can actually steer

Instead of prioritizing passive watch time, the Sora feed was built to favor active participation and remixing. The goal was to inspire people to jump in and generate their own clips after seeing what is possible.

To make that happen, OpenAI implemented steerable ranking. Users can explicitly tell the algorithm what mood they are in and adjust what surfaces. Parents can also manage continuous scroll limits and disable feed personalization for teens using ChatGPT parental controls.

The algorithm pulls from several signal layers, including likes, comments, accounts followed, and how often a post gets remixed. It also factors in general device location down to the city level. It can even tap into your ChatGPT conversation history, though users can turn off that cross-platform tracking in settings.

OpenAI also pointed out that connected content gets higher priority than unconnected, global posts. Features like Cameo, which allow friends to blend into shared video scenes, are pushed over detached viral videos.

Guardrails built into the creation engine

Moderating an AI video feed is fundamentally different from moderating TikTok or Instagram. Because every video is generated inside Sora, OpenAI set up its first layer of defense at the generation prompt itself.

If harmful prompts slip through, automated scanning and human review step in. Posts aimed at teen accounts undergo extra filtering to remove age-inappropriate media.

The distribution policy specifically bans graphic violence, explicit adult material, hate speech, and unapproved likenesses of living individuals or deceased public figures. It also goes after subtle harms, barring appearance critiques, content glorifying depression, dangerous mimicry challenges, and low-quality engagement bait.

As noted on the archived document, the standalone Sora product was later discontinued on April 26, 2026. However, the blueprint offers a rare look at how AI companies envisioned community feeds.

Why it matters

We think this feed philosophy was an ambitious attempt to fix the toxic feedback loops of traditional social media. Prioritizing remixes and user agency over pure retention metrics is a refreshing departure from platforms engineered for doomscrolling.

For creators, favoring Cameo flows and original ideas over engagement bait would have meant less algorithmic gaming and more genuine peer collaboration. Letting users steer their own feed ranking also showed genuine respect for consumer agency.

However, building an algorithmic social network entirely on top of compute-heavy diffusion models is an expensive bet. The downstream shutdown in 2026 suggests that merging a heavy generative tool with high-frequency social consumption faces brutal unit economics and retention hurdles that an ethical ranking philosophy alone cannot solve.