YouTube’s Home feed is becoming whatever you ask it to be


YouTube is adding a new discovery chip to its Home page that turns a typed request into a personalized stream of videos.

The feature, called “Your custom feed,” gives people a more direct way to break out of the usual recommendation mix. A viewer can ask for something outside their normal watch patterns, or narrow the experience around a particular moment, such as short guided meditations after work.

The YouTube Home feed change is rolling out in the US on mobile and desktop for signed-in viewers using English.

How does the chip work

The new option sits at the top of the Home page alongside YouTube’s other quick-access topic chips. Tapping “Your custom feed” opens a text box where viewers can describe what they want, or choose from suggested prompts.

Once YouTube builds the results, the request becomes a saved chip. Viewers can reopen it later instead of retyping the same prompt, which makes the feature feel closer to a reusable discovery lane than a search query.

People can also revise the prompt inside the custom feed. Changing the text generates a new space, giving the feature flexibility for shifting interests, routines, or viewing moods.

Why does this change recommendations

YouTube recommendations can feel oddly fixed once the platform decides what a viewer wants. This chip gives users a more active role when they want a temporary detour without changing their broader viewing habits.

The Home page is where many people start watching before they know exactly what they want. Letting users steer that surface with plain-language requests could make discovery feel less passive, especially for moments when the usual feed feels stale.

There’s still a limit. YouTube doesn’t say how much a prompt can override existing history, so the feature looks more like a nudge on top of the recommendation system than a full reset button.

Who gets it first

The rollout is limited to signed-in viewers in the US who use YouTube in English. It works on the YouTube mobile app and desktop.

There’s one important catch, search and watch history need to be turned on. If the chip doesn’t appear, those settings are the first place to check.

YouTube is asking for feedback through the chip’s three-dot menu when the results miss the mark. For anyone stuck in the same recommendation loops, the practical move is simple, start with a narrow prompt, then edit it until the feed feels useful.



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“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, even after we pointed out that many users may not know which model they were using in the ChatGPT interface. She also declined to comment generally about the exposure of PII by the chatbot, instead providing links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 



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