YouTube is adding AI into more parts of content creation

Influencers and content creators are many things beyond their public personas. All but the biggest figures likely do some combination of the following jobs themselves: content moderator, video editor, photographer, social media strategist, script writer, idea generator. What if they could outsource much of that work to AI? And what if it were the social media platforms themselves that provided them with the tools to do so?

At the Made On YouTube event, held Tuesday in New York, the company unveiled a slew of new AI features aimed at content creators, many of which focus on all the behind-the-scenes work that goes into a video. Unlike previous tools — like an AI background music generator or tools that create AI photos and videos — the new batch are largely content strategy features, marketed as helping creators reach new audiences (and more effectively get in front of their existing base).

Among the new tools is Ask Studio, an AI chatbot that creators can use to ask analytics questions about how their content is performing. Amjad Hanif, VP of product management, describes it as a “creative partner”: how is the audience responding to a video? What are the most compelling moments of a video? The tool pulls in data from across a YouTube channel, including longform videos and Shorts — essentially a faster and more direct analytics tool that’s built into the platform. Creators can ask the tool to do things like summarize comments and synthesize viewer sentiment, and ask for suggestions based on data: if YouTube reports a drop off in viewership at a certain part, Ask Studio will spit out advice for next time on optimizing that section of the video. Creators can also ask for things like “video ideas from comments on my latest video,” and then follow up with requests for title suggestions. For now, the tool can’t compare one channel versus another (queries like “What videos from my competitors are performing well?”).

Also rolling out is a new thumbnail and title A / B testing feature, building upon a thumbnail testing version that was announced last year. With this update, creators can pair thumbnail images and titles and run tests to see which performs the best; the “winner” is the combination that has the highest watch time.

“No matter how good the video is, the thumbnail and title is what gets people to even see the video and see if it’s good or not. It might be the most important thing, honestly,” says Ashley Alexander, a lifestyle influencer who was given early access and who has been testing some of the tools. Alexander says she uses the thumbnail-only testing feature for every video and has begun integrating the new thumbnail and title A / B testing tool into her workflow.

The influx of tools essentially meant to help creators optimize for the YouTube algorithm are something of a paradigm shift. For years, creators ran their own tests to figure out what worked best for each social media platform: how to write the most enticing title or whether to have a closed or open mouth in a thumbnail. It was trial and error, with creators trying to sort through what kind of content the platforms preferred. Now, some platforms themselves directly tell creators what they should post, and how — TikTok tells creators what topics are trending and even what their followers are searching for in the app, with explicit nudges that creators should make videos that target those searches. The effect is two-fold: it’s a way for YouTube and other platforms to more directly guide what creators make. “Optimizing” content is also potentially beneficial to both creators and YouTube itself — both parties want viewers to be spending more time on the platform, watching their videos.

YouTube is also expanding some viewer-facing AI tools like dubbing. Previously creators had access to an auto-dubbing feature — now, the feature will also sync the YouTuber’s lips to match the dubbed language. Content that uses YouTube’s AI dubbing feature will have a badge underneath the title and in the video description indicating it was auto-dubbed. Creators won’t be able to go through and correct or tweak mis-translated portions after uploading.

Separately, creators will also have the option to add multiple collaborators to a single video — essentially a cross-posting feature. Each collaborator will be able to see performance metrics for the video.

The AI creep into influencing and content creation has been happening for a while, and across industries: adult content creators are using chatbots to interact with paying clients and platforms are encouraging advertisers to use AI-generated models to sell products. When YouTube recently updated its monetization rules, many creators understood the “inauthentic content” policy was taking aim at AI-generated mass produced videos. Some creators were concerned about what exactly qualified as “inauthentic,” how YouTube would screen content, and how it would decide what to demonetize.

There’s example after example of online and hobby communities reckoning with an influx of AI content — but does it matter if it’s happening behind the scenes? Do viewers care if their favorite YouTuber uses AI to come up with video topic ideas, like the platform encourages them to do? And if everyone is perfectly optimized for YouTube’s algorithm and relying on the same built-in tools, is anyone really optimized? Who wins when everyone’s thumbnail and title are just right, or when everyone is using the same AI tool to generate the perfect video topics and scripts?

Creators like Alexander say the features are a jumping off point, not a cheat code: the AI-generated ideas are a good place to start, but she thinks ultimately she knows her audience best. For many content creators, their audiences buy into them as people making distinct creative choices, not just churning out whatever a chatbot suggests — that relationship is something AI can’t replicate.

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