Why Apps With Picture and Video Tools Get Far More Downloads Than Text AI Updates

When app makers add image and video generation features to their apps, they see 6.5 times more new downloads compared to when they improve text-based AI, according to analysis from Appfigures, a firm that tracks mobile app performance. The company looked at download patterns after different kinds of AI updates rolled out in 2024.

The difference is striking. Text improvements — like making AI write better or think through problems more carefully — bring some new users. But image and video tools bring a flood of them.

Why Pictures and Videos Spread Faster

There's a simple reason: people share visual content, but they rarely share improved text.

When you create a picture or video with AI, you want to show it to friends. It's cool, it's tangible, people can see it immediately. You post it on Instagram, TikTok, or send it to a group chat. Other people see it, think it's impressive, and download the app themselves.

Better text AI, by contrast, helps you write or think more clearly. That's useful, but it doesn't make people say "look what my app did" and send it to everyone they know. The improvement stays private, inside the app.

This pattern holds across all kinds of apps — creative tools, social networks, productivity software. Whenever they add image or video features, downloads jump. When they just improve the text part, downloads rise more slowly.

How the Technology Makes a Difference

Image generation can produce a picture in a few seconds. Video takes longer to create, but it's even more impressive when shared.

There's also a money side to this. Making an image usually costs less in computing power than running a large text AI. This means app makers can let more people use the feature without their bills going through the roof. More people trying the feature means more people telling their friends.

Consumer Apps Versus Business Tools

This download surge mainly affects apps for everyday people — the ones you'd use on your phone to make art or share on social media. Business software follows different rules. A company caring more about whether the AI helps their team work faster and more accurately than whether it creates things worth showing off.

We've seen a similar pattern before. When smartphones first arrived, regular people adopted them years ahead of most businesses. Visual AI seems to be following the same path. Consumer excitement comes first, and business adoption catches up later as the technology matures and becomes cheaper.

What This Means for App Makers

The fact that visual features drive so many more downloads creates challenges and pressures.

Building image and video features requires different technical work than text AI. It demands more computing power from servers, and apps that suddenly get popular can run into problems handling all the demand. The successful ones have figured out how to do this processing on your phone itself, rather than sending it all to company servers, because it's faster and cheaper.

The download advantage also shapes what app makers choose to build next. A team might focus on adding a visually impressive new feature rather than making the AI's core technology more reliable or accurate. The new features grab downloads right away. But if the fundamental product isn't solid, users tend to delete the app later and move on to something better.

What Happens Next

The 6.5x boost reflects the moment we're in right now, when image and video AI still feel novel to most people. As these features become normal — expected in almost every app — that advantage will likely fade. The companies that got in first and built good products will benefit most.

Over time, people will come to expect that their creative tools, social apps, and even work software can make pictures and videos. The excitement will settle down, and what matters will shift from having the feature to having the best version of it.

For app makers, the lesson is clear: users notice what they can see and do immediately. A brilliant improvement to how AI thinks works great on paper, but if users can't easily see what it does, they won't care. Visual tools work because the value is obvious. That principle is unlikely to change as AI keeps evolving.