The old/existing way is: humans produce content (writing, images, videos, music) using software and then use the internet to host and distribute the content. As the volume of information increased, aggregators of content became more important. Google helps you find anything, but primarily text and images, YouTube indexes and recommends medium length videos, etc.
A very common problem pattern is that you find multiple sources that are _nearly_ what you want, but not quite. For example you combine two stock photos in photoshop (with some editing) to produce a final image.
The future way is: large foundational models that act as the aggregators of information _and_ the editing tools. Co-pilot is the best current example we have. Once the performance of Co-pilot becomes good enough, I will never have to leave VSCode + Co-pilot, replacing the previous pattern of Google + documentation + Stackoverflow. The indexers and aggregators will be made obsolete. This is exacerbated by the fact that Co-pilot is able to do more than just aggregating the correct Stackoverflow examples. Co-pilot can do the customization and translation of the code examples into your specific use case.
These large/foundational models are nowhere near perfect yet. Once the performance is good enough to make existing workflows easier, we'll see rapid adoption in every domain. Recommendation algorithms will look entirely different in 10 years time. The 2030 version of YouTube won't be serving users the closest match video from its library, it will be generating custom edits on-the-fly tailored to the user.