In my experience, scraping the data is the easy part. Once you've scraped it you've got to get rid of all the garbage, which is where the issues arise, especially if you're just blindly scraping everything you can find.

For example, in a generative model I'm working on, I have a dataset consisting of ~5M images just blindly scraped from a website. After filtering, this drops down to ~500k images, yet a model trained on that performs worse than one trained on a set of 30k curated images (picked based on a manually evaluated list of "known good" artists), where filtering brings it down to ~18k images. The larger dataset, while containing more information, also contains more errors, many of them pretty hard to filter out.