The biggest unintended consequence of the shift from desktop to the web is that we now automatically collect vast amounts of data on just about everything, and we can ship improvements instantaneously. Those are the waves that push machine learning forward.
That sounds simple but it's hard to overstate the impact. If you were a brilliant AI researcher working on Microsoft Word in 1990, the only data you had was what you could collect in the lab. If you discovered a breakthrough, it would take years to ship it. Ten years later, the same researcher working at Google had access to a vast repository of search queries, clicks, page views, web pages, and links, and if she found an algorithmic improvement to boost click-throughs by 5%, she could ship it instantly.
The internet companies (and folks doing scientific computing) were the first to apply machine learning to huge amounts of data, which is why technologies like MapReduce and BigTable were invented at places like Google, but we're seeing the same techniques move into every other application area: genetics (where the cost of sequencing DNA is falling faster than Moore's law), health (with electronic health records), energy, finance, security, and even the army. It's pervasive enough that "Big Data" conferences like Strata have thousands of attendees.