The Legend Is Back, But No Illegal Stuff This Time
If you old enough to remember the times when music was only just becoming available to buy and listen to online, then you may well remember the name Napster. Created back in 1999 Napster was a peer to peer platform that allowed music file sharing via the internet that was encoded in MP3 format.
If you old enough to remember the times when music was only just becoming available to buy and listen to online, then you may well remember the name Napster. Created back in 1999 Napster was a peer to peer platform that allowed music file sharing via the internet that was encoded in MP3 format. However, due to certain copyright infringements, the company was forced to shut down two years later after being made bankrupt.
At a bankruptcy auction, Napster’s brand and logos were first bought by Roxio, a U.S company specializing in digital media products. Then in 2008 Napster was purchased by Best Buy for $121 million which then merged its Napster services with Rhapsody in 2011. Although the brand continued to trade under Rhapsody for a while, the company have decided to go back to Napster in the US market as well as worldwide.
One of the companies key features, as noted by its users, is that you can listen to the radio, ad-free, skip tunes you don’t want to hear and download songs that can be used offline for less than $10 per month. With this much choice and ease, who could say no?
This rebrand should only increase sales for Napster/Rhapsody as this was a very popular brand for the short while it was around before. For just a small monthly fee users have access to millions of tracks from various genres right at their fingertips. It is a genius way to sell music and keep fans interested and can only see good things for the future of streaming.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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