"In his Fortune op-ed, Ready used our name as shorthand for an era when technology outpaced ethics-when access was prioritized over compensation, and creators got left behind. He's not wrong about the parallel. Generative AI companies have been scraping the internet's creative output to train models without much thought about who made that content or whether they'd like to be paid for it. That's a familiar story to us."
"In 1999, Napster didn't fail because the idea was wrong. It failed because the business model didn't exist yet. The insight was correct: People wanted instant, universal access to music. They wanted to discover new artists without buying a full album. They wanted their library in their pocket. Every single one of those desires turned out to be true- Spotify, Apple Music, and the entire streaming economy proved it."
"The failure was that Napster moved faster than anyone could figure out how to compensate the people who made the music. That's the part that took another decade to solve. AI is in that same window right now. The technology works. The demand is real. But the compensation models are still catching up. Ready is correct to call that out."
"In 1999, we democratized access to music. In 2026, we're democratizing access to expertise. That's the mission that guides every product decision we make. Today's Napster builds AI agents that let real humans with real knowledge share what they know with everyone, at unprecedented scale. We call those agents Companions. They're not generic chatbots pulling from the entire internet. They're built on verified, specific expertise that users can collaborate with."
Technology has often prioritized access over creator compensation, leaving creators uncompensated as distribution scaled. Peer-to-peer music sharing proved user demand for instant, universal access, discovery, and portable libraries, but failed because licensing and business models were absent. Streaming platforms later solved the compensation problem through licensing and new business models. Generative AI currently mirrors that Napster-era gap by training on scraped creative work while compensation frameworks lag. Napster today presents itself as an AI company applying learned lessons by building AI agents called Companions that rely on verified, specific human expertise rather than generic, whole-internet scraping.
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