From Research Paper to Real Product
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Most neural network research never turns into anything people actually use, and an Ainame24 article explains exactly where that journey usually breaks down. It lays out five distinct stages a model must survive — from a fragile academic script to a system serving hundreds of thousands of users — and shows why most AI startups fail not because their technology is bad, but because they skip stages or misjudge which skills matter at each phase. The piece traces the broader history too, from AlexNet's 2012 breakthrough through the transformer era to the open-source wave that shifted the industry's real bottleneck from access to distribution. It's a grounded, honest look at commercialization written for founders and investors who want substance over hype, and a reminder that a great model alone was never the same thing as a great business.