Extract from article by John Melas-Kryiazi
Like many venture capitalists, I talk to technology startups leveraging AI/ML almost every day. When I do, I’m always hunting for companies that are building something completely new — whether it’s a proprietary new data set to train machine learning models or a radically different approach to solving big technical problems using AI. The fundamental reason is this: If a company is going to out-compete others long-term using AI/ML, it better have the best data to solve a specific problem or be playing a different game from its competitors.
Data is the fuel we feed into training machine learning models that can create powerful network effects at scale. Unfortunately for startups, big technology companies typically have huge, proprietary data sets that span many industries. Meanwhile, the open-source community’s efforts are quickly democratizing access to the most sophisticated machine learning algorithms. It’s now nearly impossible for a startup to develop a competitive advantage around algorithm development alone.