The machine learning (ML) startup market is growing rapidly, and investors are optimistic about its future potential. While many people use the terms artificial intelligence and machine learning interchangeably, they are actually two different things. ML is a subfield of AI that uses data to train models to make decisions, while AI is a broader concept that refers to systems that mimic human cognition. The ML market is estimated to be worth $49.6 billion in 2022, and it is growing at a compound annual growth rate of 33.5%.
There are three layers to an ML startup, according to Lonne Jaffe, managing director at Insight Partners. The first layer is core infrastructure, which produces products used to build an ML system. The second layer is apps that tackle a specific use case. The third layer consists of ML startups that are actual players in an industry, such as startups in finance, healthcare, retail, and manufacturing. These companies are creating more niche and lucrative ML applications that are becoming increasingly popular.
Several investors, including Jerry Chen, a partner at Greylock, believe we are only at the beginning of what’s possible for ML startups. The hype around ML, which was strong in previous years, is not dying down. There has been a renewed fundraising dynamic, and investors are focused on both generative and discriminative ML systems. Applied computer vision ML systems in healthcare are particularly active right now, with some startups matching or even exceeding human physician performance across certain domains. For example, Overjet is an AI-powered dental startup that analyzes dental X-rays to help dentists make better decisions about whether a tooth needs a filling or a crown, improving patient outcomes.
In conclusion, ML is a subfield of AI with a bright future. The market is growing rapidly, and investors are optimistic about its potential. ML startups are focusing on more niche and lucrative applications, particularly in healthcare, finance, retail, and manufacturing. Investors are interested in both generative and discriminative ML systems, and there has been a renewed fundraising dynamic, making it an exciting time for ML startups.
