Venture Capitalists Target Edge AI and Specialized Hardware for 2018
Investors are shifting their focus away from core machine learning tools and toward applications that bring AI to the edge and specialized hardware.
Building intelligent applications powered by machine learning becomes easier every year, prompting venture capitalists to shift their investment strategies. While core machine learning tools mature, investors actively seek opportunities in companies that move up the stack toward vertical applications, move down toward purpose-built hardware, and move out of the data center toward edge intelligence.
Bringing intelligence to the edge emerges as a major investment category as companies want to run trained models locally on devices like smart speakers. This local processing reduces power consumption, ensures privacy, and lowers latency, but it presents significant technical challenges. Current machine learning approaches rely on expensive, always-connected hardware, so innovative startups are developing new algorithms and specialized hardware to run models efficiently on cheap, power-constrained devices.
In addition to edge computing, investors show strong interest in purpose-built AI hardware designed specifically for machine learning workloads. As the demand for training capabilities skyrockets and highlighted by Nvidia's massive data center growth, the industry looks beyond traditional GPUs. Venture capitalists believe that understanding specific customer use cases remains the key to building the right solutions in both edge software and specialized AI hardware.