Data centers, which drive the apps, websites, and services that billions of people use every day, can be hazardous places for the workers that build and maintain them. Workers sometimes have to service a data center's electrical equipment while it's being energized. And they can become exposed to chemicals like chlorine, which is used as a sterilizing agent for the water circulated through liquid cooling systems for computers and servers. In June 2015, five people had to be taken to a hospital after a chlorine gas leak at an Apple data center in Maiden, North Carolina.

Data centers are safer than they used to be. But in search of forward-looking solutions, some tech giants say that they're exploring how AI can be applied to prevent safety issues. For example, Microsoft is developing an AI system that analyzes data from a range of sources and generates alerts for data center construction and operations teams to "prevent or mitigate the impact of safety incidents." A complementary but related system, also under development, attempts to detect and predict impacts to data center construction schedules.

"These initiatives are both in early testing phases and are expected to begin expanding into our production environments later this year," a Microsoft spokesperson told TechCrunch via email.

Meta also claims to be investigating ways AI can anticipate how its data centers will operate under "extreme environmental conditions" that might lead to unsafe work environments. The company says that it has been developing physical models to simulate extreme conditions and introducing this data to the AI models responsible for optimizing power consumption, cooling, and airflow across its servers.

"We have significant operational data from our data centers, in some areas at high frequency with built-in sensors in servers, racks, and in our data halls," a Meta spokesperson told TechCrunch. "Each server and network device, taking on different workloads, will consume different amounts of power, generate different amounts of heat, and make different amounts of airflow in the data centers. Our [infrastructure] team collects all the data from each server and then develops AI models which can allocate our servers and racks in the data centers and send workloads into these servers to optimize [for] performance and efficiency."