It’s simple math: Enterprise AI is already a trillion-dollar market, and it’s certain to grow much larger. Down the line, $100 billion is not out of the question, Ghodsi says-and even that could be a conservative figure.

It’s on track to near $1 billion in revenue next year, Sonsini notes. Ghodsi says Databricks is ready to go public soon.

It’s used to detect malicious actors-both on stock exchanges and on social networks. E-commerce companies use the software to suggest changes to their websites that boost sales. Databricks uses this raw data to predict when a tractor is likely to break down. John Deere, for example, installs sensors in its farm equipment to measure things like engine temperature and hours of use. Users feed in their data and the AI makes predictions about the future. “We would tell them, ‘Just take the software for free,’ and they would say ‘No, we have to give you $1 million.’ ”ĭatabricks’ cutting-edge software uses artificial intelligence to fuse costly data warehouses (structured data used for analytics) with data lakes (cheap, raw data repositories) to create what it has coined data “lakehouses” (no space between the words, in the finest geekspeak tradition). “We were a bunch of Berkeley hippies, and we just wanted to change the world,” Ghodsi says. But when the researchers offered it to companies as an open-source tool, they were told it wasn’t “enterprise ready.” In other words, Databricks needed to commercialize. It is a staggering achievement made even more incredible by the fact that many of the original founders, Ghodsi in particular, were so engrossed with their academic work that they were reluctant to start a company-or charge for their technology, a best-of-breed piece of future predicting code called Spark, at all.

The accidental billionaires