$28B Startup Says Companies Were Refusing Their Free Open-Source Code as 'Not Enterprise-Ready'

"Ali Ghodsi was happily researching AI at Berkeley when he helped invent a revolutionary bit of code — and he wanted to give it away for free," remembers Forbes India. "But few would take it unless he charged for it. "Now his startup is worth $28 billion, and the career academic is a billionaire with a reputation as one of the best CEOs in the Valley." (Literally. VC Ben Horowitz of Andreessen Horowitz calls him the best CEO in Andreessen Horowitz's portfolio of hundreds of companies.) Inside a 13th-floor boardroom in downtown San Francisco, the atmosphere was tense. It was November 2015, and Databricks, a two-year-old software company started by a group of seven Berkeley researchers, was long on buzz but short on revenue. The directors awkwardly broached subjects that had been rehashed time and again. The startup had been trying to raise funds for five months, but venture capitalists were keeping it at arm's length, wary of its paltry sales. Seeing no other option, NEA partner Pete Sonsini, an existing investor, raised his hand to save the company with an emergency $30 million injection... 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. 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 commercialise. "We were a bunch of Berkeley hippies, and we just wanted to change the world," Ghodsi says. "We would tell them, 'Just take the software for free', and they would say 'No, we have to give you $1 million'." Databricks' 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). Users feed in their data and the AI makes predictions about the future. John Deere, for example, installs sensors in its farm equipment to measure things like engine temperature and hours of use. Databricks uses this raw data to predict when a tractor is likely to break down. Ecommerce companies use the software to suggest changes to their websites that boost sales. It's used to detect malicious actors — both on stock exchanges and on social networks. Ghodsi says Databricks is ready to go public soon. It's on track to near $1 billion in revenue next year, Sonsini notes. Down the line, $100 billion is not out of the question, Ghodsi says — and even that could be a conservative figure. It's simple math: Enterprise AI is already a trillion-dollar market, and it's certain to grow much larger. If the category leader grabs just 10 percent of the market, Ghodsi says, that's revenues of "many, many hundred billions." Later in the article Ghodsi offers this succinct summary of the market they entered. "It turns out that if you dust off the neural network algorithms from the '70s, but you use way more data than ever before and modern hardware, the results start becoming superhuman."

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