When Andrew Ferguson launched Databricks’ corporate venture arm three years ago, he wasn’t expecting to be writing checks quite this fast.
Databricks Ventures has backed 25 companies already. In 2023, the fund closed an investment every month, on average, Ferguson tells Term Sheet.
Of course, Ferguson also wasn’t anticipating the frenzied rush of new entrants and new companies into the AI sector as soon as GPT hit the market at the end of 2022—nor the soaring valuations that LLM competitors would notch so shortly after. Databricks, for its part, introduced its own open-source large language model earlier this year, called DBRX. And it started making investments into other models (Mistral AI for one), and in companies that make use of multiple models, like Perplexity AI, this past fall.
Now Ferguson wants to formalize Databricks Ventures’s investment strategy accordingly. Its initial fund, launched in 2021, was squarely focused on companies that worked with the Databricks “Lakehouse” architecture—which removes traditional data silos and allows teams to pull from a single data source. Databricks’ new venture capital fund, which officially launches today, will focus on a broader subset of companies that are sitting on top or working along with the Databricks Data Intelligence Platform, which DBRX was built on top of.
Databricks’ second VC fund isn’t a specific size. The fund invests off the corporate balance sheet on a deal-by-deal basis (Databricks declined to disclose how much capital the company has invested in the 23 startups it has backed). There’s no specific deployment period, and Databricks CEO Ali Ghodsi signs off on every investment. So when Databricks says it is launching a “second fund,” you can think of it more as the company debuting a new thematic strategy. Like many corporate VCs, Databricks is on the hunt for strategic partnerships and ways to improve its customers’ product experience, rather than just a financial return. And it naturally will end up striking an M&A deal or two for portfolio companies that end up fitting into the company’s broader strategy, though that’s not the purpose of the fund, according to Ferguson. (Databricks ended up acquiring Arcion, a data ingestion company that Databricks invested in back in 2022, for example)
This strategic approach makes Ferguson a bit more comfortable coming in at some of the soaring valuations currently garnered by AI startups with comparatively slim revenue—valuations that Ferguson says make him “uncomfortable and nervous” and that he acknowledges “don’t pencil out,” at least for now.
Because these startups will be adding value to pre-existing Databricks customers, or incentivizing new users to use Databricks, there’s other financial value in it for Databricks versus just a big IPO or M&A exit. “That's valuable in a different way than when you have to think about the financial valuation at the time of our investment,” Ferguson says.
Databricks tends to come in at the Series A or Series B stages, when a company already has a product in the market. And it doesn’t ever lead deals, instead following on in rounds led by VCs, including some of its own investors such as Andreessen Horowitz or NEA, versus the other way around. Ferguson says that investors like to bring him into a round because of how Databricks can help with due diligence, given that they have either worked directly with some of the companies already or have direct access to their customer base to ask for feedback.
That came into play with Databricks’ investment into Unstructured. “We were able to validate the quality of the product offering by talking to our own customers,” Ferguson says. Alternatively, he also has passed on deals because Databricks employees didn’t personally have a good experience working with a company, or got “mixed feedback” from one of their customers.
In a nascent and rapidly evolving ecosystem of AI providers, Ferguson wants to have a finger on the pulse of the latest trends; he needs to ensure that Databricks can help create the ecosystem itself, and partner with the companies who are on the front end of sectors that “aren't even top of mind yet, because the industry is moving so quickly.”
“The use cases keep changing. The development patterns for AI keep changing. And so we want to make sure that venture is a lever we can use to help create the ecosystem of partners that our customers need,” Ferguson says.
Allie helped report this story last week, and I wrote this essay en route from her wedding in Malibu this weekend. I can confirm that vows were exchanged, that two former Term Sheet writers were in attendance, and that I’ve never seen her happier. Mazel Tov, Allie! And I hope you enjoy hearing from some of our other reporters on staff (including me!) while she’s honeymooning.
See you tomorrow,
Jessica Mathews
Twitter: @jessicakmathews
Email: jessica.mathews@fortune.com
Submit a deal for the Term Sheet newsletter here.
Joe Abrams curated the deals section of today's newsletter.
