Sources: data analytics service Databricks is raising new funding at a valuation of $38B, with Morgan Stanley leading a round of at least $1.5B
Bloomberg : Tweets: @mcbridesg , @katie_roof , and @mmcandycheng Tweets: Sarah G McBride / @mcbridesg : Valuations are rising so fast that Databricks, a startup valued at $28 billion six months ago, is now at $38 billion. Exclusive deets w @Katie_Roof https://www.bloomberg.com/... Katie Roof / @katie_roof : Raising up to $2b at a $38B pre money valuation https://twitter.com/... @mmcandycheng : Woah. I remember writing about their $6 billion valuation in 2019, back when that was considered a big number. https://twitter.com/...
Context & Ripple Effects
Databricks' valuation has been repricing at a pace few private companies see: it was valued at $6.2B in 2019, and in January was in talks for a round around $27B — a number that was itself a record for the company. Bloomberg now reports the round landing at a $38B valuation, with Katie Roof noting Databricks could raise up to $2B and Morgan Stanley leading a check of at least $1.5B.
The round matters beyond Databricks' own balance sheet because of who is writing it: a major bank taking the lead position on a late-stage private round, at a moment when the company's valuation has jumped $10B in roughly six months. The raise subsequently closed as a $1.6B Series H led by Morgan Stanley's Counterpoint Global fund, confirming the reported terms.
First-order effects
- Databricks secures at least $1.5B at a $38B pre-money valuation — a $10B step-up from the ~$28B discussed six months earlier — giving it a large capital cushion for expansion and hiring.
- Morgan Stanley converts its lead role into a marquee position in one of the hottest private data companies, the kind of relationship banks typically cultivate ahead of future underwriting mandates.
Second-order effects
- A two-year move from $6.2B to $38B resets the fundraising benchmark for every private data-infrastructure startup, as investors reprice comparables against Databricks' new mark.
- Bank-led mega-rounds at escalating valuations normalize the pattern of institutions buying late-stage private stakes directly, pulling private-market deal flow toward the same firms that dominate capital-markets fees.
Third-order effects
- The pattern held at scale: Databricks went on to raise at $134B, then closed a $5B round at $190B with a $7B revenue run rate — evidence that recurring multi-billion private rounds, not a single pre-IPO raise, became the financing model for AI-era data companies.
- Morgan Stanley's franchise followed the same arc, with the bank later reporting sharply higher capital-markets fees from AI infrastructure financing and forecasting AI-related debt issuance roughly doubling to ~$570B — the bank's Databricks lead role foreshadowing a broader Wall Street build-out around AI capital.
The trend: Late-stage private capital is becoming a standing, repeatable financing pipeline for data and AI infrastructure companies, with valuations repricing in months and Wall Street banks positioning early to capture the associated fees.