Databricks Series I

Databricks commanding a $43 billion valuation in September 2023—exactly 2.3 months before the entire market decided AI infrastructure might be overpriced—represents the kind of exquisite timing that makes you wonder if T. Rowe Price's calendar app was stuck in Q2. The Series I (yes, they're at *Series I* now, which feels like watching someone count past twenty for the first time) priced at roughly 38x ARR assuming their whispered $1.1B run rate was accurate, which in this market is like assuming your Tinder match actually looks like their photos. For context, Snowflake was trading at 18x revenue at this exact moment. Even peak-bubble Databricks believers have to squint really hard at that delta and convince themselves that "unified data lakehouse" justifies double the multiple. The $500M itself isn't offensive—it's the valuation that makes my calculator weep.

Here's what keeps this from being a complete disaster: Databricks actually ships product. Their consumption-based revenue model was genuinely humming along, posting 50% YoY growth, and Delta Lake had achieved the rare feat of making data engineers actively advocate for a technology without being paid to do so. The enterprise footprint was legitimately impressive—you couldn't throw a rock in a Fortune 500 data team without hitting someone with a Databricks dashboard open. Morgan Stanley and T. Rowe aren't known for lighting money on fire for the aesthetic, and their involvement signals that someone ran the numbers and didn't immediately burst into flames. The technical moat around their Spark implementation and the MLflow ecosystem represents real, defensible infrastructure. I'll give them credit: the fundamentals aren't fantasy.

But let's talk about what actually happened here. Traditional mutual funds leading a late-stage AI deal in September 2023 screams "we missed the NVIDIA boat and need AI exposure before EOY reporting." This wasn't strategic vision; this was FOMO with a Morgan Stanley letterhead. The deal arrived precisely when every VC was starting to quietly update their "AI infrastructure" thesis decks to include the word "rationalization" in smaller fonts. Databricks was still fighting Snowflake, Google, and increasingly AWS for the same enterprise wallet, except now everyone was whispering about OpenAI's custom infrastructure and Anthropic building their own stack. The competitive dynamics were actively deteriorating while the check was being written. A Series I in 2023 should mean you're approaching public market readiness, but their S-1 stayed suspiciously absent.

The path to exit is where this gets genuinely depressing. At $43B, Databricks needs to IPO into a market valuation of what, $50B minimum to avoid down-round optics? That's asking public market investors—who just watched every cloud stock get obliterated—to assign premium AI multiples to what is fundamentally enterprise data infrastructure with a chat wrapper. The 2024 IPO window opened and Databricks stood there checking their phone. An acquisition is laughably impossible at this price; even Microsoft's infinite capital has limits. So you're stuck in this purgatory where the company has to grow into a valuation that made sense for maybe six months in 2023, hoping that AI hype cycles back around before their employees' RSUs fully vest and they realize what strike price means. The risk-reward here is profoundly uninspiring for anyone who wasn't already on the cap table.