After months of intense building, I am super excited to launch CustomerLake today! CustomerLake is Databricks' answer to how Customer Data & Marketing will evolve in the agentic world. AdWeek sat down with @alighodsi and msyelf to dig into the details. Check it out below!

LOL

Lots of fun in the Hubspot Unbound 2026 keynote debating the future of agentic marketing with @adampc. One interesting tidbit: context fragmentation is the kryptonite of agentic workflows. Centralize, standardize and open up your business context to agents as soon as possible!

It's been amazing watching Suchi up close as she overcame so many obstacles to bring her incredible technology to life! AI is not just about more productivity and profits. This is a real, novel, and hugely impactful application of AI that we don't talk enough about.

Worth catching @rxin if you're attending #VLDB2026 this year!

For years, the advice was to move to San Francisco if you wanted a career in tech. That advice is now wrong. New York has become the most exciting place in the world to build. A few reasons why: 1. The AI labs are here. OpenAI, Anthropic, Google, and hundreds of others all run meaningful operations in NYC. You can now build a serious frontier-tech career here without giving up anything. 2. Every major industry lives here, and so does its AI counterpart. New York doesn't only have "tech," but finance, media, advertising, fashion, healthcare, real estate, law, and commerce too, with their applied-AI layer counterparts being built on top of each one. 3. The talent pool is unlike anywhere else. SF gives you engineers. New York gives you engineers, plus world-class designers, salespeople, bankers, marketers, operators, creatives, and media people, all at the top of their fields. Companies will need all of them. 4. People actually want to live here. New York is unlike any other city with its density of restaurants, art, nightlife, fashion, and media - and the people working across these sectors. If you're building something and you're more than just tech, I'd argue there's only one city for you right now.

Seeing a lot of resumes like this these days. Every time I do, I feel like I just wasted 30 seconds of my life. Sure, there's a small chance you get lucky. And a much, much bigger chance you destroy what could otherwise be an exceptional career.

Nearly 400,000 tech workers in NYC, up 8.4% from three years ago. Great news for Gotham! (Now we just gotta build enough housing for all these folks…)

Excited to share that we've acquired ElectricSQL, the team behind PGlite. Agents need super fast Postgres and this team built an amazing WASM (WebAssembly) implementation of postgres that runs in your browser, but can sync back with Postgres instances asynchronously. Exactly what blazing fast AI agents today need. Excited to supercharge our 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬 offering with these capabilities. databricks.com/blog/electric…

Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from: 1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more. 2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%. 3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%. 4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.

Paying significantly above market makes things easy in the short run but creates huge medium/long term risk. Screws up culture and can lead to a massive churn wave the minute the company hits a bump… because all the missionaries instantly abandon ship.

I know Greg means this as a good thing (and it is...) But it's also why the Chinese open source models are rising so fast, and a real challenge for the big US labs to figure out. It's also why Databricks AI Gateway is so popular - Enterprises realize this and want flexibility.

Excited to spend a couple days in the beautiful Catskill mountains strategizing and bonding with the CustomerrLake team!!

Coatue is leading the latest Databricks round at $188B valuation. Why? Obvious answer is that they believe Databricks won the Data + AI battle. I believe that's true. But there's something else going on here. Coatue published this slide a few weeks back, suggesting that companies valued between 100B-1T have a nearly 1/3 chance of 10x'ing (!!!) their valuation. Counterintuitively, you are much more likely to 10x if you are 100B-1T than if you are smaller. Coatue's conviction is clear and their actions follow their words (and slides!)

We're raising funding at $188 billion valuation to double down on our AI strategy focused on three priorities: 1️⃣ Unity AI Gateway - our multi-AI governance solution that helps control costs. 2️⃣ Genie - our AI coworkers that actually understand your business data. 3️⃣ Lakebase - our serverless Postgres database specifically for AI agents. databricks.com/company/newsr…

Happy to finally make this announcement of our Seed and Series A raises. It's been a great journey since I left Google DeepMind 2.5 years ago with a goal to democratize post-training! Post-training gets easier if you have access to good data and now, RL environments. That's why we have poured our energy into doing data research. And we will continue to make Bespoke into one of the world's best data research labs. Thank you everyone for your support and thanks to the incredible Bespoke team that has done amazing work so far! PS1: Yes, we got busy with building after our Seed (MiniCheck, Curator, OpenThoughts, Terminal Bench..) so we didn't get a chance to announce the Seed raise! PS2: I will be at ICML starting Wed!

amazing team tackling a truly challenging/exciting problem!

Agreed. HOWEVER the difference is that Palantir is ALSO proprietary - ontology, the data storage etc. So you trade one proprietary service (big labs) for another (Palantir). Databricks is the ONLY “middleman” that doesn’t hold your data hostage!