Chris Green

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing

@johncoogan @sama I keep looking for the april fools catch

TBPN has been acquired by OpenAI! The show is staying the same and we’ll continue to go live at 11am pacific every weekday. This is a full circle moment for me as I’ve worked with @sama for well over a decade. He funded my first company in 2013. Then helped us fix a serious logjam during a critical funding round a few years later. When I took my second company through YC, he was president at the time, and then when I joined Founders Fund, the first deal I saw in motion was the post-ChatGPT round in late 2022. And as we started growing TBPN last year, he was the very first lab lead to join the show. Thank you to everyone that has been a part of TBPN until now. The last year has been the most fun and rewarding part of my career and we’re excited to have more resources than ever going forward.

It's not often the co-founders and I author blog posts together, but we thought this one is important so we took time to write this together with the Neon team. In this blog we outline why we think a new type of database will slowly take over and replace all existing databases, we call it the Lakebase. We cover the WHY and the WHAT here: 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐨𝐟 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 databricks.com/blog/what-is-a…

I now constantly get questions about the SAAS meltdown, role of AI, system of records etc. I don't have an answer to all these. But I do know that we saw an acceleration in our business in Q2, Q3, and now finished the year with accelerating Q4. The question is, why? Short answer: AI. But the underlying reason is subtle. We are growing fast because we are finally removing the biggest bottleneck in data: the technical barrier to entry. For years, if you didn’t know SQL, Python, you were locked out of the value chain. That has changed fundamentally with the 𝐆𝐞𝐧𝐢𝐞 𝐟𝐚𝐦𝐢𝐥𝐲, and it is the "secret sauce" behind our recent momentum: • 𝐆𝐞𝐧𝐢𝐞: Analysts can query data without any SQL. I use this every day myself. • 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐢𝐞: Builds end-to-end AI models for you, similar to Cursor for ML on your data. • 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐆𝐞𝐧𝐢𝐞: Write Spark pipelines, does plumbing, troubleshooting. We've been talking about DATA + AI democratization, but generative AI finally enabled it in a way that wasn't possible before. That's why we're seeing a market response. Take 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. We launched this serverless engine for agents and apps recently. At 8 months into its journey, its revenue is already 2x what our Data Warehouse product was at the same stage. All this taken together, we ended up with the following stats for Q4: 🚀 $5.4B Revenue Run-Rate, growing >65% YoY 🚀 $1.4B AI Revenue Run-Rate 🚀 FCF Positive for the year 🚀 NRR >>140% databricks.com/company/newsro…

I have vibe coded with Lovable for some hackathon projects, and it is an awesome tool. Great move for Databricks!

Excited to raise $4B+ in our latest fundraise! We shared that we will primarily be using the capital to invest in: 1️⃣ Lakebase Postgres - serverless database for Agents 2️⃣ Agent Bricks - high quality agents that can reason on enterprise data 3️⃣ Databricks Apps - Data Intelligence Apps built on Lakebase and Agent Bricks As part of this we also disclosed: 🚀 Crossed $4.8B revenue run-rate, over 55% YoY growth 🚀 Crossed $1B of revenue run-rate for our Data Warehousing product 🚀 Crossed $1B of revenue run-rate for our AI products 🚀 Continued to be cash flow positive in the last 12 months Big thanks to Insight Partners, Fidelity Investments, and J.P. Morgan who led the round. databricks.com/company/newsro…

OpenAI models will now be natively available on Databricks

Big news: Databricks and @OpenAI are partnering to deliver powerful AI to the enterprise. OpenAI frontier models will now be available natively in Databricks. This means you can build, evaluate and scale production-grade AI apps and agents on your governed enterprise data, leveraging the latest OpenAI models like GPT-5. We’re excited to expand our relationship with OpenAI; Databricks was one of the first to host gpt-oss open models, they use Databricks products and now we’re offering OpenAI models natively on Databricks: databricks.com/blog/run-opena…

Shout out to my Vancouver peeps - check this out! Databricks is opening a new R&D center in Vancouver. Learn about the latest expansion and the critical projects the team will be working on sprou.tt/1wM533wfU4B

Happy Canada Day from a proud Canadian! 🇨🇦

@brianzhan1 @lancedb @ttunguz @Theoryvc @CRV @databricks @runwayml @petesoder @ZeroPrimeVC @bjwilson34 @tnachen Congrats @brianzhan1 !

Thrilled to share that @lancedb closed a $30M Series A, led by @ttunguz at @Theoryvc, with support from @CRV, @databricks Ventures, @runwayml, @petesoder at @ZeroPrimeVC, @bjwilson34 at Swift, @tnachen, and others. Our mission: make the “multimodal lakehouse” the default AI data stack. Why it matters: AI workloads, unlike BI, involve petabytes of embeddings, images, videos, and docs streaming at machine speed. Legacy systems—Parquet-on-S3 for analytics plus a standalone vector DB—can’t handle this. @changhiskhan and @eddyxu hypothesized a new format, Lance, treating vectors, images, text, and videos as first-class citizens, powered by an OLAP-grade columnar engine, with one API for search and training. One year later, Lance is the fastest-growing open-source data format, with over 20M downloads. Enterprises like Runway, Midjourney, and Character.ai use LanceDB for tens of billions of vectors and petabytes of media, simplifying ops and cutting costs. By 2025, 156ZB of global data will be video—~90% of all data. LanceDB is built for this pixel-and-embedding future. Vector-only DBs excel at similarity search but struggle with feature engineering, analysis, versioning, and lineage. Lakehouses handle OLAP but falter on low-latency retrieval. LanceDB combines search-grade latency with lake-grade scale. As a result, their customers report 30–50 % infra‑cost reduction and >2× iteration speed versus stitching together parquet lakes + bespoke vector stores + bespoke video pipelines. Seed investors dream of teams that (a) see the future early and (b) execute like it’s already here. The LanceDB team is both. We can't be more excited to double down.

We’ve released a free edition of Databricks and opened up a lot of our training materials to help developers learn data engineering, data science and AI! Check it out to rapidly get started with the latest tools and with challenging use cases.

Wonder if Elon's feud with Trump is to try to save Tesla's global business, and if the canceled SpaceX contracts are worth it

I am super excited to announce that we have agreed to acquire Neon, a developer-centric serverless Postgres company. The Neon team engineered a new database architecture that offers speed, elastic scaling, and branching and forking. The capabilities that make Neon great for developers are also great for AI agents. Together, we'll deliver an open, serverless database foundation for developers and AI agents. databricks.com/company/newsro…

700+ technical sessions await at #DataAISummit—covering everything from data intelligence and warehousing to governance, AI, and beyond Register: sprou.tt/19hLB7HGSLr

Your first look at what’s coming up for LlamaCon on April 29th! Mark will be sitting down with Microsoft Chairman and CEO @satyanadella to discuss the latest trends in AI for devs; and with @databricks Co-Founder and CEO, Ali Ghodsi on open source AI + advice for founders.

Does there exist a standard taxonomy for LLM models? i.e. Vendor / ModelFamily / Model / etc?

Excited for this partnership! sprou.tt/1RaEeLY6qhp

Getting high-quality labeled data at scale is one of the biggest enterprise challenges with fine-tuning LLMs on specific tasks. TAO (Test-time Adaptive Optimization) is a new method that uses test-time compute and reinforcement learning to improve LLMs—no labeled data required. Requiring only example inputs, TAO often surpasses supervised fine-tuning with thousands of labeled data points and makes low-cost open source models outperform expensive proprietary ones. Learn more about this research from the Mosaic Research Team. dbricks.co/420Xte1

Trying the #soundtransit Link 2 line. So far so good. Can't wait until the Downtown Redmond section opens this spring