Lei Du

This section on how dbt Labs transitioned from a purely PLG motion to layering on enterprise is a fascinating one. Two instructive passages that I have bolded (below from First Round Review's path to PMF series featuring dbt Labs) TLDR: GTM comprises your ICP, channel, and message. When you transition from PLG / bottom up to Enterprise / top down motion, naturally your ICP and channel also changes, but your messaging / proposition needs to change too, e.g., the enterprise may be multiple personas and are buying assurance as much as solution. --- "Handy found product-market fit organically for dbt as an open-source tool mostly used by data practitioners and developers. But a few years into running a commercial business, he realized he had to build a growth curve all over again with C-suite data leaders. “Even though we had an unbelievable amount of market pull, as we initially commercialized, it wasn’t easy for us to transform this open-source command line tool into a product that enterprises would pay a million dollars for,” says Handy. “When you have enough product-market fit, sometimes it allows you to get away with not being super tight on product marketing or sales motions. So around 2022, we went from this gigantic acceleration curve and overnight we realized, we have to sit at the adults’ table and figure things out real fast,” says Handy. After the PLG flame started to fizzle, Handy turned his attention to layering on a sales-led motion for the cloud platform. “We had to focus our efforts on telling cohesive stories to senior data leaders. We had to have a very clear, explainable answer to the question, ‘Why should I use the commercial product and not the open-source product? And it had to be digestible by someone with a C in their title,” he says. Handy’s answer: dbt Cloud can handle complex data for companies of every size. “The longer people used dbt, the more complex their code became,” he says. “It was a problem for the most sophisticated dbt users, who were often at the largest companies. So there was a real opportunity for us to step in and solve that for them with dbt Cloud.” To tell that story to enterprise customers, Handy relied on data, naturally. “At a user conference we presented a chart that showed the number of dbt projects that had a certain number of models in them — over 100, over 1,000, et cetera,” he says. “We watched that number climb and we knew as users ourselves, ‘Oh my God, trying to work in a dbt project with 5,000 models in it is challenging.’ So we started with that quantitative data point and asked folks in our community about their experiences with these very large, complex dbt projects, and validated that this was a pain in the ass without a cloud platform.”

23

4 Comments

It’s time to be real about what HTAP actually means and how to implement it in a way that supports agents and other similar workloads…

11

Last week, dbt Labs launched the public beta of dbt Fusion, a Rust based engine rewrite that brings a series of neat new features to our favorite data transformation tool: 🛠️ Real-time SQL validation and autocompletion 👀 Live previews of CTEs and compiled code 🔍 Rich model lineage at the colomn and table levels ⚡️ 30x faster project parse time (thanks Rust 🦀) 💻 Deep VS Code / Cursor integration At Opsima, dbt is a key component of our data stack (along with Prefect and Snowflake). dbt powers all data transformations across our cost and usage models and commitment automation engine. And being Cursor power users, the idea of a faster, smarter model development workflow within our IDE is quite exciting! That said, the reaction hasn’t been all positive. Reddit’s on fire with concerns about what Fusion means for the future of dbt Core and open source. https://lnkd.in/e9WSFARf dbt Labs insists Core stays under the Apache 2.0 license and fully supported, but Fusion is only “source-available”, not entirely open source, and licensed to limit hosting it as a service which raises some concerns. https://lnkd.in/ekVzgxcW For teams like ours though, we’re confident Fusion will help us ship faster and smarter while keeping core flows portable. What is your own take on the launch? #dbt #dbtFusion #FinOps #DataEngineering #OpenSource

13

🗓️This Week at OpenAI 1️⃣ Expands enterprise distribution via $200M Snowflake partnership embedding GPT-5.2 into governed data stacks 2️⃣ Nears $20B NVIDIA investment at ~$830B valuation, reinforcing compute-backed capital alignment 3️⃣ Launches Frontier and adds ads to free ChatGPT, opening new enterprise and consumer monetization layers. 🚀🚀🚀 #OpenAI #OAI #Weekly

4

YellowDog.ai just set a 10x benchmark uplift in scale computing, delivering 40,000 tasks per second (TPS) and managing 100,000 compute nodes in the cloud. What's even more interesting, that's 2x IBM Symphony and opens an intriguing pathway for these until-know closed/captive systems.

11

👉️ https://lnkd.in/gAUt3gZB 🧠 Writing PyTorch extensions? Your memory allocator might be the hidden bottleneck. When you're building custom operations—or integrating specialized hardware—PyTorch's default memory management won't always cut it. This blog dives deep into how to write efficient, debuggable, and performance-tuned allocators for your extensions. Key insights include: 🚀 When (and why) to use custom allocators 🎯 Memory alignment, pooling, and fragmentation strategies 🔍 Debugging tools and sanity checks you wish you had sooner 🤝 How to coexist with PyTorch’s built-in allocator without reinventing the wheel 💬 Building custom extensions or facing memory headaches? Let’s trade strategies—what’s worked for you? #PyTorch #AIEngineering #DeepLearning #MemoryManagement #CUDA #PerformanceEngineering #PythonDev #MachineLearning

1

This is why I love working at Databricks. Innovation here starts from first principles (and it’s never easy). Our product and R&D teams build “Useful AI” capabilities that enable organizations to generate real alpha, not just add to agentic hype. Add KARL to the list!

40

Document-centric tasks sit at the core of many enterprise, business, and government workflows. Search is important, but the real challenge is going beyond retrieval, enabling systems to reason across documents, verify facts, and handle multi-step information tasks. Great to see a new model from Databricks moving in this direction. I expect we’ll see many more models, including open-weight ones, designed specifically for document-driven workflows, an area with huge potential across enterprise and government use cases.

23

1 Comment

Just released my conversation with Troy Astorino, CTO of PicnicHealth, where he reveals how they built an 8B parameter model that outperforms much larger frontier models by focusing on domain expertise and building an un-breachable data moat. Troy's counterintuitive insight completely reframed how I think about AI: Most engineering leaders ask "How fast can we ship this?" when they see new models. Troy asks "Where will this break?" That mindset helped PicnicHealth engage with 7 out of the 10 largest pharma companies and collect 350 million clinician annotations, creating a data advantage that no general-purpose model can replicate. Listen here: 🎧 Substack: https://lnkd.in/gFmeKD9g 🎧 Apple Podcasts: https://lnkd.in/gAjuTvfw 🎧 Spotify: https://lnkd.in/gXCWrrdf

10

Requesty announced their $3M Seed round to build the gateway layer for the multi-model AI era. Every team working with LLMs in AI today wrestles with shifting APIs, model failures, ballooning costs, and governance gaps. Requesty fixes this in one line of code — routing across providers, standardizing access, and giving enterprises the control plane they’ve been missing. 25k developers have already taken note. We are bullish to be building with Thibault, Daniel and team; alongside 20VC, Tiny Supercomputer Investment Company, Michele Attisani and more.

58

4 Comments