Juhi Singh

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.”

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Solving Valuable Problems with ML Most excitement today centers on automation powered by fuzzy intelligence, a major driver of productivity at scale. But are there classes of high‑value problems that truly benefit from ML’s compute‑intensive capabilities? Having spent much of my career modeling complex systems with mathematics and stochastic methods—often inventing or refining algorithms to reduce dimensionality and combinatorial complexity—I chose to explore a particularly compelling challenge. Since 2008, quantitative easing and currency manipulation across major economies have added new complexities in the obfuscation of real economic interest rates. Global energy market interventions and sovereign wealth fund activity in bond markets have further complicated the picture. For quantitative finance professionals, hedging and capital allocation have become a game of speed, making this problem all the more engaging. I experimented with the Mixture of Experts (MoE) approach in ML to tackle this challenge. Here are my first results: https://lnkd.in/gfsXTBDK Summary: This path seems very compelling. #QuantFinance #ML #MOE #OptionsPricing #MacroHedging #FIIPricing #MLinFinance

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Databricks $5B Equity at $134B and $2B Debt Capacity | Download full report = www.agdillon.com/insights • Raised $5B in equity at a $134B valuation and added $2B in new debt capacity • Annualized revenue exceeded $5.4B for the recent quarter up 65% YoY • AI products contribute $1.4B in annualized revenue and the company generated free cash flow over the past year • Investors include Goldman Sachs and Glade Brook and Morgan Stanley and Neuberger Berman and Qatar Investment Authority and JPMorgan led the debt facility

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We’re learning a lot at Snowflake, very quickly, about what it actually takes to deliver high quality, high-performance agentic analytics in the real world. Rajhans Samdani and I collaborated to write this latest post, which captures some of those learnings, especially around the emerging “context layer” and why it is becoming foundational for trustworthy data agents. https://lnkd.in/g-8TMcmD What’s been most interesting is how consistently the same themes show up across customer engagements. Whether it’s Semantic Views, Cortex Analyst, our Agent Framework, or Snowflake Intelligence, the same pattern keeps reinforcing itself. You can’t get to reliable, scalable outcomes without a strong, well-defined layer of context that connects data, business meaning, and user intent. This is one of those moments where the pace of change is hard to overstate. The space is evolving quickly, and we’re seeing ideas move from early experimentation into real customer impact in a matter of weeks. Being able to take what we’re learning directly from customers, feed that back into the platform, and then see it improve results is what makes this work so exciting right now. If you’re thinking about how to build or scale agentic analytics, I hope you’ll find this perspective useful.

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Databricks hitting $100B+ valuation got me thinking about enterprise software ROI calculations we're seeing in Treasury and FP&A. The real story isn't just the valuation - it's how companies like AudioShake (fresh $14M Series A) are building scalable financial models around AI-enabled solutions. From my military and financial markets background, I know sustainable growth requires disciplined cash management and robust analytics infrastructure. These AI companies succeeding long-term will be those with: ✓ Clear unit economics ✓ Predictable cash conversion cycles ✓ Scalable Treasury operations ✓ Real-time liquidity optimization The technology is impressive, but the financial discipline separates winners from the noise. How are you evaluating AI investments in your FP&A stack? #FPA #TreasuryTech #CashManagement #FinancialAnalytics #AIInvestment #LiquidityOptimization #FinTech"

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Aakash Gupta posted a really interesting set of learnings sitting down with a fintech executive from Robinhood. One key learning that resonated with me is: re: Legal, "Assume Good Intent - They're not there to make the product worse. Their job is to make sure that we are safely shipping the products we want to ship in accordance with the regulations." A partner in Fraud once said to me, "Henry, don't think of us as a gate. Think of us as your business partners trying to give you the best advice. Imagine if our role didn't exist, it would be your job to think of all of the angles that might attack your product." That was phenomenal advice that helped me re-calibrate my working partnerships with legal, risk, compliance, and other teams.

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Snowflake pulling observability closer to the platform is a signal: data platforms (Snowflake, BigQuery, etc.) aren’t just storage + SQL anymore, they’re becoming the ops layer for AI in production. Because AI rarely fails loudly. It fails as runaway spend, spiky compute, and “why did this job suddenly run 10x?”. The 2026 skill isn’t only RAG. It’s making AI workloads predictable. And a big hidden cost is “it just works” code that wasn’t threaded into your architecture. Agents can ship fast, but they don’t carry org context: data contracts, ownership, SLOs, security boundaries, downstream impact. You get leaks, duplicates, and bills nobody can explain. My bet for 2026: winners won’t run the most AI. They’ll run it without surprises.

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$1M revenue to $500M+ in annual revenues. Mercor is building the rails for the $45T knowledge-work market. When I first met the founders, Brendan Foody, Adarsh H., and Surya Midha, they weren’t chasing hype. They achieved product-market fit and profitability without venture capital. What stood out most wasn’t just their technology or revenue traction. It was their conviction and clarity of execution to where budgets are being spent. Their platform started with an elegantly simple product with AI interviews, vector-based talent matching, and performance prediction models that are already outperforming human hiring decisions. Mercor is building the infrastructure for the next generation of labor, an API for human expertise. They understood a truth that’s easy to miss: as AI advances, the value of human expertise compounds. I wrote in Medium about Mercor’s journey, and what it means for the $45T knowledge-work economy that’s being rebuilt from the ground up. To read the full article click below. https://lnkd.in/g-zWag8h

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On the latest episode of Embracing Erosion I sat down with Suyog Deshpande, Co-Founder & CEO of Webless (a Fluvio Ventures portfolio company). Before starting Webless, Suyog spent years at Amplitude, Salesforce, and Samsara - shaping products and GTM strategy at scale as a product marketing leader. That perspective is now fueling one of the boldest bets in tech: 🏗️ rebuilding the web for LLMs. Instead of optimizing for clicks and SEO, Webless imagines an agentic web -where sites are designed to interact directly with AI models and agents. We dug into: - What an LLM-native web could actually look like - How companies can prepare to be LLM-ready - What metrics will matter beyond pageviews and clicks - Why safety and trust are core to agent-driven experiences - Lessons Suyog brings from “big tech” into this transformation If you’re curious about how discovery, trust, and value exchange will evolve online, this one’s worth your time. Link to the full episode in the comments 👇

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Big news: Snowflake Ventures is leading a strategic equity financing in AtScale. This investment validates the critical role of semantics in the modern enterprise data stack. As enterprises consolidate data on Snowflake AI Data Cloud, a unified, governed semantic layer is essential for consistent metrics and business logic. A semantic layer like AtScale makes accessing that data more accurate, accessible, and efficient by bridging the gap between business language and data language. AtScale’s Universal Semantic Layer provides this single source of semantic truth for customers like Fidelity Investments and The Home Depot. The company remains independent and interoperable, supporting a broad ecosystem of data platforms, BI tools, and AI applications. This is a good moment to reflect on the future of data and AI. New to semantic layers? I'll post a tutorial link below. And here are five big topics from the 2025 Semantic Layer Summit (link to Substack articles on these below): 1️⃣ Semantic layer standardization is coming 2️⃣ Semantic layers are BI’s Gift to AI 3️⃣ Semantic layers make AI 3X more accurate 4️⃣ Long live SQL (with semantic layers to help) 5️⃣ The great debate: Should semantics be in your database? This is a pivotal moment for the data industry, and I’m thrilled to be a part of it! https://lnkd.in/eVJ276Pp

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