Shazman Khan

The 𝗣𝗮𝗹𝗮𝗻𝘁𝗶𝗿 "𝗠𝗮𝗳𝗶𝗮" is quietly building again — this time inside 𝗬 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗼𝗿 Former Palantir engineers and operators are launching startups that touch AI infra, developer tools, and next-gen data intelligence. I pulled the data using Harmonic — my go-to startup discovery engine as a BA - and here are 6 new YC-backed startups founded by ex-Palantir talent 👇 ▫️ Clidey (YC X25) – AI-powered infra platform with containerized workflows and real-time analytics ▫️ Overstand Labs (YC W25) – AI insights from Slack, email, WhatsApp to surface product signals and churn risk ▫️ Laminar (YC S24) – LLM tracing + evaluation with blazing-fast Rust pipelines and graph-based workflows ▫️ Zeit AI (YC S24) – Natural language queries across messy enterprise data, with GDPR-safe profiling ▫️ Kenley– AI copilots for consulting workflows like RFPs, due diligence, and internal search ▫️ Parsewise – AI agents that extract structured data from messy documents, with traceability All 6 were discovered through Y Combinator’s latest batch. All have roots at Palantir. And all are solving real data problems at the edge of what's possible. Which one is your favourite? 

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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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AI breaks the data stack. Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users. This architecture worked for traditional analytics. But AI demands something different. It needs continuous feedback loops. It requires real-time embeddings & context retrieval. Consider a customer at an ATM withdrawing pocket money. The AI agent on their mobile app needs to know about that $40 transaction within seconds. Data accuracy & speed aren’t optional. Netflix rebuilt their entire recommendation infrastructure to support real-time model updates1. Stripe created unified pipelines where payment data flows into fraud models within milliseconds2. The modern AI stack requires a fundamentally different architecture. Data flows from diverse systems into vector databases, where embeddings & high-dimensional data live alongside traditional structured data. Context databases store the institutional knowledge that informs AI decisions. AI systems consume this data, then enter experimentation loops. GEPA & DSPy enable evolutionary optimization across multiple quality dimensions. Evaluations measure performance. Reinforcement learning trains agents to navigate complex enterprise environments. Underpinning everything is an observability layer. The entire system needs accurate data & fast. That’s why data observability will also fuse with AI observability to provide data engineers & AI engineers end-to-end understanding of the health of their pipelines. Data & AI infrastructure aren’t converging. They’ve already fused. References Netflix Technology Blog. (2025, August). “From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix.” https://lnkd.in/g7XhVf2u ↩︎ Stripe. (2025). “How We Built It: Stripe Radar.” https://lnkd.in/gXtkcWjq ↩︎

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Snowflake quarter: - $1,145m rev (+32% YoY) vs $1,084m consensus (6% beat) - $1,128m next Q product rev guidance vs $1,118m consensus (1% raise) - Raised full year guide 2% from last Q - 125% net dollar retention - 22 months GM adj. CAC payback - 68% GM - 5% FCF Margin Quarterly net new ARR trends below:

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Harpoon Ventures + Black Flag is teaming up with Microsoft for Startups to provide founders with access to up to $250,000 in Azure credits, empowering builders to scale faster, train models more efficiently, and access world-class AI infrastructure. What’s included: ⚡ Up to $250K in Azure credits (usable across Azure OpenAI, Grok 3, Llama 4, and more) 💻 On-demand GPU VMs for model training and fine-tuning 🧠 Access to GitHub Enterprise + Microsoft 365 Business Premium 🤝 Dedicated Microsoft technical + GTM support This partnership is designed to give deep-tech and dual-use founders the tools and resources they need to win. Because when they win, we all win. 🏴‍☠️

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A sleek software product isn’t enough to deliver real outcomes in B2B. We’re in the age of low-code, built-in guidance, and AI agents. The differentiator is no longer the tool itself. What separates lasting companies today is how well they pair tech with real operational support. Matt Verlaque gets this. He’s the CEO of Precision , a company shaped by everything he learned as COO of SaaS Academy (a multi–8-figure coaching business), co-founder of UpLaunch (acquired by Daxko), and co-builder of High Speed Ventures with Dan Martell. Precision is a prime example of Software with a Service. Matt supercharged it with a service layer to solve complex ops problems that dashboards alone can’t. He’s seen fast-moving companies from the inside and knows exactly what operational clarity looks like: – A weekly KPI review that acts as an early warning system, not a scoreboard for what already happened. – A daily 1:1 with the Chief of Staff to catch and clear bottlenecks before they slow the team down. – A broader view of metrics, because sticking to 3–5 “north stars” often hides what’s really going on. – A practical use of AI to help small teams spot the right signals and operate with big-company clarity. – Clean books and clear comp structures, handled early, because that’s how we protect our exit value. Big congratulations to Matt and his team! Thanks, Matt, for meeting to talk about what you’re building (and why). Interested in getting free access to playbooks, people, and in-person dinners like these? 👉 If you're a B2B Scaleup founder or in VC/PE/M&A, we'd love to invite you to join our community here: https://lnkd.in/gVBZ_Gvj Will be sure to share the full conversation with Matt to the private (but free) community! To your growth! 🚀

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I was talking to my friend Parul Singh about how too much VC money can be detrimental. I have been working with pre-seed/seed/A at and with companies for the last 20+ years and I have observed first hand that the saying “constraints breeds creativity” is true. In my experience, a company with 18 months of runway gets much more done in the second nine months than the first. 🪹 Pre-seed Companies: When you are in the “idea maze” (coined by Andreessen Horowitz) or “drunken walk” having too much money does not necessarily speed up progress. Of course you may need some money for founder living expenses but having a larger team wandering through the "idea maze" together doesn't necessarily make the journey go faster. There is a certain level of founder reflection that has to happen while navigating the “idea maze” and too many distractions (people, money) makes it hard. I teach an entrepreneurship class  (LeanLaunchpad) every year. Out of the eight teams we have navigating the “idea maze”, about half will test and land on a reasonable idea in just ten weeks (including a simple MVP and even some paid POCs). Total budget: zero. With the multitude of AI tools, time to PMF is accelerating. Check out https://lnkd.in/guM7Ymn9 (AI companies revenue >10x SaaS companies). 🌱 Seed Companies: When you are going from the “idea maze” to “product market fit” and are trying to first build a product that a few customers love and second build a way to acquire these customers efficiently, having too much money can actually be counterproductive. Why? Because too much money often translates to wasting time on things that don’t matter: you are not forced to focus on the features that are absolutely critical, you can spend in non-efficient marketing methods (skipping the crafting of the product-growth loop) and you can afford to over hire or to keep the wrong people onboard. 🅰️ Series A Companies: Post product market fit, when it is time to scale, money is absolutely critical. However there is one common failure mode: the “fake Series As”, the As that get overpriced without really having reached product-market fit. Take a seed in a hot space, a small team that has grown revenue to a few $100Ks or maybe even a million in just six months. There is little data on actual revenue stickiness or whether the founder-led sales motion can be replicated efficiently. There is a lot of competition to invest in the company and they end up raising $20M+ series A.  The founder feels confident, they are stretched-thin and they hire a bunch of folks. There is pressure to scale but the machine is not built yet (the product is not quite there, they can’t figure out how to get to customers efficiently) and things unravel. Venture money is like carbs: quality over quantity (not all money is equal); take in moderation (constraints bread creativity) and if you compete in endurance sports, you might need more carbs to perform well (if you are growing, you will need the cash).

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Google's co-founder Sergey Brin tried early retirement and said it was the worst decision he made. He didn’t lose ambition but he lost the systems that kept his thinking sharp: constraints, ownership, and feedback. Without them, clarity faded - so he returned to building. This is why early retirement worries me as a founder. It’s not ego or grind culture. Founders are shaped by the problems they’re forced to solve. Step away too early and you don’t just lose output- you lose judgment. Building Pearl Talent, my least clear days aren’t the busy ones. They’re the days without skin in the game. If you step back, don’t walk away from responsibility. Design systems that keep you close to hard decisions and real stakes. Early retirement isn’t the absence of work - but the absence of forces that sharpen your thinking. #startups #entrepreneurship #business #leadership #google #ceo #sergeybrin

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David George leads Andreessen Horowitz Growth, which has backed some of the most successful technology companies including Stripe, SpaceX, Waymo, Databricks, Figma, OpenAI, and Cursor. This conversation is a detailed look at how David built and runs the growth business. He shares how they win the most competitive deals, the principles behind its culture, and the framework he uses to evaluate companies. We discuss how a16z is investing across the AI stack -- from model providers to applications -- and why this platform shift creates a real window for startups to displace incumbents. Throughout the episode, David shares the models that guide his investing: why markets misprice consistent growth, what makes "pull" businesses so powerful, and why he likes to back a certain kind of founder he calls the "technical terminator." It’s fun to talk with students of the game, and David is certainly one of them. He's spent his career studying great companies, markets, and founders, and you can feel that depth in how clearly he connects patterns across cycles. Enjoy!

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📢 Do you know what the 5 most important #Snowflake features are for 2026? I've written a Medium article going through the 5 Snowflake features I'm most excited about and those which I think will have the biggest impact on how we use Snowflake: ✅Openflow ✅Managed dbt ✅Workspaces ✅Snowflake Intelligence ✅Pandas Hybrid Execution 👉Use the link pinned in the first comment of this post!

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