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CEO, Snowflake
Cupertino, California, United States
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265K followers
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About
Technologist and humanist focused on harnessing the power of software for larger social good. Learn as you go leader humbled by contact with great people.
Articles by Sridhar
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My Takeaways from WEF ’26: Making AI Real
My Takeaways from WEF ’26: Making AI Real
I just spent the week in Davos meeting with dozens of customers, partners, and peers, having important discussions…
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34 Comments -
AI isn’t the future of work. It’s the present.Nov 19, 2025
AI isn’t the future of work. It’s the present.
Everywhere I go, business leaders, engineers, and policymakers are talking about the potential of AI technology. But…
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47 Comments -
The three things I learned from customer conversations in DavosFeb 5, 2025
The three things I learned from customer conversations in Davos
2025 has kicked off with a bang and a highlight of month one was heading to the World Economic Forum in Davos…
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19 Comments -
Predictions for 2025: How AI’s real world value will come to lifeDec 10, 2024
Predictions for 2025: How AI’s real world value will come to life
Why 2025 is a make or break year for AI adoption and deployment, and what CEOs need to consider to capture value from…
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42 Comments -
The future of advanced AI is simpleNov 9, 2023
The future of advanced AI is simple
A big question, one that I certainly hear a lot, is, “What does the future of AI actually look like?” We hear a lot of…
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8 Comments
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265K followers
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisSnowflake's Cortex Code triaged three broken pipeline nodes and fixed them in a matter of minutes.. I wasn't lying when I said this is the biggest software unlock this year.. Check out the demo below where I walk through exactly how Cortex Code paired with Coalesce.io's MCP server is able to save me 𝙃𝙊𝙐𝙍𝙎. The workflow: 1. CoCo identifies the failures 2. Agents fix the broken nodes simultaneously 3. I review, test, and deploy This is just one way to use this tool, more coming tomorrow! Follow me to stay up to date. Repo: https://lnkd.in/eaBQZDMk
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisWhy do I love Snowflake CoCo? It helped me finally get my arms wrapped around Pardot and connecting funnel performance to email. I picked up and put this down 6 times over the last couple of years! 1 hour of work and I don't know if I should be excited or embarrassed how easy and inexpensive it was... If you have Pardot and frustrated with visibility and understanding, I have the right partners that can drop in an get you on right track!
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Sridhar Ramaswamy shared thisAI is no longer just surfacing data, it’s helping make decisions with it. And that changes what a data catalog must do: provide meaning and ensure governance travels with the data. That’s the idea behind a Universal AI Catalog and how we’re building Snowflake Horizon Catalog: ❄️ Semantic context that gives data business meaning ❄️ Interoperability so governance follows data everywhere ❄️ Built-in policies, lineage, and security by design Without a universal AI catalog, AI guesses. But with it, AI reasons and acts—with trusted, governed context. Read more 👉 https://lnkd.in/eNWZmsArIntelligence and Interoperability: Data Catalog Must-Haves for AI Data GovernanceIntelligence and Interoperability: Data Catalog Must-Haves for AI Data Governance
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Sridhar Ramaswamy shared thisCortex Code is now generally available in Snowsight—bringing faster, governed, agentic development directly to where your data lives. ❄️Sridhar Ramaswamy shared thisWant to build faster on your enterprise data—without the complexity? We've got you, with a number of major updates to Cortex Code. We're excited to have Cortex Code generally available in Snowsight for every Snowflake user, along with other updates including native Windows support for Cortex Code CLI. And, whether it's executing large multi-step projects or using specialized skills for cost optimization and agentic ML, we’re making it easier to bring ideas to production faster. This is enterprise AI built for speed and governance. ❄️ Dive in to the latest: https://lnkd.in/gRd6ADmC
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisSnowflake’s Cortex Code is now generally available in Snowsight. ❄️ This brings AI-assisted development directly into the environment where data work already happens. reducing context switching and tightening the loop between exploration, development and production. The result is not just faster builds, but a more integrated and scalable way to work with data.
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisWe built Snowflake Cortex Code to make it dramatically faster to go from idea to production on enterprise data. Today we're shipping three updates that push that further: ❄️ Cortex Code is now generally available in Snowsight - a persistent AI coding agent that lives where your data already is, with full awareness of your catalog, governance, and context. ❄️ The CLI now supports Windows natively, so teams can use Cortex Code in VS Code, Cursor, or any terminal they prefer. ❄️ Agent Teams let you break large projects into parallel work streams - spin up specialized agents for research, coding, and testing that coordinate automatically. The results are real. TS Imagine told us 90% of their code is now AI-generated with Cortex Code, their engineers are submitting 5x more PRs, and work that took 3-4 days now takes 2-3 hours. This is changing who gets to build. Data engineers are moving dramatically faster, and analysts are creating pipelines and apps by describing what they want. The future of development on data isn't just better models - it's AI that understands your data context from the ground up. Read more: https://lnkd.in/gb-jDypx
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Sridhar Ramaswamy shared thisLast week, I joined Anuj Mehrotra at Georgia Tech Scheller College of Business to discuss how agentic AI is starting to reshape the finance industry, and what comes next. 🚀 The organizations moving fastest right now are the ones investing in governed, auditable access to their data, building a foundation of trust and a unified view of the business. That foundation gives models and agents the context they need not just to generate insights, but to take action across the enterprise. This approach will define the Agentic Enterprise. Great to engage with such thoughtful students and guests. Thank you for the conversation!
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisI was a bit skeptical about Cortex Code — but what convinced me? Check my article to see how I started and why I’ll probably never stop. Really great job Snowflake #Snowflake #CortexCode #GenAI #DataEngineering #Analytics #SemanticLayer #AzureDevOps #DXC #DXCTechnology
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Sridhar Ramaswamy reposted thisSridhar Ramaswamy reposted thisIn the past year+, Snowflake has received a huge amount of recognition for the many advancements they've brought to the platform, and Cortex Code is among the newest and most noteworthy of these advancements. Check it out!
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Sridhar Ramaswamy liked thisSridhar Ramaswamy liked thisSnowflake's Cortex Code triaged three broken pipeline nodes and fixed them in a matter of minutes.. I wasn't lying when I said this is the biggest software unlock this year.. Check out the demo below where I walk through exactly how Cortex Code paired with Coalesce.io's MCP server is able to save me 𝙃𝙊𝙐𝙍𝙎. The workflow: 1. CoCo identifies the failures 2. Agents fix the broken nodes simultaneously 3. I review, test, and deploy This is just one way to use this tool, more coming tomorrow! Follow me to stay up to date. Repo: https://lnkd.in/eaBQZDMk
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Sridhar Ramaswamy liked thisSridhar Ramaswamy liked thisWhy do I love Snowflake CoCo? It helped me finally get my arms wrapped around Pardot and connecting funnel performance to email. I picked up and put this down 6 times over the last couple of years! 1 hour of work and I don't know if I should be excited or embarrassed how easy and inexpensive it was... If you have Pardot and frustrated with visibility and understanding, I have the right partners that can drop in an get you on right track!
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Sridhar Ramaswamy liked thisProud to see kipi.ai recognized as a Snowflake Cortex Code Preferred Partner. This milestone reflects our continued focus on helping clients move from AI ambition to real, production impact. Thank you to our teams and partners at Snowflake.Sridhar Ramaswamy liked thisWe’re proud to announce that kipi.ai has earned the Snowflake Cortex Code Preferred Partner Badge. This recognition reflects kipi.ai's cumulative expertise in leveraging Cortex Code - Snowflake’s AI-powered coding agent - to streamline engineering, machine learning, and application development workflows. Proud to help our clients move from AI experimentation to production impact. #KipiAI #SnowflakePartner #CortexCode #EnterpriseAI #GenAI #DataAI
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Sridhar Ramaswamy liked thisSridhar Ramaswamy liked thisSome big news this week that our team is very proud of! Slalom is excited to share that we're now a Snowflake Cortex Code Preferred Partner. ❄️ With Cortex Code, we’re accelerating development, automating complex workflows, and delivering AI-powered applications directly within Snowflake in a secure, scalable way. From experimentation to production, we’re helping clients turn AI into real business impact. #Snowflake #CortexCode #EnterpriseAI
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Sridhar Ramaswamy liked thisGreat article on the WHY behind Cortex Code (Coco). But I would ask everyone to read the last sentence of this article multiple times if necessary as that is the differentiator for my customers today. There is no better security of your IP (i.e. Prompts, data, etc.) then not allowing that information to leave your security and governance perimeter.Sridhar Ramaswamy liked thisThe buzz around Cortex Code (Coco) CLI is heating up in the #developer community, and for good reason. While most people associate Snowflake with data & AI, Coco is proving to be a top-tier general-purpose coding powerhouse regardless of your tech stack. Here’s why Coco is outperforming standard agents for everyday software projects: 1. Zero Model Lock-In (The Power of Choice) Some agents lock you into a single ecosystem or UI. Coco lets you toggle between the world’s best models like Claude 4.6 Opus and GPT-5.2. Need deep architectural reasoning? Use Claude. Need high-speed boilerplate? Switch to GPT. Coco gives you the best "brain" for the specific task at hand. 2. It’s Not Just a Wrapper...It’s "Secret Sauce" Snowflake’s original motto was "Type Faster." Our engineers didn't just wrap an LLM; they built a custom orchestration layer designed for speed and efficiency. It understands local project dependencies, file structures, and terminal commands better than a standard chat-to-code tool. They improved how the underlying general LLMs work for coding. 3. Full Mastery of Your Local Environment Whether you’re building a standalone .NET desktop app, a React frontend, or a Python utility, Coco has full access to your local tools:Docker & Git integration, Auto-installing dependencies, Running, testing, and self-correcting code in your terminal until it actually works. 4. Enterprise-Grade Security (By Default) This is a huge win for InfoSec. Even if you aren't using Snowflake for your app's database, all your AI calls stay within Snowflake’s Secure boundaries. You get elite AI assistance without the compliance headaches. 5. The "Unfair Advantage" for Snowflake Projects While Coco is a beast at general coding, it hits a different level of productivity if your project touches Snowflake. Because it has native skills and RBAC-controlled context, it doesn't just suggest code, it understands your schemas, data, security, compute, networking and troubleshoots why a deployment stalled. It reduces the "re-code" cycles that eat up your time and tokens by delivering a product that is truly production-ready. If you’re building a project that does touch Snowflake, Coco gives you an "unfair advantage" with native RBAC and environment awareness. But if you’re building something completely unrelated? Coco is still a superior general-purpose agent. It’s faster, more secure, and gives you the flexibility to use the best models on the market without being boxed in. Stop copying and pasting error logs. Let Coco diagnose, fix, and deploy for you. #SoftwareDevelopment #CodingAI #CortexCode #Snowflake #DeveloperTools #GenerativeAI
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Sridhar Ramaswamy liked thisSridhar Ramaswamy liked thisWant to build faster on your enterprise data—without the complexity? We've got you, with a number of major updates to Cortex Code. We're excited to have Cortex Code generally available in Snowsight for every Snowflake user, along with other updates including native Windows support for Cortex Code CLI. And, whether it's executing large multi-step projects or using specialized skills for cost optimization and agentic ML, we’re making it easier to bring ideas to production faster. This is enterprise AI built for speed and governance. ❄️ Dive in to the latest: https://lnkd.in/gRd6ADmC
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Pavel Livshiz
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Lots of news out of Hetz Ventures portfolio today - Nimble announced their $47M Series B, led by Norwest with participation from Databricks Ventures. We've been proud to back Uriel Knorovich, Menachem Salinas and the entire Nimble team since early on, turning the live web into trusted, decision-grade data for AI agents and mission-critical workflows. Tonic Security launched the Tonic Mobilization Coordinator, the industry's first agentic remediation orchestrator. It automatically drives exposure remediation end-to-end, moving security teams from knowing they have risk to actually reducing it, continuously and at scale. Well done Sharon Isaaci, David Warshavski and Gregory Ainbinder. Anima launched their UX Design Agent on Product Hunt today: https://lnkd.in/demUMrUX. Designers and product teams can now go from prompt, Figma file, or live website to on-brand, production-ready frontend code in a single shot. Deepchecks released Know Your Agent (KYA), their answer to evaluation in the agentic era. Borrowing from the KYC framework in financial services, it gives teams a structured way to understand how their agents actually behave across complex multi-agent workflows. #proudseedinvestor https://lnkd.in/dyGTAHcg
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Astasia Myers
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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz
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Jellyfish
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💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ
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Joshua Bloom
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The old SaaS pricing playbook doesn’t work for AI. Madhavan Ramanujam and I partnered with Emergence Capital and Jake Saper to write a new one—built for where AI is going, not just where it is today. We lay out what state-of-the-art AI pricing looks like now—and what it will look like: ✅ Hybrid pricing (seats + usage) is the current best practice 🎯 Outcome-based models are the future—pricing tied directly to impact 💰 The best AI companies already capture 25–50% of the value they create As autonomy and attribution improve, outcome pricing will go from rare to expected. Founders who move early will win. Full read here 👉 https://lnkd.in/gwp6tDtp
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Amber Illig
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🎙️ From Square to Mercury: How Rohini Pandhi became one of fintech’s top product leaders—and how velocity of learning at high slope companies shaped everything. Rohini dropped so many gems in our first episode of First Builders: 💎 Generalist → Specialist: In her early career, she was a generalist and tried everything. This led her to critical discoveries. She specialized in product and fintech when the “time was right” 💎 Follow the Engineers and Designers: Square was a high slope environment for Rohini, packed with talent density and learnings. She finds high slope environments by studying where smart engineers and designers are going. 💎 Fake News about PMs: PMs don’t just “move fast and break things.” Her team runs 30-50 customer interviews per quarter, providing rigor behind every decision. 💎 It’s Usually Too Early to Hire a PM: A top question she gets from founders is whether they should hire a PM. She usually says: “it’s too early.” 💎 Hiring a World Class Team is Like Tennis: When finding a tennis partner, you want someone a little better than you who can challenge you to improve your game. Give us a listen and leave a review on your favorite podcast location (see comments for links)! #FirstBuilders #StartupPodcast #ProductLeadership #TechCareers #Leadership
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Maria Palma
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On-Prem is the New Cloud: Why Enterprises are Reversing Stance in the Age of AI Remember when cloud was the battleground for innovation, agility, and scale? Everyone was tracking the % of on-prem compute moving to the cloud and it was growing every year? Well now there’s a reversal. The reality today? On-prem is the new cloud. Here’s why. AI’s Dark Side: Legal Risk and Data Governance Headaches Enterprises are excited about AI, but also uneasy as vendors like OpenAI face intensifying legal pressure. In a high-stakes lawsuit, a judge ordered OpenAI to preserve all ChatGPT chats (even deleted ones), overriding its 30-day deletion policy. Imagine that: you delete sensitive conversations, but they’re still sucked into legal limbo. The precedent is clear: AI prompts and outputs are discoverable records, demanding integration into enterprise ESI (Electronic Stored Information) policies. For large organizations handling sensitive data, the risk is simple: you can’t afford your AI tools to turn into legal liabilities. Especially when global regulations (GDPR, etc.) and internal governance collide with unpredictably broad discovery orders. Sovereignty, Control, Trust Many enterprises still ban ChatGPT or impose strict rules on usage. Even when allowed, token limits are throttled. Enterprises are cautiously letting AI in (as they should). Enterprises need to have control and trust. SAP is leaning into this with its Sovereign Cloud On-Site, deployable on customer premises with SAP-managed infrastructure. It is built to deliver data, operational, technical, and legal sovereignty in one package. Doubt it will be the last. Scaling Pains Talking to founders, three reasons stand out why cloud isn’t scaling with AI: 1/ The rate at which AWS/GCP lets you provision compute and the compute it wants you to provision both falter as AI and AI driven developers deploy a lot more code more frequently than before. So sometimes it’s not about data governance, but actually about performance and functionality. 2/ Shadow AI is rampant—employees paste sensitive info into models despite restrictions—creating demand for better permissioning tools. 3/ Data leakage remains a live risk, like the August 2025 Grok incident where hundreds of thousands of private chats became publicly accessible and indexed. One thing is for sure - founders selling into the enterprise are scaling faster if they offer the ability to run on-prem. The Future is Hybrid Don’t get me wrong - cloud is not going anywhere. But neither is on prem. A few years agoI thought we were headed for a full cloud shift, but now I think the future is truly hybrid.
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Jordan Steiner, CFA
Developer Capital • 3K followers
"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?
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Andrew Mayne
Zero Shot Fund • 2K followers
In the latest episode of the OpenAI podcast I got talk to OpenAI CFO Sarah Friar and legend himself Vinod Khosla about the AI industry. Sarah explained the relation between compute and revenue (OpenAI's revenue keeps 3x'ing each year) and Vinod provided an insightful way to tell if we're in a bubble: API calls. Are they going up or down? (Spoiler: Up, up, up!) https://lnkd.in/g2tTQBdp
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Devon O'Rourke
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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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Matt Rappaport
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Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation
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