Ishan Sinha

Why MCP Matters for the Future of AI As LLMs move from demos to real-world systems, the biggest challenge isn’t model capability—it’s context, action, and integration at scale. In this blog, I break down Model Context Protocol (MCP): • Why prompt-based integrations don’t scale • How MCP solves the M×N integration problem • Why MCP is emerging as the operating system for AI agents If you’re building agentic systems, RAG pipelines, or enterprise AI, MCP is a concept worth understanding early. 🔗 Read the blog here: https://lnkd.in/gJpcAHdt #AI #LLM #MCP #AgenticAI #Architecture #FutureOfAI

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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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Quantization in Large Language Models (LLMs) As LLMs scale, intelligence is no longer the bottleneck — memory, latency, and cost are. Quantization turns out to be one of the most powerful (and misunderstood) techniques enabling real-world deployment of large models. In this blog, I break down: • Why LLMs are surprisingly quantization-friendly • INT8 vs INT4 vs NF4 — what actually works in practice • How modern architectures absorb low-bit noise • Where quantization fails and how to design around it Quantization is no longer an optimization step — it’s foundational infrastructure for production LLM systems. Link: https://lnkd.in/gqGje75G #LLMs #AIEngineering #Quantization #MachineLearning #GenAI #MLOps

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How I Made Decisions at Amazon In 5.5 years at Amazon, I made thousands of decisions. Some were small. Scope tweaks. Deadline shifts. Meeting structure changes. Some were big. Launch calls. Escalations. Resource tradeoffs. Risk acceptance. And here’s what I learned: Indecision is usually more expensive than a wrong decision. Amazon forced me to build a decision framework…not rely on instinct alone. Here’s how I approached it: 1/ I asked: Is this a one-way door or a two-way door? ↳ A one-way door decision is hard to reverse ↳ Long-term architectural changes ↳ Big resourcing bets ↳ Major customer commitments These required deep dive, multiple perspectives, and slower motion. ↳ A two-way door decision is reversible ↳ Pilot launches ↳ Process changes ↳ Messaging experiments These required speed. Most people treat two-way doors like one-way doors. That’s how velocity dies. 2/ I defined the real risk before acting ↳ What’s the worst-case outcome? ↳ How recoverable is it? ↳ What’s the cost of waiting? Often the cost of delay was greater than the cost of being imperfect 3/ I gathered just enough signal…not perfect clarity ↳ 70% confidence was often enough ↳ The remaining 30% came from moving ↳ Over-analysis was a hidden tax on momentum 4/ I wrote my thinking down ↳ Framing the decision in writing exposed weak assumptions ↳ It also made it easier for others to pressure test it ↳ Writing sharpened judgment 5/ I owned the outcome…even when it missed ↳ If a decision didn’t land, I didn’t blame the inputs ↳ I asked: What did I miss? What pattern did I ignore? ↳ Decisions compound…so does accountability The biggest shift Amazon gave me was this: Decisiveness is a leadership trait. But structured decisiveness is a competitive advantage. 📬 I write weekly about clarity, ownership, and high-stakes execution in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc What’s one decision you’ve been overthinking that might actually be a two-way door?

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Time to stop spreading incorrect inferences of Trump's new order regarding H1B visas. It is important to cut through the confusion with facts, not speculation. ✅ The $100,000 annual fee applies to H1B entries (new and current holders), not current holders already in the US ✅ Current H1B holders can remain, but face restrictions if they travel and re-enter Displaced Talent ≠ Automatically harnessed Talent. Singing a celebratory note on how this is "good for India" is premature. Brain drain reversal requires intense, long term work to transform Infrastructure, Policy, Monetary and Innovation ecosystem. Things are changing in India, but there's still a lot of work required. 💡 Talent is Borderless and Fluid. There are many other countries, actively courting tech talent. If the US becomes prohibitively expensive or unwelcoming, talent will migrate elsewhere. ❤️ Spare a thought to families caught mid-travel, professionals with established lives, students planning their futures. Such sudden events create genuine hardship and life-changing disruption. It's good to debate policy merits but we need to maintain empathy for those affected. #H1BVisa #Immigration #TalentMigration #Policy #TechWorkers #GlobalTalent

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It is hard to understand Fabric's long-term strategy. On one hand, it was smart to build it around PBI, the leading BI platform. On the other hand, the key to Power BI's success in dethroning Tableau was its' extremely affordable licensing. Competitive licensing is not an element that Fabric's always-on pricing model can boast of at current rates, especially when the likes of Databricks, Snowflake, and other rising platforms are around. Perhaps Microsoft will be content with Fabric always being the 3rd/4th best option?

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𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝗺𝗮𝗿𝗸𝗲𝘁 𝗯𝗲𝗲𝗻 𝘂𝗽 𝘁𝗼 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟮 𝘄𝗲𝗲𝗸𝘀 𝗼𝗳 𝟮𝟬𝟮𝟲? Here are the emerging trends in the data and AI talent landscape based on my meetings with Heads of AI and CDOs along with a note on the churn at C-level ✅ High demand for hands on data and AI engineers which is also driving higher salary packages ✅ High demand for data scientists that have put GenAI products and solutions into production - this is fueled to a certain extent by AI CoEs ✅ High demand for AI governance specialists ↘️ Low demand for data scientists that have limited experience with putting AI solutions into production ↘️ Low demand for hands off managers ↘️ Low demand for pure data analysts. While the head of AI/ CDO function is still in its infancy in many organizations. Which means, data/ AI maturity is low especially from a culture perspective. However, expectations on what these teams need to deliver are sky high because of the narrative built externally based on how companies want to be perceived by their shareholders/ customers. As a result, the pressure on these teams and their leaders is immense. This often leads to a revolving door situation for CDO/ head of AI roles. The prediction I made late last year regarding change of guard at the CDO level is already playing out in many banks and FS companies. ♻️ If you found the insights valuable, please share it so that people from your network can benefit 📍 Except for the image, no AI was used to create this post!

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