Karthik Krishnan

𝐆𝐨𝐨𝐠𝐥𝐞 𝐭𝐨 𝐈𝐧𝐯𝐞𝐬𝐭 $𝟏𝟓 𝐁𝐧 𝐢𝐧 𝐀𝐈 𝐃𝐚𝐭𝐚 𝐂𝐞𝐧𝐭𝐫𝐞 𝐢𝐧 𝐕𝐢𝐳𝐚𝐠 Google Cloud CEO Thomas Kurian announced that the tech giant will invest $15 billion to build the world’s largest AI and data centre in Visakhapatnam, Andhra Pradesh. This hub will be Google’s biggest AI facility outside the US and will feature Tensor Processing Units (TPUs) for cutting-edge AI computing. “This gigawatt-scale AI hub represents a major step in supporting India’s digital infrastructure goals, and we’re grateful for the support of the Government of India and the state of Andhra Pradesh,” said Kurian. Google Cloud CEO Kurian made this announcement during Google’s event on “India’s AI-Powered Future and Advancing the Vision of Viksit Bharat 2047,” in the presence of Ashwini Vaishnaw, Minister of Electronics & IT; Nirmala Sitharaman, Finance Minister; and N. Chandrababu Naidu, Chief Minister of Andhra Pradesh. Google Cloud Thomas Kurian Ministry of Electronics and Information Technology Via: Outlook Business | Deepsekhar Choudhury (Story link in the comment)

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An awesome interview, as part of SIGPLAN blog with Prof Ranjit Jhala, Professor at University of California, San Diego. The topics ranged from philosophical discussions, grad school experiences, how he developed an interest in model checking and PL research and finally down to a cool demo of refinement types in Rust. Absolutely loved the demo and surely would be playing around with Flux. Some of my rough notes from the discussion : - Model checking and Floyd Hoare logic : FH logic is symbolic model checking where u r using assertions to represent sets of states and manipulating these sets of states symbolically (preconditions, post conditions etc.) - How lazy abstraction evolved from a talk that was led to thoughts on how to reuse parts of a search space that has already been explored in previous iterations. Frugality of abstractions - don’t do stuff unless u really need it .. did I hear “Laziness by default is not my cup of tea ?” - How liquid types evolved organically as a fusion of constraint solving (that type systems do), model checking and lazy abstractions. - Thoughts on type systems in Haskell and Rust - Rust type system is extremely complex yet the compiler produces so good error messages. And there is a compelling value proposition - you do the type checking and since there is no GC, your code runs very fast. - The cool demo on Flux, a language-integrated verifier for Rust that leverages liquid types, a form of dependent type system. It was also cool to see CoPilot auto-completing flux annotations ;-). First runs rust type and borrow checker and then take the type information and then do liquid types style reasoning using SMT solvers .. Talks about the challenges of implementing refinement types in an imperative language, and here is flux. - Philosophy of Verse as a unification of functional and logic programming. Has a very very sophisticated type system - we expect to hear more in the near future. - Interesting discussion on whether CoPilot like LLMs will stifle new programming language design and research (as we don’t have many examples in the wild to train on) ... and a lot more. Well worth the one and a half hours .. Link to the interview: https://lnkd.in/g-ygZxcw

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I tuned into Palo Alto Networks’ Q4 2025 earnings call expecting AI headlines. What stood out instead was how much time Nikesh Arora dedicated to Identity security — and how central it’s becoming. Here are my summarized notes on Identity from the call: 👇 1. Identity is hitting an inflection point in the next 12–24 months. (You could hear the conviction in his tone.) 2. Unlike firewalls or networks, Identity is a real-time enforcement point. Speed here decides whether breaches succeed. 3. He floated the idea of an “Identity Firewall” — real-time clearing across fragmented providers. (In response to Rob Owens from Piper Sandler.) 4. With Agentic AI rising, every user, machine, and AI agent must be treated as privileged. No more narrow PAM definitions. 5. 90% of breaches still tie back to stolen or mismanaged credentials. That stat wasn’t delivered lightly. 6. Identity today is too fragmented...100+ vendors. Consolidation isn’t a preference anymore, it’s survival, and critical to reducing breaches. 7. His framing was clear: security-first identity vendors, not SSO-only players, will own the future. (I couldn’t help but chuckle at this. 😅) 8. Agents (human + AI) both require privileged access controls. A shift few have fully absorbed. 9. Growth in PAM data and sensors will further solidify category leaders like CyberArk. The subtext was undeniable: Identity is no longer a supporting actor. It’s becoming the frontline pillar of AI + security in the enterprise. #IAM #IGA #IdentitySecurity

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Lessons from a Bold Move Perplexity AI, a $18B startup, just made a $34.5B bid for Google Chrome. Whether or not it happens, the play itself is a masterclass for smaller players: 1. Leverage Timing – Act when market winds or regulations are in your favour. 2. Think Bigger Than Your Size – Vision can outweigh valuation in shaping perception. 3. Own the Narrative – A bold move can put you in the same conversation as giants overnight. You don’t have to beat a giant on resources—just on speed, positioning, and story.

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Last week at #CiscoLive I sat down with Shailesh Manjrekar and Raju Datla of Fabrix.ai to talk about how the company works with Cisco The company recently rebranded to reflect its evolution from #cloud connectivity to #AI and automation. The company has a long-standing partnership with Cisco, working on #AIOps and integrating with Splunk and NVIDIA . They showcase their Agentic AI platform, which ingests large datasets, automates processes, and ensures agents adhere to company policies. Fabrix.ai aims to bring the power of agents to various IT operations, emphasizing the importance of good data for better insights and the potential of AI to enhance, rather than replace, human roles. https://lnkd.in/gmes4Qd3

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Ditto in Action: A Success Story of Real-World Adoption of NVIDIA Torch-TensorRT The latest NVIDIA blog highlights how Torch-TensorRT is enabling fast and efficient LLM inference in production, through the open-source framework Ditto developed by SqueezeBits. By effortlessly converting Hugging Face models into optimized TensorRT engines, Ditto demonstrates the practical value of Torch-TensorRT for accelerating deployment without compromising flexibility or usability. 🔗 GitHub: https://lnkd.in/ge8CQDGp 🔗 Blog: https://lnkd.in/gSUwn-N2

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From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

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🔥 SigNoz team is on fire! We shipped a core improvement recently, which led 30% higher log processing throughput without adding any infrastructure. We moved from sequential to concurrent processing architecture and that have us some good perf improvement Before : CPU Usage: 2,694m/4,000m (67% utilization) Processing Rate: 35.7k logs/second After: CPU Usage: 4,352m/4,000m (108% utilization) Processing Rate: 46k logs/second (with 66k peaks)

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🚀 TFG Founder Featured on Suman TV | India’s Chip Revolution Honored to share that our Founder & CEO Naresh Israel appeared on Suman TV, discussing TFG’s bold vision in AI-powered chip and GPU manufacturing. Key highlights: 🔹 India’s rise in semiconductor innovation 🔹 TFG’s roadmap for indigenous chip & GPU design 🔹 Reducing dependency on global imports 🔹 AI-driven hardware engineered in India 🔹 Building industry-ready semiconductor excellence TFG is committed to driving India toward chip self-reliance, intelligent hardware, and next-gen computing solutions. 🇮🇳 TFG – Building India’s Tech Future. 🔗 AI | Chips | GPUs | Semiconductor Innovation

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Ever had to deal with a binary with no source code? #GPT5 just pulled off a Wow moment for me. I had two 15-year-old binaries — one encoding, one decoding a license key for a legacy system — classic “security by obscurity”. Of course, these systems never really die as per plan - license extension was an option on the table. The day after GPT5 launched, I gave it the two binaries, a PDF explaining their usage, and asked if it could patch them to extend the date. After about a minute of deep thinking, it figured out the year was stored in 4 bits, suggested shifting the base year from 2010 → 2020, and extending validity from 2025 → 2035! I tested the patched binaries — and it worked on the first shot! 🤯 Interestingly, both #Perplexity and #Gemini had downplayed the feasibility of patching binaries with LLMs, but here was a concrete example. But this raises a question: what happens when script kiddies get access to this kind of power? #SBOM integrity has never been more critical. #BinaryAnalysis #CyberSecurity #AI #WowMoments

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