Kris Inapurapu

Most of the AI tooling conversation right now centers on code generation. Cognition, Cursor, Claude Code....these tools are genuinely useful, but engineers are drowning in production issues. At our latest Greylock Partners Change Agents event, Mayank Agarwal and Rushin Shah from Resolve AI walked through a reality most engineering teams live through: with AI coding tools "you can crank out code much faster, but teams aren't really shipping to production that much faster." Some takeaways from our conversation: 1/ The real problem isn't code generation. Mayank saw this at Splunk where engineers spent only 10-20% of their time writing new features. The rest was fighting incidents, navigating tribal knowledge across hundreds of microservices, and operating tools that don't talk to each other. 2/ Production systems are different from code. Your codebase is static. Production is emergent behavior from infrastructure, deployments, traffic patterns, to tribal knowledge buried in Slack. As Mayank stated, the issue is "a coding agent cannot predict these things without the deep understanding of what's actually happening in production." 3/ The scale required is massive. Resolve is now consuming a trillion tokens per quarter—1% of all Azure usage. That's running Stack Overflow from scratch twice per week. "Vibe debugging" is the missing piece, Rushin's term for getting clarity on production systems the way vibe coding accelerates writing code. Check out the full episode in the comments

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From building Japan's first big data analytics company to backing India’s AI future — here’s what 25+ years of tech taught me about spotting the next wave. 1997: Started my first internet company (when there were literally zero VCs in India) 2003: Built NLP solutions for Toyota & Honda 2016: Sold my big data business, went all-in on AI investing 2017: Founded SenseAI Ventures, India’s pioneering AI venture fund 2024: Invested in 18 AI-first companies What’s the pattern? Every technology revolution follows the same playbook: → Infrastructure gets built first (expensive, yield-based returns) → Applications create the real wealth (think Google, Meta vs. Cisco) → Smart money focuses on the top of the stack Today's AI boom is no different. While everyone chases infrastructure plays, the real alpha is in 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹𝗶𝗻𝗴. 𝗠𝘆 𝗰𝗼𝗻𝘃𝗶𝗰𝘁𝗶𝗼𝗻: 𝗪𝗲’𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗶𝗻𝗻𝗶𝗻𝗴 of a decade-long transformation. I had the chance to share my complete journey and investment philosophy with 🎙️ Akshay Datt on Founder Thesis — from tokenizing Japanese text in 2003 to evaluating AI startups with the VDAT framework today. A great conversation on what it takes to spot and back the next generation of 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 companies. Full episode here: https://lnkd.in/gsB_yqSS #AI #TechInvesting #Entrepreneurship #VC #StartupJourney #AIStartups #IndiaAI #Innovation #FounderThesis #FutureOfTech

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The best AI apps today are being built like infrastructure companies. Great breakdown from the awesome 💡 Yazan "Yaz" El-Baba on why technical depth, rapid iteration, and margin-aware architectures are becoming table stakes for AI startups. Definitely worth a read: https://lnkd.in/gMVFFXgH 

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