Thejo Kote

From our PearX S25 cohort, meet Pravāh, the AI-native operating system for the electric grid When Mohak Mangal, Dhruv Suri, and Aman Gupta looked at the strain on the U.S. electric grid, they saw a system pushed to its limits. Exploding demand and unpredictable supply have made blackouts increasingly common. This costs the economy more than $150B a year, a number that could 10x in the next five years. Mohak Mangal and team founded Pravāh to help fix it. Their platform helps utilities and grid operators make real-time decisions on load, generation, and congestion, reducing blackout risks, optimizing power procurement, and bringing much-needed stability to the backbone of modern life. We’re proud to support the Pravāh team as they take on one of the toughest and most important infrastructure challenges of our time.

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On February 18, during India's AI Impact Summit, a company called Sarvam shipped a 105-billion-parameter model trained from scratch in India on government-subsidized GPUs, designed for 22 Indian languages. It outperforms DeepSeek-R1 on certain benchmarks at one-sixth the size. The same summit produced $200 billion in investment pledges from Amazon, Microsoft, Google, and Reliance. The headlines were about the money. The real story was Sarvam. This post examines in detail why it took this long, what changed, and whether Sarvam is the first crack in an old equilibrium or a lonely exception. "Indians Rule Big Tech. Why Can’t India Build?": Link in comments.

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Oru'el's primary traction came in December when I met Pratyush Kumar of Sarvam at an event. "More compute without adding more GPUs, how?" he asked and we strolled off the event for 20 odd minutes. I explained to him what happens BTS, how does 'predciting' GPU failures affect this and how Foundational AI companies, and ultimately users, suffer. The sad part is, this is not something anyone could have controlled. It's really hard to predict as well. Grey failures is one of the biggest problems with GPUs and as quoted by Meta, 58% of their training run failures during Llama 3 were GPU related! Last week, Ajith and Nihith finalised the models and the architecture for our MVP. It outperforms what Bytedance did by a margin. Tho, there's no formal benchmark that exists, we would soon make one. Every SRE we have talked to told us the same 3 things : - We don't know when it would fail - We don't know why it would fail - We don't know what is the cause of failure until thorough post-mortem And since engineers don't really care about CapEx, the billions lost monthly across the world is just another headache. Oru'el's system works on pinpointing failures, telling you particular GPU about to fail, the cause of it and how much time is left to act with an actionable that they can swiftly follow. Latency? sub-milisecond. This is an SRE's dream come true. Predicting GPU failures be it for training or inference not only optimises the work flow but also calls for maintenance -- not on scheduled calendar but when you need it. This also tells you the 'health' of a GPU in real time, treating Silicon like a living being. Oru'el will be piloting soon and we have got our first prospects to work with. Everytime we cold called and talked about this to the decision makers, we never got a 'no' and that's a sign you know this industry needs physics to solve a physics problem -- not RL slapped on stats. oru-el.com

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India has abundant expertise across every domain - logistics operators, subject matter experts, local service providers, etc. What's scarce isn't capability. It's the systems that coordinate this capability at scale. Most companies see this fragmentation and default to vertical integration: build everything in-house, own all capabilities, control every aspect of delivery. This works if you have unlimited capital and time. Most companies have neither. But there's an alternative approach that we've noticed two of our portcos execute remarkably well, in completely different industries - 1. When Meesho looked at India's logistics, and they saw thousands of fragmented local operators. Instead of building warehouses and delivery fleets, they built Valmo - an orchestration platform coordinating existing partners. Individual pilots with smartphones earn sustainable incomes, small hub operators build viable businesses, and Meesho gets coverage across 15,000+ pin codes at 12% lower cost than traditional third-party-logistics. 2. Entri saw similar fragmentation in education. India has abundant teaching expertise already running offline/online programs. Entri partners with them for content while owning demand generation, platform infra, quality monitoring, and placements. This allows them to launch new categories in weeks and scale across languages without the capital intensity of building in-house teams for every vertical. If I had to pull a common thread, it's that in fragmented markets like India, the orchestration layer often matters more than asset ownership. Coordinate what exists rather than rebuilding from scratch.

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IVP is investing in LangChain because it’s a category-defining platform for AI agents — and because of the founders building it. Over the past two years, we’ve gotten to know Harrison Chase and Ankush Gola, who together have built something rare: a developer platform that’s both beloved by the open source community and commercially strong in the enterprise. They’ve galvanized thousands of developers while scaling to serve a third of the Fortune 500. Their approach is simple yet powerful — listen deeply, ship quickly, and build what customers need, just before they realize they need it. It’s the same quality we’ve seen in founders such as Datadog’s Olivier Pomel, CrowdStrike’s George Kurtz and HashiCorp’s Mitchell Hashimoto and Armon Dadgar. LangChain’s evolution — from the original framework to LangGraph and LangSmith — reflects that same instinct and timing. Harrison and Ankush are not only defining the category of agent engineering, they’re shaping how developers build the next generation of intelligent applications. That’s why IVP is leading LangChain’s $125M Series B. Read more about the Series B by Sharon Goldman in Fortune: https://lnkd.in/gMRANPbt    📷 from LangChain offices with Harrison, Ankush, and Taylor Swift. 

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Rainy days in Mumbai often inspire reflections on the happenings in the #startup ecosystem. When asked about the state of the India startup market, I sum it up concisely: - The funding winter is behind us, with green shoots flourishing, signaling the arrival of Spring. - Funding is now more concentrated, favoring startups with sound economics and mature founders for subsequent rounds. - First-time raises are decreasing, accompanied by smaller cheque sizes. - Valuation euphoria has subsided, with a shift towards a more grounded approach influenced by collective wisdom. These dynamics are fostering a favorable environment for #VentureDebt, particularly gaining traction among SaaS startups with low churn rates and defined revenue. Venture debt is increasingly recognized as an effective tool in India, enabling startups to expand round sizes, extend runways, manage dilution impacts, and optimize #CostofCapital (WACC).

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Learnings from building one of Europe’s first multi-country agentic PaaS platforms led us to create, 𝗔𝗴𝗖, Agentic Compute. Here’s the video of our announcement at The Linux Foundation OS Summit, where my cofounders Jasbir Singh and Amant Kumar share the journey and learnings. Just as EC2 changed servers, S3 changed storage, and Stripe changed payments, we asked: what could simplify building agentic ecosystems? 🚀 One Docker command for local tests. ☸️ One Helm install for Kubernetes on your infra. Fully Open source . Link in comments. Build agents on demand. Not for single-agent development - but for enterprise agentic ecosystems. Agentic compute begins here. https://lnkd.in/eDrJWgX7 

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Last year, we invested in Cognichip: what we expect to be a generational semiconductor company by making chip design as simple and accessible as vibe coding. They came out of stealth today with a vision for how we will design chips in the future. Today, building chips is nowhere nearly as simple as uttering these commands. In fact, this very task requires going through thousands of pages of documents and hundreds of hours of experienced engineer time. It gets worse with every tech node. Chip design is tedious, which is why chips cost a fortune to design, and take forever to bring to market, resulting in only a handful of (giant) chip companies. Faraj Aalaei, who personally built, grew, and took several chip companies public over the past 3 decades, founded Cognichip with Simon Sabato and Ehsan Kamalinejad to disrupt this status quo: https://lnkd.in/gAkM_k6Q He sounded the alarm a decade ago, and as a dear friend of Lux, he was kind enough to share his vision that became Cognichip: Cognichip has attracted Olympiad-medalist mathematicians and veteran chip designers to make chip design as accessible as coding apps. We expect Cognichip to enable a future where there are as many chip companies as there are software companies, rapidly accelerating compute. We are excited to embark on this journey with the fabulous team Faraj has assembled, along with our friends at Mayfield and FPV Ventures

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"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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India Just Took a Quiet but Important Step Into the Deep Tech Age The world is entering the Deep Tech age. AI, quantum, advanced materials, space and climate technologies aren’t sci-fi anymore. They’re the next industrial foundation. Globally, venture capital is already responding. In H1 2025 alone, startups raised ~$162.8B, with AI accounting for nearly 64% of all deal activity. Deep tech funding is accelerating too. Europe saw ~$7.8B flow into deep tech, up 56% YoY. The common thread behind many of these breakthroughs? Universities. In the US, companies like Facebook and Snapchat were born out of university ecosystems. In China, deep tech funding is tightly coupled with state-backed research institutions and academic labs. Capital follows research, and research compounds into companies. India, historically, hasn’t had that bridge. Our universities have done a great job preparing talent for jobs, but far less for entrepreneurship, commercialization, or patient research-led company building. That gap is precisely what makes last month’s announcement important. SINE, IIT Bombay has launched India’s first incubator-linked deep tech VC fund: Y-Point Venture Capital Fund, with a ₹250 Cr corpus. This is not just another fund launch. It’s a structural shift. Deep tech doesn’t begin with pitch decks. It begins in labs, patents, research papers, and years of experimentation. Y-Point is a SEBI-approved Category II AIF focused on pre-seed and seed investments from IITs and leading research institutions. It plans to back 25–30 companies, with ticket sizes up to ₹15 Cr, across AI, advanced computing and manufacturing, materials, defence and space, climate tech, and life sciences. It builds on IIT Bombay’s two-decade track record of nurturing 500+ startups and over 1,000 innovators. This mirrors what global ecosystems figured out early. Deep tech needs patient capital, domain expertise, and tight integration between research and venture funding. India is also seeing early signs of this shift beyond academia. Initiatives like Foundary, backed by Nikhil Kamath and Kishore Biyani, point to growing recognition that the next wave of value creation won’t come from quick consumer wins, but from building foundational technologies over time. The question now isn’t whether India can produce deep tech companies. It’s whether our institutions can become the equivalent of Y Combinator, Stanford, or Tsinghua for the next decade. IIT Bombay’s move suggests we’re beginning to build that answer. The Deep Tech age isn’t coming. It’s arriving quietly, methodically The investors who recognize this won’t chase trends. They’ll fund foundations. #DeepTech #PrivateMarkets #VentureCapital #IITBombay #IndiaInnovation #PatientCapital #Foundary #FutureOfInvesting #ChangeWealth

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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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The future of voice AI is unfolding on two fronts: 1) Speech-to-speech models that capture not just what we say, but how we feel 2) Developer-first “Voice in a Box” APIs that make conversational apps work As voice becomes a dominant interface layer, the infrastructure behind it needs to evolve fast. The demand is already here: sales agents, schedulers, care providers. Every founder building in this space is building toward a much bigger shift. “Voice AI in a box” platforms will make conversational apps and agents more personal and make developers' lives easier, like getting phone numbers, scaling calls, translation, custom-branded voices, voice cloning, and more. This area is poised to grow fast both now and well into the future as interfaces like avatars become mainstream.   If you’re building here, I’d love to meet! https://lnkd.in/dD9U3jpy

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