Bobby Srinivasan

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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Big news for PierSight and the local satellite industry in India. PierSight will be the leading Synthetic-Aperture Radar (SAR) provider in this major multi-year national government contract. In total 12 satellites will be launched, all made and operated in India. PierSight will be able to continuously monitor night and day in all weather conditions India's entire Exclusive Economic Zone (EEZ) for illegal fishing activities, oil dumping, shipping efficiency and safety. Congrats to Gaurav Seth Vinit Bansal and the team on this major milestone. CC: Sustainable Ocean Alliance Seabird Ventures #oceanimpact #oceanmonitoring #satellites #EcopreneurNetwork https://lnkd.in/dac8V5vK

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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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When Anthropic introduced the Model Context Protocol, they promised to simplify using agents. MCP enables an AI to understand which tools rest at its disposal : web search, file editing, & email drafting for example. Ten months later, we analyzed 200 MCP tools to understand which categories developers actually use. Three usage patterns have emerged from the data : Development infrastructure tools dominate with 54% of all sessions despite being just half the available servers. Terminal access, code generation, & infrastructure access are the most popular. While coding, engineers benefit from the ability to push to GitHub, run code in a terminal, & spin up databases. These tools streamline workflows & reduce context switching. Information retrieval captures 28% of sessions with fewer tools, showing high efficiency. Web search, knowledge bases, & document retrieval are key players. These systems are likely used more in production, on behalf on users, than during development. Everything else including entertainment, personal management, content creation, splits the remaining 18%. Movie recommenders, task managers, & Formula 1 schedules fill specific niches. MCP adoption is still early. Not all AIs support MCP. Of those that do, Claude, Claude Code, Cursor top the list (alliteration in AI). Developer focused products & early technical adopters are the majority of users. But as consumer use of AI tools grows & MCP support broadens, we should expect to see a much greater diversity of tool use.

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Meet the Tycoons building India's "Second Act" in DeepTech and AI - 2026 We are witnessing a fascinating transition. Serial founders are taking their IPO winnings and "war-room" experience to solve problems that were previously considered "too hard" for the Indian ecosystem. Here are the 6 heavyweight insights into what the Tycoons are building next: 1) Deepinder Goyal (Zomato) - TEMPLE He is building a brain-health wearable that tracks cerebral blood flow in real-time 🧠 He wants to prove the "Gravity Ageing Hypothesis" - the idea that gravity affects brain blood flow over decades. Deepinder wants to make brain health as trackable as heart rate. Traction: He’s invested $25M of his own wealth and recently raised a $54M round at a $190M valuation. He’s currently hiring "engineer-athletes" with <16% body fat to join the mission. Expect a limited rollout by late 2026 🧬 2) Mukesh Bansal (Myntra/Cult.fit) - NURIX He is building an AI-native services platform building "custom AI agents" for enterprises 🤖 He wants move from "Physical Wellness" to "Digital Intelligence." Nurix builds voice and text agents that handle sales and support with human-like nuance. Already raised $27.5M from Accel and General Catalyst. They are currently operational with an annual revenue run rate of approx ₹5.35 Cr as they scale their Series A ⚙️ 3) Aman Gupta (boAt) - OFFBEAT STUDIOS A new entrepreneurial "Aman 2.0" venture launched following his transition to a non-executive role at boAt 🎬 While the exact "product" is in stealth, Aman’s vision is to back "bold, hungry, and impatient" ideas that disrupt traditional consumer categories. Launched on March 3, 2026. Aman has officially stepped away from daily boAt ops to lead this, marking a shift from "Volume Hardware" to "Offbeat Innovation" 🎧 4) Mukund Jha (Dunzo) - EMERGENT An "agentic vibe-coding" platform that builds full-stack apps via AI 💻 He wants to make software development as easy as chatting. Mukund is moving from the "Logistics of Goods" to the "Logistics of Code." $100M total funding raised (Series B) as of Jan 2026. They’ve hit a $100M Annual Run Rate in record time, proving that "Vibe-coding" is the new industry standard ⚡ 5) Shashank ND (Practo) - CENT He is constructing a preventive health startup that uses AI-assisted whole-body MRI and 120+ biomarkers to screen for 300+ conditions 🛡️ To catch cancer and heart disease before you even feel a symptom. It's the "Antivirus" for the human body. Raised $5M in Seed funding (March 2026). They’ve already completed 1,500+ scans with 26% showing meaningful findings. Physical centers are opening in Bengaluru, Delhi, and Mumbai now 🏥 ➡️ The Insight: The "Second Act" of these tycoons is a masterclass in Risk Evolution. They are using their personal capital to fund scientific bets that could define the 2030s. They aren't just building companies - they are building the technological "Sovereignty" of a new India 💎

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A founder who has spent 32 Years building IP-Owned Tech in India's Defense Sector. Ashok Atluri, Founder of Zen Technologies Limited shares a growth story bootstrapped from Hyderabad: -Revenue: ₹60 Cr (FY21) → ₹930 Cr(FY24) -Market cap: 40 Cr (2012-13) → 13000 Cr today -Growth: 400X in 12 years Takeaways from our conversation: 1. India historically imported 65–70% of its defense equipment. Today, defense exports have grown more than 30× over the past decade, with the private sector contributing around 60% of exports. Domestic defense production has increased to ₹1.27 lakh crore, a 174% rise since FY 2014–15. 2. For over two decades, Zen built advanced simulators but struggled to sell them in India because single-vendor bids weren’t accepted. The IDDM policy in 2015 gave preference to IP-owning Indian companies and allocated 75% of the capital acquisition budget to local sourcing. And decades of R&D became a real market worth ₹200 billion. 3. Defense tech startups in India have raised $184 million in VC funding since 2020. Zen’s journey shows that IP-owned Tech built with self-funded R&D can build the most defensible advantage, similar to how Apple captures value through IP rather than manufacturing. 4. Zen’s anti-drone systems were proven in field operations, leading to export inquiries from over 20 countries. This mirrors the broader trend of India’s defense exports growing from ₹1,940 crore (FY14) to ₹21,083 crore (FY24). Field-tested solutions build trust far faster than lab demos or pilot projects. 5. Government fundings are rewarding innovation over manufacturing. Programs like iDEX provide up to ₹10 crore for proven concepts, while DRDO offers ₹50 crore per system under the Technology Development Fund. This shift allows companies to reinvest profits into next-generation R&D. 6. Ashok believes that not all technologies should be patented. Highly secretive or complex innovations can be reverse-engineered if patented. Instead, patent what is obvious to outsiders to protect your competitive edge. 7. Build with a focused approach on Pareto technologies with maximum impact. Instead of chasing every opportunity, Zen concentrated on the 20% of technologies delivering 80% of results, anti-drone systems and simulators. Prioritizing high-leverage areas compounds impact effectively. 8. Time market entry around overlooked gaps: In 2018, drones were a hot topic, but there was little attention on anti-drone systems. Zen entered this gap and was ready when the need became urgent, from regional conflicts to domestic security. Opportunities often lie where others are silent. 9. Create policies around your innovation: Self-funded R&D firms face unique risks that conventional procurement rules often overlook. Zen advocated for new categories rather than adapting breakthrough technologies to unsuitable frameworks, ensuring their innovations could be evaluated on merit. Full Episode https://lnkd.in/gnAFEj3f

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Cerebras is bringing its AI supercomputers to the UAE’s Stargate project ◾ Deploying megawatt-scale AI infrastructure across the Gulf, India & Pakistan ◾ Partnering with Abu Dhabi’s G42 ... part of the U.S.-UAE Stargate deal ◾ Competing head-on with Nvidia in AI compute ◾ Planning 12–15 data centers within 8 months ◾ IPO refiling “soon,” says CEO Andrew Feldman The UAE is quietly building the world’s largest AI hub outside the U.S.

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✨ Whole Vibe: Anysphere’s AI Coding Tool Hits Different Santosh Rao just published our initiation report on Anysphere, the company behind Cursor, the AI code editor that’s revolutionizing software development. With $500MM ARR achieved in just 24 months and zero marketing spend, this represents one of the most compelling growth stories in enterprise AI. 🔑 Key Highlights: 🚀 Explosive growth from $0 to $500MM ARR in 14 months, nearly doubling every two months 👥 Over 1 million daily active users and 360,000+ paying subscribers across individual developers and Fortune 500 companies 🏢 Adopted by 50%+ of Fortune 500 companies including NVIDIA, Uber, Adobe, Stripe, OpenAI, and Spotify 💰 $1.07B total funding including $900MM Series C at $9.9B valuation (281% markup from December 2024) 🧠 Leading the “vibe coding” revolution where natural language prompts generate functional code ⚡ Exceptional team productivity: ~$5MM ARR per employee vs $125K median for private SaaS companies Why This Changes Everything: 1. Anysphere is spearheading the fundamental shift in how software gets built, with AI now generating 41% of all code globally in 2024 2. The company achieved $500MM ARR with zero traditional sales or marketing, proving exceptional product-market fit through pure viral adoption 3. Their AI-native approach and developer ecosystem create compounding network effects that become stronger as adoption scales ✍🏼 What strikes me about Anysphere isn’t just the growth metrics, it’s the paradigm shift they represent. When 92% of developers now use AI coding tools and companies like Google report 25% of new code is AI-generated, we’re witnessing the industrialization of software development. Anysphere didn’t just build a better code editor, they built the infrastructure for how all future software will be created. The fact that they reached $500MM ARR without spending a dollar on marketing tells you everything about product velocity and developer stickiness. This isn’t incremental improvement, it’s fundamental transformation of the $500B software engineering industry. MVR projects their 2025 revenue between $700MM-$1B, with a trajectory toward $2.5-3.3B by 2030. When every developer becomes 10x more productive through AI assistance, Anysphere is positioned to capture that value at unprecedented scale. Talk soon, Jc 🔗: https://lnkd.in/gn6jxFps #VentureCapital #MVPAllStarFund #TechInnovation #preipo #siliconvalley #unicorn #MVPALLSTARFUND #mvpvc #mvp #therealmvpvc #venturebytes #allstar #ipo #research #technology #tommorowsipostoday #SecondaryAsAService #saas #SecondariesAsAService #Anysphere #Cursor #AICoding #SoftwareDevelopment #DeveloperTools #ArtificialIntelligence #VibeCode #ThriveCapital #Accel #AndreessenHorowitz

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I have coached 15+ startups with wildly different outcomes. Yet the billion dollar trajectories share some common DNA. Vinod Khosla calls this the difference between $0M and $0B companies. Here are six patterns that I have found useful and am actively wiring into Zeer AI from day one: 1. Turn goals into systems and automations : Relentless focus on goals can kill your venture. Hitting targets is valuable ONLY if, in the process you are also building repeatable systems and engines to hit future goals. High growth teams automate today’s work so tomorrow’s organizational complexity and growth get subsidized.  2. Iterate relentlessly without losing your plot : Great companies pivot constantly while staying consistent. Each adjustment sharpens the vision instead of derailing it. When the pace of learning is massive, it shows up as continuous refinements to the original plan. To an outsider it might appear as a distraction. At Zeer we had numerous pivots. Each pivot has clarified our original mission and uncovered smarter and more efficient ways to solve the problem. 3. Build teams that are intellectually curious : The best solutions that I've seen were powered by raw curiosity, a willingness to take risks, and a culture that made failure and risk taking safe. True breakthroughs come from first principles thinking while pursuing simplicity and not by copying someone else’s playbook. Work experience without first principles thinking can actually drag the team backwards with over reliance on cognitive bias. 4. Tackle difficult problems and a big vision : Billion dollar companies don’t (often) chase end point solutions. They build platforms or compound companies and solve difficult problems. They pursue an audacious vision while shipping a simple monetizable slice of customer experience. Balancing today’s execution with tomorrow’s dreams is a tough balancing act but the founders who nail this are the ones that make it big. 5. Craft a Simple Narrative, Build a massive distribution : Having a bold vision is not enough. Repetitive focus on simplifying the narrative and creating a massive distribution to broadcast the narrative is almost as important as the product and solution. A clear, simple repeatable narrative reduces friction and subsidizes the future. This narrative also brings in capital, early employees and customers. 6. Question everything : In most companies, the biggest resource wastage happens in the gap between a bad decision and it’s correction. When a strategy, new hire or vendor is not working out - act fast. Everybody that had to take a tough decision wishes they had done it sooner. I review my assumptions every two weeks, even the good ones. I need to be convinced that we are on the right path. In the age of AI, this cadence is not optional but key to survival and speed.  Which of these six resonates most with your team ? Please drop a note. I’m always up for swapping build-in-public lessons.

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🚨 RBI’s New Co-Lending Directions, 2025 – Effective Jan 1, 2026 The Reserve Bank of India (RBI) has issued new Co-Lending Arrangements Directions, 2025, which will come into effect on January 1, 2026. These guidelines will likely broaden the scope of co-lending 1️⃣ Mandatory Skin in the Game – Each Regulated Entity (RE) must retain minimum 10% of every loan (no shadow partnerships) 2️⃣ Blended Interest Rate – Final rate to borrower = weighted average of each lender’s rate. All fees to be factored into APR & Key Facts Statement 3️⃣ 15-Day Transfer Rule – Partner RE must book its share within 15 days of disbursement. Else, loan stays with originator 4️⃣ Default Loss Guarantee Cap – Max 5% of outstanding loans allowed 5️⃣ Borrower-Level Asset Classification – If one lender marks SMA/NPA, the other must follow 6️⃣ Escrow Account Mandate – All transactions to flow through an escrow, ensuring clear fund appropriation 7️⃣ Full Disclosure – Public list of co-lending partners + detailed reporting in financials Bottom line: Clearer rules, stronger consumer protection, tighter operational discipline >> co-lending just got a lot more transparent. #RBI #CoLending #NBFC #Fintech #Credit https://lnkd.in/gKxrz_hC

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India is not behind on talent. It is behind on risk appetite. Building LLMs or foundational AI infra is not about capability. It is about capital. Not just any capital, but capital that is comfortable with long timelines, unclear outcomes, and massive burn. Most of India’s funding still chases near-term traction. SaaS, fintech, consumer. Sectors where returns are easier to model. But AI infra does not play by those rules. It demands belief before proof. The Middle East launching a sovereign fund to accelerate AI through public private bets is not just money. That is intent. India will not build foundational AI by hoping startups will figure it out alone. It needs conviction at the system level. And a willingness to back uncertainty at scale.

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Fundamental opportunities worth solving for in AI right now: Predictability of reasoning, planning, execution of AI. Efficiency and ease of use of running AI workloads. Multiple system integrations - from DBs and APIs to infrastructure and biz systems. Trust, security, guardrails, authorization and auth. Business/science domain specific data, workflows, and optimization. AI cost understanding, control, and reduction.

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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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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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