Anmol Mohan

SANATAN LABS - Hexagon to Harmony: A Neutral Strategic Framework for Multi-Regional Alliances (Using PASSIONIT × PRUTL × KALKI AIDHARMA) When a multi-regional alliance framework emerges, it often appears as geometry — six nodes, multiple linkages, converging interests. But strategy is not geometry. It is alignment of purpose, identity, structure, and ethics. Krishna: An alliance must clearly define what it stands for, not merely what it stands against. Is the aim economic integration, technological collaboration, security resilience, or long-term stability architecture? If purpose is reactive, the framework becomes temporary. If purpose is constructive, it becomes durable. Kalki questions Alignment with Identity. Every region has foundational diplomatic principles — autonomy, balanced engagement, multi-vector partnerships. Any alliance must respect these. If a framework forces binary positioning or rigid bloc behavior, it creates long-term friction. Strategic identity cannot be outsourced. Krishna explains Structure. Rhetoric without institutional design dissolves quickly. Real alliances require interoperable systems, defined protocols, decision architecture, shared accountability, and measurable deliverables. Without structural reinforcement, enthusiasm fades. Kalki explains Intent vs Instrument. Intent must be transparent. Are partners aligned in long-term objectives, or are they temporarily converging due to situational concerns? Instruments — defence cooperation, economic corridors, technology platforms — must serve clearly articulated intent. Otherwise, instruments drift. Krishna : Operational Pathways. How are decisions implemented? What are escalation controls? What dispute resolution mechanisms exist? Strategy fails not in vision but in execution gaps. Kalki explains Narrative Coherence. If the alliance narrative feels exclusionary or polarizing, it creates external resistance. If it emphasizes shared prosperity, resilience, and stability, it builds trust beyond its core participants. Narrative determines legitimacy. Finally, through the KALKI AIDHARMA lens, the ethical audit emerges: Accountability must be mutual. Inclusivity must remain visible. Deterrence should coexist with de-escalation pathways. Harmony must remain an explicit objective. Autonomy must not be compromised. Reciprocity must be tangible. Multilateral compatibility must be preserved. Adaptability must be built into the design. The conclusion is simple: A hexagon of alliances succeeds not because six points connect, but because trust circulates among them. When purpose is clear, identity respected, structure strong, intent transparent, operations defined, narrative inclusive, and ethics integrated — the geometry transforms into coherence. And coherence, not alignment alone, sustains long-term stability.

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Vivek Raghavan, co-founder of Sarvam AI, the company that just shipped India's first foundation model trained from scratch, warned yesterday that India risks becoming a "digital colony" if it does not build foundational AI itself. At the same summit, JioStar, India's largest media conglomerate, partnered with OpenAI. I recently wrote on Substack about why this keeps happening: "Indians Rule Big Tech. Why Can’t India Build?" (https://lnkd.in/ewQBMCYf). The structural forces are thirty years deep. Source: The Economic Times (https://lnkd.in/eGEgSb95)

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🚨 𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝗯𝗶𝘁𝗲𝘀 𝘁𝗵𝗲 𝗱𝘂𝘀𝘁. That’s the 𝟰𝘁𝗵 𝗼𝗻𝗲 𝗜’𝘃𝗲 𝘀𝗲𝗲𝗻 𝘀𝗵𝘂𝘁 𝗱𝗼𝘄𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝟯 𝘄𝗲𝗲𝗸𝘀; 𝟭𝟭𝘁𝗵 𝗜 𝗸𝗻𝗼𝘄 𝗼𝗳 𝗶𝗻 𝗹𝗮𝘀𝘁 𝟭 𝘆𝗲𝗮𝗿. Subtl.ai 𝘄𝗼𝗻’𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁. 𝗨𝗻𝗳𝗼𝗿𝘁𝘂𝗻𝗮𝘁𝗲𝗹𝘆 𝗺𝗼𝘀𝘁 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗮𝗿𝗲 𝗵𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘄𝗮𝘆. 𝗪𝗵𝘆? Because just having “AI” isn’t enough. It’s like assuming all aviation companies—engine makers, airlines, defense contractors—should have the same GTM, talent, and cost structures. 𝗦𝗼𝘂𝗻𝗱𝘀 𝗿𝗶𝗱𝗶𝗰𝘂𝗹𝗼𝘂𝘀, 𝗿𝗶𝗴𝗵𝘁? Yet in AI, everyone’s copying 𝗢𝗽𝗲𝗻𝗔𝗜’𝘀 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. ⚙️ Building cutting-edge models first. 🤖 Obsessing over model accuracy before finding PMF. 💡 I’ve said this for years: 𝘈𝘥𝘥𝘪𝘯𝘨 𝘈𝘐 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘤𝘢𝘭𝘭𝘺 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘦 𝘺𝘰𝘶𝘳 𝘱𝘳𝘰𝘥𝘶𝘤𝘵’𝘴 𝘷𝘢𝘭𝘶𝘦. 𝘈𝘯𝘥 𝘶𝘴𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘤𝘢𝘳𝘦 𝘩𝘰𝘸 𝘪𝘵 𝘸𝘰𝘳𝘬𝘴—𝘰𝘯𝘭𝘺 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘪𝘵 𝘴𝘰𝘭𝘷𝘦𝘴 𝘵𝘩𝘦𝘪𝘳 𝘱𝘢𝘪𝘯. Here's the stat VCs and founders need to internalize: 📉 𝗙𝗮𝗿 𝗺𝗼𝗿𝗲 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗱𝗶𝗲 𝗳𝗿𝗼𝗺 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗣𝗠𝗙 𝘁𝗵𝗮𝗻 𝗳𝗿𝗼𝗺 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝘁𝗲𝗰𝗵. 🧠 Want to know what actually works? I break it down here → 👉 𝗔𝗻𝗮𝘁𝗼𝗺𝘆 𝗼𝗳 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗔𝗜 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 [https://lnkd.in/ghR49Q5M] Stop romanticizing the tech—and start focusing on what users really want. #AI #Startups #VC #Product #PMF #LLMs #GenAI #AIStartups

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Here's what's become increasingly less relevant: “How big is your model?” After talking to Sudarshan Kamath, founder & CEO of smallest.ai, I’m convinced the more important question is: “What real workflow can your agent run end-to-end — reliably and in real time?” 🚀 New Founder to Fortune episode is live: “Small Models, Big Impact: Why the Future of AI Isn’t Trillion-Parameter” In this conversation, we go deep on: 🛑 Why “small vs large” is a false dichotomy — and why enterprises should think in terms of specialized agents vs general-purpose agents, not just parameter counts 💪 How smallest.ai is building ultra-fast speech-to-speech models that handle multilingual, emotionally rich conversations (yes, including Hinglish and Spanish/English code-switching)  🎁 The unsexy reality of voice AI: 90–95% of the work is data, labeling, and active learning loops, not model magic 📣 A very practical playbook for CIOs/CTOs: starting with collections and outbound calls as “quick-win” voice AI use cases, before you re-architect the whole stack  🛠️ Why legacy call-center software is fundamentally designed for humans, not infinitely scalable AI agents — and who inside the org actually ends up buying these systems. Bonus! Sudarshan’s founder playbook: 💪 30K+ LinkedIn connections as a growth and fundraising engine 📣 Building the largest voice AI Discord community to shape product direction 🏇 Why you should ignore advice from people who haven’t done the thing you’re trying to do 📈 How to run asset-light experiments and pivot a thousand times before you raise a big round To kick off the episode, I’ve posted a short launch reel with one of my favorite moments from the conversation — it’s the clip where we talk about why enterprises should stop obsessing over GPT-5 vs “the next thing” and start obsessing over agents that actually close loops in production. 🎥 Watch the reel above 👆 🎧 Jump into the full episode (link in comments). If you’re: A founder building AI for the enterprise, or A CIO / head of AI trying to decide where to start with voice agents …this one will give you a very grounded way to think about small models, real-time systems, and what’s actually deployable today.

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The most interesting thing about DLF's ₹5,590 crore sellout wasn't the speed. It was who bought. 47% were under 35. Tech founders, not legacy families. And they weren't buying homes, they were buying clusters. Three, four units at once. One for living, others for their core team. The old Gurugram buyer wanted to tell people they lived near the Oberoi. These new buyers? They just want to walk to work and reach their jets fast. So DLF Limited's entire strategy is shifting. Their next launch won't have advertisements or a sales office. It's invitation-only, near Aerocity. Because when your buyers value time over heritage, you stop selling prestige and start selling proximity. I'm seeing this pattern everywhere. That Brisbane mansion with the Ferrari mural that went viral? Bought by a 32-year-old crypto founder who never even visited. In watches, buyers under 40 are driving 69% of growth above $30k. They don't care about waiting lists or heritage stories. The entire luxury playbook is being rewritten by buyers who made their money in months, not generations. And most sellers haven't noticed yet. Gurugram went from ₹1,800 to ₹180,000 per sq ft in 25 years. The next multiplication will take 5. Because speed is the new luxury. Everything else is just nostalgia.

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Recently, I got the opportunity to be on Vishal Malhotra’s podcast to talk about AI. This wasn’t a basic “AI is the future” conversation. We discussed thing like: AI voice cloning scams AI leading to war AI replacing actors Students over-depending on tools where India stands in the AI race and even whether AI could develop self-preservation instincts you won’t just learn how you can integrate AI into your life. You’ll understand what it means for media, creativity, education, and human thinking. Because AI is not just a tech upgrade anymore. It’s a shift in how we live, create, and make decisions. Watch it now Link: https://lnkd.in/dB3dgtyp

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The weekend musing, I stumbled upon the article in The Ken “Does India Risk Losing Cultural Relevance in the AI Era?” highlights a critical and overlooked truth in the global AI race. Rohin Dharmakumar of Ken and co-founder and CTO of Ozontel, Chaitanya Chokkareddy notes, while the world chases ever-larger models and faster GPUs, India is ill equipped to leverage its greatest competitive edge. Its data, the multilingual, multicultural, multimodal fabric of its society. That argument holds a mirror to the broader AI ecosystem. We have reached a stage where for developing a model, architecture is known and hardware is accessible (read NVIDIA). The one factor that remains opaque and unknown is the dataset. It is the Missing Piece in the AI Puzzle AI’s current trajectory is heavily model-centric. Every breakthrough from GPT-4 to Gemini to Claude is defined by parameter counts and benchmark scores. Yet, what underpins these models is not architecture alone, but the breadth, diversity, and quality of data they learn from. If the world’s largest models are being trained predominantly on Western, English-language, text-heavy datasets, the outputs will inevitably reflect that worldview. The result? AI systems that may excel technically but fail culturally misinterpreting context, tone, and emotion when engaging with the majority of the world’s population. India and the Global South sit on a cultural data goldmine. From regional cinema to street conversations, from folk music to memes, the region’s expression is multimodal spoken, visual, emotional not purely textual. However, because AI training still relies heavily on written corpora, many Indian and Southeast Asian languages remain invisible in the digital ecosystem. For instance, while English, Mandarin, and Spanish dominate AI training datasets, languages such as Odia, Khasi, or Malay have little to no structured digital representation because there are not enough websites for LLMs to read from. If nations in the Global South invest in capturing, annotating, and rights-managing this rich local data, they could build an entirely new economic layer: - Licensing datasets for global model training. - Developing culturally aligned agents and conversational systems. - Exporting AI grounded in local context rather than generic global models. Without intentional representation, the Global South risks becoming the backend of the AI economy, supplying labour and infrastructure but not contributing to the creative or cultural layers of intelligence. Owning culturally rooted datasets ensures not just participation, but presence the ability to shape how AI perceives identity, empathy, humour, and expression, what we envisage as AGI. In the gold rush for frontier models and AI agents, data is the last untapped mine. The nations that own their cultural data will not only shape the economics of AI they will shape its intelligence.

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100K active users in just 7 days + 12K paid conversions in week 1. That's NOT normal for a fresh AI platform — especially one building sovereign tech in India. Atomesus AI launched Jan 12, 2026 and the numbers already scream product-market fit. Here's what stands out for builders & founders 👇 🔥 Unlimited free tier (chats + image gen) hooks mass adoption fast 🔥 ~12% paid conversion right out of the gate — rare in AI land (most wrappers stay under 5% even months in) 🔥 Android live now, iOS coming soon, daily pushes from an ISRO-rooted team 🔥 Sovereign roadmap: hybrid today, full proprietary model ahead → real data-in-India + better Indic language handling This proves a few things we obsess over in 2026: → Generous free access + natural upsell beats forced subscriptions → Local talent + sovereignty focus builds actual moats in emerging markets → Iteration velocity > starting with the biggest model As someone tracking India's deep-tech moves, this milestone hits different because the monetization speed is what VCs chase in AI-native bets right now. Have you tried the app yet? → www.atomesus.com or search "Atomesus" on Play Store What's the real unlock for Indian AI teams? -Beating globals on cost + speed? -Sovereign data edges in a world of export controls? -Or hitting 10%+ paid conversion this early? Curious what devs, founders, and product folks think — drop your honest take below. Tag someone grinding on Indic AI or sovereign infra. Let's talk execution realities. 🚀 #IndiaAI #SovereignAI #DeepTech #AIStartups #ProductLedGrowth #MakeInIndia #AtomesusAI

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As a seed/pre-seed investor, our focus at Lumikai is to back founders very early on, work closely with them through the year(s) on aligning their long term ambitions with more immediate proof points (12-18 months), and then constantly adjust those plans as they work through PMF, finding a replicable model and getting to Series A. Sounds easy! For founders, this is a constant act of jugglery - there is never a time that you're out raising while everything is working - and with my own startup a little while ago, my co-founder and I made every mistake in the book in getting to that subsequent round. This podcast breaks down everything founders have to keep in mind as they go from raising the seed (on the back of their ideas and early product) to raising a Series A (where they are now judged on execution and annoying things like retention). Listen in below 👇

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TLDR: We have started shutting down subtl.ai , a big big thanks to the Hyderabad, Bangalore & Bay area ecosystems for all their support ❤️ what was subtl.ai ? We started in 2019 building infra for complex doc processing and transformer led retrieval ( what's now popularly called RAG ). We had some early success with defence PSUs, 2 airports, 1 US insurtech and State Bank of India. Over this journey and along with our colleagues, my chief scientist prof Manish and I had also published 1 patent and 1 paper around the IP we build that is super relevant for RAG ( around the doc processing and a clean approach to construct a graph and extract enriched vector embeddings that outperformed openAI for RAG on 20,000 RBI banking regulations! ). So much learning, so much of heart from myself and my colleagues. It really sucks that we have to shut down, but I understand why this happened. It's very easy to say that we got screwed over by customers, prospect investors wasted our time, few employees did not care enough about the mission... But I made up my mind that I will not act like a victim. It's completely on me, I failed my team and investors more than they failed me for sure. So what went wrong? 1. Lack of Market focus. we wanted to do openAI like things for the RAG ecosystem by letting customers build and run fine tuned SLMs for doc processing and retrieval, but I got stuck handling customers from wildly different domains with wildly different use cases. it was just not repeatable, so customers gave no shits about our other portfolio of work we had done. 2. Lack of infra GTM. if we really wanted devs to adopt our APIs for RAG, citations and everything else, we should have made it easier for them. all we did was put a message on our website saying "yo reach out if you wanna use our APIs" , we could have: - open sourced projects built on our APIs - integrated into popular frameworks like LlamaIndex and Portkey - release useful documentation for developers to understand how to use our API - dev focused PLG - support hackathons 3. I'm a better CTO than a CEO. I love building in AI, with all my heart ❤️ I have learnt to enjoy selling, but true sales chops comes from deep domain expertise. and being a vagabond across industries helped me learn alot about the world, but still not enough to convince people to pay us. In my next venture, I will be a CTO ( more on this soon! ) 4. some Investors flirt ALOT with founders, but it doesn't mean shit until they give you a term sheet. I had made decisions assuming that funding budgets would come in based on conversations I had with few good funds out of Hyderabad. This became a big reality check for me. There are investors like G Vamshi Raju Sailesh Sigatapu Mohit Gulati Gowri Shankar Nagarajan that I have so much respect for, but a lot of VCs in our ecosystem just don't care for founder time. Be ruthless with your time. It is what it is, I learnt so much. Thanks for everything 💖

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Winning High-Stakes RFPs-Part 1B/10 Solution Engineering Series for NBFCs, Healthcare & Supply Chain: Execution Playbook 🔁 10-Stage Solutioning Lifecycle 🔹 1. Discovery & Qualification → Use cases, process gaps, compliance needs → Score with BANT/CHAMP, peer tech benchmarks 🔹 2. Stakeholder Mapping → Identify decision-makers, influencers, blockers → Craft win themes for CIO, CISO, COO, CFO, Procurement 🔹 3. Solution Hypothesis → Draft modular blueprint: core, service, support stacks → Align to outcomes: SLA, compliance, cost, risk, TAT 🔹 4. Architecture & Engineering Design → Build scalable, secure, domain-ready solution → Include GenAI, APIs, analytics, RPA, microservices 🔹 5. Governance & SLAs → RACI, escalation matrix, CAB workflows → Define SLA/OLA tiers, monitoring stack (ELK, Prometheus) 🔹 6. Security & Compliance → RBI, HIPAA, ISO, SOC2, GAMP mapped upfront → Embed controls: logs, vaults, RBAC, encryption 🔹 7. Delivery & Support Model → L1–L4 model, DR/BCP, shift-left/right support ops → Uptime SLAs, prod vs non-prod flows 🔹 8. Team Structure & Delivery Pods → Architect, SME, Infra, Security, Pricing, Proposal roles → Assign onsite/offshore strategy, redundancy plan 🔹 9. Estimation & TCO/ROI → Infra, licenses, AMC, effort costing → Present ROI, breakeven, value realization 🔹 10. Final Packaging & Pitch → Build exec summary, demo storyboard, solution deck → Include SoW, pricing sheet, compliance tracker 🧠 Industry-Focused Layering 🟦 NBFCs • LOS/LMS, co-lending APIs, CRILC, CKYC • AI underwriting, RBI real-time logs, XAI scoring 🟩 Healthcare • EMR/EHR AI, FHIR/HL7, GenAI for records • HIPAA controls, anonymization, access auditing 🟨 Supply Chain • IoT edge, WMS/TMS, smart fulfillment • ISO 28000, logistics encryption, last-mile AI 👥 Stakeholder Win Themes • CIO → Cloud-native, scalable, composable architecture • CISO → Zero-trust, pre-certified compliance stack • COO → SLA-first ops, uptime, TAT metrics • Biz Head → Revenue unlock, faster GTM, automation • Procurement → ROI clarity, clean pricing, timeline confidence 🏗️ Winning Team Setup • 🧠 Architect → Solution blueprint + integration • 👔 SME → Industry KPIs, domain logic • 🛠️ Infra/DevOps → CI/CD, observability, DR/BCP • 🔐 Security → Compliance-by-design • 📊 Estimation → Cost models + buffers • 📄 Proposal → SoW, version control, submission hygiene • 🧪 Demo Lead → Storyboard, proof points 🧰 Reusable Asset Kit • 🔍 Discovery Qs + Use Case Tree • 🧱 Architecture Templates (Visio/Figma) • 🗺️ Persona Maps + Win Cards • 📉 Compliance Matrix (RBI, HIPAA, ISO) • 📊 ROI/TCO Calculator • 📑 RACI, CAB Flow, BCP Plan • 📦 Submission Tracker • 🎬 Demo Script & Storyboard 📈 Key Solution Metrics • 🏆 Win Rate • 🕒 Time-to-Solution • ♻️ Reuse % of assets • 🔒 Compliance Readiness Score • 🧪 POC-to-Win Ratio • 📦 Team Utilization #SolutionEngineering #Presales #RFP #NBFC #Healthcare #Architecture #Compliance #CoreEngineering

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Builder.ai Collapse: When “AI” Stood for “Actual Indians” The recent downfall of Builder.ai is a stark warning to the entire tech ecosystem. Once a rising star in AI-powered no-code development—backed by over $445 million from investors like Microsoft and the Qatar Investment Authority—the company is now facing insolvency. 🔍 Investigations reveal that the platform’s so-called “AI assistant” Natasha was largely powered by 700 engineers in India. In this case, “AI” stood not for Artificial Intelligence, but for Actual Indians—a term now circulating in tech circles as a blunt commentary on overhyped automation. 💸 Even more troubling, Builder.ai is accused of inflating its revenues through fabricated sales transactions with VerSe Innovation, raising serious questions around financial governance and ethical conduct. Key Takeaways for Founders, Investors & Technologists: • 🚨 Transparency over hype – Don’t overstate capabilities for funding. • 🔍 Do your due diligence – Investors must validate tech claims beyond pitch decks. • ⚖️ Ethics in marketing – Misleading customers and backers ultimately backfires. As AI transforms industries, let this be a reminder: real innovation demands real integrity. https://lnkd.in/gXHpjsH9 #AI #BuilderAI #StartupEthics #ActualIndians #TechScandal #Innovation #TransparencyInTech #EthicalAI

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Trust in new technology is not automatic. It is earned. This decision shows how clarity overcomes skepticism. A valuable leadership lesson. Full podcast link available in the original post. #AIinEducation #VoiceAI #EdTech #EdTechInnovation #EdTechTransformation #StudentEngagement #FutureOfEducation #ArtificialIntelligence #AngelX

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Israeli observability startup Coralogix has raised $115 million in a Series E round, becoming a unicorn with a valuation of over $1 billion. Coralogix offers a full-stack observability and security platform that helps companies understand and resolve complex issues in their data streams. The company has developed an AI agent, Olly, which uses a semantic layer over internal and external data to automate data observability and provide sophisticated insights through simple text prompts. The all-equity round was led by NewView Capital, with participation from the Canada Pension Plan Investment Board (CPP Investments | Investissements RPC) and NextEquity Partners, alongside existing investors. The funding will be used to expand its engineering base in India, with a planned $100M investment over five years, and to further develop its AI agent. Congratulations to co-founder and CEO Ariel Assaraf and the Coralogix team. TechCrunch: https://lnkd.in/dS3NZrrG #Observability #Data #AI #DevOps #Unicorn #SeriesE #Funding #VentureCapital #IsraelTech

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We Maneesh Agarwal just came back from a private screening of the lesser-known but deeply affecting film Human in the Loop #HumanInTheLoop, followed by a discussion with its director, Aranya Sahay At its core, the film tells the story of a tribal woman from Jharkhand, India, fighting to keep her children with her amid a custody battle. She is unemployed, vulnerable, and socially marginalized. Her entry point into the modern economy is an interview at an AI data-labeling center, where workers tag images and videos in remote facilities to “train” artificial intelligence. One of the film’s most striking parallels is the training AI alongside the act of raising a child. Both require patience, care, repetition—and both raise questions about agency, ownership, and who ultimately controls the outcomes. Another deeply moving layer comes from her childhood. As a young girl living close to nature—among forests, rocks, and trees—she is shown befriending an incredibly shy porcupine. This is not a gimmick; it becomes a metaphor. When she is calm and inwardly grounded, she is connected—to nature, to herself, to life. As an adult, she enters a relationship outside formal caste and marital norms. In Jharkhand’s tribal communities, the term “Dhukni” (or Dhuku/Dhukua) refers to a woman who enters a man’s household and lives with him as a partner without a formal, costly wedding—a practice known as Dhuku marriage. It is often a socially recognized and pragmatic arrangement for couples unable to afford traditional ceremonies. Yet, over time and especially in urban contexts, this relationship is treated as illegitimate, and she carries the burden of social judgment. Years of displacement, economic precarity, and the constant stress of defending her children’s legitimacy slowly change her. The pressure makes her anxious and angry, and she begins to lose touch with the quiet strength and inner stillness that once connected her so deeply to the world around her. What is remarkable is that so many things could have gone wrong in this story—but the film chooses hope. Not naïve hope, but one rooted in dignity, resilience, and the human need to belong, to be seen, and to stay connected. Running through it all is a subtle but powerful commentary on how technology increasingly shapes our narratives—and why those narratives must ultimately be owned and guided by people, not systems. In conversation with Aranya, hearing about his creative process and how he arrived at these themes—motherhood, marginalization, technology, and narrative ownership—made the film resonate even more deeply. The layers are rich and understated: the bond between mother and child, the hunger for acceptance, and the quiet question of who benefits from progress. Human in the Loop has received Oscar-qualifying attention for its screenplay, and it deserves a much wider audience. Highly recommend watching it. It will be screening tomorrow in San Francisco at AMC.

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The question I have been asked most over the last decade: "𝘞𝘩𝘺 𝘣𝘶𝘪𝘭𝘥 𝘢𝘯 𝘈𝘐 𝘥𝘦𝘦𝘱-𝘵𝘦𝘤𝘩 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘪𝘯 𝘐𝘯𝘥𝘪𝘢?" My answer: 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗜𝗻𝗱𝗶𝗮 𝘄𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜, 𝘄𝗲 𝘄𝗶𝗹𝗹 𝗮𝗽𝗽𝗹𝘆 𝗶𝘁 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. While the world races to build bigger models, India's true opportunity lies in applying AI to solve real-world problems at population scale. Our existing DPI gives us a unique advantage that few nations possess. Combined with our talent pool and mission-mode focus on sectors like healthcare, agriculture, and financial inclusion, we are not just participating in the AI revolution, we are defining a new category. At Avataar, we live this thesis every day: bridging the AI-ROI gap with domain/task specialized models that deliver measurable business outcomes, not just impressive demos. The shift from generalist models to purpose-driven AI systems isn't coming - it's here. And India is leading it! Thank you BW Businessworld for the platform to share this vision. #AI #IndiaAI #DeepTech #EnterpriseAI #AgenticAI #AvataarAI

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#StartupMafia 3.0… and the Coming Startup Mafia 4.0 An interesting story in the The Economic Times today, by Disha Acharya & Puran Choudhary, highlights how companies like Razorpay, Cred, Meesho and PhonePe have become “founder factories,” spawning 200+ new startups. This phenomenon—often called the Startup Mafia effect—is actually a sign that an innovation ecosystem has matured. Silicon Valley saw it first with the PayPal Mafia. India is now seeing the same pattern. Startup Mafia 1.0 (2007–2016) The first wave came from pioneers like Flipkart and Paytm. This era built India’s consumer internet economy. Focus sectors included: • e-commerce • online marketplaces • payments • consumer apps This phase proved that large digital businesses could be built in India. Startup Mafia 2.0 (2014–2021) The second wave rode the mobile internet explosion after Jio. Companies like: • Ola • Oyo • Swiggy • Zomato trained a generation of founders who understood scaling operations, logistics and hypergrowth. India’s startup ecosystem moved from experimentation to unicorn-scale companies. Startup Mafia 3.0 (2018–Present) Today’s wave is driven by companies like: • Razorpay • Cred • Meesho • PhonePe The backbone of this phase is India’s digital public infrastructure: UPI Aadhaar cheap cloud infrastructure developer ecosystems Many new startups emerging from these companies focus on: • fintech infrastructure • AI-native software • cross-border payments • SaaS tools. Startup Mafia 4.0 (Emerging) But the next wave may look completely different. Instead of consumer apps, it will likely come from deep-tech ecosystems. Places like: • IIT Madras Research Park • semiconductor clusters in Bengaluru and Hyderabad • robotics and EV startups • industrial analytics companies. Here we see companies like: • Ather Energy • AgniKul Cosmos • Detect Technologies • Gyan Data training engineers who will eventually become founders themselves. The earlier waves digitised commerce and payments. The next wave may digitise machines and infrastructure. Instead of apps connecting people… we will see platforms connecting: • factories • vehicles • industrial machines • supply chains. This is where mechatronics + IoT + AI come together. If sensor data, embedded electronics and cloud analytics are layered onto these machines, they can become connected intelligent assets. Predictive maintenance, uptime guarantees and performance optimisation become possible—even for affordable machines. Industry 4.0 for the Real Economy Germany built Industry 4.0 for high-end factories. India may build Industry 4.0 for millions of machines used across the Global South. And that next generation of founders—Startup Mafia 4.0—may emerge not from consumer apps, but from mechatronics, IoT and industrial intelligence ecosystems. The next startup revolution may not just connect people. It may connect the machines that power the real economy.

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Excited to share that our journey with DentNode has been featured in Inc42 Media's latest story: “The New Garage: OpenClaw And India’s DIY AI Agent Boom.” 🔗 Article: https://lnkd.in/gQbBHMxH The piece highlights how builders across India are experimenting with AI agents and automation to solve real-world operational problems. OpenClaw and similar frameworks are enabling a new generation of developers to build systems that don’t just chat — they actually do work. Our story in the article starts from a very personal place. I left my engineering role in Bengaluru to work with my father’s dental lab and saw the inefficiencies firsthand — labs juggling multiple scanner portals, downloading patient scans manually, and struggling with fragmented workflows. That experience led to building DentNode — a platform designed to streamline the workflow between dentists, labs, and digital dentistry tools. Being featured alongside this new wave of AI builders is exciting. We’re just getting started. If you’re working in digital dentistry, dental labs, scanners, or aligner manufacturing, I’d love to connect and learn from you. #AI #Startups #DentTech #DigitalDentistry #OpenClaw #DentNode #BuildInPublic #IndianStartups

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