Sunil Rathee

𝗜𝗳 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮, 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗔𝗻𝘆𝘄𝗵𝗲𝗿𝗲—𝗢𝗿 𝗗𝗼𝗲𝘀 𝗜𝘁? A founder pitched his SaaS Product. “India first, then global.” Investor: “Why India first?” “Indian customers are toughest. Enterprise quality at startup pricing. If we survive here, we win anywhere.” Investor nodded. Then: “Have you tried the US market?” Pause. “Not yet. We’re not ready.” — 𝗧𝘄𝗼 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝘀 𝗼𝗳 “𝗜𝗻𝗱𝗶𝗮-𝗙𝗶𝗿𝘀𝘁” One is strategic. One is avoidance disguised as strategy. ↳ Strategic: Building for extreme constraints creates innovation that scales ↳ Avoidance: Labeling difficult markets as “training” while dodging markets requiring different capabilities — 𝗧𝗿𝘂𝘁𝗵 #𝟭: Constraint-driven innovation works. Low bandwidth, price-sensitive customers, fragmented infrastructure—these force elegant solutions. WhatsApp succeeded globally solving for Indian internet. Zoom’s architecture came from emerging market connectivity. You can’t hide bloated features or mask poor UX. Legitimate strategy. 𝗧𝗿𝘂𝘁𝗵 #𝟮: Some founders confuse “hard to sell” with “makes us stronger.” What they’re avoiding: ↳ Enterprise credibility they haven’t built ↳ Regulatory expertise they don’t have ↳ Brand positioning beyond “cheaper alternative” ↳ Different buying experiences Western customers expect — 𝗧𝗵𝗲 𝗧𝗲𝘀𝘁 Your India strategy works if you’re: ↳ Building for constraints that create portable innovation ↳ Mastering unit economics that scale ($3 to $50 ARPU) ↳ Testing global markets early to learn what transfers It fails if you’re: ↳ Perfecting product before facing “sophisticated” buyers ↳ Avoiding markets requiring different sales approaches ↳ Assuming local credibility transfers globally — 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Am I solving for constraints that create innovation, or avoiding markets that expose gaps? If I launched in the US tomorrow, what breaks—and am I building those capabilities now? That reveals whether “India-first” is strategy or stalling. — 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝗰𝗮𝗹𝗲𝘀. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 𝘀𝘁𝗮𝗹𝗹𝘀. #Leadership #Startups #ProductStrategy #GlobalExpansion #SaaS

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Who doesn't like discounts and bargain offers? When it comes to AI.. Biggest AI sale is in India... Reliance and Google are offering 18 months of free Gemini 2.5 Pro (worth ₹35,100 i.e. USD 400) to 18–25-year-olds with their unlimited 5G plan. Meanwhile, OpenAI is giving a year of ChatGPT Go free for Indian users, and the earliest mover in India Perplexity teamed up with Airtel for its premium plan giveaway ( around USD $250). Reliance’s move is especially smart: target young users, build daily habits, and make AI part of growing up. The AI wars are moving into the next phase it seems.. Now it is not just about who builds the smartest model , it’s about who builds the deepest connections and captures the market...

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The Hidden Complexity Behind a Simple UPI Transaction. Most users see a UPI transaction as a simple three step process: Open App --> Enter UPI ID / Scan QR --> Enter PIN Money moves in seconds. What users don't see is the sophisticated ecosystem working behind the scenes to make that experience possible. A single UPI transaction typically involves: • The customer application (PSP App) • The remitter bank • The beneficiary bank • NPCI's UPI switch • Fraud and risk monitoring systems • Device binding and authentication services • Reconciliation and settlement systems • Dispute management frameworks Within a few seconds, multiple API calls are exchanged, validations are performed, fraud checks are executed, account balances are verified, and transaction statuses are synchronized across participating entities. What makes this even more challenging is the expectation of near perfect availability. Users expect payments to work instantly, whether they're paying ₹10 for tea or ₹1 lakh for a business transaction. Behind every successful UPI payment lies years of infrastructure investment, regulatory compliance, operational monitoring, and collaboration between banks, fintechs, and technology providers. As digital payments continue to grow in India, the focus is no longer just on enabling transactions but it's about delivering speed, reliability, security, and trust at scale. The next time you hear the familiar "Payment Successful" notification, remember that a complex network of systems worked together in milliseconds to make that happen. #UPI #DigitalPayments #FinTech #ProductManagement #Banking #Payments #NPCI #Technology #IndiaStack #ProductLeadership

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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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#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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This Bengaluru AI startup's headcount is smaller than single client accounts at Indian IT services firms, and still a $65M valuation. A 15-person team in Bengaluru just raised $21 million, or approx 200 crore. Their product does what most companies still pay a full department to do. 📍 Runable's agent, Grow, runs ads on Meta, Google, and ChatGPT without a media buyer. 📍 It cold calls and emails leads from a real phone number, without an SDR. 📍 It handles SEO and AI search visibility, without an SEO lead. So now the company has 15 people for 1.5 million users and over $65 million valuation, backed by titans like Susquehanna and Nexus Venture Partners.  That is a smaller team than most single client accounts at an Indian IT services firm. For 20 years, Indian tech scaled the same way.  A client had a problem We staffed people against it because more scope meant more headcount.  That math worked when execution was the bottleneck. Runable stands out because it is different.  > 1 agent owns the outcome end to end, building product, then running growth on top of it.  > Their team is at 15 because the work that needed 5 departments now needs maybe 5 prompts and one person watching the output. You can see the same shift inside GCCs right now.  Nasscom counted over 420 India-based GCCs whose parent companies make under $100 million in revenue, small teams, high leverage, built around ownership instead of scale.  Runable is that same logic, built as a product instead of a captive centre. I believe engineers should train for exactly this world and become people who own an outcome, not people who wait to be staffed onto one.  Because companies that used to need 50 people before to hit this scale now only need 15.  That is not a one-off. That is where hiring and the future of work in India is headed. Are you prepared for the new era?

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