Richard Lin

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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"Hiring really good AI researchers is incredibly important." Xiao Zhang, CEO and Co-founder of Collov AI, talks about hiring for AI product teams on Tech Scenes Unplugged. #venturebacked #vcbacked #venturecapital #leadership #ceo #founder #operatingsystem #peakOS #foundermode

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Bolna - the Cursor for Voice AI development, purpose built for Indian Businesses. Maitreya and Prateek are building the control plane for voice agents solving for the hard, non-glamorous engineering behind production-grade calling. Built on the strong pillar of reliability across latency, languages, accents, interruptions, and failure modes. When abstraction is the promise, deep systems engineering and a robust product is a must. That’s how voice gets truly democratized.

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Design is where the real value in electronics gets created, it’s where IP is built, roadmaps are defined, and margins are won. In a $3.5T global electronics market, owning design is the next frontier. India has made huge strides in scaling electronics output now ~$120B, but design-led manufacturing remains a gap. That’s the opportunity iDO Devices is going after. They're building full-stack capabilities, from firmware and hardware design to system integration — to enable world-class consumer tech to be designed and developed from India. Thrilled to back this vision at Merak Ventures alongside Blume Ventures. #Electronics #ODM

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Ankur Capital is excited to invest in Turiyam AI, powering the future of AI inference. It's playing into the need for edge use silicon, that's going to optimise use of resources as AI becomes all pervasive. We look forward to partner with Sanchayan Sinha Parag Jain Praveen Jain in building the Indian semicon sector Ritu Verma Krishnan Neelakantan Vishal Katariya Vishal Aman Suraj Nair Sama Jashnani Burzin Dubash Yagna Sreeramaneni Debansh Sahoo

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Rishi Taparia and I Garuda Ventures are thrilled to announce our partnership with Adarsh Kulkarni and the Foundry Robotics team as they build the “Everything Factory” - the AI+robotics first, assembly-focused, dual-use contract manufacturer to power the next century of American manufacturing. Adarsh is a founder who was built for this moment, where AI, hardware, and the national interest are aligning. He’s a technical builder with deep robotics expertise, proven commercial execution at scale, defense credibility, and the will and drive to tackle a problem of this scale. Foundry’s bet is that foundation models, modern robotics, and manufacturing software expertise unlock the ability to do generalized robotic assembly across diverse operations and different products at scale, and that the right business model entry point is to serve as a dual-use contract manufacturer. If they are right, the implications are transformative: a factory that can assemble many different products, switching between them without traditional costly and time-consuming retooling overhead, while supporting the re-shoring of American manufacturing. Excited to be investing alongside an incredible group of partners and friends including Khosla Ventures Red Glass Ventures Hanabi Capital Recall Capital Zero Shot Fund E14 Fund Embark Ventures Read more about our thesis in our blog post (first comment below). Let's go Foundry Robotics! (And thank you to Pawan Deshpande for the intro!)

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Tried my first attempt at crafting a full 3 year product vision but using all the usual LLMs as a thinking partners. My observations on current LLM tech (I suspect software engineers face the same issues) - - Gave me the ability to think in paragraphs rather than sentences knowing it would handle the nitty gritty of "filling out" text, sometimes to a fault. - Excellent at any kind of reformatting and tonal shifts- "Make this half page length, combine these two sections, reframe this through the lens of a future investor/engineering manager/health plan CMO/arch nemesis, make it less negative, make it into a table" - Poor in picking "key insights" from a concept - things that need ground-level domain knowledge - (e.g. what is the key moat we are building here or what was the common insight after actually talking to patients and hearing their emotions) . - Great when you give it parameters to optimize against "make sure I don't get too technical" or "check me when I over-explain things" or "review guidelines and make sure I am actually answering the questions" - Okayish in mirroring tones (my writing style tends to be more direct, LLMs do have their own writing style that I can best describe as "government memo" that humans seems to detect easily) - Still needs sentence by sentence review to ensure that the true thought to be communicated comes through. Overly defaults to prosaic and filler text - Gets caught in the dreaded text loop where it keeps editing and making the same text worse - role of human is to help it break out of that Continues to highlight for me that we should be over-indexing on spending all our times with other humans and understanding their true problems.

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Adverse Selection and Moral Hazard in Product Strategy and M&A Yesterday, I was speaking with Sri Malladi - a business school senior, IIT alum, and good friend. He has done really well, switching from a corporate career to starting and scaling a solopreneurial investment bank in just over 5 years. Check it out here: https://lnkd.in/gvb-cvkj During our conversation, the topic of sell side investment banking pitches to corporates came up. In the large, mid, and small company corporate development and strategy teams that I have been in, we received many of these - from the bulge brackets and the lower middle market banks alike. Across the many years, depending on the size of the company, we would typically get between ~5 ( small co.) and ~100 (large co.) such inbound pitches a month. That’s a lot! We rarely spent much time on these. In my entire career, I have probably seen < 5 deals going through out of multiple thousand such unsolicited inbounds. When I bring in the mix hundreds of venture investment deals we received, the story remains the same. That’s a major productivity loss on both sides. The #1 reason behind that is the information asymmetry problem. The risk of what the target knew but we didn’t at the front end was a major concern - questions like - “why is this company up for sale?”, “why do they need to hire a banker to sell themselves if they are good”? These were all the right questions to ask by thoughtful product and strategy teams. We call this the Adverse Selection Bias. It’s a real problem before any transaction. As product strategists we know, best companies are bought, and not sold. The best product managers and business strategists understand at depth the gaps in the portfolio relative to what the market and competitive needs are over a 3-5 years horizon, and which of those gaps can be built organically vs. which need to be bought via acquisitions based on time to market, better-together arts of the possible, risk vs. return appetite, opportunity loss of not doing anything among the key criteria. I offer more depth on these biases in tech strategy and m&a here on my SubStack blog: https://lnkd.in/gDd7g6VQ What other biases have you seen?

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My co-founder Kulapradip Bharali and I go back over 16 years—we first met as Computer Science undergrads at NIT. Since then, we’ve been close friends and collaborators, building and scaling ambitious ideas together. Previously,we co-founded Gobillion, an e-commerce platform tailored for India’s Tier 2+ towns. Backed by Y Combinator and top global investors, Gobillion grew rapidly, serving small-town consumers across India. Through that experience, we got a front-row seat to the realities of building and scaling technology in real-world environments. As founders obsessed with operational efficiency, we naturally turned to AI to optimize our business across customer support, lead generation, sales outreach, and internal ops. But we kept hitting the same wall most businesses face today: AI products, in reality, often fail to deliver meaningful business impact. Most of them are too generic, do not integrate well with workflows, or simply not built for production-grade use. The gap between the Gen AI hype and business outcomes became painfully clear. As we spoke to multiple business leaders and founder friends, we witnessed the same frustration. Businesses didn’t need another chatbot, or off-the-shelf voice agent or just another generic self- serve AI tool - they needed a domain-specific, AI automation stack that could actually do things and deliver results. These insights became the inspiration for launching MyriadAI! At MyriadAI, we build Agentic AI products designed for real-world complexity. We help companies automate their entire Conversational stack and allied workflows - using domain tuned, production grade AI agents. Alongside our flagship products, we also offer tailored AI advisory and automation services to accelerate enterprise adoption and drive measurable impact. With a deep focus on the BFSI / Fintech, and Retail / E-commerce sector, we solve high-value problems where precision and performance matter - boosting revenue, increasing productivity, and optimizing costs. We're not just building generic tools—we're building the AI agent stack that can deliver outcomes across complex workflows. Our goal is simple but ambitious - Be a trusted AI partner from POC to Production and beyond! #AI #ArtificialIntelligence #GenAI #GenerativeAI #AgenticAI #AIAgents #Startup #Entrepreneurship #AIForBusiness #DigitalTransformation #ConversationalAI #VoiceAI #Fintech #BFSI #Ecommerce #Retail #RetailTech #MyriadAI

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Parag Agarwal challanges GPT's with Parallel 'Parallel is backed by Silicon Valley names, including Khosla Ventures, First Round Capital, Index Ventures, and others. Its founding team includes veterans from Google, Twitter, Stripe, Airbnb, and Waymo, many of whom worked on large-scale infrastructure and applied machine learning systems. Though still early in its journey, Parallel already claims to be powering millions of research tasks daily.' https://lnkd.in/gs_gDRqm

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Turns out being sleep deprived at Y Combinator HQ has more benefits than being drunk at a bar. Built at the Y Combinator Hackathon and WON the Top Dev Tool award. Built with Varun Venkatesh and Udaya Vijay Anand. Powered by Browser Use, Anthropic, Vercel, Convex, Daytona, and supermemory. We built a multi-agent AI pentesting system that finds real vulnerabilities with proof.

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Emergent doubled its ARR from $50M to $100M in 30 days, becoming one of the fastest-growing AI startups in history. Founded by Mukund Jha and Madhav Jha, the Y Combinator S24 company has raised $100M from Khosla Ventures, SoftBank, Lightspeed, and Google AI Futures Fund. Over 6 million builders across 190+ countries have created 7 million+ apps on the platform. Emergent's vision is to make app building accessible to everyone through AI. No coding is required, just describe what you want, and the AI builds fully functional web, iOS, and Android apps that you can publish to the App Store and Play Store. The product is exceptional. The growth is undeniable. They just launched their mobile app, positioning themselves as India's answer to global AI app builders. But when I looked at their AI discoverability, I saw a major gap. Emergent is invisible when people ask AI for solutions. I tested this. Ask ChatGPT or Perplexity "What's the best AI app builder?" right now. Emergent doesn't show up. Competitors like Lovable, Bolt.new, and Replit Agent do. Every missed AI recommendation is a user who signed up for a competitor. Here's how Emergent can scale by fixing AI discoverability: 1. Own the narrative. When someone asks "best no-code AI app builder" or "fastest way to build an app with AI," Emergent should be the answer. Position as the India-built platform that went from zero to $100M ARR faster than any competitor. 2. Get recommended by AI. ChatGPT and Perplexity pull recommendations from Reddit, dev forums, comparison sites, and YouTube reviews. Emergent needs presence across these platforms so AI tools cite them when builders ask for app-building recommendations. 3. Build visibility beyond the website. AI doesn't just look at product sites. It pulls from tech reviews, tutorial videos, developer communities, and Product Hunt discussions. Emergent needs to be everywhere developers are researching solutions. 4. Educate the market. Help builders understand what they're getting. Show real examples of apps built on Emergent, explain how the AI works, and demonstrate the publishing process. Educational content builds trust and gets cited by AI. Emergent has the product, the traction, the funding, and the fastest growth in the category. They just need AI discoverability so millions of builders searching for app-building solutions actually find them. Congrats to Mukund Jha, Madhav Jha, Prashant Sharma, Aseem Sharma and the Emergent team on the milestone.

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The fastest path into healthcare AI isn’t where most founders think. At Vertical Velocity in San Mateo today, I hosted a panel with seed investors Jyotika Gupta of z21 Ventures, Anuradhika A. Of CommonSpirit Health Ventures and Varsha Rao of Flare Capital Partners on healthcare x AI. One pattern emerged clearly: *Win the periphery first*. The founders winning today aren’t battling Epic or Cerner head-on. They’re entering through administrative workloads—supply chain optimization, scheduling, pre-authorization. Areas where you’re adding capability, not displacing entrenched solutions. The same logic applies to care settings: home health and mental health offer lower friction than acute care. You avoid the “rip and replace” conversation entirely. *Voice is the wedge product right now* Every investor on the panel is seeing voice AI traction across clinical documentation, patient engagement, and care coordination. The tech has hit the adoption curve. My colleague Rohil Bagga has written deeper dives on both our healthcare AI thesis and why voice AI is reaching inflection. Worth your time if you’re building in this space: • Voice AI: https://lnkd.in/g2eJqe4J • Healthcare AI: https://lnkd.in/gHFsb-zM SaaSBoomi

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Some podcasts entertain. A few make you think. Even fewer genuinely expand how you see both technology and humanity. Brett A. Hurt’s podcast Love Conquers Fear sits firmly in that last category. As a serial entrepreneur, Brett has spent years building companies and navigating the intersection of technology, leadership, and human nature. What makes the podcast special is how he brings those experiences into thoughtful conversations about courage, values, and the choices we make as builders and leaders. The show launched last fall, and 45 episodes later, it has only grown stronger. The topics are wide-ranging but consistently insightful, combining practical lessons with deeper reflection on what drives us. I’ve personally found Brett’s work both inspiring and thought-provoking, and I highly recommend giving it a listen. If you are building something, leading a team, or simply thinking about how technology and humanity shape each other, Love Conquers Fear is worth your time!

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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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🤖 Meet Varun Vummadi & Esha Dinne: the 24-year-old IIT duo who turned down $675,000+ in offers to build AI that does enterprise ops better than humans. They’re the founders of Giga - an AI operations platform that’s being used by companies like DoorDash. Enterprises get millions of calls, chats, and tickets every month. Most are repetitive. Many require context. They’re all expensive. GigaML said: what if AI could not only talk like a human… but also think like an ops engineer? Unlike a lot of “LLM consultants” in the market, GigaML is a product-first platform. You upload your transcripts, policies, and workflows… and the agent builds itself. It’s not like a 3-month consulting project, and doesn’t need a 10-person integrations team. ⚙️ To start Giga, Varun turned down a $525k quant trading role & a Stanford PhD. Esha also turned down a $150k HFT systems offer. They were convinced that people who build AI products will own the future, not the people who price options. In production, GigaML’s agents are achieving 90%+ resolution accuracy on real customer calls, matching or exceeding human agents on difficult workflows. Their voice system is optimized for ~400ms latency, creating a “genius within a second” experience that feels faster and smoother than speaking to a human. The platform supports 90+ languages across voice, chat, and SMS, and enterprises can go live in under 2 weeks, which is significantly faster than legacy call center automation tools. For large B2C enterprises, GigaML’s automation delivers up to 70% cost savings on support, which can translate into $100M-$200M per year in avoided human-agent spend. 💰 On November 5th, Giga raised a $61M Series A led by Redpoint Ventures with participation from Y Combinator and Nexus Venture Partners. Check out the full deep dive - link in the comments 👇

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Exciting news from the Crane Venture Partners portfolio: 𝗦𝗥𝗘.𝗮𝗶 𝗵𝗮𝘀 𝗿𝗮𝗶𝘀𝗲𝗱 𝗮 $𝟳.𝟮𝗠 𝘀𝗲𝗲𝗱 𝗿𝗼𝘂𝗻𝗱, led by Salesforce Ventures and Crane Venture Partners 🚀 Edward Aryee and Rajsekhar Kadiyala are tackling one of the biggest blind spots in enterprise tech, bringing 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝘃𝗢𝗽𝘀 to the systems and applications that companies rely on every day 🙌 A perfect example of the trend of AI enabled service-as-software disrupting a legacy market dominated by consultants ☠️ Read more: https://lnkd.in/e8UrSbsa

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