Biswaroop Palit

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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Today I’m excited to introduce Wood Wide AI, founded by Pradeep Ravikumar and Varsha Raj, from our PearX S25 cohort! So many companies today are drowning in numeric data. Generative AI tools can’t make sense of it, but Wood Wide AI can. Pradeep and Varsha are building a table-native numeric AI that combines the flexibility of LLMs with the accuracy of ML and the reasoning power of neuro-symbolic AI to deliver real insights from any table, in any domain. Instead of relying on custom data science tooling and data teams, business users can directly run data analysis and predictions. The next evolution of tabular ML is here, and I’m excited to see what’s next for Wood Wide AI.

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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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Agilitas x Virat Kohli partnership is one of the masterstroke deals I have seen in recent times. Virat invests 40 CR in Agilitas for a 1.94% stake, putting Agilitas’s valuation around 2000 CR. 📈 In turn, Agilitas acquires one8world, making Virat a co-founder and shareholder. They now get two very important growth levers: ⬇️ 1. A powerful manufacturing (via Mochiko Shoes - a 600 CR+ footwear brand that Agilitas acquired in 2008 ), 2. India’s biggest athlete — exclusively aligned. Easily becomes a 4000 CR revenue brand in the next 5 years. Footwear + retail scale requires capital — but this partnership compresses customer acquisition, product cycles, and brand-building like few others. Even Virat said in his podcast with Abhishek Ganguly, the co-founder of Agilitas that, “I didn’t want a brand deal… I wanted to build something that outlives me.” And Abhishek said something even beautiful - “An ambition to build from India but be globally relevant.” This isn’t marketing. This is legacy building with shared skin in the game. And that is how startups work - shared ambition with one goal (to make the company big) and food (read stake) in the table for everyone! A company that can be built in the long term with culture, capital, and conviction aligned. This might be the first time in India, where an athlete joins hands with a sport company to re-imagine and build the sport ecosystem of India - truly revolutionalising the game with a clear vision. 🙌

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Filling the AI Infrastructure Gaps: Sharad Sanghi on What Clients Really Want In this reel, Sharad Sanghi, CEO of Neysa, talks about how he’s addressing the critical gaps in India’s AI infrastructure. He shares key insights into what clients are truly looking for — from scalable GPU access to clients having full control — and how Neysa is uniquely positioned to deliver. Watch the Full Episode here: https://lnkd.in/g2jbfbKy #aipodcasts #ai #technology #techleadership #podcasts #datacenters

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Grateful to join Shripati Acharya and the Prime Ventures Podcast to discuss Uniphore’s journey and what it really takes to bring agentic AI into production at the world’s largest businesses. We covered a few themes that keep coming up with CIOs and business leaders I speak with: what industrializing AI looks like beyond pilots, why a horizontal enterprise platform (data, models, security, agents) is essential for scaling responsibly, and the challenge of making AI agents more deterministic through architecture, guardrails, and workflow design. I also shared why small, fine-tuned models can be the right approach for many business use cases, balancing accuracy, control, and cost at scale. At the center of this is the Uniphore Business AI Cloud: an end-to-end platform that helps businesses bring their data together, fine-tune models, and build and deploy AI agents with the security and sovereignty large organizations require. And, it’s increasingly enabled by a strong ecosystem of strategic partners across compute, data, and cloud, working together to help customers move from experimentation to measurable outcomes. I’m confident the next chapter of Business AI will be defined by disciplined execution and real impact at scale. Thank you Shripati for a thoughtful conversation. Let’s do it again soon. #BusinessAI

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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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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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re:Invent Day 1 is done and it was packed! My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases. After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know) But my highlight was launching the new AI Competency and Agentic AI categories for partners. I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community. We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims. We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions. Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work. For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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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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2% of the mission's total ₹10,300 crore budget -while 44% has gone to compute. "Investments solely in compute are inherently transient- their value fades once consumed. On the other hand, investments in datasets build durable, reusable assets that continue to deliver value over time," said Chaitanya. Shared my thoughts on voice AI and datasets. Paywall article. https://lnkd.in/gbSRxmSQ

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