Neena Batrra

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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Excited to share my conversation with Marc Baselga and Ben Erez on the Supra Insider podcast. We went deep on one of the biggest shifts happening in product leadership right now: why many experienced PMs and product execs are returning to individual contributor (IC) roles in the AI era, and why that’s not a step back, but a strategic leap forward. Other topics we discussed: -- Why the leverage equation has changed—and how managing AI agents now rivals managing people -- The critical importance of hands-on credibility in AI-native companies -- Why great PMs in the future will blend product, design, analytics, and frontend execution -- What it takes to deliver real customer outcomes (not just launch features) -- How to evaluate breakout companies—and what founders often get wrong when hiring product leaders I also shared my thoughts on: -- Joining the right company: mission, trajectory, talent density, and founder courage -- Crushing it as a “super IC”: the power of speed, ownership, and infectious energy -- Transitioning from product leader to GM or founder with the right mindset If you’re navigating your next chapter in product (or just trying to stay sharp in the AI-native world), I hope you’ll find this useful. Episode links: Spotify: https://lnkd.in/gK48PFWK Apple: https://lnkd.in/gmT265BY YouTube: https://lnkd.in/gtr4RAKd Substack: https://lnkd.in/gM38FSWc

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"This space moves so fast that every quarter, everything you do can be obliterated," said Glean founder Arvind Jain. "We had to shift to a mindset of constantly rebuilding. Your moat is not your tech but the value you provide to your customer, those deep relationships you build, and the team that’s with you to build and execute in that kind of environment." An intimate, thoughtful gathering at last night’s AI Pioneers Summit by Decibel Partners and Latent Space Podcast with speakers that included (photo, left to right) Eric Simons (bolt.new), Behnam Neyshabur(Anthropic), swyx ​(inc a celebration of 10M downloads), Arvind Jain (Glean), and Laura Burkhauser (Descript). Simons: “We’re seeing more developer-adjacent product people building direct experiences.. that will lead to developers doing deeper thinking & reviewing PM work, as PMs also go deeper to shape the front-end.” Burkhauser spoke to the complexity of AI video editing, where context goes far beyond the script to understand how humans see, feel and sense, and uncover the reasoning behind what a viewer expects. Neyshabur's pursuit of creating an AI scientist is rooted in his curiosity about how a model learns from human interaction and the nature of learning itself. Our breakout session was insightful, inclusive, and energetic. Good to spend time with founders like Bella Liu (Orby AI - A Uniphore Company) and catch up with Ajay Singh. A great mix of technical depth and openness for conversations that stay with you.

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🎙️ From Square to Mercury: How Rohini Pandhi became one of fintech’s top product leaders—and how velocity of learning at high slope companies shaped everything. Rohini dropped so many gems in our first episode of First Builders: 💎 Generalist → Specialist: In her early career, she was a generalist and tried everything. This led her to critical discoveries. She specialized in product and fintech when the “time was right” 💎 Follow the Engineers and Designers: Square was a high slope environment for Rohini, packed with talent density and learnings. She finds high slope environments by studying where smart engineers and designers are going. 💎 Fake News about PMs: PMs don’t just “move fast and break things.” Her team runs 30-50 customer interviews per quarter, providing rigor behind every decision. 💎 It’s Usually Too Early to Hire a PM: A top question she gets from founders is whether they should hire a PM. She usually says: “it’s too early.” 💎 Hiring a World Class Team is Like Tennis: When finding a tennis partner, you want someone a little better than you who can challenge you to improve your game. Give us a listen and leave a review on your favorite podcast location (see comments for links)! #FirstBuilders #StartupPodcast #ProductLeadership #TechCareers #Leadership

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Day 1: NeurIPS Met up with the incredible Jigar Doshi. He is one of the few using AI not for profit or hype, but to tackle poverty in India. Here are some remarkable initiatives he has led: 1. Copilot for Front Line Workers: LLM-powered co-pilot into an existing learning and support application to improve the training of auxiliary nurses and midwives in India so they can better manage high-risk pregnancies (in native languages). One woman dies in childbirth every twenty minutes in India. Many maternal and infant deaths could be prevented by improving access to critical care information and ensuring that health workers can detect risk factors and treat complications early on. 2. CottonAce app: Wadhwani AI’s initiative to help cotton farmers fight pest infestations. It is easily accessible, works offline and is available in nine languages. Farmers install pheromone traps amongst their crops, which trap pests. They then upload images of the pests to the app and the AI algorithm identifies the pest and calculates how severe the infestation is. CottonAce generates instantaneous recommendations on which pesticide to use and how to best apply it. The app then shares the data with nearby farmers, creating an early-warning system for the area. https://lnkd.in/eYCZb7hz https://lnkd.in/eSwSFVHy https://lnkd.in/eh8HY6Sd

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"Product managers are the new prompt engineers." – Tim Simmons, Chief Product Officer, Walmart International In a standout conversation with our Head Resident CPO, Renee Niemi, Tim shared a perspective that every product leader should consider as they navigate the AI era. Prompt Engineering is Now Core PM Craft If your team learns just one new skill this year, make it prompt engineering. Tim sets the expectation that PMs and UXers must be the best prompt engineers in the enterprise. The ability to interact with AI systems fluently is now as essential as understanding user needs. This shift reframes the PM role from problem solver to AI orchestrator. Implication: CPOs must actively upskill their teams and hire with prompt fluency in mind. Build with AI, Not Just for AI From copilots to custom GPTs, Tim emphasizes the massive productivity unlock when AI is embedded in how product teams work, not just what they build. Walmart’s PMs are already using AI assistants to double speed and accuracy across the product lifecycle. Implication: You need two AI strategies: 1) Build AI into your product 2) Build with AI in your processes Both matter. The former drives differentiation. The latter drives efficiency. Bottom Line: The future of product leadership demands fluency in AI, not just as a feature but as a collaborator. The podcast conversation touches upon a few other topics such as 1) how product management has become the de facto enterprise strategy, 2) Effective product management requires platform thinking, and 3 ) Embedding curiosity into the everyday work is a key function for CPOs. Thank you Tim for an excellent conversation! https://lnkd.in/gYV3XSxq Jordan Carriere Renee Niemi SC Moatti Floyd Given Kristin Kobey - ACC Henu Kotia Gaurav Dutta Parul Jain

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AI has fundamentally changed how marketplaces offer help. In the traditional model, the handoff from a platform to a human can lack context about where they are in their workflow and what they are trying to accomplish. With AI, that happens instantly, and with intent. That means there is no room for a trust gap because everything has to go right in a split second. Perfecting this handoff requires more than just good code and a smart person. It requires a unified ecosystem under one roof. There are only a handful of companies equipped to deliver this world class experience, and I enjoyed listening to my colleague Alex Balazs discuss the topic with Aprio's Jordan Fladell. Give it a listen and let me know your thoughts > https://lnkd.in/ggdrxdzK

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This Scale AI story might matter more than you think. Meta just invested $14.3B to take a 49% stake in Scale AI — and brought Scale’s CEO into its internal “superintelligence” unit. On the surface, it looks like a major bet on data. But look a little deeper, and it’s a shift in how control is consolidating across the AI value chain. Why it matters: – Scale was one of the few trusted, neutral data providers for labeling and evaluation – Now that neutrality is in question, contracts are reportedly getting pulled by OpenAI and Google – Startups and enterprises alike are suddenly reconsidering where their training signal comes from What comes next: – A surge of boutique data providers — decentralized, domain-specific, and more transparent – Startups reframing data control as part of their moat: “We don’t just use models — we own the context” – Infra teams re-evaluating dependencies — either proactively, or reactively when vendor dynamics shift As training data usage becomes more regulated and controlled — moving away from the wild west of early foundation model training — the pressure to secure differentiated data pipelines only increases. When we talk about defensibility in AI, it’s tempting to focus on model IP or feature differentiation. But data leverage — how it’s sourced, labeled, evaluated, and governed — is becoming a more important part of the strategic equation. It’s not quite infrastructure. But it’s no longer a commodity either. It’s a layer of increasing consequence — and one worth watching closely. Are you rethinking your data leverage yet? https://lnkd.in/gx9GPefp

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We all assumed GenAI’s breakout use case would be productivity. Turns out, it might actually be… therapy. A recent HBR piece shows that people are turning to AI not to write memos or crunch data — but to reflect, vent, and seek emotional support. Sure, it’s based on a limited sample — but it resonates. Funny enough, the very first GPT I built (when OpenAI released GPTs) was called Life Coach — giving career advice and more. There’s still so much we’re discovering about how LLMs can be used — and plenty of risks to unpack along the way especially for that kind of use cases. But it does reveal something powerful: We’re not just building tools. We’re building companions. #AI #GenAI #FutureOfWork #EmotionalIntelligence

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It was great speaking with Dr. Vinesh Sukumar, VP of Product Management at Qualcomm, during Web Summit! In this clip, Vinesh shared key insights from his Masterclass on Edge AI, highlighting how #AI improves user experience through personalization, context, responsiveness, and privacy. Thanks for the chat, Vinesh and hope you found that pastel de nata!

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