Shreya Bhargava

Looking for a Product Leadership role? Explore these top opportunities to drive innovation, lead teams, and shape strategy at these companies: 1. SVP, Product Management – MasterControl, Salt Lake City, UT (Remote) Lead product strategy and development for MasterControl's quality and compliance software portfolio while managing the product management team. MasterControl provides cloud-based quality and compliance software to over 1,000 companies in life sciences and regulated industries. 2. Group Product Manager - Microsoft Security Service Edge – Microsoft, San Francisco, CA (Remote) Lead Microsoft's Security Service Edge (SSE) offering, driving product strategy and development for Microsoft Entra Internet Access and Private Access solutions. Microsoft is a global technology leader with 228,000 employees across 190 countries, focused on empowering every person and organization to achieve more through technology. 3. VP of Product Management – Benevity, Seattle, WA Lead product strategy and development for Benevity's Finance Platform, managing a product team while driving vision, roadmap execution, and platform operations. Benevity is a B Corporation that provides social impact technology to Fortune 100 companies, enabling corporate giving, volunteering, and other social action initiatives. 4. VP of Product – Banyan Software, Chicago, IL Serve as a key executive leader at SoftSmiths (a Banyan Software portfolio company) responsible for driving product strategy, innovation, and execution in the energy sector, initially working independently with potential to build a product team. Reports to the CEO of SoftSmiths, working closely with VP Technology. Banyan Software is one of fastest-growing private software companies in the US that acquires and grows enterprise software businesses with dominant positions in niche vertical markets, operating with a permanent "buy and hold for life" strategy. 5. Director of Product Management – Mark43, New York, NY (Remote) Lead the Computer Aided Dispatch (CAD) product line and Mobile applications, managing a team of 2-3 Product Managers while driving product strategy and development. Mark43 is a technology company that develops cloud-native software solutions for law enforcement and public safety agencies to improve community safety and quality of life. 6. Head of Product – 0x, San Francisco, CA (Remote) Lead Product team overseeing consumer and developer platforms while driving product strategy and fostering team growth. Reports directly to the CEO. 0x is a Web3 company providing developer APIs for token access and liquidity, having processed over $130B in trading volume through major platforms like Coinbase and MetaMask. Want more Product Leadership roles? Access the weekly updated database: https://lnkd.in/gq9qY6s4

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Paul Graham wrote an excellent essay titled “Founder Mode,” based on a talk given at Y Combinator by Brian Chesky. (You can read it here: https://lnkd.in/gXwk7EB8) I agree with Paul’s and Bryan’s thinking and experiences, yet there is a natural question about what follows after founder mode? Assuming that the company is successful, founders eventually have to pass the baton to someone else. Should founder mode continue, even though no founder is at the helm? How can the transition happen in a way that gives continuity to employees, customers, and ecosystem in general? In my opinion, one foundational item for the transition is that managers and leaders at the company are properly prepared to do their jobs. An old adage says that people join companies, but quit managers. (Although this is not the sole reason, but it is a strong factor.) The last act of founder mode is to enable to company to continue being successful long after they hang up their cleats. #leadership #management

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I love YC Demo Day season. It’s an insane concentration of driven founders building something they deeply believe in at the frontier of tech. I know it's more trendy for VCs to complain about YC. But I believe Garry Tan when he says 5-10% of companies become worth a billion dollars. Maven’s hit rate from YC is even higher: 16% have had $B+ exits, and we’re trending toward a 40% graduation rate from Seed to Unicorn with investments we’ve made alongside YC. Since Maven’s founding in 2013, we’ve funded 12 YC startups out of the 66 companies we’ve invested in. Here are a few: Cruise: acquired by GM for $1B+ Embark: $4B SPAC, acquired by Applied Intuition Chariot: acquired by Ford before Series A May Mobility: raised $300M+ in follow on funding, including from Toyota Carrot Fertility: raised $100M+ in follow on funding, including from CRV and USVP Daybreak Health: Series A from Lightspeed, Series B from USV Moment Robotics: acquired by Cruise founder Kyle Vogt’s new company, The Bot Co (Maven is an investor) Our strategy differs from other YC investors. Many funds build a YC “index fund”, investing in many startups per batch. It’s not a bad move. But we focus on concentration, meeting all the companies in our thesis area and investing in one or two. Gratefully, I think every company from the list above can point to Maven as one of their most trusted partners that made a difference: intros to customers, closing follow-on funding from top VCs, connecting key hires, and offering genuine care and advice in both the best and toughest of moments. What are we looking for in a YC investment? 1) Team - a technical product team with startup DNA and hustle that can achieve a bold vision. Brilliant, determined founders no one would bet against who are ambitious enough to build an iconic company. 2) Vision worth fighting for - what are you bringing into the world that is worth all the time, effort and money required for success? Why aren’t existing solutions good enough? Are we proud to help bring this idea to the world? 3) Massive market - because of the valuation premium YC demands, even a $B outcome may not return today’s average Seed fund. Honestly, that math sucks. In order to pay the premium, we have to believe that if we’re right, we’ll be wildly successful. 4) Consumer trend - Maven invests in tech companies addressing emerging consumer trends. Even if some investments are B2B or B2B2C, they are always grounded in a consumer insight: autonomous vehicles in 2014, fertility care in 2017, mental health in 2020. Today, we’re looking at consumer applications of AI and frontier consumer health. Many successful companies we fund are outside of YC as well – like Zoom, Hello Heart, Epic!, Class, Wildtype. So we match YC’s Unicorn % across the rest of our portfolio, too 💪 YC founders in consumer tech and digital health: if you want to build an iconic company that delivers a positive impact for millions of consumers - I can’t wait to talk to you!

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👉 Howdy LinkedIn! Tomorrow, on "Building Products with Generative AI" - I will be hosting 🚘 Nitin Kartik at 11:30AM CST/12:30 PM EST! We're going to be digging into the intersection of Product Marketing & Generative AI, as well as other topics that will branch from Nitin's experience in the space. I'm excited for another interesting episode with a great thought leader in the space - we hope to see you join for tomorrow's episode! #GenerativeAI #AI #ProductMarketing

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AI is reshaping how enterprise software delivers—and prices—value. Companies like Zendesk, Intercom, and Forethought are shifting to outcome-based pricing, charging only when AI autonomously resolves issues. I've been investing in AI for over a decade. The shift to outcome-based pricing in enterprise software is a seismic change. As I told Jon Victor at The Information, software companies now face a critical decision: Does a company decide to change business models at the expense of losing revenue? It's a necessary risk. With cheap access to LLMs, businesses can build their own enterprise apps. Adapt or die. Why this pricing shift matters: → Vendor success ties directly to customer outcomes → Short-term revenue risks enable long-term market expansion → Industry faces widespread pressure to demonstrate measurable value Pitching AI-powered automation software as a clear cost-reduction tool could ultimately lead to greater revenue for vendors. This is because customers may view AI solutions as fundamentally different from traditional enterprise apps, whose business value is often harder to quantify directly. It's a huge market expansion opportunity. Vendors that nail outcome-based pricing could see massive growth as AI transforms enterprise operations. Those that don't? 😬🫣 How do you see this impacting sectors beyond customer support? Leave a comment. And if you're building in this space, send me a message.

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Local context has long been the key to business success across geographies. As the world goes digital, that truth remains - especially in AI. To win, we must build not just culturally intelligent LLMs, but embed that cultural understanding across AI applications and tools in every industry. India's massive domestic market is hungry for AI solutions that truly speak its language. By infusing local context into our models and solutions, we can unlock a new era of AI innovation and entrepreneurship right here at home. The opportunity is clear - it's time for India's AI ecosystem to take the lead and show the world what culturally intelligent AI can do, across every sector and domain. Read more at The Economic Times: https://lnkd.in/gCKUyUq7

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India is not just catching up in the AI race; we're innovating and leading in ways that are uniquely ours. The Economic Times recently highlighted how our approach to Generative AI goes beyond just Large Language Models (LLMs), addressing the specific challenges and opportunities within our diverse market. Rahul Agarwalla, Managing Partner at SenseAI Ventures, shared his invaluable insights into this AI revolution. His leadership and vision are truly driving innovation in the field, and it's an honour to be part of this journey with him. For anyone interested in the developments in AI and how India is making its mark on the global stage, this is a must-read.

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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My end-of-the-year reading? LLM Reliability by Rush Shahani. I've recently started a new job working on search at Causaly. Causaly is a platform for research in the life sciences domain. A bit different from my most recent experience focused largely on building out search for areas like e-commerce and media. Perhaps the biggest difference is how important precision is in this new domain. With e-commerce search, if a user is looking for sporty shoes and gets a result or two for socks, well, it's not that bad. But a biomedical scientist doing research trying to find a cure for the diseases that plague humanity can't afford to get inaccurate information. At Causaly, we use LLMs for a lot. Our users search through natural language and we translate those questions into queries. We summarize research papers individually and as a group. We let scientists ask questions about that research. We do a lot of work to make sure we're returning accurate answers, and there's always more we can do, and ways to increase our efficiency. Shahani's book goes in depth on how to approach these problems. Solutions like red-teaming, monitoring platforms, and human evaluation techniques. It also talks about techniques to minimize hallucinations and leveraging RAG to ground responses. The book is in early release and is still just about 1/3 complete, but I'm already learning things I'll be taking back to my job. And it looks like this will be my start-of-the-year reading, as well. https://lnkd.in/eFZfJSyp

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It took me 8 months to set up company LinkedIn profile. Because we’re hyper focused on actually shipping what’s valuable. 😉 ⬇ Results so far: ✅ Proprietary LLM outperforming top models like Llama 3.1 and Goliath in context adherence and role-play benchmarks 💸 Clients generating real subscriptions and revenue with their AI personas 🔮 AI persona creation experience that’s fully self-serviced and does not require any training or manual fine-tuning Follow for more of what's coming: https://lnkd.in/eMK8wBRn #ai #genai #generativeai #aipersona #aiclone #aiagent #aistartup #aiforcreators

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🚀 *Big news* 🚀 (no, really) In 2019, my co-founder Gil Makleff and I had an intuition that AI was more powerful than most imagined at the time. We thought deeply about what this meant and how it would change our daily lives. An idea was born: AI that undestands work conversations and, knowing the outcomes the participants are pursuing, supports them in achieving those results through guidance, just-in-time information, and full-on task automation. We called this idea "Sembly AI". Sembly has evolved over the years, as the first product to accurately identify tasks of different types, the first to accurately outline meetings in a "headline" format, the first to support multiple languages in the same meeting, the first to generate meeting notes while taking into account the type of the meeting being summarized. And today, I am excited to present another "first". Please welcome Semblian 2.0 to the Sembly AI product family. https://lnkd.in/dDWn3YiX Semblian 2.0 brings a suite of powerful features designed to enhance productivity and automate the most tedious aspects of your work: ✨ Multi-Meeting AI Chat: Engage in AI-powered chats across multiple meetings. Ask questions across meetings, enrich context by adding relevant discussions, and receive personalized assistance—as if you had a team member who attended every meeting. ✨ Artifacts: Automatically generate both short-form and long-form documents based on your meetings—project plans, contract proposals, product strategies, and more. Save time with personalized outputs tailored to you and your specific work situation. ✨ Personalized Post-Meeting Insights: Get actionable insights tailored to your role and involvement in projects. Receive recommendations on next steps and deliverables, enhancing your decision-making. Semblian 2.0 is available NOW on all paid plans, and I can't wait for you to try it out. 👉 If you're as excited as I am, please upvote us on Product Hunt here: https://lnkd.in/dAEs2nap 👉 Experience Semblian 2.0 for yourself here: https://lnkd.in/dd45zb-H Thank you to the more than 1,000 organizations and thousands of users who have enjoyed Sembly through the years. For those of you who haven't tried Sembly yet -- today is a great day to start! #ainews #aitools #newtech #futureofwork #semblyai

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Never got around to sharing my thoughts on the Applied LLMs paper that came out back in June - my key takeaways and questions below, lmk what you think: For LLM-based products, shiny demos have become WAY easier to build than before, but full working products have become much harder — and still remain elusive when they are non-deterministic with so many edge cases. This means as product leaders we will have to start changing the questions that we ask our teams and what we assume about product readiness. We’ll need to ask and dig into the data around the following questions: How are you designing your evaluation architecture? What is your golden set and how did you come up with it? What are the n problems that came up in your latest eval that you’re tackling now? This is because strong evaluation and guardrails actually IS the product and is what creates a sharp and clear vision — this means designing more in spreadsheets than figmas/short docs even at an early stage of the product. In many ways, having a clear operationalized system for evaluation and guardrails is the only way to future-proof against models constantly changing, since being clear about what good looks like for a golden set and beyond should stay relatively stable even as models change. I’m also quite interested in how bullish the authors are about RAG as way to avoid the costs and pitfalls of finetuning. They are bullish even with increased context windows (e.g. Gemini 1.5 at 10M tokens) — the theory being that keeping the prompt simpler and easier to debug will always have value and improve performance. But one thing we’ll have to avoid is over-engineering, replicating complexity that exists in traditional recommender systems that might be not as useful in this context (e.g. testing how much re-rankers matter in your RAG architecture before building them too early). Who has experience with this and has thoughts? Big thanks to the authors for spending the time to put this together - from the Vanishing Gradients podcast it also sounds like it was a lot of fun to put together Eugene Yan Bryan Bischof Charles Frye Hamel H. Jason Liu Shreya Shankar. Also, h/t to Tomer Cohen for sharing it early back in June with a bunch of folks working on this at LinkedIn

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Kim Burgaard Rachel Perez Jay Leverett David Bellotti -- I'll be listening to this one for sure! Sergio H. Serge Madenian Episode Summary: The episode features an in-depth interview with Mike Krieger, the new chief product officer at AI company Anthropic, discussing his background, the decision to join Anthropic, the company's focus on responsible AI development, the challenges and opportunities in building AI products, and his vision for the future of AI technology and its applications. https://lnkd.in/guWW8cku

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Picks Picks Picks! VCs using Harmonic find stealth and seed-stage founders 6-12 months ahead of those using the competition. Here are three exciting examples found on Harmonic and shared by Data-Driven VC in their Picks of the Week. 🇺🇸 Palantir Technologies, McKinsey & Company, and Stanford University alum Sam Bolian left her post as Head of Product at Plural in April to start something new. 🇩🇰 Serial entrepreneur Jakob Olund Kristensen was an early employee at Pleo, where he last served as Product Lead. He's now working in stealth after moving on from Weld, which he co-founded. 🇫🇷 Osium AI (YC S23) enables the development of better materials, 10x faster. Founders Sarah Najmark and Luisa Bouneder have added six people to their team since raising a seed round in late 2023. Investors include Kima Ventures, Collaborative Fund, Singular, Y Combinator, Atomico, and more. Check out the full Picks of the Week by Dr. Andre Retterath and Jérôme Jaggi at DDVC in the comments.

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I built my first generative AI product back in 2018 called Soundspot that could turn any blog into a podcast. We were using GPT-1 for text summarization and Google's text-to-speech transformer called Tacotron. Since then, I've been at the forefront of the AI transformation in marketing, from investing in early stage companies to co-founding Tofu. Here are some key learnings I've developed for applying generative AI to your GTM workflows: 1. Embrace personalization, but beware of the "personalization blindness" trap. Are you still sending the same generic email to every prospect? Want your audience to actually engage? Use AI to craft hyper-personalized messages that speak directly to their pain points. But be careful - when everything is personalized, nothing is. The key is to make it genuinely relevant, not just mail merge on steroids. 2. Let AI handle the grunt work, but keep humans in the driver's seat. Sure, AI can write 100 variations of an ad in seconds. But can it capture the essence of your brand? Can it understand the nuances of your target audience? That's where you come in. Use AI as your sidekick, not your replacement. 3. Think omnichannel, not just multi-channel. Are you still siloing your marketing efforts? Your customers don't live in one channel, so why should your marketing? Use AI to create consistent, personalized experiences across all touchpoints and make sure you're present where your customers live in the physical and digital worlds. 4. Repurpose content like a pro. If you're not squeezing every drop of value out of your content, you're leaving money on the table. That white paper you spent months on? Turn it into blog posts, social media content, email campaigns, and more. AI can help you do this at scale, without manual effort or expensive agencies. 5. Measure, iterate, repeat. The beauty of AI is its ability to learn and improve. Are you taking advantage of that? If you're not constantly testing, measuring, and refining your AI-driven campaigns, you're missing the point. Remember, the machines are learning - make sure they're learning the right things. 6. Stay human in an AI world. In the rush to adopt AI, don't forget the most important element: the human touch. AI can generate content, but it can't replace genuine human connection, creativity, or strategy. Use AI to free up time for more face-to-face interactions, not less. TAKEAWAY: AI is transforming how we approach GTM but it's not a magic wand. The winners will be those who strategically integrate AI into their workflows, amplifying human creativity and strategy rather than replacing it. Want to dive deeper into applying generative AI to your GTM workflows? I'm teaching a session called "Where and How to Apply Generative AI in GTM Workflows" at Pavilion's CMO School. It will be a practical, tactical session with takeaways you can start implementing immediately. Join here: https://lnkd.in/gSF8Jjmj

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LinkedIn team (Juan Pablo Bottaro Karthik Ramgopal) shared a helpful deep dive into their deployment of an LLM-powered job search assistant. Highly recommend this read! Few highlights:  💡 Relatively easy to setup RAG-based pipeline. Achieved 80% of the basic experience in 1 month, then 4 more months to reach 95% mark. 💡 Evaluating the quality of application outputs is hard. I hear this from many enterprises developing LLM applications. (check out Okareo by Matthew Wyman Boris Selitser) 💡 Balancing latency with quality is important. While certain techniques like Chain of Thought could improve accuracy and reduce hallucinations, it will increase response times, which can affect user experience. https://lnkd.in/gtGGwf5P #largelanguagemodels #RAG #EBR #LLM #ml #ai #hallucinations #evaluations #

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We are just at the beginning of the AI led digital transformation! Lots more questions than we answers on adoption, evolution in Enterprise AI but that’s exactly what creates white spaces, opportunities for innovation. Let’s play!

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Bold prediction for 2025: Investors will pay founders to access their venture data. Founders own their data and grant access to investors for a fee. This shift promotes transparency, trust, and efficiency in fundraising, while giving startups a new revenue stream.

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