Shyvee Shi

Today, I met Aravind Srinivas, the CEO & Co-founder of Perplexity AI at Harvard Innovation Labs - and I walked away reminded of how calm, clear thinking is often the biggest superpower in a chaotic, fast-moving world. In the middle of all the noise, hype, and panic around AI, Aravind stands out for his rare clarity, humility, and strategic maturity. What struck me most was not just what he’s building, but how he’s building it — with a long-term, first-principles mindset. 3 lessons I took away from our conversation: 1) You don’t need to build everything to win: While most players rushed to build their own base models, Aravind made the bold, unconventional call to stay model-agnostic and focus on post-training and retrieval-augmented generation (RAG) — creating an experience-first product. 2) In a gold rush, don’t sell shovels. Build what people actually use: Perplexity focused not on infrastructure, but on giving people a real-time, accurate, and conversational search experience. It’s working — they are now doing 100M+ monthly visits without burning billions. 3) Stay calm when everyone is panicking. In a world where everyone is hyped up or fearful about AI’s future, Aravind’s level-headedness was refreshing. Leadership, after all, is about seeing clearly when others can’t. The future of AI will not just be shaped by the loudest voices — but by people like Aravind who quietly, consistently, and thoughtfully build. Thank you, Aravind, for the conversation and the wisdom. You have a fan in me. If you’re building in AI or trying to navigate this fast-changing space, I highly recommend following what Perplexity is doing. @Aravind Srinivas @Perplexity AI #AI #Leadership #StrategicThinking #Startups #Innovation #CalmInTheChaos #ProductThinking #PerplexityAI

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The number of tech GTM jobs requiring AI skills reached a new high in July & has soared by 14x in only two years. I was shocked at how varied the job titles & requisite AI skills were. Sure, there are next-gen roles like GTM/growth engineers. But you know who also wants AI skills? Folks hiring BDRs, social media producers, marketing analysts, content marketing managers, product marketers and even CMOs. Clearly, AI skills are in demand. But the next frontier is figuring out who *has* AI skills (and, if you’re looking for a job, proving *you* have AI skills). To help, I put together a six-step FLUENT framework for hiring AI-native talent. This includes the go-to interview questions of 🔥 operators from Clay, ClickUp, Vercel, Wiz, Zapier & more. Read the full piece in today’s Growth Unhinged newsletter: https://lnkd.in/eZCrFNCt Hope you find it useful 🙏 - Kyle PS, huge shoutout to Sumble for the data & to Everett Berry, Gaurav Agarwal, Meg Scheding, Paul Stansik, Philip Lakin, Sam Kuehnle, Tom Orbach, Yash Tekriwal 🤔 and Zeb Hermann! #ai #gtm #genai

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I was talking to my friend Parul Singh about how too much VC money can be detrimental. I have been working with pre-seed/seed/A at and with companies for the last 20+ years and I have observed first hand that the saying “constraints breeds creativity” is true. In my experience, a company with 18 months of runway gets much more done in the second nine months than the first. 🪹 Pre-seed Companies: When you are in the “idea maze” (coined by Andreessen Horowitz) or “drunken walk” having too much money does not necessarily speed up progress. Of course you may need some money for founder living expenses but having a larger team wandering through the "idea maze" together doesn't necessarily make the journey go faster. There is a certain level of founder reflection that has to happen while navigating the “idea maze” and too many distractions (people, money) makes it hard. I teach an entrepreneurship class  (LeanLaunchpad) every year. Out of the eight teams we have navigating the “idea maze”, about half will test and land on a reasonable idea in just ten weeks (including a simple MVP and even some paid POCs). Total budget: zero. With the multitude of AI tools, time to PMF is accelerating. Check out https://lnkd.in/guM7Ymn9 (AI companies revenue >10x SaaS companies). 🌱 Seed Companies: When you are going from the “idea maze” to “product market fit” and are trying to first build a product that a few customers love and second build a way to acquire these customers efficiently, having too much money can actually be counterproductive. Why? Because too much money often translates to wasting time on things that don’t matter: you are not forced to focus on the features that are absolutely critical, you can spend in non-efficient marketing methods (skipping the crafting of the product-growth loop) and you can afford to over hire or to keep the wrong people onboard. 🅰️ Series A Companies: Post product market fit, when it is time to scale, money is absolutely critical. However there is one common failure mode: the “fake Series As”, the As that get overpriced without really having reached product-market fit. Take a seed in a hot space, a small team that has grown revenue to a few $100Ks or maybe even a million in just six months. There is little data on actual revenue stickiness or whether the founder-led sales motion can be replicated efficiently. There is a lot of competition to invest in the company and they end up raising $20M+ series A.  The founder feels confident, they are stretched-thin and they hire a bunch of folks. There is pressure to scale but the machine is not built yet (the product is not quite there, they can’t figure out how to get to customers efficiently) and things unravel. Venture money is like carbs: quality over quantity (not all money is equal); take in moderation (constraints bread creativity) and if you compete in endurance sports, you might need more carbs to perform well (if you are growing, you will need the cash).

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"Best advice for an MIT undergraduate interested in entrepreneurship - go" Had a blast yesterday getting to guest lecture in Paul Cheek and Nagarjuna Venna 's Venture Creation Tactics class at MIT. We talked founder equity splits, advisor comp, first hire grants — all the fun Carta data cuts. And then they politely ambushed me in this cool makeshift recording studio and hit me with some questions for undergrads. Am I qualified to tell college kids what to do with their lives? Not at all 😁 Anyways, my (unsolicited) advice: 1. The best ideas come from connecting seemingly unrelated things. Take the philosophy course and comp sci. Try creative writing and advanced math. Mix disciplines. 2. Try as best you can to separate your self-worth from your job. This is particularly challenging for founders, who often equate valuation and self-worth. Feel free to ignore 🙏 #startups #founders #entrepreneurs  

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Can Startup Societies Rebuild Community IRL? Balaji Srinivasan argues that universities like Harvard, Texas A&M, and Stanford were early “nodes” of American society—training talent and shaping regions. Today, he’s pioneering “Network Schools” as 21st-century nodes: physical communities where like-minded people gather to build companies and lives together. Network Schools attract remote workers, visa-challenged builders, and ideological pioneers. • The motivation? Discontent with existing societies and a desire to start fresh. • Hundreds are already moving in; thousands have applied, showing a massive appetite for new models of community. #StartupSocieties #Crypto #RemoteWork #CommunityBuilding #Innovation #Founders #ThisWeekInStartups

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What will it take to make foundation models 100x better - without spending billions on compute or turning the planet into a giant data center? This is one of the questions I explore with Rohan Taori, a researcher at Anthropic and co-creator of Alpaca - the project that demonstrated how fine-tuning @Meta’s LLaMA could achieve ChatGPT-level performance for under $600. Rohan shares his journey from early work in computer vision at UC Berkeley to his Ph.D. at Stanford, where he focused on making AI more accessible. He breaks down the key innovations behind Alpaca, including self-instruct, a method that uses a stronger model (OpenAI’s text-davinci-003) to generate synthetic data for fine-tuning a weaker one (LLaMA). We also discuss: The potential and limitations of synthetic data - how far can it take us, and where does it fall short? What’s needed to make foundation models 100x better - beyond just scaling compute The future of multimodal AI and the research directions he's most excited about Our full conversation here: https://lnkd.in/ghYaWYnn

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🦄 The H-1B visa has long been a cornerstone of U.S. tech innovation, quietly powering the rise of Silicon Valley’s most iconic startups. Its impact goes far beyond American borders. 🦄 It is reshaping how Asian investors and VCs in China, India, and Singapore engage with the U.S. startup ecosystem, especially in the AI space. 🦄 From OpenAI to Anthropic, many of today’s AI breakthroughs are built by global talent such as engineers, researchers, and founders who entered the U.S. through the H-1B program. These individuals create companies, drive innovation, and attract billions in venture capital. 🦄 For Asian-based VCs, especially those active in cross-border investments, the H-1B pipeline is a strategic asset. It ensures that the startups they back in the U.S. have access to world-class talent, diverse perspectives, and scalable global ambitions. 🦄 The truth is most promising AI startups in the U.S. have founders or key engineers from India and China or supported by Asian capital. 🦄 Here is the catch. Tightening H-1B policies or delays in visa processing directly impact startup velocity, talent retention, and investor confidence. 🦄 For VCs in Singapore and across Asia, this is not just a U.S. immigration issue. It is a portfolio risk and a strategic blind spot. 🦄 As AI continues to redefine industries, from healthcare to finance to national security. The ability to attract and retain global talent will determine which startups thrive and which stall. 🦄 Asian investors must stay informed & engaged about policies that affect the innovation pipeline they are betting on. #AIStartups #VentureCapital #H1BVisa #GlobalTalent #TechPolicy #StartupInvesting #SingaporeVC

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AI agents are the future of the internet. Join Kristopher Floyd and Miles Maggio for a hands-on workshop where you'll learn how to create AI agents from scratch! We'll break down the key concepts in simple terms and guide you step by step—no advanced coding experience needed. In this session, you'll discover: ✅ What AI agents are and how they work ✅ The basics of training and improving AI models ✅ How to build and test your own AI agent By the end, you'll have a solid foundation to start building your own AI-powered projects. Perfect for beginners and curious minds! 🚀 RSVP: https://lu.ma/tlpvx1yq

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We had a great convo with Sree Sreenivasan about the rapid advancements and pervasive impact of AI across various aspects of life and work, including job displacement, business transformation and societal shifts. *Next AI chat on April 18; link in comments. Key themes included the transformative power of generative AI, the potential for widespread job displacement and the lack of adequate societal preparedness, the evolving nature of work and organizational structures, the ethical considerations and risks associated with AI, and the need for continuous learning and adaptation. Sree touched on: - The Pervasive and Accelerating Impact of Generative AI  "In the next version the agent, your agent and the other agent will just get together and just write the reports." - Concerns about Widespread Job Displacement and Lack of Preparedness "The latest studies say uh that if you look at the world Japan 27% of jobs will be replaced by AI US 25% You keep going down China 15% India 10%." - The Evolving Nature of Work and Organizational Structures Ethan Mollick's point about the "value of experience being deflated" due to AI was mentioned. - Ethical Considerations and Risks Associated with AI: Sree cautioned about tools like Replica and Character.ai, "It's so addictive that you will start talking to it and it'll be like you know what whatever you were afraid to share with your spouse or significant other you're sharing. It's kind of scary." - The Need for Continuous Learning and Adaptation: Daniel Pink's quote was shared: "AI won't replace humans but humans using AI will replace humans not using AI right..." While recognizing the transformative potential of AI to enhance productivity and innovation, the participants express significant concerns about its societal implications, particularly regarding employment and ethical considerations. The conversation underscores the urgent need for proactive planning, societal adaptation, and continuous learning to navigate the complex landscape shaped by artificial intelligence. Summary courtesy of Notebook LM. Digimentors Thanks for supporting the event and ecosystem: Citrin Cooperman Gearhart Law, LLC

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