Vishal Kapoor

🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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I've been thinking a lot about how Tesla's vehicle infrastructure can leverage inference compute, distributing computational load and reducing data center energy usage. Dr. John Gibb does a nice job of summarizing these concepts in his video (Starting around 8:10). Inference Arbitrage: A Game Changer for AI Compute As AI continues to evolve, the demand for computational power is skyrocketing. Giants like OpenAI, Meta, and Google are pouring billions into training and inference compute, facing looming shortages of transformers and electricity. Here's where inference arbitrage, a novel concept leveraging Tesla's infrastructure, comes into play. The Challenge: Soaring AI Compute Costs Leading AI companies are investing billions to power their AI programs. However, the energy and computational needs are outpacing infrastructure capabilities. This bottleneck isn't just financial; it's physical. The growing demand for AI services could soon exceed available power and hardware. The Solution: Inference on the Edge xAI, in collaboration with Tesla, offers a groundbreaking solution: using Tesla vehicles for AI inference tasks. Tesla's fleet, equipped with powerful hardware and large batteries, can perform AI inference during idle times (about 90% of the time), reducing strain on central data centers How Inference Arbitrage Works Distributed Computing: Instead of relying solely on massive, centralized compute farms, inference requests (e.g., generating an email or image) are processed by idle Tesla vehicles. This distributed approach taps into the existing infrastructure efficiently. Energy Optimization: Tesla vehicles can perform inference tasks using their batteries during peak electricity demand times, avoiding high energy costs. At off-peak times, they recharge at lower costs, ensuring energy-efficient operations. Commercial Benefits: This model allows Tesla owners to earn money by utilizing their vehicle's idle time for AI computations. Tesla benefits from additional revenue streams with minimal overhead, and xAI gains a scalable, cost-effective solution for AI service delivery. Impact on the AI Industry By integrating AI inference with Tesla's distributed compute network, xAI can deliver services faster and more economically than competitors. This innovative use of existing resources not only addresses the infrastructure bottleneck but also offers a sustainable path forward for AI growth. Conclusion Inference arbitrage provides a strategic edge for xAI and Tesla, combining AI innovation with smart energy use. As the demand for AI compute continues to rise, this approach could redefine the landscape, positioning xAI and Tesla as leaders in efficient, scalable AI service delivery. Stay tuned as this exciting development unfolds and reshapes the future of AI. #ElectricVehicles #InferenceCompute #DataCenters #Electrification #AI #FrontierTechnology #FutureFrontier

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The magic of AI will only be unlocked for the agtech ecosystem once we have good ag data to feed the beast. We need digitized, harmonized, standardized, interoperable, machine-readable data for the magical AI tools to do meaningful work in agriculture. Let's get to work!

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AI is everywhere. CTOs are responsible for deploying these technologies in a way that does not jeopardise the security or reputation of their companies. We believe Responsible AI is a critically important topic for practitioners to understand & have been spending a lot of time deep diving into the area with our research team leading the charge. In Part I of our research series, we explored why Responsible AI matters. Now, Advika Jalan, Mina Samaan & Nitish Malhotra highlight the early stage companies shaping this landscape. Check it out!

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Three takeaways here: 1. #VentureIQ is insanely good at analyzing dense #RandD and #IP reports and detailing the potential applications of the underlying IP. That said, the reports still skew towards a technical audience... 2. #NotebookLM is insanely good at taking technical writing and producing "podcasts" that a 10 year-old can understand. If you don't believe me, just listen to the first 30 seconds: https://lnkd.in/ep-FNc_z Together #VentureIQ and #NotebookLM can take complex material seemingly written in a foreign language, distill what it does and why it matters, and then explain it to a 10 year-old. And it only takes 5 minutes. Pretty cool, right? This brings me to item #3.... Neal Ghosh, is crazy smart (he ran this on his own #ML patent) and has been working on #AI since before it was cool. Everyone should have a Neal on their team.

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🚀 Accelerating Insights with #VentureIQ 🚀 One of the most exciting things about #VentureIQ, as Neal Ghosh and 9point8 Collective say, is how quickly it distills complex concepts across diverse fields—whether it’s #AI, #deeptech, or patent submissions. By leveraging contextual learning, VentureIQ rapidly uncovers key business insights for investors and founders alike. The ability to move from dense technical material to actionable commercial strategies in minutes is a game-changer for venture-building. Curious how it works? DM me and let’s chat! Check out the original post for a deeper dive ⬇️

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Cornami, Inc. CEO Walden C. ( Wally ) Rhines will provide a keynote at 9/9 Kisaco Research event on next innovation wave in generative AI: Private LLMs. The rise of private Large Language Models (LLMs) represents a significant next phase in the growth of generative AI, as organizations seek to leverage advanced AI capabilities while maintaining control over their data and customization needs. Private LLMs enable companies to build proprietary models fine-tuned to specific industries, processes, or use cases without relying on third-party cloud services, ensuring data privacy and compliance. As generative AI becomes more integral to business operations, especially in sectors like finance, healthcare, manufacturing, government, and law, where data sensitivity is paramount, private models provide organizations with greater flexibility and security. This trend aligns with the growing demand for specialized, domain-specific LLMs tailored to unique workflows and datasets. Major tech companies along with some extraordinary new startups are leading the charge in facilitating private LLM adoption. OpenAI, for example, offers enterprise solutions allowing businesses to customize and deploy GPT models on-premises or within dedicated cloud environments, ensuring tighter control over data. Microsoft, a key partner of OpenAI, has integrated Azure OpenAI Service to provide businesses with private deployment options. Additionally, Google and Meta are focusing on developing LLM platforms such as Google Cloud's Vertex AI and Meta's LLaMA (Large Language Model Meta AI), which allow enterprises to fine-tune models on their own infrastructure while leveraging advanced capabilities for text generation, summarization, and more. This shift towards private LLMs is part of a broader industry movement towards democratizing AI while addressing concerns over data sovereignty, security, and model ownership. By empowering businesses to deploy generative AI models within secure, customizable environments, Cornami, Inc. is positioning itself as an enabler of the next wave of AI-driven innovation. Gordon (Gordie) A. Campbell, Jan Geldmacher, Alexander Fortmüller, John Wei, PhD. MBA, Pankaj K., Quinn Li, Yatish Mishra, Bindu Madhavan, Heejin Chung https://lnkd.in/g2Rttm2p

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📈 AI Startup Sierra Expands HQ in San Francisco (4K to 41K SF) 🌁 Artificial intelligence firm Sierra is making a bold move by relocating to a larger office within San Francisco. The startup has signed a lease for a 41,104-square-foot space at 235 Second St., significantly upsizing from its current 4,000-square-foot office. 🏢 This move to San Francisco's South of Market neighborhood, where office vacancy rates have started to decline for the first time since 2020, is a positive sign for the city's hard-hit office market. The area's office vacancy rate, though still high at 28.8%, is slightly lower than six months ago. 📉 The deal contributes to the increase in leasing activity in San Francisco, which is up by about 1 million square feet compared to last year. This uptick is driven by the region's reputation as a leading national hub for AI companies. 💼 Sierra joins other AI firms expanding in the South of Market area, including Scale AI, which signed the city's largest office lease of 2024 with a 178,000-square-foot deal. So far this year, office tenants have signed leases totaling 750,000 square feet in South of Market, a significant increase from last year's 370,000 square feet. 🚀 Co-founded in 2023 by former Salesforce CEO Bret Taylor and former Google executive Clay Bavor, Sierra raised $85 million earlier this year to develop a customer support AI tool for businesses. The firm is set to relocate from its current space at 215 Second St. by next month. San Francisco's reputation as an AI hub, bolstered by its talent pool and six major AI research institutions, continues to attract companies. Recent significant deals include OpenAI’s sublease of over 400,000 square feet of Uber’s former headquarters and Anthropic’s takeover of Slack's former headquarters. These developments reflect a resilient and adaptive real estate market. As a commercial real estate professional, I’m excited to see how these trends shape the future of office space in our vibrant city.

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