Jay Dawani

Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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Groq Partners with Aramco to Propel Saudi AI Ambitions In a groundbreaking move, Groq, the innovative chip startup renowned for its cutting-edge technology, has inked a significant deal with aramco, the world’s leading oil company. This strategic partnership is set to be a game-changer for Saudi Arabia's burgeoning AI landscape. What This Means: Groq’s advanced chip technology will play a crucial role in supporting Saudi Arabia's ambitious plans to position itself as a global leader in artificial intelligence. The collaboration is expected to significantly enhance the computational power available for AI applications, paving the way for breakthroughs in various sectors including energy, healthcare, and smart cities. Key Highlights: Boosting AI Capabilities: Groq’s state-of-the-art chips are designed to handle complex AI tasks with unprecedented speed and efficiency, aligning perfectly with Saudi Arabia's AI objectives. Strategic Alignment: This partnership reflects a broader trend of technological alliances aimed at fostering innovation and economic diversification in the region. Future Impact: By leveraging Groq’s technology, aramco and Saudi Arabia are positioning themselves at the forefront of the AI revolution, potentially setting new standards in the industry. As the tech world watches closely, this deal underscores the growing intersection of AI and semiconductor technology, and how strategic collaborations are shaping the future of these industries. For more information: Bloomberg https://lnkd.in/dr89qeCV #AI #ChipTechnology #Innovation #TechPartnerships #Groq #Aramco #SaudiArabia #FutureOfAI #TechNews

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Spent a wonderful week in Canada with family! Now back to business.. Here's your weekly AI update: 🔙 𝐓𝐡𝐞 𝐁𝐚𝐜𝐤𝐰𝐚𝐫𝐝 𝐏𝐚𝐬𝐬: 𝐑𝐞𝐯𝐞𝐫𝐬𝐢𝐧𝐠 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐀𝐈 𝐖𝐞𝐞𝐤 Week 51 | August 5 - August 11 The OpenAI Brain Drain Industry: 🚨OpenAI co-founder John Schulman leaves for Anthropic; President Greg Brockman is taking extended leave through the end of year. What's interesting is that three of their most technical co-founders (Ilya Sutskever, John Schulman, Andrej Karpathy) have all left the company now. This then begs the question - is this a result of natural silicon-valley attrition, or is there something deeper going on here? And moreover, how will this impact the technical moat that OpenAI has built over the years? (https://lnkd.in/gztRKhwV) 🚨Google announced that new Gemini AI-powered features will be incorporated in a variety of home devices, such as Google Home and Nest (https://lnkd.in/gV2QacAQ?). It also announced a significant reduction in token cost of Gemini 1.5 Flash (https://lnkd.in/gbNe6yNU?) 🚨Figure releases Figure 02, it's newest humanoid robot. Figure has been actively testing robots with BMW at a manufacturing plant. Figure last raised $675M Series B in February 2024, valuing the company at $2.6 billion. (https://lnkd.in/gZnPSke4) 🚨Mark Zuckerberg said on Meta's Q2 earning call that to train Llama 4, the company will need 10x more compute than what was needed to train Llama 3. (https://lnkd.in/g5pEPrTw?) 🚨Elon Musk filed a lawsuit against Sam Altman and Greg Brockman, renewing claims that the company is betraying its founding aims of benefiting the public good rather than pursuing profits. (https://lnkd.in/gAk5grU6) Financing and M&A: 🚨Groq raises $640m in Series D at a $2.8B valuation. The round was led by Blackrock Private Equity Partners with participation from other investors such as Neuberger Berman, Type One Ventures, Cisco, KDDI, and Samsung Catalyst Fund. (https://lnkd.in/gRtKQKvW?) 🚨Hugging Face acquires Seattle data storage startup Xethub. The HF team plans to integrate Xethub's backend storage technology, enabling developers to host more large models and datasets than currently possible. CEO Clem Delangue 🤗 confirmed this was the largest acquisition the company has made thus far. (https://lnkd.in/g8fJ-BjT) 🚨OpenAI is leading a $60m Series B funding round for Opal Camera, a maker of professional-grade webcams(https://lnkd.in/gMT9dkTG) 🚨ProRata.ai a company that has invented an attribution technology to properly compensate content owners, has raised a $25m Series A, led by Prime Movers Lab, Mayfield Fund and Revolution Ventures. (https://lnkd.in/gumjbWmj) R&D: 🚨Alibaba Group releases Qwen2-math, a SOTA model for math-specific tasks. (https://lnkd.in/g8Jh3M7T?) Other Interesting Resources: 🚨 Between May and August, more Humane AI Pins were returned than purchased (https://lnkd.in/gy56qpKm?). 😯

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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There is a new #searchengine in town going after #Google and #Perplexity out of Canada A new AI-first search engine, Upend, has emerged from stealth, aiming to democratize access to advanced generative AI models. Upend is a Canadian startup that has just launched its AI-powered search platform, offering users a unique and powerful search experience. Access to 100+ Large Language Models (LLMs): Upend provides users with a wide selection of LLMs, including models from #OpenAI , #Claude , #Mistral , and more, allowing for diverse and specialized search capabilities. Upend offers the option to base search results on Wikipedia, providing users with grounded and cited information. Affordable Pricing: Upend aims to democratize access to advanced AI search by offering team plans starting at $20/month, significantly lower than competitors. Upend's mission to make AI search more accessible and affordable is a significant step towards empowering students, professionals, and businesses to harness the power of generative AI. _______ 🤩 Enjoy being the first to get news like this? ♻️ Repost it to your network and follow Ben Silverman for more! Want to stay up to date on all of the latest news and tools and get them directly to your inbox every week? ⛏️ Join our community of 2,000 + subscribers today https://lnkd.in/gMDQ6CDd https://lnkd.in/dneXZDbK https://lnkd.in/gfMvRbyq

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Collision Conf is one of the biggest tech+AI events in North America and I am looking forward to learning more in this exploding space. If anyone is heading that way, feel free to ping and we can connect! #AI #generativeAI #digitaltransformation

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💲The Founders Weekly BOOST - Elon "Patents are for the weak", How VC's Really Works, All the Newly Minted Ca$h Announced by VCs and Lots lots more 🔥 💲SaaS Legend DHH on saving $10m in Cloud Costs 🤯 Can you follow his lead? 💲The 2024 Accel Euroscape - "Is AI Eating Software..😱 Or is AI the Transformation Lever for SaaS? You decide 🫣 💲I've been an LTV/ +ve DRR focused investor for a decade - Tom Tunguz "The Year Of LTV" - better late than never 🤣 who knew keeping customers was a fashion? 🤔 💲Dig in, share with friends & colleagues and always feel free to repurpose the content in my feed or our Newsletter ✅ 🎯 Don't Forget To Subscribe so you never miss a 💲 of value 💙 "The Founders Corner" Rubén & I work hard all week with our collaborators to make this happen & so if you want to collaborate with us feel free to DM and maybe join the gang 🌟

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🧩 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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Last week Asian founders did... 🚀 Aditya Rajagopal PhD launched nCompass Technologies Inc.'s first service, an API that supports any open-source, fine-tuned, or custom LLM with no rate limits while maintaining a high QoS. 🚀 Danielle J. Yoesep and Shuying Zhang launched Verse Therapy (YC W24), a healthcare platform to connect patients with speech therapists, and empower speech therapists to start their own practices. 🚀 Vidy Thatte launched Webclips, a no-code platform to build mobile apps on your phone. 🚀 Stephen Chau and Mike Chu launched Cove, a new type of collaborative AI that directly edits content with you, learns from your content, proactively suggests next steps, and more. 💸 Ankur Goyal raised a $36M Series A for Braintrust, an end-to-end platform for building AI applications which focuses on making LLM software development robust and iterative. 💸 Jeff Chang raised a $50M Series B for Rad AI, an AI workflow platform enabling radiologists to save time, reduce burnout, and improve patient care. 💸 Hamzah Chaudhary raised an $11M Series A for Lightdash, a platform which turns business data into a self-serve full-stack BI platform. 💸 Annie Liao 🇦🇺 raised a $1.75M Pre-Seed round for Build Club, an AI e-learning platform empowering builders through hands-on, gamified AI education. 🎙️ Jason Patel, founder of OpenForge, shares his journey building a an AI-powered email sales tool on AI Innovaters with Rob May 🎙️ Arvind Jain, co-founder of Rubrik and Glean, talks about Glean and the future of AI-work on the Venture with Grace podcast Links to the podcasts in the comments ⬇️

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Exciting to see startups aiming to use AI to discover chemicals, materials, drugs, etc. Semi companies have taken notice: AMD is investing in and Cadence is acquiring them. Semi/EDA companies have broken out by becoming critical path, whether it’s through critical software, packaging, IP, design kits from foundries, or user communities. Aside from just making it easy to make new materials, these “AI for making X” companies also need to articulate a vision as to how they can establish monopolistic positions in their industries.

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As LLM's get increasingly more sophisticated, with OpenAI's o1 model now including reflection before answering, I am increasingly investigating the emergence of autonomous AI Agents. My path here was somewhat circuitous in that I was going through Y Combinator's Summer 24 cohort which had a plethora of AI use cases that were very industry specific. I found that curious in that earlier SaaS offerings should have already automated and/or digitized most of these process, and I couldn't figure out why AI startups would focus on basic workflows such as inventory management and book keeping. As I dove into the matter, with the help of my now indispensable Perplexity, I landed on a very insightful post by Bessemer Venture Partners (https://lnkd.in/gHtQQRha). The thesis is that Vertical AI companies are going to be magnitudes bigger than general solutions, especially as they focus on industries that were earlier inaccessible to SaaS given very high language based workflows. Within that thesis, there was also a mention of AI Agents and how these would come to dominate workflow automation going forward. This neatly tied in with a great conversation between Peter H. Diamandis and Emad Mostaque (https://lnkd.in/g-ccxbma) that talked about 10 billion agents!! I have spent the last few weeks diving into AI Agents with the help of content from interesting voices such as Adam Silverman (Hiring) and David Ondrej. Over the next few months, I will try to create and deploy AI Agents on some basic use cases to get hands on experience on these systems. In the meantime, I will close this post with a link to a video from Andrew Ng, a pioneer in AI systems that provides a short overview of AI Agents (https://lnkd.in/g8v4pUQu). I hope you enjoy the content and wish everyone a productive week ahead.

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Generative AI is not just a Silicon Valley game. The core computer science was developed at the University of Toronto (Geoffrey Hinton), University of Montreal (Yoshua Benigo) and Yann LeCun (Bell Labs and NYU). DeepMind was based in London. And Chinese companies have been hyperactive in the space. It is important to keep an open mind on where the innovations will come from. https://lnkd.in/gCTVNFJ3 #AI #generativeAI #innovation

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Just finished reading "The Next Giant Leap" by Space Capital - a pioneering VC investing in the emerging Space Economy. The report speaks to how SpaceX’s Starship is more than just the most powerful rocket ever built—it is poised to reshape the global space economy in profound ways. With its immense payload capacity, reusable design, and lower cost per launch, Starship will unlock entirely new industries and transform existing business models. But how should industries and businesses evolve to align with this paradigm shift? 📈 Rethinking Business Strategies & Supply Chains - For businesses, Starship represents an era of mass-production over precision engineering. This means moving away from the focus on mass efficiency toward cost-effective, redundant, and scalable production. - In terms of supply chains, this opens opportunities for new players in manufacturing, materials, and space components. The demand for off-the-shelf parts and non-aerospace suppliers will rise dramatically. 💡 New Use Cases & Opportunities   The opportunities Starship unlocks go beyond launching satellites. - In-space manufacturing of high-value products like pharmaceuticals and advanced materials that cannot be created on Earth. - Mega-constellations of satellites for global broadband, expanding on what Starlink has already begun. - In-orbit assembly and servicing, allowing for larger space structures like telescopes or even habitats to be built in orbit, radically reducing costs. - Lunar infrastructure and resource extraction will become more viable, paving the way for sustainable human presence on the Moon and, eventually, Mars. 📊 What Should Businesses Do Now?   1. Embrace Redundancy & Scale – Instead of focusing on one-off, high-cost solutions, businesses should embrace redundancy and mass production.    2. Simplify Design – With Starship’s payload capacity, there is less pressure to optimize for mass efficiency. Simplified, scalable designs will allow for faster production and iteration cycles, akin to how SpaceX rapidly tests and evolves Starship itself. 3. Develop Strategic Partnerships – Collaborating with forward-thinking space startups, suppliers, and even non-aerospace sectors will be essential to capitalize on the opportunities Starship provides. 🌌 A New Era of Innovation   Starship will democratize access to space, lowering the barriers for businesses to create and scale. The next decade will witness breakthroughs in scientific exploration, energy production, and in-space transportation that we can only begin to imagine today. #SpaceEconomy #Starship #Innovation #SupplyChain #Manufacturing #SpaceTech #Entrepreneurship #FutureOfSpace https://lnkd.in/gNdbwXdq

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Benjamin Bolte from K-Scale Labs just posted his 'Robotics Predictions for 2025' containing some hot takes 🦾🔥 By the end of 2025.... 1. Humanoid hardware is commoditized - Hardware components will converge, but robot designs will remain different - Actuators no longer differentiators; industry moves towards open-source like MIT Cheetah - BOM for a humanoid will come down to a shocking <$8k! Question: What happens to incumbents like Telsa, Figure, 1X, Apptronik, etc., who have spent billions of $ in R&D to create robots with a $50 - $150k BOM? 2. No ChatGPT moment for robotics - A LOT of data (millions of hours) is needed to train a general-purpose robotics model - Language + multi-modal models will not be able to control a robot sufficiently - Progress will be incremental rather than spiky Question: If building a robot for a domain and bootstrapping to generality is a losing strategy, what's a better one? 3. More than half of humanoids will be sold to consumers - The 'killer apps' will be speech and vision - Traditional SLAs for business customers will force robotics teams away from building good generalist models - "Robotics companies will start looking more like SV tech companies, caring about user engagement and retention." Question: What are the 'killer' consumer use cases that will drive this adoption? What are the jobs to be done? Link to the full blog in the comments. What are your thoughts?

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Seeing lots of great founders disrupting EDA with AI Most EDA tools don’t do real “engineering,” but rather take the rote tasks out of implementing engineering output from centuries of “experience” Can AI collapse the heuristics of the best engineers that ever lived to actually automate engineering? Hard to beat EDA at its own game by making it better with AI Generating the boundary conditions that engineers normally use to start the engineering process and better optimized at the end would be a great complement to existing EDA

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I’ve been fortunate enough to do AI shows with Sam Altman, Yann LeCun & Arthur Mensch. But this show today with David Cahn at Sequoia Capital is the best of all of them: 🧠 AI’s $600BN question 🏃 Why no one will never train a frontier model on the same data center twice 🏗️ Servers, steel & power: The 3 pillars of the future of AI Not one to be missed, my 8 key takeaways 👇 1. The Oligopoly That is Blowing Billions on AI - Microsoft, Azure, & Google now represent $7T market cap. - Of course they will spend billions to protect their oligopoly - What is most interesting is Meta is the only incumbent that is financing this not with cloud being their cash cow. 2. How Startups Could Win with the Cost of Compute Coming Down - Big tech companies are producers of compute & startups are consumers of compute. - Overproduction of compute = lower prices.  - This gives startups higher margins & makes them more valuable. 3. Is Compute the Currency of the Future or Do the Costs Come Down and It Is Commoditised? - We are forgetting “compute” is a euphemism for a $2BN data center in the middle of nowhere. - They have GPUs & liquid cooling systems that need to be constantly optimized & upgraded. - We cannot simply build 15 years worth of compute all at once.  No one's ever going to train a frontier model on the same data center twice. 4. Data Centre Teams and Model Teams Need to Be Coupled - You cannot have separate teams running data centers & building models. - Elon & Zuck control all their data centres. - It will not work as these models get better & better. 5. Why the Data Center Is the Most Important Asset - There is not much difference between these models today. - AI researchers are jumping from one lab to another. - Scaling laws will become more dominant as these models get bigger. 6. Servers Steel and Power: The Three Bottlenecks of AI Three things: 1. Servers: NVIDIA, AMD, Broadcom… 2. Steel: Construction & real estate developers for data centers. 3. Power: All the energy required for AI. - More factories will be built in 12 months for AI and we will see another industrial revolution. 7. You Have to Have a Cash Machine to Play the Big Model Game - Amazon has AWS - Meta has Instagram - Microsoft has Azure - You need a cash machine to compete and it CANNOT be the AI business. 8. There is One Definition of Success in Venture: Are you a Slugger? - If you are generating billion dollar gains, you are a slugger. - Doug Leone, Pat Grady, Andrew Reed are all sluggers. - You cannot be good at this business without being a slugger. (links in comments) #founder #funding #business #investing #vc #venturecapital #entrepreneur #startup #seed #funding #sequoia

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There are still VC scale hardware opportunities in the age of GPU and cloud, you just have to have an open mind and know where to look. 👇🏻

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Apple’s Private Cloud Compute and the business of privacy With Private Cloud Compute, Apple has gone to extraordinary lengths to safeguard user privacy with AI-related tasks. In doing so, Apple has surely also incurred an extraordinary expense. How can Apple justify the cost of providing PCC if it doesn’t directly monetize its Apple Intelligence features? Either through the improved brand perception captured in a broader privacy narrative or by monetizing PCC with other non-AI use cases. While the narrative explanation is clear, the more interesting theory about Apple’s motivation in building PCC is that it can be expanded to other use cases that can be directly monetized. The first alternative use case that springs to mind is advertising attribution. PCC sounds conceptually similar to a data clean room, albeit far more sophisticated than most implementations, which merely use IP addresses and other identifiers to match publisher-observed ad interactions with advertiser-observed conversions. While the PCC nodes seem hardware-optimized for processing AI-related tasks, other nodes could be built that aren’t.

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