Otto von Zastrow’s Post

Today, Vincent Weisser and I are excited to share what we’ve been working on. Prime Intellect is building the infrastructure for decentralized AI development at scale. We aggregate global compute resources and enable researchers to collaboratively train state-of-the-art models through distributed training across clusters. https://lnkd.in/d39i9ux8 We’ve raised a $5.5m seed round, co-led by Distributed Global and CoinFund, with participation from Compound, Collab+Currency, Protocol Labs, and an incredible group of angel investors incl. Clem Delangue 🤗 (HuggingFace Founder), Dylan Patel (SemiAnalysis) and many others. After reading the Scaling Hypothesis in 2020, I shifted my entire focus to LLMs. I joined Aleph Alpha over two years ago, helping build a new, distributed training framework with state-of-the-art efficiency. Following endless discussions on the latest AI research, traveling together to ICML, NeurIPS and more, Vincent Weisser and I decided four months ago to go all in on Prime Intellect to build our vision of an open and decentralized future of AI. Seeing that even the largest organizations begin to train their models across multiple clusters and scale up to hundreds of thousands of interconnected GPUs, it became obvious to us that the open-source community must overcome the limitations of traditional compute infrastructure and find ways to train on thousands of distributed clusters across the globe. Learn more about state-of-the-art decentralized training techniques and our development plans on our blog: https://lnkd.in/dCRjQwfF Our masterplan is as follows: 1. Aggregate global compute (live in beta) 2. Enable globally distributed training across clusters 3. Collaboratively train open AI models in high-impact domains 4. Create a decentralized protocol for collective AI model ownership Our platform aggregating the most cost-effective GPU providers is live in private beta! Sign up for early access at https://primeintellect.ai/ Next, we'll build an open, decentralized training framework to enable collaborative model development across globally distributed GPUs. We’ve started assembling a world-class team across AI foundation model training, novel transformer architectures and distributed systems to execute on this mission. If you’re excited about what we are building, we are actively hiring for founding team members! https://lnkd.in/dxH8WPj8 I'm immensely grateful for the hard work of our early founding team Sami Jaghouar, Kemal Erdem, Johannes Weniger, Mario Sieg and contributors Leonardo (Openprest), Max H., @goth600 over the last months! Back to building 🫡

Introducing Prime Intellect – democratizing AI development at scale, from compute to intelligence. https://lnkd.in/eMJMG8rd We are excited to announce our $5.5M Seed Round co-led by Distributed Global and CoinFund, with participation from Compound, Collab+Currency, Protocol Labs, and an incredible group of angel investors incl. Clem Delangue (Hugging Face Founder), Dylan Patel (SemiAnalysis Founder), Riva Tez (Layerzero), Erik Voorhees (ShapeShift), Shivani Mitra (Nous Research), Dominik Clemente (Worldcoin), @rjdrost (Eigen Labs), Ben Fielding (Gensyn), ID Theory, Scott Moore (Gitcoin), Andrew Kang (Mechanism Capital), Joe Lallouz (ex Coinbase Cloud), Tyler Golato and Paul Kohlhaas (Molecule), Bool Capital (ex Wormhole), DCFGod, Fabian Wetekamp (Tribute Labs), Steven W. (Orchid), Sami Kassab (OSS), Yonatan Ben Shimon Justin Mares, Janine Leger, Gmoney and many more. 🎉 Fortune covered the exciting news, and we've shared the article in the comments below. Johannes Hagemann and Vincent Weisser started Prime Intellect with the belief that decentralizing AI development is crucial for driving unprecedented progress in high-impact domains such as language models, coding agents, and scientific breakthroughs. By making large-scale AI development more accessible, we aim to unlock a new wave of open AI innovation that benefits all of humanity. Over the past four months, we built Prime Intellect into a platform that aggregates global compute resources and enables researchers to collaboratively train state-of-the-art AI models through distributed training across clusters. Our vision is to democratize AI development, allowing anyone to contribute compute, capital, and code to train open AI models and share in their ownership and benefits. This is just the beginning of our journey, and we are excited to work with our community to build the open future of AI. Our roadmap includes developing robust distributed training frameworks, collaboratively training impactful AI models, and launching a decentralized protocol for collective ownership and governance of AI. We are grateful to have an amazing team that is growing quickly with Kemal Erdem, Sami Jaghouar, Johannes Weniger and Mario S. and early collaborators (researchers, compute providers, and capital contributors), and investors for believing in our mission and joining us on this incredible journey!

🌐 AI Collaboration on Ethereum and Ethereum Swarm 🤖 There's an ongoing debate in the AI community between open-source and closed AI systems. OpenAI advocates for closed AI due to safety concerns, while Meta champions open-source AI, arguing that closed systems hinder progress. However, their motivations may not be purely altruistic. Explore how these dynamics are playing out on the Ethereum platform and what it means for the future of AI. https://lnkd.in/eSiPzXUC #ArtificialIntelligence #Ethereum #MachineLearning #DataScience #Innovation #OpenSourceAI #TechTrends

🌟 Exciting News! I've been working on something that I think could truly make a difference. It all started with a simple question: What if we could use all the idle computing power around the world to make AI more accessible? This question led to an idea, which has now evolved into a project I'm incredibly passionate about. 🚀 If you're curious to learn more about this journey and the potential of decentralized AI, I’ve shared my thoughts in a Medium article. I’d love to hear your feedback and ideas! Check it out here:  https://lnkd.in/eAv2HptX #AI #Decentralization #Innovation #TechCommunity #MachineLearning #ArtificialIntelligence #GlobAI

Three leading decentralized AI projects are merging with the intent to become a more competitive, democratized approach.

Great essay from Sequoia Capital! A summary: 🚀 Two Years Into the Generative AI Revolution: A New Frontier Emerges 🔑 Key Milestones: As the LLM market stabilizes with players like Microsoft/OpenAI, AWS/Anthropic, Meta, and Google, the focus is shifting from rapid pre-trained responses (“System 1” thinking) to reasoning at inference time (“System 2” thinking). 🌐 Market Consolidation: The foundation layer is now dominated by hyperscalers, with pre-training compute well-understood. But the next frontier is inference-time compute, where AI models “stop and think” before generating responses—ushering in a new era of agentic applications. 💡 o1 & Inference-Time Compute: OpenAI’s new model “Strawberry” (o1) demonstrates true general reasoning. This leap is akin to AlphaGo’s deliberate search for the best move in Go, opening possibilities for AI to reason through complex, real-world problems like coding, math, and even creative tasks. 📈 New Scaling Law: The more inference-time compute you provide, the better the model reasons—paving the way for a new class of “killer apps” across industries like healthcare, cybersecurity, and legal tech. 🤖 Agentic Apps on the Rise: AI startups are no longer just “wrappers” on LLMs. Companies like Harvey (AI lawyer) and Factory (AI software engineer) are leading the charge by building custom cognitive architectures that scale reasoning capabilities tailored to specific tasks. 💼 Investor Takeaway: The most exciting opportunities lie in the application layer. As reasoning becomes more embedded in models, the potential for disruptive, AI-native businesses expands—targeting trillion-dollar services markets, not just software. 🌟 Looking Forward: Multi-agent systems and sophisticated cognitive architectures may define Generative AI’s next act. The race is on for that game-changing “Move 37” moment—when AI surprises us with capabilities that feel superhuman. #GenerativeAI #AIRevolution #VentureCapital #InferenceCompute #AgenticApplications https://lnkd.in/eQY5tWiY

We are building the future of Decentralized AI with privacy-preserving technology and scalable solutions. Our ROFL framework will enable decentralized agents powered by AI t have private “thoughts” (state) and interact with the world (including other agents) via transactions, queries & oracles. Learn more about Decentralized AI in our latest interview below—featuring Vishwa Raman, Head of Enterprise Solutions of Oasis Labs:

📣🤖 Introducing 'The AI Monitor' 📰🔍: Your Source for the Latest in AI Innovation and Automation 💥 Hello there 👋, AI enthusiasts and tech junkies! We are thrilled to welcome you to 'The AI Monitor,' a weekly newsletter brought to you by LangLabs, the ultimate AI Automation Agency 🚀. In this edition, we're diving deep into the most exciting developments in the world of artificial intelligence, from groundbreaking innovations to noteworthy funding news. So, grab your caffeine fix ☕, sit back, and let's explore the latest AI buzz! 🔮 Emerging Technologies: Laying the Foundation for AI Revolution 💡 The world of AI is growing at an unprecedented rate, and emerging technologies are among its foundational building blocks. One such technology is quantum computing 🕹️, which has been gaining significant attention lately. IBM recently unveiled Quantum Experience, a project aimed at making quantum computing accessible to the masses. This breakthrough has the potential to transform industries like drug discovery and cryptography. Exciting times ahead! 🌈 💰 Funding Frenzy: Dollars Pouring into AI Startups 💸 In the AI landscape, funding is the fuel that drives innovation. This week, AI startup Onfido secured a whopping $100 million in Series D funding, bringing its total funding to $200 million! Onfido specializes in AI-powered identity verification, and this fresh capital infusion will help them expand globally and accelerate their growth. Onfido is primed to disrupt the identity verification market! 🆕 New Kids on the Block: AI Product Launches 🚀 The AI space is a hotbed of constant product launches, with companies vying to out-innovate one another. This week, Cognite launched their new AI-powered platform, Cognite Data Fusion, unlocking the full potential of industrial data. With real-time data analysis capabilities, this platform has the potential to revolutionize industrial operations and drive efficiency. Keep an eye on Cognite, folks! 🚁 📡 Updates and Upgrades: Improving AI Software 🔄 As AI evolves, so does the software powering it. OpenAI recently announced OpenAI Codex, a powerful AI language model trained on a massive dataset. This upgraded version of GPT-3 assists software developers by generating code and providing suggestions. Developers, get ready to level up your game! 🚀👩💻 That's a wrap for this edition of 'The AI Monitor' 📰🔍. We hope you enjoyed this glimpse into the exciting world of AI and automation. Stay tuned for our next issue, where we'll continue to keep you updated on all things AI-related. Keep exploring the possibilities that AI has to offer, and have a fantastic week ahead! 🌟🤖 P.S. Got any burning AI questions or topics you'd like us to cover? Let us know by replying to this email! We'd love to hear from you. 💡📧 Click here to learn more: https://lnkd.in/eR5u3dqj

🍿 Unlocking the #Future: How #Crypto Can Power Open-Source #Generative #AI 🍩 The #integration path between #Web3 and AI may be more #financial than #technical https://lnkd.in/eq2eYm7R

Have you heard of Jevons' Paradox? It's a paradox discovered by British economist William Stanley Jevons (1835-1882) during the Industrial Revolution in 1865. After James Watt introduced an efficient steam engine that required much less coal than previous methods, people assumed that Watt's engine would eventually reduce the total amount of coal consumed. But the exact opposite happened! Coal consumption in the UK skyrocketed. This is the phenomenon of how increasing the efficiency of a resource as technology continues to advance does not lead to less use of that resource, but rather more. In the generative AI space, the token cost of using LLMs is rapidly decreasing, especially as LLM technology development accelerates and open-source LLMs proliferate. Professor Andrew Ng wrote a piece a few days ago about the rapid decline in token costs, why it's happening, and what AI companies should be thinking about going forward. Here's a quick summary of his thoughts: First, I continue to hear from teams that are surprised to find out how cheap LLM usage is when they actually work through cost calculations. For many applications, it isn’t worth too much effort to optimize the cost. So first and foremost, I advise teams to focus on building a useful application rather than on optimizing LLM costs. Second, even if an application is marginally too expensive to run today, it may be worth deploying in anticipation of lower prices. Finally, as new models get released, it might be worthwhile to periodically examine an application to decide whether to switch to a new model either from the same provider (such as switching from GPT-4 to the latest GPT-4o-2024-08-06) or a different provider, to take advantage of falling prices and/or increased capabilities. [source: Turing Post]