Switzerland's Open LLM: A Breakthrough in AI Technology

The Day AI Infrastructure Became a $500B Story It started with a headline: OpenAI partners with South Korean tech giants to build Stargate a $500 billion AI infrastructure project. At first, it felt like another big tech announcement. But then the ripple hit. Chip stocks surged. AI forums lit up. Data Scientists, ML engineers, and startup founders began asking the same question: What does this mean for us? Because Stargate isn’t just a data center. It’s a signal. A signal that the future of AI won’t be built in labs alone it’ll be built on infrastructure. Floating data centers. High-bandwidth memory chips. Global compute networks that make today’s LLMs look like dial-up. For those of us in Data Science, this changes everything. Model training won’t be bottlenecked by compute. Deployment pipelines will stretch across oceans. And the next breakthrough in generative AI might come from Seoul, not San Francisco. This is the infrastructure moment. And if you’re building in AI, it’s time to think bigger.

Breaking Tech Realities: What’s Actually Happening in 2025🔍 --- 🚀Tech 2025 is not just about innovation, it’s about transformation. AI has quietly moved from being a tool to becoming the invisible infrastructure that powers our world. ⚛️ Quantum computing is stepping out of labs into billion-dollar projects, while AI supercomputers are doubling their power every nine months, consuming energy on a massive scale. 🔒 At the same time, the race for post-quantum security has already begun. The future isn’t waiting — it’s unfolding right now, shaping how we work, live, and build tomorrow. Readout blog : https://lnkd.in/gxSPH_67 @ Medium

𝐓𝐡𝐞 𝐀𝐈 𝐆𝐨𝐥𝐢𝐚𝐭𝐡𝐬 𝐀𝐫𝐞 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐍𝐞𝐫𝐯𝐨𝐮𝐬. 🎯The rulebook is being rewritten. Forget "bigger is better." The new mantra is "𝐬𝐦𝐚𝐫𝐭𝐞𝐫, 𝐧𝐨𝐭 𝐡𝐚𝐫𝐝𝐞𝐫." A groundbreaking MIT study reveals the law of diminishing returns is hitting AI's giants. Hard. While they pour billions into compute, a revolution is brewing in the shadows. 𝐓𝐡𝐞 𝐍𝐞𝐰 𝐌𝐚𝐭𝐡 𝐨𝐟 𝐀𝐈: • 𝐒𝐦𝐚𝐥𝐥𝐞𝐫, "𝐦𝐞𝐞𝐤" 𝐦𝐨𝐝𝐞𝐥𝐬 𝐚𝐫𝐞 𝐜𝐥𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐠𝐚𝐩. 𝐅𝐚𝐬𝐭. • Algorithmic elegance is starting to crush raw computational brute force. • The cost? Millions vs. thousands. DeepSeek already proved it: you don't need a model the size of a moon to reason like one. 𝐓𝐡𝐞 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐒𝐭𝐮𝐧𝐧𝐢𝐧𝐠: 🔹 The moat of tech titans? Suddenly looking like a puddle. 🔹 Startups can now compete on intellect, not just infrastructure. 🔹 The real value shifts from GPU stockpiles to genius-level algorithms. As Jamie Dimon hinted, the economics are circular—GPUs depreciate, but smart code appreciates. 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐛𝐞𝐥𝐨𝐧𝐠𝐬 𝐧𝐨𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐦𝐨𝐝𝐞𝐥𝐬, 𝐛𝐮𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐝𝐞𝐬𝐢𝐠𝐧𝐬. So, the billion-dollar question is shifting: Are you betting on 𝐬𝐜𝐚𝐥𝐞 or 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲? 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/eiWDiyT4

Today's Top AI Stories (10/08/2025) Section 1: Top 3 Trends Today: 💡 AI is increasingly being applied to scientific research, with models like Gemini classifying astronomical events and new strategies promoting AI in science. 💡 Enterprise AI adoption is accelerating for operational efficiency, financial compliance, and cryptography management. 💡 Significant focus on AI infrastructure, education, and literacy is emerging to support widespread integration and innovation. Section 2: Top 3 Investment/Funding Stories: 💰 Elon Musk's xAI raised $20 billion, with Nvidia investing $2 billion for AI development. 💰 GreyLabs AI secured ₹85 crore to expand its voice-based AI for the BFSI sector. 💰 London-based startup Dragonfly raised €3 million to simplify software decision-making with AI. Section 3: Top 5 Product/Service Announcements: 🚀 Eventus: Frank AI, for secure and transparent financial compliance and surveillance analytics. 🚀 Zendesk: Enhanced Resolution Platform with autonomous Voice AI Agents and video calling for customer service. 🚀 iTmethods: ONEagent Studio, empowering enterprises to create and deploy custom AI agents with governance. 🚀 IBM: Guardium Cryptography Manager, an AI-powered system for managing encryption and quantum security risks. 🚀 AlphaSense: Financial Data, integrating quantitative and qualitative insights into a single chat interface for market intelligence. Section 4: Research & Breakthroughs (Top 3): 🔬 University of Oxford and Google Cloud: Gemini LLM achieved 93% accuracy in classifying transient astronomical events. 🔬 European Commission: Launched AI in Science Strategy and RAISE virtual institute to advance AI-driven research. 🔬 OpenAI: Opened applications for its $50 million People-First AI Fund to support AI literacy and mission-focused organizations. Link to learn more at praxie.com #AI #ArtificialIntelligence #BusinessAI #TechNews #AIDevelopment

Today's Top AI Stories (10/08/2025) Section 1: Top 3 Trends Today: 💡 AI is increasingly being applied to scientific research, with models like Gemini classifying astronomical events and new strategies promoting AI in science. 💡 Enterprise AI adoption is accelerating for operational efficiency, financial compliance, and cryptography management. 💡 Significant focus on AI infrastructure, education, and literacy is emerging to support widespread integration and innovation. Section 2: Top 3 Investment/Funding Stories: 💰 Elon Musk's xAI raised $20 billion, with Nvidia investing $2 billion for AI development. 💰 GreyLabs AI secured ₹85 crore to expand its voice-based AI for the BFSI sector. 💰 London-based startup Dragonfly raised €3 million to simplify software decision-making with AI. Section 3: Top 5 Product/Service Announcements: 🚀 Eventus: Frank AI, for secure and transparent financial compliance and surveillance analytics. 🚀 Zendesk: Enhanced Resolution Platform with autonomous Voice AI Agents and video calling for customer service. 🚀 iTmethods: ONEagent Studio, empowering enterprises to create and deploy custom AI agents with governance. 🚀 IBM: Guardium Cryptography Manager, an AI-powered system for managing encryption and quantum security risks. 🚀 AlphaSense: Financial Data, integrating quantitative and qualitative insights into a single chat interface for market intelligence. Section 4: Research & Breakthroughs (Top 3): 🔬 University of Oxford and Google Cloud: Gemini LLM achieved 93% accuracy in classifying transient astronomical events. 🔬 European Commission: Launched AI in Science Strategy and RAISE virtual institute to advance AI-driven research. 🔬 OpenAI: Opened applications for its $50 million People-First AI Fund to support AI literacy and mission-focused organizations. Link to learn more at praxie.com #AI #ArtificialIntelligence #BusinessAI #TechNews #AIDevelopment

Everyone thinks building AI models is rocket science. It’s not. It’s finally simple. 1️⃣ One API. 2️⃣ Train any LLM-big or small. 3️⃣ Forget the hardware hell. Focus on your data and algorithms. Just discovered 𝗧𝗶𝗻𝗸𝗲𝗿 𝗯𝘆 Thinking Machines Lab, a platform that’s rewriting how models get built. Most teams? They rent AI copilots. With Tinker, you can engineer models, custom-fit to your needs. ✔️ Distributed training? Automated. ✔️ Fine-tuning (LoRA)? Four lines of code. ✔️ Hardware scaling? Invisible. And it’s not just hype. Princeton, Stanford, Berkeley, Redwood Research, these are early adopters. India, global teams, students, solo hackers, everyone’s welcome. Advancing science shouldn’t need $2M of GPUs. Favorite part? The Tinker Cookbook: https://lnkd.in/d9jstQVw An open-source library with ready-to-use recipes for hacking, fine-tuning, and iterating on modern AI models, with no guesswork. ⚡ Faster. 💰 Cheaper. 🧠 Smarter. This isn’t just an upgrade. It’s a shift in who controls AI. Tinker’s in private beta. Free to start. Usage pricing coming soon. Question is: Will you build your own edge? Or keep renting someone else’s? 👇 Comment your thoughts. 👇 Save this for your next AI project. 👇 Follow for more frontier tech. Try Tinker: https://lnkd.in/dGZEBN9M #Tinker #ThinkingMachinesLab #MiraMurati #AI #LLM #AIDevelopment #TechInnovation

In a world where frontier AI is often locked behind massive compute and ops overhead, Thinking Machines Lab just pulled back the curtain, releasing Tinker to make custom large model adaptation more accessible. Tinker enables the creation of custom frontier AI models as a managed service. The initial release of Tinker supports fine-tuning of Alibaba Qwen 3 and Meta Llama 3 and offers LoRA-based adapter tuning to train a streamlined adapter instead of updating the base model’s weights. The service automates much of the operational burden, from tool orchestration to GPU setup, so researchers and organizations can dedicate their time to curating and running the best training datasets. While still early and in private beta, this new service opens the door for companies and academics to fine-tune foundational models for domain-specific or niche tasks, further increasing the value of large language models. I’m looking forward to seeing how early adopters push Tinker’s capabilities. If you’re experimenting with Qwen or Llama models, I’d love to hear your thoughts, feel free to connect or drop a comment.

China’s AI Power Surge in LLMs (Alibaba Group's Qwen Moonshot AI's Kimi AI K2 DeepSeek AI #ZhipuAI's GLM-4.5 Baidu, Inc.’s #Ernie) is supported by Huawei #BaiduKunlun #BirenTechnology and #Cambricon on the chips side. China's technology sector has now achieved self-sufficiency in artificial intelligence, motivated by domestic competition and the need to circumvent international chip export restrictions. The Chinese LLM Landscape: An Open-Source Buffet Chinese companies are not just competing on model size, but on efficiency, context length and open-source availability, often utilising the highly efficient Mixture-of-Experts (MoE) architecture. Alibaba's Qwen Series is as a versatile and powerful all-rounder. Distinguished by its strong open-source commitment, Qwen excels in multilingual tasks (supporting over 100 languages) and features strong multimodal capabilities, handling text, images, and audio. Moonshot AI's Kimi K2 is gaining attention for its exceptional ability to handle extremely long context windows (e.g., 128K tokens). Kimi is a leader in agentic AI, meaning it can plan and execute complex, multi-step tasks autonomously. DeepSeek AI has carved out a niche by demonstrating coding and mathematical reasoning performance that rivals top global models, but at a reported fraction of the traditional training cost. Zhipu AI's GLM-4.5 is highly regarded for its robust tool-use and reliable function calling, which are critical for developers building sophisticated AI agents. Baidu’s Ernie Series, one of China’s original foundation models, continues to be a major player, using its extensive knowledge graph for enhanced reasoning in its latest multimodal versions. Ultimately, the Chinese LLM ecosystem is driving forward on efficiency and accessibility, with many top models being released as open-source alternatives to proprietary Western systems. The Hardware Race: Bypassing Restrictions with Domestic GPUs Necessity is the mother of invention thanks to US controls on NVIDIA's H100 forcing Chinese tech giants to heavily invest in domestic alternatives. Huawei Ascend is the most crucial domestic line of AI accelerators, with chips like the Ascend 910/920 being the go-to alternative. DeepSeek are specifically optimizing their LLMs to run efficiently on Ascend hardware. Baidu Kunlun develops its own chips primarily to power its proprietary LLMs and cloud infrastructure. Biren Technology and Cambricon are two other domestic startups that are quickly advancing their general-purpose computing GPUs to fill the void left by international sanctions. The primary strategy being employed is software optimization. By finding smarter, more efficient ways to train and run their LLMs, China is proving that they can extract performance comparable to top-tier models with less advanced or export-restricted hardware. The hardware race is therefore a dual challenge of both chip manufacturing and software ingenuity.

Technically and economically, most of what is called “AI” today is a function of scale in compute, data and optimization. The architecture—matrix multiplication across large tensor networks—has not changed fundamentally since the early 2010s. What has changed is the magnitude: orders-of-magnitude increases in compute throughput, parallelization and training duration. From this perspective, “AI progress” is largely the industrialization of statistical pattern learning. — Each major breakthrough—transformers, diffusion models, large language models—derives its gains from higher compute budgets, denser data corpora, and more efficient scaling laws. Intelligence, in this frame, becomes an emergent property of computation at scale rather than a qualitatively new algorithmic paradigm. — My take is that AI equals stronger compute, which implies the next frontier is constrained by space, time, physics, cost and energy rather than theory. This aligns with current signals: frontier-model training now costs hundreds of millions of dollars and consumes megawatt-scale energy. Future differentiation would then depend more on hardware innovation, power efficiency and data-center geopolitics than on software originality. #ai

AI’s next frontier isn’t intelligence — it’s energy. When we met Clark Alexander, Co-Founder & Head of AI at Argentum AI, his first-principles view of compute efficiency made it clear they weren’t just another GPU marketplace — they were building a market mechanism for the trillion-dollar energy problem in AI. As Clark shared in his recent DL News interview today: “An LLM’s computational needs grow quadratically, but its energy demand grows cubically. We’re running out of electricity before we run out of ideas.” That insight is exactly why Banyan AI Fund I led the pre-seed round and Sam Awrabi joined as founding advisory partner. Argentum AI is creating a decentralized, compute-agnostic marketplace that intelligently matches workloads to the most efficient hardware — saving cost, energy, and time while securing the future of compute through post-quantum encryption. At Banyan, we back AI-Native infrastructure that solves foundational bottlenecks, not just symptoms. Argentum AI is one of them. https://lnkd.in/g9gEfTUz