Haggai Klorman-Eraqi
United States
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Simon Lancaster 🇺🇸🇨🇦🇵🇹
The LLM era is peaking. The SLM era is just getting started. Why care? 🐧 Tencent just dropped their 0.5B-parameter Hunyuan Small Language Model that can run offline on a smartphone or edge/constrained device — no cloud, no Wi-Fi, no “please hold while your AI is thinking.” 📈 It’s tiny compared to models like o4 (~200B) or o4-mini (~20B), yet it supports a 256K context window and both “fast” and “slow” thinking modes. Translation: it can produce high-quality outputs while living entirely on-device. Our AIOT portfolio companies like Atym, Mimiq and Tripolar Industries (stealth) are primed to take advantage of this wave. 🏁 Why SLMs > LLMs in certain domains: - Speed: Millisecond responses. - Offline operation: Works in connectivity deserts. - Privacy: No data leaves the device. - Focus: Perfect for specialized tasks. 🤖 Industry will be a killer app for SLMs: - Edge AI in factories: Local analysis of production data without risking IP leaks. - Aerospace & automotive: On-device AI guidance for additive manufacturing. - Frontline productivity: Real-time troubleshooting without a network tether. 🔥 Hot take: Within 3 years, most “AI in manufacturing” will not be powered by giant LLMs in the cloud — it’ll be nimble SLMs at the edge. The next AI arms race isn’t about who has the biggest model. It’s about who can make the smartest model that fits in your pocket. What’s your bet?
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Eze Vidra
Elad Gil is a solo GP raising $1.5 billion. Some of his early bets include Harvey, Perplexity, Character.AI, Mistral AI, Braintrust and others. In a recent post, he says: “We’ve entered an era where the first set of AI markets have solidified.” 🔍 Which AI markets have crystallised? 1. Foundation Models (LLMs) – OpenAI, Anthropic, Mistral AI, Meta, xAI, Google Gemini. 2. Code Generation – GitHub Copilot, Cursor, Cognition, Claude Code, Replit. 3. Legal AI – Harvey, Casetext, Part of Thomson Reuters, EvenUp. 4. Medical Scribing – Abridge, Ambience Healthcare, Nuance Communications. 5. Customer Support – Decagon, Sierra, Forethought. 6. Search + IR – Perplexity, OpenAI, Google, Meta. These markets have matured from chaos to clarity, with revenue ramping fast and agentic workflows (AI doing tasks for you) reshaping how value is captured. 🧭 What’s next? Elad sees the next frontiers forming in: – Accounting – Compliance – Financial tools – Sales agents – Security – …and AI-driven rollups of legacy service businesses. He also introduces the concept of the "GPT Ladder" where new markets open as models cross capability thresholds (e.g., GPT-5 or Claude X enabling previously impossible use cases). An interesting thought is on usage based pricing; “We’re shifting from selling seats to selling units of cognition.” Read the full post in the first comment 👇
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Sriram Chidambaram
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Lo Toney
AI is not just hype...it is a layered ecosystem. From Nvidia’s chips, to Anthropic's innovative models and Perplexity’s interface, we are witnessing a reshaping of how tech value is created and captured. I broke this down live on CNBC and followed up with a Medium post that goes deeper into the AI stack: infrastructure, models, and applications and the players leading each layer (Medium post in comments). https://lnkd.in/gaYixDjp
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Claus Zibrandtsen
We are learning so rapidly about AI right now, and new and valuable insights are published daily. A new report from MIT’s NANDA Initiative presents a couple of conclusions, which resonated with me and our current thinking at People Ventures. First off, 95 % of the surveyed companies do not achieve rapid acceleration of revenue through their use of AI at this point. Although the story they themselves present is often about regulatory issues or the tools’ performance, the key challenge is actually flawed enterprise integration, not the AI models themselves, while generic tools such as ChatGPT are still struggling with how to adapt well to enterprise use and workflows involving several parties. Also, there is a lot of money going into sales and marketing, while MIT’s research shows that the largest gains are reaped in back-office automation and streamlining operations. What do we learn from these findings? Mainly that it’s still early days. None of these questions addresses the fundamental potential of these tools, and the report also mentions that the most advanced companies are already experimenting with AI systems operating much more independently, within set boundaries. I think of it mainly as a privilege to be witnessing these shifts as they unfold in fits and starts, and my team at People Ventures and I are more excited and eager to learn than ever.
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