Greg McNutt

I have some personal news I'd like to share. I started an AI company with my dear friend and college roommate, Wayne Pan. It took over two decades, but everything has fallen into place, personally and professionally. At Cisco I led edge compute in industrial IoT.  At NEOM I helped shape nation-building technology strategies.  At SambaNova I scaled AI infrastructure globally and built deep partnerships across the Americas and the Middle East. That journey taught me something simple: AI is no longer just an important piece of technology. It is national infrastructure. And that realization is what led me here, to building haimaker. And before I give you the pitch, I wanted to say a few things. → Get your children involved in AI. Teach them how to use the tools. Let them use it to express their creative side. Or maybe show off their tech-y side. And maybe teaching is the wrong word, because in many respects, we’re all still learning. AI is a superhuman ability. Let’s get our kids involved in using these tools. → We are living through one of the greatest technological innovations of our lifetime. It’s a blessing to be able to witness the impact that AI has had on my life and career. I’ve never felt the vibe like this before. It’s something bigger than all of us. AI brings out the little kid in me a lot of times. It’s fun to tinker and create new things with the endless tools that are available. → We are so early. So, so, so early. When I think of AI. I think of what’s possible. I find myself discovering new things every day that I can’t believe are possible because of AI. I don’t think we are anywhere near our limit of discoveries. Don’t ever feel that you’re too late to get involved with AI as a career path. → AI isn’t a feature. It’s critical national infrastructure. Just like airports, hospitals, and telco networks, AI is essential to the growth and security of every nation. And it should be treated as such. Back to haimaker. haimaker is a Sovereign AI Compute Orchestration Platform. haimaker enables nations and telcos to launch sovereign AI Compute as a Service, while giving enterprises and developers the ability to run workloads in compliant regions. This is just the beginning. We have a lot of great news coming very soon and we can’t wait to share what we’ve been working on.

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I've found that most devs have rarely looked at GPU compute dies. There's a tremendous amount of innovation that happens at the silicon level that can help in understanding optimization paths when considering different platforms. This blog, authored by Kyle Aubrey and Nick Stam, takes a deep dive into the Blackwell Ultra GPU architecture including the high-speed interconnects, SMs, and other critical components responsible for driving critical workload performance at scale. Blog link: https://lnkd.in/eUXNFb_m #nvidia #gpus #blackwell #hardware #ai

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For the first time, Google is sharing data on the energy and water consumption of each Gemini prompt. 🌳 At the median, each prompt consumes the equivalent of running a microwave for about a second (or 9 seconds of watching TV). 📺 ➡️ And, since AI data centers consume water for cooling, the estimate is that each prompt consumes about 5 drops of water per prompt (0.26 milliliters). ➡️ Even surprising, the total energy per Gemini query has also fallen dramatically over time with prompts using 33x more energy in 2024 than 2025. 🙂 It's pretty awesome that MIT Technology Review called this "most transparent estimate yet from a Big Tech company". Now the data is in front of you, too. You can read their summary piece below: https://lnkd.in/dSQ7xsu2 Or, if you want to see all the data, I have that for you, too. https://lnkd.in/diD2njdA #google #lifeatgoogle #ai #research #data #environment

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I was honored to host a fireside chat with Sherwin Wu, head of engineering for OpenAI API at last week's Fellows Forum 2025 organized by Fellows Fund. Sherwin shared his incredible journey at OpenAI in scaling OpenAI's developer platform and building its vibrant ecosystem, and his deep insights into various topics we covered.   A few quick notes:   Model Evolution & Platform Development: Outlined 3 major model capability inflection points 📍GPT-3: Limited to copywriting applications 📍GPT-3.5/4: Enabled the post-ChatGPT explosion (Perplexity, Snapchat integrations) 📍Reasoning paradigm (O1): Unlocked long-term planning for coding and autonomous agents   Lessons from GPT-5 launch 📍Latency concerns: Model's extensive thinking time frustrated users 📍Product showcase mismatch: ChatGPT interface didn't effectively demonstrate the model's capabilities 📍Learning: Future major launches will pair API releases with first-party products that better showcase model strengths   Unexpected use cases 📍Consumer apps like enabling toys for kids to chat with   Common patterns in startups that successfully leverage OpenAI models and API 📍Most successful startups "swim with the current" - building products that push models to their limits and position for future capabilities rather than current limitations   Common patterns in enterprises that successfully leverage OpenAI models and API 📍Common use cases include internal productivity enhancement (e.g., using Codex) and customer interaction transformation (support, sales automation)   Barriers in enterprise Adoption 📍Lack of both top-down and bottom-up organizational buy-in (top-down alone is not sufficient) 📍Insufficient AI-ready infrastructure and data platform investment   Responsible use: OpenAI is investing in 📍Pre-launch Red-teaming in Model Development 📍Post-launch monitoring 📍Tools to help developers manage safety risks, build guiderails, and setup evals   Exciting future breakthroughs 📍Excitement centers on advancing the reasoning paradigm (longer thinking periods) and multimodal breakthroughs, particularly in voice interactions that feel more naturally conversational Full fireside chat video: https://lnkd.in/gJzjzMBm

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𝗛𝗼𝘄 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝗠𝗮𝗻𝗮𝗴𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 Managing sessions in LLMs isn’t just about keeping track of prompts. It is about orchestrating memory, context, and tools seamlessly across multiple agents. That’s where MCP servers come in. Here’s the flow I captured visually → Context Window as Working Memory: Holds system prompt, user inputs, assistant responses, tool calls, and rolling context → MCP Servers: Each server maintains its own session state, ensuring consistency across interactions → System Prompt + Tools + Code Loops: Work together to power agentic behavior while grounding outputs in real tasks → LLM Integration: The context is constantly summarized and updated before being sent to the model This structure ensures scalable, multi-session orchestration, which is critical for enterprise AI where multiple tasks and tools interact simultaneously.

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Scientists across Amazon are revolutionizing how we verify AI systems with automated reasoning. Our teams are making AI more trustworthy, applying the same mathematical logic we’ve used for years to verify security at Amazon Web Services (AWS). It’s called “neurosymbolic AI”, and you can see this in action with robots like the one below navigating Amazon warehouses more efficiently, Rufus providing shoppers with more accurate answers, and AWS teams using this approach for everything from verifying AI-generated code correctness before shipping new Kiro features publicly, to Graviton chip optimization. Last week’s launch of Automated Reasoning checks in Amazon Bedrock Guardrails—catching AI errors with up to 99% accuracy—is another proof point in how we’re making AI safer for everyone. Learn more in the The Wall Street Journal about this game-changing verification technology. https://lnkd.in/g9H-hXnS

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