Check out the latest article in my newsletter: 🚀 🧠 88% of companies use AI — but only 1 in 3 are actually scaling it. McKinsey’s latest State of AI 2025 report proves what most in enterprise tech already feel: We’ve moved past the AI hype… but we’re still taxiing on the runway. The real differentiator now isn’t who uses AI — it’s who’s redesigning workflows, building agents, and governing risk at scale. From cloud to telecom to digital health, agentic AI is shifting from experiment to execution. This isn’t the “future of work” — it’s the infrastructure of the modern enterprise. ✈️ Read my latest newsletter breakdown:linkedin.com/pulse/state-ai-… via @LinkedIn

The greatest rap battle that never happened ⚡🎤 AC vs DC. Tesla vs Edison. Genius meets ego, innovation meets ambition — and the world gets electrified. Who won? Well… your house runs on AC. 😉 #HistoryOfTech #Tesla #Edison #Innovation

Who won the rap battle?

Frankenstein for president

A rendering of what it could be! The possibilities of XR MXR AI Haptics ! Surgery Education and Training ! @NextMedHealth @BrennanSpiegel @virtualmedconf @EvanKirstel @FundamentalVr sora.chatgpt.com/p/s_690f961…

🧠 Neuroscientists discover a key brain signal that predicts reading fluency in children By @PsyPost 📍The speed of a #child’s neural response to words predicts their #reading #fluency. Scientists @Stanford have found a precise way to measure this in individual children. #Neuroscience #Children #Learning #CognitiveScience #Education 👉psypost.org/neuroscientists-… @Khulood_Almani @Shi4Tech @drsharwood @pierrepinna @IanLJones98 @mvollmer1 @Eli_Krumova @CurieuxExplorer @HaroldSinnott @ahier @RosyCoaching @Eli_Krumova @JoannMoretti @dinisguarda @PawlowskiMario @FrRonconi @RLDI_Lamy @Nicochan33 @Sharleneisenia @EvanKirstel @Timothy_Hughes @gvalan @NevilleGaunt @SusanFourtane @domingonarvaez1 @postoff25 @margaretsiegien @bamitav @Fabriziobustama @IngridVasiliu @TanyaSinha_ @Analytics_699 @andresvilarino @baski_LA @marcusborba @anand_narang @AkwyZ @TerenceLeungSF @Hana_ElSayyed @sonu_monika @SabineVdL @TravelFoodiesTV @BetaMoroney @GlenGilmore @SpirosMargaris @pchamard #Edtech #EEG #CES #Mentalhealth 🧠“Because we collaborated with a school in designing and carrying out this study, we know that we can now measure this neural speed incredibly precisely and reliably, like a mechanic timing the sparks in your car’s carburetor, in nearly every school child, within schools, without missing anything more than a single class,” McCandliss explained. “This means kids can see the results of their hard work as learning to read progressively refines this core neural function.

Life on the shire

The Private AI Revolution: @Broadcom's #VMwareExplore Highlights The explosion of generative AI has created an urgent need for enterprises to run AI workloads on-premises to protect sensitive data, maintain governance, and safeguard intellectual property. In this illuminating conversation with Tasha Drew, who leads the engineering team for private AI services within VMware Cloud Foundation, we explore how Broadcom is transforming enterprise AI infrastructure following their landmark announcements at VMware Explore. Tasha reveals how @VMware's AI platform, now baked directly into VCF, enables organizations to implement comprehensive model governance, scale models efficiently as a service, and programmatically prepare data for RAG applications. What's particularly fascinating is #VMware's commitment to "dogfooding" their own platform by using it to deliver VCF Intelligent Assist, ensuring the technology is constantly tested and improved under real-world conditions. One surprising trend Tasha highlights is how organizations are increasingly migrating bare metal AI workloads to VCF for superior enterprise manageability. While data science teams may start with bare metal for experimentation, production AI workloads demand the scheduled maintenance, workload mobility, and scalability that VCF has perfected over decades. This shift underscores a fundamental truth: as AI matures within enterprises, infrastructure management becomes just as critical as model performance. The conversation explores VMware's partnerships with @AMD, @NVIDIA, @IntelBusiness , @ibm and @Canonical , emphasizing their commitment to an open ecosystem approach that lets customers choose the hardware and software stack that best suits their specific AI use cases. Looking toward the future, Tasha shares exciting developments around Model-Context-Protocol (MCP) integration, agent builder capabilities, and tools for enterprises to safely implement agentic AI workflows with proper authentication and governance. With real-world examples from customers like Walmart and manufacturing environments that require air-gapped AI infrastructure, this episode provides a comprehensive look at how VMware is making enterprise-grade AI infrastructure accessible, manageable, and secure. Don't miss Tasha's insights on what's coming next, including the technical preview of Intelligent Assist expected early next year. @AIatAMD @ubuntu @vmwarevcf @VMwareExplore

Check out the latest article in my newsletter: 🚀 🧠 88% of companies use AI — but only 1 in 3 are actually scaling it. McKinsey’s latest State of AI 2025 report proves what most in enterprise tech already feel: We’ve moved past the AI hype… but we’re still taxiing on the runway. The real differentiator now isn’t who uses AI — it’s who’s redesigning workflows, building agents, and governing risk at scale. From cloud to telecom to digital health, agentic AI is shifting from experiment to execution. This isn’t the “future of work” — it’s the infrastructure of the modern enterprise. ✈️ Read my latest newsletter breakdown:linkedin.com/pulse/state-ai-… via @LinkedIn

Welcome to our latest gated community, Ironwood reserve

Rafael Grossmann, MD FACS @ZGJR — the first surgeon to use Google Glass in an operating room — continues to redefine what’s possible at the intersection of surgery, AR/VR, and AI. An immigrant from Venezuela, educator, and relentless innovator, he’s proving that the future of healthcare isn’t just high-tech — it’s deeply human. #DigitalHealth #XR #MedTech Now on sora! @IrmaRaste

Ranked: Countries With the Largest Forests in 2025 visualcapitalist.com/ranked-…

Some people love AI, others hate it. Here's why. Whether you love or hate AI has a lot to do with how your brain processes risk and trust. livescience.com/technology/a…

Airport security

I just pre-ordered mine on Temu

#AI powered diabetes prevention program shows similar benefits to those led by people buff.ly/yEwHdY4 via @HubJHU #HealthTech Cc @helene_wpli @jblefevre60 @YuHelenYu @mvollmer1 @Fabriziobustama @ahier @EvanKirstel

There Is No Mine. Stop Buying Shovels. Executives are buying GPUs like they're staking gold claims. The pitch deck says “AI gold rush,” and suddenly you’re spec’ing DGX clusters. But inference isn’t mining — it’s manufacturing. And most of you haven’t built the factory. Gold has value the moment you dig it up. Oil has value once you refine it. Inference only has value when you build a system that turns it into outcomes — repeatedly, measurably, and safely. Until you can show me yield rates and cost per completed task, you’re not doing AI at scale. You’re just burning tokens. The Test If you can’t answer these seven questions with numbers, you’re not ready to buy more hardware: 1️⃣ Unit of value: What’s a completed task worth — ticket deflection, minutes saved, conversion uplift, cycle time reduction? 2️⃣ Cost per outcome: Total cost per accepted task, not per token. 3️⃣ Yield & quality: How often do you accept model output without human correction? 4️⃣ Policy placement: How do you enforce residency and compliance before the inference runs? 5️⃣ Throughput under constraint: What happens at 2× load? What actually breaks first? 6️⃣ Change management: How do you promote and roll back changes to prompts, models, and tools? 7️⃣ Attribution: Which component — RAG, prompt, model, or tool — moved a KPI by 5% last quarter? If those questions make you uncomfortable, good. That’s the gap between demos and production. Why the Metaphors Fail The “gold rush” line isn’t just imprecise — it’s expensive. Gold is valuable when you extract it. Inference isn’t. Each output is situational, ephemeral, and non-transferable. You can’t deposit it. You can’t trade it. You can’t hold it as an asset. The value only comes from the system that turns those inferences into outcomes — with control, measurement, and evolution. What Actually Works ✅ Define 3–5 repeatable, measurable tasks. ✅ Instrument the line — cost per accepted output, quality gates, feedback loops. ✅ Enforce placement before orchestration — that’s your missing layer between “schedule it” and “run it.” ✅ Budget by outcome, not tokens. ✅ Kill the brittle demos. If it can’t survive regression tests, it’s not production. Where Vendors Fit Dell, HPE, Supermicro, NVIDIA, AMD — they matter. Power, cooling, and interconnects determine your throughput ceiling and unit economics. But they can’t build your factory. That’s on you. Hardware doesn’t print money when your line design is wrong. Bottom Line There’s no gold rush in AI. There’s work. The executives who can answer those seven questions with numbers will create moats. The ones chasing metaphors will buy shovels for a mine that doesn’t exist. Build the factory. Then scale.

Real-Time Audio Deepfakes Are Now a Reality Real-time Audio Deepfakes Have Arrived A cybersecurity firm has created convincing voices on the fly spectrum.ieee.org/real-time-…

China has revealed the world’s first wind-powered underwater data center off the coast of Shanghai, marking a major step toward sustainable high-performance computing. Built at a cost of ¥1.6 billion, the facility operates on over 95% offshore wind energy and uses cold seawater for cooling, dramatically reducing energy consumption and completely eliminating the need for freshwater. The underwater center is designed to power AI computing, 5G networks, and industrial data processing, all while occupying far less land than traditional data centers — a crucial advantage in densely populated coastal regions. Although still in its early development phase, China plans to expand the system’s capacity as the technology evolves, positioning itself at the forefront of green digital infrastructure innovation. #ChinaTech #DataCenter #RenewableEnergy #AIInfrastructure #Sustainability