Claude Mythos 5 & Capabara: 10 T Params Meet Efficient AI 🚀 10-trillion parameters, sub-2-second latency, and a safety-first rollout - the AI landscape just shifted. • ✅ Claude Mythos 5 targets high-stakes tasks: code generation, cybersecurity analysis, academic reasoning. • 📈 Leaked benchmarks show 28% SPEEDUP over GPT-4 on coding suites and 0.32% HALUCINATION RATE on the latest safety tests. • ⏱️ End-to-end latency ≈1.9 s per 1 k tokens, ~20% faster than Anthropic's Opus. • 💡 Capabara delivers ~70% of Mythos 5's performance with 40% lower compute, ideal for edge-AI and real-time analytics. • 🛡️ Phased rollout emphasizes alignment checks and misuse mitigation before full public release. 🔗 @Anthropic's phased deployment strategy aims to balance breakthrough capability with responsible AI governance. @CapabaraAI positions the model as a versatile middle ground for enterprises that need power without the full resource footprint. What benchmark would you love to see next? Drop your ideas in the comments - let's crowdsource the next test suite. Found this useful? Share it with someone who needs to see this. #ClaudeMythos5 #Capabara #GenerativeAI #LLM #AIInnovation REFERENCES: 1. TechCrunch - "Anthropic's Claude Mythos 5 leak reveals 10 T parameters", March 2026 2. The Verge - "Capabara: Anthropic's mid-tier model aims for efficient AI", April 2026

🔻 The era of a single global AI stack is ending. 🔻 Here is a good read from the World AI Summit team for your weekend 👇 Governments worldwide are accelerating efforts to build “sovereign AI” — reshaping how data, compute, and models are owned, controlled, and deployed. For global organisations, this isn’t just policy noise. It’s a structural shift that raises critical questions: 🔴 Where should your AI workloads live? 🔴 Who controls your infrastructure? 🔴 How many versions of your stack can you sustain? In our latest insight, we explore how sovereign AI is redefining global architecture — from fragmented regulation and geopolitical pressures to the emergence of region-first and hybrid AI strategies. This is no longer a future scenario. It’s already influencing how leading enterprises design their AI systems today. 👉 Read the full analysis we made in our News Hub: https://hubs.li/Q04b95MV0 #AI #SovereignAI #DigitalInfrastructure #TechStrategy #AILeadership #WorldSummitAI #WSAI26

DeepSeek V4 Challenges AI Leaders Amidst Global Race for World Models and Compute 🛰️ [LLMS] DeepSeek's V4 model emerges as a strong open-source competitor, intensifying the global AI race. Why it matters: The emergence of DeepSeek V4 signifies China's increasing capability in advanced AI, particularly with its optimization for domestic hardware, challenging Western dominance. Simultaneously, the escalating investment in foundational models and the strategic focus on 'world models' highlight the industry's push towards more capable, physically-grounded AI, while geopolitical tensions complicate global tech collaboration. 🤔 How will the dual forces of open-source innovation and geopolitical competition shape the future trajectory of global AI development? #DeepSeek #WorldModels #AICompetition #ChinaAI #GenerativeAI 📡 Follow DailyAIWire for high-signal AI news.

The era of “AI training at all costs” is over. 📉 It’s time for an Inference-First strategy. In a recent Nasdaq #TradeTalks session, Blaize CEO Dinakar Munagala joined Tanium CTO Harman Kaur and host Jill Malandrino to break down how the AI stack is being redefined, shifting from “bigger is better” to practical, scalable, and profitable deployment. The industry is hitting a tipping point where efficiency matters more than hype: 🚀 Inference > Training | The value of AI isn’t in how it’s built—but how it performs in the real world. Inference workloads are rapidly outpacing training. 🔋 Energy is the New Constraint | Power availability is now a limiting factor. The future belongs to AI infrastructure built for efficiency. 🎯 Specialization over Scale | Smaller, purpose-built models can deliver comparable accuracy—without the cost and overhead of massive general models. 🌍 Distributed & Edge-First | AI is moving beyond centralized cloud into real-world environments—closer to where data is created and decisions are made. The bottom line: if your AI strategy doesn’t deliver measurable ROI and strong unit economics, it won’t scale in the post-hyperscaler era. At Blaize, we’re building the hardware and software to power this shift. Watch the full interview: https://lnkd.in/gpU8_EZZ #AI #Inference #EdgeComputing #Semiconductors #Nasdaq #Sustainability #BZAI

The AI "Training" era is maturing. The "Inference" era is here. 📉 Value is shifting from how AI is built to how it is deployed. My top takeaways from the recent Nasdaq #TradeTalks with Dinakar Munagala and Harman Kaur: 🔹Performance over Size: Inference now creates more value (and profit) than training. 🔹Power is the Ceiling: Energy efficiency is the new prerequisite for scaling. 🔹Precision > Scale: Smaller, specialized models are winning on unit economics. 🔹The Edge Reality: AI is moving out of the cloud and into the real world—where data actually lives. Great discussion, Jill Malandrino. Full video video discussion: https://lnkd.in/gpU8_EZZ #AI #EdgeComputing #TechTrends #Nasdaq #BZAI

The era of “AI training at all costs” is over. 📉 It’s time for an Inference-First strategy. In a recent Nasdaq #TradeTalks session, Blaize CEO Dinakar Munagala joined Tanium CTO Harman Kaur and host Jill Malandrino to break down how the AI stack is being redefined, shifting from “bigger is better” to practical, scalable, and profitable deployment. The industry is hitting a tipping point where efficiency matters more than hype: 🚀 Inference > Training | The value of AI isn’t in how it’s built—but how it performs in the real world. Inference workloads are rapidly outpacing training. 🔋 Energy is the New Constraint | Power availability is now a limiting factor. The future belongs to AI infrastructure built for efficiency. 🎯 Specialization over Scale | Smaller, purpose-built models can deliver comparable accuracy—without the cost and overhead of massive general models. 🌍 Distributed & Edge-First | AI is moving beyond centralized cloud into real-world environments—closer to where data is created and decisions are made. The bottom line: if your AI strategy doesn’t deliver measurable ROI and strong unit economics, it won’t scale in the post-hyperscaler era. At Blaize, we’re building the hardware and software to power this shift. Watch the full interview: https://lnkd.in/gpU8_EZZ #AI #Inference #EdgeComputing #Semiconductors #Nasdaq #Sustainability #BZAI

The case for “AI is a bubble” has collapsed. For the last two years, skeptics had a reasonable argument. The AI capex buildout was the largest private-sector infrastructure project in history. Every similar-but-smaller project eventually experienced a bubble collapse. Why would AI be any different? The answer — it turns out — is demand. Derek Thompson made a version of the bubble argument himself, but this week he changed his mind. The shift he’s documenting is from demand scarcity to supply scarcity: AI companies can’t provide enough compute to keep up with how much people actually want to use these tools. Anthropic quadrupled revenue since December. Projects are being canceled and usage is being throttled not because companies are pulling back, but because they physically cannot keep pace with how much people want to use these tools. I’ve worked with teams over the past year who were in “wait and see” mode. Slow-walking pilots. Hesitating to build out workflows. Hedging on whether to invest real time in figuring out how AI fits into their work. That posture made sense when the outcome was genuinely uncertain. The outcome is no longer genuinely uncertain. The question now isn’t whether this technology is changing how people work. The question is how far behind you’re willing to fall while you finish deciding. #AIAdoption #FutureofWork #digitaltransformation https://lnkd.in/eZh8TZeT

What a milestone. Today, VAST Data announced our Series F funding, bringing our valuation to $30 billion. In my daily conversations with the analyst community, the consensus is clear: the market doesn't just need faster storage; it requires a fundamentally new approach to how systems are architected. That’s exactly what we are delivering. By unifying data, compute, and real-time processing into a single system, VAST is building the true AI Operating System. It's an incredible time to be shaping this narrative and watching VAST serve as the foundation for the world's AI leaders. Check out the video below for a glimpse at how AI infrastructure is being rebuilt. 🚀 #VASTData #AI #SeriesF #AIInfrastructure #DataPlatform #TechNews

If you thought - that AI was slowing down - Guess Again! AI capability is not plateauing. It is accelerating and reaching more people than ever. After going through the Stanford AI Index Report 2026, three shifts stand out: Number 1: The moat is no longer the model Open-source has caught up. Closed models are converging in capability. The real value is moving to: Applications. Distribution. Proprietary data. The winners won’t be the ones with the smartest model — but the ones who apply it best. Number 2: The real race is infrastructure The United States has ~10x more data center capacity than others — even ahead of China. This isn’t a marginal lead. It’s a structural advantage. If models are becoming interchangeable, then compute becomes the bottleneck — and the new moat. Number 3: AI is still “jagged” Models can solve Olympiad-level math… and still fail at simple tasks like telling time. This isn’t a glitch. It’s the nature of current AI. Brilliant in narrow domains. Unreliable in the general case. The takeaway? Stop obsessing over models. Start paying attention to infrastructure. Build where real-world usage happens. AI isn’t a finished product. It’s an evolving system — and the opportunity is wide open. #AI #ArtificialIntelligence #AITrends #AIIndex #StanfordAIIndex #FutureOfWork #TechTrends #Innovation #DigitalTransformation #AILeadership #DataCenters #Compute #OpenSourceAI #AIApplications #EmergingTech #BusinessStrategy #Leadership #TechLeadership #AIForBusiness #NextGenTech

Why your AI is still living in the past. Most companies are building "Librarian AI"—systems that are great at finding old documents (RAG) but terrible at handling the now. In the world of Sovereign Intelligence, "knowing" isn't enough. If your system can't react to a real-time market shift, a supply chain break, or a sudden Gochara transition, it’s not an intelligence; it’s an archive. RAG is a library; EAG is a central nervous system. We are moving toward the EAG-RAG Logic Flow. While RAG provides the context of your history, Event-Augmented Generation (EAG) provides the pulse of the present. A Sovereign Intelligence doesn't just "know"; it "reacts" within the precise bounds of your business logic. 👉 Stop building static bots. Start building reactive engines. By: Krishna Moorthy M, Founder-CEO. #SovereignIntelligence #EAG #GenerativeAI #FutureOfTech #ECCI