AI as the fourth pillar of infrastructure: implications for developers and software

What is that AI really costing you? The rise of LLMs and agent-based apps is overwhelming cloud environments that were never built for this kind of compute. CIOs are scrambling to adapt. John Pettit, CTO of Promevo, shared insights on simplifying Google technology for clients to thrive in the digital realm. He discussed the competitive tech scene, focusing on AI's hurdles like energy usage and data complexities. Managing agentic AI workflows demands fresh strategies, moving beyond conventional cost approaches to assess value over past expenses. Enhancing observability and monitoring AI systems emerged as crucial aspects. Delving deeper, John addressed AI inference costs and the escalating energy needs tied to AI operations. He highlighted how AI agents drive search requests independently, elevating electricity consumption, and outlined challenges around GPU availability. John stressed the importance of evaluating AI compute costs based on value creation, urging organizations to craft robust AI strategies. Emphasizing outcome quantification and small-scale experimentation, he underlined the shift towards value-centric AI investment assessments. #AI #cloud

Great to be featured in this post by Julian Lee after our recent discussion on the real economics of AI. We're moving past the initial "wow" factor of LLMs and agents, and now CIOs are facing the staggering operational realities. In our conversation, we explored why the narrative needs to shift from initial expenses to long-term value creation. The main question is "What quantifiable outcome will this achieve?". This calls for robust AI strategies built on small-scale experiments and a clear focus on ROI. Thanks again to Julian Lee and E-ChannelNews.com for amplifying this important conversation. The full discussion is linked in the original post below. I'd love to hear how your organization is tackling these challenges. #AI #Cloud #AIEconomics #CIO #TechLeadership #LLMs #Observability #Promevo

Thanks for featuring our CTO, John Pettit, on eChannelNews, Julian Lee! This conversation digs into one of the most pressing challenges in today’s AI landscape — how organizations can balance innovation with observability, cost management, and sustainability. Listen to the full episode here: https://lnkd.in/gTPSSwvF

Unleash the Power of AI with VAST Data: Read Our Latest Article! Dive into our exciting new article, VAST Data: How the First AI Operating System Is Transforming Businesses, and discover how VAST Data is revolutionizing the business landscape. As the world’s first AI Operating System, VAST Data empowers organizations with unparalleled data management, enabling lightning-fast insights and seamless AI integration. Learn how its innovative platform unifies storage, database, and compute to drive efficiency, scalability, and performance for enterprises worldwide. From accelerating AI workloads to transforming industries like finance, healthcare, and tech, VAST Data is redefining what’s possible. Explore real-world success stories, cutting-edge features, and the technology behind its meteoric rise. Don’t miss this deep dive into how VAST Data is shaping the future of business innovation. Read now to see why it’s the ultimate game-changer for AI-driven enterprises! https://lnkd.in/e_dvu2su #AI #ArtificialIntelligence #CNBC #DataBase #GraphicProcessingUnits #GPU #HIPAA #JohnsHopkins #MayoClinic #NVIDIA #OperatingSystem #OS #VASTData #Technology #Businesses

This Week in AI: Enterprise Infrastructure Gets Strategic The AI landscape is maturing rapidly, with major players making strategic moves that signal a shift from experimentation to serious enterprise deployment. 🏗️ The Infrastructure Reality Microsoft decision to develop proprietary AI models marks a significant departure from their OpenAI partnership. This isn't just about competition—it's about enterprise control, compliance, and the recognition that owning your AI stack matters when you're serving Fortune 500 clients with stringent requirements. Meanwhile, OpenAI's GPT-5 launch brings genuine improvements in reasoning and code generation, already integrated into production enterprise tools. The gap between announcement and real-world deployment continues to shrink. ⚙️ Platform Wars Heat Up Google DeepMind Gemini 2.5 "Deep Think" demonstrates serious mathematical reasoning capabilities, while Amazon Web Services (AWS) Bedrock AgentCore signals that cloud providers are moving beyond simple model hosting toward comprehensive AI deployment platforms. These aren't just technical updates—they're infrastructure plays for the enterprise AI market. DataRobot and NVIDIA's "Agent Workforce" concept reflects growing demand for AI systems that can handle complex, multi-step business processes autonomously. 💰 Investment Reality Check NVIDIA's Jensen Huang forecasts $3-4 trillion in AI infrastructure spending by 2030—a number that sounds astronomical but reflects the genuine transformation happening across industries. With 2024 GenAI funding tracking toward $22.7 billion, we're seeing sustained, serious investment rather than speculative bubbles. 🌍 Global Competition Intensifies India's push for AI infrastructure and data sovereignty highlights how nations are treating AI capabilities as strategic assets. The geopolitical dimensions of AI development are becoming impossible to ignore for multinational enterprises planning their technology strategies. The Enterprise Takeaway: We're witnessing the transition from "AI experimentation" to "AI infrastructure." The companies building robust, compliant, and scalable AI capabilities today are positioning themselves for the next decade of competitive advantage. The question isn't whether AI will transform your industry—it's whether your infrastructure strategy will keep pace with the transformation. #AI #EnterpriseAI #DigitalTransformation #TechStrategy #ArtificialIntelligence #Innovation #AIInfrastructure #TechLeadership #StrongholdSystems #AIPulse

💡 AI + Data Science are powerful. But without Data Quality, they’re powerless. Imagine building the world’s smartest AI model… 🚫 If the data is incomplete → the prediction fails. 🚫 If the data is inconsistent → the insights mislead. 🚫 If the data is inaccurate → the decisions cost millions. ✅ That’s why Data Quality is not optional—it’s mission critical. At FirstEigen and Cloud Life, we help organizations ensure their AI and Data Science initiatives are built on clean, trusted, and reliable data—so that insights are not just fast, but also accurate and actionable. 🔹 AI & DS = The engine. 🔹 Data Quality = The fuel. Together, they drive innovation, efficiency, and business growth. 🚀 The future belongs to companies that treat Data Quality as a strategic advantage. #AI #DataScience #DataQuality #TrustedInsights #Innovation #FutureOfWork #DigitalTransformation #DataBuck FirstEigen

What is AI Data Storage? AI storage is a specialized infrastructure that supports machine learning and artificial intelligence workloads. It provides high-performance, scalable storage choices for managing huge data volumes generated by AI applications, ensuring fast access and processing of the data.

⚡️ The AI Revolution: 2025 → 2045 ⚡️ Today: 🤖 Generative AI creates. 🌐 Data Engineering powers intelligence. ☁️ Cloud makes it accessible. But imagine 20 years from now… ✨ AI that self-heals and optimizes data pipelines. ✨ Real-time decision-making with zero latency. ✨ Humans + AI as true co-creators, not competitors. ✨ AI woven into every profession — not as a tool, but as a partner. We’re not heading into an era of automation. We’re heading into an era of co-intelligence. 🚀 👉 What do you think AI + Data will look like in 2045? #AI #FutureOfAI #MachineLearning #GenerativeAI #DataEngineering #CloudComputing #FutureOfWork #Innovation #BigData #ArtificialIntelligence

🔍 Excited to share some insights from my recent work in building AI-powered applications! Over the past year, I’ve focused on developing Generative AI solutions using GPT-4, LangChain, and vector databases like Pinecone and Qdrant to enable smarter, context-aware applications. One key project involved designing a RAG (Retrieval-Augmented Generation) pipeline that reduced response latency by 35% and cut hallucinations by 28%, improving real-time conversational experiences. I also fine-tuned LLMs for domain-specific tasks like legal document analysis and automated HR workflows. ☁️ Combining cloud services (AWS, Azure) with AI has been a game-changer in delivering scalable, production-grade solutions that drive real business impact. 📢 Always eager to collaborate and exchange ideas on how AI is transforming industries. Let’s connect and discuss the future of intelligent systems! 📩 Reach me anytime at jaganadari0825@gmail.com 📞 +1 (806) 429-2130 #AI #MachineLearning #GenerativeAI #LLMs #VectorSearch #LangChain #RAG #PromptEngineering #MLOps #CloudComputing #Innovation

Agentic AI + Edge Computing = 🤝 Agentic AI marks an important shift in how we think about artificial intelligence. No longer confined to fixed instructions, agentic AI systems can act with autonomy and adapt to changing conditions. When paired with edge computing, this evolution unlocks new opportunities for real-time intelligence, enabling decisions to be made closer to where data is generated, reducing latency, and strengthening resilience in distributed environments. 💡 Read more about how #agenticAI at the #edge is impacting the future of distributed computing: https://lnkd.in/dZykmU2k