Ai

Investors poured over $9.5 billion into AI processor startups in 2024, betting on architectures that could reshape inference economics. NVIDIA itself projects the broader AI-infrastructure market could reach $3,4 trillion by 2030. That kind of capital rarely gathers around incremental improvements, it usually signals an architectural inflection point. Yet GPUs still dominate both training and most inference workloads today, so any transition will be evolutionary before it is disruptive. ...

AI Factories are EU-supported ecosystems that combine EuroHPC supercomputing capacity, data and expertise to develop trustworthy, cutting‑edge generative AI models and applications. They connect supercomputing centres, universities, SMEs, industry and finance to accelerate AI innovation across sectors such as health, manufacturing, climate, finance and space, with an emphasis on access for startups and SMEs. Key elements: Purpose: provide high-performance, AI-optimised computing resources and support services to train and deploy advanced AI models while promoting trustworthiness and compliance with EU values and the AI Act. Governance and support: the EuroHPC Joint Undertaking (JU) offers access to computing time and services; the European Commission, European AI Office and related initiatives (Testing and Experimentation Facilities, European Digital Innovation Hubs) coordinate and network resources. Scale-up instruments: the InvestAI Facility proposes a €20 billion fund to support up to five AI Gigafactories—large facilities for training trillion-parameter models requiring massive processor counts, power capacity, networking and energy efficiency. Deployment timeline and scope: through 2025–2026 the EU aims to have at least 15 AI Factories and several Antennas operational and to procure at least nine new AI‑optimised supercomputers, more than tripling current EuroHPC AI capacity. Selection rounds: the EuroHPC JU selected seven consortia in December 2024 (Finland, Germany, Greece, Italy, Luxembourg, Spain, Sweden), six more in March 2025 (Austria, Bulgaria, France, Germany, Poland, Slovenia), and announced another six in October 2025 (Czech Republic, Lithuania, Netherlands, Romania, Spain, Poland) alongside 13 Antennas including several partner countries (Iceland, Moldova, North Macedonia, Serbia, Switzerland, UK). Investment: combined public and member/state investments in supercomputing and AI Factories are expected to reach about €10 billion for 2021–2027. The initiative aims to create a pan‑EU AI ecosystem that balances technological leadership (via compute, training, and Gigafactories) with regulatory compliance, access for innovators, and coordinated public‑private investment. ...

At a time when most technology leaders are forecasting the collapse of white-collar work, Box CEO Aaron Levy offers a different vision: one where AI amplifies human productivity instead of replacing it. His central insight is deceptively simple, jobs aren’t tasks. While AI systems can automate individual tasks with extraordinary speed, work itself is a web of judgment, context, and coordination that still requires people. Lawyers may review contracts twice as fast, but that doesn’t erase the legal department; it raises throughput and expands the company’s capacity to act. Engineers may ship code more quickly, but that acceleration creates new bottlenecks, and new roles, elsewhere. Automation doesn’t extinguish work; it increases demand for it. ...

Agentic RAG goes beyond traditional RAG by adding the ability to reason, plan, and act. Instead of merely fetching and producing information, it autonomously determines what to retrieve, how to apply it, and when to adjust the context, enabling adaptive, goal-oriented systems that improve through continuous learning.

The software industry’s latest fantasy- that AI will deliver a tenfold leap in developer productivity- is now colliding with reality. As the author of the piece notes, the trillion-dollar AI sector still cannot produce reliable, production-grade code. Behind the flood of “breakthrough” announcements lies an epidemic of unmaintainable software, riddled with four recurring categories of error. The classical ones- false positives and false negatives- are joined by two new cognitive pathologies born from model mechanics themselves: entangled logic that turns architectures into spaghetti, and memory collisions that make unrelated functions misfire. What was once branded “hallucination” now looks less like quirk and more like structural failure. ...

Over the last year the workflows vs. agents debate has turned from a niche engineering question into something every product team seems to argue about. New tooling lowers the barrier to spin up an AI agent that can call tools in a loop, so leaders start asking whether they should rebuild processes around these agents, or stick with the safer structure of step-by-step workflows. The conversation heats up because both sides have real wins and real failure modes: agents feel magical when they solve fuzzy, open-ended tasks, but they can be slow, costly, and unpredictable at scale; workflows are efficient and auditable, but can feel rigid when the job needs exploration. That tension between flexibility and control is why this topic is hot and why teams keep getting stuck. ...

Bloomberg just published this fascinating map of the AI power network, showing how companies like NVIDIA, OpenAI, Microsoft, AMD, Oracle, and Intel are now intertwined through billions in deals, compute, and equity. It’s not just a supply chain anymore. It’s a feedback loop — where hardware, software, and capital keep feeding each other. NVIDIA sits at the center with a $4.5 trillion market cap, investing up to $100 billion in OpenAI, while selling GPUs to Oracle, AMD, xAI, and everyone else. OpenAI, in turn, signs a $300 billion cloud deal with Oracle, deploys 6 gigawatts of AMD GPUs, and gives AMD an option to buy 160 million OpenAI shares. Microsoft is still the connective tissue, part investor, part service provider, part enabler.

I came across a video by Dr. John Padfield, a former engineer and state representative, talking about something that stuck with me. He recalls being told, “never get in a fight with people who buy ink by the barrel.” It was a warning for politicians — don’t mess with newspapers. Then he updates it for our time: “Never get in a fight with people who buy network servers by the acre.” ...

Time Between Disengagements is a concept I came across in a recent article from Gitpod, and it offered an interesting new way to think about AI’s role in software development. It compares the evolution of AI in engineering to the progression of self-driving cars—where the key metric is how long an autonomous system can operate before a human needs to step in. That simple but powerful analogy really clicked with me. It reframes how we should think about the future of AI-assisted development—not just in terms of raw capability, but in how independently and safely these systems can work. ...

As the agent ecosystem rapidly evolves, interoperability is emerging as the foundation for scalable, secure, and collaborative AI. Industry leaders—including Google, Anthropic, Cisco, and others—are actively building open standards like Agent2Agent (A2A), Model Context Protocol (MCP), and AGNTCY to ensure agents from different vendors and platforms can discover, communicate, and work together seamlessly We’re at a pivotal moment where these standards are becoming the essential building blocks for the “Internet of Agents,” much like HTTP and TCP/IP did for the web. This collaborative push for interoperability aims to break down silos, accelerate innovation, and create a robust, trustworthy AI ecosystem that benefits enterprises and developers alike ...