Ai

Cursor raises $2.3 billion at a $29.3 billion valuation. Nvidia and Google both joined the round. Cursor launched its own in-house mixture-of-experts model, rewrote the kernels directly, skipping high-level CUDA libraries and working directly in raw CUDA and PTX (link). They got up to four times faster than comparable models. For engineers, that’s not just a speed bump, it’s a different category of tool. Coding tasks that used to take minutes are now completed in under 30 seconds. According to Cursor, their own model is already the most-used on the platform, which means they’re not just riding on OpenAI or Anthropic anymore. They’re building the rails themselves. ...

Anthropic has just surfaced what appears to be the first large-scale, mostly autonomous, AI-orchestrated cyber espionage campaign. In mid-September 2025, they detected suspicious behaviour that turned out to be a sophisticated operation run by a Chinese state-sponsored group. AI wasn’t just “helping.” It was doing most of the work. The attackers jailbroke Claude Code, framed it as doing defensive testing, and then drove it through an automated framework aimed at ~30 global targets, big tech, finance, chemicals, and government. A few intrusions succeeded. ...

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.” ...