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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. ...

Patrick Bet-David argues that Larry Ellison (and his son David Ellison via Skydance) is quietly assembling one of the most powerful media empires. Through a chain of high-profile deals, Skydance’s acquisition of Paramount and its pursuit of Warner Bros., plus Oracle’s role buying 80% of TikTok’s U.S. operations, the Ellison family could combine IP, networks, streaming, and social data into a unique, vertically integrated media powerhouse. Bet-David compares the potential combined revenue to Disney and Netflix, highlights Oracle’s technical role (data storage, algorithm oversight, equity stake), emphasizes strategic partnerships (Nvidia, OpenAI), and warns this consolidation will yield massive market and political influence. ...

fascinating once again, the universe reminds us that all abstractions eventually resolve to DNS Monday’s Massive AWS Outage Explained: Looks Like It’s Finally Over - CNET Amazon brain drain finally caught up with AWS • The Register

The shift away from the public cloud by companies like 37signals marks a moment of reckoning for an industry that spent the last decade preaching infinite scalability and “as-a-service” convenience. What once seemed like the inevitable future of computing, outsourcing infrastructure entirely to hyperscalers, is now being reassessed through the lens of cost, performance, and control. The so-called “great unclouding” is not a rejection of cloud technology itself, but of the assumptions that it is always cheaper, simpler, or strategically wiser. For mature companies with predictable workloads, owning the hardware again is beginning to look less like nostalgia and more like discipline. ...

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. ...

In 1957, the launch of Sputnik jolted the United States into an era of scientific ambition, triggering massive investment in research and higher education. That anxiety- the fear of falling behind- proved constructive, fueling decades of technological leadership. Today, however, the situation feels inverted. As China quietly consolidates its lead in areas such as energy production and advanced manufacturing, the U.S. response is not urgency but denial. Despite China now generating more than twice America’s electricity, Washington’s political discourse has turned inward, cutting funding for education, science, and renewable energy in the name of culture wars. ...

In an intriguing article, Jeferson Borba discusses the Dead Internet Theory, which suggests that the internet has been overrun by bots, AI-generated content, and algorithmic manipulation since around 2016. He reflects on how platforms like LinkedIn are filled with AI-generated posts that mimic genuine human interaction, making us question whether the internet is truly alive. This theory, which originated in niche online forums, has gained traction as users observe a shift in the nature of engagement on social media and the prevalence of automated accounts that shape public opinion without real human discourse. ...