Nor does every form factor make sense for every PC game. McWhirter points to triple-A gaming's struggles on smartphones: controller requirements, limited storage, and games designed for sustained attention don't disappear merely because the software technically runs. "Play anywhere" doesn't mean will run on a toaster.

The Dell Laptop Buying Guide 2026: Which XPS or Alienware Laptop Should You Buy? The Dell Laptop Buying Guide UK: Which Dell Laptop Should You Buy? When you purchase through links in our articles, we may earn a small commission. The Steam Deck, a $599 MacBook Neo, and Nvidia's RTX Spark all point the same way: Windows no longer sets the PC agenda. For most of my life, talking about "the PC" basically meant talking about Windows. Weirdos like us knew you could build Hackintoshes, boot obscure Linux distros, and spend an entire Saturday compiling something because it's fun and interesting, dammit!

The engineering system surrounding an AI model increasingly determines whether its intelligence can be delivered reliably, economically, and at scale. The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, higher benchmark scores. Those advances have been extraordinary. But as AI moves from experimentation to production, the question facing enterprise leaders has changed--it's no longer, "what can the model do?"--it's now, "Can we deliver that intelligence reliably, economically, and at scale?" Increasingly, the answer depends on the serving harness--the engineering system surrounding the model, including the architecture, memory management, scheduling, and optimization decisions that determine how efficiently model intelligence becomes a production capability. For technical executives, that changes where AI investment should go--and what they should demand from the architecture supporting it.

This Startup Is All In on Recursive Self-Improvement. Recursive makes the case that the benefits of using AI to build AI outweigh the risks. For the past few days I've been testing an impressive open-weight AI coding model called Flint. It suggests that fully recursive self-improvement, or RSI--an exciting and controversial approach that uses AI to build AI--is getting closer. Flint is the first offering from Recursive, a company that aims to use RSI to advance both AI itself and other areas of science using AI .

CXMT said it had made good progress on new DRAM architecture that would boost chip memory density. CXMT said it had made good progress on new DRAM architecture that would boost chip memory density. Tue 13 Oct 2026 05.48 EDTFirst published on Tue 13 Oct 2026 04.01 EDT Shares in semiconductor companies fell on Tuesday after the boss of Broadcom compared many AI investors to "casino" gamblers, and the Chinese memory chip maker CXMT said it had made good progress on a new chip architecture. Japan's Nikkei fell 1.8% and the Taiwan exchange lost almost 2%, with the country's chip maker TSMC down by a similar amount. Samsung Electronics shares dropped 7.4% on the South Korean exchange, while the memory chip maker SK Hynix was down 10%.

What can we learn from the brain about building better computers? Oliver Rhodes discusses how neuromorphic computing draws on the brain's architecture to develop new approaches to more efficient information processing. Can you give me an overview of your background and the research that you're involved with? Neuromorphic computing is quite a broad subject, which essentially looks to biology as inspiration to develop next-generation computing systems. We work off this principle: we know the brain is this really amazing computer, and it's able to do things that a lot of modern computing systems aren't able to do, and it also does them in an incredibly energy efficient way. We'd like to try to replicate that with some of the systems we build. In the context of the current climate around AI, while we've seen that it's made really big steps forward and is able to do really impressive things, there are certain things that it still can't do that the brain is able to do. So we look to the brain for inspiration to try and solve some of those next generation challenges. Neuromorphic computing covers all aspects of this: from looking at algorithms that might be solving a particular problem, to the systems and subsystems that would be running those algorithms, right down to a chip or devices level. We often use neural networks, which are also employed in artificial intelligence models, such as in deep learning and transformer models.

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With AI inference now driving enterprise workloads, organizations must rethink infrastructure for speed, efficiency, scalability, and performance per watt to unlock AI's real-world potential. The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs. This shift changes what infrastructure must deliver.

Agentic AI is no longer a research artifact. These systems are embedded in live infrastructure, clinical workflows, and legal processes: deleting databases, advising patients, and generating legal documents. Wrong outputs cannot simply be taken back. The central question has shifted from whether these systems are ready to make consequential decisions to who is accountable when they get those decisions wrong. The standard apparatus of fault-finding (identifying an actor, establishing a duty, connecting a breach to an injury) was built for a world in which agents are human, decisions are sequential, and causation is legible.

Tech Visionary Says the Big AI Labs Don't Get What People Want Tim O'Reilly built a publishing empire that AI is helping to destroy. Yet he loves AI--as long as it's open source. Tim O'Reilly's yardstick for measuring the worth of a company, person, or society has long been It's no surprise that O'Reilly-- publisher, internet pioneer, VC, conference organizer, and dispenser of tech wisdom --is applying that metric to the way people design and use AI. Specifically, he's pushing for a future where open-source AI is an elixir for the masses. He worries that, like Microsoft in the 1990s, today's hyperscalers are trying to lock users into their products.