Kepler Computing claims a new approach to chip design--and a proprietary material--can help end the supply bottlenecks that have sent memory prices surging. An ambitious chip startup that has spent more than seven years quietly trying to redesign the architecture for computer memory has just come out of stealth mode and believes its new approach can help ease the global memory-chip shortage --provided it can produce its technology at scale. Kepler Computing, a San Jose, California-based startup founded in 2018 by a team of physicists and computer scientists, says it has developed a new architecture for high-bandwidth memory (HBM) that directly addresses some of the chip supply bottlenecks that are constraining the computing market. While chipmakers typically rely on expensive extreme ultraviolet lithography (EUV) to shrink the transistors on a chip, thereby packing more technology into the same amount of space, Kepler claims that its "3D stacking" approach and a proprietary new material allow it to increase density without relying on EUV at all--and it can work with existing semiconductor fabrication plants. Kepler says it has made similar gains for the high-speed cache memory typically used in CPUs, GPUs, and XPUs.

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.

Watch a subscriber-only conversation to learn about what this idea means for the future of American democracy and the internet. The "censorship-industrial complex" is an idea that a network of government, tech, and research groups is collaborating to suppress conservative online speech. This was fodder for the right-wing information sphere for years--then it began making its way into US policy. Watch a conversation exploring how it started, where it's going, and what it means for the future of democracy and the internet. A startup claims it broke through a bottleneck that's holding back LLMs Will Douglas Heaven A startup claims it broke through a bottleneck that's holding back LLMs Subquadratic has now shared more details about its new model. But some are still skeptical.

Plus: Nvidia has secured $500 billion from Wall Street for AI infrastructure. Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large language model. But transformers are starting to show their age. As LLMs get bigger and better, transformers have become a bottleneck. Their dense attention mechanism becomes increasingly expensive as the amount of text grows, and they're not great at keeping track of a lot of information at once. Here are four new ideas for how to solve the transformer problem --innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.

Click here for the September/October 2026 Puzzle Corner, brought to you by Michael S. Branicky, ScD '95, of the Puzzle Corner Puzzle Crew (aka PC), which also includes Edward Faulkner '03, MEng '04, and Abe Kunin '03. This column includes solutions to the May/June issue. Editor emeritus Allan Gottlieb '67 launched Puzzle Corner in 1966. A startup claims it broke through a bottleneck that's holding back LLMs Will Douglas Heaven The "steroid olympics" were a circus--and a window into our culture Amit Katwala A startup claims it broke through a bottleneck that's holding back LLMs Subquadratic has now shared more details about its new model. But some are still skeptical. The "steroid olympics" were a circus--and a window into our culture Dozens of athletes on performance-enhancing drugs competed in the Enhanced Games.

As generative AI captures public attention, a different kind of AI is reshaping drug discovery. Machine learning models are helping to compress decade-long timelines and cracking problems that were previously unsolvable. Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to treat conditions across most major acute and chronic diseases), the complexity is even greater.

As AI reshapes how work gets done, organizations with strong process frameworks are best positioned to lead and maintain operational rigor at scale. Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos--a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to embed habits of measurement, analysis, and accountability into day-to-day company culture. But today, those time-tested playbooks are evolving as companies seek to embed AI into established process excellence methodologies. By some estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade.

Message Passing Neural Networks (MPNNs) are the building block of graph foundation models, but fundamentally suffer from oversmoothing and oversquashing. There has recently been a surge of interest in fixing both issues. Existing efforts primarily adopt global approaches, which may be beneficial in some regions but detrimental in others, ultimately leading to the suboptimal expressiveness. In this paper, we begin by revisiting oversquashing through a global measure - spectral gap λ- and prove that the increase of λleads to gradient vanishing with respect to the input features, thereby undermining the effectiveness of message passing. Motivated by such theoretical insights, we propose a local approach that adaptively adjusts message passing based on local structures. To achieve this, we connect local Riemannian geometry with MPNNs, and establish a novel nonhomogeneous boundary condition to address both oversquashing and oversmoothing. Building on the Robin condition, we design a GBN network with local bottleneck adjustment, coupled with theoretical guarantees. Extensive experiments on homophilic and heterophilic graphs show the expressiveness of GBN. Furthermore, GBN does not exhibit performance degradation even when the network depth exceeds 256 layers.

Plus: Amazon workers who backed data center limits face potential termination. A startup claims it broke through a bottleneck that's holding back LLMs AI startup Subquadratic came out of stealth last month with a huge claim: it had solved a mathematical bottleneck that had held back large language models for almost a decade. The purported breakthrough comes from slashing the number of computations transformers need to carry out to generate answers. The result is a faster and cheaper LLM that uses far less energy than any other model on the market. Many experts remained skeptical--but Subquadratic has started to share the receipts. They suggest that their approach might be worth paying attention to.

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the results of an independent evaluation of its new tech. The results suggest that the company's claims might be worth paying attention to.