Latest articles on AI, technology, and software development.
AI's power appetite has grown beyond what wind and solar can reliably deliver at scale. The technology industry's answer is an unlikely one: nuclear energy, both by reviving old plants and betting on small modular reactors still years from completion.
Two years into the $52.7 billion US semiconductor independence push, TSMC Arizona is producing 4nm chips, Intel 18A is in customer validation, and Micron is building the first US DRAM fab in 20 years. Here's the honest scorecard.
The AI boom has created an unprecedented demand for electricity that is straining power grids, reviving nuclear plants, and reshaping energy markets in ways that will outlast any individual AI model. The numbers are extraordinary — and the industry's responses reveal deep tensions between speed and sustainability.
Microsoft, Google, and Amazon have all signed nuclear power deals in the last 18 months. The AI compute buildout is consuming electricity faster than renewables can supply it, and the industry is increasingly betting that small modular reactors and restarts of dormant plants are the fastest path to reliable, carbon-free power at scale.
WebAssembly started as a way to run C++ games at near-native speed in Chrome. In 2026 it runs production workloads on Cloudflare's global network, powers plugin systems in editors and databases, executes AI inference at the edge, and is standardizing a system interface — WASI — that lets the same binary run in a browser, a server, or a microcontroller. Here is what that actually looks like.
The global internet runs on a network of around 550 fiber optic cables on the ocean floor, carrying virtually all international data traffic. Recent incidents in the Red Sea and Baltic Sea have highlighted how this critical infrastructure can be disrupted — by accident, by anchor, or by design.
What began as a manufacturing tool for simulating jet engines and assembly lines has become infrastructure for urban planning, hospital operations, and disaster response. Digital twins are growing up — and the complexity is growing with them.
AI datacenters are consuming electricity at a rate that is straining power grids and alarming climate researchers. The bottleneck is not the chips — it's moving data between them. Silicon photonics replaces copper wires with light, using up to 80% less power for data transmission. In 2026, it is moving from research labs into mass production.
Meta acquired Rivos, Qualcomm bought Ventana Micro, and RISC-V silicon is appearing in data centers and AI accelerators. After a decade of IoT dominance, the open architecture is moving to where the real compute budget lives.
Three laptop manufacturers have shipped or announced RISC-V-based consumer laptops in the first half of 2026. The open instruction set architecture finally has real hardware — but the performance benchmarks and software ecosystem reveal a gap that enthusiasts should understand before buying.
Intel's Hala Point system, unveiled in April 2024 and now shipping to research partners in 2026, demonstrates that brain-inspired computing architectures can achieve energy efficiency three orders of magnitude beyond conventional GPU compute. The technology is leaving the lab and entering specific workloads where it has no practical competitor.
The open-source instruction set architecture born at UC Berkeley in 2010 now runs in billions of devices, from Espressif's $1 WiFi chips to NVIDIA's GPU microcontrollers to Western Digital's SSD controllers. The next question is whether it can move up the stack into servers and laptops.