Custom AI chips are converging fast — but Nvidia still owns training. Every major hyperscaler and AI lab is now designing its own silicon. Google, Amazon, Microsoft, Meta, and now …

Taiwanese startup Tranxform builds low-power edge processors for on-device AI In sum – what we know: Stephen Huang has spent decades building chips for some of the biggest names in …

Oxmiq’s CUDA-compatible architecture targets sovereign computing customers Oxmiq Labs, the AI semiconductor startup founded by former Intel chief architect Raja Koduri, has raised $35 million in a Series A round co-led by …

The Anthropic chip would use Samsung’s advanced 2nm process In sum – what we know: Anthropic is in exploratory talks with Samsung Electronics about manufacturing a custom AI processor, according …

Samsung’s 2029 target represents a two-year delay from original plans In sum – what we know: Samsung Foundry has officially resumed commercialization and R&D work on its 1.4nm (SF1.4) process node, with …

Everyone’s making custom ASICs — so why all the talk about Nvidia dependency? Nvidia arguably powered the AI boom, but well and truly into it, hyperscalers and other big players …

Qualcomm’s Dragonfly chips and HBC memory take aim at Nvidia’s inference lead In sum – what we know: Qualcomm has been expanding. At its 2026 Investor Day in New York, …

OpenAI’s new chip is already running GPT-5.3 workloads In sum – what we know: OpenAI and Broadcom are showing off the initial fruits of their partnership to build custom silicon. The two …

TSMC’s leading-edge chips and advanced packaging are spoken for In sum – what we know: There’s no single bottleneck when it comes to AI. A few years ago, perhaps there …

MEXT’s Predictive Memory software uses AI to anticipate which data a workload needs next In sum – what we know: AMD has acquired MEXT, a privately held AI memory-optimization startup …