AI
Last week was Samsung Unpacked, specifically Unpacked focusing on the Galaxy Z Fold 7, Z Flip 7, and Galaxy Watch 8 series. These launches mark a pivotal moment for the broader smartphone market, signaling Samsung’s drive to make foldables mainstream by marrying a familiar phone feel with flagship‑g
AI
In the age of agentic AI, a common misconception persists that AI will be a “winner-takes-all” market, where a single dominant agent will rule across every use case and every user. This belief misunderstands the evolution and purpose of agentic AI. The future will involve multiple specialized agents
AI
I hate to steal Google’s tagline for this I really don’t but this year felt like Apple trying to make platforms more helpful. There’s this general consensus that Apple is behind in AI, missing out on chatbots and ignoring what most consider to be the next generation of experiences. The common saying
AI
All AI factories are data centers, but not all data centers are AI factories. For much of the last two decades, enterprise infrastructure strategy has been grounded in cost control. Data centers were built to support core IT functions—storage, compute, and networking—with an emphasis on efficiency,
AI
Key Takeaways on Google’s 1Q25 Earnings Sustained AI Momentum Across Products and Infrastructure: Gemini 2.2 is now embedded across 15 products with 500M+ users, while AI Overviews in Search surpassed 1.5B MAUs. Visual and voice-first inputs like Google Lens and Circle to Search are driving deepe
AI
Key Takeaways: Google Cloud unveiled Ironwood (TPUv7), its first inference-optimized TPU, offering massive leaps in compute (5x), HBM capacity (6x), and power efficiency (2x) over the previous generation. Performance is estimated to be within 5% of an Nvidia B200, despite TPUv7’s single primary comp
AI
We are in a huge AI compute shortage right now. At this very moment! When you hear most people talk about this, it’s generally in regards to model scaling and compute scaling for training. Not enough compute so labs can’t scale up the scaling laws. Right now is different, it’s the Ghibilification of
AI
Executive Summary Custom CPU development is essential for maximizing the efficiency and performance of accelerated computing architectures. NVIDIA’s strategic approach to CPU design, specifically its purpose-built Arm-based CPUs, delivers significant competitive advantages by addressing the limitati
AI
The GPU vs. Custom ASIC debate is easily one of the most talked about dynamics regarding AI compute infrastructure. I have dug deeply into the debate in this article here (Understanding the Hyper Scaler Custom ASIC Strategy). There is no doubt this is a central question NVIDIA executives get from i
AI
A closer look at NVIDIA’s Q4 earnings call highlights key investor concerns that management tackled head-on throughout the discussion. The conversation sheds light on the company’s strategic direction and how the market perceives its leadership in the rapidly evolving AI infrastructure space. 1. Bla
AI
A few days ago, xAI announced Grok 3 and it’s easily the best LLM I’ve used. Good vibes, great quality, fast token generation, and happened to be trained on the most compute for any model (200,000 H100s). During the stream, Elon Musk said xAI is already working on Grok 4’s data center, which will be
AI
China’s smartphone market, the world’s largest, could be at an inflection point not seen since the initial smartphone boom. In recent quarters, the market has shown signs of recovery, with local brands benefiting from a surge in premium offerings as more Chinese consumers are willing to spend on new