Neoclouds vs. Hyperscalers: The Infrastructure Gap is Closing The AI infrastructure market is entering a new phase. What began as an urgent scramble for GPUs has evolved into a more structured battle between hyperscalers and a new class of GPU-native cloud providers. These neoclouds—companies like CoreWeave, Lambda, and Fluidstack—emerged by moving faster, taking on more…

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, uptime, and total cost of ownership (TCO). These environments…

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 compute die design. New Agent Development Kit (ADK) and Agent2Agent (A2A) protocol…

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, there’s…

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…

The GPU vs. Custom ASIC debate is one of the most talked about in our circles of tech executives and investors. It is also a debate that requires more in-depth analysis to fully understand. I want to look at the custom ASIC strategy in two ways relative to this debate. The first is how custom…