Brian Costello

Californians already pay 50% more for gas than the rest of the country, and, thanks to Gavin shutting down the state’s refineries, they are estimated to pay another $.50 a gallon on top. Add math and basic economics to the list of subjects Gavin struggles with.

The victim of the terrorist attack at Old Dominion has been identified as Lt. Col. Brandon Shah-- who was a Professor of Military Science and the leader of the university's ROTC program. He was a veteran of Operation Iraqi Freedom, Operation Enduring Freedom, and Atlantic Resolve. His awards included two Bronze Stars, Senior Army Aviator Badge, Combat Action Badge, Parachutist Badge, Air Assault Badge, Defense Meritorious Service Medal, Meritorious Service Medal, Air Medal with Valor, Joint Service Commendation Medal, Army Commendation Medal, and the Joint Service Achievement Medal. RIP. 🙏

🚨 BREAKING: A Google researcher and a Turing Award winner just published a paper that exposes the real crisis in AI. It's not training. It's inference. And the hardware we're using was never designed for it. The paper is by Xiaoyu Ma and David Patterson. Accepted by IEEE Computer, 2026. No hype. No product launch. Just a cold breakdown of why serving LLMs is fundamentally broken at the hardware level. The core argument is brutal: → GPU FLOPS grew 80X from 2012 to 2022 → Memory bandwidth grew only 17X in that same period → HBM costs per GB are going UP, not down → The Decode phase is memory-bound, not compute-bound → We're building inference on chips designed for training Here's the wildest part: OpenAI lost roughly $5B on $3.7B in revenue. The bottleneck isn't model quality. It's the cost of serving every single token to every single user. Inference is bleeding these companies dry. And five trends are making it worse simultaneously: → MoE models like DeepSeek-V3 with 256 experts exploding memory → Reasoning models generating massive thought chains before answering → Multimodal inputs (image, audio, video) dwarfing text → Long-context windows straining KV caches → RAG pipelines injecting more context per request Their four proposed hardware shifts: → High Bandwidth Flash: 512GB stacks at HBM-level bandwidth, 10X more memory per node → Processing-Near-Memory: logic dies placed next to memory, not on the same chip → 3D Memory-Logic Stacking: vertical connections delivering 2-3X lower power than HBM → Low-Latency Interconnect: fewer hops, in-network compute, SRAM packet buffers Companies that tried SRAM-only chips like Cerebras and Groq already failed and had to add DRAM back. This paper doesn't sell a product. It maps the entire hardware bottleneck and says: the industry is solving the wrong problem. Paper dropped January 2026. Link in the first comment 👇