Signal
Intelligence
Have you ever tried keeping up with the AI industry? It's a massive wall of noise. SGNL Intelligence is fixing that using something completely fascinating called GIKE. We send out a literal swarm of AI agents to process every claim in tech, and then a human editor steps into the loop to verify the truth. We don't just summarize the news; we find the signal.
See how GIKE works under the hood →- Jevons Paradox: Why Every AI Optimization Makes the Hardware Shortage Worse
OpenRouter data shows coding tokens grew from 11% to over 50% of all AI usage in one year. Every efficiency gain -- TurboQuant, DeepSeek Engram, cheaper models -- creates new use cases that consume more compute than was saved. The semiconductor industry is building for a demand curve that accelerates when costs drop.
- DeepSeek's Memory Divorce: What Happens When AI Learns to Separate Knowing from Thinking
DeepSeek's Engram paper offloads 100 billion knowledge parameters to cheap host DRAM with only 2% throughput loss. If adopted at scale, it would double DRAM demand per AI server rack — and the DRAM shortage is already the worst in a decade.
- The HBM4 Yield Game: More Memory, Less Power, Cheaper Silicon — Pick All Three
NVIDIA needs the top 20-30% of Samsung's HBM4 output to hit 10 Gbps for Vera Rubin. AMD only needs the floor bin at 6.5 Gbps. That gap isn't a weakness — it's AMD's greatest supply chain advantage.
- Samsung Is Juggling Knives. One Might Drop.
Samsung is making NVIDIA's memory, fabricating NVIDIA's inference chips, developing next-gen HBM4E, and building two Texas fabs — all while its workforce moves toward a strike. One company, four critical roles, zero backup on the most important one.
- The AI Memory Stack, Now and Future
A single Vera Rubin NVL72 rack needs 20 TB of HBM4, 100 TB of CXL memory, and petabytes of flash — and the memory bill may exceed the GPU cost. Every layer of the AI memory hierarchy is being rebuilt in 2026. Here's the full stack, the products, and the math.