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 →- 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.
- 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.
- 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.
- NVIDIA's Rubin Is Late. Here's Who Wins and Who Gets Squeezed.
NVIDIA's next-gen Vera Rubin GPU — promising 5x inference over Blackwell — is reportedly delayed one quarter because the world can't make HBM4 fast enough. Google TPUs rise, AMD gets more time to close the software gap, and AI labs scramble for compute. The memory wall just hit NVIDIA's roadmap.