The enterprise takeaway is simple: AI performance is now a system-level challenge. The winners will optimize chips, memory, interconnects, software, and architecture together. Less latency means faster intelligence.
Less movement means lower cost.
Less waste means AI that can
COMPUTING
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AI Performance as System-Level Enterprise Challenge
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LogicFolding: 3D Chip Architecture for AI Inference Optimization
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One concept that makes this practical is LogicFolding. Traditional chips spread logic across a flat surface. LogicFolding brings related logic closer together by moving toward more 3D structures. Less distance means less delay. And in AI workloads, small delays compound fast.
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Tau Scaling Law: Beyond Chip Size in AI Systems
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This is the idea behind Tau Scaling Law, also known among peers as Her’s Law. Instead of asking only, “How small can the chip get?” We now have to ask: → Where is time being lost?
→ Where is data waiting?
→ Where are signals traveling too far?
→ Where is the system -
ByteDance Reportedly Building AI Inference Chip Modeled on Groq’s LPU Architecture
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ByteDance is reportedly building its own inference chip modeled on Groq's LPU, the same architecture Nvidia paid roughly $20B to license in December. The LPU keeps the model in on-chip SRAM and skips high-bandwidth memory. HBM is the component the US restricts most tightly for
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System built with Codex due to Claude’s mistakes
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nah, it’s built with codex. Claude makes too many mistakes.
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Opus 4.8 more cooperative but still expensive
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Worked on some code this morning using Opus 4.8 and so far I'm really liking it. Much more cooperative than 4.7 and less "over agentic". Stops and asks for my input when needed in places 4.7 (and GPT 5.5) would just foolishly blast ahead. (Still WAY too expensive.)
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Jax vs C: inefficient implementation comparison
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IIUC the previous training code at xai was super inefficient. Perhaps the 10x is comparing to that? So it's not really "jax vs C" but "crappy jax impl vs less crappy C impl"?
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Edge-Cloud Hybrid Architecture for AI Development
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The architecture is typically hybrid: edge handles latency-sensitive control, cloud platforms handle analytics and AI development at scale.
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General Compute Builds Efficient AI Inference Cloud Infrastructure
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@TimFernholz at @TechCrunch breaks down how General Compute is building its inference cloud with SambaNova, and why faster, more efficient inference infrastructure is becoming critical for the next wave of AI. Featuring insights from General Compute CEO @FPuklowski and CTO
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JAX GPU scaling and pipeline parallelism for training
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Yes. It’s not that we’ve discovered some magic bullet, but rather that JAX, or at least the open source version of it, is mostly optimized for small to medium-sized training runs on Google TPUs, whereas we need to massive training runs on Nvidia GPUs. Pipeline parallelism is