LIVE · THU, JUN 25, 2026 --:--:-- ET
Issue Nº 65 COST TOTAL $14514.13 ARTICLES TODAY 4 TOKENS TOTAL 9.10B
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Running the wire
Breaking Visionaries GP Judith Dada joins Langdock as co-CEO; AI model platform hits $40M ARR, eyes 2026 fundraise Market Anthropic aggressively expands Asia-Pacific data centers: hiring 13 compute roles in Australia, Japan amid infrastructure strain Chips OpenAI, Broadcom unveil Jalapeño: custom LLM inference chip designed in 9 months Funding British Business Bank commits £90M to 10 first-time UK VCs backing deeptech, defence, climate at pre-seed/seed Funding SK Hynix files for record $29.4B Nasdaq ADR listing; stock surges 12% on Micron supply-tight signal Market Micron hits record 84.9% gross margin as memory shortage props up pricing power Breaking Anthropic accuses Alibaba of largest distillation attack on Claude, 28.8M model queries via 25K fake accounts Market Micron posts $41.5B Q3 revenue, guides $50B for Q4 on AI memory supercycle Funding Qualcomm acquires Modular for ~$4B to build hardware-agnostic AI stack against NVIDIA CUDA Market AWS launches EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell; 4.6x inference gains Chips Qualcomm unveils Dragonfly C1000 data-center CPU; Meta commits to 2028 production volumes Chips OpenAI unveils Jalapeño inference chip with Broadcom, targets late-2026 deployment Breaking Huang tells shareholders black-market data centers from smuggled chips are a "dead end" Research Google integrates computer use natively into Gemini 3.5 Flash for agentic automation Research Google OpenRL: Self-hosted Kubernetes API for LLM post-training; decouples RL from infrastructure Market Micron Q3 earnings beat on record DRAM margins; HBM supply fully allocated through 2026 Policy US secures Netherlands for Pax Silica chip alliance; ASML tensions persist over MATCH Act export restrictions Chips OpenAI & Broadcom unveil Jalapeño: Custom LLM inference chip targets gigawatt-scale deployment by end of 2026 Breaking Gemini 3.5 Flash adds native computer use; agent framework now default across Search Research AI rapidly designs novel radio-frequency chips beyond human intuition, reducing years of work to hours Breaking Visionaries GP Judith Dada joins Langdock as co-CEO; AI model platform hits $40M ARR, eyes 2026 fundraise Market Anthropic aggressively expands Asia-Pacific data centers: hiring 13 compute roles in Australia, Japan amid infrastructure strain Chips OpenAI, Broadcom unveil Jalapeño: custom LLM inference chip designed in 9 months Funding British Business Bank commits £90M to 10 first-time UK VCs backing deeptech, defence, climate at pre-seed/seed Funding SK Hynix files for record $29.4B Nasdaq ADR listing; stock surges 12% on Micron supply-tight signal Market Micron hits record 84.9% gross margin as memory shortage props up pricing power Breaking Anthropic accuses Alibaba of largest distillation attack on Claude, 28.8M model queries via 25K fake accounts Market Micron posts $41.5B Q3 revenue, guides $50B for Q4 on AI memory supercycle Funding Qualcomm acquires Modular for ~$4B to build hardware-agnostic AI stack against NVIDIA CUDA Market AWS launches EC2 G7 instances with NVIDIA RTX PRO 4500 Blackwell; 4.6x inference gains Chips Qualcomm unveils Dragonfly C1000 data-center CPU; Meta commits to 2028 production volumes Chips OpenAI unveils Jalapeño inference chip with Broadcom, targets late-2026 deployment Breaking Huang tells shareholders black-market data centers from smuggled chips are a "dead end" Research Google integrates computer use natively into Gemini 3.5 Flash for agentic automation Research Google OpenRL: Self-hosted Kubernetes API for LLM post-training; decouples RL from infrastructure Market Micron Q3 earnings beat on record DRAM margins; HBM supply fully allocated through 2026 Policy US secures Netherlands for Pax Silica chip alliance; ASML tensions persist over MATCH Act export restrictions Chips OpenAI & Broadcom unveil Jalapeño: Custom LLM inference chip targets gigawatt-scale deployment by end of 2026 Breaking Gemini 3.5 Flash adds native computer use; agent framework now default across Search Research AI rapidly designs novel radio-frequency chips beyond human intuition, reducing years of work to hours
Chips

China's LineShine supercomputer tops TOP500 with 2.198 exaflops CPU-only, ending US El Capitan's reign

China's LineShine supercomputer has taken the #1 spot on the TOP500 list with 2.198 exaflops of double-precision Linpack performance, pushing AMD-powered El Capitan (1.809 exaflops) into second place. Critically, LineShine achieved this using no GPUs or accelerators of any kind—only 13.78 million cores of domestically designed Armv9-based silicon (the LX2 processor), the first CPU-only machine to clear the two-exaflop threshold. The system uses SMIC's 7nm-class domestic process, proprietary LingQi interconnects, and the Kylin OS, representing an all-domestic technology stack.

This is the first China-based system to lead the TOP500 since Sunway TaihuLight in 2017. More tellingly, China stopped submitting its fastest machines to the list around 2021 after entity-list sanctions; the decision to submit LineShine now signals a deliberate geopolitical posture change. Observers believe China has operated undisclosed exascale systems (OceanLight, Tianhe-3) for years without submitting. LineShine's disclosure is a statement that indigenous design and fabrication can work without Western components, breaking dependency on TSMC, EDA, and export controls.

However, LineShine dominates only on high-precision FP64 workloads. On the mixed-precision HPL-MxP benchmark that approximates AI training math, it scored only 22% of El Capitan's relative performance gain, ranking fourth at 7.92 exaflops versus El Capitan's 16.7 exaflops. LineShine also consumes 42,220 kW (pulling 42% more power than El Capitan for similar-magnitude FP64 output), indicating that the architecture favors traditional HPC over AI-scale matrix math.

For infrastructure teams: LineShine proves China can build sovereign exascale systems but raises questions about frontier-AI workloads. The discrepancy between FP64 and FP32/BF16 performance highlights why GPUs and specialized accelerators still rule training and inference. This is less a threat to NVIDIA's AI dominance than a statement about HPC independence. Watch whether China's next systems prioritize reduced-precision throughput, and whether sanctions-evasion via custom silicon becomes table stakes for other frontier players.

Sources