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Thursday, November 12, 2026

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ASML ($ASML) Deep Dive

ASML monopolizes EUV lithography, a critical tool for leading-edge chips, with production capacity stretched through 2028 and customer planning extending into 2030.

ASML $591B
Jimmy's Journal

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@convequity 1 click

Convequity on X

$NVDA, $CBRS, and Huawei's Kirin 9050 Pro are three different bets on the same turning point: what the chip industry does when shrinking transistors stops paying. In AI chips, most energy goes into moving data, not into the calculation itself. In our view, as transistors shrink, calculations get cheaper much faster than the wires around them, so the industry's centre of gravity is shifting from ever-smaller transistors to shorter paths for data. That shift is why Huawei's logic folding — designing a circuit vertically to cut the distance data travels — looks potentially transformative and disruptive for the semiconductor industry to us. Here is the comparison in one line: one 16-bit floating-point calculation is the baseline, 1×, about 0.52 picojoules in NVIDIA's published GH200 energy model. Every figure below is the energy spent moving data, measured against that one calculation. 1. Moving data to local SRAM next to the computing units: Cerebras uses about 2× the baseline. NVIDIA's highly optimized local-memory path uses about 5–15× the baseline. 2. Moving data across the chip: we estimate Cerebras's core-to-core network at about 12–37× the baseline. NVIDIA's optimized path through L2, its shared on-chip cache, is about 60–90× the baseline. 3. Moving data off the chip to external memory: Cerebras's MemoryX path is about 330–800× the baseline. NVIDIA's optimized path to HBM, its external high-bandwidth memory, is about 230–290× the baseline. The energy cost is set by how far the data travels: a short hop costs single-digit multiples of one calculation, a trip across the chip costs tens of times, and a trip to external memory costs hundreds of times. If moving data is the real constraint, then shortening paths, which is what Huawei is doing with logic folding, matters more than another process node — for NVIDIA and Cerebras as much as for Huawei. These are engineering estimates, workload-dependent, not measurements of complete systems. The optimized block transfers behind NVIDIA's figures are already counted above; what remains is the physical cost of the wires and memory themselves. In our view, Cerebras's commercial case depends on whether its compiler can keep data close to computation across enough workloads. NVIDIA's advantage is a broader range of software, not a way around the energy cost of moving data.

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ON THE TAPE with Guy Adami

Guy Adami discusses Nvidia, Anthropic's IPO prospects, energy, gold and the AI trade.

NVDA $4.2T
WHAT ARE WE DOING? “Contrarians At The Gate” 5 clicks

DoorDash’s FBA Moment

DoorDash’s $17 billion Instacart deal targets DashMart warehouse scale, where 99%+ order accuracy and faster delivery address the limits of…

DASH $78BKR $41B
MBI Deep Dives 3 clicks

Cat-Scratch Fervo

Fervo Energy raised $2 billion in its May IPO, then fell nearly 70% below issue price after a Morpheus short report, despite targeting 1 GW…

FRVO $3.8B
Doomberg 5 clicks
OSW · short

One Spa World

OneSpaWorld (OSW) faces about 37% downside as its valuation requires unattainable growth in average revenue per customer after major-market…

OSW $3.1B
Value Investors Club

Sea Ltd Earnings Update: Q3 2026

Sea Ltd reported Q3 2026 revenue of $8.6 billion, up 43.4% year over year, while net income rose 34.0%.

SE $45B
SixSigmaCapital: Navigating the Financial Markets 7 clicks

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