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Thursday, November 12, 2026

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Expert Call | Lumentum & Marvell Deal

Marvell’s all-stock acquisition of Lumentum targets optical supply, DSP synergies and AI data-center demand, while adding fab integration, customer-retention and regulatory risks.

LITE $59B
P Equity Research 2 clicks

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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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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 4 clicks

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 6 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

The AutoNation Story

AutoNation shed rentals and megastores, focused on dealerships, and retired 91.1% of its shares through buybacks after its 1990s roll-up…

AN $6B
Cannibal Stocks 4 clicks

No title

SpaceX’s $24 billion Starbase power plant, Microsoft’s Oracle capacity cuts, Riot’s $72 billion AI lease and Quanta’s sell-off frame…

PWR $88BRIOT $6.9B
Monte Independent Investment Research 3 clicks

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