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Monday, November 9, 2026

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@DeepSailCapital

Deep Sail Capital on X

$CRDO - Others have tried this already. $MRVL and $BDCM have offered merchant AEC DSPs for years. A cheaper merchant DSP hasn’t broken the integrated model so far. Credo’s moat is the system not just the DSP. Credo owns qualification, SerDes, AEC patents, ETC which allow it to keep the full cable end to end. It has deep relationships with five hyperscalers and growing engagement with neoclouds. Anyone that knows the market knows $MXL might try to get a piece of the market but unlikely to get a material piece.
@convequity 19 clicks

Convequity on X

Cerebras ($CBRS) is a taxi built for very fast inference while Nvidia ($NVDA) runs the bus network. Most of the confusion around CBRS comes from reviewing the wrong vehicle. In our view, Cerebras' wafer-scale design and abundant on-chip SRAM let it reach high utilization at batches of roughly 3–4. That makes it exceptionally fast for low-batch inference. GPUs need much larger batches and tightly connected clusters to get there, but their flexibility, scalability and throughput make them the better mass-market token factories. The bus wins on economics when serving many users at once. We think Cerebras is mostly attractive over the next two to three years, provided demand grows enough for premium-priced, high-speed inference rather than mostly low-cost, high-volume tokens. How big that premium segment gets is the main variable in our thesis.
@convequity 7 clicks

Convequity on X

Nvidia's DSX POD container shortens the gap to system-level competitors like Google and Huawei, but it does not eliminate that gap. At GTC Berlin on 21 October, $NVDA introduced DSX POD, a partner-built container for Vera Rubin AI factories. Nvidia says it cuts deployment at a prepared site from six months to as little as 14 days, with worldwide delivery targeted for early 2027. Our latest Nvidia report, published a few days ago, flagged one drawback: Nvidia is not expanding into the full system solution quickly enough. Huawei, Google and Broadcom are leveraging system-level optimization, where the chip, the racks, the network between racks, power, cooling and software are designed together for one owner's workload. That is where they are finding further efficiency gains in the competition against Nvidia. Nvidia has stayed within rack-level optimization: the GPU, the NVLink switch and the rack they sit in. That is understandable. Nvidia serves many customers with many different designs and criteria, so it has to find the most common denominator those customers can accept. It also means this handicaps Nvidia's competitiveness against vertically integrated competitors like Google and Huawei. A container sits between a rack and a full system. Partners integrate the Vera Rubin racks, the networking, the power and the cooling inside the container and validate them together before delivery, so the customer receives a tested unit rather than racks to assemble. In our view that is why the gap shortens. It is still a gap, because Nvidia is not a vertical player and still does not provide the system-level solution. What DSX POD does show is that Nvidia is adaptive and very eager to stay in the game. We expect it to try whatever it can to alleviate this drawback.

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Roblox: Capture This Moment

Roblox’s Q3 FY26 DAUs fell year over year for the first time, while bookings declined 12% to $1.7 billion.

RBLX $35B
TSOH Investment Research 1 click

DoorDash and Google

DoorDash and Google.

DASH $86B
Stratechery by Ben Thompson – On the business, strategy, and impact of technology. 4 clicks

NEW POSITION: Google LEAPS

Alphabet bought long-dated GOOGL calls after a 12.6% earnings drop, betting AI monetization and distribution overcome capex pressure and…

GOOGL $3.5T
Hated Moats 1 click

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