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Tuesday, October 27, 2026

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@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.
@convequity 2 clicks

Convequity on X

$META's Muse progress confirms our view that Meta owns the essential assets for frontier AI, and it is progressing better than most people believe. Zuckerberg called Muse Wonder Meta's first frontier model on the 21 October Q3 2026 call. More than 10 million people set up Muse in about six weeks, and shopping is the largest use case. We expected Meta to come back, and we are delighted to see it back among the frontier labs. Two things concern us. 1. Meta appears to have recognized it will not catch up on frontier enterprise use cases, coding above all. So it is differentiating on consumer. In our view that is a double-edged sword. Consumer is where Meta's assets are strongest. But we think the next big shift is silicon-based consumption squeezing out carbon-based consumption: spending by AI agents replacing spending by humans. Humans have finite attention and a limited addressable market, so human demand grows roughly linearly. AI agents have a near-unlimited spending demand, and we think it is about to go exponential. A consumer-first AI is built for the side that grows linearly. That is a longer-term concern for Meta. 2. We are disappointed that Muse is only available in the US, not in the rest of the world. That is cautious big-company behavior, closer to Google than to SpaceX. Our guess is that the legal warnings and threats Meta keeps receiving have made it extra careful on the legal side. That caution has a cost: it handicaps Muse against the other leading frontier apps.
@convequity 12 clicks

Convequity on X

$ORCL Spends More of Its Cash Flow on Capex Than $AMZN or $MSFT — Does the Backlog Justify the Stretch? We hold Oracle, so we pulled the numbers from our ABBX dashboard to check whether the contracted backlog supports the heavier spending. Capex intensity (last four quarters) Oracle spent about 161% of operating cash flow on capex (to 31 Aug 2026). Amazon was about 107% and Microsoft about 63% (both to 30 Jun 2026). Oracle put USD 75.7B into capex against USD 46.9B of operating cash flow. Debt and lease load Add borrowings, lease liabilities, and the undiscounted value of leases signed but not yet started. Purchase obligations are excluded for all three. - Oracle: about USD 457B, or 3.6x its USD 125B of borrowings. - Amazon: about 2.9x. - Microsoft: about 11x (its borrowings are small relative to lease commitments). Oracle stands out on size versus cash flow: roughly 9.7x trailing operating cash flow, versus 2.5x at Microsoft and 2.3x at Amazon. The backlog counterweight Oracle’s RPO (remaining performance obligations) is USD 664B — close to Microsoft’s USD 678B commercial RPO, while generating only about a quarter of Microsoft’s operating cash flow. RPO covers about 1.45x Oracle’s debt-plus-leases figure, versus 1.48x at Microsoft. Those contracts provide revenue visibility, though delivery still depends on margins and timing. Amazon’s USD 496B backlog is AWS-only, so it is not a clean comparison. ABBX capacity estimates show Oracle rising from about 4 GW to about 12.8 GW by 2029, Microsoft from 8.5 to 17 GW by 2030, and Amazon from 10.6 to 16 GW by 2028. That implies roughly USD 52B of RPO per planned GW for Oracle versus about USD 40B for Microsoft (rough scale comparison given different RPO scopes and time horizons). Our take The bigger risks at Oracle are timing, execution, and funding rather than a lack of demand. Oracle expects about 13% of its RPO to convert to revenue in the next 12 months while capex is being spent now. Its 12.8 GW figure is an ABBX estimate, not company guidance; it carries ABBX’s lowest confidence of the three, and grid approval is still pending at one or more sites. Oracle also sold USD 20B of stock in FQ1 FY27. We think the backlog justifies holding the position, but sized for funding risk. What we watch: RPO converting to revenue on schedule, new capacity coming online, and whether more equity is needed.

Investor Letters

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Right-Sizing Memory

Micron’s AI memory demand depends on workload-specific accelerator designs that shift bits among HBM, GDDR and LPDDR, changing wafer…

MU $946BNVDA $5.2T
Chipstrat 4 clicks

Are Neoclouds Dead?

Neoclouds face tighter debt financing as AI infrastructure capex outruns cash flow, leaving cash-burning GPU providers more exposed than…

NBIS $42B
Daniel Romero 9 clicks

Reddit: Deep Dive Analysis

Reddit generated $805 million in Q2 2026 revenue, 95% from ads, but slowing Google search referrals expose AI-driven traffic and…

RDDT $18B
Hated Moats 6 clicks

Qualcomm Inc. (QCOM)

Qualcomm supplies chips for on-device AI agents and data center inference, with Meta and AWS agreements expanding its role across both…

QCOM $195B
SixSigmaCapital: Navigating the Financial Markets 5 clicks
WULF · long

Terawulf INC

TeraWulf is shifting from bitcoin mining toward data centers, seeking a higher-quality and more defensive business model.

WULF $5.4B
Value Investors Club

The Data Center Bridge

Small- and mid-cap data center stocks trade as one despite sharply different funding bridges: 12 of 15 cover fixed costs on signed…

IREN $11BFRMI $1.9B
CV's Substack 16 clicks

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