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Sunday, October 25, 2026

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@convequity 16 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 10 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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Tower Semiconductor Deep Dive

Tower Semiconductor’s low-loss photonics chips halve lasers in 1.6T optical modules, supporting a $680 million annualized photonics run…

TSEM $19B
CV's Substack 8 clicks

Deep Dive: $ASML

ASML’s €230 million EUV machines underpin advanced AI chip production, with demand booked through 2028 after nearly $9 billion and two…

ASML $616B
Rebound Capital 8 clicks

Margin Call

Petrobras’s Morpho-1 well revived hopes that Brazil’s Equatorial Margin can become a major new oil frontier after hydrocarbons were…

PBR $96B
Doomberg 9 clicks
JBFCY · long

Jollibee Foods CORP

Jollibee Foods trades at PHP 144, about 56% below its January 2019 high, framing a long thesis on the Philippine restaurant operator.

JBFCY $2.5B
Value Investors Club 1 click

Our Views on CoreWeave

CoreWeave's financial statements point to a business model familiar to the authors after a closer review of the AI infrastructure company.

CRWV $34B
WHAT ARE WE DOING? “Contrarians At The Gate” 12 clicks

Capital Wars

CoreWeave’s $35 billion to $39 billion 2026 spending plan relies on debt as unsecured yields reach 15%, making its GPU cloud expansion a…

CRWV $34B
Net Interest 10 clicks

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