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Convequity on X

$NET's AI hosting is limited by the physics of its edge footprint and by the role of its products. Carrier-hotel sites built for fast network traffic are a different proposition from purpose-built AI datacenters. Cloudflare’s own engineering disclosures acknowledge power constraints, and management distinguishes its strategy from the hyperscalers’ GPU spending race. Our assessment starts with a two-H100-per-server edge configuration. H100 PCIe and NVL-class cards operate around 350–400 watts per GPU, versus up to 700 watts for H100 SXM. Hyperscalers commonly pack eight SXM GPUs into a machine and connect those machines into dense clusters. The distinction is power, cooling, memory bandwidth and GPU interconnects — not simply whether a model can run. The existing edge footprint cannot acquire hyperscaler-class cooling and power density through server upgrades. Dense, liquid-cooled Blackwell and Vera Rubin systems require a different infrastructure commitment. We see little chance that NET’s existing edge footprint becomes a competitive home for those dense clusters. For sustained, large-model inference, our assessment is that sparse edge GPUs cost more per token than dense AI clusters. Geographic proximity saves network transit time, but it does not guarantee a faster first token or a cheaper response. Greater compute capacity, faster interconnects and efficient batching can outweigh the edge’s network advantage. We expect heavy training, frontier-scale hosting and the most demanding next-generation inference to remain concentrated with hyperscalers. Workers AI’s progress with larger open models is real. Hosting those models does not establish an economic advantage over dense clusters. AI Gateway also lets customers route requests to external providers, matching the model and infrastructure to the task. Cloudflare can still win substantially. More agents mean more requests to secure, more Workers executions, more orchestration and more demand for fine-grained access controls. Those workloads build on infrastructure and capabilities Cloudflare already has. On the SaaS Survival Scorecard, we score $NET at 2.5 for Model-Layer Leverage. That reflects its substantial work building an agent harness across inference, orchestration and execution, while stopping short of model post-training and reinforcement learning.
@StableBread

Fajasy on X

My investing thesis in a nutshell (w/examples): 1) Buy with a strong margin of safety or stock price near/below cash and/or book value. 2) Buy in industries and/or markets where the money is flowing. Makes it a lot easier. 3) Buy the best early and/or the most overlooked in that particular industry. 4) Pay up for unfair advantages, excellent mgmt teams, and companies with huge latent pricing power. 5) Sell when the thesis breaks. Or a much better risk/reward setup hits your desk. Otherwise continue to add/hold. Don't track stock prices. Track the main business KPIs like any competent mgmt team would. Example #1: Bought $NBIS 4x mid $20's (best overall). Then bought $BRUN common and warrants pre-merger (most overlooked). Example #2: Bought $FTEK $1.39. Cash floor new excellent operator CEO. Strong upside potential w/data centers. Example #3: Bought $BABA $70's. Strong margin of safety, market leader, unfair advantages. Sold $170 for better risk/reward. Example #4: Bought $ELMT @$19 and @$14. Unfair advantage. Strategic. Cheap. Critical in strong growth industries. Example #5: Bought $TOST @$24. Beaten down due to macro headwinds and new/early $DASH competition. But almost everything in business stronger and expanding to new verticals. Lastly, if you're unsure or don't fully understand the business. Just say no. In fact, say no to most stocks. Most probably your best idea is already in your portfolio. Don't lockup your money in a 3/5 setup, you're trying to make outsized returns! And sub to my newsletter (link in bio) if you want my deep dives. 😉

Investor Letters

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“Is Ralph Lauren Still Relevant?”

Ralph Lauren’s 2017 CEO change put Patrice Louvet in charge of reversing nine quarters of same-store sales declines and restoring the…

RL $22B
TSOH Investment Research 1 click

A temporary glitch?

Coupang’s November 2025 cybersecurity breach tests whether a selloff is temporary despite its 28% Korean e-commerce share, 14 million-plus…

CPNG $27B
Asian Century Stocks 9 clicks

TSOH Weekly Roundup (10/09/2026)

Paramount Skydance’s debt-heavy Warner Bros. Discovery acquisition, streaming bundle pricing and alcohol-industry diversification anchor…

WBD $78B
TSOH Investment Research
DB · long

Deutsche Bank AG

Deutsche Bank trades at 1.0x tangible book, with projected ROTCE above 13% and a favorable setup ahead of earnings.

DB $63B
Value Investors Club

Zscaler Powered By Agentic AI Growth

Zscaler's $3.8 billion ARR grew 25% year over year as AI-driven security demand and operating leverage support a path to doubling ARR by…

ZS $37B
Andres Cardenal, CFA

Boyz in the Hood

Robinhood’s AI agents, 24/7 equity trading and 10:1 crypto perpetual futures encourage higher retail trading velocity, leverage and…

HOOD $91B
WHAT ARE WE DOING? “Contrarians At The Gate” 1 click

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