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@convequity 3 clicks

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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Modine Deep Dive

Modine, soon Modexus Solutions, has a $4 billion hyperscaler cooling contract but must ramp factory output and restore margins as data…

MOD $9.2B
CV's Substack 18 clicks

AppLovin Deep Dive

AppLovin rebuilt its ad model with AXON 2.0, lifting LTM revenue to about $7 billion and margins to 77% as Unity competition threatens…

APP $88B
Speedwell Research 26 clicks

Constellation: Stars Aligning?

Constellation Brands trades near $127 at roughly 11x next-twelve-month EV/EBIT, with recent business results offsetting concerns over…

STZ $22B
TSOH Investment Research 27 clicks
SPHR · long

Sphere Entertainment CO

Sphere Entertainment is shifting from construction and startup spending to a high-margin, cash-generative venue-operator model, with…

SPHR $3.7B
Value Investors Club 2 clicks

Lennar, Part One: The Story

Lennar grew from a Miami builder into one of America’s two largest homebuilders, controlling land largely through options and land banks…

LEN $17B
Latticework by MOI Global 15 clicks

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