puffin

trump got mythos to hack the voter machines

trump is live on TV blatantly laying the groundwork for claiming fraud on the midterms

IF YOU ARE GOING TO POST “YOUR” FINANCIAL MODELS ON X PLEASE TELL CLAUDE FOR EXCEL TO BE LESS OBVIOUS OR EDIT THE FORMATTING YOURSELF THANK YOU

i am giga bullish on ai but this company should not be one of the leaders

this apple lawsuit is further proof of the incompetence of @OpenAI and its leadership

$meta selling compute externally is the top

@CoorsLightCEO tbpn appearance will be gold

ed zitron doing bank/research group expert calls now lol

@BillKrackman +money max(EV) hedge is (ticket profit)/((odds/100)+1) (60-5)/((170/100)+1) 55/2.7 = 20.37k

I’m not a hedge guy but this was a unique situation. Congratulations to this Knicks ticket holder. 👏💰

@KawzInvests the div is suspended FYI and your anthropic % is too low

Jensen Huang called $SKM the backbone of Korea's AI ecosystem. $SKM just bought more Anthropic in the Series H and has no intention of selling before the IPO. The CEO said it himself in Tokyo yesterday on June 10. That same week Anthropic pulled $SKM into Project Glasswing, the invite-only program for access to Claude Mythos Preview, a model so advanced Anthropic has not released it publicly over cybersecurity concerns. And buried in the Korean coverage, $SKM's CEO revealed Anthropic is actively seeking compute infrastructure and wants SKT to provide it. The $39 stock is not just an Anthropic proxy. It is becoming Anthropic's infrastructure partner. Gigawatt-scale AI Cloud with $NVDA coming online 2027. 4.4% dividend. 6.1x EV/EBITDA. Every Western telco peer clears 7.5x to 9x.

$msgs

6/7

@saso_capital are you retarded? this literally makes no sense. AECs are for scale up. “$AAOI’s exposure is concentrated in exactly the undifferentiated short-reach laser module” is completely false. the datasheet for $aaoi 800G transceiver is below where you can clearly see distance = 500m

$CRDO earnings just signaled a major problem for $AAOI? On AECs: Credo said active electrical cables are now “the preferred solution for rack connectivity and for many multi-rack deployments up to 7 meters.” 1000x greater reliability than commodity laser-based optical modules. At lower power. That is $AAOI’s core market. Then the ALC bombshell. Active LED Cables. Optical reach up to 30m using micro-LED instead of traditional lasers. AEC-class reliability with optical range. Management said ALCs could ramp with “very much similar” dynamics to AEC and ZeroFlap. Meaning fast. And big. That is the rest of $AAOI’s market. Credo also said hyperscalers are no longer optimizing for lowest module cost. The priority is now “reliability, power efficiency, signal integrity and telemetry.” ZeroFlap optics carry 3-digit ASPs vs 2-digit for commodity components. “Meaningful improvement in network reliability, time to cluster stability, and long term uptime.” Hyperscalers are telling you they will pay more for better. That is the opposite of what commodity transceiver suppliers need to hear. $AAOI’s exposure is concentrated in exactly the undifferentiated short-reach laser modules that: AEC displaces under 7m ALC targets out to 30m As buying criteria shift from unit cost to uptime and power, the addressable market for commodity optics at short distances is shrinking. $CRDO is not just growing. It is eating the market that $AAOI needs to survive.

Sundar receives an A+ for the equity raise, but $goog and da 7 must go down

ok us gov giving $2B to quantum … $intc is right there, this is like 5 high-NA machines we are lighting on fire

been saying since $30B ARR anthropic is likely EBIT positive. where the fuck is that delusional Ed Zitron guy and his gang of retards now? don’t even have to go off an EBTIT number

@bubbleboi $ is worthless SPY to 1k

Believe it or not every semiconductor company could have amazing earnings and still be down 20-30% by end of year this is what we call in finance the “discount rate.”

fuckkkk just got tapped to degross my semis because some autist wasn’t holding my book as of 3/31 fuckkkk

unphased by the semis puke today but did buy a thing of zyns and proceed to immediately drop half of them #itsallover

EBTIT (earnings before training, interest & taxes)

Grok got my back: Yes, your speculation is directionally correct—and a sharp intuition, not “retarded” at all.1 Here’s why it holds up in practice for serving (inference) large MoE models: •Sparsity/MoE fundamentally decouples total model size from per-token active compute and memory traffic. A model like DeepSeek-V3 has ~671B total parameters but activates only ~37B per token. The router picks a small subset of experts (typically top-2 or top-8 out of dozens/hundreds). This means the FLOPs and the weights that actually need to be loaded from HBM per forward pass are a small fraction of the total model.2 •Expert Parallelism (EP) + high interconnect bandwidth lets you distribute those experts across many GPUs/nodes. Each GPU only holds and serves its own slice of experts. When a token routes to an expert on another GPU, you do an all-to-all exchange.
Larger interconnect bandwidth (NVLink domains, NVSwitch, high-speed fabrics, etc.) makes this exchange cheap and scalable. NVIDIA’s own Wide Expert Parallelism on GB200 NVL72 racks explicitly uses the massive coherent 130 TB/s aggregate NVLink bandwidth to make wide EP practical without blowing up communication overhead.1 •Result: total model size can grow super-linearly relative to per-GPU HBM bandwidth.
You add more experts → larger model.
You spread them with EP → each GPU’s local expert weights stay roughly the same size.
HBM bandwidth demand per GPU therefore stays roughly constant (it’s still dominated by loading its own active experts + activations), not scaling linearly with the overall model size. The extra scale is “paid for” by the interconnect and by the sparsity itself.23 This is exactly how frontier MoE serving works today (Mixtral, DeepSeek, Grok-1 style models, etc.). Without sparsity you’d be forced into either (a) much bigger individual GPUs with proportionally higher HBM bandwidth or (b) heavier tensor parallelism that also hammers interconnect. Sparsity + EP + fat interconnect is the trick that breaks the linear scaling. A couple of real-world caveats (they don’t invalidate the direction) •Memory capacity still scales with total parameters (all experts have to live somewhere in the cluster), but that’s aggregate cluster memory, not per-GPU HBM pressure.21 •Communication can still bite if your interconnect isn’t up to the task—especially in multi-node setups or at huge batch sizes. All-to-all routing traffic is real (Meta has seen it eat 10-30% of latency). But that’s precisely why more interconnect bandwidth is valuable, not a “tax on stupidity.”26 So yeah—you nailed the core insight. The bubble-boi take that extra inter-cluster bandwidth is pointless ignores exactly this dynamic. Sparsity and MoE don’t eliminate bandwidth bottlenecks, but they do let you serve dramatically larger models without a matching linear explosion in per-GPU memory-bandwidth demands. That’s how the industry is actually scaling inference right now.

@bubbleboi thru sparcity tho you can serve a larger model (enabled by larger interconnect) without some equal (linear) increase in memory bandwidth i would imagine? Or am i just retarded