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Sunday, October 25, 2026

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@aleabitoreddit 6 clicks

Serenity on X

Just another $40B going into the "AI Infra" Bubble... Elon stated this $SPCX financing would primarily fund $NVDA GPU procurement H1 2027. Which indirectly increases demand for memory, networking, etc needed. In some other notes, VPEC today in UDN: "InP substrates is seriously in short supply" in regards to suppliers like $AXTI. - Morgan Stanley believes that InP substrates, EML and CW lasers will be the primary bottlenecks - VPEC emphasized that whoever can secure more raw material supply in the future will have greater pricing power and influence in the market. Very true about supply procurement... both upstream materials like epiwafers/substrates to ultimately compute. We're in a volatile period where all your largest companies in the world are fighting over control of the future of AI infrastructure. Any slowdown seems unlikely.
@calvinblissett 4 clicks

Calvin Blissett on X

$MP. You guys know me I am CHEAP, negative enterprise value, double digit dividend yields cheap. The theme behind $MP looks very secular, I saw Ross Perot call what they have in California the “Saudi’s Arabia” of magnets. Analysts price targets are virtually all 100% above today’s price. The selloff is not justified based on the underlying commodity price (aka fundamentals) is my takeaway from some reading. I also spoke with someone who worked at BofA this summer and per him “on a call with a senator the senator was essentially just repeating buy $MP and the moderator said shut up and stop”. May someone with a bigger brain then me give their thoughts
@calvinblissett 1 click

Calvin Blissett on X

$MP. You guys know me I am CHEAP, negative enterprise value, double digit dividend yields cheap. The theme behind $MP looks very secular, I saw Ross Perot call what they have in California the “Saudi’s Arabia” of magnets. Analysts price targets are virtually all 100% above today’s price. The selloff is not justified based on the underlying commodity price (aka fundamentals) is my takeaway from some reading. May someone with a bigger brain then me give their thoughts
@RPavuluri 8 clicks

Rohan Pavuluri on X

Always amazed when I learn about a new public company for the first time. Especially one that did YC and is based in SF/NYC. @BillionToOneInc is a $5B+ public company ($BLLN) that I never heard of before today. Despite it being YC S17 and the fact that I did YC myself a couple batches afterwards, I had no clue it existed. As far as I can tell, it's @ycombinator's biggest bio success story too. Checked the X account and it has 1,302 followers. Have never seen on a podcast, a wsj article, on the timeline, etc. but yet a monster bio company making very real revenue! Goes to show that there are likely multiple companies founded in last 10 years (not just the sleepy under the radar midwest ones from decades ago) still based in hot areas (BillionToOne is in Menlo Park) that still don't show up on the "timeline" and are even public (BillionToOne IPOed last year) that fly very under the radar...looks like the buzz makes no difference to its success!
@convequity 1 click

Convequity on X

Guess what is a more severe bottleneck than HBM? Grid transformers, switchgear and EPC, and electrical steel, both severity 94, are more severe than HBM at 93. Those numbers come from our AI Infrastructure Bottleneck Map, an AI infrastructure supply chain map. Severity runs from 0 to 100 and measures how hard a node constrains new AI capacity in 2026 to 2028. It measures scarcity, not the size of the market. On each company we score return potential and risk, each from 1 to 10 over the same 2026 to 2028 window. Return potential is the upside still left in the company. Risk is company risk, and 10 is the riskiest. The goal is to identify the companies with high remaining return potential and relatively lower risk. Not investment advice. The five most severe nodes, and what each one is: Grid transformers, switchgear and EPC, severity 94 Medium- and high-voltage equipment, grid interconnect, and the construction services that install them. The backlog runs multiple years, and the constraint is forming through 2028. • $HTHIF Hitachi, Ltd. Return potential 6.2. Risk 1. This is the lowest risk on the list, the return potential is still high, and the grid business sits inside Hitachi rather than in a separate listing. • $POWL Powell Industries. Return potential 7.5. Risk 3.2. This is the highest return potential on the list, the risk is still moderate, and Powell sells the medium- and high-voltage equipment on this node. • $600089.SS TBEA Co., Ltd. Return potential 6.5. Risk 6.1. Return potential is high and risk is high, and TBEA builds high-voltage transformers in China. Electrical steel, severity 94 The core steel inside transformers. Supply is tight, and this constraint is also forming through 2028. • $5401.T Nippon Steel. Return potential 6.4. Risk 3.2. High return potential with relatively lower risk, from a major mill for the electrical steel that goes into transformer cores. • $005490.KS POSCO Holdings. Return potential 6. Risk 3.8. This is the holding company rather than a separate steel listing, POSCO is a major electrical-steel mill, and the return potential is lower than Nippon Steel's with higher risk. • $CLF Cleveland-Cliffs. Return potential 6.5. Risk 4.2. Return potential is high, the risk is higher than Nippon Steel's, and Cleveland-Cliffs is the only US producer of grain-oriented electrical steel, the steel in transformer cores. HBM, severity 93 High-bandwidth memory stacks, which constrain accelerator performance. Supply is extremely tight and the vendors are few. • $005930.KS Samsung Electronics. Return potential 6.5. Risk 3.5. High return potential with relatively lower risk, from one of the three companies that make HBM. • $000660.KS SK hynix. Return potential 6.5. Risk 4. SK hynix is the second of the three HBM makers, with the same return potential as Samsung and higher risk. • $MU Micron Technology. Return potential 6.5. Risk 5. Micron is the third HBM maker, with the same return potential and the highest risk of the three. Gas turbines and on-site generation, severity 88 The turbines that make prime and backup power on site, and the hot-section parts those turbines need. This is the next power constraint forming in the 2026 to 2028 window, and this node has two listed names, not three. • $HWM Howmet Aerospace. Return potential 6. Risk 2.3. High return potential with relatively lower risk, and Howmet makes the castings that large turbines wait on. • $603308.SS Anhui Yingliu Electromechanical. Return potential 7.2. Risk 7.8. Return potential is high and risk is high, and Anhui Yingliu makes castings for industrial gas turbines in China. Leading-edge foundry capacity, severity 86 Manufacturing capacity at 7 nanometers and tighter. The map calls this the main AI hardware bottleneck. • $TSM TSMC. Return potential 6.2. Risk 3.8. The scarce capacity on this node is the capacity TSMC already runs. • $INTC Intel Corporation. Return potential 7.4. Risk 6.8. Return potential is high and risk is high, and the foundry work sits inside Intel with no separate foundry listing. • $0981.HK SMIC. Return potential 6.2. Risk 6.8. SMIC, Semiconductor Manufacturing International Corporation on the Hong Kong line, is the third listed foundry on this node, with a higher risk than TSMC.
@aleabitoreddit 1 click

Serenity on X

Few things: - $SIVE is targeting T1 hyperscaler deployments for pluggables, CPO, NPO. Not quite sure what you mean by Tier 2. They have direct engagements with hyperscalers but currently are more upstream by qualifying with companies like Ayar, $JBL, $GFS, O-Net that eventually likely ends up -> $AMZN / $MSFT for example. On financing: - Colo players like $CIFR still need $1B+ of financing for their sites. - Neoclouds like $CRWV need billions of financing to buy GPUs. Higher rates + less support for financing makes it more challenging for these players. $SIVE already raised 700M SEK for their buildout and largely rely on external foundries, so they don't have financing constraints. There's pressure on players that need currently need financing for capex, but markets group up everyone in AI sector indiscriminately. The second nuance is whether or not this effects the end customers in terms of demand. I'd argue optical sector is insulated, even if overall AI capex from hyperscalers decreases, I see optical content still rising and aggregate demand rising. And this is especially the case with new incoming players going through their first qual cycles.

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