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Markos
@MarkosAAIG
Founder of AAIG. Research built across the stack. A 7-person team spanning engineering, finance and strategy. One investable view.
236 Following    17.5K Followers
Of my three holdings in the neo-cloud space ($NBIS , $IREN and $WYFI ) Nebius is the clear strategic winner from SemiAnalysis’ new ClusterMAX 3.0. Which isn’t a suprise ofc. Nebius is proving it can serve both ends of the market: multi-hundred-megawatt hyperscaler contracts and smaller, shorter-term clusters for AI labs and startups. Its move into Platinum validates the full-stack strategy, strong infrastructure, orchestration and customer experience translating into premium pricing and higher revenue per MW. Build by a strong, experienced team. IREN’s result is clearly weaker, but it is a bit more nuanced on site and older DC infra imo. Most of the negative feedback relates to Prince George and Mackenzie, while the newer Childress and Sweetwater builds appear materially stronger. I had hoped to see clearer validation from Sweetwater but we have to wait when first cluster is online. IREN’s strategic strength remains its ability to build large-scale, competitively priced capacity. The next step is proving that its managed-cloud layer can catch up or remaining disciplined around bare metal plus where demand is already strong. For WhiteFiber, there is not enough recent evidence for a high-level judgment. ClusterMAX still relies largely on earlier testing. WhiteFiber is now signing new cloud contracts on an improved platform, so the next meaningful signal must come from actual customer experience. Can’t judge it properly with recent developments but the road they are taking is promising.
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Interesting for $NUAI pipeline.
Anthropic is in early talks to lease up to 1GW of capacity from Stream Data Centers, majority-owned by Apollo, per The Information. The facilities could be filled primarily with Google/Broadcom TPUs, with Nvidia GPUs also possible. Google has also been discussed as a potential credit guarantor for the project. A 1GW buildout would require at least $40B in capital investment, according to data center developers. By leasing facilities directly and sourcing chips itself, Anthropic could gain more control over compute costs, hardware supply, networking and server infrastructure. Anthropic has already signed smaller direct data center leases with Hut 8 and TeraWulf, a 2GW chip agreement with AMD, and a $35B TPU leasing deal backed by Apollo and Blackstone. The company has signed roughly $531B of compute agreements over the past 11 months.
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Saw this move coming. These are the fastest to bring online. Can be retrofit or modulair building. You can purchase existing factory load sites with a smaller MW grid connection much easier due to availability. Much more for sale. There has already been a lot of site inspections by players like Krambu, Duos technologies and much more. Private company Giga energy also sees huge growth in their modulair building. We talk with the CTO soon. $WYFI $DUOT $IREN
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Morning Developments: Anthropic & OpenAI hunt for smaller data center deployments of 20-30 MW Northland Capital starts $IREN with Outperform rating and $99 price target Morgan Stanley maintains $CIFR Overweight rating and raises price target to $54 from $43.50
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Still long power with $IREN $NUAI , We wil release a thesis this week on Landbridge. $LB owned it for a while now. We need a lot more MW.
We see more risk that NVDA misses estimates because its customers cannot get power than that the miners fail to lease their megawatts. Our re-underwritten base case implies ~83% average upside across our powered-land holdings.
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If you want to play the Nscale IPO, look at the JV partner $AKER.OL They still own a 22.7% ownership position in Nscale. Nscale scaling will be very bumpy though because they have a huge backlog, and they will carry very large losses before their infrastructure has reached scale. You also have some dilution risk. CEO of Aker is still on the board though. If the IPO is valued at a big higher valuation it gives immediately upside for Aker also. They online have 5% of $NVDA GPUs online tho so they will be very power hungry and aggresive.
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NVIDIA-BACKED NSCALE FILES FOR NYSE IPO UNDER $NSCL; REVEALS $103.4B OF CONTRACT VALUE Here’s everything you need to know: Nscale is a UK-based AI cloud company headquartered in London. It builds and operates data centers, GPU infrastructure and cloud software that customers use to train AI models and run them in production. Its filing reports $103.4B in total contract value as of Aug. 31, up from $38B at the end of 2025. These are multi-year contract amounts, not annual revenue or cash already collected. ANTHROPIC: UP TO $44.6B Agreements signed Aug. 25 cover Nvidia Vera Rubin computing systems at Nscale’s planned West Virginia campus. The key financing disclosure: Nscale says it does not yet have binding commitments for the financing needed to deliver the Anthropic buildout. The filed agreement also includes a right to terminate if the required financing is not secured by an agreed deadline. MICROSOFT: UP TO $43.8B THROUGH 2033 Microsoft is Nscale’s other major customer. Its previously announced deployment program includes roughly 200,000 Nvidia GB300 GPUs across the U.S., UK, Portugal and Norway, with Dell participating in the infrastructure rollout. The disclosed Microsoft and Anthropic contract amounts together reach $88.4B, equivalent to roughly 85% of Nscale’s total contract value. How much capacity is actually running? Nscale had approximately 461,000 GPUs active or contracted at the end of August, but only about 25,000 were operating. That distinction matters: the contracted footprint is substantially larger than the infrastructure already delivering services. First-half 2026 financials • Revenue: $140.6M, up from $10.4M a year earlier • Operating loss: $492M • Net loss: $1.02B, including $457M in fair-value losses Nvidia backing and fresh financing Nvidia agreed to invest $1B as part of a $3.1B convertible financing signed Sept. 15. Earlier this year, Nscale raised $2B at a $14.6B valuation, with backers including Nvidia, Dell, Nokia, Citadel, Jane Street and Point72. That was its March funding valuation, not an announced IPO valuation. The offering Goldman Sachs, J.P. Morgan and Morgan Stanley are leading the proposed NYSE listing. The share count and price range have not yet been determined. The filing puts the opportunity and the execution challenge side by side: substantial customer commitments, but much of the capacity still needs to be financed, built and brought online.
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A small extension on yesterday. I personally found the memory market to be one of the most difficult markets to research out of all the research I have done, because there are just so many moving parts. I have heard the same from members, subscribers and clients. I also see it daily on my X feed: 10 or 20 posts about Samsung expanding here, SK hynix moving to this technology, Micron moving to that, Samsung taking the lead on yield, then SK hynix having the yield lead, then Samsung not being qualified, and then substrates being in a huge shortage… But it is impossible to tie all of this together if you do not put in the work to map it from every angle and put it into a live system. For most private investors, depending on how much money you have exposed to memory, it is simply too big of a task. Ideally and this is what I built for our members and clients you use all the most important angles that actually move something, allowing you to cancel out the noise and focus on one overview. Otherwise, the entire memory trade, at a high level, is almost impossible to follow. $SKHY $MU $SNDK
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Little late night personal commentary
Like I said 3 days ago. Before we talk about full AI ROI we need to learn the basics.✍️ (must read for the people that need to bridge all these tech post on models, GPUs, etc to human psychology and adoption) Yesterday at AI Infra Summit, I listened to a panel called “Operationalizing AI: Turning Data, GPUs, and Inference into Enterprise ROI.” It was moderated by Radhika Malik of Dell Technologies Capital, with Kaushik Shirhatti of NVIDIA, Boris Lukashev of WhiteFiber, Ruben Bryon of Verda and Alex Saroyan of Netris on the panel. A lot of things where so inline with my own thinking that i feel the industry is moving in the right direction. I was very clear on something a while back: you need to give people time to adopt AI and get used to the basic ways of using the tools. Otherwise, it becomes way too fast, and that essentially stops adoption. Thats not rocket science. Kaushik compared generative AI to a vending machine. You press a button and get an answer. An agent, on the other hand, is more like a new employee. It needs to be onboarded. It needs the most current information, access to the right tools and an introduction to the systems. It also needs boundaries, security, supervision and all of that. Kaushik also compared the complete team to a relay team so the compute, network and software may all be super good individually, but if you have four sprinters on your team, you need to practise the handoff. Its as simple as that. That is where a lot of performance, and therefore ROI, is won or lost for the enterprise and, to keep it even simpler, for people using AI. I’m going to show you a few angles here. Ruben focused on speed and adaptability. He compared a neocloud to a fighter jet and a conventional cloud more to an aircraft carrier. This is also so spot on because I’ve been talking about the agility that, for example, Nebius $NBIS provides and WhiteFiber $WYFI also. This panel showed again that you need to adapt fast, with physical deployment, software and operations at the same time, even when billions in capital have just been spent. A lot of people misinterpret the agility that is needed for this build-out. Ruben’s advice was to get people using the tools quickly, including in areas where the return is not yet completely clear. The cost of producing intelligence continues to fall while the models keep improving. I couldn’t agree more with that. This session made so much sense to me, based on how I was already thinking, that I had to share it with you. Boris, for example, said: “Preemptive optimization is the work of the devil.” You need to reach the required level of accuracy first and then optimize for performancenot earlier. So how much proof of ROI should we demand before allowing people to experiment? We can measure GPU utilization, inference speed, token volume, everything but those numbers don’t tell you anything about whether AI improves a decision imo. @tengyanAI talked about it this week as well. I personally see that it is a really fine line that you need to walk. If you ask too early, you are killing promising applications before people really understand what the technology can do. I think we are now at the point where we should start that.And if you leave experimentation unfocused for too long, you burn through a lot of resources without actually learning anything from feedback. The panel agreed, and I was already fully aligned with this. The right approach is to let people explore what becomes useful and then concentrate investments on three or four workflows that materially change businesses. I found this such a good panel discussion. Everybody should read it if it comes outor when it comes out. I’m not sure.👌👌 $IREN $DELL
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Some personal Investing lesson on how I research and judge. My investing approach starts by mapping a market in its early stages, then working out what it could look like when it matures. From there, I reverse engineer what a company needs to do to succeed throughout that transition. For example on the AI buildout (subsector power) Early on, securing power and grid access is critical. As the market develops, constraints could increase the need for on-site generation, battery storage and software that coordinates power supply and demand to use available capacity efficiently. I then bring that back to the company level: what is it building today? Which capabilities is it developing? Do those investments position it for the next stage of the market? How does management align their strategy to my end state market vision. That shows me if it’s just a quick trade because they lack it. That’s one part on how I invest: i map the starting point, map the potential destination, and test the companies building the capabilities needed to get there. If you reverse engineer it all will become so much clearer for you all. Give it a try.
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Everybody stressing out about the Dario comment and Sam Altman and Elon Musk publicly backing it, but it has zero to do with the AI buildout demand. This is from the model side and even if so it’s temporary. We still need a vast amount of compute. Nothing has changed there, but potentially the market will overreact tomorrow as always.
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WhiteFiber Management Call: The Focus on Strategy and Execution This week, as promised, we are releasing our full @WhiteFiber_ management call analysis for everyone to read. We had a very constructive conversation with Michael Francisco, Vice President of Cloud Services, and Cameron Schnier, Senior Vice President of Capital Markets and Corporate Strategy. I know the WhiteFiber retail community has been highly anticipating this one. $WYFI We went into the call wanting to understand how the different parts of WhiteFiber fit together and what kind of company management is actually trying to build over the long term. We also wanted to discuss the broader market and what is currently happening across the data center space. This gives us an even deeper understanding of companies we have already followed for a long time, including Nebius and IREN. We discussed everything from the strategy behind NC1, NC2 and NC3 to colocation, financing, WhiteFiber’s efforts in cloud and managed services, the Token Factory it is building, and the partnerships management wants to use to scale faster. This call gave us much more than an update on individual projects. It gave us a clearer view of the model management is building. In short: own selected strategic sites, create stable colocation income, selectively deploy cloud capacity, and scale beyond the company’s own balance sheet through managed services and partner locations. This was a good conversation to pick management’s brain about WhiteFiber’s strategy, while also testing our own view of where neoclouds and the broader AI infrastructure market are heading against Whitefibers model. A very good conversation and, in my opinion, a highly complete write-up for anyone who wants to understand what WhiteFiber is trying to build looked trough my strategic lens. 17 pages of company insights! Enjoy! Link in comments 👇 $WYFI $NBIS $IREN $CRWV
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I did not see this coming at all. Wow.
*CITRINI FOUNDER VAN GEELEN SELLS FIRM TO SEMIANALYSIS @citrini is selling selling to SemiAnalysis Did not see that coming
My base case for memory. Huge report incoming. $MU $SKHY
@pequityresearch Yet is related to the accelerated adoption of AI and that you don’t assume a rebound but additional capacity gets consumed immediatly with bigger models, longer context etc etc. Its my base case of my HBM research
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@pequityresearch Yet is related to the accelerated adoption of AI and that you don’t assume a rebound but additional capacity gets consumed immediatly with bigger models, longer context etc etc. Its my base case of my HBM research
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Future concept I have in mind is called AAIG Hive: a living intelligence network that connects our research across every layer of the AI infrastructure stack. So the work we do from power and data centers to cooling, chips, memory, networking, etc etc. The goal is to show how a development in one part of the ecosystem ripples through companies, and investment theses turning separate research into one connected system. In love already.
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GPT-6 Astra with the Higgsfield plugin looks insane. You can upload your CCTV footage and have Astra track parcels and activity.
Hmm, a lot of ambition, but yields can’t make it. As you can see @jukan05 shows reports indicate approximately 30% front-end yield and 70% back-end yield, so you get only about 21% final yield. I think estimates were around 25%. SemiAnalysis was much higher, at 35% and 70%. So it’s more difficult than they anticipated, for sure. $MU $SKHY
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"Three out of four are defective"... China's CXMT struggles with HBM yields, with immature TSV technology to blame ChangXin Memory Technologies (CXMT), China's largest DRAM maker, has begun trial production of fourth generation high bandwidth memory (HBM3), but initial yields are barely improving. Yields are reported to have stalled at 25%, roughly one third of the so called "golden yield" of 80% that the semiconductor industry treats as the threshold for volume production. The gap is stark compared with SK hynix, which has been mass producing the same product since 2022 and has secured yields above 90%. Industry sources point to the gap in maturity of through-silicon via (TSV) technology, the core process for stacking and connecting multiple DRAM layers, as the root cause. ◇ "DRAM has caught up, but HBM stacking is a different problem" According to a senior official at a semiconductor equipment company familiar with CXMT's situation on the 9th, the yield of CXMT's HBM3 8-High product is stuck at around 30% in the front end process. Of the products that survive that stage, only about 70% are recognized as final good units after passing through the back end process. In simple terms, if 100 HBM3 units are started, close to 80 of them fail the final test. CXMT is reported to be supplying the small volumes of HBM samples it produces this way to Chinese companies such as Alibaba's T-Head and Cambricon while continuing its yield improvement work. The problem is that the issue does not lie in the fine process technology of the DRAM itself. A semiconductor equipment industry official explained, "There is no major problem with the standard DRAM that CXMT makes on its 'G4' (17nm class) process used for HBM. However, the DRAM dies used for HBM are larger in area than standard products and have more demanding electrical specifications, so even on the same process they are much harder to pass the acceptance criteria." In other words, CXMT's fundamental capability in making standard DRAM has risen to a considerable level, but a bottleneck is emerging at the stage of converting it into the high performance product that is HBM. At the heart of that bottleneck, according to industry sources, is the TSV process. ◇ The real hurdle is TSV... "Impossible to catch up without years of accumulated know how" TSV stands for "Through Silicon Via" and refers to the microscopic copper wiring that passes vertically through each layer to carry electrical signals when DRAM is stacked in multiple layers, as in HBM. It is a highly demanding process in which a DRAM wafer is thinned down to several tens of micrometers, a fraction of the thickness of a human hair, after which thousands of tiny holes are drilled through that thin silicon plate and filled completely with copper. A single hole that is misaligned or not properly filled can cause the entire layer to be rejected, making it one of the semiconductor processes with the most stringent precision requirements. The consensus in the industry is that this is the process where the technology gap between CXMT and the leading companies is widest. According to analysis by semiconductor research firm Nomad Semi, Samsung Electronics' HBM2 (second generation HBM) has more than 5,000 TSVs per die and SK hynix's HBM3 has more than 8,000, while CXMT's is understood to have only around 3,000. A smaller number of TSVs means sacrificing bandwidth (data processing speed) in exchange for lower process difficulty, yet even so CXMT's yields still fall far short of Samsung and SK hynix. TSV is a process that is challenging even for the industry leader: SK hynix itself publicly disclosed in 2024 that the yield of the standalone TSV process was only 40 to 60% at the time. On top of this, yield losses also occur in the back end (stacking and bonding) stage where the dies are actually stacked and joined. If even one of the eight dies is misaligned, if a microscopic void forms at a bonding interface, or if a layer warps during the thermocompression bonding process (warpage), the entire stack is scrapped. Because the number of possible failure points grows with each additional layer, the difficulty rises exponentially. Given that CXMT is already showing such poor yields at 8-High, some expect it to face even greater difficulties when moving to higher stacks such as 12-High. A semiconductor industry official explained, "The TSV process is an area that only stabilizes after years of accumulated wafer handling know how. Chinese companies have rapidly closed the gap in the fine process technology of DRAM itself, but back end know how such as TSV and bonding is difficult to catch up on in a short period." He added, "That said, Samsung Electronics also had initial HBM4 (sixth generation HBM) production yields below 60% in February this year and raised them to 80% within six months, so it is too early to declare CXMT's 25% yield a 'failure.'"
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I love Nebius, I really do, but I would also like to keep our data center expert. 😉 Mark’s journey of posting on X has only just begun, and I believe he is massively underfollowed. If you see the quality he brings to our team in terms of both his knowledge and his network relating to everything around data center construction you’ll understand how valuable he is. From power and cooling architecture to high-density rack layouts, equipment lead times, construction sequencing, how suppliers operate, and where shortages will hit.. name it all. He understands the entire path, from initial design and program planning through deployment, commissioning, handover, and day-to-day operational reliability. We have seen so many experts on X provide massive value over the past year, including @damnang2 and @PhotonCap Within his own area of expertise, I think @Mark_AAIG definitely belongs among them. He brings enormous value to our team, and if you read his technical papers, you will understand why. We are super happy to have him. Make sure you give him a follow! $NBIS $IREN $CRWV $WYFI
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