“Without data centers, GDP growth was 0.1% in the first half of 2025. Investment in “information-processing equipment and software” (which includes data-centers) represented about 4 % of U.S. GDP but accounted for ~92 % of GDP growth in the first half of 2025” /Harvard economist Jason Furman/
🟩 Silicon & Data: 1 > 2
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“THE SINGULARITY IS NEAR: WHEN HUMANS TRANSCEND BIOLOGY”
@TOzgokmen/status/1985330454778527774 : Which is these still companies exist today?
Why should NVDA have a 10-year lead?
Who knew about NVDA 10 years ago?
☑️ #106 Oct 29, 2025
American (and swedish) lithography
@OmerCheeema: I live in Eindhoven, the ASML town. I was chatting with an employee at a gym last night. He works on the mechanical engineering side of the wafer table for EUV machines. The job is to align wafers to the source and ready it for exposure. The alignment needs to be accurate at a micrometer level. And about 900 people at ASML work on it. Substrate, the startup, has 12 employees as seen on LinkedIn. ASML should offer them jobs.
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@BloombergTV: @BloombergTV: A secretive American startup says it has chipmaking technology to rival ASML and take on TSMC.
Bloomberg’s @EdLudlow breaks the story on Substrate’s particle accelerator-powered lithography machine and plans for an American foundry http://bloom.bg/48Q0LG7
news.bloomberglaw.com (10/28/25): [Excerpt] Thiel-Backed Chip Startup Is Seeking to Crack ASML Dominance (1) A secretive American startup has emerged with aspirations to challenge two titans of the semiconductor industry: ASML Holding NV and Taiwan Semiconductor Manufacturing Co.
The company, Substrate, has developed a new chipmaking machine that uses particle acceleration to handle lithography — the crucial process of etching microscopic circuitry onto silicon wafers. Claiming to have solved one of technology’s toughest problems, Substrate aims to compete with ASML, whose equipment is currently the only option for making advanced processors.
+ Related content:
substrate.com & read our purpose: [Excerpt] Return to Solicon Valley.
Substrate was founded to return the United States to dominance in semiconductor fabrication on three core beliefs:
The cost of semiconductor fabrication is out of control, and it will only continue to trend upward without radical new technological innovation
The United States is dangerously reliant on sources of geopolitical and supply chain risk that we do not directly control
The only chance the United States has to return to dominance in semiconductor production would be to create a new type of more vertically-integrated foundry, one that continues pushing Moore’s Law in both performance and cost
To achieve this, Substrate is building next-generation semiconductor fabs to return America to dominance in semiconductor production and will use our technology—a new form of advanced X-ray lithography—to power them.
Leading-edge semiconductor fabrication facility cost over time which follows a steady upward trend. Source: Substrate linkedin.com: Atomic Layer Etching for Nano Device Fabrication
We provide an ALE-based method of manufacturing nanostructures with characteristic size below 20nm.
This is a new method of nanostructure fabrication using the atomic layer etching process, which is inherently a damage-free etch process. Owing to the etching process’s selectivity to inclined surfaces, which has been recently discovered, the inclined surfaces can be used as a mask and in this way walls of tapered structures, which can be readily fabricated by e.g., dry etching or epitaxial growth, will provide masking during the atomic layer etching process. This is why this process provides access to fabrication of extremely small structures in a very precise and efficient way.
alixlabs.com (10/7/25) [Excerpt] AlixLabs Secures Notice of Allowance for US Patent for Innovative Semiconductor Manufacturing Technology.
Lund, Sweden – October 7, 2025 – AlixLabs, a pioneering Swedish semiconductor equipment startup, today announced that the United States Patent and Trademark Office (USPTO) has issued a Notice of Allowance for the continuation of its internally named “Tetris” patent. The new patent strengthens and broadens AlixLabs’ intellectual property portfolio, covering key innovations in selective side wall masking technology that integrate self-aligned double patterning (SADP) with the company’s proprietary Atomic Layer Etching-based Pitch Splitting (APS™) process.
Source: AlixLabs AB
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☑️ #105 Oct 18, 2025
Data (digital) tasks: new data wave
@bearlyai: Uber will give its drivers in the US an option to make money by doing “digital tasks”. These short minute-long tasks can be done anytime including while idling for passengers:
data-labelling (for AI training)
uploading restaurant menus
recording audio samples of themselves
narrating scenarios in different languages
The opportunity is quite larges based on fact that two AI data-labelling startups (Scale AI, Surge AI) each reached ~$30B valuations.
Earlier in October, Uber — which has 1m+ drivers in the US — had acquired Belgian data-labelling startup Segments AI for an undisclosed amount.
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☑️ #104 Oct 17, 2025
Scaling Compute as a Moat: Going from 0 to 200,000 GPUs in just over 200 days was practically unheard of. That puts xAI’s Colossus ahead of all but a few hyperscalers in terms of raw compute, making this infrastructure the central competitive moat for LLM training and inference at scale.
The core idea: Whoever owns (or controls) the most compute wins.
In only 122 days, xAI built its first huge data center, Colossus in Memphis, Tennessee, equipping it with 100,000 Nvidia GPUs. 92 days later, capacity was boosted to 200,000 units. Now, Antonio Gracias (Valor Equity Partners) anchors xAI Corporation’s plan to raise $20 billion for a deal using a special purpose vehicle (SPV) that would acquire semiconductors from Nvidia Corporation and then lease them to Colossus 2. perplexity.ai and phind.com (Phind-70B) > [Sources not verified. Do your own research]: In the AI arms race, models matter—but scale, speed of iteration, and compute density are increasingly the differentiators. xAI clearly sees this and is making infrastructure its front line.
xAI’s rapid execution in scaling its compute infrastructure is one of the most striking industrial feats in recent tech history. Building and doubling capacity at Colossus in just over seven months illustrates the extreme speed and capital availability shaping the current AI arms race. A few key implications stand out:
Vertical coordination and financing: By using a special purpose vehicle (SPV) backed by Antonio Gracias and Valor Equity Partners, xAI is effectively separating asset ownership (semiconductors) from operational use (Colossus 2). This structure minimizes balance sheet strain while ensuring access to scarce Nvidia GPUs—an ingenious financial engineering move under today’s hardware constraints.
xAI’s infrastructure strategy for its “Colossus 2” data center in Memphis, Tennessee, represents a significant departure from traditional AI development. The initial “Colossus 1” site already boasts over 200,000 Nvidia H100 GPUs. For “Colossus 2,” the focus is shifting to even more advanced hardware, with plans for 550,000 Nvidia GB200 and GB300 GPUs, aiming for an eventual total of 1 million GPUs within the entire Colossus ecosystem. Elon Musk has publicly stated an audacious goal for xAI to deploy 50 million “H100 equivalent” AI GPUs within the next five years. This scale is unprecedented, requiring a “gigawatt-scale” facility – one of the largest, if not the largest, AI-focused data centers globally, with xAI constructing its own dedicated power plant, Stateline Power, in Mississippi, to supply over 1 gigawatt by 2027. (wral news)
phind.com: The financial breakdown is sourced fromn markets.financialcontent.com which mentions “$7.5 billion in new equity and up to $12.5 billion in debt.”
Strategic positioning vs. hyperscalers: This model could allow xAI to scale faster than Big Tech peers locked into more traditional data center expansion timelines. If successful, it may pioneer a new hybrid approach—AI companies as “GPU tenants” rather than infrastructure owners.
Market dynamics and geopolitical influence: The Memphis facility marks a strategic domestic placement, likely aligned with U.S. policy goals to repatriate critical computing capacity. It also signals Nvidia’s continuing dominance in AI training chips, though at the cost of creating systemic dependency between the two firms.
Capital and risk outlook: Raising $20 billion through a single-purpose leasing vehicle signals investor appetite is still robust for foundation-model infrastructure, but it raises questions about long-term ROI and operational leverage—especially as new architectures (custom ASICs, emerging competitors) evolve.
Overall, this move looks like a high-risk, high-return bet designed to secure xAI’s independence in compute supply while cementing its position as a world-scale AI developer. It shows Elon Musk’s consistent playbook: integrate critical capabilities early and finance with partners willing to think in decades, not quarters.
+ Related content:
@markets: Nvidia to Invest in Musk’s xAI.
Elon Musk’s artificial intelligence startup xAI is raising more financing than initially planned, tapping backers including Nvidia to lift its ongoing funding round to $20 billion, according to people familiar with the matter. Nvidia is investing as much as $2 billion in the equity portion of the transaction, the people said. Bloomberg’s Annabelle Droulers reports.
PrateekJ17 (10/12/25): So we started this because we needed a way to provision infrastructure for AI agents at scale. Turns out nobody had really solved this properly. We wanted agents that could just... spin up a VM when they needed one, do their thing, and shut down. Simple idea, but getting it to actually work reliably was a whole journey. What we ended up building:
Agents that can deploy and control their own virtual machines An orchestration layer that doesn’t fall over when you scale to 100+ agents Support for pretty much any LLM (GPT-5, Claude, local models, whatever) Real monitoring because debugging invisible agents is a nightmare
The whole thing is open source. Apache license. No strings attached. We’re using it for automated testing and web scraping stuff, but honestly people are probably going to use it for things we haven’t even thought of. If you’ve ever tried to run computer-use agents in production, you know the pain points. That’s what we tried to fix. GitHub: https://github.com/LLmHub-dev/open-computer-use
PrateekJ17 (10/14/25): We open-sourced XAI’s Macrohard, an autonomous computer-using agent.
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☑️ #102 Oct 15, 2025
Sustainable and adaptive Scake Data Centers and Builld-to-Scale solutions for global hyperscale, AI/HPC, and enterprise customers
aligneddc.com: [Excerpt] AIP was founded by BlackRock, Global Infrastructure Partners (GIP), a part of BlackRock, MGX, Microsoft, and NVIDIA to expand capacity of AI infrastructure and help shape the future of AI-driven economic growth. Its financial anchor investors include the Kuwait Investment Authority and Temasek.
In less than a decade, Aligned has evolved into one of the largest and fastest growing data center companies globally. The Company designs, builds, and operates cutting-edge data campuses and data centers for the world’s premier hyperscalers, neocloud, and enterprise innovators. Aligned’s portfolio includes 50 campuses and more than 5 gigawatts of operational and planned capacity, including assets under development, primarily located in key Tier I digital gateway regions across the U.S. and Latin America including Northern Virginia, Chicago, Dallas, Ohio, Phoenix, Salt Lake City, Sao Paulo (Brazil), Queretaro (Mexico), and Santiago (Chile).
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☑️ #101 Oct 14, 2025
Beginning in calendar Q3 2026, Oracle will be the first hyperscaler to offer a publicly available AI supercluster powered by 50,000 AMD Instinct MI450 Series GPUs
oracle.com: [Excerpt] Oracle and AMD Expand Partnership to Help Customers Achieve Next-Generation AI Scale.
AMD Instinct MI450 Series GPUs Coming to OCI
AMD Instinct MI450 Series GPU-powered shapes are designed to deliver high-performance, flexible cloud deployment options and provide extensive open-source support. This provides the ideal foundation for customers running today’s most advanced language models, generative AI, and high-performance computing workloads. With AMD Instinct MI450 Series GPUs on OCI, customers will be able to benefit from:
Breakthrough compute and memory.
AMD optimized “Helios” rack design.
Powerful head node.
DPU-accelerated converged networking.
Scale-out networking for AI.
Innovative UALink and UALoE fabric.
Open-source AMD ROCm™ software stack.
Advanced partitioning and virtualization.
To give customers that build, train, and inference AI at scale more choice, OCI also announced the general availability of OCI Compute with AMD Instinct MI355X GPUs. These will be available in the zettascale OCI Supercluster that can scale to 131,072 GPUs. AMD Instinct MI355X-powered shapes are designed with superior value, cloud flexibility, and open-source compatibility. Learn more here and here.
+ Related content:
chat.openai.com: [Sources not verified. Do your own research] Key Specifications & Architecture
The MI450 Series are AMD’s next‑generation data center / AI training & inference GPUs, built on CDNA “Next” architecture (sometimes referred to CDNA 5 in reporting). They will be manufactured using TSMC’s 2 nm (N2) process technology, which gives performance/power/density advantages over previous nodes. (Datacenter Dynamics)
AMD is positioning variants within the MI400 family: for example, MI450X, aimed at large‑scale AI workloads (low‑precision / inference), and MI430X, for HPC/high‑precision workloads (FP32/FP64). These variants will have differentiated logic (dropping certain precision units in each to optimize die space).
The MI450X is expected to lean heavily on Ethernet‑based “Ultra Ethernet” (scale‑out networking), rather than relying immediately on UALink / dedicated accelerator fabric switches, which may lag in availability. (Tom’s Hardware)
“Helios” Rack Design
AMD is developing a rack‑scale architecture called Helios, which couples MI450 GPUs with EPYC CPUs (codename “Venice”) and networking via AMD Pensando (codename “Vulcano”) to deliver large scale AI training + inference clusters.
A Helios rack holds 72 MI450 GPUs, is liquid cooled, and is designed for high performance density, power efficiency, and scalability.
The Helios rack design signals AMD’s push to provide end‑to‑end solutions (GPU + CPU + networking) and compete more directly with Nvidia, especially in hyperscale cloud / AI supercluster infrastructure. (AMD)
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