Cole

@chamath TATRASUPERCOMPUTE.COM we have power in CEE...

We are massively power constrained. This market, if functioning properly like every market before it, should be moving in the exact opposite direction.

Every trader can now take a position on the most important commodity of the AI infrastructure buildout: compute. @OrnnExchange x @ICE_Markets are partnering to list the first transaction-based compute index on a regulated exchange today. Compute is a commodity. ir.theice.com/press/news-det…

Coatue CIO of Public Investments Jaimin Rangwalla explains why the market is rewarding AI infrastructure suppliers ..and punishing the hyperscalers buying the shortage: YTD Return: Sellers ~107% Buyers ~4% "Amazon & Google are in an unusual camp" Full list: Sellers of Products in Shortage: - Broadcom - Coherent - Marvell Technology - GE Vernova - Lumentum - Samsung Electronics - Micron Technology - NVIDIA - TSMC - SK hynix - Western Digital - Seagate Technology - SanDisk Buyers of Products in Shortage: - Amazon - Google - Meta Platforms - Oracle - Microsoft @coatuemgmt

The robotics supply chain is bigger than just Tesla. Here are 10 stocks building the components that go inside every humanoid robot: 1. $MP - MP Materials Every robot motor needs rare earth magnets. China controls over 90% of the global supply. MP is building the American alternative. Q1 revenue $90.6M, up 49%. Record NdPr oxide production of 917 metric tons, up 63%. The Fort Worth facility has been producing finished NdFeB magnets since December 2025. Just broke ground on the 10X magnetics campus. No magnets, no motors. No motors, no robots.

A framework to understand how value accrues across the AI stack. This is a blueprint for understanding what builds AI into its pragmatic parts: what each layer is, where it ends, and where value is accrued. So here’s how you can think about it: 1. Layer 1 - Infrastructure Before any AI model trains or any robot moves, an industrial foundation must exist. Land, energy grids, cooling systems, critical minerals, and fabrication facilities. Infrastructure is the constraint that all the other layers depend on. 2. Layer 2 - Chips Transistors that are etched onto silicon wafers using extreme ultraviolet light. This is what allows both physical and digital AI to take an input, process it, and return a predictive output. The more transistors that fit on a chip, the more computation it can perform. 3. Layer 3 - Data Both digital and physical models train on data. Digital models train on text, code, and images; physical models train on gravity, friction, depth, and sensor streams. The more accurate the data, the more accurate the output. 4. Layer 4 - Models A model is a system that learns from examples. Feed it enough examples of inputs paired with correct outputs, and it adjusts its internal structure until it can predict correct outputs on inputs it has never seen before. LLMs represent a specific class trained on text. They learn by processing billions of examples of human language, developing the ability to write, reason, summarize, and generate code. 5. Layer 5 - Execution This is what lets models take actions on behalf of users. The execution layer lets models pursue objectives through sequential action: observing the environment, reasoning about the next step, acting, and looping until the goal is reached. 6. Layer 6 - Application All of the AI Stack’s revenue originates at the application layer, then goes to the layers below. Every dollar paid for AI is paid for an outcome, a task completed, and an answer delivered. Nobody wants H100s for their own sake. They want H100s because someone, somewhere, wants to run an application. These are the different layers that make up the entire ecosystem of AI. We did a full study on the AI stack. If you want to read about it, head over to my Substack (chamath.substack.com/p/the-ai-stack)

Not sure if people realized this but unless a thesis completely breaks, companies like $NBIS can keep growing. Just look at $AMZN or $GOOGL over the past 15 years. If people "trim" it often triggers taxes. And a lot of corrections are typically less than those taxes paid. By the time a "50% crash happens", it's probably already compounded hundreds of even thousands of percent. If people need to pay expenses, once you hit 7-8-9 figures, you can always borrow against those assets and keep letting them appreciate. NFA, just personal opinion. You all do you, but it's highly, highly, dependent on the companies you pick. Can't do this with something trash like $IREN. But I do believe $NBIS is positioned to be the next hyperscaler.

*CEREBRAS IPO IS MORE THAN 20 TIMES OVERSUBSCRIBED: SOURCES *AI CHIPMAKER CEREBRAS RAISES PRICE RANGE TO $125-135 (PRIOR $115-125): BLOOMBERG

@elonmusk @SawyerMerritt tatrasupercompute.com

@SawyerMerritt xAI will be dissolved as a separate company, so it will just be SpaceXAI, the AI products from SpaceX

SpaceX/xAI has just announced that it has signed an agreement with Anthropic to provide access to its Colossus 1 supercomputer. "Anthropic plans to use this additional compute to directly improve capacity for Claude Pro and Claude Max subscribers. As part of this agreement, Anthropic also expressed interest in partnering to develop multiple gigawatts of orbital AI compute capacity. The compute required to train and operate the next generation of these systems is outpacing what terrestrial power, land, and cooling can deliver on the timelines that matter. Built from the ground up in record time, Colossus delivers unprecedented scale for AI training, fine-tuning, inference, and high-performance computing workloads. Colossus 1 features over 220,000 NVIDIA GPUs, including dense deployments of H100, H200, and next-generation GB200 accelerators. The cluster delivers extreme parallel performance for large language models, multimodal systems, scientific simulations, and generative AI at frontier scale. SpaceX is the only organization with the launch cadence, mass-to-orbit economics, and constellation operations experience to make orbital compute a near-term engineering program rather than a research concept. If engineering challenges can be overcome, space-based compute offers near-limitless sustainable power with less impact on Earth." I think this is smart for SpaceX/xAI, as it seems to have some excess capacity as Colossus, so why not make a some money on the side by renting that compute capacity out. I think we'll see some more similar partnerships soon.

$FCEL FuelCell Energy is surging on the AI data-center power narrative. High-temp fuel cells for clean baseload + carbon capture + hydrogen. Market Cap: ~$650M 🤯 Financials (TTM) • Revenue: $170M (+growth, Q1 +61% YoY) • Still unprofitable: Net loss ~$186M, but narrowing • Cash: $312M | Debt low | Strong liquidity Backlog ~$1.17B. Manufacturing ramping to 350MW. Bull Case 🔥 • 12.5MW FuelCell Blocks scaling for hyperscale data centers (gas + CMM partnerships) • AI power demand tailwinds lifting the whole sector • Pipeline exploding on off-grid solutions Risks ⚠️ • Negative gross margins • Cash burn & dilution risk • Execution on big contracts unproven • Analysts target ~$8.50 (Hold rating) Backlog: ~$1.17B (Generation dominant). Focus now on converting massive pipeline into firm orders.

@scottmelker @Micro2Macr0 @OrnnExchange

WATCH: BLACKROCK CEO LARRY FINK SAYS "I ACTUALLY BELIEVE A NEW ASSET CLASS WILL BE BUYING FUTURES OF COMPUTE, WE JUST DON'T HAVE ENOUGH COMPUTE POWER RIGHT NOW."

@michaelsikand INFQ is great pick 👍

You can make money buying consensus stocks. But the asymmetric returns happen when you understand an asset better than the market. Of course, this strategy is harder and riskier but here's a few on my radar. 1. $INFQ - I call it the anti-quantum quantum stock because they're guiding for $40M in revenue by commercializing their quantum clocks for defense/space applications while others wait for commercial adoption. Trades at a fraction of its peers on P/S and the post-SPAC lockup doesn't appear to be as apocalyptic as expected per my convos with a few sharp traders. Forthcoming $20B Quantuinuum IPO re-rates sector? 2. $QCOM - Best ARM CPU IP outside of Apple / ex-Intel XEON architect hired now pivoting to building a data center CPU and ASIC. Hyperscaler customer confirmed yesterday. Tailwinds in smartphone and robotics inference chips. Priced like a distressed asset and reorganizing its valuable resources for an AI future. Investor update in June / new details are catalysts. 3. $FLY - 40% of their revenue base is EBITDA-positive missile tracking software not cash burning space projects. Thought of as a space stock but I think better described as a prime contractor on $185B golden dome program. Aims to nearly triple revenues this year. Has launch and lunar economy upside with rockets + lander. 4. $AVEX - Drone stock with a real, established business shipping diverse hardware to the battlefield with $430M in 2025 revenue. Guiding half that in Q1 2026 alone. It trades at a fraction of $ONDS market cap even though it did more revenue in 2025 than $ONDS guides for in 2026, and has much deeper ties in the ISR and U.S. defense ecosystem. IPO overhang and volatility creates uncertainty but doesn't change fundamentals to comps. What about you? Share your ticker and thesis below!

@USDai_Official Congrats! What’s the LTC?

USDAI's largest loan to date is now active. - $34M. - 96 B200 servers, 768 GPUs in Sweden. Funds are earning 5% in escrow. 12% APR post-execution.

@kellyjgreer I have capacity on B300s … 🫡

just talked to a lab with a war chest of a balance sheet. they actively contract compute across all major clouds and neos no one has any material capacity coming online between now and September hourly compute pricing is going much higher in the coming 5 months

$NVDA $INFQ

Infleqtion CEO Matthew Kinsella has explicitly modeled the company’s strategy on NVIDIA’s playbook: deploy real products (atomic clocks, RF receivers, inertial sensors) that already outperform classical alternatives, use that revenue to fund longer-term quantum computing milestones, and deepen integration with NVIDIA’s GPU ecosystem across data centers and the edge.

the market is treating $INFQ, $RGTI $QUBT and other quantum stocks like the same trade, they’re not. $INFQ is the only one that hasn’t fully rerated yet. It actually has revenue and contracts, but the stock hasn’t had the same hype move as the others in 2025.. Quantum will matter one day. Right now, most of this is just people getting ahead of themselves.

Insane, OpenAI is now planning for 30GW of compute by 2030. Just one lab, $1T TAM for Jensen, but $NVDA wont move because people are still debating should hopper be sold to China.

@NWischoff Location just in US?

Co-founded an AI data center co last year. Had an early take that hyperscalers would begin to focus on tier II, III cities and smaller increments of power (sub 100MW), question was how quickly. Went from "call back when you have 300MW" to "we will take whatever you have" within 12 months. Giddy up.

@SmallCapSnipa @AnthropicAI can come see tatrasupercompute.com

ANTHROPIC PLANS TO SIGN DATA CENTER CAPACITY DEALS IN AUSTRALIA & EUROPE The company has only publicly announced data center plans in the United States, but job listings show that may change soon 🇦🇺

@ChrisCamillo Own @AnthropicAI @LambdaAPI @perplexity_ai @jobyaviation .....

Amazon isn’t simply “investing” another $5B + up to an additional $20B into Anthropic. They’re buying deeper lock-in to Tranium and the Amazon stack. The 2x+ return they likely get from this investment is just the bonus. Love this for $AMZN + my favorite Anthropic proxy $SKM