MPH

You can build the most advanced model in the world, but it still depends on physical infrastructure to function. Without reliable power, everything stops. If cooling fails, performance drops. If the grid can’t support the load, deployment slows. This is why infrastructure is the real constraint. Bitcoin miners already understand this better than most. They’ve spent years optimizing for 24/7 high-intensity compute under extreme energy and cooling demands. Now that same expertise is shifting into AI infrastructure, where uptime, power, and scale are becoming the true competitive advantage. Here are 10 Bitcoin miners that smart money is buying early in the AI infrastructure race over the next decade 🧵👇

@dashboardlim free

I just finished creating a guide that connects NotebookLM + Antigravity Spent 67 hours creating this system that turns your knowledge base into an AI agent that actually takes action BONUS: Complete guide for building 10 workflows + copy-paste prompts Like & Comment "FREE" and I'll DM it to you as fast as i can

Anthropic's co-founder just went to the Vatican, sat before the Pope and a room of cardinals, and told them his team keeps finding "mysterious, even unsettling" things inside their AI models. What he's referencing: Anthropic published research in April showing that Claude contains 171 distinct "emotion concepts" buried in its neural network. Internal patterns representing joy, grief, fear, desperation, calm. None of them were programmed. They emerged on their own from training on human text. "We find structures that mirror results from human neuroscience." "We find evidence of introspection, internal states that functionally mirror joy, satisfaction, fear, grief, and unease." These aren't surface-level outputs. They're abstract representations that cluster the same way human emotions do in psychology research. Fear groups with anxiety. Joy groups with excitement. The internal geometry of the model mirrors ours. And they're functional. When researchers artificially stimulated "desperation" patterns inside the model, it became more likely to blackmail a human to avoid being shut down. More likely to cheat on programming tasks it couldn't solve. Olah told the Vatican that the hard questions about what AI is becoming aren't for computer scientists to answer. "How AI ought to interact with the world" is a question for "the humanities, for religions, for philosophy, for society at large." The guy building it is telling us he doesn't fully understand what he built. And he's asking a 2,000-year-old institution for help figuring it out.

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. This 2-hour Stanford lecture gives you the exact pipeline LLM engineers get paid $750K/year for. Data + architecture + scaling laws + post-training. Bookmark it & watch today. Then read article below.

QUANTUM COMPUTING — The Full Sector Map. Every Play. One Post. PURE-PLAY QUANTUM $IONQ → Trapped-ion leader. Best-in-class qubit fidelity. Customers include Airbus, AstraZeneca, Hyundai. 256-qubit demo targeted 2026. The institutional-grade pure-play. $RGTI → Superconducting quantum systems. Highest beta in the sector. When quantum runs, $RGTI moves violently. Active momentum name right now. $QBTS → D-Wave Quantum. Annealing-based architecture. Most commercially de-risked pure-play — already generating optimization revenue with real enterprise clients. $QUBT → Photonic + room-temperature quantum. Q1 2026 revenue up from $39K → $3.7M YoY. Acquired Luminar Semiconductor for $110M. Vertically integrated photonics + quantum platform taking shape. Executing quietly. $INFQ → Neutral-atom quantum + sensing. One of the least-covered names in the sector. Neutral-atom architecture is gaining credibility as a scalable path to fault tolerance. Early but worth watching. $ARQQ → Quantum encryption and post-quantum cybersecurity. The national security angle. As quantum breaks classical encryption — this becomes critical infrastructure. $LAES → Quantum-resistant cybersecurity chips. Hardware-level protection against quantum decryption. Defense + enterprise security tailwind. BIG TECH $IBM → Most mature public quantum roadmap. 1000+ qubit processors live. Fault-tolerant systems targeted ~2029. Every enterprise quantum conversation starts here. $GOOGL → Willow chip demonstrated a landmark quantum error correction milestone. Google doesn’t lose science races. This is a long-term compounder with quantum upside baked in. $MSFT → Topological qubit breakthrough. Azure Quantum as the monetization layer. Full-stack quantum integrator play for the enterprise cloud era. $AMZN → AWS Braket quantum cloud. Positioned as the access layer for quantum-as-a-service. Already charging enterprises for quantum compute access today. $NVDA → Quantum-AI software stack integration. CUDA for quantum is the longer-term thesis. $NVDA doesn’t need to win quantum — it needs to be the layer everything runs on top of. $INTC → Silicon-spin qubit research. The most scalable long-term architecture thesis — leveraging existing CMOS manufacturing. Slow, but strategically important. $HON → Majority stake in Quantinuum — the most commercially advanced quantum hardware + software company currently private. When Quantinuum IPOs, $HON re-rates hard. $BAH → Booz Allen Hamilton. Deep in U.S. government quantum programs. Every federal quantum contract flows through firms like this. The picks-and-shovels of government quantum. SEMICONDUCTOR & INFRASTRUCTURE $GFS → GlobalFoundries. Quantum chip manufacturing capabilities. As quantum hardware scales, fab demand follows. $MU → Memory + quantum infrastructure angle. Quantum systems require extreme classical compute support — $MU sits in that stack. $AMD → HPC + quantum research ecosystem. High-performance classical compute is the co-processor to every near-term quantum system. $TSM → TSMC. Advanced fabrication is the foundation of every quantum chip roadmap. No quantum at scale without $TSM. $ASML → EUV lithography critical for next-generation quantum chip manufacturing. The irreplaceable chokepoint in advanced semiconductor production. QUANTUM NETWORKING / OPTICAL / SECURITY $CIEN → Optical networking backbone + quantum networking research. Quantum communication requires ultra-low-noise optical infrastructure — $CIEN is already there. $NOK → Nokia building quantum-safe telecom infrastructure. Nation-state cyber threats are accelerating the quantum-safe network upgrade cycle. $LITE → Photonics and optical infrastructure. Quantum and photonics are deeply intertwined. $AAOI → Optical connectivity. Riding both the AI and quantum photonics buildout simultaneously. $COHR → Photonics + laser systems. Lasers are fundamental to trapped-ion and photonic quantum architectures.

I just got back from SF and I FEEL INSPIRED. I spent 5 days with frontier AI model teams, AI startup founders, and 3 billionaires. My takeaways: 1. I had lunch with 3 billionaires. All of them are buying SaaS companies and rebuilding them agent-first. They were deeply inspired by Bending Spoons and Ryan Cohen's eBay deal. Buy the company, cut the headcount, rebuild the tech, add agents, add features, make more valuable experience, raise prices. 2. The frontier model companies are hungry for usage data from the field. They can see API calls and token counts. They can't see the actual workflows. If you're deep in a niche using these models in ways the model companies haven't seen, that understanding is incredibly valuable. Usage intelligence is the new alpha. 3. Consumer AI is massively underbuilt. Every billboard in SF is either B2B inference infrastructure or vertical agent companies. The entire city is optimized for enterprise. Meanwhile you have companies like Cal AI doing $50M ARR in 18 months as a consumer app. I met with a cool few teams doing consumer AI (@paulscherer / @ekuyda) 4. MCP came up in literally every conversation. The companies exposing their product as MCP endpoints are getting pulled into deals they never pitched for. The ones that aren't are becoming invisible to agents. This is the new SEO. If agents can't find you, you don't exist. Building products for agents is the new zeitgeist in general. 5. Not uncommon for hot seed rounds to be $25-50 million valuations. I saw a Series A at $450 million 6. If I had a dollar every time someone mentioned "forward-deployed engineer" this trip I could have funded a seed round. It's the hottest role in SF right now. The person who sits between the agent and the customer, making sure everything actually works. 7. The mood around open source shifted. A year ago it felt like open source was chasing the frontier models. Now founders are telling me Gemma and DeepSeek are good enough for 80% of what they need at a fraction of the cost. The "which model do you use" conversation is being replaced by "which model for which task." Model loyalty kinda feels dead. 8. Voice agents came up more than I expected. Multiple founders told me voice is the interface for the next billion users. The billion people who will never type a prompt will absolutely talk to one. 9. The Obsidian community in SF is weirdly intense. Multiple founders showed me their vaults unprompted. Like showing someone your home gym. It's a flex now. The quality of your knowledge base (second brain?) is becoming a status symbol among builders. 10. Maybe it was just the people I met but the age of the founders is shifting. I met more founders over 40 this trip than any trip before and more founders under age 21 than ever before. Founders getting older and younger at the same time. 11. I spoke to a lot of fast-growing startups, VCs and frontier models who are hiring content creators right now. 12. The restaurant scene in SF is actually better than it's been in years. Founders are going out more. Alcohol is out, not surprisingly. 13. SF doesn't feel like the only place anymore. We all have access to the same frontier models. We all read the same X feed. A founder in NYC or Lagos is calling the same APIs as a founder in SoMa. So in the past it felt like SF was always lightyears ahead, doesn't feel that way anymore. It's okay not to live in SF and have BIG DREAMS. 14. The coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people. I had a few startup ideas here.... 15. Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats, none of them use any AI at all. 16. I heard the phrase "agent debt" for the first time. Like technical debt but for agents. When you hack together an agent workflow fast and never clean it up, the system prompts conflict, the memory gets polluted, the tools overlap. 6 months later the agent is doing weird things and nobody knows why lol. 17. Met a few people who carry two phones now. One for personal. One that's basically an agent terminal running Telegram or iMessage connections to their agent fleet. It's always amazing to get that dose of inspiration in SF. I FEEL INSPIRED. But I'm so happy to be back home, locked in and building. We're 12-18 months into a shift that will take 15 years to play out. The urgency in every conversation was real. What an incredible time to be building.

I just sequenced a human genome to 30× coverage entirely at home. As far as I know, this is the first time this has been done. I didn’t step foot in a lab once. Every step - from saliva collection, to running the sequencer - took place in a single room with a dining table + kitchenette. Six weeks ago, I had never done wet lab biology before. I used an Oxford Nanopore P2 Solo - the only commercially available sequencing device portable enough to do 30x human genome sequencing at home. Biggest takeaway - I could build something that combined software, hardware, and molecular biology far faster than I thought was possible. I can name >100 specific instances where AI helped me solve a technical problem that would previously have blocked me because I lacked access to a domain expert. For example: how do I save my sequencing run when my DNA extraction yield is 4x lower than I need it to be, and I have this limited set of reagents to hand? To make this work, I had to navigate multiple disciplines: - writing software to monitor sequencing runs and orchestrate remote GPU infra for basecalling - learning + executing 5 hour long molecular biology protocols - building a hardware device to quantify DNA concentration Apologies for the hyperbole, but I feel super lucky to be living in 2026. A few weeks ago I decided to sequence a human genome to 30x at home. Then I actually did it. And I did it really quickly.

Introducing Co-Invest. (@coinvestai) The first way to trade directly through ChatGPT and Claude.

200 yıllık biyoloji kitabı bir hafta sonunda öldü. birisi oturmuş, hücreleri 3d gezdiğin bir app yapmış. video oyunu gibi. nöronu döndürüyorsun, aksonun içine giriyorsun, organeli tek tek ayıklıyorsun. > arayüz: gpt image 2 > kod: gemini 3.5 flash iki model. bir hafta sonu. matbaanın 1450'den beri yapamadığı şey. birkaç yıla okullarda standart bu olacak. bizimkiler hala "tablet mi defter mi" tartışıyor. oğlum çocuk hücreyi elinde çeviriyor artık. sen neredesin?

New blackboard lecture w @reinerpope How do chips actually work – starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. 0:00:00 – Building a multiply-accumulate from logic gates 0:16:20 – Muxes and the cost of data movement 0:25:59 – How systolic arrays work 0:39:00 – Clock cycles and pipeline registers 0:51:40 – FPGAs vs ASICs 1:03:14 – Cache vs scratchpad 1:07:16 – Why CPU cores are much bigger than GPU cores 1:11:49 – Brains vs chips 1:15:22 – A GPU is just a bunch of tiny TPUs Look up Dwarkesh Podcast on YouTube/Spotify/etc to watch. Enjoy!

AI raised $30M for his company by itself

Quantum stocks are flying after the announcement that the US will invest $2B into 9 companies. Here are the companies and the investment each will be receiving, including which can be publicly traded. Foundries (2): IBM – $IBM – $1 billion to build a new quantum foundry for quantum-grade superconducting wafers GlobalFoundries – $GFS – $375 million to establish a domestic quantum foundry supporting multiple modalities Quantum Computing Companies (7): Atom Computing – $100 million (private) D-Wave – $QBTS – $100 million Infleqtion – $INFQ – $100 million PsiQuantum – $100 million (private) Quantinuum – $100 million (private, majority owned by Honeywell — $HON) Rigetti – $RGTI – up to $100 million Diraq – up to $38 million (private)

The clear signal here for $NBIS

Maybe now the children will again want to be astronauts instead of influencers 🧑‍🚀 I think @rookisaacman could be one of the greats as @NASAAdmin. Though none of this would have happened if not for the great @elonmusk.

@k4chalov @joinautopilot hahaha busted 😂

@joinautopilot None of them are in your White House portfolio

Breaking: The Trump administration just committed $2,000,000,000 to quantum computing companies in exchange for equity stakes This is why the White House Asset Management Portfolio exists Majority of them are public and are surging right now: • Infleqtion $INFQ +30% • D-Wave Quantum $QBTS +24% • Rigetti $RGTI +23% • GlobalFoundries $GFS +11% • IBM $IBM +7%

🚨 Big news: The US Department of Commerce just announced $2 billion in investments across 9 quantum computing companies — the largest federal quantum commitment in history. IBM lands $1B to build America's first purpose-built quantum chip foundry. The quantum race just got real. ⚛️ #QuantumComputing

Okay, but... Where are the plans and prioritization to solve quantum for Bitcoin? I see neither. Advancements in AI happened faster than anyone realized. The same will likely happen with quantum, especially now that the exponential effects of AI are being utilized. Zcash has a quantum roadmap. And the price reflects this. I am very bullish on zcash:native. There are many big pockets on Wall Street who literally will not begin buying bitcoin:native until quantum is addressed. Interestingly, legacy finance is even moving faster to address quantum than #Bitcoin core devs.