NEW: The NEXUS S1 BM1373 ASIC is a desktop Bitcoin miner. This little beast produces 10 TH/s of hashrate while consuming just 100W of power, about the same as a standard light bulb.

Interesting. According to FundaAI, Qualcomm is expected to begin shipping an LPU-like AI ASIC to a Chinese CSP by the end of 2026. Estimated shipment volume is around 1 million units, with an ASP of roughly $4,000. For general-purpose server CPUs, shipments are expected to begin in the second half of 2027, or toward the end of 2027. 3 million units… that’s a lot. Qualcomm is also reportedly working with two U.S. CSPs. $QCOM

Right now, the majority of AI workloads are executed on GPUs, but that could soon be changing with market adoption of AI application specific integrated circuits (ASICs). @Luxor's Mike San Miguel said that hyperscalers have been utilizing these ASICs for some time, but now neoclouds are making moves to leverage them as well. "They're just starting to hit the open market now The hyperscalers have been doing versions of these for about a decade. What's different now is that, as opposed to being bespoke chips for hyperscalers, we're starting to see new manufacturers that are producing these AI ASICs, and as a result these are going to start hitting the neoclouds. "You're probably going to see them hitting the market in scale in 2027 when they're going to start entering fleets for a lot of these neoclouds." This raises a a business question for AI companies, however, because unlike GPUs which can be used for inference and training, AI ASICs can only be used for one or the other. "A GPU can be used for inferencing training. But with ASICs, they're purpose built for one or the other -- they can't do both. "But that's the advantage. You get a much higher output...what it's probably going to look like in the next 2 to 5 years -- beecause these things take time to roll out -- is a hybrid approach."

Moats in our neocloud theme: $APLD Ellendale, Macquarie financed $BTDR Bhutan power, SEALMINER ASIC $CIFR AWS anchor tenant validation $CLSK low cost power per MW $CORZ multi-billion hosting backlog $CRWV GPU operator, OpenAI $DGXX 400MW, Blackwell ready pods $HIVE BUZZ HPC GPU subsidiary $HUT vertically integrated power $IREN cleanest HPC revenue ramp $KEEL $533M liquidity, three campuses $MARA largest scale, biggest footprint $NBIS lowest cost per MW operator $RIOT Texas grid scale power assets $SLNH behind the meter, 4.3 GW $WULF Core42 deal, nuclear adjacent $WYFI $865M Nscale anchor contract The important stuff? Secured power capacity, signed anchor tenant contracts, cost per MW competitiveness, and execution speed on conversion. I think #SLNH# offers deep value. NFA. Will keep updating...

🔥Iran-linked disruptions are reportedly pushing key industrial resin ingredients up more than 40% since March, continuing to pressure #CCL# prices — a critical material for #AI# #GPU# and ASIC #PCBs#.💡More: 🔗

Dr. Yang, Founder and CEO of MicroBT, will speak at Money Frontier 2026. MicroBT is one of the world’s leading Bitcoin mining hardware manufacturers, best known for its WhatsMiner @Whatsminer_MBT series of ASIC miners. Join him for a discussion on the latest developments in Bitcoin mining hardware and the future direction of Bitcoin mining. At Money Frontier, you will also meet factory-direct manufacturers and solution providers behind the infrastructure upgrade, helping mining farms evolve faster and more cost-effectively from single-purpose Bitcoin mining sites into hybrid compute infrastructure for next-generation Bitcoin mining and AI/HPC workloads.

The Underpriced Truth: Agentic AI Is a Paradigm Shift Centered on Memory 1/ The market will slowly realize: Agentic AI is memory-centric, not compute-centric. The new hardware stack is: ① Memory — HBM / DRAM / NAND ② Parallel compute — GPU / ASIC ③ Coordinator — CPU CPUs stopped doing the heavy lifting a long time ago. This isn't a cycle. It's a paradigm. 🧵👇 2/ First principles Humanity's ultimate pursuit of intelligence has always been two things: Infinite memory + infinite compute. When we say someone is smart, we mean two things: "good memory" + "fast thinking." Machine intelligence is walking the exact same path. 3/ The story the market already understands: HBM LLM inference's decode stage is a textbook memory-bound workload. Every token generated → drag the entire KV cache across memory. Bandwidth too low → expensive GPUs sit idle. That's why every new GPU generation ships with more HBM bandwidth and capacity. 4/ The story the market is missing The "1M context" you keep hearing about? It is not assembled inside the GPU inference cluster. So where is it actually built? 5/ It's built on the traditional servers running the agentic system Those CPU + huge-DRAM servers are quietly doing the heaviest lifting: • loading user long-term & short-term memory • loading the agent's system spec / prompt • loading skill / tool / subagent definitions • compressing the context once it overflows 1M tokens All of this lives in DRAM, not HBM. 6/ Compare this to the previous era In the web / mobile era, we barely stored any user context at all. Only search / recsys / ads kept a small user profile — maybe 1/20, even 1/100 of the data volume an agentic system needs today. That asymmetry is the real overlooked inflection point. 7/ The supply chain is already telling this story Server CPU : DRAM ratio is climbing fast: • Web / Mobile era: 1 core : 4 GB • Agentic AI today: 1 core : 16 GB • Deep agentic future: 1 core : 64 GB and beyond 8/ And it's NOT just "4x more memory" Under agentic workloads, a single CPU serves a fraction of the users it used to. When the entire IT stack migrates to agentic: • CPU count grows several-fold to ~10x • DRAM total grows tens-fold to ~100x That's the part nobody is pricing in. 9/ The conclusion Agentic AI is a paradigm shift centered on storage + parallel compute. The software paradigm changed. The hardware paradigm changed with it. Only those who deeply understand the technology will see it: This isn't a memory cycle. It's a memory paradigm. 10/ Time horizon Given how early we still are on: • user adoption rate • depth of usage per user We are at least 5 years away from the cyclical top of this memory wave. (Zoom out far enough and everything is a cycle — but this one is nowhere near peak.) $MU $DRAM $SNDK

After blasting into the semi sector to go long the full 9 yard of AI stuff (CPU, GPU, ASIC, XRAM, LLM, etfs) with the thesis that we are chronically short the raws to power AI + AI demand continues to rock, where would you put incremental dollars to work?

BofA Reiterates $NVDA at Buy, PT $320; ER Preview: Analyst comments: "Nvidia reports post-close on Wednesday, May 20, and we expect the usual historical 2–4%/$2–$4 billion sales outperformance relative to current sell-side expectations. However, beyond headlines, we expect the focus to be on: 1) potential for enhanced cash returns; 2) Vera Rubin ramp timing (2H26E); 3) gross margin durability (~75% amid continued memory/other cost inflation); 4) update to the $1 trillion CY25–27 forecast, especially contribution from LPU racks, CPU, and Vera Rubin Ultra, which were not included before; and 5) competitive landscape changes against Google TPU, agentic CPU, and other ASICs. We maintain our Buy rating, top-pick designation, and $320 price objective on the company’s dominance in the fastest-growing tech market and its compelling valuation at <20x CY27E P/E, or only 0.4x PEG relative to 46%+ CY25–28E EPS CAGR. As discussed in our recent note, NVDA’s large existing positioning — 8.3% of the S&P 500 Index and ~78% active fund manager ownership — often acts as a headwind. Other large-cap tech names in the same position have added incremental investors by boosting cash returns and appealing to dividend/income-oriented investors. NVDA hasn’t done this yet, with only 47% of free cash flow from CY22–25 allocated to dividends/buybacks versus peers returning around 80% of free cash flow. NVDA’s investments have instead been diverted to investing in the ecosystem — OpenAI, Anthropic, and tech partners — and have been unfairly, in our view, characterized as circular/vendor financing. Boosting shareholder returns could expand ownership, close NVDA’s valuation gap, and minimize circularity concerns, a second-half catalyst." Analyst: Vivek Arya