Nutty Atlas is where I keep my research on the AI build-out, from power and interconnects to packaging and memory. You can browse by topic or company. If you’re curious about how the technology works and what it means for investors, have a look around. atlas.nuttycld.workers.dev/?…

I just started using Muse from Meta. So… what exactly can I do with it? 😅

I’ve spent 20 years designing silicon chips. Now I’m building Silicon Atlas to help readers understand what new AI hardware changes, what the evidence supports, and what it could mean for the industry. Paid subscriptions are now open. Expect deep dives connecting chip architecture, system constraints, and industry economics, plus follow-up analysis as the evidence develops. Everything I’ve published so far stays free, and I’ll continue sharing free essays and insights here. If my work has been useful to you, I’d appreciate your support. $10/month or $100/year. siliconatlas.substack.com/su…

I happened to be working on an OCS article when @PhotonCap published this piece on the same topic. It explains how skipping optical-to-electrical conversion can reduce costs and power consumption, and why slow-moving mirrors can still be fast enough for AI training. A useful look at both the technology and the economics behind Lumentum’s rapid OCS revenue growth. My piece takes that discussion a little further, looking at how incumbents’ manufacturing experience stacks up against newcomers’ technologies. Coming soon.

I agree. The very term “goodput” implies delivering genuinely useful value, not just raw speed. Measuring it is far from simple, and inference companies will compete fiercely to prove it. With inference chips and racks only now reaching the market, we should soon see their real utility.

The competition in inference chips goes beyond speed. The system a customer chooses also determines which memory, networking components, and servers it buys. In Part 2, I look at why customers might choose Cerebras or d-Matrix, how Jalapeño takes a different approach, and where companies like Broadcom and Celestica fit into these systems.

Even though I understand that AI-created images are difficult to protect under copyright, as an article writer who knows how challenging and time-consuming it is to generate such useful images, I find it very uncomfortable when they are shared without the original author's permission.

This is the first time I am publicly listing accounts I follow for signal. (The order is alphabetical.) @aleabitoreddit — AI and semiconductor supply chains @Alisvolatprop12 — Memory and AI hardware @AnalysisOp — Financial analysis, value investing, and GARP @citrini — Thematic, cross-asset research @damnang2 — Semiconductor engineer. HBM, memory, optics @demian_ai — From silicon to tokens @Frenchie_ — Trading, fundamentals, and technology @FundaAI — Research platform for public-market investors @Gaetano2026 — Photonics / Physical AI @iamfabian — Test and measurement, photonics, data-center interconnects @jmartinprin — Networks and optical infrastructure. SemiAnalysis @Joule14 — AI infrastructure research @jukan05 — Semiconductors and AI infrastructure. Citrini @KawzInvests — Photonics, AI, defense @Midnight_Captl — AI and semis. Joule14 @NURadu_ — Small caps and overlooked opportunities @NuttyCLD — Analog IC. Semiconductors, power, optics @outliercapx — Asymmetric longs @ParadisLabs — AI, tech, and macro @Pep_Invest — Technology, industry structure, supply chain @pequityresearch — Semiconductor and tech deep dives @ren_stocks — The AI buildout @rwang07 — Ex-SemiAnalysis. Semiconductors and AI infrastructure @Semicon_player — Semiconductors, written from the ground @Silicon_Atlas — AI silicon through a systems lens @StormDirac — Former Sivers CEO. Optical semiconductors and lasers @SVTrivo — Semiconductor designer. AI silicon and architecture @TheTechInvest — AI infrastructure @vikramskr — Physics-first semiconductor research Not chart accounts. Until recently I was the one being introduced. Here are the accounts I actually read. I used Grok to help compile the list. If I missed someone, that is on me. People who write semiconductors, photonics, and AI infrastructure through structure. I do not agree with all of them on every name. I do learn from all of them. Markets react to earnings calls. Physics doesn't.

I have introduced quite a few Silicon Valley insiders here on X, and here is another person who deserves your attention. @Silicon_Atlas is a longtime semiconductor industry veteran based in the Bay Area. The quality of his research and analysis is exceptional, yet his follower count is still criminally low. Get in early and follow him now!!! I am meeting him in person tomorrow, and I am really looking forward to hearing the insights he has to share🤗

Quantum computing is clearly still a sector where traditional valuation based on revenue or earnings is difficult. But that does not mean the right conclusion is to ignore the space or come back to it later. If anything, this is a time when we need to study it seriously. Knowing @PhotonCap's technical background well, I can confidently say that he is one of the best tutors I know for doing exactly that. I am learning a lot from him myself. From an investment perspective, I think we should at least start by separating the opportunities into a few buckets: whether to bet directly on quantum companies, whether to bet on the infrastructure and physical bottlenecks that will be needed regardless of which technology wins, or whether to bet on large companies where quantum may still be a small part of the business but which could end up owning critical platforms or standards across the ecosystem. His latest piece goes much deeper than that, breaking down individual companies and analyzing their strengths and weaknesses in detail. But more than the individual scores, the main takeaway for me is a broader question: Can we keep investing in this industry without having to predict the technology winner? In a field like quantum, where technological uncertainty is still extremely high, trying to identify what will ultimately win may be less useful than asking what will still be necessary no matter what wins. To me, that is the most important idea in this piece.

Memory allocation is becoming a key topic, and we will likely continue to see many different combinations and architectures emerge. From an investment perspective, that may actually make this a period that requires extra caution. Even with a player as dominant as NVIDIA, smaller companies with more unconventional architectures may continue to prove highly effective for certain applications. Still, a technology win does not necessarily translate into an economic win. It is still unclear whether today’s divergence is simply a path toward convergence around one dominant architecture, or the beginning of a more fragmented market shaped by different workloads. But for memory makers, the implication seems relatively clear. Rather than going all-in on HBM, it may be safer to maintain a broad lineup and optionality across HBM, DDR, LPDDR, NAND, CXL, and other parts of the memory hierarchy. At this stage, the key may be less about picking the final winner and more about identifying who has the most options as the architecture continues to evolve.

Really looking forward to seeing @damnang2 and @Midnight_Captl create even more value with @joule14! 🎉

New, with Ozeco: The Advanced Packaging Primer. Value in AI chips is moving off the wafer. This piece follows where it lands: who owns the yield loop, why HBM switches to hybrid bonding last, what the quoted yield numbers actually measure, and which suppliers survive the panel reset.

The Advanced Packaging Primer The value in AI semiconductors is increasingly moving off the wafer. Better transistors alone are no longer enough. Multiple compute dies and HBM have to be connected as one system, powered, cooled, and assembled without losing yield. That increasingly determines both performance and how many systems can actually ship. And the money is following. Processes that once sat quietly in the back end, including grinding and dicing, interposers and substrates, bonding, thermal materials, inspection, and test, are becoming much more equipment and capital intensive. But the economics are very different at each step. Some suppliers earn more every time another wafer or die passes through their installed base. Others are tied much more closely to a specific packaging format and its capex cycle. That distinction matters more as packages get larger, stacking gets more complex, and manufacturing moves toward new formats. Ozeco and I have been working on The Advanced Packaging Primer, a deep dive into where value is accumulating across this stack and which parts can hold onto it as the architecture changes. Publishing soon.

SoftBank-backed SB Energy ($SBE) filed for a U.S. IPO on September 1. In the first half, it posted $138.7M in revenue, a $3.21B net loss, roughly $439B in backlog, and 8.8 GW of data center capacity contracted or under construction, with zero data centers operating today. OpenAI is a major customer, and Nvidia plans to invest $1.5B at the IPO price. cnbc.com/2026/09/01/sb-energ…

A great primer for getting the big picture on memory. SRAM, DRAM, NAND, DDR, and HBM are all laid out clearly and connected in a way that’s easy to follow.