This is my first time putting together a list like this. I’d like to share some independent researchers who are active on X and consistently provide me with valuable insights. They have given me a lot of valuable insights, so I highly recommend checking them out! And if there’s anyone I missed, please let me know in the comments! @PhotonCap : One of the best experts in optics, without a doubt. His Substack has a lot of deep and insightful analysis on the optical industry. @vikramskr : A semiconductor researcher with a background at Qualcomm who now also provides research for institutional clients. He’s not only extremely knowledgeable, but also a genuinely great person. I’ve learned a lot from him, both professionally and personally. @Frenchie_ : A France based market analyst who also runs a large community of his own. I feel like I’m always receiving insights and help from him, so I’m simply grateful. @ParadisLabs : An excellent source for timely market news and insights on X. He also shares insightful articles every week, so definitely give him a follow. @jukan05 : Do I even need to explain? His subscription is only $1. Just go subscribe already. @jmartinprin : An optics and semiconductor expert formerly with SemiAnalysis. I’ve learned a tremendous amount of technical knowledge from him and personally really appreciate all the insights he has shared with me. @demian_ai : A market expert currently with Nebius. I’m really excited to see the AI bottleneck product he’s building. @pequityresearch : A professional investor and market analyst. Personally, I think his X subscription is way too cheap for the value he provides. If you want to follow analysis across the broader market, definitely check him out. @benitoz : I was particularly impressed by his recent critique of SemiAnalysis. Looking forward to seeing more from him. @ShortSeller : He gives me a lot of insight into technical chart analysis, which is definitely one of my weaker areas. Always appreciate what I learn from him. @ren_stocks : I don’t know. I just really like Pikachu. 😂 And his posts too, of course. @NuttyCLD : Personally, I think he’s one of the best semiconductor industry professionals at explaining technical topics in a simple and understandable way. Please post a little more on X! @AurelionRsch : They run what I consider one of the best models for an independent research firm. I’ve gained a tremendous amount of insight from their work. @BigBerbowski : He provides thoughtful research across AI infrastructure, semiconductors, and the broader ecosystem. His work has personally helped me a lot, and I’m very grateful for it. If there’s anyone I missed, please let me know in the comments! And once again, thank you to everyone on this list for all the insights and support!

Last month I wrote about how we can build a positive and safe future for everyone: Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.

I joined David Goldman on @CelestaCapital's TechSurge podcast to talk about the race to build the next trillion-dollar AI chip company. The main idea we dig into is that no one has yet brought a chip to market designed specifically for LLMs. Blackwell is a GPU that morphed toward the workload. Nobody has yet stood up a gigawatt of clean-sheet LLM silicon, and that gap is where the next trillion-dollar chip company comes from. We cover: • Why AI infrastructure now sells as full systems, and how Nvidia got to rack scale first • Prefill vs. decode, and why the SRAM bets Groq and Cerebras made years ago suddenly paid off • How neoclouds built $125B+ of public-market value while most investors missed them • Circular financing, and why I land on the demand being real • My four conditions for the next trillion-dollar chip company • Clean-sheet architectures: @OpenAI's Jalapeno, @TensordyneInc's log math, @Etched's low-voltage inference • Why enterprise and on-prem inference could end up bigger than the cloud Full episode:

Lumentum have a really good laser, but being first might not matter in this case. It's always healthy to assume the flip side scenario and not just live in an echo chamber hugging your $LITE shares for dear life and refuting any counter argument. Many experts in the field if you talk to them mention seeing multiple technology options emerge in the future, both for scale up and scale out, without any real conversion to one single technology taking over.

@vikramskr What if, by the time CPO matures and becomes widely adopted, LITE's most with high power EML get heavily eroded? Am I missing something here that might not make this possible other than linewidth and InPh shortages, and maybe engineering challenges by other EML manufacturers?

This might sound a little arrogant, but I’ll say it anyway. Lately, I’ve been looking at the quality of some institutional research reports and expert calls, and then hearing what people actually pay for them. Honestly, some of it feels absurdly expensive. At the same time, it made me understand a little better why my Substack has been able to grow so quickly. I really believe independent research from talented individuals is going to grow much faster from here, especially as AI continues to improve. AI gives individuals access to tools and leverage that used to require an entire research organization. And this is definitely not just about me. There are already many excellent independent researchers on Substack producing genuinely great work in their own fields. So if you have some time, I highly recommend exploring Substack and finding a few independent researchers you like. And of course, you are also very welcome to visit mine. Ahem. 😌 FYI, The Intel expert call report will be published on my Substack this evening. I’m also running a Substack promotion right now, so please come check it out 🫡

Is Lumentum's laser moat disappearing? Co-packaged optics (CPO) shifts modulation onto silicon photonics chips (like Nvidia's), replacing complex EMLs with simpler, high-power (300-400mW) continuous-wave lasers. Essentially, a flashlight. Does the real moat still exist?

In this episode, you will notice that the video looks ... different. We hired a designer to come up with a beautiful looking theme that is inspired around campfire colors, outdoors looking at the night sky. We take aesthetic very seriously, because it provides a certain look to the end product. And we wanted it to have a premium feel. Our editor then took all the brand design, and implemented it here, and will continue to do so every episode from now on. We have a small team of dedicated, and hardworking folks who tirelessly help us with this podcast. We could never do this alone, and it always helps to take a step back and acknowledge those who make it possible. 🙏🏽

🎙️ NEW EPISODE: TSMC Will Buy High NA After All: ASML's New 6x12-Inch Mask Austin and Vik break down ASML's High NA EUV, whose anamorphic optics cut the exposure field in half, the new 6x12-inch masks that restore the full field size, and what the change means for the mask supply chain. - High NA uses anamorphic optics with 4x by 8x demagnification - A 12-inch mask restores the full 26mm x 33mm field size - Samsung targets High NA for DRAM in 2028, ahead of TSMC. TSMC is interested now though! - Intel is a million wafers deep on its High NA learning curve - Nearly 30% of ASML's revenue is recurring services income Chapters: 0:00 Episode Opening 2:23 The Rise of AI Agents 6:00 Instinct: The Autonomous Agent 10:50 Productizing AI for Mass Adoption 14:03 From AI Agents to ASML 24:29 The Photomask Stitching Problem 31:49 High NA's Anamorphic Optics Tradeoff 36:39 The Cost of a Halved Reticle 37:26 The 6x12-Inch Mask Solution 39:08 A Full Supply Chain Problem 41:17 TSMC, Samsung & Intel Timelines 48:24 Intel's EUV History Lesson Get more of Austin and Vik daily, free! Sign up: Connect with Vik and Austin: Vik's Paid Substack: Austin's Paid Substack: @austinsemis @vikramskr