Serenity@aleabitoredditPost2026-06-07

The author describes a discretionary, inference-based investing style that relies on unstr

The author describes a discretionary, inference-based investing style that relies on unstructured real-world observations, supply-chain bottlenecks, and speculation on emerging AI / photonics trends rather than mainstream analysts. They highlight specific picks and their rationale, stressing high-conviction guesses that may prove right or wrong.

“I think my personal style of investing is a bit different, just some reflection: It's inherently discretionary, based on stuff markets don't know yet. And a culmination of life experiences? If you look at $AXTI, $RPI, $SIVE, $IQE and others. Lot of it is guessing on unstructured relationships then seeing if it's right or not down the line. $RPI is the perfect example: 1. Nobody really thought of Raspberry Pis for AI growth. Mainly people bought one or two just for class + education + hobbyist. 2. After OpenClaw, just noticed all my friends and people just buying Apple Mac Minis / RPIs for AI applications. 3. Found validation of that trend online with lot of people sharing video tutorials on AI orchestration with RPI. 4. AI was their ideal perfect growth vector, did some modeling, and thought it was compelling. Earnings comes out and I was right. Everyone in media was calling it a meme stock because there's nothing online that shows revenue growth from AI (was 14% forecasted revenue growth, turned out to be 58%, my projection was around 55%). So it was a mix of guessing next industry trend (AI using lightweight hardware instead of GPU clusters), real life trends, then revenue forecasting off my guess. For stuff like $AXTI: 1. Everyone called it a joke when I bought at ~$12. LLMs would hallucinate and say "hyperscalers/govs would have known about this by now and fixed this vulnerability with InP substrates" 2. Or would conflate very nuanced parts of InP substrate stack, where there's multiple different chokepoints in upstream processing. 3. So part of this was just discretionary based on what I've seen over InP substrate breakdowns, industry trends, etc. 4. Then also guessing the major supercycle was photonics (this was before everyone caught onto $LITE, and others). Or before you saw the $141B TAM projections from GS. 5. AXT owned 40% of InP supply chain, without them the supply chain just gets cripped). 6. All the "analysts" were forecasting steady InP substrate growth, few hundred million TAM, etc. or export controls. 7. Everyone kept trying to say $AXTI was overvalued based on TAM estimates. But if it's a few hundred million TAM you just think that's a joke and go into game theory over allocations. 8. Then I just had to guess, how much would this be worth if it were a NAND style bottleneck, what MC could it reach based on control, how much would hyperscalers price it as, etc. A lot of the current research outputs from Goldman Sachs, or earnings reports from the Epiwafer companies, were confirmed after I published my piece on AXT. If you did research back then, lot of the same material /framing wouldn't have come up. With stuff like $XFAB as you're seeing now, a lot of it is just pure guessing: 1. Not really any CPO materials, how much their MTP process makes in revenue, etc. Everyone online keeps saying they're not a photonics player. 2. But if you go through ASE docs or Gov websites, they all kinda cite XFAB as a major emerging player here. 3. $NVDA also evaluating them right now (maybe it's successful who knows). 4. No clear revenue around this area because their main silicon photonics process is still precommercial, but if you guess it's trying to create a EU supply chain to compete with $TSEM, once pre-commercial shifts to commercial, maybe similar but less volume contracts? 5. Then just seeing updates over the next few months to see if anything confirms this thesis guess. _ I think a lot of information discovery still can be done with LLMs I'm seeing online. But it's also really hard to make a bunch of unstructured inferences based on unrelated material or even just trends you're seeing in real life. So probably better to just do what's standard, eg. do valuation forecasting based on current numbers Stuff like $AAOI, if they're projecting $471m/M h1 2027 and you see MC at $12B, probably undervalued might be a good idea to go long for next years. Stuff like Samsung Electronics is easier, see what people are modeling for operating profits for 2027, 2028 then just seeing if it's undervalued or not at current levels. Maybe something harder is $JBL. I haven't really seen any great volume numbers around 1.6T LRO, but you can just make a guess on how popular that might be then project how that might impact current MCs. Or picking just good names everyone kinda agrees like $TSM, $INTC, $MRVL is also solid. So a lot of things is just building up your life skills then applying that to markets. I don't think it's that can be taught with courses and stuff. Of course, much of what I'm doing is just high conviction inference based on unconnected parts. Could always be wrong.”