I am invested in Tesla and used to work there but I have to say that I'm going to switch from Cursor to Codex. Composer 2.5 isn't main model material, Fable 5 has data retention issues, Opus 5 never has the GPU capacity runs at full capabilities. None of the Open Source models match in real life usage. ChatGPT-5.6 is completely essential for work right now.

Neoclouds: Nvidia Reserving Powered Datacenter Capacity to Sublease Nvidia is now buying datacenter capacity so it can sublease it out to it’s Neoclouds in the future. This seems to me a play to carve out market share in power away from XPU (ASICS) and AMD.

Neoclouds: The Kimi K3 Scare Kimi K3 caused a large scare in the AI trade as this Chinese open source model matched frontier models on benchmarks. Let me unpack what's actually going on. Chinese Labs have much less GPUs than American Labs and yet are able to train "just as good" of a model. This implies that Chinese Labs have huge efficiencies that allow them to use much less GPUs in training. This is would imply less HBM, less datacenters, less cloud bills - the whole capex heavy buildout that the AI trade is predicated upon. Now here's the big hole in all this logic. MoonshotAI, the Lab that made Kimi K3, is supposedly a magnitude more efficient in training than American Labs yet their inference compute consumption is the same or less efficient! Kimi K3 cost exactly the same as GPT 5.5 and slightly less than Claude 4.8 Opus High. Some people are misunderstanding what expensive tokens mean. Yes the cost of the open source weights/topology is 0 but the amount of the compute/GPUs that you need to run the model is a metric of a efficient your inference is. Compute/GPU time is very expensive and cost of open source inference is very not free. Now, it makes absolutely zero sense that MoonshotAI Kimi is so much more efficient in training but slightly less efficient in inference. Why? Training is a the forward pass plus backward pass and inference is the forward pass. This means that training efficiency improvements lead to inference efficiency improvements. You know why MoonshotAI training and inference efficiencies are asymmetric? Because their "training efficiencies" come from distilling American models. If MoonshotAI had true training efficiencies they would also show inference efficiencies but they have no advantage in inference efficiencies! AI Capex will still continue because: 1. If American Labs stop training capex, then Chinese models will also stop improving. AI progress will have stopped. American companies have never given up just because Chinese are trying to copy them. 2. Chinese model still consume alot of compute/GPUs for inference. Inference demand will outstrip training demand anyways.

5/15/26 Portfolio $IREN: 95.66% | Closed all Covered Calls I closed all my covered call positions previous ~20% of my shares. From this one week, I netted more than my monthly salary. I am closing the covered calls because I see a 35% of a deal being signed late May or June. This is because IREN just raised a 3B convertible at 1% interest and high conversion premium. From IREN's side I cannot see why they would need 3B except to get SW1 going. From the investor side, I cannot see why they would agree to lend IREN on high conversion premium unless IREN showed them that the 3B will be used to move the stock price up. At the same time I'm at a 35% chance instead of 50%+ because IREN will be signing 2027 capacity which I originally thought would be in the fall. Very high bulk transactions and call options also suggest deal. Never the less 35%+ chance of deal means that it's obviously not worth it to have cover calls on for rest of May and most of June. Last time it took ~1 month from IREN's 1B convertible to MSFT deal. I use the profits from covered call play to open $52 weekly puts as I am fine with them b eing assigned. Cash: 1.84% Cash position remained same but I didn't include my rainy day fund from my calculations like I did last time. $TSLA: 1.83% Funnily enough, TSLA is basically like my other cash position nowadays. Very long consolidation with $410-$450 resistance. My last tranche of shares is 7.3x so tax is very high if I sell. My buys earliest would be 20x+ but I traded in and out of those long ago. $INV: 0.58% Thanks to @MarkosAAIG, I wrote about it here: $OUST: 0.08% Thanks to @daniel_koss. I started a start position at $26 but this one moved faster than I could accumulate a sizable position. My target for this year is $40-$45 and I'll keep studying to see what prices I want to keep accumulating it and write a post on it.