To The Moon

16 years ago, $Tesla Motors(TSLA)$ IPO'd at $17. It returned 445x and transformed the car industry. Here's 5 robotics companies that will 100x-200x exactly like TSLA: 1. $Ouster Inc.(OUST)$ — Digital lidar + AI cameras giving robots 3D vision to see and navigate the physical world. Customers: Komatsu, FieldAI, AIM Intelligent Machines, warehouse/logistics operators. Special note: Only US-aligned, at-scale lidar+camera perception stack after acquiring Stereolabs (90K+ deployed ZED cameras), now the sole non-Chinese option in federal procurement. 2. $Aeva Technologies Inc.(AEVA)$ — FMCW 4D lidar-on-chip that measures instant velocity, not just distance, for real-ti

The biggest bottleneck of AI is memory right now. Memory stocks like $SanDisk Corp.(SNDK)$ spiked 4661% already. Here's the bottleneck company for robotics: First, this is the bottleneck: precision actuation (motors + gearboxes + integrated force/torque sensing) Not compute, not lidar, not even batteries it's the actuator/joint system. Here's why: 1. It's the single biggest cost and engineering line item. McKinsey breaks the humanoid hardware stack into five domains, and actuators alone account for 40–60% of the bill of materials more than double any other subsystem. 2. The supplier base is tiny and not built for volume. Fewer than 10 global suppliers can currently produce actuators with the precision required, and the humanoid supply chain is exp

99% of traders will never tell you where to sell. I will. This year, Kate turned $1000 into $215,000 in 1 month. I don't sell early to make 50%-100% when you can make 700%-2000%. Here's the 1 cheatcode to hold any stock longer: It all comes down to TRUST. You sell early because you don't trust the price action, your setup, or yourself. The market didn't force you out. Fear did. Every time you cut a winner short, you're telling yourself: "I don't believe this trade can reach my target." The best traders don't make more predictions. They trust their edge enough to let probabilities play out. Why traders don't trust their trades: 1. They never fully accepted the risk before entering. 2. Their position size is too large. 3. They don't have a written profit target. 4. They focus on every candle

I think there are two big questions hitting AI markets right now: If $SpaceX(SPCX)$ (via xAI) and Meta are all of a sudden turning around and renting out their compute capacity, are we really in a compute crunch or is the system starting to fill up with excess? What does the rise of open source models and the end of tokenmaxing mean for Anthropic and OpenAI? On point 1 - SpaceX famously created ~$2.32B of monthly revenue by selling (ie renting out) about 450k of GPUs to Anthropic, Google and Reflection. Details below. SpaceX lands another computing deal, this time with Reflection, an open source model development company. $150m / month for GB300s. SpaceX the Neocloud! Deal 1 with Anthropic Colossus 1 and Colossus 2. Anthropic took all of Colossus

3 years ago, 5 trillion tokens were consumed per month. By 2030, $JPMorgan Chase(JPM)$ estimates 8 quadrillion tokens will be used per day so 49000x more. These 3 companies will benefit the most: 1. $NVIDIA(NVDA)$ Higher tokens/sec/GPU makes each GB300 more valuable per dollar spent better effective ROI for buyers, which reinforces continued capex cycles and pricing power. Also validates Nvidia's push toward system-level architecture (NVLink, rack-scale) since wideEP/disaggregation depends on high-bandwidth GPU-to-GPU interconnect, not just compute. 2. $Micron Technology(MU)$ / SK Hynix / $SanDisk Corp.(SNDK)$ (Memory suppl