Called $BTC bullish on Jan 15, 2023: $BTC rose 46% in 6 months.
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@Robostrategy Not investment advice
Cite asCryptoQuant has tracked the directional calls on NVDA by @Rewkang on X since Jan 2021: not yet scored on NVDA (as of Nov 11, 2026).
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1 NVDA call on record, a thin history.
Relative view by default: fewer than 30 NVDA calls or days on record.
Sentiment timeline shows relative sentiment within this analyst's history. A perma-bull showing 8 bullish : 2 bearish in a bear market sets that as their baseline. If you notice any errors, claim @Rewkang to submit corrections.
Called $BTC bullish on Jan 15, 2023: $BTC rose 46% in 6 months.
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Dominance of closed source models is an existential threat to Nvidia Jensen has been clear since earlier this year that strong open source models are essential It’s game theoretically optimal for Nvidia to build frontier open source models optimized for its own hardware https://t.co/vq9laGOZ6e https://t.co/3dw9Tz4ErK
View original →Dyna just dropped a world model for robotics achieving a new record for scale of pretraining using human video data - 1 million hours This is 2 orders of magnitude larger than NVIDIA's Egoscale dataset (~20k hours) They established scaling laws parallel to the scaling laws identified in GPT-3. Pretraining on more human video data increased robot performance and generalization across 4 orders of magnitude Most importantly, the scaling fit power laws with no plateau
View original →NVIDIA went public at a $625M Valuation Now companies like OpenAI, Anthropic, SpaceX do not go public until they are $1 Trillion + Public Markets need Venture Capital Access https://t.co/IEBopXLnzq
View original →A big insight from this podcast is that intelligence will be commoditized and open source. It is an existential risk to Nvidia for intelligence to be closed source as it would make them captive to a few model companies who could chose to optimize on a different hardware stack. Nvidia needs the most used models to be optimized for their stack. It explains why Nvidia is putting so much effort into research and there is a clear trajectory for their open source models to saturate the biggest capability benchmarks that matter. SWE, general knowledge work, autonomous driving, physical AI. There are still a lot of domains where the frontier of capabilities can continuously expand and for closed source models to be dominant. However, the vast majority of tokens produced in the future will be produced by open source models.
View original →2025 was an insane year for robotics research Long time model architecture/training challenges were solved and major progress was made on data collection techniques, understanding data quality, and data recipe. This gives Physical AI companies the confidence to finally start investing in large scale data collection. You saw companies like Figure, Dyna, and PI reach >99% success rates in real life deployments in diverse settings by leveraging RL innovations. Many frameworks were developed for self improving and self-recovering robot models. Researchers figured out how to prevent overfitting in VLA fine-tuning while retaining generalist capabilities. Which means we can build toward generalist models by merging specialist models. Robots can also move much more agilely from methods like action chunking and FAST tokenization. We see robots able to exhibit smooth full body control at human speeds and not slow or choppy movement. Roboticists showed how to effectively fuse multi-modal sensor data for huge policy improvements. Integrating vision, language and tactile data was challenging, but doing so opens the door for many contact rich tasks that require a granular sense of force. Force awareness also solves for common issues like visual occlusions. System 1/2 architectures were hardened to handle long-horizon planning/ task orchestration which enable robots to perform jobs that require series of tasks. Gemini Robotics-ER 1.5 introduced Chain-of-Thought reasoning to physical agents, allowing them to parse constraints and evaluate Semantic Safety. Memory advancements allowed robots to maintain long-term spatio-temporal reasoning, breaking the "memory wall" with brain-inspired algorithms. NVIDIA's ReMEmber used memory-based navigation, while Titans + MIRAS enabled test-time memorization for sustained performance. Advancements in the foundation model space also continue to compound progress in Robotics. Better VLMs means VLAs with better spatial understanding and data labeling and processing pipelines that can massively increase in throughput. World models are starting to show promise in data augmentation and policy evaluations. 2025 gave us a small taste of what data scale could do. A glimpse into the future with robots exhibiting emergent intelligence such as zero-shot affordance mapping, visual force sensitivity, and all sorts of general physical reasoning 2026 we get to experience physical AI with 100x the data scale
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