According to @gdb, results that persist across many orders of magnitude and decades suggest deep learning is uncovering something fundamental, signaling continued confidence in scaling-law-driven progress in AI research; Source: https://twitter.com/gdb/status/1962594235263427045 This view aligns with neural scaling laws showing predictable performance gains as model size, data, and compute scale, which were documented in large language models and vision tasks; Sources: https://arxiv.org/abs/2001.08361, https://arxiv.org/abs/1712.00409 Compute-optimal training findings indicate that balancing parameters and tokens improves efficiency, reinforcing demand for AI infrastructure and compute supply; Source: https://arxiv.org/abs/2203.15556 For crypto traders, AI catalysts have historically moved AI-linked tokens such as RNDR, FET, and AGIX alongside major AI equity news like Nvidia earnings beats; Source: Reuters, Feb 2024, AI-linked crypto tokens surged after Nvidia’s results AI compute demand has also intersected with crypto infrastructure, as bitcoin miner Core Scientific rallied on an AI services deal with CoreWeave, tying GPU demand to digital asset businesses; Source: Reuters, June 2024, Core Scientific shares jumped on AI deal with CoreWeave No project-specific or token-specific details were disclosed in the post, so the tweet should be treated as a broad AI sentiment signal rather than a direct catalyst; Source: https://twitter.com/gdb/status/1962594235263427045