Dylan Patel: GPT4.5's Flop, Grok 4, Meta's Poaching Spree, Apple's Failure, and Super Intelligence

AI Model Development & Strategy - Meta delayed the release of its Behemoth model due to training problems and questionable architectural decisions, and may not release it at all [1] - The industry believes super intelligence is the ultimate goal, driving companies to prioritize it over AGI [1][3] - OpenAI's GPT-4.5% (Orion) failed due to overparameterization, insufficient data scaling, and training bugs, leading to its deprecation [7] - Reasoning breakthroughs, like OpenAI's "strawberry," demonstrate that generating high-quality data is crucial for model efficiency and performance [7][8] Talent Acquisition & Competition - Meta acquired Scale AI primarily for its talent, particularly Alexander Wang, to lead its super intelligence efforts, signaling a strategic shift [3] - Meta is offering substantial bonuses, reportedly up to $100 million or even over $1 billion for some individuals, to attract top AI researchers from companies like OpenAI [3][4] - Apple faces challenges in attracting top AI talent due to its secretive culture, aversion to Nvidia, and lack of competitive compute resources [8] Cloud & Compute Infrastructure - OpenAI's exclusivity agreement with Microsoft for compute has ended, with OpenAI now diversifying its compute resources through partnerships with Oracle, CoreWeave, and others [5] - Nvidia is prioritizing smaller cloud companies, potentially creating tension with major players like Amazon and Google, who feel marginalized in GPU allocations [10] - AMD is employing strategies such as renting back GPUs to cloud providers to encourage adoption of its chips, fostering relationships and driving interest [17][18][20] Market Dynamics & Future Trends - The analyst believes closed source AI will ultimately dominate, raising concerns about the concentration of power among a few companies [57] - The analyst estimates that 20% of jobs could be automated by the end of this decade or the beginning of the next, but the implementation and deployment will take years [48] - The analyst is bearish on on-device AI, arguing that cloud-based AI offers better performance, access to data, and cost-effectiveness for most valuable use cases [9]