3 6 Ke·2025-08-19 09:42Core Insights - Tesla's Dojo supercomputer project, initially aimed at enhancing fully autonomous driving capabilities, has been officially shut down after significant investment exceeding $1 billion, marking a shift in strategy towards purchasing AI chips from Nvidia instead of continuing self-development [1][4][6][10]. Group 1: Project Overview - The Dojo project was introduced by Elon Musk in 2019 with the goal of creating a powerful computing system specifically for training autonomous driving models using Tesla's proprietary D1 chip [4]. - Despite initial ambitions, the project faced significant challenges in performance and stability, leading to its eventual discontinuation [8][10]. Group 2: Strategic Shift - Tesla plans to invest billions in Nvidia AI chips, increasing its stock from 35,000 to 85,000 units by the end of 2025, indicating a strategic pivot from self-reliance to leveraging established industry solutions [6][15]. - This decision reflects a broader industry trend where companies are recognizing the importance of platform ecosystems over isolated technological breakthroughs [11][13]. Group 3: Industry Context - The competitive landscape is dominated by Nvidia, which has built a robust software ecosystem (CUDA) that supports AI development, making it challenging for new entrants to compete without similar infrastructure [9][11]. - The closure of Dojo highlights the difficulties faced by companies attempting to innovate in isolation, as seen in the case of Graphcore, which failed to establish a competitive software ecosystem [13]. Group 4: Future Implications - The end of the Dojo project may allow Tesla's engineers to focus on their strengths in neural network algorithms and data processing, rather than hardware challenges, potentially leading to more effective advancements in AI [12][14]. - This strategic retreat from self-development to collaboration with established players like Nvidia may ultimately position Tesla to achieve its goals more efficiently [16].