智通财经网·2025-08-12 08:23Core Viewpoint - Morgan Stanley maintains an "Overweight" rating on Tesla (TSLA.US) with a target price of $410, highlighting a strategic shift involving the dissolution of the in-house Dojo supercomputer team to optimize AI program cost-effectiveness [1] Group 1: Strategic Changes - Tesla is reportedly disbanding its Dojo supercomputer team, with team leader Peter Bannon leaving the company, although Tesla has not confirmed this news [1] - The Dojo supercomputer was designed to process vast amounts of data and video generated by Tesla vehicles for training Full Self-Driving (FSD) and Optimus machine learning models [1] - Elon Musk has ordered the termination of the Dojo project, with plans to increase collaboration with external tech partners like NVIDIA (NVDA.US) and AMD (AMD.US) for computational support [1] Group 2: Financial Implications - Analyst Adam Jonas suggests that this move reflects significant strategic and financial considerations, potentially part of Tesla's cost-cutting plan to reduce capital and operational expenditures associated with the Dojo project [2] - Tesla's Q2 report indicated that AI initiatives have increased operational expenses, with capital expenditures expected to exceed $9 billion in fiscal year 2025, primarily directed towards AI-related fields [2] Group 3: Focus on Robotics and Edge Computing - The strategic shift may benefit Musk's xAI company, which is taking on more responsibilities for developing Tesla's "AI brain" and utilizing data from social media and real-world vehicle operations [2] - Tesla appears to be refocusing on robotics technology and edge inference capabilities, with Musk emphasizing the potential strategic value of Tesla's global fleet as a distributed inference network [2] - As the commercialization of Optimus accelerates, analysts believe Tesla may redirect capital expenditures and R&D investments towards reducing robot production costs and optimizing manufacturing systems [2] Group 4: Market Context - The timing of Tesla's spending reduction is notable, as the GPU shortage that previously pressured the company to develop in-house computing resources has significantly eased [2] - Joe Moore from Morgan Stanley notes that developing ASIC chips that can outperform NVIDIA's offerings is becoming increasingly difficult due to NVIDIA's annual R&D spending exceeding $15 billion and the expanding scope of AI investments [3]