Dojo AI News & Updates
Tesla Shuts Down Dojo AI Supercomputer Project, Pivots to AI6 Chips
Elon Musk confirmed Tesla has disbanded its Dojo AI training supercomputer team and shelved the second-generation D2 chip development. Tesla is now consolidating resources to focus on AI5 and AI6 chips manufactured by TSMC and Samsung, which are designed for both inference and training across self-driving cars and humanoid robots.
Skynet Chance (-0.03%): Tesla's resource consolidation and focus on more practical AI chips suggests a more controlled, commercially-driven approach to AI development rather than pursuing potentially less controllable experimental architectures. The shift away from custom supercomputer infrastructure reduces one potential vector for uncontrolled AI scaling.
Skynet Date (+0 days): The project shutdown and resource reallocation likely creates short-term delays in Tesla's AI capabilities development, as teams are disbanded and strategic direction shifts. This temporary disruption could slow the pace of AI advancement in autonomous systems.
AGI Progress (-0.03%): The cancellation of Dojo represents a setback in specialized AI training infrastructure development, which is crucial for AGI advancement. Tesla's retreat from custom supercomputing solutions indicates challenges in scaling specialized AI hardware, potentially slowing broader industry progress.
AGI Date (+0 days): The shutdown of a major AI training project and disbanding of specialized teams creates delays in Tesla's AI development timeline. Resource reallocation and strategic pivots typically result in slower near-term progress as new approaches are implemented and teams are restructured.
Tesla Discontinues Dojo AI Supercomputer Project, Shifts to External Partners
Tesla is shutting down its Dojo AI training supercomputer project and disbanding the team, with lead engineer Peter Bannon leaving the company. The company is pivoting to rely more heavily on external partners like Nvidia and AMD for compute power, while signing a $16.5 billion deal with Samsung for AI6 inference chips. This represents a major strategic shift away from in-house chip development that CEO Elon Musk had previously touted as crucial for achieving full self-driving capabilities.
Skynet Chance (-0.03%): Tesla's shift away from developing proprietary AI hardware reduces potential concentration of advanced AI capabilities under a single company's control. Increased reliance on established vendors like Nvidia creates more distributed oversight and standardization in AI development infrastructure.
Skynet Date (+1 days): The abandonment of Dojo represents a setback in Tesla's AI ambitions and suggests slower progress toward autonomous systems that could pose control risks. This strategic retreat likely delays aggressive AI capability development in the automotive sector.
AGI Progress (-0.04%): Tesla's retreat from custom AI hardware development represents a step back from vertical integration in AI systems. The failure of Dojo, which was designed to process vast amounts of video data for autonomous driving, suggests challenges in scaling specialized AI compute infrastructure.
AGI Date (+0 days): While Tesla's pivot to external partners may provide access to more mature hardware, the abandonment of Dojo likely delays Tesla's specific contributions to AGI through autonomous vehicle AI. However, increased reliance on Nvidia may accelerate overall progress through established infrastructure.
Tesla's Dojo and Cortex: Elon Musk's Custom AI Supercomputers for Self-Driving Cars
Tesla is developing custom supercomputers Dojo and Cortex to train AI models for its Full Self-Driving technology and humanoid robots. The company aims to reduce dependency on Nvidia chips by creating its own D1 chips, with plans to scale Dojo to 100 exaflops by October 2024, though recent communications suggest a pivot toward Cortex as the primary training infrastructure.
Skynet Chance (+0.08%): Tesla's development of immense AI training capabilities aimed at creating "synthetic animals" with human-like perception increases the risk of advanced autonomous systems that could eventually operate beyond human comprehension or control. Tesla's emphasis on proprietary AI hardware-software integration creates potential for uniquely capable systems with limited external oversight.
Skynet Date (-1 days): The massive investment in proprietary AI compute infrastructure specifically designed for training autonomous systems suggests an acceleration in the development timeline for human-level AI perception and decision-making in physical environments. Tesla's commitment to deploy robotaxis by mid-2025 puts pressure on rapidly advancing these capabilities.
AGI Progress (+0.05%): Tesla's development of custom AI hardware optimized for neural network training represents significant progress in scaling AI computing infrastructure toward AGI-necessary levels. The company's integrated approach to hardware and software, combined with real-world data collection from millions of vehicles, creates a uniquely powerful capability focused on perception and decision-making.
AGI Date (-1 days): Tesla's massive investment in custom AI compute infrastructure (targeting 100 exaflops) and its aggressive timeline for unsupervised FSD by 2025 suggests an acceleration in the development of AI systems capable of human-level visual perception and decision-making in complex environments.