According to Sawyer Merritt on X, newly posted photos show Tesla Cybercabs without steering wheels, indicating a fully autonomous interior layout aligned with Tesla’s planned robotaxi service. As reported by Sawyer Merritt, the cabin lacks driver controls, implying reliance on Tesla Full Self-Driving software and onboard compute for Level 4 style service operations, pending regulatory approval. According to Sawyer Merritt, the design suggests cost-optimized fleets for ride-hailing with higher passenger space utilization, which could lower per-mile costs for urban mobility providers if Tesla scales production. As reported by Sawyer Merritt, the images reinforce Tesla’s push to commercialize autonomous ride services, presenting opportunities for fleet operators, city pilots, and mobility-as-a-service platforms that integrate Tesla FSD APIs once available.

According to Sawyer Merritt on X, Tesla has begun hiring AI Hardware Engineers in India for the first time, with roles focused on custom silicon and optimized architectures to power its autonomous driving and energy products; this move suggests localized talent scaling for AI chips and systems design (as reported by Sawyer Merritt). According to the job description excerpt cited by Sawyer Merritt, the team’s mandate is to build custom silicon and architectures to keep Tesla leading in AI-driven automotive and energy solutions, indicating potential growth of in-house accelerators and hardware-software co-design for Full Self-Driving and Dojo-class compute. As reported by Sawyer Merritt, establishing AI hardware roles in India could lower R&D costs, expand 24x7 engineering coverage, and tap India’s semiconductor design talent pool, creating supplier and hiring opportunities for EDA tools, verification, and physical design services in the region.

According to Sawyer Merritt on X, a new image post drew attention to Tesla’s AI stack and data collection, highlighting the role of on-vehicle compute and centralized training. As reported by Tesla’s 2023–2024 AI Day materials and earnings calls, Tesla is investing in Dojo to scale video model training for Full Self-Driving with billions of real-world miles as training data. According to Tesla’s 2024 Q4 update, the company continues to expand its autolabeled video datasets and multi-camera neural networks for end-to-end driving. Based on The Information’s reporting, Tesla is procuring Nvidia H100 clusters in parallel with Dojo for model training throughput. These developments create five business implications: 1) lower per-mile data acquisition costs through fleet learning; 2) faster iteration on end-to-end driving models via vertically integrated training; 3) potential licensing of autonomy stacks to OEMs once safety metrics are validated; 4) margin expansion from software subscriptions such as FSD; and 5) defensible moat from proprietary, large-scale driving video corpora. All statements are drawn from the above sources; the image post by Sawyer Merritt serves as a topical pointer to Tesla’s ongoing AI strategy.