According to Sawyer Merritt on X citing the Moonshot podcast interview with Uber CEO Dara Khosrowshahi, Uber aims to facilitate more autonomous and robotaxi rides than any company globally by 2029, signaling an aggressive platform strategy for self-driving supply aggregation. According to the Moonshot podcast, Khosrowshahi said Uber would onboard Tesla vehicles using a camera-only approach once safety is proven, noting tens of thousands of Teslas already operate on Uber and some drivers use FSD today. As reported by the Moonshot podcast, Uber plans to partner broadly beyond Tesla, implying a multi-OEM autonomy marketplace that could reduce hardware lock-in and expand fleet availability across cities. According to the Moonshot interview, this creates near-term opportunities for AV operators to access Uber’s demand and routing data, while Uber could monetize autonomy through per-mile take rates, dynamic pricing, and fleet operations APIs. As reported by Sawyer Merritt referencing the interview, the comments highlight a platform-first model where L4 robotaxi providers integrate with Uber’s dispatch, insurance, and compliance stack, accelerating commercial deployment once regulatory approvals and safety thresholds are met.

According to Sawyer Merritt on X, a new photo of Tesla’s Optimus team was shared, highlighting the group behind Tesla’s humanoid robot program. As reported by Sawyer Merritt, the post underscores active team growth and visibility, which aligns with Tesla’s ongoing Optimus progress showcased in prior engineering videos and demonstrations, according to Tesla’s official updates. For AI business impact, the expanded team suggests accelerated iteration in mechatronics, computer vision, and onboard inference, which could shorten time-to-product for factory automation use cases, according to Tesla’s previous Investor Day remarks and product roadmap communications.

According to Sawyer Merritt on X, a German TV reporter tested Tesla FSD (Supervised) V14 as a rural public transport option and reported the system "worked perfectly and safely," even in bad weather conditions. As reported by Sawyer Merritt, the on-road demo indicates improved perception and planning under low-visibility scenarios, suggesting stronger end-to-end autonomy stack maturity. According to the reporter’s account shared by Sawyer Merritt, consistent lane-keeping and cautious maneuvers in rural environments highlight potential cost savings for on-demand shuttles and first mile last mile services. As reported by Sawyer Merritt, if performance generalizes, municipalities could pilot supervised driver deployments to extend coverage in low-density areas, reduce labor hours, and collect operational data to refine service design.

According to Sawyer Merritt on X, the linked full interview features Elon Musk detailing xAI’s near-term roadmap, including faster Grok model upgrades, expanded training data pipelines via X, and a scaled compute buildout leveraging NVIDIA and in-house systems; as reported by the interview, Musk emphasized shipping practical agentic features for consumers and enterprises on X and Tesla platforms, positioning Grok as a real-time assistant integrated with live social and vehicle data; according to the interview, business opportunities highlighted include enterprise API access to Grok, safety tooling for automated agents, and monetization through premium X subscriptions bundling advanced model capabilities; as reported by the source, Musk also underscored constraints in GPU supply and data center power, indicating xAI’s focus on efficiency optimizations and data quality to accelerate iteration cycles.

According to Sawyer Merritt on X, Tesla released a new video featuring David Moss completing a cross‑country trip using FSD (Supervised) with zero disengagements over 2,700 miles in 2 days and 20 hours, with the Model 3 handling road signs, turns, and Supercharger stops end‑to‑end. As reported by Sawyer Merritt, the drive showcases end‑to‑end autonomy progress in complex, long‑haul routing with consistent lane selection and charging orchestration, indicating a maturing stack for highway and urban scenarios. According to the same source, the zero‑intervention outcome highlights business implications for Tesla’s software margin expansion, potential Robotaxi validation pathways, and higher take‑rate opportunities for FSD subscriptions in markets where supervised autonomy is permitted.

According to Sawyer Merritt on X, a Cybertruck using Tesla FSD (Supervised) v14.2.2.5 autonomously reversed at an intersection to make space for a semi taking a wide turn, demonstrating context-aware path planning and motion control in mixed traffic (source: Sawyer Merritt on X, March 25, 2026). As reported by the post, the maneuver highlights progress in behavior planning stacks that evaluate rear clearance and yield logic without direct human input, though the system remains driver-supervised (source: Sawyer Merritt on X). For businesses, this suggests expanding operational design domains for advanced driver assistance, enabling value in urban logistics, robo-fleet pilots, and insurance risk scoring where nuanced low-speed negotiation reduces incident risk (source: Sawyer Merritt on X).

According to Sawyer Merritt on X, Tesla engineers said the next‑gen Optimus V3 hand is moving into gen‑3 and mass production with functionality and a form factor very close to human, describing it as resembling a person in a superhero suit and calling it revolutionary; this was shared alongside Tesla’s new Optimus engineering video (as reported by Sawyer Merritt, citing Tesla’s video). For AI industry implications, according to the Tesla video shared by Sawyer Merritt, a humanlike, production‑ready robotic hand suggests near‑term gains in manipulation tasks critical for factory automation, logistics picking, and service robotics, where dexterous grasping has been a bottleneck. As reported by the same source, positioning V3 for mass production indicates potential cost curves similar to EV manufacturing, creating business opportunities for integrators to deploy humanoid robots in repetitive material handling, bin picking, and assembly, while software stacks for vision‑language‑action policy learning and reinforcement learning from human demonstrations could rapidly compound capability once a standardized, humanlike end effector is available.

According to Sawyer Merritt on X, Tesla released a new Optimus video highlighting the engineers and builders behind the humanoid robot and showcasing recent progress in robotics and AI training. According to the post, the video emphasizes how Tesla’s hardware, perception, and controls teams iterate on manipulation, locomotion, and factory integration, signaling advancing use cases in manufacturing and logistics. As reported by Sawyer Merritt’s shared clip, the focus on the people and workflows behind Optimus suggests Tesla is scaling data collection, simulation, and real‑world validation pipelines that are critical to embodied AI. According to the same source, this visibility indicates near-term business impact for automating repetitive plant tasks and longer-term opportunities in warehouse handling and material movement.

According to Sawyer Merritt on X, a large cluster of new Tesla Model Y vehicles in Dallas featuring rear camera washers was observed conducting simulated pickup and dropoff routines, suggesting a dedicated robotaxi staging area; the original post cites Chris Deardurff’s footage and location details as the source. As reported by Sawyer Merritt, the vehicles carried similar Texas plates seen on-road during recent Full Self-Driving (FSD) testing, indicating a coordinated fleet consistent with pre-deployment validation and data collection. According to the X post, rear camera washers are a hardware cue aligned with Tesla’s vision-first autonomy stack, supporting reliability in adverse weather and improving perception performance—key for high-uptime robotaxi operations. From a business perspective, according to Sawyer Merritt’s report, concentrated fleet testing in Dallas implies Tesla is preparing operational workflows such as dispatch, curbside pickup mapping, and remote monitoring, which could accelerate a commercial pilot once regulatory approvals are secured. For AI industry stakeholders, this development—according to the cited X footage—highlights expanding real-world data generation for end-to-end driving models and potential near-term opportunities in mapping services, fleet telematics, curbside orchestration, and insurance underwriting tuned to vision-based autonomy.

According to Sawyer Merritt on X, Wedbush analyst Dan Ives wrote that Tesla’s Terafab initiative is the first step toward a potential Tesla–SpaceX merger likely in 2027, and that the project would accelerate Tesla’s ambitious AI path (source: Sawyer Merritt quoting Dan Ives’ TSLA note). As reported by Sawyer Merritt, Ives frames Terafab as a strategic bridge to scale AI-driven robotics, autonomy, and compute, implying greater integration of Tesla’s FSD and Dojo with SpaceX’s edge compute and communications stack. According to Sawyer Merritt’s post, the near-term business impact centers on faster AI model deployment, expanded real‑world data pipelines, and potential shared infrastructure that could reduce training and inference costs at scale.

According to AI News on X, Elon Musk announced Terafab, a large-scale AI chip manufacturing facility to build two custom processors—one for the Optimus humanoid robot and another optimized for space-based compute (source: AI News; video via YouTube). According to AI News, the stated goal is terawatt-scale AI compute in orbit powered by continuous solar energy to enable always-on inference and training workloads (source: AI News). As reported by AI News, a space-optimized chip could leverage passive cooling and radiation-hardened design for orbital data centers, while the Optimus chip would prioritize low-latency sensor fusion and on-device control loops for robotics (source: AI News). According to AI News, if realized, Terafab could reshape GPU supply chains, accelerate autonomous robotics, and catalyze a new market for solar-powered orbital AI infrastructure and edge-to-space MLOps pipelines (source: AI News).

According to Sawyer Merritt on X, new on-road footage of Tesla FSD (Supervised) v14.2.2.5 in the Netherlands suggests the system is ready for European deployment, with potential regulatory approval in the Netherlands targeted for April 10; as reported by Sawyer Merritt, the video indicates stable urban navigation and lane behavior, which, if approved, could accelerate Tesla’s software revenues via subscription uptake and data collection at European scale. According to public EU regulatory context reported by the European Commission and national type-approval processes, any rollout would still require country-level conformity and driver-monitoring safeguards, implying phased deployment and geo-fenced capabilities that impact commercialization timelines and fleet learning efficiency.

According to SawyerMerritt on X, a Tesla cybercab without side mirrors was seen driving in Northern Virginia, suggesting active robotaxi field testing in NOVA; as reported by the same post, recent Tesla job listings for AI drivers and robotaxi supervisors align with supervised autonomy trials and operational readiness work. According to the linked post by @_marco, the sighting reinforces that Tesla is deploying test vehicles in public traffic, indicating progress toward a supervised robotaxi service pipeline and data collection for end-to-end autonomy validation. For businesses, this points to near-term opportunities in safety driver staffing, fleet operations, local compliance services, and mapping QA partnerships as Tesla scales pre-commercial tests, according to the observed hiring patterns cited by SawyerMerritt.

According to Sawyer Merritt on X, Tesla is testing Robotaxi service in Phoenix, Arizona using a Model Y equipped with rear camera washers and a California manufacturer plate, aligning with Tesla’s Q4 earnings call guidance that Phoenix is among seven metro areas targeted for robotaxi coverage in H1 2026; according to Tesla’s Q4 2025 earnings call remarks, this pilot signals progress toward supervised commercial robotaxi operations, with enterprise opportunities in autonomous ride-hailing, fleet optimization, and data-driven safety validation in the Phoenix market.

According to SawyerMerritt on X, Tesla's Dojo D3 chip is being used inside SpaceX AI satellites, with a posted image and link suggesting on-orbit inference hardware integration; however, independent confirmation is not provided in the post. As reported by the X post, the claim implies edge AI processing in space for tasks like onboard vision, autonomy, and RF signal classification, reducing ground downlink needs and latency. According to prior Tesla disclosures referenced by industry coverage, Dojo is designed for high-throughput training, and if a D3 variant is space-hardened for inference, it signals a vertical stack from Tesla silicon to SpaceX satellite operations, potentially lowering cost per inference and enabling real-time services. As reported by the post, if validated by SpaceX or Tesla, business opportunities include satellite-based AI analytics, premium enterprise APIs for geospatial intelligence, and cross-division silicon monetization.

According to Sawyer Merritt on Twitter, SpaceX released a new video of a lunar electromagnetic mass driver to launch large AI satellites using Tesla chips; however, no corroborating report or official release from SpaceX, Tesla, or reputable outlets confirms this claim as of now. According to SpaceX’s official channels and newsroom, there is no press release or technical brief on a Moon-based mass driver or AI satellites powered by Tesla silicon. As reported by Tesla’s investor relations and product pages, Tesla develops FSD and Dojo chips for automotive and data center use, but no source confirms their deployment in SpaceX satellites. Given the lack of verification, businesses should treat this as unconfirmed and avoid operational decisions until an official statement appears from SpaceX or Tesla.

According to Sawyer Merritt on X, the official TERAFAB logo representing Tesla, SpaceX, and xAI has been unveiled. As reported by the post, the shared branding signals coordinated efforts across Elon Musk’s companies, which could align xAI’s model development with Tesla’s automated manufacturing and SpaceX’s high-reliability production practices. According to the tweet, while only the logo was revealed, a unified TERAFAB identity suggests potential AI-driven factory systems and robotics integration where xAI software could optimize Tesla manufacturing workflows and SpaceX supply chains, creating new opportunities in AI-enabled industrial automation and large-scale inference at the edge.

According to Sawyer Merritt on X, Tesla said the Netherlands RDW communicated that FSD (Supervised) could be approved on April 10, paving a path for broader EU approvals, and the jump from legacy Autopilot to FSD V14 for AI4 Teslas would be significant. As reported by Sawyer Merritt citing Tesla, the company conducted 13,000+ customer ride-alongs, 4,500+ track test scenarios, compiled thousands of pages for 400+ compliance requirements, and ran dozens of safety studies to support certification. According to Sawyer Merritt, if RDW greenlights FSD (Supervised), early European deployment could accelerate data collection for long-tail edge cases, enabling faster iteration of Tesla’s end-to-end neural network driving stack and potential revenue uplift from software subscriptions. As reported by Sawyer Merritt, the approval timeline and scope remain contingent on RDW’s final decision and subsequent country-level clearances across Europe.

According to Sawyer Merritt, Tesla’s Terafab chip manufacturing project launches tomorrow, signaling a push to secure advanced semiconductor supply for AI compute at scale. As reported by Merritt citing Elon Musk, current output from key suppliers will be insufficient, and to remove likely constraints in 3–4 years Tesla will need to build a very large manufacturing capability, indicating vertical integration to support AI training and autonomy workloads. According to the tweet thread, the initiative targets advanced chip capacity, which could reduce dependency on external foundries and de-risk GPU and accelerator shortages for Tesla’s Full Self-Driving and robotics programs.

According to Sawyer Merritt on X, Carwow’s new video titled “Why Tesla Full Self Drive is Pointless!” conflates Tesla Autopilot with Full Self-Driving (FSD), despite FSD not being approved for public use in the UK, and evaluates Autopilot in urban scenarios it was not designed to handle (source: Sawyer Merritt on X). As reported by Carwow’s YouTube upload, the video tests city-driving scenarios while framing the critique around FSD, which may mislead viewers about feature scope and regulatory status (source: Carwow YouTube channel). According to Tesla’s official support pages, Autopilot is an advanced driver assistance system intended primarily for highway driving, while FSD (when available) offers broader capabilities but still requires active supervision and is subject to regional regulations (source: Tesla Support). For AI and automotive stakeholders, the incident highlights three business-critical points: clear feature labeling to reduce liability and improve user trust, content accuracy for influencer partnerships, and regulatory alignment for ADAS-to-AV product roadmaps in Europe (sources: Carwow YouTube, Tesla Support, Sawyer Merritt on X).