According to Sawyer Merritt on X, Tesla updated its Full Self-Driving (FSD) miles tracker to reflect a larger fleet and higher utilization, reporting an average of 28.8 million FSD miles per day, up from 14.4 million a few months ago, equivalent to roughly 1,000 miles every 3 seconds. As reported by Sawyer Merritt, this doubling of daily FSD miles materially expands Tesla’s real‑world driving dataset, which is critical for training end‑to‑end neural networks and improving long‑tail reliability. According to the same source, the scale-up indicates stronger user engagement with FSD, creating opportunities for faster model iteration, regional feature rollout, and potential progress toward supervised autonomy services that could precede broader robotaxi deployment.

According to Sawyer Merritt on X, Tesla’s paid robotaxi program has logged 1.7 million miles, up from 610,000 at the end of Q4 2025, indicating rapid expansion of supervised commercial autonomy trials. As reported by Sawyer Merritt, the scale-up suggests higher route density for Tesla’s supervised autonomy fleet and increased rider supply, which can improve model learning through real-world edge cases and drive per-mile cost reductions. According to industry coverage by Electrek and previous Tesla earnings calls, Tesla is developing end-to-end neural networks and planning an Optimus and Dojo-aligned stack; this new mileage milestone implies more labeled driving data volume that can accelerate model iteration cycles and reduce disengagement rates in geofenced operations. As reported by Tesla’s past FSD updates in release notes and discussed by investors on earnings calls, expanding paid rides can validate pricing, utilization, and safety KPIs crucial for regulatory dialogs and market entry sequencing. According to Sawyer Merritt, the jump from 610,000 to 1.7 million paid miles in roughly one quarter highlights potential network effects for marketplace liquidity, opening opportunities for city-by-city launches, driver-partner programs, and fleet optimization software revenues.

According to Sawyer Merritt on X, a second Tesla robotaxi operating in Houston appears to run in an unsupervised mode, indicating a potential expansion of Tesla’s autonomous pilot testing footprint in real-world urban conditions. As reported by the X post, the sighting suggests Tesla is iterating on end-to-end neural network driving stacks and large-scale on-road data collection, which could accelerate model training and validation cycles. According to publicly shared company updates referenced by Electrek and previous Tesla AI Day materials, Tesla’s approach centers on vision-based end-to-end models trained with fleet data, implying that unsupervised street operation—if confirmed by Tesla—would have notable implications for regulatory approvals, safety benchmarks, and commercial robotaxi deployment timelines in the U.S. market.

According to Sawyer Merritt on X, citing RtaxiTracker, Tesla has added a second Model Y robotaxi to its Houston fleet, signaling expanded on‑road testing of autonomous capabilities (source: Sawyer Merritt post referencing RtaxiTracker on X). According to Sawyer Merritt, the deployment underscores Tesla’s push to validate Full Self-Driving in real-world urban operations, a prerequisite for scalable robotaxi services and potential ride-hailing revenue streams (source: Sawyer Merritt on X). As reported by RtaxiTracker via Sawyer Merritt, incremental fleet growth in one metro allows Tesla to collect diverse edge-case data, improve neural network training, and iterate on safety and reliability KPIs critical for regulatory approvals and commercial launch (source: RtaxiTracker via Sawyer Merritt on X). According to Sawyer Merritt, Houston’s expansion may enable Tesla to test pricing models, dispatch logic, and utilization metrics ahead of broader rollouts, creating near-term business opportunities in autonomous mobility and fleet management software (source: Sawyer Merritt on X).

According to Sawyer Merritt on X, every Unsupervised Model Y operating in Austin, Houston, and Dallas is running Tesla’s AI4 stack. According to Merritt’s post, this indicates Tesla has standardized its next‑gen autonomy software across key Texas pilot markets. As reported by Sawyer Merritt, broader AI4 deployment could accelerate data collection for end‑to‑end neural networks and reinforcement learning at scale, improving Full Self-Driving model iteration cycles. According to prior Tesla disclosures cited by investor reports, concentrated regional rollouts enable rapid telemetry feedback, lowering validation costs and shortening release cadences, which can translate into faster pathway to supervised-to-unsupervised transitions and potential regulatory engagement advantages. For suppliers and ecosystem partners, this AI4 footprint in Texas signals near-term opportunities in edge AI compute, high-bandwidth connectivity, and fleet data labeling operations supporting autonomy model training, as indicated by industry analyses referencing Tesla’s incremental city-by-city activation strategy.

According to Yann LeCun on X, the Lush SN system used a homegrown Lisp interpreter with a compiler added in the early 1990s, and it was a distinct language rather than Common Lisp, as echoed in a thread with Artur Chakhvadze; according to the official Lush manual, Lush combined a Lisp-like syntax with efficient C and CUDA extensions for numerical computing and machine learning, influencing early neural network research workflows. According to the Lush manual, this design enabled rapid prototyping with compiled performance for matrix operations and signal processing, a pattern later mirrored in modern AI frameworks that couple high-level scripting with optimized kernels. As reported by the Lush documentation, the language’s mixed interpreted compiled pipeline offered practical advantages for early deep learning experiments, providing a historical blueprint for today’s hybrid JIT and graph compilers used in model training.

According to Sawyer Merritt on X, both Houston and Dallas are now confirmed to have unsupervised Tesla Robotaxi rides, indicating active driverless operations beyond supervised FSD testing in two major Texas metros. As reported by Merritt’s post, this suggests Tesla is progressing toward commercial robotaxi service zones that could accelerate ride-hailing monetization and fleet utilization in high-demand corridors. According to prior Tesla statements cited by multiple industry trackers, unsupervised operation would rely on FSD v12-class end-to-end neural networks, implying expanded real-world data capture and potential regulatory engagement with Texas city authorities for operating permits.

According to Sawyer Merritt on X, Tesla has begun offering unsupervised Robotaxi rides to regular customers in Dallas, Texas, marking a public pilot of driverless ride-hailing under Tesla’s supervised autonomy roadmap (source: Sawyer Merritt, X). As reported by Merritt, the ride was completed without a human safety driver, indicating Tesla is testing a fully driverless operational design domain in a major U.S. metro (source: Sawyer Merritt, X). According to prior company statements covered by Reuters, Tesla’s Robotaxi strategy is expected to leverage its end to end neural network FSD stack trained with large scale fleet data, positioning the company to compete with incumbents like Waymo in urban ride-hailing. For businesses, this signals near term opportunities in fleet operations, mapping data partnerships, insurance underwriting for AV risk, and curbside logistics, while regulators and municipalities in Texas—known for permissive AV policies per state DOT guidance—could accelerate commercial permits and geofence expansion.

According to SawyerMerritt on X, Elon Musk stated that Tesla's AI4 hardware is sufficient to achieve better-than-human safety for Full Self-Driving (FSD), citing Optimus and Tesla's supercomputer clusters as enabling factors (source: Sawyer Merritt post referencing Elon Musk on X). According to Elon Musk on X, this implies current AI4 Tesla owners could see substantial FSD performance and safety improvements without immediate hardware upgrades, which may accelerate feature rollouts and fleet-wide validation. As reported by Sawyer Merritt, the emphasis on in-house supercomputer clusters suggests Tesla will continue scaling end-to-end neural networks and video training pipelines, reinforcing a vertically integrated strategy with potential cost efficiencies and faster iteration cycles for autonomy software.

According to Sawyer Merritt, citing Reuters, the Dutch vehicle authority RDW has notified the European Commission of its plan to seek EU approval for Tesla’s Full Self-Driving (FSD) features by 2026, initiating a formal pathway to assess automated driving functions under EU type-approval frameworks. As reported by Reuters, RDW’s move could centralize technical evaluation for over-the-air software, computer vision stacks, and neural network–based driving policies used by Tesla, aligning with UNECE regulations and EU General Safety Regulation timelines. According to Reuters, potential EU-level approval would expand Tesla’s commercial rollout of supervised autonomy across member states, while imposing transparent validation for corner-case performance, data logging, and human-machine interface safeguards. For AI vendors and Tier 1 suppliers, this signals growing demand for ISO 26262/21448-compliant perception models, scenario simulation, and safety case tooling. As reported by Reuters, regulatory scrutiny on model behavior, dataset provenance, and continuous learning updates could create opportunities for European test platforms, synthetic data providers, and edge AI compute optimization targeting FSD-class workloads.

According to Sawyer Merritt on Twitter, Tesla updated its Full Self-Driving miles tracker to reflect a larger fleet and higher usage, with the fleet now averaging 19.2 million miles per day on FSD, up from 14.4 million a few months ago (a pace of roughly 1,000 miles every 4.5 seconds). As reported by Sawyer Merritt, this rapid increase signals accelerating real-world data collection that can improve model performance and safety validation for Tesla’s end-to-end autonomy stack. According to Sawyer Merritt, the usage surge expands the training corpus for vision-based neural networks and could shorten iteration cycles for software updates, creating business advantages in operating cost reduction, feature reliability, and regulatory readiness.

According to Sawyer Merritt on X, Tesla has officially released FSD v14.3 with upgrades including improved parking location pin prediction displayed with a P icon on the map and greater decisiveness in selecting and maneuvering into parking spots. As reported by Sawyer Merritt, the update also refines lane selection and merging behavior, aiming for smoother, more humanlike driving in complex traffic. According to Tesla’s historical release notes practices cited by Sawyer Merritt, these enhancements target urban drivability and low-speed confidence, which are critical for wider consumer adoption and lower driver interventions. For businesses, as reported by Sawyer Merritt, the improvements could reduce last‑mile inefficiencies for ride‑hailing, delivery fleets, and valet operations, while reinforcing Tesla’s end‑to‑end neural network approach and data advantage from its fleet. According to Sawyer Merritt, broader rollout cadence will determine near‑term impact on safety metrics, user satisfaction, and potential subscription uptake for FSD.

According to OpenAI on Twitter, the company closed a new funding round with $122 billion in committed capital at an $852 billion post-money valuation, stating the fastest way to expand AI’s benefits is to put useful intelligence in people’s hands early and compound access globally. As reported by OpenAI’s official post, the new capital provides resources to accelerate model training, deploy safer, more capable systems, and expand distribution, which could lower inference costs and speed enterprise adoption. According to the OpenAI announcement, the scale of this raise signals intensified competition for advanced compute, potential strategic GPU and custom accelerator investments, and broader commercialization of AI assistants across consumer and enterprise channels.

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 on X, Tesla has added another Unsupervised Model Y to its robotaxi fleet operating in Austin. As reported by Merritt, the vehicle is labeled for unsupervised operation, signaling continued on‑road validation of Tesla’s end‑to‑end neural network autonomy stack and data engine. According to prior Tesla disclosures cited by Reuters and Tesla’s 2023–2024 AI Day materials, the company’s Full Self-Driving approach relies on vision-only perception, large-scale fleet learning, and inference on the FSD computer, and additional fleet units can accelerate corner-case collection and model retraining. For mobility operators and city partners, as noted by The Verge’s coverage of Tesla’s robotaxi plans, incremental fleet growth in a single market like Austin can inform permitting pathways, safety metrics, and unit economics before broader deployment. According to Bloomberg’s analysis of autonomy pilots, concentrated testing regions enable faster software iteration cycles, improved mapping priors from camera-only systems, and clearer business KPIs such as rides per vehicle per day and intervention rates.

According to Sawyer Merritt on X, citing a new The All-In Podcast interview, Travis Kalanick said Waymo is “obviously ahead” in self-driving but faces challenges in manufacturing, scale, urgency, and fierceness, while Tesla is tackling “fundamentals, science, hard mode times 100,” and he questioned when a “ChatGPT moment” will arrive for vision AI. According to The All-In Podcast interview referenced by Sawyer Merritt, this framing highlights two distinct go-to-market strategies: Waymo’s robotaxi-first approach with geo-fenced deployments and deep safety validation, and Tesla’s consumer-scale software-first Full Self-Driving strategy that bets on end-to-end neural networks and fleet learning. As reported by Sawyer Merritt referencing The All-In Podcast, the business implications are clear: Waymo’s constraint is industrialization and rapid city expansion, whereas Tesla’s key risk is the timeline for vision-only breakthroughs to achieve broadly reliable autonomy. According to the same source, Kalanick also noted many smaller players “don’t really have the stuff yet,” underscoring consolidation risk and a capital-intensive path to Level 4 at scale.

According to Sawyer Merritt on Twitter, the post highlights an autonomous future, pointing to Tesla’s continued push toward robotaxi services powered by its end to end neural networks and Full Self Driving stack; as reported by Tesla’s AI Day materials and investor communications, Tesla trains vision only models on fleet data to improve planning and perception for autonomy at scale, which creates business opportunities in on demand mobility and AI software margins; according to Tesla filings and earnings calls cited by outlets like The Verge and Reuters, the company targets a vertically integrated autonomy platform spanning custom inference compute and data engines, positioning it for recurring software revenue and fleet utilization economics; as reported by industry analyses from Bloomberg and ARK Invest, widespread autonomy could unlock cost per mile reductions and new logistics use cases, underlining why autonomous AI stacks and scalable datasets are central to commercialization.

According to DeepLearning.AI on X, everyday services already rely on AI, including face unlock on smartphones, spam and priority email filtering, and route optimization in navigation apps. As reported by DeepLearning.AI, these workloads typically use on-device neural networks for face recognition, server-side machine learning models for email classification, and graph-based reinforcement learning or predictive models for real-time traffic routing, illustrating mature, revenue-scale AI deployment in consumer products. According to DeepLearning.AI, this underscores business opportunities for edge inference (e.g., mobile NPUs), model optimization (quantization and pruning), and privacy-preserving ML, while vendors can capture value via improved latency, lower cloud costs, and tiered AI features.

According to Sawyer Merritt on X, Tesla plans to launch FSD (Supervised) in Japan by the end of 2026 and has added a Model Y to its local testing fleet; as reported by Nikkei, the initiative signals active groundwork for regulatory validation and localization testing. For AI businesses, this points to a near-term expansion of supervised driver-assistance powered by Tesla’s end-to-end neural networks and vision stack, with opportunities in HD mapping partnerships, data labeling, and fleet compliance tools, according to Nikkei and Sawyer Merritt. According to Nikkei, a 2026 target implies an 18–24 month window for Japan-specific training data collection, safety case preparation, and over-the-air readiness, creating demand for local simulation, telematics analytics, and insurance risk models tailored to FSD (Supervised).

According to Sawyer Merritt on X, the first Tesla FSD (Supervised) ride-alongs have officially started in Japan, with the system handling routes smoothly during demonstrations. As reported by Merritt’s post, this marks Tesla’s initial public on-road exposure for FSD in Japan, a market known for dense urban traffic and complex road rules, offering a high-signal test bed for vision-only autonomy. According to the original tweet, these are supervised trials, indicating human oversight remains required, which aligns with Tesla’s staged deployment playbook aimed at local validation and regulatory acceptance. From an AI-industry perspective, this deployment showcases Tesla’s end-to-end neural network stack and on-vehicle inference optimized by the FSD computer, creating business opportunities in localization data, mapping-free navigation, and model fine-tuning for Japan’s left-hand traffic, as evidenced by the Japan-specific ride-along context reported by Merritt. According to Merritt’s post, early positive handling claims point to maturing perception and planning, which could accelerate regional partnerships, insurer telematics pilots, and fleet trials as Tesla gathers country-specific edge cases under supervision.