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Tesla FSD Beta 10.69.2.2 extending to 160k owners in US and Canada: Elon Musk
It appears that after several iterations and adjustments, FSD Beta 10.69 is ready to roll out to the greater FSD Beta program. Elon Musk mentioned the update on Twitter, with the CEO stating that v10.69.2.2. should extend to 160,000 owners in the United States and Canada.
Similar to his other announcements about the FSD Beta program, Musk’s comments were posted on Twitter. “FSD Beta 10.69.2.1 looks good, extending to 160k owners in US & Canada,” Musk wrote before correcting himself and clarifying that he was talking about FSD Beta 10.69.2.2, not v10.69.2.1.
While Elon Musk has a known tendency to be extremely optimistic about FSD Beta-related statements, his comments about v10.69.2.2 do reflect observations from some of the program’s longtime members. Veteran FSD Beta tester @WholeMarsBlog, who does not shy away from criticizing the system if it does not work well, noted that his takeovers with v10.69.2.2 have been marginal. Fellow FSD Beta tester @GailAlfarATX reported similar observations.
Tesla definitely seems to be pushing to release FSD to its fleet. Recent comments from Tesla’s Senior Director of Investor Relations Martin Viecha during an invite-only Goldman Sachs tech conference have hinted that the electric vehicle maker is on track to release “supervised” FSD around the end of the year. That’s around the same time as Elon Musk’s estimate for FSD’s wide release.
It should be noted, of course, that even if Tesla manages to release “supervised” FSD to consumers by the end of the year, the version of the advanced driver-assist system would still require drivers to pay attention to the road and follow proper driving practices. With a feature-complete “supervised” FSD, however, Teslas would be able to navigate on their own regardless of whether they are in the highway or in inner-city streets. And that, ultimately, is a feature that will be extremely hard to beat.
Following are the release notes of FSD Beta v10.69.2.2, as retrieved by NotaTeslaApp:
– Added a new “deep lane guidance” module to the Vector Lanes neural network which fuses features extracted from the video streams with coarse map data, i.e. lane counts and lane connectivities. This architecture achieves a 44% lower error rate on lane topology compared to the previous model, enabling smoother control before lanes and their connectivities becomes visually apparent. This provides a way to make every Autopilot drive as good as someone driving their own commute, yet in a sufficiently general way that adapts for road changes.
– Improved overall driving smoothness, without sacrificing latency, through better modeling of system and actuation latency in trajectory planning. Trajectory planner now independently accounts for latency from steering commands to actual steering actuation, as well as acceleration and brake commands to actuation. This results in a trajectory that is a more accurate model of how the vehicle would drive. This allows better downstream controller tracking and smoothness while also allowing a more accurate response during harsh maneuvers.
– Improved unprotected left turns with more appropriate speed profile when approaching and exiting median crossover regions, in the presence of high speed cross traffic (“Chuck Cook style” unprotected left turns). This was done by allowing optimisable initial jerk, to mimic the harsh pedal press by a human, when required to go in front of high speed objects. Also improved lateral profile approaching such safety regions to allow for better pose that aligns well for exiting the region. Finally, improved interaction with objects that are entering or waiting inside the median crossover region with better modeling of their future intent.
– Added control for arbitrary low-speed moving volumes from Occupancy Network. This also enables finer control for more precise object shapes that cannot be easily represented by a cuboid primitive. This required predicting velocity at every 3D voxel. We may now control for slow-moving UFOs.
– Upgraded Occupancy Network to use video instead of images from single time step. This temporal context allows the network to be robust to temporary occlusions and enables prediction of occupancy flow. Also, improved ground truth with semantics-driven outlier rejection, hard example mining, and increasing the dataset size by 2.4x.
– Upgraded to a new two-stage architecture to produce object kinematics (e.g. velocity, acceleration, yaw rate) where network compute is allocated O(objects) instead of O(space). This improved velocity estimates for far away crossing vehicles by 20%, while using one tenth of the compute.
– Increased smoothness for protected right turns by improving the association of traffic lights with slip lanes vs yield signs with slip lanes. This reduces false slowdowns when there are no relevant objects present and also improves yielding position when they are present.
– Reduced false slowdowns near crosswalks. This was done with improved understanding of pedestrian and bicyclist intent based on their motion.
– Improved geometry error of ego-relevant lanes by 34% and crossing lanes by 21% with a full Vector Lanes neural network update. Information bottlenecks in the network architecture were eliminated by increasing the size of the per-camera feature extractors, video modules, internals of the autoregressive decoder, and by adding a hard attention mechanism which greatly improved the fine position of lanes.
– Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
– Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
– Enabled creeping for visibility at any intersection where objects might cross ego’s path, regardless of presence of traffic controls.
– Improved accuracy of stopping position in critical scenarios with crossing objects, by allowing dynamic resolution in trajectory optimization to focus more on areas where finer control is essential.
– Increased recall of forking lanes by 36% by having topological tokens participate in the attention operations of the autoregressive decoder and by increasing the loss applied to fork tokens during training.
– Improved velocity error for pedestrians and bicyclists by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network.
– Improved recall of object detection, eliminating 26% of missing detections for far away crossing vehicles by tuning the loss function used during training and improving label quality.
– Improved object future path prediction in scenarios with high yaw rate by incorporating yaw rate and lateral motion into the likelihood estimation. This helps with objects turning into or away from ego’s lane, especially in intersections or cut-in scenarios.
– Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
– Reduced latency when starting from a stop by accounting for lead vehicle jerk.
– Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
Press the “Video Record” button on the top bar UI to share your feedback. When pressed, your vehicle’s external cameras will share a short VIN-associated Autopilot Snapshot with the Tesla engineering team to help make improvements to FSD. You will not be able to view the clip.
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News
Tesla intertwines FSD with in-house Insurance for attractive incentive
Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.
Tesla intertwined its Full Self-Driving (Supervised) suite with its in-house Insurance initiative in an effort to offer an attractive incentive to drivers.
Tesla announced that its new Safety Score 3.0 will automatically have a perfect score of 100 with every mile driven with Full Self-Driving (Supervised) enabled.
The change is designed to boost customers’ average safety scores and deliver noticeably lower monthly premiums.
The move marks the clearest link yet between Tesla’s autonomous driving technology and its proprietary insurance product. Tesla Insurance already relies on real-time vehicle data—such as acceleration, braking, following distance, and speed—to calculate a Safety Score between 0 and 100. Higher scores have long translated into cheaper rates.
Under the previous system, however, even brief manual interventions could drag down the average, frustrating owners who rely heavily on FSD. Version 3.0 eliminates that penalty for supervised autonomous miles, effectively treating FSD-driven segments as the safest possible driving behavior.
The incentive is immediate and financial. Drivers who keep FSD engaged for the majority of their trips will see their overall score rise, potentially shaving hundreds of dollars off annual premiums.
Tesla framed the update as a direct response to customer feedback, many of whom had complained that the old scoring model punished the very behavior it was meant to encourage.
For now, the program applies only to new policies in six states: Indiana, Tennessee, Texas, Arizona, Virginia, and Illinois.
Existing policyholders are not yet included, a point that drew swift questions from the Tesla community. Many owners in other states, including California and Georgia, expressed hope that the benefit would expand nationwide soon.
The announcement arrives as Tesla continues to roll out FSD Supervised updates and push for regulatory approval of more advanced autonomy. By tying insurance savings directly to FSD usage, the company is putting its own actuarial weight behind the technology’s safety claims.
Every mile logged under FSD now carries a documented financial value—lower risk, lower cost—based on Tesla’s internal driving data rather than external crash statistics alone.
Tesla has not disclosed exact premium reductions or the full rollout timeline beyond the six launch states.
Still, the message is clear: the more drivers trust FSD Supervised, the more Tesla Insurance will reward them. In an era when legacy insurers remain cautious about autonomous tech, Tesla is betting that its own data will prove the safest miles are the ones driven hands-free.
Elon Musk
Tesla finalizes AI5 chip design, Elon Musk makes bold claim on capability
The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.
Tesla has finalized its chip design for AI5, as Elon Musk confirmed today that the new chip has reached the tape-out stage, the final step before mass production.
But in a brief reply on X, Musk clarified Tesla’s AI hardware roadmap, essentially confirming that the new chip will not be utilized for being “enough to achieve much better than human safety for FSD.”
He said that AI4 is enough to do that.
Instead, the AI5 chip will be focused on Tesla’s big-time projects for the future: Optimus and supercomputer clusters.
Musk thanked TSMC and Samsung for production support, noting that AI5 could become “one of the most produced AI chips ever.” Yet, the key pivot came in his direct answer: vehicles no longer need the bleeding-edge silicon.
And thank you to @TaiwanSemi_TSC and @Samsung for your support in bringing this chip to production! It will be one of most produced AI chips ever.
— Elon Musk (@elonmusk) April 15, 2026
Existing AI4 hardware, which is already deployed in hundreds of thousands of HW4-equipped Teslas, delivers safety metrics superior to human drivers for Full Self-Driving. AI5 will instead accelerate Optimus robot development and massive Dojo-style training clusters.
The Tesla CEO’s words mark a strategic shift. Tesla has long emphasized software-hardware co-design, squeezing maximum performance from every transistor. Musk previously described AI5 as optimized for edge inference in both Robotaxi and Optimus.
Now, with AI4 proving sufficient, the company avoids costly retrofits across its fleet while redirecting next-generation compute toward higher-value applications: dexterous robots and exponential training scale.
But is it reasonable to assume AI4 enables unsupervised self-driving? Yes, but with important caveats.
On the hardware side, the claim is credible. Tesla’s FSD stack runs end-to-end neural networks trained on billions of miles of real-world data. Internal safety data reportedly shows AI4-equipped vehicles already outperforming average human drivers by a significant margin in controlled metrics (collision avoidance, reaction time, edge-case handling).
Dual-redundant AI4 chips provide ample headroom for the driving task, leaving bandwidth for future model improvements without new silicon. Musk’s assertion aligns with Tesla’s pattern of over-provisioning compute early, then optimizing ruthlessly, exactly as HW3 once sufficed before HW4 scaled further.
Optimus and our supercomputer clusters.
AI4 is enough to achieve much better than human safety for FSD.
— Elon Musk (@elonmusk) April 15, 2026
Unsupervised autonomy, meaning Level 4 or higher, is not solely a compute problem. Regulatory approval remains the primary gate.
Even if AI4 achieves “much better than human” safety statistically, agencies like the NHTSA demand exhaustive validation, liability frameworks, and public trust.
Tesla’s supervised FSD has shown rapid gains in recent versions, yet real-world edge cases, like construction zones, emergency vehicles, and adverse weather, still require driver intervention in many jurisdictions. Competitors like Waymo operate limited unsupervised fleets, but only in geofenced areas with extensive mapping. Tesla’s vision-only, fleet-scale approach is more ambitious—and harder to certify globally.
In short, Musk’s post is both pragmatic and bullish. AI4 is likely capable of unsupervised FSD from a technical standpoint. Whether regulators and consumers agree, and how quickly, will determine if Tesla’s bet pays off.
The company’s capital-efficient path keeps existing cars relevant while pouring future compute into robots. If the safety data holds, unsupervised autonomy could arrive sooner than many expect.
Elon Musk
Elon Musk signals expansion of Tesla’s unique side business
Long envisioning the Tesla Diner as more than a charging stop, Musk has clearly adopted the idea that the Supercharger and Restaurant combo is a good thing for the company to have. It’s a blend of classic American drive-in culture with futuristic Tesla flair, complete with a 1950s-inspired design, movie screens, and on-site dining.
Elon Musk has signaled an expansion of Tesla’s unique side business, something that really has nothing to do with cars or spaceships, but fans of the company have truly adopted it as just another one of its awesome ventures.
Musk confirmed on Wednesday that Tesla would build a new Diner location in Palo Alto, Northern California. After hinting last October that it “probably makes sense to open one near our Giga Texas HQ in Austin and engineering HQ in Palo Alto,” it seems one of those locations is being set into motion.
Sure
— Elon Musk (@elonmusk) April 15, 2026
Long envisioning the Tesla Diner as more than a charging stop, Musk has clearly adopted the idea that the Supercharger and Restaurant combo is a good thing for the company to have. It’s a blend of classic American drive-in culture with futuristic Tesla flair, complete with a 1950s-inspired design, movie screens, and on-site dining.
He first floated broader expansion plans shortly after the LA opening in July 2025, noting that if the prototype succeeded, Tesla would roll out similar venues in major cities worldwide and along long-distance Supercharger routes.
Earlier hints included a confirmed second site at Starbase in Texas, tied to SpaceX operations, underscoring the Diner’s role in enhancing Tesla’s ecosystem behind vehicles.
The Los Angeles location on Santa Monica Boulevard in West Hollywood has served as a high-profile test case. Opened in July 2025 at 7001 Santa Monica Blvd., it features the world’s largest urban Supercharging station with 80 V4 stalls open to all NACS-compatible EVs, over 250 dining seats, rooftop views, and 24/7 service.
The retro-futuristic building replaced a former Shakey’s and quickly became a destination. Tesla reported selling 50,000 burgers in the first 72 days—an average of over 700 daily—drawing crowds with Cybertruck-shaped packaging, breakfast extensions until 2 p.m., and movie screenings.
Palo Alto stands out as a logical next step for several reasons. As Tesla’s longstanding engineering headquarters in the heart of Silicon Valley, the city is home to thousands of Tesla employees, engineers, and executives who could benefit from a convenient, branded gathering spot.
The area boasts high EV adoption rates, dense tech talent, and heavy traffic along key corridors, making a large Supercharger-diner an ideal fit for both daily commuters and long-haul travelers.
Proximity to Stanford University and the innovation ecosystem would amplify its appeal, potentially serving as a showcase for Tesla’s vision of integrated mobility and lifestyle experiences. It could be a great way for Tesla to recruit new talent from one of the country’s best universities.
If Tesla and Musk decide to move forward with a Palo Alto diner, it would build directly on the LA prototype’s momentum while addressing Musk’s earlier calls for expansion near core Tesla hubs.
Whether it materializes as a full confirmation or evolves from these hints remains to be seen, but the pattern is clear: Tesla is testing ways to make charging stops memorable. For EV drivers and enthusiasts alike, a Silicon Valley outpost could blend cutting-edge tech with nostalgic comfort, further embedding Tesla into everyday culture. As Musk’s comments suggest, the future of the Diner looks promising.
Tesla intertwines FSD with in-house Insurance for attractive incentive
Tesla finalizes AI5 chip design, Elon Musk makes bold claim on capability
