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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.
Don’t hesitate to contact us with news tips. Just send a message to simon@teslarati.com to give us a heads up.
Elon Musk
SpaceX’s newest logo confirms everything about what it’s become
SpaceX officially absorbed xAI under the SpaceXAI brand, completing the largest private merger in history.
SpaceX made its corporate transformation official in May 2026 when Elon Musk posted on X that xAI would cease to exist as a standalone company. “xAI will be dissolved as a separate company, so it will just be SpaceXAI, the AI products from SpaceX,” he wrote.
A new SpaceXAI logo was announced today, visually embedding the xAI letters inside the SpaceX identity, which can be seen as a deliberate design choice that signals the merger is not a partnership but a full absorption and XAi a core function of the same company. The same way Starlink is not a separate brand but a SpaceX product. The announcement closed the loop on a process that began February 2, 2026, when SpaceX acquired xAI in the largest private merger in history, valued at $1.25 trillion. SpaceX at $1 trillion and xAI at $250 billion.
We are now @SpaceXAI. pic.twitter.com/ema66xDWC9
— SpaceXAI (@SpaceXAI) July 6, 2026
The reason SpaceX bought xAI was stated plainly by Musk at the time of the deal: to build orbital data centers. SpaceX had simultaneously filed with the FCC to launch up to one million satellites designed to function as AI compute nodes in low Earth orbit, escaping what Musk described as the energy constraints limiting AI development on Earth.
xAI provided the AI software stack, with Grok, the X platform, and the Colossus supercomputer infrastructure in Memphis with over 220,000 NVIDIA GPUs, while SpaceX provided the rockets, Starlink, and the capital base to fund it. The two companies needed each other. xAI was burning $2.5 billion in losses on $250 million in revenue. SpaceX was generating an estimated $8 billion in profit on $15 billion in revenue and needed an AI narrative to command the valuation it was targeting for its IPO.
What SpaceX has done, regardless of how the orbital AI vision ultimately plays out, is walk into a public market as something no company has been before: a rocket manufacturer, satellite internet provider, AI software company, social media platform, and supercomputer operator under one ticker. Whether that combination is worth $2 trillion depends entirely on which of those businesses you believe in most.
Tesla brought its innovative Cybercab robotaxi to the National Federation of the Blind (NFB) Annual Convention in Austin, Texas, on July 3 at the JW Marriott Austin.
The hands-on demonstration highlighted the vehicle’s thoughtful design for blind and visually impaired users, underscoring Tesla’s commitment to inclusive autonomous mobility. Attendees, many using white canes or accompanied by service dogs, experienced the steering-wheel-free Cybercab firsthand.
Cybercab at the National Federation of the Blind’s Annual Convention in Austin for a hands-on experience of its accessibility features for blind or visually impaired customers⁰⁰For example:⁰– Braille lettering on physical controls
– Space for service animals & assistive… pic.twitter.com/8wrJcDHkw7— Tesla Robotaxi (@robotaxi) July 6, 2026
The showcase emphasized practical features tailored to the needs of the blind community. Braille lettering appears on physical controls, including door releases and emergency buttons, allowing users to navigate interfaces independently through touch. Generous interior space accommodates service animals and assistive devices such as canes, guide dogs, or mobility aids without compromising comfort.
Wheelchair-height seating facilitates easier transfers for users with additional mobility challenges. Photos from the event captured blind attendees approaching the vehicle confidently, service dogs relaxing inside, and hands exploring Braille-equipped handles.
Tesla Robotaxi’s official account detailed these elements, noting the Cybercab’s focus on accessibility, especially noting the Braille lettering and additional space for service animals.
How Tesla Will Transform Mobility for the Blind
Autonomous vehicles like the Cybercab promise revolutionary independence for the roughly 2.2 million visually impaired Americans. Traditional barriers—reliance on sighted drivers, costly paratransit, or limited public transit—often restrict spontaneous travel. Tesla Full Self-Driving aims to eliminate the need for a human operator, enabling on-demand, door-to-door rides via simple app hailing with voice guidance.
Users gain freedom to work, socialize, shop, or attend events anytime without scheduling hassles or safety concerns. This reduces isolation, boosts employment opportunities, and enhances quality of life, turning mobility from a dependency into true personal autonomy.
The NFB demonstration not only gathered valuable feedback but also generated excitement about a future where technology levels the playing field. By prioritizing inclusive design, Tesla advances a vision of transportation that serves everyone, potentially reshaping daily life for blind individuals and setting a standard for the autonomous industry.
As Cybercab deployment scales, these accessibility innovations could mark a significant step toward equitable mobility.
Investor's Corner
Tesla challenges startups to score a gig inside its most advanced European factory
Tesla is challenging startups to bring their best battery tech directly to Gigafactory Berlin.
Tesla has issued an open challenge to startups across Europe, inviting them to bring their best battery technology directly to the floor of Gigafactory Berlin. The program, called the JUNI x Tesla Battery Cell Giga Challenge, opened applications this month with a deadline of July 24, 2026, and is targeting startups with solutions that can make battery cell manufacturing faster, cheaper, safer, and more scalable at an industrial level.
The timing of the challenge is directly tied to Tesla’s most aggressive European battery investment yet. On May 12, 2026, Giga Berlin plant manager André Thierig announced a $250 million investment to scale the factory’s annual 4680 cell production capacity from 8 GWh to 18 GWh, more than doubling the previous target set just months earlier in December 2025. Thierig confirmed the expansion on X, saying the investment “will enable 18 GWh of annual 4680 cell production and create more than 1,500 new jobs.” Combined with a previously announced battery investment at the Grunheide site now approaches $1.2 billion.
Today, we announced a $ 250m investment for our Giga Berlin Cell factory. This will enable 18GWh of annual 4680 cell production and create more than 1500 new jobs. Good news during challenging times for the German industry. pic.twitter.com/ou4SWMfWh9
— André Thierig (@AndrThie) May 12, 2026
The challenge is looking specifically for startups with proven solutions across five categories: materials, equipment, operations, automation, and artificial intelligence. Applications are screened directly by Tesla’s cell manufacturing team in Grunheide, and the strongest submissions move through technical discussions, a pitch day in front of Tesla stakeholders, and potentially a paid pilot project with the cell team. Tesla is not looking for ideas at concept stage. The program requires applicants to demonstrate working prototypes, test data, or prior pilots before being considered.
The historical context matters here. Elon Musk first announced plans for what he called the world’s largest battery cell production facility alongside the Giga Berlin car factory back in 2020, targeting up to 250 GWh of annual capacity. Those plans were shelved in 2022 when Tesla shifted its battery investment focus to the United States to take advantage of Inflation Reduction Act incentives. The revival of cell production at Giga Berlin, now backed by over $1 billion in committed capital, represents a return to an ambition that was set aside for three years. As Teslarati has reported, the 4680 format is central to Tesla’s long-term cost reduction strategy across vehicles, energy storage, including the Tesla Semi and Cybercab.
By opening the challenge to outside startups, Tesla is acknowledging that reaching 18 GWh at Grunheide will require technology it does not currently have in-house, and it is willing to pay for the right solutions. For a startup in the battery supply chain, a paid pilot with Tesla’s European cell team is as close to a direct commercial path as the industry offers.
SpaceX’s newest logo confirms everything about what it’s become
Tesla flexes how it will help the blind with Cybercab
