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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
Elon Musk teases previously unknown Tesla Optimus capability
Elon Musk revealed over the weekend that the humanoid robot should be able to utilize Tesla’s dataset for Full Self-Driving (FSD) to operate cars not manufactured by Tesla.

Elon Musk revealed a new capability that Tesla Optimus should have, and it is one that will surely surprise many people, as it falls outside the CEO’s scope of his several companies.
Tesla Optimus is likely going to be the biggest product the company ever develops, and Musk has even predicted that it could make up about 80 percent of the company’s value in the coming years.
Teasing the potential to eliminate any trivial and monotonous tasks from human life, Optimus surely has its appeal.
However, Musk revealed over the weekend that the humanoid robot should be able to utilize Tesla’s dataset for Full Self-Driving (FSD) to operate cars not manufactured by Tesla:
Probably
— Elon Musk (@elonmusk) October 5, 2025
FSD would essentially translate from operation in Tesla vehicles from a driverless perspective to Optimus, allowing FSD to basically be present in any vehicle ever made. Optimus could be similar to a personal chauffeur, as well as an assistant.
Optimus has significant hype behind it, as Tesla has been meticulously refining its capabilities. Along with Musk’s and other executives’ comments about its potential, it’s clear that there is genuine excitement internally.
This past weekend, the company continued to stoke hype behind Optimus by showing a new video of the humanoid robot learning Kung Fu and training with a teacher:
🚨 Some have wondered if this is ‘staged’ or if Optimus is teleoperated here
Elon Musk said this is completely AI https://t.co/N69uDD6OVM
— TESLARATI (@Teslarati) October 4, 2025
Tesla plans to launch its Gen 3 version of Optimus in the coming months, and although we saw a new-look robot just last month, thanks to a video from Salesforce CEO and Musk’s friend Marc Benioff, we have been told that this was not a look at the company’s new iteration.
Instead, Gen 3’s true design remains a mystery for the general public, but with the improvements between the first two iterations already displayed, we are sure the newest version will be something special.
Investor's Corner
Cantor Fitzgerald reaffirms bullish view on Tesla after record Q3 deliveries
The firm reiterated its Overweight rating and $355 price target.

Cantor Fitzgerald is maintaining its bullish outlook on Tesla (NASDAQ:TSLA) following the company’s record-breaking third quarter of 2025.
The firm reiterated its Overweight rating and $355 price target, citing strong delivery results driven by a rush of consumer purchases ahead of the end of the federal tax credit on September 30.
On Tesla’s vehicle deliveries in Q3 2025
During the third quarter of 2025, Tesla delivered a total of 497,099 vehicles, significantly beating analyst expectations of 443,079 vehicles. As per Cantor Fitzgerald, this was likely affected by customers rushing at the end of Q3 to purchase an EV due to the end of the federal tax credit, as noted in an Investing.com report.
“On 10/2, TSLA pre-announced that it delivered 497,099 vehicles in 3Q25 (its highest quarterly delivery in company history), significantly above Company consensus of 443,079, and above 384,122 in 2Q25. This was due primarily to a ‘push forward effect’ from consumers who rushed to purchase or lease EVs ahead of the $7,500 EV tax credit expiring on 9/30,” the firm wrote in its note.
A bright spot in Tesla Energy
Cantor Fitzgerald also highlighted that while Tesla’s full-year production and deliveries would likely fall short of 2024’s 1.8 million total, Tesla’s energy storage business remains a bright spot in the company’s results.
“Tesla also announced that it had deployed 12.5 GWh of energy storage products in 3Q25, its highest in company history vs. our estimate/Visible Alpha consensus of 11.5/10.9 GWh (and vs. ~6.9 GWh in 3Q24). Tesla’s Energy Storage has now deployed more products YTD than all of last year, which is encouraging. We expect Energy Storage revenue to surpass $12B this year, and to account for ~15% of total revenue,” the firm stated.
Tesla’s strong Q3 results have helped lift its market capitalization to $1.47 trillion as of writing. The company also teased a new product reveal on X set for October 7, which the firm stated could serve as another near-term catalyst.
Elon Musk
Elon Musk’s xAI becomes Memphis’ 2nd largest taxpayer in just one year: report
Elon Musk’s artificial intelligence startup, xAI, is reshaping Memphis’s economic landscape.

Elon Musk’s artificial intelligence startup, xAI, is reshaping Memphis’s economic landscape. In just twelve months, the company has become the city and county’s second largest taxpayer.
The update was related in a report from The Wall Street Journal.
Memphis’ second-largest taxpayer
xAI is currently transforming a defunct Mississippi power plant into a crucial hub for AI, supplying electricity to its Colossus supercomputer cluster and its successor, Colossus 2. Together, the Colossi supercomputers will host more than half a million Nvidia chips that would be used for the development and improvement of Grok, xAI’s large language model.
The buildout has injected billions into the region, making xAI one of Memphis’s most significant private investors and a symbol of the city’s high-tech aspirations. Bill Dunavant III, a Memphis businessman who sits on the board of directors of the city’s chamber of commerce, highlighted xAI’s contribution to the city’s economy in a comment to the WSJ.
“In one year, xAI has become the second largest taxpayer in the city and county after FedEx,” he said. A spokesman for the Greater Memphis Chamber of Commerce has also stated that xAI has demonstrated “substantial economic commitment to our region, without any tax incentives.”
Not without controversy
Despite the economic boost, xAI’s footprint has drawn scrutiny. The company’s natural-gas-powered turbines are expected to consume a substantial amount of water and electricity. Critics have also expressed worries about pollution and increased utility costs, though others see Musk’s wastewater recycling plans and cleanup initiatives as meaningful offsets.
As per the WSJ, xAI’s positioning in the market may be quite different than what Musk is typically used to, considering that the CEO tends to become a first mover in key industries, such as the EV segment with Tesla and private spaceflight with SpaceX. With xAI, however, he is catching up to competitors, the most notable of which is a company he co-founded, OpenAI, and its ubiquitous large language model, ChatGPT.
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