Tesla’s AI Day is here. In a few minutes, Tesla watchers would be seeing executives like Elon Musk provide an in-depth discussion on the company’s AI efforts on not just its automotive business but on its energy business and beyond as well. AI Day promises to be yet another tour-de-force of technical information from the electric car manufacturer. Thus, it is no surprise that there is a lot of excitement from the EV community heading into the event.
Tesla has kept the details of AI Day behind closed doors, so the specifics of the actual event are scarce. That being said, an AI Day agenda sent to attendees indicated that they could expect to hear Elon Musk speak during a live keynote, speak with Andrej Karpathy and the rest of Tesla’s AI engineers, and participate in breakout sessions with the teams behind Tesla’s AI development.
Similar to Autonomy Day and Battery Day, Teslarati would be following along on AI Day’s discussions to provide you with an updated account of the highly-anticipated event. Please refresh this page from time to time, as notes, details, and quotes from Elon Musk’s keynote and its following discussions will be posted here.
Simon 19:40 PT – A question about the use cases for the Tesla Bot was asked. Musk notes that the Tesla Bot would start with boring, repetitive, work, or work that people would least like to do.
Simon 19:25 PT – A question about AI and manufacturing is asked and how it potentially relates to the “Alien Dreadnaught” concept. Musk notes that most of Tesla’s manufacturing today is already automated. Musk also noted that humanoid robots would be done either way, so it would be great for Tesla to do this project, and safely as well. “We’re making the pieces that would be useful for a humanoid robot, so we should probably make it. If we don’t someone else will — and we want to make sure it’s safe,” Musk said.
Simon 19:15 PT – And the Q&A starts. First question involves open-sourcing Tesla’s innovations. Musk notes that it’s pretty expensive to develop all this tech, so he’s not sure how things could be open-sourced. But if other car companies would like to license the system, that could be done.
Simon 19:11 PT – There will really be a “Tesla Bot.” It would be built by humans, for humans. It would be friendly, and it would eliminate dangerous, repetitive, boring tasks. This is still petty darn unreal. It uses the systems that are currently being developed for the company’s vehicles. “There will be profound applications for the economy,” Musk said.
Simon 19:06 PT – New products! A whole Tesla suit?! After a fun skit, Elon says the “Tesla Bot” would eventually be real.
Simon 19:00 PT – What is crazy is that Dojo is not even done. This is just what it is today. Dojo is still evolving, and it is going to be way more powerful in the future. Now, it’s Elon Musk’s turn. What’s next for Tesla beyond vehicles.
Simon 19:00 PT – Venkataramanan teases the ExaPOD. Yet another revolutionary solution from Tesla. With all this, it is evident that Tesla’s approach to autonomy is on a whole other level. It would not be surprising if it takes Wall Street and the market a few days to fully absorb what is happening here.
Simon 18:55 PT – The specs of Dojo are insane. Behind its beastly specs, it seems that Dojo’s full potential lies in the fact that all this power is being used to do one thing: to make autonomous cars possible. Dojo is a pure learning machine, with more than 500,000 training nodes being built together. Nine petaflops of compute per tile, 36 terabytes per second of off-tile bandwidth. But this is just the tip of the iceberg for Dojo.
Simon 18:50 PT – Ganesh Venkataramanan, Project Dojo’s lead, takes the stage. He states that Elon Musk wanted a super-fast training computer to train Autopilot. And thus Project Dojo was born. Dojo is a distributed compute architecture connected by network fabric. It also has a large compute plane, extremely high bandwidth with low latencies, and big networks that are partitioned and mapped, to name a few.
Simon 18:45 PT – Milan Kovac, Tesla’s Director of Autopilot Engineering takes the stage. He notes that he would discuss how neural networks are run in the company’s cars. He notes that Tesla’s systems require supercomputers.
Simon 18:40 PT – Ashok notes that simulations have helped Tesla a lot already. It has, for example, helped the company identify pedestrian, bicycle, and vehicle detection and kinematics. The networks in the vehicles were traded to 371 million simulated images and 480 million cuboids.
Simon 18:35 PT – Ashok notes that these strategies ultimately helped Tesla retire radar from its FSD and Autopilot suite and adopt a pure vision model. A comparison between a radar+camera system and pure vision shows just how much more refined the company’s current strategy is. The executive also touched on how simulations help Tesla develop its self-driving systems. He states that simulations help when data is difficult to source, difficult to label, or in a closed loop.
Simon 18:30 PT – Ashok returns to discuss Auto Labeling. Simply put, there is so much labeling that needs to be done that it’s impossible to be done manually. He shows how roads and other items on the road are “reconstructed” from a single car that’s driving. This effectively allowed Tesla to label data much faster, while allowing vehicles to navigate safely and accurately even when occlusions are present.
Simon 18:25 PT – Karpathy returns to talk about manual labeling. He notes that manual labeling that’s outsourced to third-party firms is not optimal. Thus, in the spirit of vertical integration, Tesla opted to establish its own labeling team. Karpathy notes that in the beginning, that Tesla was using 2D image labeling. Eventually, Tesla transitioned to 4D labeling, where the company could label in vector space. But even this was not enough, and thus, auto labeling was developed.
Simon 18:23 PT – The executive states that traffic behavior is extremely complicated, especially in several parts of the world. Ashok notes that this partly illustrated by parking lots and how they are actually complex. Summoning a car from a parking lot, for example, used to utilize 400k notes to navigate, resulting in a system whose performance left much to be desired.
Simon 18:18 PT – Ashok notes that when driving alongside other cars, Autopilot must not only think about how they would drive, they must also think about how other cars would operate. He shows a video of a Tesla navigating a road and dealing with multiple vehicles to demonstrate this point.
Simon 18:15 PT – Director of Autopilot Software Ashok Elluswamy takes the stage. He starts off by discussing some key problems in planning in both non-convex and high-dimensional action spaces. He also shows Tesla’s solution to these issues, a “Hybrid Planning System.” He demonstrates this by showing how Autopilot performs a lane change.
Simon 18:10 PT – Karpathy’s discussion notes that today, Tesla’s FSD strategy is a lot more cohesive. This is demonstrated by the fact that the company’s vehicles could effectively draw a map in real-time as it drives. This is a massive difference compared to the pre-mapped strategies employed by rivals in both the automotive and software field like Super Cruise and Waymo.
To solve several problems encountered over the last few years with the previous suite, Tesla re-engineered their NN learning from the ground up and utilized a multi-head route, camera calibrations, caching, queues, and optimizations to streamline all tasks.
(heavily simplified) pic.twitter.com/LG2TRgjxip
— Teslascope (@teslascope) August 20, 2021
Simon 18:05 PT – The AI Director discusses how Tesla practically re-engineered their neural network learning from the ground-up and utilized a multi-head route. These include camera calibrations, caching, queues, and optimizations to streamline all tasks. Do note that this is an extremely simplified iteration of Karpathy’s discussion so far.
Simon 18:00 PT – Karpathy covers more challenges that are involved in even the basics of perception. Needless to say, AI Day is quickly proving to be Tesla’s most technical event right off the bat. That said, multi-camera networks are amazing. They’re just a ton of work, but it may very well be a silver bullet for Tesla’s predictive efforts.
Simon 17:56 PT – Karpathy showcases a video of how Tesla used to process its image data in the past. He shows a popular video for FSD that has been shared in the past. He notes that while great, such a system proved to be inadequate, and this is something that Tesla learned when it launched Smart Summon. While per-camera detection is great, the vector space proves inadequate.
Simon 17:55 PT – Karpathy noted that when Tesla designs the visual cortex in its car, the company is modeling it to how a biological vision is perceived by eyes. He also touches on how Tesla’s visual processing strategies have evolved over the years, and how it is done today. The AI Director also touches on Tesla’s “HydraNets,” on account of their multi-task learning capabilities.
Simon 17:51 PT – Karpathy starts off by discussing the visual component of Tesla’s AI, as characterized by the eight cameras used in the company’s vehicles. The AI director notes that AI could be considered like a biological being, and it’s built from the ground up, including its synthetic visual cortex.
Simon 17:48 PT – Elon Musk takes the stage. He apologizes for the event’s delay. He jokes that Tesla probably needs AI to solve these “technical difficulties.” The CEO highlights that AI Day is a recruitment event. He calls Tesla’s head of AI Andrej Karpathy. There’s no better person to discuss AI.
Simon 17:45 PT – We’re here watching the AI Day FSD preview video and we can’t help but notice that… are those Waypoints?!
Simon 17:38 PT – Looks like we’ve got an Elon sighting! And a preview video too! Here we go, folks!
We’ve got an Elon sighting
— Rob Maurer (@TeslaPodcast) August 20, 2021
Simon 17:30 PT – A 30-minute delay. We haven’t seen this much delay in quite a bit.
Simon 17:20 PT – It’s a good thing that Tesla has great taste in music. Did Grimes mix this track?
Simon 17:15 PT – We’re 15 minutes in. “Elon Time” is going strong on AI Day. To be honest, though, this music would fit the “Rave Cave” in Giga Berlin this coming October.
Simon 17:10 PT – A good thing to keep in mind is that AI Day is a recruitment event. Some food for thought just in case the discussions take a turn for the extremely technical. AI Day is designed to attract individuals who speak Tesla’s language in its rawest form. We’re just fortunate enough to come along for the ride.
Tesla Board Member Hiro Mizuno sums it up in this tweet pretty well.
Anybody passionate about real world AI !! https://t.co/ydaWQlkE4O
— HIRO MIZUNO (@hiromichimizuno) August 20, 2021
Simon 17:05 PT – I guess AI Day is starting on “Elon Time?” We’re on to the next track of chill music.
Simon 17:00 PT – And with 5 p.m. PST here, the music is officially live on the AI Day live stream. Looks like we’re in for some wait. Wonder how many minutes it would take before it starts? Gotta love this chill music though.
Simon 16:58 PT – While waiting, I can’t help but think that a ton of TSLA bears and Wall Street would likely not understand the nuances of what Tesla would be discussing today. Will Tesla go three-for-three? It was certainly the case with Battery Day and Autonomy Day.
Made it pic.twitter.com/aAWqxgf0bP
— Johnna (@JohnnaCrider1) August 19, 2021
Simon 16:55 PT – T-minus 5 minutes. Some attendees of AI Day are now posting some photos on Twitter, but it seems like photos and videos are not allowed on the actual venue of the event. Pretty much expected, I guess.
Simon 16:50 PT – Greetings, everyone, and welcome to another Live Blog. This is Tesla’s most technical event yet, so I expect this one to go extremely in-depth on the company’s AI efforts and the technology behind it. We’re pretty excited.
Don’t hesitate to contact us with news tips. Just send a message to tips@teslarati.com to give us a heads up.
Tesla Vice President of Vehicle Engineering, Lars Moravy, recently revealed the company has thought about introducing a Plaid powertrain on the Model 3, but there could be some challenges involved.
On the Ride the Lightning podcast, Moravy revealed that he thinks about a Plaid Model 3 “all the time,” and it certainly has a place in Tesla’s potential lineup of future vehicles.
Now that the Plaid powertrain is technically defunct due to the newfound absence of the Model S and Model X, Tesla could find a way to reintroduce the lightning-quick trim level to its mass-market vehicles.
But there are going to be some challenges with it. Moravy said that the Model 3 Plaid would likely adopt the carbon-sleeved motors that the Model S Plaid had. However, packaging would be a major challenge, as Moravy said on the podcast, it would be a “tight engineering squeeze.”
It’s important to note that there are no active production plans for the Model 3 Plaid at this point, but it’s also worth noting that with the Model S and Model X Plaid no longer available, Tesla would likely be willing to introduce something that is even more white-knuckle than the Model 3 Performance, which already boasts a 2.9-second 0-60 MPH acceleration rate and a top speed of 163 MPH.
Of course, there is the Roadster, but we don’t know when that will exactly make it to market, and we know that, for sure, it will not be accessible to many.
Tesla unveils juicy new detail on the Roadster and hints at new unveil timeline
Tesla has prided itself in building some of the best cars out there, but they’re also interested in building cars that are simply fun to be in.
A Plaid Model 3 could truly push the limits and could end up being one of the best cars Tesla will ever build, especially if it can shave off at least half of a second from its 0-60 MPH time and increase its top speed slightly.
More than anything, the real changes will be in the ride and aerodynamics. Tesla improving things like the suspension, handling, and downforce will be the true trademarks of its Plaid powertrain; putting it in the Model 3 could be a great move for the company and for customers interested in high-end performance.
Elon Musk
NASA’s first human outpost on the Moon starts now – SpaceX on deck
NASA named the rovers, landers, and vendors that will build America’s first Moon Base.
NASA has laid out its most detailed Moon Base plan to date, describing a permanent outpost near the Moon’s south pole that the agency intends to build over the coming decade as a direct stepping stone to Mars. “The Moon Base will be America’s and humanity’s first outpost on another celestial world,” NASA Administrator Jared Isaacman said, adding that every mission crewed and uncrewed “will be a learning opportunity as we return to the lunar surface, build the infrastructure to stay, and master the skills required to live and operate in one of the most demanding and dangerous environments imaginable.”
The plan is structured in three phases involving both uncrewed and crewed missions to deliver equipment, vehicles, and infrastructure to the surface, with the first three moon base missions targeted to launch before the end of 2026.
Moon Base I, targeting fall 2026, will use Blue Origin’s Blue Moon Mark 1 lander to deliver scientific instruments to the Shackleton Connecting Ridge, the same region where Artemis astronauts will land. Moon Base II will send Astrobotic’s Griffin lander carrying more than 1,100 pounds of cargo including Astrolab’s FLIP rover to begin developing mobility systems on the surface. Moon Base III will carry the Lunar Vertex science mission on Intuitive Machines’ Nova-C Trinity lander to study lunar swirls near the south pole, with ESA and Korean science payloads aboard.
On the rover side, NASA awarded Astrolab $219 million and Lunar Outpost $220 million to build the first phase of Lunar Terrain Vehicles, with both rovers targeted for deployment to the lunar surface by 2028. Astrolab’s crewed rover weighs roughly 2,000 pounds and can reach over 6 mph. Lunar Outpost’s Pegasus rover can operate autonomously or via remote control at over 9 mph. Blue Origin separately received $188 million with an option worth $280.4 million to deliver cargo landers for rover transport.
NASA also confirmed that MoonFall, a mission deploying four survey drones to scout Artemis landing sites, has selected Firefly Aerospace to build the transport spacecraft, with a 2028 launch target.
SpaceX sits at the center of that commercial layer. SpaceX holds the NASA Human Landing System contract for the Starship-derived lander that will put astronauts on the surface under Artemis IV, currently targeting 2028. Before that can happen, SpaceX must demonstrate in-orbit propellant transfer at scale, a process requiring multiple Starship tanker launches to fuel a single mission. Water ice at the lunar south pole is central to the base’s long-term viability, as it can be converted into drinking water, breathable oxygen, and rocket fuel, directly reducing dependence on Earth resupply. That resource loop becomes far more practical if Starship can land and be refueled on or near the Moon itself.
Elon Musk has publicly stated that Starship V3, which recently completed its first flight, should be capable enough for initial Mars missions. The Moon Base plan announced Tuesday is the infrastructure layer that connects everything between those two ambitions, and SpaceX is the only American company currently contracted to build the rocket that gets humans to either destination.
A new Tesla patent that has been granted to the company this week has revealed a potential strategy for solving a major issue that could impact both the Full Self-Driving suite and Optimus.
The patent, which is No. 12,636,684, describes a “Lens Cleaning System,” and was submitted by Tesla in May 2025.
The language in the patent details a lens cleaning system that can dispense fluid and wipe it away with a wiper assembly.
Optimus can see you now… 🤖👁️
The patent for @Tesla_Optimus‘s eye structure just dropped. $TSLA pic.twitter.com/Jac4VhDmKH
— SETI Park (@seti_park) May 26, 2026
This would effectively clean any debris that would potentially impact the visibility of the cameras on Tesla automobiles or Optimus’s camera eyes. Perhaps the most pertinent example is through the Full Self-Driving suite, as debris that can accumulate on the vehicle’s exterior cameras can impact the suite’s ability to operate effectively.
This requires a remedy through manual cleaning, but this patent hints that Tesla could be planning to implement this new technology on its upcoming vehicles.
Interestingly, we have started to see it on some Robotaxi vehicles, and it will likely be included in the Cybercab, especially as that vehicle will enable full autonomy.
Back in January, the first Model Y Robotaxi units were spotted with camera washers on the side repeaters, as the video below shows fluid squirting and rinsing off any debris that is limiting visibility.
🚨 Tesla looks to have installed Camera Washers on the side repeater cameras on Robotaxis in Austin
pic.twitter.com/xemRtDtlRR— TESLARATI (@Teslarati) January 23, 2026
This hardware patent does bring up an interesting question for those of us who own Teslas with AI4 and have been told that our cars will one day be capable of full autonomy: Will this washer be available as a retrofit on already-built cars?
Perhaps the “Lens Cleaning System” patent is a good look at one way Tesla plans to combat one of the most obvious issues of autonomy that utilizes a camera-based system. For Optimus, it could be less needed as it could be manually cleaned by owners. For cars, it seems like a bigger necessity, especially as autonomy nears and Tesla gets close to launching a feature-complete FSD suite.
Tesla teases going Plaid Mode with the Model 3
NASA’s first human outpost on the Moon starts now – SpaceX on deck
