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.
News
Man credits Grok AI with saving his life after ER missed near-ruptured appendix
The AI flagged some of the man’s symptoms and urged him to return to the ER immediately and demand a CT scan.
A 49-year-old man has stated that xAI’s Grok ended up saving his life when the large language model identified a near-ruptured appendix that his first ER visit dismissed as acid reflux.
After being sent home from the ER, the man asked Grok to analyze his symptoms. The AI flagged some of the man’s symptoms and urged him to return immediately and demand a CT scan. The scan confirmed that something far worse than acid reflux was indeed going on.
Grok spotted what a doctor missed
In a post on Reddit, u/Tykjen noted that for 24 hours straight, he had a constant “razor-blade-level” abdominal pain that forced him into a fetal position. He had no fever or visible signs. He went to the ER, where a doctor pressed his soft belly, prescribed acid blockers, and sent him home.
The acid blockers didn’t work, and the man’s pain remained intense. He then decided to open a year-long chat he had with Grok and listed every detail that he was experiencing. The AI responded quickly. “Grok immediately flagged perforated ulcer or atypical appendicitis, told me the exact red-flag pattern I was describing, and basically said “go back right now and ask for a CT,” the man wrote in his post.
He copied Grok’s reasoning, returned to the ER, and insisted on the scan. The CT scan ultimately showed an inflamed appendix on the verge of rupture. Six hours later, the appendix was out. The man said the pain has completely vanished, and he woke up laughing under anesthesia. He was discharged the next day.
How a late-night conversation with Grok got me to demand the CT scan that saved my life from a ruptured appendix (December 2025)
byu/Tykjen ingrok
AI doctors could very well be welcomed
In the replies to his Reddit post, u/Tykjen further explained that he specifically avoided telling doctors that Grok, an AI, suggested he get a CT scan. “I did not tell them on the second visit that Grok recommended the CT scan. I had to lie. I told them my sister who’s a nurse told me to ask for the scan,” the man wrote.
One commenter noted that the use of AI in medicine will likely be welcomed, stating that “If AI could take doctors’ jobs one day, I will be happy. Doctors just don’t care anymore. It’s all a paycheck.” The Redditor replied with, “Sadly yes. That is what it felt like after the first visit. And the following night could have been my last.”
Elon Musk has been very optimistic about the potential of robots like Tesla Optimus in the medical field. Provided that they are able to achieve human-level articulation in their hands, and Tesla is able to bring down their cost through mass manufacturing, the era of AI-powered medical care could very well be closer than expected.
News
Tesla expands Model 3 lineup in Europe with most affordable variant yet
The Model 3 Standard still delivers more than 300 miles of range, potentially making it an attractive option for budget-conscious buyers.
Tesla has introduced a lower-priced Model 3 variant in Europe, expanding the lineup just two months after the vehicle’s U.S. debut. The Model 3 Standard still delivers more than 300 miles (480 km) of range, potentially making it an attractive option for budget-conscious buyers.
Tesla’s pricing strategy
The Model 3 Standard arrives as Tesla contends with declining registrations in several countries across Europe, where sales have not fully offset shifting consumer preferences. Many buyers have turned to options such as Volkswagen’s ID.3 and BYD’s Atto 3, both of which have benefited from aggressive pricing.
By removing select premium finishes and features, Tesla positioned the new Model 3 Standard as an “ultra-low cost of ownership” option of its all-electric sedan. Pricing comes in at €37,970 in Germany, NOK 330,056 in Norway, and SEK 449,990 in Sweden, depending on market. This places the Model 3 Standard well below the “premium” Model 3 trim, which starts at €45,970 in Germany.
Deliveries for the Standard model are expected to begin in the first quarter of 2026, giving Tesla an entry-level foothold in a segment that’s increasingly defined by sub-€40,000 offerings.
Tesla’s affordable vehicle push
The low-cost Model 3 follows October’s launch of a similarly positioned Model Y variant, signaling a broader shift in Tesla’s product strategy. While CEO Elon Musk has moved the company toward AI-driven initiatives such as robotaxis and humanoid robots, lower-priced vehicles remain necessary to support the company’s revenue in the near term.
Reports have indicated that Tesla previously abandoned plans for an all-new $25,000 EV, with the company opting to create cheaper versions of existing platforms instead. Analysts have flagged possible cannibalization of higher-margin models, but the move aims to counter an influx of aggressively priced entrants from China and Europe, many of which sell below $30,000. With the new Model 3 Standard, Tesla is reinforcing its volume strategy in Europe’s increasingly competitive EV landscape.
News
Tesla FSD (Supervised) stuns Germany’s biggest car magazine
FSD Supervised recognized construction zones, braked early for pedestrians, and yielded politely on narrow streets.
Tesla’s upcoming FSD Supervised system, set for a European debut pending regulatory approval, is showing notably refined behavior in real-world testing, including construction zones, pedestrian detection, and lane changes, as per a recent demonstration ride in Berlin.
While the system still required driver oversight, its smooth braking, steering, and decision-making illustrated how far Tesla’s driver-assistance technology has advanced ahead of a potential 2026 rollout.
FSD’s maturity in dense city driving
During the Berlin test ride with Auto Bild, Germany’s largest automotive publication, a Tesla Model 3 running FSD handled complex traffic with minimal intervention, autonomously managing braking, acceleration, steering, and overtaking up to 140 km/h. It recognized construction zones, braked early for pedestrians, and yielded politely on narrow streets.
Only one manual override was required when the system misread a converted one-way route, an example, Tesla stated, of the continuous learning baked into its vision-based architecture.
Robin Hornig of Auto Bild summed up his experience with FSD Supervised with a glowing review of the system. As per the reporter, FSD Supervised already exceeds humans with its all-around vision. “Tesla FSD Supervised sees more than I do. It doesn’t get distracted and never gets tired. I like to think I’m a good driver, but I can’t match this system’s all-around vision. It’s at its best when both work together: my experience and the Tesla’s constant attention,” the journalist wrote.
Tesla FSD in Europe
FSD Supervised is still a driver-assistance system rather than autonomous driving. Still, Auto Bild noted that Tesla’s 360-degree camera suite, constant monitoring, and high computing power mark a sizable leap from earlier iterations. Already active in the U.S., China, and several other regions, the system is currently navigating Europe’s approval pipeline. Tesla has applied for an exemption in the Netherlands, aiming to launch the feature through a free software update as early as February 2026.
What Tesla demonstrated in Berlin mirrors capabilities already common in China and the U.S., where rival automakers have rolled out hands-free or city-navigation systems. Europe, however, remains behind due to a stricter certification environment, though Tesla is currently hard at work pushing for FSD Supervised’s approval in several countries in the region.
Man credits Grok AI with saving his life after ER missed near-ruptured appendix
Tesla expands Model 3 lineup in Europe with most affordable variant yet
