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.
Elon Musk
Musk forces Judge’s exit from shareholder battles over viral social media slip-up
McCormick insisted in a court filing that she harbors no actual bias against Musk or the defendants. She claimed she either never clicked the “support” button, LinkedIn’s version of a “like,” or did so accidentally.
Many Tesla fans are familiar with the name Kathaleen McCormick, especially if they are investors in the company.
McCormick is a Delaware Chancery Court Judge who presided over Tesla CEO Elon Musk’s pay package lawsuit over the past few years, as well as his purchase of Twitter. However, she will no longer be sitting in on any issues related to Musk.
Elon Musk demands Delaware Judge recuse herself after ‘support’ post celebrating $2B court loss
In a rare admission of potential optics issues in one of America’s most powerful corporate courts, Delaware Chancery Court Chancellor Kathaleen McCormick stepped aside Monday from a cluster of shareholder lawsuits targeting Elon Musk and Tesla’s board.
The move came just days after Musk’s legal team highlighted her apparent “support” on LinkedIn for a post that mocked the billionaire over his 2022 tweets about the $44 billion Twitter acquisition.
McCormick insisted in a court filing that she harbors no actual bias against Musk or the defendants. She claimed she either never clicked the “support” button, LinkedIn’s version of a “like,” or did so accidentally.
She wrote in a newly published memo from the Delaware Chancery Court:
“The motion for recusal rests on a false premise — that I support a LinkedIn post about Mr. Musk, which I do not in fact support. I am not biased against the defendants in these actions.”
Yet she granted the reassignment anyway, acknowledging that the intense media scrutiny surrounding her involvement had become “detrimental to the administration of justice.”
The consolidated cases will now be handled by three of her colleagues on the Delaware Court of Chancery, the nation’s go-to venue for high-stakes corporate disputes. The lawsuits accuse Musk and Tesla directors of breaching fiduciary duties through lavish executive compensation and lax governance oversight.
One prominent claim, filed by a Detroit pension fund, challenges massive stock awards granted to board members, alleging the payouts harmed the company. The litigation also overlaps with issues stemming from Musk’s turbulent 2022 Twitter purchase.
McCormick’s history with Musk made her a lightning rod. In 2022, she presided over the fast-tracked lawsuit that ultimately forced Musk to complete the Twitter deal after he tried to back out.
Then in 2024, she struck down his record $56 billion Tesla compensation package, ruling the approval process was flawed and overly CEO-friendly. The Delaware Supreme Court later reinstated the pay on technical grounds, but the ruling fueled Musk’s long-standing criticism of the state’s judiciary.
Musk has repeatedly urged companies to reincorporate elsewhere, arguing Delaware courts have grown hostile to visionary leaders. Monday’s recusal hands him a symbolic victory and underscores how personal social-media activity can collide with judicial impartiality standards.
Delaware law requires judges to step aside if there’s even a “reasonable basis” to question their neutrality.
Court watchers say the episode highlights growing tensions in corporate America’s legal epicenter. While McCormick maintained her impartiality, the appearance of bias proved too costly to ignore. The cases will proceed without her, but the broader debate over Delaware’s dominance in business litigation is far from over.
Elon Musk
Elon Musk has generous TSA offer denied by the White House: here’s why
Musk stepped in on March 21 via a post on X, writing: “I would like to offer to pay the salaries of TSA personnel during this funding impasse that is negatively affecting the lives of so many Americans at airports throughout the country.”
Tesla and SpaceX CEO Elon Musk made a generous offer to pay the salaries of Transportation Security Administration (TSA) employees last week, but the offer was denied by the White House.
In a striking display of private-sector initiative clashing with federal bureaucracy, the White House has turned down an offer from Elon Musk to personally cover the salaries of TSA officers amid an ongoing partial government shutdown. The rejection, reported last Wednesday by multiple outlets, highlights the legal and political hurdles facing unconventional solutions to Washington’s funding gridlock.
The impasse began weeks ago when Congress failed to pass funding for the Department of Homeland Security (DHS), leaving TSA employees, essential workers who screen millions of travelers daily, without paychecks while still required to report for duty.
Frustrated travelers have endured record-long security lines at major airports, with reports of chaos and delays rippling across the country.
Musk stepped in on March 21 via a post on X, writing: “I would like to offer to pay the salaries of TSA personnel during this funding impasse that is negatively affecting the lives of so many Americans at airports throughout the country.”
I would like to offer to pay the salaries of TSA personnel during this funding impasse that is negatively affecting the lives of so many Americans at airports throughout the country
— Elon Musk (@elonmusk) March 21, 2026
But it was not for no reason.
White House spokesperson Abigail Jackson responded on behalf of the Trump administration, expressing appreciation for Musk’s gesture.
However, the legal obstacles, which would be insurmountable, would inhibit Musk from doing so. Jackson said:
“We greatly appreciate Elon’s generous offer. This would pose great legal challenges due to his involvement with federal government contracts.”
Musk’s companies hold significant federal contracts, including NASA launches through SpaceX and potential Defense Department work, raising concerns about conflicts of interest, ethics rules, and anti-bribery statutes that prohibit private payments to government employees. Administration officials also indicated they expect the shutdown to end soon, making external funding unnecessary.
The episode underscores deeper tensions in Washington. Musk, who has advised on government efficiency efforts and maintains a close relationship with President Trump, has frequently criticized wasteful spending and bureaucratic delays.
His offer came as airport security lines ballooned, drawing public frustration toward both parties. TSA officers, many of whom rely on paychecks to cover mortgages and family expenses, have continued working without compensation, a situation that has drawn bipartisan concern but little immediate resolution.
Critics of the rejection argue it prioritizes red tape over practical relief for frontline workers and travelers. Supporters of the White House position counter that allowing private funding sets a dangerous precedent and could undermine congressional authority over the budget.
The White House eventually came to terms with the TSA on Friday and started paying them once again, and lines at airports instantly shrank. The Department of Homeland Security (DHS) said that TSA staf would begin receiving paychecks “as early as” today.
Elon Musk
Tesla FSD mocks BMW human driver: Saves pedestrian from near miss
Tesla FSD anticipated a BMW driver’s lane drift before the human behind the wheel could react.
A video posted to r/TeslaFSD this week put a sharp spotlight on Tesla’s Full Self-Driving (FSD) software being able to react to pedestrian intent than an actual human driver behind the wheel. In the Reddit clip, a BMW driver can be seen rolling through a neighborhood street completely unaware of a pedestrian stepping in to cross. At the same time, a Tesla driving on FSD had already begun slowing down before the pedestrian even began their attempt to cross the street The BMW kept moving, prompting the pedestrian to hop back, while the Tesla came to a stop and provide right-of-way for the human to safely cross.
That gap between what the BMW driver saw and what FSD had already processed is the story. Tesla FSD wasn’t reacting to a person in the street, rather it was reading the signals that a person was about to enter it based on the pedestrian’s movement, trajectory, and their trajectory to telegraph intent.
Tesla’s FSD is now built on an end-to-end neural network trained on billions of real-world miles, learning to interpret subtle human behavioral cues the same way an experienced human driver does instinctively. The difference is consistency. A human driver distracted for two seconds misses what FSD does not.
Tesla sues California DMV over Autopilot and FSD advertising ruling
Reddit commenters in the thread were blunt about the BMW driver’s failure, with several pointing out that the pedestrian was visible well before the crossing. One response put it plainly that the car on FSD saw the situation developing before the human in the other car had registered there was a situation at all.
Tesla has published data showing FSD (Supervised) is 54% safer than a human driver, accumulated across billions of miles driven on the system. Elon Musk has said FSD v14 will outperform human drivers by a factor of two to three, and that v15 has “a shot” at a 10x improvement. Pedestrian safety is where the stakes are highest, and where intent prediction closes the gap fastest. At 30 mph, a car covers roughly 44 feet per second. An extra second of awareness from reading a person’s body language rather than waiting for them to step out is often the difference between a near miss and a fatality.
Video and community discussion: r/TeslaFSD on Reddit
FSD saves man from becoming a pancake. BMW driver nearly flattens him.
by
u/Qwertygolol in
TeslaFSD
Musk forces Judge’s exit from shareholder battles over viral social media slip-up
Elon Musk has generous TSA offer denied by the White House: here’s why
Tesla FSD mocks BMW human driver: Saves pedestrian from near miss
Elon Musk says Tesla is developing a new vehicle: ‘Way cooler than a minivan’
Elon Musk launches TERAFAB: The $25B Tesla-SpaceXAI chip factory that will rewire the AI industry
