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
SpaceX Starship Flight 13 faces wrath of the Texas skies
SpaceX pushed Starship Flight 13 to Friday, blaming weather instead of the previous engine issues.
SpaceX called off Thursday’s launch attempt of Starship Flight 13, pushing the mission to Friday because of weather tied to Tropical Storm Bertha. The company confirmed the delay on X, noting “Now targeting Friday, July 24 for Starship’s thirteenth flight test, due to weather. A key objective for the flight test is to get clear imagery from the ground of Starship’s heatshield as it flies at a higher dynamic pressure during ascent, which won’t be possible with today’s weather conditions.”
This is the second delay for Flight 13 in two weeks. SpaceX first tried to launch the mission on July 16, but the countdown ended in an automated abort at T-0 when four of Super Heavy Booster 20’s 33 Raptor engines failed to ignite. Musk said at the time that two Raptors would need to be removed and replaced, as Teslarati reported. The company spent the following week destacking Ship 40 and Booster 20, swapping engines, and running leak checks before restacking the vehicle on Pad 2 Wednesday night, according to Spaceflight Now’s live coverage.
Unlike the engine problem, Thursday’s delay has nothing to do with the hardware. SpaceX wants clean footage of Starship’s heat shield captured from the ground as the vehicle flies through max dynamic pressure, something the storm’s cloud cover over South Texas would not allow. The company said visibility should improve for Friday’s attempt, with the same 90 minute window opening at 5:45 p.m. CT.
Flight 13 will be the second outing for the V3 versions of Starship and Super Heavy, following their debut on Flight 12 in May. The mission carries 20 production Starlink V3 satellites, the first time SpaceX has flown operational satellites rather than mass simulators on Starship. Six of those satellites are fitted with cameras to inspect the heat shield from a different angle during ascent, giving engineers a second data source beyond the ground imagery the weather is currently blocking.
Booster 20 will attempt a boostback burn and a splashdown landing burn in the Gulf of America, while Ship 40 follows a suborbital trajectory toward a landing in the Indian Ocean. The flight plan largely mirrors Flight 12, though the booster will run a more aggressive ascent burn after max Q this time, and the ship’s heat shield includes load sensing tiles meant to measure stress at the higher dynamic pressure SpaceX is targeting.
If Friday’s attempt succeeds, Flight 13 could be the last suborbital test in the program. SpaceX is already looking to push for an orbital flight on Flight 14.
Tesla stock (NASDAQ: TSLA) endured one of its sharpest single-day declines in years on July 23, tumbling approximately 14.5 percent and closing near $320 after opening the session around $374. The drop erased more than $140 billion in market value amid heavy trading volume and left the shares at multi-week lows.
The sell-off followed the company’s second-quarter 2026 results, released the previous evening. Tesla reported record revenue of $28.2 billion, up 26 percent year over year, driven by a Q2-record 480,126 vehicle deliveries. Energy storage deployments also rose strongly.
Tesla (TSLA) Q2 2026 earnings results: miss on EPS, beat on revenue
Yet profitability disappointed sharply. Operating income fell 57 percent to $398 million, compressing the operating margin to just 1.4 percent. Non-GAAP earnings per share came in at $0.33, well below the roughly $0.53 analysts had expected. Free cash flow turned negative by $1.1 billion as capital expenditures surged 142 percent to $5.8 billion, largely tied to accelerated spending on artificial intelligence, robotics, and autonomous systems.
The losses on capex were expected, as Tesla said it would be spending heavily in 2026.
Investors also reacted to lingering uncertainty surrounding key product timelines. During the Earnings Call, management reiterated ambitions for Robotaxi deployment and the Optimus humanoid robot, but offered limited new concrete milestones, renewing questions about execution pace that have long accompanied Tesla’s ambitious roadmap.
The magnitude of the decline places it among Tesla’s more severe one-day percentage losses since its 2010 initial public offering. Historically, the two largest single-day drops (split-adjusted) remain September 8, 2020, when shares fell 21.1 percent amid broader market volatility and valuation concerns, and January 13, 2012, with a 19.3 percent plunge during the company’s early growth struggles.
Other notable declines include an 18.6 percent drop on March 16, 2020, at the onset of pandemic-related market turmoil. Thursday’s move ranks roughly ninth on the all-time list but stands out as the steepest in more than a year.
Despite the short-term pain, Tesla’s long-term trajectory has repeatedly recovered from such volatility. The latest results underscore both the strength of its core automotive and energy businesses and the near-term costs of heavy investment in next-generation technologies.
Elon Musk clapped back at France’s decision to withhold the approval for Tesla’s Full Self-Driving (FSD) Supervised system, projecting a clear and blunt message to French Transport Minister Phillippe Tabarot, after he publicly rejected the technology in its current form.
Tabarot outlines several concerns with Tesla Full Self-Driving in a detailed video statement, where he said, “The safety trade-offs are not yet sufficient to authorize it as it currently stands,” he said. He emphasized that FSD is not a true self-driving system and that the driver remains fully responsible.
Key issues Tabarot also brought up included allowing speeding when surrounding traffic exceeds limits and what he believes are insufficient guarantees of driver attention during complex urban maneuvers such as lane changes, intersections, and roundabouts.
Delaying the approval of FSD in France will cost lives
— Elon Musk (@elonmusk) July 22, 2026
While acknowledging technological progress and France’s support for autonomous innovation, Tabarot stressed that deployment must prioritize road safety. He noted ongoing technical discussions with Tesla, the Netherlands, and other European partners, with further ecosystem meetings planned for the fall.
Musk’s rebuke highlights the human cost of regulatory caution. Tesla’s latest safety reports provide compelling data supporting accelerated adoption. In the most recent 12-month period, vehicles using FSD (Supervised) recorded one major collision per approximately 5.1 million miles driven, dramatically better than the U.S. national average of one crash per 698,000 miles.
Even Tesla vehicles driven manually with active safety features outperform the average by a wide margin. These figures come from billions of real-world miles of telemetry, showing FSD vehicles involved in far fewer incidents than both manual Teslas and the broader U.S. fleet.
Critics argue Tesla’s comparisons require careful scrutiny regarding reporting thresholds and fleet demographics, yet the data consistently positions FSD as a potential lifesaver. With road fatalities remaining a leading cause of death worldwide, Musk contends that proven safer technology should not face prolonged bureaucratic hurdles.
France’s measured approach reflects the broader European regulatory caution, which many, especially Musk, have been critical of in the past. However, as autonomous systems from Tesla and competitors like Waymo demonstrate superior safety in independent studies, pressure is mounting for harmonized approvals.
Musk’s warning carries the belief that every month of delay may equate to avoidable tragedies on European roads.
SpaceX Starship Flight 13 faces wrath of the Texas skies
Tesla stock tumbles after earnings, one of its sharpest single-day declines
