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.ย
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.ย
Similar to Autonomy Day and Battery Day,ย 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 โAlien Dreadnaughtโ concept. Musk notes that most of Teslaโs 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. โWeโre making the pieces that would be useful for a humanoid robot, so we should probably make it. If we donโt someone else will โ and we want to make sure it’s safe,โ Musk said.
Simon 19:15 PT – And the Q&A starts. First question involves open-sourcing Teslaโs innovations. Musk notes that itโs pretty expensive to develop all this tech, so heโs 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 โTesla Bot.โ 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 –ย 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 Teslaโs 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 Dojoโs 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 Dojoโs 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, Teslaโs Director of Autopilot Engineering takes the stage. He notes that he would discuss how neural networks are run in the companyโs cars. He notes that Teslaโs 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 companyโs 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 itโs impossible to be done manually. He shows how roads and other items on the road are โreconstructedโ from a single car thatโs 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 thatโs 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 Teslaโs solution to these issues, a โHybrid Planning System.โ He demonstrates this by showing how Autopilot performs a lane change.
Simon 18:10 PT – Karpathy’s discussion notes that today, Teslaโs FSD strategy is a lot more cohesive. This is demonstrated by the fact that the companyโs 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 Karpathyโs 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 Teslaโs most technical event right off the bat. That said, multi-camera networks are amazing. Theyโre just a ton of work, but it may very well be a silver bullet for Teslaโs 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 Teslaโs visual processing strategies have evolved over the years, and how it is done today. The AI Director also touches on Teslaโs โHydraNets,โ on account of their multi-task learning capabilities.
Simon 17:51 PT – Karpathy starts off by discussing the visual component of Teslaโs AI, as characterized by the eight cameras used in the companyโs vehicles. The AI director notes that AI could be considered like a biological being, and itโs built from the ground up, including its synthetic visual cortex.
Simon 17:48 PT – Elon Musk takes the stage. He apologizes for the eventโs delay. He jokes that Tesla probably needs AI to solve these โtechnical difficulties.โ The CEO highlights that AI Day is a recruitment event. He calls Teslaโs head of AI Andrej Karpathy. Thereโs no better person to discuss AI.
Simon 17:45 PT – Weโre here watching the AI Day FSD preview video and we canโt help but notice thatโฆ are those Waypoints?!
Simon 17:38 PT – Looks like we’ve got an Elon sighting! And a preview video too! Here we go, folks!
Weโve got an Elon sighting
— Rob Maurer (@TeslaPodcast) August 20, 2021
Simon 17:30 PT – A 30-minute delay. We havenโt 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 – Weโre 15 minutes in. โElon Timeโ 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 Teslaโs language in its rawest form. Weโre 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 toย tips@teslarati.comย to give us a heads up.ย
News
Three things Tesla needs to improve with Full Self-Driving v14 release
These are the three things I’d like to see Tesla Full Self-Driving v14 improve.

As Tesla plans to release Full Self-Driving version 14 this week after CEO Elon Musk detailed a short delay in its rollout, there are several things that continue to plague what are extremely well-done drives by the suite.
Tesla Full Self-Driving has truly revolutionized the way I travel, and I use it for the majority of my driving. However, it does a few things really poorly, and these issues are consistent across many drives, not just one.
Tesla Full Self-Driving impressions after three weeks of ownership
Musk has called FSD v14 “sentient” and hinted that it would demonstrate drastic improvements from v13. The current version is very good, and it commonly performs some of the more difficult driving tasks well. I have found that it does simple, yet crucial things, somewhat poorly.
These are the three things I’d like to see Tesla Full Self-Driving v14 improve.
Navigation, Routing, and Logical Departure
My biggest complaint is how poorly the navigation system chooses its route of departure. I’ve noticed this specifically from where I Supercharge. The car routinely takes the most illogical route to leave the Supercharger, a path that would require an illegal U-turn to get on the correct route.
I managed to capture this yesterday when leaving the Supercharger to go on a lengthy ride using Full Self-Driving:
You’ll see I overrode the attempt to turn right out of the lot by pushing the turn signal to turn left instead. If you go right, you’ll go around the entire convenience store and end up approaching a traffic light with a “No U-Turn” sign. The car has tried to initiate a U-turn at this light before.
If you’re attempting to get on the highway, you simply have to leave the convenience store on a different route (the one I made the vehicle go in).
It then attempted to enter the right lane when the car needed to remain in the left lane to turn left and access the highway. I manually took over and then reactivated Full Self-Driving when it was in the correct lane.
To achieve Unsupervised Full Self-Driving, such as navigating out of a parking lot and taking the logical route, while also avoiding illegal maneuvers, is incredibly crucial.
Too Much Time in the Left Lane on the Highway
It is illegal to cruise in the left lane on highways in all 50 U.S. states, although certain states enforce it more than others. Colorado, for example, has a law that makes it illegal to drive in the left lane on highways with a speed limit of 65 MPH or greater unless you are passing.
In Florida, it is generally prohibited to use the left lane unless you are passing a slower vehicle.
In Pennsylvania, where I live, cruising in the left lane is illegal on limited-access highways with two or more lanes. Left lanes are designed for passing, while right lanes are intended for cruising.
Full Self-Driving, especially on the “Hurry” drive mode, which drives most realistically, cruises in the left lane, making it in violation of these cruising laws. There are many instances when it has a drastic amount of space between cars in the right lane, and it simply chooses to stay in the left lane:
The clip above is nearly 12 minutes in length without being sped up. In real-time, it had plenty of opportunities to get over and cruise in the left lane. It did not do this until the end of the video.
Tesla should implement a “Preferred Highway Cruising Lane” option for two and three-lane highways, allowing drivers to choose the lane that FSD cruises in.
It also tends to pass vehicles in the slow lane at a speed that is only a mile an hour or two higher than that other car.
This holds up traffic in the left lane; if it is going to overtake a vehicle in the right lane, it needs to do it faster and with more assertiveness. It should not take more than 5-10 seconds to pass a car. Anything longer is disrupting the flow of highway traffic.
Parking
Full Self-Driving does a great job of getting you to your destination, but parking automatically once you’re there has been a pain point.
As I was arriving at my destination, it pulled in directly on top of the line separating two parking spots. It does this frequently when I arrive at my house as well.
Here’s what it looked like yesterday:
Parking is one of the easier tasks Full Self-Driving performs, and Autopark does extremely well when the driver manually chooses the spot. I use Autopark on an almost daily basis.
However, if I do not assist the vehicle in choosing a spot, its performance pulling into spaces is pretty lackluster.
With a lot of hype surrounding v14, Tesla has built up considerable anticipation among owners who want to see FSD perform the easy tasks well. As of now, I believe it does the harder things better than the easy things.
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
โ 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:
๐จ Some have wondered if this is ‘staged’ or if Optimus is teleoperated here
Elon Musk said this is completely AI https://t.co/N69uDD6OVM
โ 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 companyโs record-breaking third quarter of 2025.ย
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 Teslaโs 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.ย
โOn 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,โ the firm wrote in its note.
A bright spot in Tesla Energy
Cantor Fitzgerald also highlighted that while Teslaโs full-year production and deliveries would likely fall short of 2024โs 1.8 million total, Teslaโs energy storage business remains a bright spot in the companyโs results.
โTesla 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,โ the firm stated.
Teslaโs 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 Musk2 weeks ago
Tesla FSD V14 set for early wide release next week: Elon Musk
-
News1 week ago
Elon Musk gives update on Tesla Optimus progress
-
News2 weeks ago
Tesla has a new first with its Supercharger network
-
News2 weeks ago
Tesla job postings seem to show next surprise market entry
-
News2 weeks ago
Tesla makes a big change to reflect new IRS EV tax credit rules
-
Investor's Corner1 week ago
Tesla gets new Street-high price target with high hopes for autonomy domination
-
Lifestyle1 week ago
500-mile test proves why Tesla Model Y still humiliates rivals in Europe
-
News1 week ago
Tesla Giga Berlin’s water consumption has achieved the unthinkable