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
Tesla Cybercab gets crazy change as mass production begins
Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.
Tesla Cybercab has evidently received a pretty crazy change from an aesthetic standpoint, as the company has made the decision to offer an additional finish on the vehicle as mass production is starting.
Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.
VIN Zero—the very first production Cybercab—showcases a vibrant champagne gold exterior with a high-gloss finish, a dramatic departure from the flat, matte-wrapped prototypes that debuted at the 2024 “We, Robot” event.
Presenting VIN Zero — the very first production Cybercab built at Giga Texas. pic.twitter.com/8bXo4CJAlr
— TechOperator (@TechOperator) April 23, 2026
This glossy sheen is a pretty big pivot from what was initially shown by Tesla. The company has maintained a pretty flat tone in terms of anything related to custom colors or finishes.
A specialized clear coat or process delivers the deep, reflective gloss without conventional painting. The result is a premium, mirror-like shine, and it looks pretty good, and gives the compact two-seater a more luxurious and futuristic presence than the subdued matte prototypes.
Photos shared by Tesla community members reveal VIN Zero in a showroom-like setting at Giga Texas, highlighting refined panel gaps, large aero wheel covers, and the signature no-steering-wheel, no-pedals interior optimized for full autonomy.
The open frunk in some images offers a glimpse of practical storage, while the overall build quality appears more polished than that of test mules.
This glossy evolution aligns with Tesla’s broader production ramp. After the first unit in February 2026, the company has shifted to volume manufacturing, with dozens of units already spotted in outbound lots. CEO Elon Musk and the team aim for hundreds per week, paving the way for unsupervised FSD robotaxi networks that could slash ride costs to pennies per mile.
The Cybercab holds Tesla’s grand ambitions of operating a full-service ride-hailing service without any drivers in its grasp. Tesla has yet to solve autonomy, but is well on its way, and although its timelines are usually a bit off, improvements often come through the Over-the-Air updates to the Full Self-Driving suite.
Tesla has confirmed today that its steering wheel-less and pedal-less Cybercab, the vehicle geared toward launching the company’s autonomous ride-hailing hopes, has officially entered production at its Giga Texas production facility outside of Austin.
The Cybercab is a sleek two-door, two-passenger coupe engineered from the ground up as an electric self-driving vehicle. It features no steering wheel or pedals, relying instead on Tesla’s advanced vision-only Full Self-Driving system powered by multiple cameras and artificial intelligence.
Purpose-built for autonomy
Cybercab in production now at Giga Texas pic.twitter.com/Y9qG3KyWBa
— Tesla (@Tesla) April 23, 2026
The minimalist cabin centers on a large display screen that serves as the primary interface for passengers, creating an open, futuristic space optimized for comfort during unsupervised rides. A compact 35-kilowatt-hour battery pack delivers exceptional efficiency at 5.5 miles per kilowatt-hour, providing an estimated 200-mile range.
Additional innovations include inductive charging compatibility and a lightweight design that enhances aerodynamics and performance.
Production at Giga Texas builds on earlier prototypes and initial units completed earlier in 2026. The facility, already a hub for Model Y and Cybertruck assembly, now ramps up dedicated lines for the Cybercab.
This shift to volume manufacturing reflects Tesla’s strategy to scale affordable autonomous vehicles rapidly.
By focusing on a dedicated platform rather than adapting existing models, the company aims to keep costs low while prioritizing safety and reliability through continuous AI improvements.
The Cybercab’s debut in production carries broad implications for urban mobility. As the cornerstone of Tesla’s Robotaxi network, it promises on-demand, driverless rides that could slash transportation expenses, reduce traffic accidents caused by human error, and lower emissions through its all-electric powertrain.
Accessibility features, such as space for service animals or assistive devices, further broaden its appeal. Regulators and cities worldwide will soon evaluate its deployment, but the vehicle’s design already addresses key hurdles in scaling unsupervised autonomy.
Challenges persist, including full regulatory clearance and building charging infrastructure. Yet this production launch signals momentum. With Cybercabs poised to roll out in increasing numbers, Tesla edges closer to a future where personal ownership meets shared fleets of intelligent vehicles.
The start of Cybercab production is more than just a new vehicle entering mass manufacturing for Tesla, as it’s a signal autonomy is near. Being developed without manual controls is such a massive sign by Tesla that it trusts its progress on Full Self-Driving.
While the development of that suite continues, Tesla is making a clear cut statement that it is prepared to get its fully autonomous vehicle out in public roads as it prepares to revolutionize passenger travel once and for all.
News
Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much
There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.
Tesla Full Self-Driving v14.3.2 began rolling out to some owners earlier this week, and there are some notable improvements that came with this update.
There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.
Overall operation saw a handful of slight improvements, especially with parking performance, which has been the most notable difference with the arrival of FSD v14.3. However, there are still some very notable shortcomings, most notably with region-specific signage and navigation.
Tesla Assisted Smart Summon (ASS) improvements
There are noticeable improvements to ASS operation, which has definitely been inconsistent in terms of performance. Tesla wrote in the release notes for v14.3.2:
“Unified the model between Actually Smart Summon, FSD, and Robotaxi for more capable and reliable behavior.”
As recently as this month, I used Summon with no success. It had pulled around the parking lot I was in incorrectly, leaving the range at which Summon can be operated and losing a signal while moving in the middle of the lot.
This caused me to sprint across the lot to retrieve the vehicle:
It was pouring when I left the gym so I tried to Summon my Model Y
It turned the opposite way and drove out of range, stopping here and forcing me to walk even further across the lot in the rain for it 🤣
One day pic.twitter.com/iD10c8sriB
— TESLARATI (@Teslarati) April 5, 2026
Unfortunately, Summon was not dependable or accurate enough to use regularly. It appears Tesla might have bridged the gap needed to make it an effective feature, as two tests in parking lots proved that Summon was more responsive and faster to navigate to the location chosen.
It also did so without hesitation, confidently, and at a comfortable speed. I was able to test it twice at different distances:
🚨 Tesla FSD v14.3.2 ASS testing part 1
This was a significant improvement than recent tries using ASS. The parking lot was pretty empty but getting it to come to my location in one singular motion and maneuver was encouraging. https://t.co/vF7TS48GGV pic.twitter.com/sYt8tyHgNn
— TESLARATI (@Teslarati) April 23, 2026
Tesla Full Self-Driving v14.3.2 ASS testing part 2 https://t.co/lxfWfnLUxf pic.twitter.com/2R0r3ohI3M
— TESLARATI (@Teslarati) April 23, 2026
I plan to test this more thoroughly and regularly through the next few weeks, and I avoided using it in a congested parking lot initially because I have not had overwhelming success with Summon in the past. I wanted to set a low baseline for it to see if it could simply pull up to the place I pinned in the Tesla app.
It was two for two, which is a big improvement because I don’t think I ever had successful Summon attempts back-to-back. It just seems more confident than ever before.
New Disengagement Categories
This is a really good idea from Tesla, but there are some issues with it. The categories you can select are Critical, Comfort, Preference, and Other.
I think the reasons why people choose to take over would be a better way to prompt drivers, like, “Traveling Too Fast,” “Incorrect Maneuver,” “Navigation Error,” would be more beneficial.
I say this because it seems that how we each categorize things might be different. For example, I shared a video of an intervention because the car had navigated to an exit to a parking lot and put its left blinker on, despite left turns not being allowed there.
I disengaged and chose Critical as the reason; it’s not a comfort issue, it’s not a preference, it’s quite literally an illegal turn, and it’s also dangerous because it cuts across several lanes of traffic and is 180 degrees.
I chose to label this Navigation error as “Critical” while testing FSD v14.3.2
Here’s why:
✅ This intervention wasn’t “preference,” as the maneuver FSD routed was illegal
✅ If a police officer saw this maneuver, it would result in a ticket https://t.co/znhHb4haAo pic.twitter.com/bZOiLwWmQa— TESLARATI (@Teslarati) April 23, 2026
Some said I should not have labeled this as Critical, but that’s the description I best characterized the disengagement as.
Categorizing interventions is a good thing, but it’s kind of hard to determine how to label them correctly.
Inconsistency with Regional Traffic Patterns
Tesla Full Self-Driving is pretty inconsistent with how it handles regional or local traffic patterns and road rules. The most frequent example I like to use is that of the “Except Right Turn” stop sign, which has become a notorious sighting on our social media platforms.
In the initial rollout of v14.3, my Model Y successfully navigated through one of these stop signs with no issues. However, testing at two of these stop signs yesterday proved it is still not sure how to read signs and navigate through them properly.
🚨 Tesla FSD v14.3.2 attempts the “Except Right Turn” stop sign: https://t.co/W5MjAybaNK pic.twitter.com/P6oeUsk4PN
— TESLARATI (@Teslarati) April 23, 2026
Off camera, I approached another one of these signs and felt the car coming to a stop, so I nudged it forward with the accelerator pedal pressed.
This helped the car go through the sign without stopping, but I could feel the bucking of the vehicle as the car really wanted to stop.
Musk said on the earnings call earlier this week that unsupervised FSD would probably be available in some regions before others, including a state-to-state basis in the U.S.
“It’s difficult to release this like to everyone everywhere all at once because we do want to make sure that they’re not unique situations in a city that particularly complex intersection or — actually, they tend to be places where people get into accidents a lot because they’re just — perhaps there’s — and like I said, an unsafe intersection or bad road markings or a lot of weather challenges. So I think we would release unsupervised gradually to the customer fleet as we feel like a particular geography is confirmed to be safe.”
This could be one of those examples that Tesla just has to figure out.
Highway Operation
Full Self-Driving is already pretty good at routine roadway navigation, so I don’t have too much to report here.
However, I was happy with FSD’s decision-making at several points, including its choice not to pass a slightly slower car and remain in the right lane as we approached the off-ramp:
🚨 Tesla FSD v14.3.2 highway operation: generally happy with the performance here, especially behavior near the exit
Love that the car got over in the right lane after its final pass, and stayed there as the off ramp was approaching https://t.co/qVRVhg6XGR pic.twitter.com/1ELwHf2XKS
— TESLARATI (@Teslarati) April 23, 2026
Better Maneuvering at Stop Signs
Many FSD users report some strange operations at stop signs, especially four-way intersections where there is a stop sign and a line on the road, and they’re not even with one another.
I experienced this quite frequently and found that FSD would actually double stop: once at the stop sign and again at the line.
This created some interesting scenarios for me and I had many cars honk at me when the second stop would happen. Other vehicles that had waved me on to proceed through the intersection would become frustrated at the second stop.
FSD seems to have worked through this particular maneuver:
🚨 Tesla FSD v14.3.2 with a singular stop at the correct spot
No double stopping anymore in my experience https://t.co/Wd0TaNjc1R pic.twitter.com/CdQPvJHaAM
— TESLARATI (@Teslarati) April 23, 2026
FSD should know to go to the more appropriate location (whichever provides better visibility), and proceed when it is the car’s turn to move. The double stop really ruined the flow of traffic at times and generally caused some frustration from other drivers.
Tesla Cybercab gets crazy change as mass production begins
Tesla confirms Cybercab with no steering wheel enters production
