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
Elon Musk’s Boring Company opens Vegas Loop’s newest station
The Fontainebleau is the latest resort on the Las Vegas Strip to embrace the tunneling startup’s underground transportation system.
Elon Musk’s tunneling startup, The Boring Company, has welcomed its newest Vegas Loop station at the Fontainebleau Las Vegas.
The Fontainebleau is the latest resort on the Las Vegas Strip to embrace the tunneling startup’s underground transportation system.
Fontainebleau Loop station
The new Vegas Loop station is located on level V-1 of the Fontainebleau’s south valet area, as noted in a report from the Las Vegas Review-Journal. According to the resort, guests will be able to travel free of charge to the stations serving the Las Vegas Convention Center, as well as to Loop stations in Encore and Westgate.
The Fontainebleau station connects to the Riviera Station, which is located in the northwest parking lot of the convention center’s West Hall. From there, passengers will be able to access the greater Vegas Loop.
Vegas Loop expansion
In December, The Boring Company began offering Vegas Loop rides to and from Harry Reid International Airport. Those trips include a limited above-ground segment, following approval from the Nevada Transportation Authority to allow surface street travel tied to Loop operations.
Under the approval, airport rides are limited to no more than four miles of surface street travel, and each trip must include a tunnel segment. The Vegas Loop currently includes more than 10 miles of tunnels. From this number, about four miles of tunnels are operational.
The Boring Company President Steve Davis previously told the Review-Journal that the University Center Loop segment, which is currently under construction, is expected to open in the first quarter of 2026. That extension would allow Loop vehicles to travel beneath Paradise Road between the convention center and the airport, with a planned station located just north of Tropicana Avenue.
News
Tesla leases new 108k-sq ft R&D facility near Fremont Factory
The lease adds to Tesla’s presence near its primary California manufacturing hub as the company continues investing in autonomy and artificial intelligence.
Tesla has expanded its footprint near its Fremont Factory by leasing a 108,000-square-foot R&D facility in the East Bay.
The lease adds to Tesla’s presence near its primary California manufacturing hub as the company continues investing in autonomy and artificial intelligence.
A new Fremont lease
Tesla will occupy the entire building at 45401 Research Ave. in Fremont, as per real estate services firm Colliers. The transaction stands as the second-largest R&D lease of the fourth quarter, trailing only a roughly 115,000-square-foot transaction by Figure AI in San Jose.
As noted in a Silicon Valley Business Journal report, Tesla’s new Fremont lease was completed with landlord Lincoln Property Co., which owns the facility. Colliers stated that Tesla’s Fremont expansion reflects continued demand from established technology companies that are seeking space for engineering, testing, and specialized manufacturing.
Tesla has not disclosed which of its business units will be occupying the building, though Colliers has described the property as suitable for office and R&D functions. Tesla has not issued a comment about its new Fremont lease as of writing.
AI investments
Silicon Valley remains a key region for automakers as vehicles increasingly rely on software, artificial intelligence, and advanced electronics. Erin Keating, senior director of economics and industry insights at Cox Automotive, has stated that Tesla is among the most aggressive auto companies when it comes to software-driven vehicle development.
Other automakers have also expanded their presence in the area. Rivian operates an autonomy and core technology hub in Palo Alto, while GM maintains an AI center of excellence in Mountain View. Toyota is also relocating its software and autonomy unit to a newly upgraded property in Santa Clara.
Despite these expansions, Colliers has noted that Silicon Valley posted nearly 444,000 square feet of net occupancy losses in Q4 2025, pushing overall vacancy to 11.2%.
In Pennsylvania, we got between 10 and 12 inches of snow over the weekend as a nasty Winter storm ripped through a large portion of the country, bringing snow to some areas and nasty ice storms to others.
I have had a Model Y Performance for the week courtesy of Tesla, which got the car to me last Monday. Today was my last full day with it before I take it back to my local showroom, and with all the accumulation on it, I decided to run a cool little experiment: How long would it take for Tesla’s Defrost feature to melt 8 inches of snow?
Tesla’s Defrost feature is one of the best and most underrated that the car has in its arsenal. While every car out there has a defrost setting, Tesla’s can be activated through the Smartphone App and is one of the better-performing systems in my opinion.
It has come in handy a lot through the Fall and Winter, helping clear up my windshield more efficiently while also clearing up more of the front glass than other cars I’ve owned.
The test was simple: don’t touch any of the ice or snow with my ice scraper, and let the car do all the work, no matter how long it took. Of course, it would be quicker to just clear the ice off manually, but I really wanted to see how long it would take.
Tesla Model Y heat pump takes on Model S resistive heating in defrosting showdown
Observations
I started this test at around 10:30 a.m. It was still pretty cloudy and cold out, and I knew the latter portion of the test would get some help from the Sun as it was expected to come out around noon, maybe a little bit after.
I cranked it up and set my iPhone up on a tripod, and activated the Time Lapse feature in the Camera settings.
The rest of the test was sitting and waiting.
It didn’t take long to see some difference. In fact, by the 20-minute mark, there was some notable melting of snow and ice along the sides of the windshield near the A Pillar.
However, this test was not one that was “efficient” in any manner; it took about three hours and 40 minutes to get the snow to a point where I would feel comfortable driving out in public. In no way would I do this normally; I simply wanted to see how it would do with a massive accumulation of snow.
It did well, but in the future, I’ll stick to clearing it off manually and using the Defrost setting for clearing up some ice before the gym in the morning.
Check out the video of the test below:
❄️ How long will it take for the Tesla Model Y Performance to defrost and melt ONE FOOT of snow after a blizzard?
Let’s find out: pic.twitter.com/Zmfeveap1x
— TESLARATI (@Teslarati) January 26, 2026
Elon Musk’s Boring Company opens Vegas Loop’s newest station
Tesla leases new 108k-sq ft R&D facility near Fremont Factory
