Missed Tesla's Q2 Earnings Call? Read Our Bullet-Point Summary of Everything Announced

By Karan Singh
Not a Tesla App

Did you miss Tesla’s Earnings Call, or just want to see a summarized version? We’ve got you covered. Below is an outline of everything talked about during Tesla’s earnings call and Q&A session.

EV Market and Giga Factories

  • Strong EV adoption, despite short-term challenges.

    • Positive long-term outlook

  • Future vision for an all-electric future, including boats and planes.

  • Possible vehicle tariffs for Mexico means that Giga Mexico is on hold.

  • Giga Berlin could serve as a new export point as tariffs are placed on Chinese-built vehicles in Europe.

Production

  • Affordable Tesla model expected to be revealed in the first half of 2025.

  • Expansion of vehicle lineup, including new trims and paint options in 2024 has helped sales

  • Cybertruck production has tripled so far – 1,400 per week, and ramping continues.

    • Expected to be profitable by the end of 2024.

  • Model 3 and CT are still being impacted by tariffs as they scale up.

  • 4680 cell production improvements

    • 51% more 4680 cells in Q2 over Q1, with a COGS reduction.

    • 1,400 CTs per week on 4680.

    • Tesla is reaching cost-parity with other cells by the end of 2024.

    • First Validation Cybertruck on dry-cathode process has been built and is being tested.

    • Production launch for dry-cathode process in Q4, should drive costs down by up to 50%.

  • Tesla Semi factory on track for large-scale production by the end of 2025.

  • Giga Berlin has begun producing and delivering RHD vehicles, including to the UK.

  • Roadster engineering is complete and expected to see production sometime in 2025.

  • Tesla’s guideline for production is 300mi on a single charge.

    • Tesla expects to expand its Supercharging network globally to meet this goal.

    • This seems to be the data-driven distance that Tesla has found most suitable for general driving uses.

FSD, Autonomy, AI

  • Tesla continues to work towards unsupervised FSD. Aiming to see unsupervised FSD by the end of 2024, if not the end of 2025.

    • Elon has admitted he’s been overly optimistic in the past.

    • This new estimate is based on current trends in miles per intervention growth.

  • Robotaxi event to take place on 10/10/2024.

    • Elon wanted to improve Robotaxi a bit more, and also show off “some other things”

  • Tesla is looking to seek FSD approval with V12.5 or V12.6 in Europe, China, and other countries, hopefully by the end of the year.

  • Tesla is in talks with multiple OEMs for FSD licensing.

    • OEMs will need 360* camera coverage, a gateway, and Tesla’s AI computers at minimum.

    • Tesla is looking for OEMs to produce over 1m vehicles per year.

    • Disclosure of an agreement will happen in conjunction with the signing OEM.

  • Optimus is working in Tesla’s factories in a limited capacity.

    • Limited initial production to begin at Giga Texas in early 2025.

    • Tesla expects to use V1 to iron out bugs internally.

    • V2 is expected through 2026 and will be sold to outside customers.

  • Tesla will continue working on DOJO, as acquiring Nvidia GPUs is becoming more and more difficult.

    • Tesla aims to be competitive with Nvidia in the AI GPU space in the future.

  • Tesla aims to launch distributed compute alongside its AI5 hardware (formerly HW5).

    • AI5 is expected to launch in late 2025 and be in mass production by early 2026.

    • Distributed compute could use about 100 hours of idle time per week to generate income.

  • Grok in Tesla could be a thing of the future, Tesla has learned a lot from xAI.

    • A proposed shareholder vote on investing in xAI could happen soon.

Tesla’s Financial Performance in Q2 2024

  • Record quarter for regulatory credits.

  • High interest rates globally have impacted sales and revenue per unit.

    • Tesla has offset rates in the US through competitive financing rates and expects to continue this into Q3 2024.

  • Service and Merchandise profits have improved incrementally this quarter.

  • Energy Storage deployments doubled between Q1 to Q2, leading to record revenue and profits.

  • Tesla has a positive cash flow of $1.3 billion after restructuring this year.

    • Restructuring cost approximately $622m

    • Total free cash float of $30B.

  • Capital expenditures of approximately $10B this year but beginning to come down.

    • This includes the money already spent on the new AI supercomputer cluster.

Energy Storage and Deployment

  • Tesla’s Megapack factory production continues to ramp up, new Shanghai Megafactory is well in progress.

    • After the completion, current production is expected to double or triple.

    • Tesla is currently constrained by production.

  • Powerwall 3 is now available in multiple countries, and demand is exceptionally high.

  • There is a long pipeline between purchase and delivery for Megapack, Tesla has good pricing leverage and is working with global energy providers.

  • Chinese OEMs are competitive, but Tesla offers a full software stack, including Auto Bidder with its Megapacks.

  • New Megapack demand lines:

    • Buffer for power plants – Megapack can buffer power plants so they can run at a steady state, improving power production and efficiency by 2-3x.

    • AI and Data Center backup – AI compute is power-hungry, and data centers are now looking to Megapack to provide battery backup.

Watch Earnings Call

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Tesla Included FSD V12.6.1 and V13.2.4 in the Same Update: What Caused This and What It Means

By Karan Singh
Not a Tesla App

Tesla launched two FSD updates simultaneously on Saturday night, and what’s most interesting is that they arrived on the same software version. We’ll dig into that a little later, but for now, there’s good news for everyone. For Hardware 3 owners, FSD V12.6.1 is launching to all vehicles, including the Model 3 and Model Y. For AI4 owners, FSD V13.2.4 is launching, starting with the Cybertruck.

FSD V13.2.4

A new V13 build is now rolling out to the Cybertruck and is expected to arrive for the rest of the AI4 fleet soon. However, this build seems to be focused on bug fixes. There are no changes to the release notes for the Cybertruck with this release, and it’s unlikely to feature any changes when it arrives on other vehicles.

While this update focuses on bug fixes, Tesla’s already working on bigger features for FSD V13.3, which we have already confirmed to include improvements to highway following and speed control.

FSD V12.6.1

FSD V12.6.1 builds upon V12.6, which is the latest FSD version for HW3 vehicles. While FSD V12.6 was only released for the redesigned Model S and Model X with HW3, FSD V12.6.1 is adding support for the Model 3 and Model Y.

While this is only a bug-fix release for users coming from FSD V12.6, it includes massive improvements for anyone coming from an older FSD version. Two of the biggest changes are the new end-to-end highway stack that now utilizes FSD V12 for highway driving and a redesigned controller that allows FSD to drive “V13” smooth.

It also adds speed profiles, earlier lane changes, and more. You can read our in-depth look at all the changes in FSD V12.6.

Same Update, Multiple FSD Builds

What’s interesting about this software version is that it “includes" two FSD updates, V12.6.1 for HW3 and V13.2.4 for HW4 vehicles. While this is interesting, it’s less special when you understand what’s happening under the hood.

The vehicle’s firmware and Autopilot firmware are actually completely separate. While a vehicle downloading a firmware update may look like a singular process, it’s actually performing several functions during this period. First, it downloads the vehicle’s firmware. Upon unpacking the update, it’s instructed which Autopilot/FSD firmware should be downloaded.

While the FSD firmware is separate, the vehicle can’t download any FSD update. The FSD version is hard-coded in the vehicle’s firmware that was just downloaded. This helps Tesla keep the infotainment and Autopilot firmware tightly coupled, leading to fewer issues.

What we’re seeing here is that HW3 vehicles are being told to download one FSD version, while HW4 vehicles are being told to download a different version.

While this is the first time Tesla has had two FSD versions tied to the same vehicle software version, the process hasn’t actually changed, and what we’re seeing won’t lead to faster FSD updates or the ability to download FSD separately. What we’re seeing is the direct result of the divergence of HW3 and HW4.

While HW3/4 remained basically on the same FSD version until recently, it is now necessary to deploy different versions for the two platforms. We expect this to be the norm going forward, where HW3 will be on a much different version of FSD than HW4. While each update may not include two different FSD versions going forward, we may see it occasionally, depending on which features Autopilot is dependent on.

Thanks to Greentheonly for helping us understand what happened with this release and for the insight into Tesla’s processes.

Nvidia’s Cosmos Offers Synthetic Training Data; Following Tesla’s Lead

By Karan Singh
Not a Tesla App

At the 2025 Consumer Electronics Show, Nvidia showed off its new consumer graphics cards, home-scale compute machines, and commercial AI offerings. One of these offerings included the new Nvidia Cosmos training system.

Nvidia is a close partner of Tesla - in fact, they produce and supply the GPUs that Tesla uses to train FSD - the H100s and soon-to-be H200s, located at the new Cortex Supercomputing Cluster at Giga Texas. Nvidia will also challenge Tesla’s lead in developing and deploying synthetic training data for an autonomous driving system - something Tesla is already doing.

However, this is far more important for other manufacturers. We’re going to take a look at what Nvidia is offering and how it compares to what Tesla is already doing. We’ve done a few deep dives into how Tesla’s FSD works, how Tesla streamlines FSD, and, more recently, how they optimize FSD. If you want to get familiar with a bit of the lingo and the background knowledge, we recommend reading those articles before continuing, but we’ll do our best to explain how all this synthetic data works.

Nvidia Cosmos

Nvidia’s Cosmos is a generative AI model created to accelerate the development of physical AI systems, including robots and autonomous vehicles. Remember - Tesla’s FSD is also the same software that powers their humanoid robot, Optimus. Nvidia is aiming to tackle physical, real-world deployments of AI anywhere from your home, your street, or your workplace, just like Tesla.

Cosmos is a physics-aware engine that learns from real-world video and builds simulated video inputs. It tokenizes data to help AI systems learn quicker, all based on the video that is input into the system. Sound familiar? That’s exactly how FSD learns as well.

Cosmos also has the capability to do sensor-fused simulations. That means it can take multiple input sources - video, LiDAR, audio, or whatever else the user intends, and fuse them together into a single-world simulation for your AI model to learn from. This helps train, test, and validate autonomous vehicle behavior in a safe, synthetic format while also providing a massive breadth of data.

Data Scaling

Of course, Cosmos itself still requires video input - the more video you feed it, the more simulations it can generate and run. Data scaling is a necessity for AI applications, as you’ll need to feed it an infinite amount of data to build an infinite amount of scenarios for it to train itself on.

Synthetic data also has a problem - is it real? Can it predict real-world situations? In early 2024, Elon Musk commented on this problem, noting that data scales infinitely both in the real world and in simulated data. A better way to gather testing data is through real-world data. After all, no AI can predict the real world just yet - in fact, that’s an excellent quantum computing problem that the brightest minds are working on.

Yun-Ta Tsai, an engineer at Tesla’s AI team, also mentioned that writing code or generating scenarios doesn’t cover what even the wildest AI hallucinations might come up with. There are lots of optical phenomena and real-world situations that don’t necessarily make sense in the rigid training sets that AI would develop, so real-world data is absolutely essential to build a system that can actually train a useful real-world AI.

Tesla has billions of miles of real-world video that can be used for training, according to Tesla’s Social Media Team Lead Viv. This much data is essential because even today, FSD encounters “edge cases” that can confuse it, slow it down, or render it incapable of continuing, throwing up the dreaded red hands telling the user to take over.

Cosmos was trained on approximately 20 million hours of footage, including human activities like walking and manipulating objects. On the other hand, Tesla’s fleet gathers approximately 2,380 recorded minutes of real-world video per minute. Every 140 hours - just shy of 6 days - Tesla’s fleet gathers 20 million hours of footage. That was a little bit of back-of-the-napkin math, calculated at 60 mph as the average speed.

Generative Worlds

Both Tesla’s FSD and Nvidia’s Cosmos can generate highly realistic, physics-based worlds. These worlds are life-like environments and simulate the movement of people and traffic and the real-life position of obstacles and objects, including curbs, fences, buildings, and other objects.

Tesla uses a combination of real-world data and synthetic data, but the combination of data is heavily weighted to real-world data. Meanwhile, companies who use Cosmos will be weighting their data heavily towards synthetically created situations, drastically limiting what kind of cases they may see in their training datasets.

As such, while generative worlds may be useful to validate an AI quickly, we would argue that these worlds aren’t as useful as real-world data to do the training of an AI.

Overall, Cosmos is an exciting step - others are clearly following in Tesla’s footsteps, but they’re extremely far behind in real-world data. Tesla has built a massive first-mover advantage in AI and autonomy, and others are now playing catch-up.

We’re excited to see how Tesla’s future deployment of its Dojo Supercomputer for Data Labelling adds to its pre-existing lead, and how Cortex will be able to expand, as well as what competitors are going to be bringing to the table. After all, competition breeds innovation - and that’s how Tesla innovated in the EV space to begin with.

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