Omead Afshar, who was previously Elon Musk’s “Fixer” and the Head of Operations for North America and Europe, has left the company, according to reports from Forbes and Bloomberg.
While some sources have claimed he was fired, others say he voluntarily left, but his exit isn’t exactly an isolated event.
Afshar’s departure is the second high-level exit this month, following Optimus' lead, Milan Kovac. When viewed together, alongside Elon's full-time return to Tesla, these changes may offer some insight into the pressures the Tesla executive team is facing during a transitional period.
Transition from what, you may ask? Well, from the world’s largest EV company to an AI and robotics-first company. This transition has been looming for years, and with Elon’s vision of a future powered by autonomous vehicles and humanoid robots. It’s the path that Tesla is determined to forge, ahead of anyone else, and despite the immense challenges of real-world AI.
Two Competing Narratives
Two primary theories have emerged to explain the timing of Afshar’s exit, and each paints quite a different picture.
The first, supported by the reporting from Forbes, frames him as a casualty of Tesla’s current sales issues. With sales having declined for five consecutive months in Europe and dropping in the US, the second quarter of 2025 has been rough for Tesla. In conjunction with recent factory shutdowns, a lot is happening behind the scenes, with Robotaxi taking the limelight and the missing Affordable Model in the backseat. Afshar’s departure could be the result of a move to show accountability for the performance drop of the core business he managed.
The second narrative is one of “mission accomplished.” Just days before his abrupt exit, Afshar posted a celebratory message on X about the successful launch of the Robotaxi Network.
Absolutely historic day for Tesla.
This has been years of hard work and focus by so many people within the company.
He followed up with a second celebratory-styled message the day after - it was a project he was deeply involved in as the do-it-all executive for Elon. This has led to speculation that his departure was planned, and potentially tied to compensation vesting with the launch of the Robotaxi Network, allowing him to leave on a high note after seeing the kick-off of one of Tesla’s most critical projects. This follows other recent departures of Tesla’s executive team, many of whom have gone to full-time retirement following years of hard work.
The Bigger Picture: What Is Tesla, really?
While both theories are plausible, the truth may be that Afshar’s departure is the symptom of a much larger challenge. Tesla is balancing two very different corporate identities.
On one hand, it's a manufacturing and sales powerhouse, responsible for the world’s best-selling electric vehicles, a business facing intense competition and brand perception challenges that even Elon has acknowledged.
On the other hand, Tesla is the only company shipping real-world AI for consumers, and betting its future on robotics and AI with massive investments in capacity for both future businesses.
The recent executive churn suggests that this balancing act is creating some strain, especially for Tesla’s senior executives. The departure of Milan Kovac signaled pressure on the future side of the business, where progress has been slow but consistent. Now, the exit of Afshar, who ran the “legacy” automotive side of the business, shows there’s pressure there, as the automotive business navigates a period of flattening growth and intense global competition.
So, we ask again - What is Tesla, really? Is it an AI and Robotics company? Kind of, but not really. Is it an EV company? Once again, kind of.
In our eyes, it is no longer just an EV company, but it’s at a critical point where it is transitioning to an AI and robotics company.
Tesla’s messaging to the outside world is similarly conflicted. On the one hand, the launch of the refreshed Model Y, a massive boost for the business, went seemingly unnoticed by Elon, who only posted a single update on the Model Y after its launch. On the other hand, we’ve seen consistent and non-stop posts about Robotaxi, which is likely years away from generating a significant portion of Tesla’s profits.
A Company in Transition
Ultimately, Omead Afshar’s departure is more than a single personnel change; it’s a reflection of Tesla navigating a crucial and challenging transition. The evidence of an abrupt halt, with internal sources reporting his account has been removed from internal company directories, suggests that there’s more to this than meets the eye.
Whether he was fired for declining sales or chose to leave after the successful launch of the Robotaxi Network, the outcome is the same. A key leader, tasked with managing the core business of the present, is gone at the very moment when the company is changing its path towards AI and robotics.
Being both a car company in a tough market and an AI company on the verge of a breakthrough is a monumental challenge, and the path forward is likely to see even more changes.
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Tesla has unveiled its 6-seat Model Y variant in China, known as the Model Y L. This new variant of one of the world’s best-selling vehicles comes with a longer wheelbase, adjusted C-pillar design, and most importantly, a six-seat interior layout.
The vehicle’s specifications have been officially listed in a filing with China’s Ministry of Industry and Information Technology (MIIT), confirming a launch for this fall.
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The addition of a longer wheelbase and a more spacious third row is a fantastic addition for the Model Y’s family utility, and positions this variant as sort of a mini Model X, but let’s compare the sizes to really know how this new Model Y compares to a Model X.
Meet the Model Y L
The defining feature of the new Model Y L is its six-seat configuration. This layout has previously been exclusive to the larger and more expensive Model X. While Tesla has offered the Model Y in a 7-seat configuration before, the third row was much too small to be utilized by anyone but small children.
Comparing Model Y L to the Model X
@xiaoteshushu on X
Let’s compare this upcoming Model Y L to the regular Model Y and the Model X.
Vehicle/Dimension
Wheelbase
Overall Length
Model Y
2,890mm / 113.8 in
4,797mm / 188.9 in
Model Y L
3,040mm / 119.7 in
4,976mm / 195.9 in
Model X
2,965mm / 116.7 in
5,060mm / 199.2 in
The new wheelbase of 3,040mm is a significant stretch from the standard wheelbase, and in fact, is longer than the Model X’s wheelbase of 2,965mm. However, the overall length of the vehicle is 84mm (~3 inches) shorter than the Model X. This means the vehicle sits neatly between the current Model Y and Model X, filling a much-needed gap.
While this Model Y L is slightly smaller than the Model X, it doesn’t necessarily mean that it’s smaller inside. The Model X features a much larger front end than the Model Y, accounting for several inches. When you line up the front wheel base of the Model X with this new Model Y, the vehicles are almost exactly the same length.
Tesla has designed this Model Y to be a bit more compact and efficient than the Model X, and likely much cheaper, while featuring the well-loved design of the new Model Y.
Other Specifications and Price
The MIIT filing also provided a detailed look at some additional specifications. The Model Y L is a dual-motor, AWD variant, so it will likely be more expensive than the current Model Y AWD that’s available in China today. Tesla charges an additional $6,500 USD when upgrading the Model X from a 5-seat configuration to a 6-seat layout, so we may see something similar here.
The extra length has been added behind the C-pillar, resulting in a longer rear profile for the Model Y L. To accompany this, Tesla has added an updated rear spoiler, similar to the one found on Performance variants, but not carbon fiber. There is also a new wheel design to complement the updated look, along with unique Model Y L badging and a new light gold paint option.
In classic Tesla fashion, no Tesla is slow - and the Y L has a 0-100 km/h (0-60mph) time of 5.9s, with a top speed of 217km/h. Alongside an 82.5 kWh LFP battery pack, the Model Y L boasts an impressive CLTC range of 688 km (427 mi).
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Launch & Availability
According to posts from Tesla China on Chinese social media, the new Model Y L is scheduled to launch in the fall of 2025. Its official listing in the MIIT database is essentially the final regulatory step required before sales can begin, which means the launch is really just around the corner. For now, it appears that Tesla intends to launch this vehicle only in China, as no other filings have been made in other regions. However, these could be revealed in the coming months.
The new Model Y L is a huge addition to Tesla’s lineup - one that addresses the Chinese preference for vehicles with longer wheelbases and additional passenger room in a compact SUV package. The question is - will this variant make its way to North America and Europe?
Solving real-world artificial intelligence - whether for autonomous driving, real-world robotics, or advanced reasoning - requires an almost unfathomable amount of computational power. To meet this challenge, Tesla has been developing its own custom AI training hardware while simultaneously purchasing hardware in the open market.
Now, the next-generation Dojo 2 chip has reportedly entered mass production with the world’s largest semiconductor manufacturer, TSMC. While many may consider this a side quest, expanding Tesla’s computing base will be necessary to achieve exascale supercomputing, which will be crucial for all of Tesla’s AI ambitions.
Elon Musk called Dojo 2 “a good computer,” and then followed up with a classic computer performance joke - Dojo 2 can indeed play Crysis at a billion frames per second.
While Tesla has effectively utilized powerful third-party GPUs to train its models to date, the Dojo supercomputer is a ground-up, application-specific solution designed for a single purpose. It will efficiently process massive amounts of video data for training neural networks. The Dojo 2 chip itself is the key that unlocks this potential.
Dojo 2 will train the vision-based neural nets that FSD relies on, allowing Tesla to process video from its massive global fleet of vehicles even faster. As Tesla continues to improve FSD, one of the biggest challenges has been the intake of video for handling difficult edge cases.
Hundreds of thousands of miles of training data may pass by before an edge case is identified and trained on, but it all needs to be analyzed, labeled, and processed, which is key for Dojo 2. Each new useful piece of training data will help Tesla proceed down the march of 9s, making FSD just that little bit better every time.
This process requires massive amounts of compute and training time - but it is an absolute necessity to improve FSD. Of course, this goes beyond just FSD in vehicles. Tesla’s humanoid robot, Optimus, also runs on FSD to navigate and interact with the physical world.
While it may be a custom version of FSD, it remains FSD at its core, which means the same neural nets that analyze the environment and build a 3D map of the world for your car perform the same work for Optimus.
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Custom Approach to AI Hardware
Dojo 2’s power doesn’t just come from raw compute; it comes from a series of architectural choices that make it excel at training FSD and differentiate it from general-purpose hardware, or even other AI-specific hardware.
To this end, Tesla is using TSMC’s new Integrated Fan-Out with Silicon-on-Wafer (InFO-SoW) packaging technology. For massive AI workloads, heat and the speed at which data moves between chips are often the biggest bottlenecks.
This new packaging technique allows for high-bandwidth connections directly between processing dies, which lowers latency and dramatically improves heat dissipation, all key to building massive and dense compute clusters.
Unlike general-purpose chips, Dojo 2 is designed with a custom instruction set, specifically built to train FSD. The cores are specifically made to accelerate the exact mathematical operations, like matrix multiples and systolic arrays, which form the backbone of Tesla’s vision-based neural networks.
By building its own hardware, Tesla can then integrate its own software and compilers directly with the silicon, optimizing for specific workloads and avoiding the performance penalties that can result from using third-party software, such as Nvidia’s CUDA.
The start of Dojo 2 may seem like a side quest for some, but it’s actually a key step for Tesla’s AI technologies that give them an advantage over the competition using off-the-shelf hardware. They’ll need to continue investing in custom hardware to improve FSD at a reasonable pace, rather than the current glacial pace we’ve seen over the last few months.
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