News
Tesla’s Neural Network adaptability to hardware highlighted in new patent application
Tesla’s developments in the artificial intelligence arena are one of the most important aspects of its current and future technology, and this includes adapting neural networks to various hardware platforms. A recent patent publication titled “System and Method for Adapting a Neural Network Model On a Hardware Platform” provides a bit of insight into how the electric car maker is taking on the challenge.
In general, a neural network is a set of algorithms designed to gather data and recognize patterns from it. The particular data being collected depends on the platform involved and what kind of information it can send to the network, i.e., cameras/image data, etc. Differences between platforms mean differences in the neural network algorithms, and adapting them is something time consuming for developers. Just as apps have to be programmed to work based on the operating system or hardware on a phone or tablet, for example, so too do neural networks. Tesla’s answer to the adaptation issue is automation (of course).
During the adaptation process of a neural network to specific hardware, decisions must be made by a software developer based on available options built into the hardware being used. Each of these options, in turn, usually requires research, hardware documentation review, and impact analysis, with each set of options chosen, eventually adding up to a configuration for the neural network to use. Tesla’s application calls these options “decision points,” and they are a vital part of how their invention functions.
According to the application, after plugging in a neural network model and the specific hardware platform information for adaptation, software code traverses the network to learn where the decision points are, then runs the hardware parameters against those points to provide available configurations. More specifically, the software method looks at the hardware constraints (such as processing resources and performance metrics) and generates setups for the neural network that will satisfy the requirements for it to operate correctly. From the application:
“In order to produce a concrete implementation of an abstract neural network, a number of implementation decisions about one or more of system’s data layout, numerical precision, algorithm selection, data padding, accelerator use, stride, and more may be made. These decisions may be made on a per-layer or per-tensor basis, so there can potentially be hundreds of decisions, or more, to make for a particular network. Embodiments of the invention take many factors into account before implementing the neural network because many configurations are not supported by underlying software or hardware platforms, and such configurations will result in an inoperable implementation.”
Tesla’s invention also provides the ability to display the neural network configuration information on a graphical interface to make assessment and selection a bit more user friendly. For instance, different configurations could have different evaluation times, power consumption, or memory consumption. Perhaps an analogy for this process would be selecting configurations based on differences between Track Mode and Range Mode but instead for how you’d want your AI to work with your hardware.
This patent application looks to be one of the products of Tesla’s reported acquisition of DeepScale, an AI startup focused on Full Self Driving and designing neural networks for small devices. The listed inventor, Dr. Michael Driscoll, was a Senior Staff Engineer for DeepScale before transitioning to a Senior Software Engineer position at Tesla. Prior CEO of DeepScale, Dr. Forrest Iandola, also transitioned to Tesla as a Senior Staff Machine Learning Scientist before moving on to independent research this year.
In early 2026, Tesla made a bold strategic pivot: it largely eliminated the option to purchase Full Self-Driving (FSD) software outright and shifted to a subscription-only model. The change, effective around mid-February, ended the one-time fee that had previously ranged as high as $15,000 and later dropped to $8,000. Instead, customers would access FSD (Supervised) for $99 per month in the U.S.
At the time, skeptics questioned whether locking customers into recurring payments would hurt adoption or alienate buyers who preferred ownership of the feature. Tesla bet that a lower barrier to entry, seamless integration at purchase, and the ability to cancel at any time would drive higher uptake.
The results from Q2 2026 speak for themselves: the decision has been a resounding success, delivering the largest quarterly growth in FSD subscriptions in the company’s history.
Tesla FSD subscriptions went up 56% in Q2 2026 to 1.48 million, an increase of 200,000 from Q1 2026.
Tesla added more FSD subscribers in Q2 than in any quarter in its history. pic.twitter.com/jTciTD2JqW
— Sawyer Merritt (@SawyerMerritt) July 22, 2026
According to Tesla’s Q2 shareholder update, active FSD subscriptions reached 1.48 million globally by the end of June 2026. That represents a 56 percent increase year-over-year and a 15.6 percent jump from the prior quarter. Tesla added roughly 200,000 new subscriptions in the period alone—the biggest single-quarter gain on record.
North America led the charge, with more than 55 percent of new vehicle deliveries including an FSD subscription at the time of purchase, a record attach rate for the region.
Tesla explicitly noted that “more customers [are] opting for subscription at the time of vehicle purchase,” crediting the model shift and prominent placement of the option in the ordering process. Subscriptions now contribute meaningfully to ancillary revenue, helping offset pressure elsewhere in the business.
The financial upside is substantial: At $99 per month, 1.48 million active subscriptions generate approximately $146.5 million in monthly recurring revenue. Over a full year, that equates to roughly $1.76 billion in annualized recurring revenue (ARR) from FSD subscriptions alone, assuming steady retention and no major pricing changes.
These figures represent pure, high-margin software revenue. Unlike vehicle sales, which carry production costs, warranty obligations, and supply-chain risks, FSD subscriptions flow largely to the bottom line once the software is developed and deployed over-the-air.
Tesla does not break out exact FSD subscription revenue in its filings (it sits within “Services and Other”), but the category grew 50 percent year-over-year in Q2, with executives highlighting subscriptions as a key driver.
The subscription model offers several structural advantages. It lowers the upfront cost of a new Tesla, potentially broadening the buyer pool and supporting vehicle demand, especially important amid fluctuating EV market conditions. It creates a predictable revenue stream that compounds as the fleet grows and more owners try (and stick with) the software.
Legacy one-time purchasers still exist, but new growth is overwhelmingly subscription-based following the February cutoff.
Early data also suggests improving retention and satisfaction, as well. Tesla has rolled out iterative FSD updates, including v14 features, and expanded availability to additional markets. Recent regulatory approvals in parts of Europe have further boosted interest, with owners in newly enabled countries eager to activate the software they had been waiting for.
FSD is still supervised; regulatory hurdles for true unsupervised autonomy persist in many regions, including the United States, and competition in advanced driver-assistance systems is intensifying. Yet the Q2 numbers validate Tesla’s bet: by removing the large upfront commitment and making FSD accessible via subscription, the company has accelerated adoption faster than many anticipated.
What began as a controversial switch-up has become a clear win. With nearly 1.5 million subscribers, record attach rates, and nearly $1.8 billion in potential annual recurring revenue already in view, Tesla’s FSD business is transitioning from a promised future to a tangible, fast-growing profit engine.
If the momentum continues, and especially if unsupervised capabilities unlock robotaxi opportunities, the subscription flywheel could become one of the most valuable assets in Tesla’s portfolio.
Tesla Robotaxi is among its biggest projects currently, but many have been quick to point out the fact that the company has definitely been slow to expand its fleet.
However, there is definitely a method to that madness. CEO Elon Musk answered several concerns during last night’s quarterly earnings call that some might have about that slow rollout of the Robotaxi suite, maintaining the company’s narrative on prioritizing safety and wanting to avoid injuries to anyone, including animals.
Musk said:
“With Robotaxi, our goals are very ambitious for Robotaxi, but we do need to be cautious about causing any accidents or causing any harm to anyone. Although there are, I think, 30,000 to 40,000 automotive deaths per year in the U.S. alone, most of those do not generate any press or maybe, you never really read about almost any of those. If we injure even one person, it’ll be worldwide headline news, and regulators will immediately clamp down on our activities.
We don’t want to injure anyone. We’re going as fast as humanly possible in scaling Robotaxi, but while trying to ensure that we do not harm anyone at all, and ideally do not even run over a pet. That’s really the constraint is we want to grow as fast as possible with Robotaxi without harm to anyone.”
Tesla has maintained an exemplary safety record with its Robotaxi suite, according to internal data. VP of AI, Ashok Elluswamy, said that the Robotaxi suite has driven more than 380,000 miles unsupervised without any incidents.
0 notable incidents across over 380,000 miles traveled by Robotaxi
— Tesla (@Tesla) July 22, 2026
Analyst Colin Langan of Bank of America also pushed Tesla executives for answers regarding the company’s decision to add cities across several states with dozens of vehicles “as opposed to hundreds.”
Elluswamy said there’s a bigger advantage to do it the way Tesla has been because it ensures that its software stack “is a very general one:”
“The reason we have been expanding across different cities instead of just doubling down on a single city, is that we want to make sure that our stack is a very general one. It is a general one. We just want to both prove to ourselves and to other folks that it is working across a lot of different cities without too much effort per city. That’s what we see internally.”
In the past, we have written about Tesla’s decision to be incredibly conservative with its Robotaxi rollout, especially with the incredibly small fleet size compared to competitors. However, there really is not a price anyone can put on safety for those utilizing the platform or pedestrians, so what Tesla is doing is justified.
A year into the Robotaxi program being active, Tesla has made major strides, but many investors and fans would like to see the fleet expand as quickly as the program has to other cities and states.
Elon Musk
Tesla Semi finally has an FSD timeline and it’s waiting on the Cybercab
Elon Musk told investors Semi self-driving should start working by early 2027, per today’s earnings.
During Wednesday’s’ Tesla Q2 earnings call, an analyst asked Elon Musk when Tesla would look at autonomy for the Semi. His answer set a real timeline for the first time, noting that self-driving on the Tesla Semi is expected to start working “around the end of this year or early next year”.
Musk framed the delay as a matter of priority, not capability. Tesla’s self-driving team is currently focused on Model 3, Model Y, and Cybercab, the vehicles that make up the overwhelming majority of Tesla’s fleet. Since Semi trucks on the road remain a small fraction of that total even after the recent Nevada factory ramp, Musk said it made more sense to keep the software team’s attention on what he called “the march of nines of safety” for the higher volume vehicles first. Autonomous Semi development is “taking a bit of a backseat for the next six months or so,” he said, before adding that it “will definitely be working next year and in time for the scale-up to high production of the Tesla Semi.”
Tesla Semi’s official battery capacity leaked by California regulators
The timeline lines up with what’s already been showing up on public roads. In June, a Tesla Semi was spotted in Sunnyvale wearing a full validation rig, the same rooftop sensor array Tesla mounts on vehicles ahead of an FSD milestone.
A second unit was seen near Fremont days later with a matching camera suite and lens washers. Separately, Tesla analyst Nic Cruz Patane posted video this month of the production Semi’s exterior camera array, ten AI4 based units built directly into the truck rather than added later.
Tesla Semi AI4 cameras. The production version has 10 cameras on its exterior.
These trucks are designed to be autonomous. pic.twitter.com/GH3BamxIBQ
— Nic Cruz Patane (@niccruzpatane) April 14, 2026
Musk also gave the reason autonomy on the Semi matters in the first place, a persistent shortage of qualified truck drivers. “There is a really serious shortage of truckers,” he said on the call, framing a self-driving Semi as important both for addressing that shortage and for improving safety and comfort for the drivers running the truck today.
The timing also tracks with the Semi’s production reality. Tesla’s Q2 shareholder letter, dropped language promising the Semi would reach volume production this year. Musk pointed to 4680 battery cell output as the near-term constraint on Semi and Cybercab production. A software timeline landing in early 2027 gives Tesla’s autonomy team room to work while the hardware ramp catches up behind it.
It’s worth nothing that this isn’t necessarily a promise the Semi ships driverless next year. Musk’s own language, self-driving “working” by early 2027, describes internal validation catching up to hardware already riding on every production truck, not a public unsupervised rollout.
Tesla’s switch-up on selling Full Self-Driving has paid off big time
Tesla Robotaxi’s slow rollout gets explanation from Elon Musk
