Apple’s self-driving car program, known internally as Project Titan, never resulted in a commercial vehicle, but its engineering efforts left a lasting impact on the company’s chip design. The project, which spanned roughly a decade before being officially shuttered in early 2024, required immense on-device AI processing capabilities. To meet these demands, Apple began developing a specialized processor that would later evolve into the Neural Engine, the backbone of modern Apple silicon.

The first iteration of the Neural Engine debuted with the iPhone X and the A11 Bionic chip in 2017. Initially, it handled tasks like Face ID, Animoji, and augmented reality—functions that relied on computer vision. But the technology quickly expanded beyond mobile devices. By the time Apple introduced its M1 chip for Macs in 2020, the Neural Engine had become a standard component across all product lines. This move gave Apple a distinct advantage in on-device AI processing, enabling features like real-time language translation, photo analysis, and privacy-preserving machine learning.

While Apple’s software AI strategy has often been seen as slower compared to competitors like Google and Microsoft, its hardware investments have been consistently impressive. The Neural Engine allowed Apple to differentiate its products on privacy: because more AI tasks were processed locally, less user data needed to be sent to the cloud. This became a key selling point, especially as concerns about data security grew.

Origins in Project Titan

Project Titan was first reported in 2014, with Apple aiming to build a fully autonomous electric vehicle. The car required a powerful onboard computer capable of running AI algorithms for perception, planning, and control in real time. Apple’s chip engineers recognized that off-the-shelf processors wouldn’t meet the strict power and performance requirements. They began designing a custom chip that could execute neural network models efficiently.

Initially, the chip was intended solely for the car. However, as the project evolved and faced leadership changes and strategic pivots, Apple decided to repurpose the technology. The first public evidence of this came with the A11 Bionic’s Neural Engine, which could perform up to 600 billion operations per second. Over the years, each new generation of Apple silicon brought significant improvements: the A12 doubled performance, and the M1 Neural Engine handled 11 trillion operations per second.

By the time Apple canceled the car project, the chip team had already gained invaluable experience. The knowledge gained from designing a high-performance AI processor for a vehicle translated directly into the chips used in iPhones, iPads, and Macs. In many ways, the car was the catalyst that forced Apple to invest heavily in AI hardware earlier than it might have otherwise.

The M7 Ultra and beyond

According to recent reports from industry analyst Mark Gurman, Apple is now accelerating its chip development roadmap. The company plans to skip the Pro, Max, and Ultra versions of the upcoming M6 chip. Instead, it is focusing on the M7 series, with the M7 Ultra expected to ship in the first half of 2027. This chip will feature a significantly upgraded Neural Engine, designed to handle even larger AI workloads.

One of the most notable specifications of the M7 Ultra is its support for up to 1.5TB of RAM. This massive memory capacity points to Apple’s ambitions in server hardware. For years, Apple has been building its own AI data centers, and the M7 Ultra could serve as the foundational chip for a new line of Apple servers. By using its own silicon in servers, Apple can better control performance, power efficiency, and—critically—privacy. It also reduces reliance on third-party vendors like Nvidia and Intel.

The M7 Ultra’s Neural Engine is expected to be several times more powerful than the current M4 generation. This would enable more sophisticated on-device AI features, such as real-time video analysis, advanced natural language understanding, and generative AI tasks that currently require cloud connectivity. Apple has been rumored to be developing its own large language model (LLM) internally, and the M7 series would provide the necessary compute power to run such models efficiently on devices.

Historical context of Apple’s AI chip evolution

Apple’s journey with AI chips did not start with the car project. The company had been using general-purpose CPUs and GPUs for machine learning tasks before the Neural Engine. However, the limitations of those components—especially in terms of power consumption—became apparent as AI models grew more complex. A dedicated neural processing unit (NPU) was the logical next step.

The car program provided the perfect use case: real-time processing of camera feeds, LiDAR data, and sensor fusion. The chip had to operate with low latency and minimal power draw, both of which are critical in a vehicle. These same requirements apply to mobile devices, which are battery-powered. Thus, the early work on the car chip directly informed the design of the mobile Neural Engine.

Apple’s competitors took different paths. Google introduced the Pixel Visual Core in 2017 and later the Tensor Processing Unit (TPU) for cloud and mobile. Qualcomm integrated AI accelerators into its Snapdragon chips. However, Apple was the first to bring a dedicated NPU to a smartphone, and it has maintained a lead in raw performance per watt ever since.

Impact on Apple’s product strategy

The Neural Engine has become a central pillar of Apple’s product lineup. From the iPhone to the Mac, Apple Watches, and even AirPods Pro (which use the H1 chip with a neural network accelerator), every device benefits from on-device AI processing. Features like live text recognition in photos, voice isolation in calls, and the new Apple Intelligence suite rely heavily on this hardware.

Apple Intelligence, introduced in 2024, uses a combination of on-device processing and cloud servers. The on-device component runs on the Neural Engine, handling simpler tasks like summarizing notifications or suggesting replies. More complex queries are sent to Apple’s private cloud servers, which also use Apple silicon. This hybrid approach ensures both performance and privacy, as data is processed without being visible to Apple.

Looking ahead, the M7 Ultra could enable entirely new categories of applications. For example, developers could run large generative AI models directly on the iPhone, without any cloud dependency. This would open up possibilities for real-time language translation, AI-powered photography, and advanced accessibility features.

Apple’s ability to control both hardware and software gives it a unique advantage. Unlike many competitors who rely on third-party AI accelerators, Apple can optimize every layer of the stack. The car project may have failed as a product, but it succeeded in forcing Apple to think differently about how AI should be implemented. The result is a family of chips that now define the company’s technological identity.

As of mid-2026, Apple continues to refine its chip designs. The M7 series is on track for a 2027 release, and early prototypes are already being tested. Engineers are reportedly working on even more advanced architectures for beyond 2027, potentially incorporating new materials like graphene or 3D stacking. The legacy of Project Titan lives on, not in a car, but in the silicon that powers devices used by billions.

Source: The Verge News