Apple is taking a bold step into the future of hardware engineering by incorporating generative artificial intelligence into the chip design process. Johny Srouji, Apple’s Senior Vice President of Hardware Technologies, confirmed the shift during a recent speech in Belgium, revealing the tech giant’s growing reliance on AI to make chip development faster, smarter, and more efficient.

This rare glimpse into Apple’s internal engineering processes offers insight into how one of the world’s most secretive and successful companies is evolving its approach to chip development. From the early days of the A4 chip to the complex silicon powering the Vision Pro and upcoming AI servers, Apple is now reimagining what chip innovation looks like—driven by the power of generative AI.

Apple uses AI to design chips, builds custom cloud chip ‘Baltra’.

Generative AI and Chip Design: A Natural Fit

In his speech, Srouji pointed out that “generative AI techniques have a high potential in getting more design work in less time, and it can be a huge productivity boost.” With semiconductor designs becoming increasingly intricate and resource-intensive, Apple is turning to AI to help reduce complexity and accelerate time to market.

Generative AI can be used to simulate and optimize thousands of chip layouts, test scenarios, and power efficiency models in a fraction of the time it would take human engineers. AI doesn’t replace designers; instead, it augments their capabilities, allowing them to focus on high-level design while the AI handles the repetitive and detail-heavy tasks.

This shift also reflects a broader trend in the semiconductor industry, where companies are racing to integrate AI into Electronic Design Automation (EDA) tools to handle today’s enormous chip design challenges.

The Role of EDA Tools from Synopsys and Cadence

Apple, despite designing its chips in-house, relies heavily on EDA software from third-party vendors like Synopsys and Cadence Design Systems. These tools are essential to simulate, verify, and implement silicon designs for Apple’s full range of devices—iPhones, iPads, Macs, Apple Watches, and now Vision Pro.

Both Synopsys and Cadence are modernizing their platforms with AI-powered capabilities. Synopsys recently launched a tool called AgentEngineer, which leverages AI agents to automate tasks such as timing closure, power optimization, and design rule checking. This allows human engineers to offload repetitive tasks and focus on strategy and creativity.

Cadence is also investing heavily in AI-based design environments. The company has been integrating deep learning and reinforcement learning models into its chip workflow software. These tools aim to reduce chip development time while improving performance and efficiency—goals aligned closely with Apple’s ambitions.

Apple’s Chip Evolution: From A4 to Vision Pro

Srouji’s remarks traced Apple’s silicon journey, beginning with the A4 chip in the iPhone 4, which debuted in 2010. That chip marked Apple’s first major foray into custom silicon and kicked off more than a decade of steady hardware innovation.

From there, Apple expanded its custom silicon to power the iPad, Apple Watch, HomePod, and Apple TV. In 2020, the company made a historic leap by transitioning the Mac from Intel processors to its own Apple Silicon, starting with the M1 chip. This move allowed Apple to unify its ecosystem with tighter hardware-software integration and far better performance per watt.

Most recently, Apple developed high-performance silicon for the Vision Pro mixed-reality headset, pushing the envelope of spatial computing. That required deep integration between neural engines, image sensors, and AI processing units—all of which benefit greatly from automated, AI-assisted chip design.

The Growing Complexity of Chip Design

As Srouji noted, chip design is no longer just about transistors and clock speed. It's about integrating hardware and software in a seamless, intelligent way. Each generation of chips must meet increasingly stringent demands around power efficiency, thermal control, security, and machine learning capabilities.

This complexity is exactly where generative AI excels. It can generate and test multiple design configurations, validate against thousands of parameters, and find edge cases that might be missed by humans. AI also helps reduce the time required for regression testing, chip floor planning, and error detection.

The result? More robust designs in less time—a critical advantage when product cycles are shrinking and the demand for AI-enabled features is exploding.

Apple’s Baltra: A Custom AI Server Chip

Apple’s AI ambitions go beyond just on-device hardware. In late 2024, the company quietly began working with Broadcom, one of its long-time chip suppliers, to design its first AI server chip. Internally code-named Baltra, this chip is intended to power Apple’s private cloud infrastructure and handle intensive AI workloads.

Baltra is reportedly optimized for running AI models tied to Apple Intelligence, a suite of new AI features recently announced for iPhones, iPads, and Macs. These tools range from on-device personalization to powerful image generation, and Baltra will handle the tasks that are too demanding for mobile chips.

What makes this move especially strategic is Apple’s emphasis on user privacy. Baltra will run in Apple-controlled data centers, supporting a system called Private Cloud Compute. This approach allows Apple to deliver advanced AI experiences without compromising user anonymity, as no login or data retention is required.

By designing its own server chips, Apple gains full control over the security, performance, and energy efficiency of its backend AI infrastructure—key pillars in maintaining its privacy-first brand.

On-Device vs. Cloud AI: A Balanced Approach

Apple’s decision to split AI workloads between on-device and cloud-based chips like Baltra reflects a growing need to balance performance with privacy.

On-device AI—powered by Apple Silicon in iPhones, Macs, and iPads—offers fast and private processing. But some AI features require massive computation power and memory that only data centers can provide. That’s where Baltra fits in.

With Private Cloud Compute, Apple promises that:

• Users will not need to log in to access cloud AI services.

• Data is not stored or linked to user identities.

• The entire system is built on custom hardware and verified software.

This unique setup helps Apple sidestep the privacy pitfalls that many competitors face. It also shows how deeply Apple is investing in the full AI pipeline—from training to inference, across both edge devices and servers.

No Plan B: Apple’s Bold Hardware Philosophy

In his speech, Srouji also revealed something striking: When Apple moved the Mac to Apple Silicon, there was no backup plan. “We went all in,” he said. “There was no split-the-lineup plan, so we went all in, including a monumental software effort.”

That same level of commitment now seems to apply to Apple’s AI strategy. The company isn’t just dabbling in AI-enhanced chip design—it’s restructuring its entire silicon pipeline around it. Whether it’s for on-device chips or server-grade processors like Baltra, Apple appears to be going all in again.

This willingness to take calculated risks—and trust in its engineering teams—is a core part of Apple’s DNA. And with the addition of generative AI, that engineering power may now be even more amplified.

The Road Ahead: Talent, Tools, and Testing

As Apple pushes deeper into AI-based chip design, it will need more than just advanced software—it will need new kinds of talent.

That means hiring engineers who not only understand VLSI (Very Large Scale Integration) design and system architecture but are also fluent in machine learning frameworks, generative models, and EDA software with AI extensions.

In parallel, Apple must ensure that all AI-designed chips pass rigorous testing, both in simulation and in silicon. While generative AI can speed up development, chip verification, and real-world validation still require precision and patience. For that, Apple continues to rely on TSMC, its key manufacturing partner, to bring those designs to life with cutting-edge fabrication processes.

AI, Hardware, and Apple’s Vertical Integration

Ultimately, Apple’s move toward AI-driven chip design fits neatly into its broader strategy of vertical integration. The company has always sought to control every major component of its ecosystem, from custom silicon and software to supply chain logistics and now, backend infrastructure.

By using AI to design its chips, Apple can:

• Accelerate innovation cycles.

• Reduce time-to-market for new products.

• Enhance hardware-software synergy.

• Maintain strict data privacy standards.

• Push the boundaries of what’s possible with personal computing.

This approach is not just about making better chips—it’s about future-proofing the entire Apple experience.

Final Thoughts: The Next Era of Silicon Starts Now

Apple’s embrace of generative AI in chip design marks a new chapter in the evolution of computing. No longer content to rely solely on human ingenuity, the company is pairing its top engineering minds with AI-driven tools to develop chips that are more powerful, efficient, and aligned with its privacy-first philosophy.

With Baltra on the backend, Apple Silicon on the frontlines, and Synopsys and Cadence helping pave the way, Apple is building a hardware future that’s smarter, faster, and more autonomous. As these chips find their way into everything from iPhones to AI data centers, the entire Apple ecosystem will become more intelligent—and more tightly integrated—than ever before.

In Srouji’s own words, Apple is “all in.” And if history is any guide, that means the next generation of Apple devices will not just be better—they’ll be a leap forward in what’s possible when AI meets hardware.