NVIDIA leads with approximately 5,000 patents in GPU architecture, memory hierarchies, and interconnects for AI supercomputers. While AI hardware startups may not reach those numbers, the principle is clear: patents protect physical implementations beyond reverse-engineerable code. The good news for AI infrastructure startups? The USPTO's 2026 guidance is now favoring exactly these kinds of innovations.

Why AI Infrastructure Needs Patents More Than Ever

What is AI infrastructure? It refers to hardware (GPUs, TPUs, supercomputers) and supporting systems enabling large-scale AI model training and deployment. Startups in this space compete not just on algorithms, but on novel hardware optimizations like interconnect technologies and memory hierarchies.

Here's the fundamental advantage: software algorithms can be reverse-engineered overnight; hardware integrations require physical replication. NVIDIA's GPU clusters for Argonne's Solstice supercomputer, Intel Gaudi accelerators, and Huawei Ascend processors are patented precisely because they can't be easily copied without legal exposure.

Patents as the Ultimate Moat

A patent portfolio provides legal exclusivity for 20 years—a sustainable competitive advantage that extends beyond what competitors can reverse-engineer from your hardware. As industry experts note, "patents on hardware configurations provide defensible protection in AI infrastructure beyond easily reverse-engineered code."

Consider Selector, which gained 8 USPTO patents on AI causal inference for network operations and LLM training. These patents don't just protect algorithms; they protect specific technical solutions that create barriers against competition lasting two decades.

Key Patent Opportunities for Hardware Startups

AI infrastructure companies typically have patentable innovations in:

• Power efficiency: Methods for reducing energy consumption in AI workloads, a critical concern as models grow larger
• Thermal management: Innovative cooling or heat dissipation systems that improve performance and reliability
• Custom interconnects: Novel ways of connecting processing elements to reduce latency and increase bandwidth—exactly what NVIDIA patents
• Memory hierarchies: Specialized memory architectures optimized for AI tensor operations
• Tensor Processing Units: Custom ASICs designed for specific AI workloads, like Google's TPU for optimizing tensor operations
• Neuromorphic processors: Brain-inspired chips mimicking neural structures for improved efficiency

How Hardware Patents Beat Software Patents in AI

The comparison is stark. Software patents have become increasingly difficult to obtain due to abstract idea rejections under Section 101. But hardware patents tie to physical improvements—a GPU with 30% better power efficiency, or an interconnect design that reduces latency by milliseconds—and these specifics avoid Section 101 problems.

Moreover, hardware patents are harder to design around. A competitor might rewrite your software, but they can't easily replicate your custom silicon without infringing your patents.

The USPTO's 2026 Guidance Changes Everything

The August 2025 USPTO memorandum expanded Section 101 eligibility for AI innovations, directly overturning prior restrictions on model improvements and applied technologies. This guidance is particularly favorable for hardware companies:

• Technical improvements to AI systems—even using generic computer components—can integrate a judicial exception into a practical application
• Close calls now favor applicants; examiners should only reject when it's "more likely than not" (greater than 50%) that a claim is ineligible
• Claims that merely "involve" a judicial exception (but don't explicitly recite one) are eligible without further eligibility analysis

Proven Filing Strategies for AI Hardware

To maximize your patent portfolio:

• Target hardware-specific claims: Focus on power efficiency metrics, thermal management innovations, custom interconnect latencies, and memory bandwidth improvements
• Leverage 2026 USPTO guidance: Emphasize "applied technologies" and specific technical problems solved—this is exactly what the new guidance rewards
• Use AI tools strategically: Tools like DeepIP can assist with invention harvesting and prior art analysis, but remember that human inventors remain required per USPTO rules
• Counter obviousness rejections: When faced with rejection, respond with specific hardware metrics proving your innovation isn't obvious to someone in the field

The Startup Advantage Right Now

Larger competitors like NVIDIA have already built their patent moats. But the favorable USPTO environment creates an opportunity for well-positioned startups to establish strong patent positions before the landscape shifts again. The window won't stay open forever—policy at the USPTO can change with administrations.

Companies that build comprehensive hardware patent portfolios during this favorable period will have significant advantages: defensive protection against larger competitors, leverage in partnership and acquisition negotiations, and credible assets for attracting VC investment.

Why VCs Care About Hardware Patents

VCs investing in AI infrastructure are particularly focused on IP because hardware startups represent significant capital requirements. Patents provide the only reliable way to defend that investment against well-funded competitors who could otherwise reverse-engineer and commoditize your technology.

Action Items for AI Infrastructure Startups

• Audit your current innovations in power efficiency, thermal management, and interconnect design
• Document your technical achievements with specific metrics—performance improvements, energy reductions, latency gains
• File provisional applications immediately to establish priority dates while the favorable USPTO guidance is in effect
• Build an inventor disclosure process to capture innovations as they're developed
• Work with patent counsel experienced in hardware-AI integration to maximize claim scope