Competitive Landscape of AI Chip IP Licensing Models (ARM/RISC-V)

As AI chips move from niche accelerators to pervasive compute foundations in data centers, edge devices, and embedded systems, the way CPU and AI cores are licensed has become a strategic question rather than a mere technical detail. ARM’s established commercial IP licensing model and the rising ecosystem around RISC-V represent two contrasting philosophies: one offers mature, standardized cores under proprietary licenses;

This article examines the competitive landscape of AI chip IP licensing with a focus on ARM and RISC-V, exploring how their models work in practice, how they influence AI chip architecture decisions, and why many companies increasingly treat IP licensing as a core part of their business strategy rather than a purely engineering choice.

IP licensing in AI chips: from supporting role to central driver

In earlier generations of SoCs, CPU IP licensing was often a supporting consideration: designers selected a mainstream core, integrated their peripheral subsystems, and focused differentiation on software and system integration. With AI chips, the story is different. The CPU subsystem and AI accelerators must work tightly together, and their licensing terms can determine how freely companies can modify, extend, and scale their designs.

AI chip vendors today typically license CPU IP plus additional system components—interconnects, security blocks, sometimes even AI accelerators—under packages that define everything from royalties and upfront fees to derivative work rights and access to future upgrades. This shifts IP licensing from a background contract to a central driver of product cost, roadmap flexibility, and competitive positioning.

Against this backdrop, ARM and RISC-V ecosystems embody different approaches to controlling and monetizing CPU IP, creating a competitive landscape in which AI chip firms must evaluate not only core performance but also licensing model fit.

ARM’s established commercial model: predictability with constraints

ARM’s licensing model is built around proprietary IP cores and system components. Companies typically pay upfront license fees plus ongoing royalties per chip or per core integrated. In return, they gain access to a broad portfolio of CPU designs, reference subsystems, development tools, and ecosystem support spanning compilers, operating systems, and software frameworks.

This model offers predictability. AI chip designers can rely on well-characterized cores with long histories of deployment across mobile, consumer, and infrastructure products. The licensing structure is familiar to investors and finance teams, making cost planning straightforward at scale. ARM’s roadmap and ecosystem commitments also give designers confidence in long-term support.

The trade-off lies in constraints. ARM’s proprietary nature limits how deeply companies can modify core microarchitectures and instruction sets without moving into more complex and costly “architectural license” arrangements. Royalties add recurring cost that scales with volume. For AI chip firms seeking radical customization or very low marginal cost at massive scale, these constraints weigh heavily.

RISC-V’s open ISA model: flexibility with ecosystem variability

RISC-V centers on an open instruction set architecture (ISA), allowing anyone to implement compatible cores without paying ISA royalties. Implementations can be fully open-source, proprietary, or a mix, and multiple vendors offer commercial cores, verification suites, and design services built around the ISA.

For AI chip designers, this openness translates into flexibility. Companies can tailor core microarchitectures to fit their AI workloads—adding custom extensions, tweaking pipeline structures, and optimizing for specific power/performance envelopes—without negotiating ISA-level permissions. Licensing costs shift toward implementation IP or internal development rather than ISA royalties.

The variability comes from ecosystem maturity. While RISC-V tooling and software support have grown rapidly, they are not as uniformly deep or standardized as ARM’s in all segments. AI chip firms must evaluate specific IP vendors, tool chains, and software stacks rather than relying on a single dominant provider. This diversity can be a strength, but it also requires more integration and validation effort.

Cost structures: license fees, royalties, and internal development

Cost is a central axis of competition between ARM and RISC-V licensing models. ARM-based designs typically incur upfront license fees plus per-unit royalties. These costs are clear and predictable, and can be justified by ecosystem richness and reduced internal design burden. For high-volume AI chips, however, royalties can significantly affect margin, especially when hardware is sold into price-sensitive markets.

RISC-V’s open ISA reduces or eliminates ISA royalty costs, but companies still face expenses for commercial implementations, verification IP, and tools—or for building their own cores in-house. Internal development can be capital-intensive, requiring experienced CPU architects and robust verification flows, but it can also reduce marginal cost per unit over time and support deeper differentiation.

AI chip vendors thus weigh trade-offs: ARM may offer lower upfront development effort with known licensing costs, while RISC-V can enable lower long-term per-unit costs at the price of higher initial engineering investment. The optimal choice depends on volume projections, pricing strategies, and the role CPU IP plays in the overall differentiation story.

Differentiation and custom extensions: AI-centric CPU design

AI chips often need CPUs that can efficiently orchestrate accelerators, handle control logic, manage memory hierarchies, and sometimes participate directly in AI workloads. Differentiation opportunities arise in custom instructions, vector extensions, and close coupling between CPU and AI cores.

ARM offers some room for customization through architecture licenses and optional extensions, but deep modifications to the ISA or core internals come with complexity and licensing nuance. Many AI chip firms using ARM focus customization on accelerators and interconnects rather than the CPU’s fundamental structure.

RISC-V’s open ISA enables more direct AI-centric CPU tailoring. Designers can add domain-specific instructions—for tensor operations, address generation patterns, or specific AI control flows—while maintaining compatibility with base RISC-V toolchains. This approach can reduce overhead between CPU and accelerators and enable optimization of AI workloads in ways that are harder under more constrained proprietary ISAs.

As AI chips increasingly integrate heterogeneous compute, the ability to co-design CPU and AI subsystems becomes a significant competitive lever, and IP licensing models that support flexible customization gain appeal.

Ecosystem and software: maturity vs agility

Software ecosystem maturity influences licensing decisions as much as hardware features. ARM’s long presence in mobile, embedded, and infrastructure means that operating systems, compilers, libraries, and AI frameworks are extensively optimized for ARM cores. This maturity reduces time-to-market and integration risk for AI chip vendors, especially when targeting platforms already running ARM software.

RISC-V’s ecosystem, while growing rapidly, is more uneven. Core toolchains and OS support exist, and AI-related frameworks increasingly offer RISC-V ports, but coverage and optimization levels vary by segment. AI chip designers must sometimes invest more in software porting, performance tuning, and ecosystem development, particularly in niche or cutting-edge use cases.

On the other hand, RISC-V’s agility allows faster community-driven evolution. As AI workloads change, RISC-V-based ecosystems can adopt new extensions and optimizations without waiting on a single vendor’s roadmap. This openness can accelerate innovation for companies willing to actively participate in ecosystem development rather than simply consume it.

Strategic independence and geopolitical considerations

IP licensing models have strategic and geopolitical dimensions, especially for AI chip designers in regions focused on technology sovereignty. ARM’s IP is governed by a central commercial entity and subject to contractual and regulatory constraints. Changes in ownership, policy, or export controls can influence access to certain cores or technologies.

RISC-V’s open ISA framework is perceived by many as a path toward greater strategic independence. While individual IP implementations may still be subject to commercial and regulatory constraints, the ISA itself is not owned by a single company. This allows regions or companies concerned about external dependencies to invest in domestic or internal core implementations while retaining ISA compatibility.

For AI chip vendors whose long-term business depends on stable access to core IP, these strategic considerations weigh heavily. Some may adopt ARM for certain markets and RISC-V for others, or move gradually from proprietary IP toward open ISA-based designs as internal capabilities mature.

Hybrid and multi-ISA strategies in AI chip portfolios

The competitive landscape is not a simple ARM versus RISC-V binary. Many AI chip companies pursue hybrid portfolios that combine multiple ISAs and licensing models across products or even within a single system. For example, a vendor might use ARM cores for general-purpose control and RISC-V-based microcontrollers or AI co-processors for specialized tasks.

Multi-ISA strategies allow firms to balance ecosystem maturity and cost. ARM-based designs can serve markets where software compatibility and established tooling are critical, while RISC-V cores can underpin products where customization and cost control matter more. Over time, successful multi-ISA integration frameworks—across compilers, OS abstractions, and interconnects—make such heterogeneity more manageable.

This hybrid approach reflects a broader trend: instead of choosing a single licensing model for all AI chip needs, companies treat IP choices as a portfolio problem, optimizing per segment and per generation while maintaining overarching architectural coherence.

Impact on startup vs incumbent dynamics

IP licensing models also shape the dynamics between startups and incumbents in AI chips. Established companies often have existing ARM licenses, deep familiarity with its ecosystem, and the scale to absorb royalties into their cost structure. For them, ARM’s predictability and support can outweigh the benefits of switching to RISC-V, at least in the short term.

Startups, by contrast, may view RISC-V as a way to reduce dependence on large IP vendors and control long-term cost. Building in-house RISC-V cores or leveraging open-source implementations can provide more room for architectural experimentation and differentiation, albeit with higher technical risk.

Over time, this dynamic can lead to a landscape where incumbents dominate ARM-based AI platforms in certain segments, while RISC-V-enabled designs emerge more aggressively in new or specialized markets. Licensing models thus indirectly influence where innovation and disruption originate in AI hardware.

AI-specific IP beyond CPUs: accelerators and system IP

While ARM and RISC-V discussions often focus on CPU IP, AI chips rely on broader IP portfolios: vector units, matrix engines, interconnect fabrics, memory controllers, and security modules. ARM offers system-level IP and some AI-related blocks, which can be licensed alongside CPUs under integrated models. This can streamline design but deepen dependence on a single vendor.

RISC-V ecosystems tend to feature more fragmented IP landscapes. Multiple vendors and open projects provide AI accelerators, interconnects, and system components compatible with RISC-V cores. This diversity allows AI chip designers to mix and match IP providers, potentially optimizing each block for their workloads, but it increases integration and verification complexity.

Licensing models for AI-specific IP—whether bundled with CPU IP (as in some ARM offerings) or sourced from multiple RISC-V ecosystem players—directly affect development cost and time-to-market. Companies must decide how much integration burden they are willing to absorb in exchange for flexibility and cost leverage.

Long-term trends: convergence, competition, and standardization

Looking ahead, several trends are likely to shape the competitive landscape of AI chip IP licensing. First, ARM and RISC-V ecosystems are converging in some areas: both support rich OS stacks, AI frameworks, and system IP, reducing purely technical barriers to choosing one over the other.

Second, competition will push each model to evolve. ARM may offer more flexible licensing options or enhanced customization pathways for AI-centric designs, while RISC-V vendors will continue improving toolchains, verification, and turnkey solutions to reduce integration friction. This mutual pressure benefits AI chip designers by expanding their options.

Third, standardization around AI-related extensions—vector operations, security, and virtualization—may emerge across ISAs, allowing software to target common capabilities regardless of underlying CPU IP. In such a world, licensing models and strategic factors could weigh even more heavily than raw ISA differences when AI chip firms make IP decisions.

In this evolving landscape, AI chip designers who treat IP licensing as a strategic pillar—balancing cost, differentiation, ecosystem access, and independence—will be better positioned to navigate the shifting competitive terrain between ARM and RISC-V.

Conclusion: IP licensing as a core strategic lever in AI chips

The competitive landscape of AI chip IP licensing models, exemplified by ARM and RISC-V, is about much more than instruction sets. It touches cost structures, customization freedom, ecosystem maturity, and geopolitical strategy. ARM offers proven, well-supported cores under proprietary licenses that many companies trust for fast, reliable deployment. RISC-V provides an open ISA that invites deeper customization and potential cost advantages, at the price of greater integration responsibility and ecosystem variability.

For AI chip designers, the most resilient approach is rarely to bet everything on a single model. Instead, treating IP licensing as a core strategic lever—combined with careful technical evaluation—enables portfolios that match ARM’s predictability where it matters and RISC-V’s flexibility where it creates competitive edge. As AI hardware continues to diversify and specialize, the companies that skillfully navigate this licensing landscape will be the ones that turn IP choices into durable advantages rather than constraints.