Patent Licensing Bottlenecks in AI Infrastructure: The Case for Antitrust Intervention

Patent Licensing Bottlenecks in AI Infrastructure: The Case for Antitrust Intervention 

Nick Cipriani

1. Introduction

   The artificial intelligence market presents novel challenges for antitrust enforcement, particularly regarding patent licensing arrangements and vertical integration within AI infrastructure.[1] The AI industry's structure creates unique competitive dynamics through the convergence of specialized hardware, proprietary software frameworks, and essential patent portfolios.[2] Understanding these dynamics requires rigorous application of established antitrust principles to define relevant markets, analyze competitive effects, and evaluate potential regulatory solutions. Accordingly, current patent law provides insufficient constraints on exclusionary licensing practices in AI markets where control over essential components determines downstream competitiveness.

    

2. Defining the Market: The AI Stack and Hardware

   "The outer boundaries of a product market are determined by the reasonable interchangeability of use or the cross-elasticity of demand between the product itself and substitutes for it."[3] AI infrastructure encompasses three distinct product markets that must be analyzed separately under established antitrust principles.[4] Each market presents unique substitution patterns and competitive dynamics that shape the broader competitive landscape for artificial intelligence development.

   First, the AI accelerator chip market consists of specialized semiconductors optimized for machine learning computations. This includes NVIDIA's graphics processing units (GPUs), Google's tensor processing units (TPUs), and emerging application-specific integrated circuits (ASICs). These processors handle the parallel mathematical operations essential for training and deploying AI models that traditional CPUs cannot efficiently perform.[5]

   Second, the CPU architecture licensing market involves fundamental processor instruction sets and architectural designs that semiconductor manufacturers license to create compatible processors. This market differs fundamentally from chip manufacturing itself, focusing instead on the intellectual property rights that enable chip production across the industry.[6]

   Third, the AI software framework market encompasses development platforms and programming interfaces that enable AI model creation. NVIDIA's CUDA platform dominates this space, though alternatives like OpenCL and emerging frameworks attempt to provide competitive options.[7]

   1. Barriers to Entry

      The AI infrastructure markets exhibit substantial barriers to entry that support and maintain market concentration across multiple dimensions. First, patent portfolios create consolidation of intellectual property barriers.[8] Second, research and development costs require multi-billion dollar investments sustained over multiple years before competitive products can reach market.[9] Third, network effects in software ecosystem development create positive feedback loops that reinforce market leadership.[10] Finally, manufacturing capacity constraints create additional bottlenecks for potential competitors.[11]

      NVIDIA holds thousands of GPU-related patents, while Arm's architectural patents are used for intellectual property licenses with clauses that eliminate potential competitors.[12] As both companies rack up patent portfolios, they’re able to better control the market, so others do business with them.[13] The Federal Trade Commission's analysis in Rambus Inc. demonstrated how essential patents can create "patent thickets" that systematically exclude potential competitors from entire market segments.[14] Advanced semiconductor production requires specialized facilities controlled by a small number of foundries, creating supply chain dependencies that limit competitive entry.[15]Therefore, stifling the promotion of innovation in the free market.

       

3. Legal Framework: Enhanced Analysis of Patent-Antitrust Intersection
   1. Licensing Boundaries and Post-Acquisition Rights

      “The longstanding doctrine of patent exhaustion provides that the initial authorized sale of a patented item terminates all patent rights to that item.”[16] Quanta Computer v. LG Electronics (2008) should be used as the landmark case for antitrust issues concerning the regulation of AI patents. Currently allowing AI chip manufacturers to include clauses in their licenses that revert the intellectual property rights back to the AI chip manufacturer is the crux of the bottleneck issue.

      “Many of Arm’s licensees, including NVIDIA, are ‘fabless’ semiconductor companies. [Meaning] they design and market computer chips . . . but outsource the physical manufacturing of these chips . . ..”[17] Similarly, in Quanta Computer, LG Electronics (“LG”) licensed CPUs. Quanta used Intel components licensed to Intel in its computers along with non-Intel components without getting a separate license from LG. LG asserted that the combination of the licensed Intel components with the non-Intel components infringed on its patent.[18] The Supreme Court concluded that, “Intel's authorized sale to Quanta thus took its products outside the scope of the patent monopoly, and as a result, LGE can no longer assert its patent rights against Quanta.”[19] However, the scope of these limits is yet to be established.

      Throughout its complaint, the FTC makes it clear that its strategy is to go after the NVIDIA-Arm merger under Guideline 5 of the DOJ 2023 Merger Guidelines and the Clayton Act § 7.[20]  The FTC goes as far as claiming, “[t]here are numerous full or partial foreclosure strategies that [Nvidia-Arm combined firm] could use to disadvantage its rivals . . . without the rival ever knowing the strategy was executed.”[21] This acknowledges that there is a barrier to market entry developing in the specialized chip manufacturing sector of the AI stack. The AI market downstream participants may be subjected to unfavorable terms through the Arms Processor Technology licensing model, or experience reduced access to essential AI chips and hardware. This presents an opportunity to discover where AI giants are acting under the guise of innovation.

      Arm is the industry standard for CPUs.[22] Its Arm Processor Technology licensing business model creates an industry that is dependent on one entity.[23] In fact, Arm does not manufacturer chips itself but generates its revenue from the licensing fees it collects when customers either (i) get an architecture license, allowing licensee to create their own Arm-based CPUs, or (ii) implementation license, only allowing licensee to use Arm’s chip design in its products.[24] Given the burgeoning AI market, regulators should clearly establish whether to allow one player to control an essential component of AI technology or whether they will implement the patent exhaustion doctrine, where the patent licensing power stop after an authorized sale to a third-party, to avoid the manifestation of bottlenecks that stifle innovation. The DOJ/FTC Guidelines Antitrust Guidelines for the Licensing of Intellectual Property advises, “[a]ntitrust analysis takes differences among [the] forms of intellectual property into account in evaluating the specific market circumstances in which transactions occur, just as it does with other particular market circumstances.”[25] However, it does not address the scope in which licensing could be used before it infringes the Sherman Act § 1. This requires a case-by-case analysis under the rule-of-reason standard.[26]

   2.  Rule of Reason Analysis: Structured Antitrust Framework

      The rules-of-reason standard states, “the factfinder weighs all of the circumstances of a case in deciding whether a restrictive practice should be prohibited as imposing an unreasonable restraint on competition.”[27] Using the FTC v. NVIDIA/Arm as a benchmark for AI antitrust enforcement, one would first define the market as specialized AI chip manufacturer.[28] Despite the different roles NVIDIA and Arm take, both are still in the business of specialized AI chip manufacturing, as demonstrated through Arm’s patent licensing and NVIDIA’s chip production.[29] Afterwards, one will have the burden to show the anticompetitive effects of the infringing party’s conduct. Here, NVIDIA will have the ability to “undermine its competitors” and “reduce product quality.”[30] Arm Processor Technology licenses could “include restraints that adversely affect competition in goods markets by dividing the markets among firms that would have competed using different technologies.”[31] The adverse effects of such licensing schemes is the prevention of nascent competitors from having a fair opportunity to fully compete. Afterall, the competitive AI chip market is starting out where NVIDIA and Arm each dominate over 70% of their respective chip market. 

      The NVIDIA/Arm merger would have reduced the market to one key player creating both the CPUs and GPUs necessary to operate foundational models’ software.[32] Therefore, the combined company would have a monopoly on who gets to enter the AI market and control the participants’ innovation. Whether in a combined power or as a solo entity, granting IP licensing power without a scrutinized framework during the initial phases of a market could lead to a future where the existing competitors collude to raise prices and create impenetrable wall gardens.[33]

       

4. Conclusion: Toward Comprehensive AI Antitrust Framework

1. Synthesis of Legal Principles

Among the most concerning challenges facing the nascent AI market is the formation of bottlenecks at the AI chip development layer. These bottlenecks have shown to increase prices through patent trolling exercised through the guise of licensing agreements.[34] Courts have taken the position of not “punishing success” at the cost of consumers.[35] When private businesses acquire patents and use their licensing power to control the market, it is not the technology manufacturers that are ultimately harmed but the consumer who is paying multiple times more for the final good. Licensing can adequately protect intellectual property rights but can also promote a weakening of antitrust law by preventing access when unchecked. Access and a fair market can be protected through the consolidation of IP and antitrust law.

To successfully implement these, and similar doctrines, regulators must update their guidelines and work with legislators to acknowledge the evolving nature of gatekeepers in the AI emerging markets. The goal of amended IP and antitrust policies should be to encourage innovation of products and competition. The Future of AI will be shaped by the winner of the tension between protecting conglomerates’ IP and protecting consumers. The future competitiveness of AI markets depends on maintaining the balance between rewarding innovation and preserving competitive processes.