How the IP Asset Class Answers Challenges for Investors in the Era of Applied AI

AI has entered a phase of deeper interplay with the rest of the economy, with huge profit zones at stake; but AI is more likely to destroy an old competitive moat than provide a new one. This has major implications for investors both within and outside the technology sector.

In a series of white papers,¹ we explored how AI is simultaneously: (1) setting the stage for new architectures and systems that will reshape many industries and (2) accelerating competitive forces, destroying the competitive moats throughout industry, and forcing leaders and investors to seek durable alternatives. We proposed a solution: businesses can adopt closed-loop, agentic architectures and systems to unlock large performance gains and acquire foundational patents to wrap them with a durable moat.

Below we turn to how AI challenges investors to: (1) rethink asset allocations due the extreme concentration of public equity around a small number of companies that have undertaken massive infrastructure and R&D investments that so far have largely yielded parity; (2) prepare for the threat of widespread commoditization across industries driven by how AI compresses R&D cycles; (3) reevaluate correlation within their portfolios, as AI is poised to reshape many categories and markets; and (4) find complementary mechanisms to participate in the potential that AI will create more value than any technology in history without creating unacceptable problems of correlation and volatility.

We propose that the intellectual property asset class provides a good answer for these challenges, but in Part II we highlight a gap in the asset class that exists for historical reasons, which Strong Force seeks to fill.

PART I Addressing the Challenges of AI for Investors

Asset Allocation

Investors need to rethink asset allocation in a market where both public and private equities are increasingly concentrated in a small number of technology companies² whose extraordinary valuations reflect massive investments in AI infrastructure, computing capacity, and R&D—investments that, to date, have often produced competitive parity rather than durable differentiation. As leading firms spend unprecedented sums to keep pace with one another, the risk is that capital expenditures and innovation become the price of admission rather than sources of lasting economic rent, leaving returns increasingly dependent on monetization, pricing power, and the ability to establish defensible competitive advantages. This creates a potential mismatch between the concentration of capital in a small number of dominant equities and the dispersion of future economic value across the broader economy, where applied AI may generate substantial productivity gains but where the companies capturing those gains have yet to be fully identified. Investors may need to reassess whether conventional market-cap-weighted allocations provide adequate diversification and consider opportunities in underappreciated assets and businesses with durable competitive moats—including defensible intellectual property—that can capture value from the next phase of AI deployment rather than simply finance its infrastructure.

Widespread Commoditization

Investors must prepare for a world in which this AI-driven compression of R&D cycles accelerates commoditization across industries. For AI providers and their customers alike, AI is more likely to destroy an existing moat than to provide a new one. AI dramatically accelerates development cycles. This undermines the benefits of speed, proprietary knowledge, organizational scale, and technology breakthroughs. Before, a first mover could enjoy a period of exclusivity during which it could establish user loyalty, network effects, and other long-lasting advantages. Now, a competitor can replicate functionality within days that once would have taken years to develop. This effect is most pronounced in software, but AI has shown formidable capabilities in deep research, from drug discovery to semiconductor design. Today’s differentiated offering can become tomorrow’s baseline capability, and the capital invested to achieve parity may generate diminishing economic rents. No industry is untouched.

Investors should therefore look beyond revenue growth, market share, and R&D spending to assess what actually protects a company’s future cash flows from competitive erosion. Businesses with durable control points—including defensible intellectual property, deeply embedded customer relationships, switching costs, proprietary data advantages, and control over critical infrastructure or distribution—may be better positioned to preserve margins as the pace of imitation accelerates. In our white paper on Competitive Strategy in the Era of Applied AI we raised significant questions about the durability of a number of these control points; for example, will customer relationships remain as important in an age where agents start to take over discovery and procurement functions?

This calls for a reassessment of asset allocation toward companies whose advantages are structurally difficult to replicate, rather than those whose valuations assume that current technological leadership will persist indefinitely. In an AI-compressed economy, the central investment question shifts from who can innovate fastest to who can sustainably capture the value of that innovation.

Reevaluating Correlation Risk

Investors may wish to reevaluate not only the individual assets in their portfolios, but also the correlations among them as AI reshapes the economics of entire industries. Investments that appear diversified by sector may increasingly share the same underlying risk factors: exposure to AI infrastructure spending, dependence on a handful of model providers, vulnerability to AI-enabled competitors, or reliance on business models whose pricing power and margins may be eroded by rapid replication.

As AI compresses R&D cycles and accelerates commoditization, traditional diversification across industries may provide less protection than expected if multiple holdings are exposed to the same technological disruption.

Intellectual property offers a potentially differentiated source of diversification. IP investments have historically been treated as an alternative asset class with return drivers distinct from public equities, although their actual correlation varies by strategy, valuation methodology, liquidity and market conditions. More importantly, carefully selected patent portfolios may offer a potential structural hedge against AI-driven commoditization: as AI makes products and capabilities easier to replicate, enforceable patent rights covering essential systems, architectures and methods may help preserve the economic value of innovation, support licensing income, and provide leverage to companies seeking freedom to operate.

The benefits of an IP investment opportunity are therefore twofold: adding an asset class with different return drivers (reducing correlation), while acquiring rights that should become more valuable as AI intensifies competitive pressure across industries and alternative durable control points become more scarce.

In an economy increasingly transformed by a common technological force, investors should seek diversification not merely across sectors, but across the underlying sources of economic value and competitive protection. IP is an excellent candidate for that diversification.

The AI Investment Dilemma

Investors face a historic dilemma: the opportunity to participate in the economic value created by AI may be too large to ignore, yet the obvious ways to invest may introduce unacceptable levels of concentration, correlation and volatility. AI could become a general-purpose technology that transforms productivity, reshapes entire industries and creates tens of trillions of dollars in economic value over time. McKinsey, for example, estimated that generative AI alone could add $2.6 trillion to $4.4 trillion annually to the global economy across the use cases it analyzed. Broader estimates of AI’s potential economic impact reach into the tens of trillions, although these are projections of economic value, not forecasts of investor returns. The scale of the opportunity makes standing aside a consequential decision: investors who avoid AI exposure altogether could miss one of the most significant waves of value creation in modern economic history.

Yet the opposite strategy carries its own dangers. Capital rushing into a narrow group of AI model developers, chipmakers, cloud platforms and infrastructure providers can leave portfolios dependent on a small number of companies, shared growth assumptions and a common technological investment cycle. Even holdings spread across different sectors may become highly correlated if their valuations depend on the same AI spending boom, or if AI-driven disruption threatens their margins simultaneously. The result is a difficult asymmetry: investors may feel compelled to participate in AI, but the conventional routes to participation can amplify the very concentration and volatility they need to manage.

The solution is not necessarily to own less AI, but to find complementary mechanisms for capturing AI-driven value. That means looking beyond the most visible public-market beneficiaries toward assets and strategies with different economic drivers: applied AI across multiple industries, essential systems and infrastructure, and intellectual property that may help determine who captures the profits as AI capabilities diffuse through the economy. In particular, carefully selected patent portfolios may provide exposure to the architectures and methods that enable AI applications across many companies and industries, rather than requiring investors to predict which individual model, platform or application provider will ultimately prevail. Such investments offer a distinct way to participate in the transformation.³

The imperative is to separate participation in AI’s economic upside from dependence on a handful of AI winners. Investors who remain entirely on the sidelines risk missing an extraordinary expansion in productivity and enterprise value. Those who participate only through crowded, highly correlated positions risk paying too much for that opportunity—and suffering severe losses if expectations, capital spending or market leadership change. The dilemma calls for a broader investment framework: one designed not simply to capture AI growth, but to diversify the mechanisms through which that growth becomes durable, defensible cash flow.

The scale of the opportunity is substantial.⁴ The central question for investors is not whether to participate in AI, but how to participate without allowing one transformative technology to become a single, concentrated bet across an entire investment portfolio. IP offers a potential solution, both as a focus in target company investing and as a diversifying element or hedge, but as we discuss in the next sections, the IP asset class is still maturing, so investors may need to familiarize themselves with its unique dynamics.

PART II Patent Investing

The Patent Investing Asset Class Today

Patent investing is an established, specialized alternative-investment market in which capital is deployed to acquire, finance, license, enforce or otherwise monetize intellectual property. Historically, much of the institutional market has focused on non-exclusive licensing and enforcement: investors provide debt, equity or structured capital to companies that acquire patent portfolios and seek licensing revenue from businesses whose products or services may practice the patented inventions.

Notably, this is almost entirely a secondary market, the underlying assets are sourced from a primary market in which patents are generated by product companies, universities and others with diversified goals that aren’t necessarily aligned with using patents for monetization. In most cases, a patent holder in the primary market goes through a market cycle (or more) before even considering a sale. Many patents come on the market only very late in life.

As a result of these dynamics, most investments have targeted older patent portfolios covering widely adopted technologies—such as wireless communications, semiconductors, networking, video compression and software functionality. The investment thesis is that a technology can become ubiquitous long after the original invention, creating a large potential licensing market. The challenge is to establish that specific patents remain valid, enforceable and applicable to commercial products, and that the expected recoveries justify the costs, time and risk of monetization.

Capital can enter at several levels: financing a patent owner, purchasing a portfolio outright, funding a particular litigation campaign, or investing in a platform that aggregates patent rights and licenses them to many users. Fortress, for example, operates a business spanning patent-backed financing, portfolio acquisitions, joint licensing, company acquisitions and litigation funding. Intellectual Ventures has likewise built a large licensing business by acquiring patents from companies, universities, inventors and other sources. RPX aggregates patent rights from disparate owners and syndicates them to multiple customers, reducing the inefficiency of the many-to-many bargaining process. SIM helps patent owners monetize their portfolios through licensing campaigns and acquiesce portfolios for its own monetization. Large patent pools are operated by the historical Telcos, Interdigital, SISVEL, and others.

The Technical Focus of Large Patent Pools

A useful distinction is that these models do not all represent the same investment exposure. Some depend primarily on successful enforcement against alleged infringers; others generate fees for reducing patent risk, or royalties through standardized licensing programs.

Beyond the basic challenges of finding high quality patents that apply to commercial products in an increasingly competitive secondary market, there are fundamental limitations with licensing into mature markets. First, profit margins in mature, standardized markets are typically much lower than in high growth markets. The presence of many players is attractive for a wide royalty base, but those players typically compete away much of the profit margin. Second, “improvement” patents tend to accumulate over the years, covering incremental improvements to products or standards. These patents form part of a “royalty stack.” As the stack grows, the room for any one player shrinks. Together, these factors leave room only for low royalty rates. For example, royalties in cellular standards are often measured in basis points.

A Massive Gap in the Patent Investing Asset Class

We believe a large investment opportunity exists in the widening gap between the future-directed trajectory of Applied AI and the backward-looking orientation of most patent investing.

Applied AI is About the Future

More than $1T has been invested since 2022 in R&D for AI model development and data center infrastructure. As a fraction of GDP, the annual investment is the largest in history, exceeding the railroad, the electrical grid and the highway system. Much of the investment has been in the form of private debt, creating enormous urgency to translate infrastructure and model capabilities into real world applications.

So far, most revenue generation from AI has come from consumer-facing applications, like search and chatbots. This year, the dramatic improvement in agentic reasoning and coding capabilities has set the stage for higher-stakes industrial and enterprise use cases, where the convergence of AI, connectivity, sensing, and software-defined assets will create entirely new architectures, workflows, and business models. Industry leaders project a TAM exceeding $20T for enterprise use cases. If they are right, enterprise/industrial Applied AI will be the largest market in history.

Patent Investment is About the Past

Ironically, given the inherent intersection of patents with technology, patent investing is about the prior generation of technology, not the future. As noted above, institutional patent investment has focused almost exclusively on patents that already read on existing products, with clearly identified infringement claims, effectively looking backward at yesterday’s technology.

The reason for the backward-looking focus is two-fold:

First, as noted above, in the absence of a primary market that is focused on monetization (as opposed to product-company goals), patent investments rely heavily on purchasing patents. Patents available for purchase tend to come on the market later in their lifecycle, such as from poorly performing product lines or distressed companies. By the time the patents are available, the market has usually matured and commoditized, only allowing for low royalty rates.

Second, patent investors seek to minimize future technology risk. A further irony is that AI is very likely to erode competitive moats around older technologies, or in some cases displace them entirely. As a result, current patent investments are much more exposed to future technology risk than investors may have considered.

Patent Investors Are Missing the Largest Technology in History

As a result of the above, investors in the patent asset class have largely ignored AI, a technology widely expected to be among the most impactful in history. The AI market will eventually mature, standardize and commoditize, at which point the standard patent investment playbook will be available, but that could take years.

The historical analogy is investing exclusively in horse-and-carriage patents during the buildout of the railroad: the strategy might monetize existing activity, but it misses the far greater opportunity created by the new infrastructure.

The Opportunity

The much greater opportunity is to identify and acquire foundational patents on the architectures through which Applied AI will actually be deployed—not merely the applications that exist today—positioning investors to participate in the value created as those architectures become essential to the next generation of industrial and enterprise systems.

Among other advantages, holding unencumbered patents during the growth phase of a market opens a much greater playbook for the patent holder to generate profits. During that phase, where industry alliances are still forming, market share is up for grabs, and margins are typically high, patents can be used in wide-ranging ways: as a legal monopoly to defend market share and high margins; to negotiate equity and revenue share positions in alliances; and to generate much higher royalties than possible after a technology matures.

We believe timing is excellent for Applied AI. The infrastructure buildout is strongly underway, models have become very powerful, architectures that interconnect them with the rest of the economy are clarifying, and companies are developing powerful new solutions upon them. Opportunities for large performance gains and high-margin profits are widespread, but incremental patents have not yet accumulated, so a strong patent holder can still capture the lion’s share of IP-based value. Strong patent holders have the opportunity to establish IP pricing terms that can endure for years.

Strong Force

Strong Force sought to fill the gap in Applied AI. Over the last decade, it created a portfolio of more than 1,000 largely unencumbered patent assets on Applied AI infrastructure, architectures and systems across major industry verticals, which are extensible to many more. While secondary market players wait for patents to come on the market, the Strong Force patents are ready to be monetized now, through a wide playbook of value creation.⁵