The Growing Complexity of Digital Economies over the GenAI Waterfall: Challenges and Policy Implications

GenAI is already percolating through businesses and tasks, transforming how we create, innovate, and consume content (information), products, and services.¹ Being deployed more widely across all layers and components of value chains, it brings new affordances that have transformed (or are slated to transform) nearly all conceivable social and economic contexts. Changes will affect online and offline worlds directly and indirectly. Users of AI models, tools, and services have been interacting with GenAI for some time already, but the indirect effects of GenAI are inherently less obvious and harder to assess, especially at this early stage. End users are often unaware of how the products and services they use are produced, and even for domain experts, the challenge of assessing the social and economic impact of ICTs has always proved difficult.² Those challenges will only become more challenging with GenAI because its ability to operate in the background (a direct result of automation) means that many of those affected by GenAI will be unaware that—or how—GenAI is already impacting them.

All of this adds to the recognition that the locus of digital technology-driven change is distributed and diffused. Adding the opacity of AI systems to the picture further amplifies the fundamental information and measurement challenges. Taken together, it is reasonable to assume that we are approaching an inflection point in human and economic history, a potential Fourth Industrial Revolution.³ This inflection point can be described as a ‘waterfall moment’—we see the falls but have no anticipation of what comes after it, how deep the fall is, and so on. Despite this considerable uncertainty characterized by ‘known unknowns’ and ‘unknowables’, it is worth considering what this means for GenAI policymaking, a question we explored in greater detail elsewhere.⁴

To address those challenges, a necessary first step is to acknowledge that service provision in today’s increasingly digital economy is based on a complex and evolving fabric of interconnected and complementary resources, or collectively, digital infrastructures. These include networked computing and communications resources and data that are organized into complex and overlapping stacks of hardware and software platforms.⁵

Many of those resources are owned, managed, and controlled by different entities, which have imperfectly aligned and sometimes opposing interests.⁶ AI technologies offer novel options for provisioning almost everything as a service (XaaS).⁷ Enabled by softwarization and modularization, AI has enhanced the options to seamlessly combine (i.e., mix-and-match) these resources, rendering industry value chains and system architectures more fluid and dynamically changeable. That implies changing dependencies and more complex inter-dependencies within and across firms and industries. These may not be always apparent or visible⁸ and further defy traditional efforts in economics to define clear boundaries between industries and markets or to classify inter-firm relations as vertical or horizontal. A first insight is that this calls for a more nuanced model of co-opetition rather than competition or coordination in the traditional sense.⁹

These developments have been coming for a long time. Well before ChatGPT ushered in mass recognition of the GenAI future in November 2022, leading tech firms like Google, Amazon, Microsoft, and Apple have been investing in digital infrastructures and developed multi-layered ecosystems that span various layers of service and network platforms.¹⁰Key elements of each of these ecosystems are highly manageable, extensible, and scalable ‘infrastructure substrates’ comprised of digital infrastructure resources that have supported the development and delivery of a widening variety of services. These make possible the rise of pervasive computing, where distributed sensing (IoT), communications (wireless everywhere) and robotic automation (actuators, computer vision, etc.) enshroud us in a digitally augmented world. A future where everything may be connectable to and augmented by digital technologies, often without direct human attention or even awareness, is coming. The capabilities of AI are determined by this infrastructure substrate, which in turn shapes AI’s potential for use and innovation, creating feedback loops that lead to further enhancements in the substrate. Investment, innovation spillovers, and feedback loops shape the trajectory of AI-driven ecosystems.¹¹

In view of these co-dependencies, it is hardly surprising that leading tech platforms have emerged as key players in the GenAI sphere.¹² Tech giants are engaged in a corporate arms race for AI supremacy, with several companies (e.g., from the US¹³ and China¹⁴) investing heavily in acquiring, building, and developing essential resources like data centers, AI chips, and AI models. A wave of novel collaborations and partnerships between leading tech companies and AI firms (e.g., providers of foundation models) have accompanied these investments. As a CMA report from 2024¹⁵ showed, many of the leading tech companies now have multiple AI partners. Moreover, many companies are additionally building, developing, and using their own AI tools and resources.¹⁶ A second insight is that these developments emphasize the fluidity and complexity of the technical and business relationship fabric that underlies AI-driven digital ecosystems.

The launch of DeepSeek’s low-resource and low-cost R1 model and the shock wave it sent caused stock market prices of several key AI players,¹⁷ including AI chip giant NVIDIA, to plummet. While DeepSeek was reported to have fueled a rebound effect that boosted demand for NVIDIA chips,¹⁸ the launch of xAI’s Grok 3—a model that has garnered much attention as it was reportedly trained on the company’s Colossus supercluster (which offers up to 200,000 GPUs)¹⁹ and immediately ranked among the top-performing AI models—arguably sent the opposite signal.²⁰ A third insight is that these developments, the continuous improvements of existing models (i.e., new releases and versions (including reasoning models) by players like OpenAI, Anthropic, Google, or Meta, but also DeepSeek and Alibaba Cloud),²¹ the rapidly growing variety of ‘open source’ models,²² and the fact that leading players like Google,²³ OpenAI,²⁴ Microsoft,²⁵ Amazon,²⁶ and others²⁷ are ushering in the ‘agentic era’²⁸ indicate that the future of GenAI is both highly uncertain and dynamic.²⁹

As the above shows, the rise of GenAI as an engine for the Fourth Industrial Revolution further accelerates, complexifies, and renders more dynamic the interdependencies, competition challenges, and fluidity of industry relationships in the ICT sector. It may alter the power dynamics between ecosystem stakeholders (e.g., providers of telecoms infrastructure, cloud infrastructure, AI foundation models, and AI enhancements) and increase the prevalence of unintended consequences associated with ‘waterbed effects.’³⁰ With GenAI, those effects are not limited to the ICT sectors that provide the infrastructure but impact wider parts of the (global) economy.

That being said, the co-opetition dynamics and the implications of those for contestability at different levels³¹ are harder to assess or manage with existing policy tools and institutions. Indeed, policymakers are challenged to identify appropriate interventions and organize consensus on how to implement them in ways that do not create problems that are worse than the ones they sought to mitigate.³² We need to acknowledge the inherent second-best efficiency of government interventions and the infeasibility of government micro-management outperforming market-competition directed industrial performance. At most, regulators can aspire to framing ex-ante guardrails and more targeted ex-post regulatory interventions to try and manage competition and remedy any market failures.

The key ecosystems contending for GenAI relevance, if not dominance, are global. The key technologies are software or algorithm based, and those are geographically footloose and increasingly difficult to regulate on any sovereign basis. Coordinating international policy interests is challenged by the divergent alignment of the ecosystems with national interests that are not collectively aligned. Each nation recognizes the risks of being left behind and some more than others see hope in potentially leading in the race to the digital future. This is illustrated by the significant support and investments national governments are devoting to AI development and industrial policy initiatives. For example, in the first two months of 2025, the US announced the 500 billion USD Stargate³³ joint venture and the EU its 200 billion EUR InvestAI³⁴ initiative. Amidst these initiatives, geopolitical tensions³⁵ surrounding AI supremacy and tech regulation grow,³⁶ with AI safety concerns looming larger.

If managing digital automation was a difficult problem before GenAI, it is a much more difficult challenge going forward. As the options for transferring agency and control to digital systems expand, these systems must be more directly involved in the management challenge. The future of the digital economy in the GenAI era will require using GenAI to oversee GenAI and coordinate human responses globally. If the model for effective policymaking in a complex society or nation-state aspires to the principles of sound Evidence-Based Decision Making (EBDM), that already difficult challenge becomes even more complex in a world of GenAI, especially if we want to keep humans in the loop.³⁷

To realize the potential of GenAI and to manage it effectively —regardless of who is in charge, whether policymakers, companies, or individual end-users—a much more advanced measurement ecosystem³⁸ offering the capability for much more granular and detailed data collection, management, and interpretation (at scale) is needed. Only then can users be empowered to make informed decisions and GenAI be effectively used, managed, and regulated.

A first set of issues relates to AI opacity and openness. AI systems rely on models built on neural networks and deep learning—technologies that are known to create explainability and auditing challenges.³⁹ This is also true for GenAI and is arguably aggravated by agentic AI and workflows where (several) AI systems act (interact) autonomously.⁴⁰Expanded options for human-AI collaborations and AI-to-AI interactions potentially offer many benefits, yet they come at the cost of transparency challenges.

When discussing AI transparency, examining foundation models is beneficial. Notably, the Foundation Model Transparency Index⁴¹ has indicated that several widely deployed AI foundation models are significantly opaque regarding various subdomains of transparency, such as data, human labor, compute, model training-related subdomains, and mitigations (e.g., guardrails). Similar issues have been highlighted by the AI Data Transparency Index.⁴² The transparency challenges examined here are closely intertwined with AI model openness, resulting from entrepreneurial decisions, which create a source of ‘avoidable’ opacity beyond what is inherent to the technology. That being said, contrary to some public discourse that promotes a dichotomy between closed and open models, this issue is not binary. A recent study identified and explored 14 dimensions of model openness. Beyond providing sobering results for leading AI models, it demonstrates that model openness should be characterized as “composite” and “gradient,” emphasizing how a binary understanding of openness can lead to misclassifications and “open-washing.”⁴³ Schrepel and Potts critically review AI model openness, presenting a framework to assess model openness beyond purely technical perspectives. Notably, the authors illustrate how model openness and AI licenses influence competition and innovation.⁴⁴

A second insight is that the sources cited above point to technical, systematic, and structural hurdles for auditors (including academic researchers) to detect, evaluate, and mitigate the causes of model performance and potentially undesirable outcomes (including biases).⁴⁵ The costs and benefits of collecting, sharing, and responding to such information at different time-scales vary across stakeholders for the AI systems to operate as expected and desired, which includes complying with industry regulations. It is clear that the providers of the digital infrastructure substrates will be tapping into the expanding (agentic) capabilities of GenAI to acquire the situational awareness needed to operate in the more fluid, dynamic, and complex market environments but with variable success. There is no presumption that industry value chains or platform ecosystem participants will collect or share the information needed for good system-level decision-making, let alone to address the challenges of enforcing digital governance. For instance, platform providers may be held liable for moderating content (e.g., combating fake news or preventing cybercrime), or if exempt from liability, may abuse their tools to modulate and manipulate consumer responses for private rather than their end-users’ or society’s benefit.⁴⁶ Concurrently, regulators need to monitor and audit markets to detect and enforce violations and to promote industrial policies (e.g., competition, universal accessibility, prohibited use of copyrighted material in training data, etc.). The watchers and the watched do not and should not trust each other without care. Fundamental and strategic asymmetric and imperfect information challenges will persist.

The design and implementation (including enforcement) of digital governance policies depends on stakeholders having the right information, which requires a robust measurement ecosystem. Without the ability to access and act upon timely and accurate market intelligence, key policies and regulations (e.g., the DSA) cannot be effectively enforced. Lastly, without the ability to measure the usage of GenAI and the infrastructure resources it depends on, it will be impossible to effectively govern GenAI’s economic and social impact.⁴⁷

A second set of issues relates to what can be subsumed under the term ‘GenAI digital literacy’. Here, we adopt a more human-centric perspective to asymmetric information challenges. The latter are aggravated by the fact that many GenAI tools and pre-trained AI models are easy to use and interact with, lowering skill-related adoption and innovation costs and barriers for end-users (e.g., individuals including prosumers as well as companies). On the one hand, the ease of use conceals much of the complexity inherent to these technical systems. While we explained above several transparency challenges, this is arguably causing a mismatch between the ability to use GenAI tools and models and the literacy required to understand these complex technologies fully and use them appropriately.⁴⁸ The unprecedented pace of AI adoption and technology evolution (as the rise of AI agents demonstrates) further complicates the picture. Beyond privacy and security-related threats, it is hard for end-users to detect growing threats related to deception or manipulation (e.g., via synthetic content scams and fake news), or nudging through selective (i.e., biased) recommendations or AI-driven dark patterns.⁴⁹ In many contexts, we will directly confront or interact with GenAI tools, fully aware of their involvement. Here, asymmetric information challenges are complex. However, in even more contexts, we may lack this same awareness and will indirectly confront or engage with products, services, and content that may, in some shape or form, be influenced by GenAI.⁵⁰ Here, the challenges are even more complex.

GenAI systems are designed to be interactive with both the environment and users. Through this interactivity, they derive much of their potential power. Many GenAI tools are responsive and adaptive to user input, but not necessarily in ways that humans may understand or desire. We will illustrate this with a few examples. GenAI tools may be used to extract consumer surplus based on first-degree price discrimination. Additionally, in-context learning enables end-users to customize or personalize GenAI tools (such as ChatGPT) and/or their outputs, whether intentionally or unintentionally.⁵¹

The adaptation of AI systems may yield data feedback loops, attracting users, incentivizing engagement, and enabling data network effects through both cross-user learning and within-user learning, customization, and personalization.⁵²This can influence end-user behavior and choices, based on nudging or manipulation,⁵³ as mentioned earlier, and can raise switching costs. For some users, the acquisition of GenAI digital literacy may lower switching costs. For GenAI tools, how they are used may determine which of these countervailing effects will prevail.

From a policy perspective, the growing complexity of digital literacy issues raises questions about what it means to be an informed consumer (or user more broadly) and what measures are necessary to protect them effectively.⁵⁴

None of those are easy problems, but it is clear that solving them will require multi-stakeholder engagement. Waterbed effects will require consideration of system-level and component-level interventions; and those will be national and transnational in scope. To improve hopes of successful results, policymakers should aspire to the principles of sound EBDM.⁵⁵ That requires information, which requires data, and its process and ingestion into the policy-making process. Measurement is the acquisition of that data, and undertaking the requisite measurements will require access to the measurement ecosystem discussed earlier.