In the ongoing push to shape how artificial intelligence is developed and governed, Google has unveiled a policy proposal that sits between OpenAI’s recent stance and broader federal ambitions. The document makes a case for “balanced” copyright rules and argues that the government should do more to support AI advancement through funding and policy changes. It arrives in a context where high costs and tight profit margins have not slowed the rapid expansion of generative AI, and where policy debates are increasingly centered on who owns the data that fuels these models and how regulations should be structured across different jurisdictions. Google’s framing echoes its competitors in seeking a path that encourages innovation while addressing legitimate concerns from creators and rights holders, though critics question whether the proposed balance truly tilts in a way that protects rights without stifling progress. The broader backdrop includes a presidential push for a national AI Action Plan and a judiciary landscape in which litigation over training data usage continues to unfold, potentially setting important precedents for how AI developers may be held responsible for using copyrighted material without permission. Against this landscape, Google seeks to chart a course that it says will accelerate AI capabilities while maintaining a governance framework that is workable for industry, regulators, and the public.
Context and Policy Landscape
The policy moment around artificial intelligence is dominated by a mix of national strategy, industry proposals, and evolving state and international rules. The Trump administration’s call for a national AI Action Plan spotlights the state’s interest in guiding the growth of AI across sectors, with a clear emphasis on building strategic capacity and ensuring competitive advantage. In parallel, major AI developers are trading ideas about how copyright enforcement should intersect with machine learning training. OpenAI has argued that current copyright enforcement can hinder AI development, presenting a worry that rights holders could impose rigid constraints that would limit the use of data needed to improve models. Google’s own policy proposal aligns with some of OpenAI’s concerns about copyright while pushing for public funding and policy reforms that would smooth the path for AI research and deployment. This alignment signals a broader industry push for policy clarity that can reduce legal risk and speed innovation, even as stakeholders debate legitimate protections for creators.
Google’s document explicitly frames the question of copyright within the broader objective of enabling robust AI systems. It acknowledges the central tension between access to large-scale data—often publicly available and legally protected by copyright—and the rights of content owners who might object to the use of their works in training datasets. The company asserts that a carefully designed framework can permit the use of public data for AI development without triggering disruptive negotiations or unpredictable terms that could impede progress. In effect, Google is arguing for a set of rules that clarify when and how copyrighted material can be used in training while limiting the potential downstream harms to rights holders. The claim is that with the right policy levers, AI developers can access essential data inputs without triggering a cascade of licensing disputes that would slow advancement.
Beyond copyright, Google’s policy stance addresses the practical realities of maintaining viable AI systems. The document emphasizes that access to data and computing power must be reliable and predictable for researchers and engineers who are building and refining generative technologies. It argues that current processes—such as energy procurement and data-center permitting—are often slow or opaque and that these frictions can bottleneck innovation. In this context, the proposal calls for modernization of energy infrastructure to ensure that AI firms have the power capacity required to train large models and to run inference at scale. The core message is that policy should not only govern rights and responsibilities but also create a supportive infrastructure environment that makes ongoing AI work sustainable and scalable.
The policy also advances a vision of federal leadership in AI that emphasizes interoperability and a multi-vendor ecosystem. Google wants the government to set an example by adopting AI systems that can operate across platforms and providers, reducing vendor lock-in and encouraging broader collaboration. This approach is paired with a call for public data sets to be made available for commercial training, along with funding for early-stage AI development and research initiatives. Public-private partnerships are highlighted as critical mechanisms for accelerating innovation, with the proposal arguing that closer collaboration between industry, government, and research institutions will yield faster breakthroughs and more robust products.
Copyright, Training Data, and the Fair Use Debate
A central theme in Google’s policy proposal is the treatment of copyrighted material used to train AI models. The company argues that access to public data—whether freely available or copyrighted—should be allowed for AI development under a clarified legal framework. The aim is to reduce unpredictable, imbalanced, and lengthy licensing negotiations that can stall research and product development. In this framing, Google contends that the incremental impact of using copyrighted works in training AI is often mischaracterized and can be managed through well-defined rules that protect creators while not blocking innovation. The emphasis is on establishing a fair use standard that is practical for machine learning while safeguarding the core interests of rightsholders.
The policy makes concrete assertions about the nature of training data, insisting that a significant volume of useful material exists in publicly accessible sources. Google argues that fair access to such data is essential for improving the quality and capabilities of generative AI systems. The claim is that without access to this data, the ability of models to generalize and perform across diverse tasks could be compromised. In practice, this means proposing a framework in which AI developers can ingest broad swathes of public content to train models, subject to safeguards and governance that mitigate the risk to rights holders. The company also notes that not all uses of copyrighted material will have the same impact, and the policy seeks to differentiate between permissible training inputs and uses that would require additional permissions or compensation.
A critical dimension of this debate is the tension between transparency and trade secrecy. Some global competitors, including those in Europe, are advancing regulatory proposals that would demand disclosure of training data and disclosed risks associated with AI products. Google recognizes the tension that emerges here: openness can be valuable for safety and accountability, but it also risks revealing sensitive information that could erode competitive advantages or expose trade secrets. The policy thus signals a preference for a transparent yet carefully bounded approach to data disclosure, designed to protect proprietary methods while enabling regulators and researchers to assess risk and safety.
In practical terms, Google’s proposal calls for a clear, federal baseline that can replace the current patchwork of state rules. The fragmentation of state-level regulations creates compliance challenges and adds complexity for companies operating nationwide or globally. By advocating for a unified framework, Google argues that AI developers and deployers would face fewer regulatory ambiguities, enabling more predictable planning and investment. The policy also suggests that a comprehensive federal approach should address not only copyright and data use but also the broader governance of AI, including accountability, safety, and risk management.
Energy Infrastructure, Data Centers, and Power Reliability
A notable portion of Google’s policy position centers on energy and the infrastructure necessary to sustain AI development. The company projects that global data center power demand will rise significantly in the near term, highlighting an estimated increase of tens of gigawatts from 2024 to 2026. This forecast underscores the scale of power required to train, run, and maintain large language models and other sophisticated AI systems. The implication is that without a reliable, well-structured energy supply and permitting environment, the industry could face bottlenecks that slow down or inhibit progress.
Google argues that current U.S. energy infrastructure and permitting processes are not sufficiently aligned with the needs of the AI sector. The policy contends that a more predictable and expedited approach to energy provisioning—combined with streamlined regulatory approvals for data centers and related facilities—would help ensure that AI initiatives can scale efficiently. This emphasis on infrastructure is not merely a matter of capacity; it also touches on resilience, grid stability, and the ability to sustain intensive computing workloads during peak training periods and ongoing inference tasks. The underlying claim is that policy makers must recognize the energy dimension as a core element of AI strategy, not a peripheral concern.
In addition to securing power, Google calls for government leadership in adopting AI tools across federal agencies with a focus on interoperability and vendor diversity. The proposal advocates for the federal government to set an example by employing multi-vendor AI systems that can function together effectively, reducing dependency on a single provider and fostering a more dynamic ecosystem. This approach would require data-sharing initiatives and the provision of data sets to support commercial AI training, as well as active investment in early-stage AI research and development. The broader objective is to synchronize policy with the computational demands of advanced AI and to ensure that public institutions are constructive buyers and testbeds for AI innovation.
Federal Leadership, Interoperability, and Public-Private Collaboration
Central to Google’s suggested framework is the notion that federal leadership should catalyze rapid, balanced advancement in AI. The document argues that the United States should not merely fund AI research but also implement policy measures that facilitate practical deployment, collaboration, and interoperability across a multi-vendor landscape. By promoting a government-wide approach that emphasizes openness and collaboration, Google envisions a federal ecosystem where data sets and research outputs are shared to spur innovation while maintaining safeguards for safety, privacy, and intellectual property.
A key part of this vision is the call for data sets that can support commercial AI training. The policy suggests that the government has a role in making certain data resources available to developers and researchers, thereby lowering the barriers to create and refine AI systems. This would be complemented by increased investment in public-private partnerships, where industry players work alongside federally funded institutions to accelerate breakthroughs, scale up solutions, and bring innovations to market more quickly. The proposed framework would also involve public funding mechanisms and competitive drives such as government-funded competitions and prizes to incentivize AI innovation and problem-solving across sectors.
In practice, achieving multi-vendor interoperability requires clear rules about interoperability standards, data formats, and interface protocols. Google argues that interoperability should be a guiding principle for both policy and procurement decisions, enabling a broader set of players to contribute to AI development and deployment. This includes ensuring that research institutions, startups, and established tech companies can participate meaningfully in the AI ecosystem and cooperate with federal agencies on pilot projects, pilots, and long-term initiatives. The executive branch, in this view, would act as a catalyst, not a bottleneck, by providing a stable policy environment that supports experimentation and cross-organizational collaboration.
The policy also highlights the federal role in promoting responsible AI development through research partnerships and prize-based initiatives. Such programs would be designed to spur breakthroughs in safety, efficiency, and novel applications of AI, while ensuring that government-funded research remains accessible to a broad spectrum of actors. The aim is to create a virtuous cycle in which public money supports private innovation, and results from that collaboration feed back into the public sector to improve policy, safety standards, and governance. The net effect, according to the proposal, would be a more agile, risk-tolerant environment that can accommodate rapid advances in AI capabilities while still upholding core norms and safeguards.
Global Regulation, Trade Secrets, and Diplomacy
The Google policy narrative also delves into the international regulatory terrain and the delicate balance between openness and secrecy in AI development. Some regulatory efforts abroad, including the European Union’s AI Act, are framed as potentially requiring companies to publish overview information about training data and associated risks. Google expresses concern that such disclosures could effectively reveal trade secrets and openings for foreign competitors to replicate or imitate its innovations, which could undermine competitive advantages. The policy thus advocates a measured approach to transparency that protects essential corporate intellectual property while still enabling regulatory scrutiny and safety assessment.
The document’s stance on international governance is also pragmatic about diplomacy. It urges the U.S. government to push back against stringent regulatory measures that could hamstring global deployment of AI products. By advocating for a light-touch regulatory regime that aligns with perceived U.S. values and approaches, Google seeks to preserve the ability to bring AI products to markets worldwide without facing fragmented or contradictory standards. This stance reflects a broader tension in global AI governance: how to harmonize safety, transparency, and accountability with the competitive imperatives and economic interests of leading technology nations. The policy calls for a framework that supports innovation while maintaining enough guardrails to manage risks and protect user safety.
Within this regulatory discourse, there is a recurring reference to California’s SB-1047, a proposed bill that would have imposed certain AI safety requirements but was vetoed. The mention serves to illustrate the political dynamics that shape how AI rules evolve at the state level and the potential friction between innovative aims and prescriptive safety mandates. The policy contends that a national framework could mitigate these frictions by offering a cohesive baseline that preempts a patchwork of state-level constraints, enabling smoother cross-border deployment and more predictable compliance for companies operating nationwide or internationally.
Liability, Responsibility, and Accountability in AI
A recurring question in the policy debate concerns who should bear responsibility when AI systems err or cause harm. Google’s document frames the issue around the non-deterministic nature of generative AI: models can produce outputs that are hard to predict or control, making it difficult to assign liability with precision. The policy argues for clearly defined responsibilities among AI developers, deployers, and end users, but it also emphasizes a preference for distributing most responsibilities to entities other than the original model creators. The underlying rationale is that the developer may not have visibility into how the model is ultimately used once it leaves the lab or production environment, complicating direct accountability.
This position contributes to a larger debate about whether liability should be anchored to the model creators, the deployment channels, or the organizations that implement AI solutions in real-world settings. The policy suggests that a nuanced approach is necessary—one that incentivizes responsible design and monitoring by developers while also ensuring that deployers and end users implement appropriate governance, oversight, and risk controls. In practice, this could translate into regulatory requirements for risk assessments, ongoing monitoring, auditing, and transparency disclosures that help stakeholders understand how AI systems are trained, what data were used, and what safeguards are in place to mitigate bias, misinformation, or safety risks.
Industry Perspective, Public Perception, and Implications
Google’s policy stance sits within a broader industry conversation about how to balance innovation with safeguards. The document frames the policy dialogue as a need to advance AI capabilities without neglecting the potential risks that come with powerful technologies. The tone suggests a confidence that a well-designed regulatory framework, paired with adequate funding and a commitment to interoperability, can unlock rapid progress while preserving essential protections for creators and users alike. Yet the framing has drawn scrutiny. Critics may argue that calls for “balanced” rules can mask a preference for lenient regulation that prioritizes speed and commercial advantage over stricter safeguards. Others contend that the emphasis on public-private collaboration, while beneficial, could result in policy capture or slow-moving bureaucracies if not paired with clear accountability mechanisms and measurable outcomes.
The policy’s emphasis on energy infrastructure, data access, and multi-vendor interoperability could reshape the competitive landscape in AI. By advocating for government-backed datasets, easier permitting, and robust incentive structures for research and development, the proposal might accelerate certain types of innovation, especially in sectors with heavy data needs and significant computational demands. At the same time, the push to maintain light-touch regulatory regimes on a global scale raises questions about how to maintain consistent safety standards, protect sensitive information, and ensure ethical use across borders. The resulting policy environment could influence funding priorities, procurement strategies, and collaboration models for years to come, affecting startups, established tech giants, research institutions, and public agencies alike.
Conversations around these proposals also touch on transparency and openness. On one hand, there is a clear push to streamline data access and create shared resources that can propel AI forward. On the other hand, concerns about trade secrets and competitive advantage remind policymakers and industry stakeholders that some level of secrecy may be necessary to sustain innovation ecosystems. The tension between openness and protection is likely to shape ongoing negotiations, regulatory proposals, and enforcement approaches as AI technologies become more capable and more deeply integrated into everyday life and critical industries.
Conclusion
The policy dialogue surrounding Google’s AI wishlist reflects a broader struggle to define a practical, ambitious, and responsible path for AI development. By advocating for balanced copyright rules, robust funding and policy support, and a national framework that prioritizes interoperability, Google positions itself as a stakeholder seeking to harmonize innovation with governance. The proposal emphasizes the importance of reliable energy infrastructure, streamlined data access, and sustained public-private collaboration as essential ingredients for accelerating AI progress. It also grapples with complex questions about liability, transparency, and international regulation, signaling a preference for a governance approach that protects trade secrets and competitive capabilities while enabling responsible use and safety oversight.
As the AI policy landscape evolves, the interplay between federal leadership, state experimentation, and international regulatory activity will continue to shape how AI is trained, deployed, and governed. The balance between encouraging rapid, scalable innovation and protecting the rights and safety of individuals and content creators remains at the heart of these debates. The outcome of this evolving policy environment will influence not only the pace of AI advancement but also the standards, incentives, and collaborations that determine which companies, researchers, and communities can participate most effectively in this transformative field.
