Regulatory uncertainty is what actually holds back innovation

President Trump signed an executive order last fall aimed at ensuring artificial intelligence (AI) companies are “free to innovate without cumbersome regulation.” In pursuit of this goal, the order established an AI Litigation Task Force to challenge “burdensome” state AI laws, directed the administration to evaluate state AI laws, and threatened to restrict remaining funding from the Broadband Equity Access and Deployment (BEAD) Program to states with onerous AI laws.

The directive followed the president voicing concerns about a patchwork of state AI regulations and attempts to regulate the technology harming American innovation—one of the most common arguments against new AI rules. While there is some empirical support for this view, there is stronger evidence that regulatory uncertainty—a lack of clarity over what the rules are or how they will be applied or enforced—is far more disruptive to innovation than regulation itself. The recent executive order threatens to create such an environment and may itself be disruptive to American innovation.

The arguments against regulation

Companies, think tanks, and politicians regularly claim that privacy and AI laws suffocate competition and slow technological progress, often drawing on three main sets of evidence: studies of the impact of the EU’s General Data Protection Regulation (GDPR), broad comparisons between Europe and the United States, and disproportionate compliance burdens borne by smaller firms.

The GDPR has reshaped data handling practices across Europe and imposed compliance obligations on firms of all sizes. Broadly, empirical research confirms these new obligations have likely negatively impacted innovation. Studies show new compliance costs and legal complexity have diverted resources from product development, and the regulation has led to reductions in revenue and venture funding. After the GDPR took effect, some firms shifted from innovating radical new products toward more incremental product improvements, suggesting that compliance requirements may redirect resources away from breakthrough innovation. Yet others identify notable positive impacts of the GDPR on innovation, including a growing demand for technologies designed to enhance privacy, and linked some privacy regulations to small enhancements in consumer trust.

Though studies of the GDPR provide useful data, researchers should be cautious about generalizing its effects to all technology regulation. Changes ascribed to regulation are usually accompanied by economic, cultural, or political shifts, and measuring innovation itself is difficult; researchers must choose proxies—such as research and development (R&D) spending, patent counts, or the share of new products—which each capture different aspects of innovation. The GDPR literature is also still evolving, and findings are mixed, leading some to note that there is little evidence and no consensus on whether the GDPR is beneficial or detrimental to innovation.

In addition to evidence from the GDPR, critics frequently make broad cross‑Atlantic comparisons—yet there are a number of significant differences. The U.S. has produced many of the world’s largest technology firms, while Europe has fewer unicorns. Europe accounts for roughly 5% of global AI computing capacity, something often attributed to stricter European regulation.

Yet structural and institutional factors, such as fragmented legal regimes and smaller venture capital markets, also can impede European start‑ups’ growth. Simple comparisons are, therefore, misleading; regulation interacts with broader economic and social conditions instead of acting as the sole cause of slower innovation.

These structural factors also shape start‑up ecosystems. Most start-ups fail for reasons that are usually financial, market-based, or team-related rather than regulatory—such as poor product-market fit, lack of funding, or inexperienced leadership—so disentangling regulatory effects from these factors requires careful work. The limited evidence shows heterogeneity: Some start‑ups pivot toward privacy‑enhancing services or exploit regulatory niches, while others exit markets or abandon data‑intensive models. Given that successful firms are the only ones to survive long enough to be observed, analyses of regulation’s impact on start‑ups must account for survival bias and variations in business models.

Assessing regulation’s impact on innovation is further complicated by difficulties in defining and measuring innovation. Metrics like R&D spending and patent counts often capture only the inputs or focus on narrow outputs, rather than measuring overall impact. Surveys like the EU’s Community Innovation Survey and the U.S. Business R&D and Innovation Survey ask firms whether they introduced new‑to‑market or new‑to‑firm products, among other questions. Each proxy has limits: R&D spending may reflect compliance costs rather than creativity; patent counts vary by industry; and surveys can be unreliable because firms sometimes over‑ or under‑report their activities, and response rates are often low, so respondents may not represent the broader population. Since no single proxy fully captures innovation, scholars triangulate multiple indicators—such as product‑level data on radical versus incremental innovation, venture‑capital flows and start‑up formation rates—to build a more comprehensive picture. Even with these indicators, establishing causal relationships remains difficult because regulations rarely change on their own; they are typically part of broader economic, political, or social shifts, and their impacts may take years to materialize.

Critics of AI rules also argue that regulation disproportionately burdens smaller firms. There is some evidence that GDPR compliance costs fall more heavily on smaller companies, including one paper covering 61 countries that found firms heavily exposed to EU markets experienced an average 8% decline in profits and a 2% reduction in sales after the GDPR took effect. The negative effect was larger for small tech firms, while big IT service providers showed no significant decline.

Regulatory uncertainty and innovation

While the overall effects of regulation are complex, one of the clearest and most consistent findings is that regulatory uncertainty discourages investment and innovation, especially among smaller firms. Unpredictability increases perceived risk and often encourages a wait-and-see approach. And when investment projects are irreversible and future conditions are uncertain, it can be rational for firms to delay commitments. In some cases, firms may scale back long-term investment when future policy conditions are difficult to predict, even when the immediate regulatory burden is not particularly high.

But uncertainty results in more than delayed investment; there is evidence of firms employing a range of strategies in response. The findings that companies shifted from radical to incremental innovation under the GDPR may partially reflect uncertainty about how to comply. Others have observed firms increasing their engagement in public policy processes and increasing strategic flexibility. Moreover, there is wide variation in the capacity of companies to respond to uncertainty. Larger companies likely have more ability to pivot in the face of uncertainty.

Certain industries have demonstrated the importance of regulatory certainty. In medical technology, regulatory ambiguity can slow first-of-their-kind innovation. One study finds that firms introducing the first device in a new category spend about one-third longer in the U.S. approval process than follow-on entrants, and these delays fall when regulators issue objective guidance and clearer compliance pathways. In nanomedicine, similar dynamics appear: Regulatory uncertainty slows the translation of scientific discoveries into technical applications and suppresses related business activity.

Evidence beyond the technology sector further suggests that regulatory uncertainty stifles innovation. For example, researchers have linked regulatory uncertainty to reductions in research investment, risk-taking, corporate innovation investment, and even patenting activity. One recent study finds that monetary policy uncertainty can intensify financing constraints and reduce firms’ willingness to take risks, which in turn suppresses corporate innovation investment.

These cross-sector findings underscore the importance of clear, stable rules and predictable enforcement. When businesses can anticipate compliance costs and timelines, they invest more readily in new products and markets. Unpredictable enforcement or changing rules raise financing costs and dissuade entry, especially for small and mid‑sized firms.

Moving forward

Our findings reveal the literature does not support facial claims that regulation either consistently suppresses, or consistently promotes innovation. Rather, the relationship between regulation and innovation is mixed and context dependent. Technology regulation can raise costs and create obstacles for firms, which can push them to focus on smaller, incremental improvements instead of pursuing bold, breakthrough innovations. At the same time, regulation can support innovation by clarifying intellectual property rights, creating standards that promote interoperability, and building consumer trust. Moreover, challenges in how to define, operationalize, and measure both innovation and regulation further complicate the picture.

Yet, there is strong evidence that regulatory uncertainty consistently undermines, or at minimum, delays innovation. Such uncertainty is demonstrated through investments, stale business activities, deterred market entry, and reduced patenting. And these effects occur across scale and sector— both small and large firms suffer when unpredictability raises perceived risk and increases expected adjustment costs.

Policymakers should focus on reducing regulatory uncertainty to foster innovation while meeting public policy goals. Stable national laws and clear enforcement can reduce surprises, while giving businesses more predictable rules. Well‑designed pilot programs, regulatory sandboxes, and policy experiments allow regulators and firms to test and refine approaches before full implementation, which also supports consumer confidence. They provide structured guidance and generate evidence to inform future legislation, though they are not a substitute for stable legislated frameworks.

Ongoing dialogue between regulators and stakeholders can also help to clarify how rules will apply in practice and allow adjustments as technologies evolve. Firms that integrate legal strategy early in the design process, treating regulation as a design parameter, are better positioned to build compliance into products and scale them.

Governments should also provide tailored assistance to small and medium-sized enterprises, which often lack the resources to navigate complex laws. Regulatory uncertainty and fixed compliance costs tend to fall disproportionately on smaller firms, while larger firms are better positioned to absorb costs, delay investment, or adapt through internal legal and compliance capacity. EU-style digital regulation can therefore reinforce these asymmetries, particularly for firms whose business models depend heavily on personal data. Clear guidance, compliance support, and predictable enforcement timelines can help mitigate these burdens.

Good regulation and innovation are not mutually exclusive. What matters is not simply the presence of regulation, but whether the rules governing markets are clear, predictable, and stable.