Dan Herbatschek, CEO of New York-based Ramsey Theory Capital, released new analysis today indicating that global AI regulation has evolved from a compliance checkbox into a primary driver of enterprise budgets and board-level accountability.

The firm’s latest research reveals a pivotal shift in corporate finance: more than 60% of large organizations now tie AI project funding directly to demonstrable regulatory readiness. This trend marks a new era where governance maturity is a prerequisite for scaling technology, rather than a post-deployment consideration.

The New Default Operating Environment

According to Herbatschek, enterprise leaders who view compliance as a distant concern are already losing their competitive edge. Global AI laws have quietly become the default operating environment, fundamentally reshaping how intelligent systems are designed and deployed.

"The biggest myth is that regulation slows AI," says Dan Herbatschek, CEO of Ramsey Theory Capital. "What actually slows AI is poor governance; regulation simply makes the cost of that inefficiency visible. The question for leaders is no longer if regulation applies, but whether their operating model is mature enough to comply by default across every market they touch."

Navigating a Fragmented Global Landscape

The Ramsey Theory Capital analysis highlights a growing divergence in regional regulatory philosophies, requiring enterprises to adopt "regulation-aware" operating models:

• United States: A decentralized, enforcement-driven environment where governance gaps translate into significant legal and reputational liability.
• European Union: A prescriptive, risk-based approach that mandates transparency and lifecycle controls as core engineering requirements.
• Asia-Pacific (APAC): A complex mix of pro-innovation policies and strict national data sovereignty controls that require high operational flexibility.

From "Compliance Theater" to Operational Maturity

The research suggests that 2026 is the year of regulation-by-design. Leading organizations are now embedding auditability and accountability directly into their AI workflows. This shift is influencing every level of the enterprise, from vendor selection and capital allocation to long-term architecture decisions.

Organizations that fail to operationalize these controls early risk escalating deployment costs, significant rollout delays, and increased exposure to global risk.