NEW YORK, May 21, 2026 (GLOBE NEWSWIRE) -- Labrynth, a global AI-services and regulatory intelligence platform behind the Red Tape Index (RTI), announced the international rollout of RTI alongside the launch of the Data Center Readiness Index, a new U.S. benchmark evaluating state readiness for hyperscale AI infrastructure, cloud computing campuses, and large-scale digital industrial development.

The Red Tape Index has signed partnerships to expand into more than 30 countries by the end of 2026 through a growing series of country-level and sector-specific indexes designed to measure infrastructure competitiveness, regulatory friction, economic efficiency, and operational readiness using transparent public data.

The U.S. launch of the DCRI represents the first deployment within RTI’s broader global benchmarking initiative, which is designed to measure how operational, regulatory, and infrastructure conditions shape economic competitiveness across sectors and geographies.

In its inaugural rankings, Texas emerged as the top-ranked state overall, followed by Oregon, Illinois, and Florida, reflecting a broader shift in where AI-era infrastructure can scale most efficiently across the United States.

Top 10 States in the 2026 Data Center Readiness Index

TexasOregonIllinoisFloridaGeorgiaOhioMarylandConnecticutMassachusettsVirginia

The DCRI is designed to create a transparent, comparable framework for evaluating operational readiness across states as AI infrastructure investment accelerates globally. The index evaluates states across nine dimensions that are critical to large-scale AI and cloud infrastructure deployment, including energy supply, grid reliability, interconnection, natural disaster risk, permitting, workforce, water supply, economic climate, and public sentiment.

The scoring framework consolidates publicly available federal and institutional datasets into a single composite readiness ranking designed for hyperscalers, infrastructure investors, utilities, developers, site-selection teams, policymakers, and analysts evaluating large-scale infrastructure deployment conditions.

Built using regulatory intelligence infrastructure from Labrynth, the DCRI is designed to create a standardized framework for comparing operational readiness across states using consistent, auditable inputs.

As AI infrastructure deployment accelerates globally, the limiting factor is increasingly no longer compute demand, but whether states can deliver the power, permitting capacity, infrastructure, workforce, and operational readiness needed to support large-scale deployment.

Utilities, developers, hyperscalers, and state economic development agencies are facing growing pressure around generation capacity, transmission expansion, permitting timelines, water availability, and workforce readiness as AI-scale infrastructure demand expands.

According to BloombergNEF, annual capital expenditures from major data center operators are projected to approach $750 billion globally by 2026, with cumulative AI infrastructure investment expected to exceed $3 trillion by 2029.

“The rankings suggest that future AI infrastructure growth may increasingly shift toward states capable of combining power availability, operational certainty, workforce depth, and infrastructure scalability rather than relying solely on legacy data center concentration,” said Stuart Lacey, founder and CEO of Labrynth.

“For years, every state has told its own story about how ready it is,” Lacey added. “The Data Center Readiness Index puts all 50 states on the same map, scored across the same dimensions using transparent public data. It’s a productivity tool. When everyone is working from the same evidence, capital moves faster, projects break ground sooner, and infrastructure decisions become easier to compare across markets.”

The rankings highlight widening differences in operational readiness across states as infrastructure demand accelerates. While some states demonstrate stronger infrastructure and operational indicators, others face constraints related to transmission access, interconnection timelines, industrial infrastructure, or resource availability.

For large-scale site-selection teams evaluating multi-billion-dollar campus developments, the index is designed to replace what has traditionally been a months-long process of reconciling energy, permitting, workforce, infrastructure, and environmental data across multiple federal and third-party sources with a single comparable framework.

States are evaluated using both rank-based and continuous scoring methodologies. The scoring framework is designed to prevent severe weakness in one operational category from being fully offset by strength in another. The methodology, weighting structure, source datasets, and scoring framework are publicly available through the RTI dashboard.

The Data Center Readiness Index is available free to the public at https://redtapeindex.com/.

Governments, enterprises, industry associations, and institutions interested in developing country-level or sector-specific indexes in partnership with RTI are encouraged to contact the organization via the platform.

About Labrynth

Labrynth is a global AI-services and regulatory intelligence platform that builds compliance engines for governments and businesses navigating complex, multi-agency regulatory environments. The platform accelerates approvals, reduces compliance risk, and unlocks faster growth by turning regulatory bottlenecks into operational advantages.

Labrynth is currently deployed across state and local government, construction, energy infrastructure, supply chain, disability and care, and payroll assurance sectors, with active engagements across four continents.

The Red Tape Index, published by Labrynth, serves as a source of record for regulatory performance and infrastructure-readiness data across U.S. jurisdictions.

Spun out of Invisible Technologies, Labrynth is founded and led by serial entrepreneur Stuart Lacey, who brings more than 20 years of experience bridging public- and private-sector innovation globally.

Learn more at labrynth.ai.

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Source: Labrynth