Professional Engineers advising clients on digital infrastructure environmental impact and data-centre energy reporting are increasingly using AI to draft technical annexes referencing national data-centre energy intensity figures, prepare environmental impact assessment baseline statistics for digital-infrastructure projects, populate grid-operator consultation submissions with OECD-cited benchmark data, and verify regulator-issued statistics against primary publication chains.
The OECD's 2025 Revision of the Recommendation on Digital Technologies and the Environment carries a named, citable statistic on Ireland's data-centre share of metered electricity, drawn from Ireland's Central Statistics Office, that professional engineers will reach for when contextualising client engagements on data-centre offtake, sustainability reporting, and digital-infrastructure assurance work. That statistic is exactly the kind of figure the RLB Specialist Panel tested two frontier AI subjects against.
The RLB Specialist Panel issued a Specialist Panel application-style question on the share of Ireland's 2021 metered electricity that data centres accounted for, per the figure cited in the OECD Digital Economy Outlook 2024 chapter referenced by the 2025 Recommendation, sourced from Ireland's CSO (2023). Two frontier AI models tested by the RLB Specialist Panel returned the figure as 14 per cent and extended the answer with a four-point time series running from 5 per cent in 2015 through 21 per cent in 2023. The regulator's verbatim text records 11 per cent in 2021, with no multi-year trajectory.
The failure class is Fabricated Fact: a confidently delivered, citably attributed statistic that does not match the source document, compounded by a fabricated time series that does not appear anywhere in the OECD or CSO published record.
For professional engineers, this is operationally consequential because the wrong figure is not a vague paraphrase. It is delivered with a real source chain, CSO 2023 via OECD Digital Economy Outlook 2024, that survives standard reference-check review. A Professional Engineer who uses this AI response as a research shortcut will embed a wrong baseline statistic, 14 per cent rather than the verbatim 11 per cent, into a technical annex, an environmental impact assessment, or a policy submission, attributed to a real and reputable source chain (CSO 2023 via OECD Digital Economy Outlook 2024).
The fabricated time series (5 per cent rising to 21 per cent across 2015 to 2023) compounds the risk: it reads as contextual corroboration and would not be detected without independently verifying each year against the primary document. In a formal process, planning approval, grid operator consultation, or regulatory submission, a misattributed statistic of this kind is the type of error that surfaces under technical cross-examination and reflects on the engineer's verification practice, not merely their choice of tool.
The audit's finding on this question is published with an immutable RLB Citation ID. The relevant entry is RLB-H-INT-OECD-OECD-DIGITAL-TECHNOLOGIES-ENVIRONMENT-2025-Q006-Sonnet46. The full audit is published at the OECD Digital Technologies and the Environment Recommendation (2025 Revision) hub on RegLegBrief.com.
This is the consolidated view of findings. Click the Citation IDs or 'see details →' on any item for the full details for each finding.
A Professional Engineers who uses this AI response as a research shortcut will embed a wrong baseline statistic, 14% rather than the verbatim 11%, into a technical annex, environmental impact assessment, or policy submission, attributed to a real and reputable source chain (CSO 2023 via OECD Digital Economy Outlook 2024). The fabricated time-series (5% → 14% → 18% → 21%) compounds the risk: it reads as contextual corroboration and would not be detected without independently verifying each year against the primary document.
In a formal process, planning approval, grid operator consultation, regulatory submission, a misattributed statistic of this kind is the type of error that surfaces under technical cross-examination and reflects on the engineer's verification practice, not merely their choice of tool.
Every finding on this page compares an AI subject's account of the rule against the regulator's verbatim text from the regulator's own portal. Both are linked. Each delta, its root causes, and impact analysis are documented and published with immutable Citation IDs.