Friday, August 28, 2026 · Daily edition
BLS’s new 2025–35 jobs map, liquid-cooling efficiency, and draft high-risk rules
Today’s edition centers a new BLS projection cycle for 2025–35 — slower total job growth, AI-linked power and compute hiring, office support as the steepest decline, and the agency’s first official AI-exposure categories. Beside the demand headlines of recent days, IEA 4E maps liquid cooling’s efficiency potential and why PUE understates the gain. On day 26 of live AI Act transparency, the Commission’s draft high-risk classification guidelines spell out the two Article 6 routes. WHO/Europe’s first EU-27 health-AI snapshot and an OECD education note on general-purpose GenAI polish versus exam learning round out the ledger.
BLS’s new 2025–35 map: slower total growth, AI power hiring, and office support as the steepest decline
What happened. The Bureau of Labor Statistics employment-projections news release for 2025–35 (USDL-26-1422, for release 10:00 a.m. ET Thursday, 27 August 2026) is a new projection cycle — not the prior-cycle TED/MLR package. Total employment is projected to rise from about 170 million to about 176 million — +3.5%, or about 5.9 million jobs — slower than the prior decade’s double-digit gain. Utilities is the fastest major sector (+9.8% / +58,800), with nearly all of that gain in electric power generation, transmission, and distribution, including AI power demands. Private healthcare and social assistance adds the most jobs (+9.5% / more than 2 million, about 37% of all new jobs). Professional, scientific, and technical services grow +8.6% / +926,700 on demand for AI-based systems, R&D, and consulting. Computing infrastructure / data processing / web hosting grows +25.1% / +120,400 on accelerated AI adoption. Computer and mathematical occupations are the fifth-fastest major group (+7.3%), with data scientists +34.6%. Office and administrative support is the fastest-declining major group (−4.0% / −752,100), with AI and automation tools named among demand dampeners. These are long-horizon projections with embedded judgment, not a 2026 layoff census.
What to watch. The measurable record is whether hiring and openings track the power/compute expansion and the office-support decline — not slogans that “half of all jobs” vanish. Do not mash these 2025–35 levels with the prior-cycle table.
Read the BLS 2025–35 projections →
BLS’s first official AI-exposure categories: relative ranks, not a job-loss forecast
What happened. Alongside the 2025–35 projections, BLS published a new supplement that sorts detailed occupations into four relative AI-exposure categories — Low, Moderate, High, and Very high — for the 831 occupations in the projections matrix. The product combines five external sources: three theoretical measures (Felten et al.; Eloundou et al.; Eisfeldt et al.) and two observed-usage measures (Anthropic Claude traffic; Microsoft Copilot applicability). Exposure means AI could assist or complete some of the work, or that observed AI interactions map onto occupational tasks. The agency is explicit: exposure does not imply job loss, productivity gains, automation probability, wage effects, or replacement. Categories are relative, do not split automation from augmentation, and the theoretical sources conceptualize AI capabilities no later than mid-2023.
What to watch. Career planners finally have an official relative ranking — and a hard disclaimer against treating the ranking as a displacement model. The measurable follow-through is whether later BLS products connect exposure ranks to actual employment change.
Read the BLS AI exposure categories →
IEA 4E maps liquid cooling’s efficiency potential — and why PUE understates the gain
What happened. An IEA 4E EDNA publication, Liquid Cooling in Data Centres, maps liquid-cooling technologies and the barriers to wider use. The HTML summary puts energy-savings potential around ~8% at servers, 30–40% at facility level, and overall on the order of 10–21%. Current use is low because of missing standardisation, high upfront cost, and long-term reliability concerns. A further barrier: Power Usage Effectiveness (PUE) systematically understates efficiency gains from liquid cooling. The report argues for policy intervention and for retrofit solutions suited to existing multistorey halls. This is a 4E/EDNA efficiency product — not an IEA global TWh demand path.
What to watch. After a week of demand-path headlines, the efficiency layer is the missing half of the ledger. Potential is not booked savings; the measurable record is deployed liquid-cooled capacity, better metrics than PUE alone, and whether retrofits reach legacy sites.
Read the IEA 4E liquid-cooling report →
Commission draft high-risk classification guidelines: two Article 6 routes, still non-binding
What happened. On day 26 of live AI Act transparency enforcement, the European Commission’s draft guidelines on classifying high-risk AI systems under Article 6 are the fresh product — not the enforcement-architecture map. The draft is meant to help providers, deployers, and market-surveillance authorities decide whether a system is high-risk and to support uniform application of Article 6. Two routes are locked on the page: Article 6(1) — the system is a safety component of (or is itself) a product covered by Annex I harmonisation law and required third-party conformity assessment; and Article 6(2) — Annex III use cases. The guidelines include practical examples of systems that should or should not be high-risk; the examples are not exhaustive and may be updated. This is Commission interpretation in draft form, not adopted final guidelines.
What to watch. Classification is the gate before most high-risk duties bite. Getting Annex I product routes confused with Annex III use-case routes is how compliance calendars go wrong. Draft examples are not final obligations.
Read the draft high-risk classification guidelines →
WHO/Europe’s first EU-27 AI-in-health snapshot — and OECD on GenAI polish vs exams
What happened. WHO/Europe released a first snapshot of AI in health care across the 27 EU Member States. All 27 countries name improved patient care as a driver; 74% report AI-assisted diagnostics; 63% use chatbots for patient engagement; 81% involve stakeholders in AI governance. This is country-reported adoption from a mid-decade survey window, not a patient-outcome RCT. Separately, OECD Director Andreas Schleicher writes that general-purpose GenAI can raise assignment quality while exam performance does not follow — risking weaker consolidation when productive struggle is removed — while specialised pedagogical tools show more promise in early trials.
What to watch. Adoption shares are infrastructure facts, not mortality or equity wins. In classrooms, the measurable record is exam and mastery evidence under tools built for pedagogy — not chatbots treated as homework machines.
Read the WHO/Europe health-AI snapshot →
Read the OECD education note →
|