---
id: "lib-2026-10-14-beige-book-october-2026"
type: "library"
title: "Beige Book, October 2026"
lang: "EN"
org: "Federal Reserve"
source_type: "Survey"
date: "2026-10-14"
week: "2026-W43"
url: "https://nosov.ai/library/lib-2026-10-14-beige-book-october-2026/"
original_url: "https://www.federalreserve.gov/monetarypolicy/beigebook202610.htm"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "Federal Reserve's official summary of business-contact commentary across 12 districts; authoritative and broad, but anecdotal by design and AI is a minor thread."
summary_basis: "full-text"
original_minutes: "60"
tags: ["labour market", "ai and hiring", "us economy", "attrition"]
---

# Beige Book, October 2026

Tier B (Useful, limited): Federal Reserve's official summary of business-contact commentary across 12 districts; authoritative and broad, but anecdotal by design and AI is a minor thread.

Summary based on the full text.

## In short

The Federal Reserve's October 2026 Beige Book, compiled by the Philadelphia Fed from contacts outside the Fed and collected on or before 5 October 2026. Activity was flat to modestly up, led by manufacturing, with employment largely unchanged. On AI, five of twelve districts reported some firms reducing staff through attrition, delaying hiring or planning less job creation after AI productivity gains. Reports of layoffs in general remained limited, and the AI remarks describe slower hiring rather than mass cuts.

## Key numbers

- 5 of 12: districts reporting firms that cut staff through attrition, delayed hiring or expected less job creation after AI productivity gains
- October 5, 2026: cut-off date for the information collected
- 180+ hours: interviews conducted by AI agents in two days at one staffing firm, which then hired recruiters to review them

## What it reports

- National: two districts reported moderate growth, three modest, four slight and three no change. Most reported little or no net change in employment; shortages persisted for specialised, technical and skilled-trade workers.
- Five districts: some firms reduced staffing through attrition, delayed hiring or expected less job creation after investing in AI.
- Examples from district reports: larger organisations cutting selected jobs or holding off hiring while integrating AI; two banking contacts saying automation and AI reduced staffing needs; a business-services firm reducing employment through attrition; IT staffing contacts saying AI was not causing layoffs but was dampening new job creation.
- Some large accounting firms were delaying new employment contracts because they may need fewer entry-level hires than planned, partly due to AI efficiency.
- One staffing firm hired recruiters to review the output of AI agents that ran more than 180 hours of interviews in two days.
- One district said AI implementation broadened among surveyed firms, but most did not expect significant workforce reductions in the near term.
- AI also shows up as demand: data centres driving manufacturing, utility load and construction labour costs; rising costs of software and AI tools for banking and professional-services contacts.

## How they got it

Each Reserve Bank gathers mostly qualitative information from its directors and from interviews and online questionnaires with businesses, community organisations, economists and market experts, then summarises it by district; the national summary aggregates the district reports. Contacts are curated, not selected at random.

## Limits

- Anecdotal and qualitative; district counts are not weighted by employment or representative.
- It summarises contacts' comments and is not a view of Federal Reserve officials.
- AI remarks are scattered across sections and not systematically asked about.

## Why it matters for companies

The pattern contacts describe matches what administrative data show in France: AI effects arrive through attrition and fewer new roles, especially entry-level, not mass layoffs. Firms benchmarking their own plans can expect peers to be quietly shrinking junior intake. The staffing example also shows a new review workload created by agent output, which should be counted when estimating savings.

Original: https://www.federalreserve.gov/monetarypolicy/beigebook202610.htm