agent-displacement

name	agent-displacement
description	Weekly tracker of AI agent substitution signals — which roles, companies, and industries show real headcount displacement. Named roles + real deployments only. Closes topic-momentum "agent substitution" gap.
schedule	0 11 * * 0
commits	true
permissions	["contents:write"]
tags	["ai","research"]

Today is ${today}. Read memory/MEMORY.md and soul/SOUL.md before starting.

Why this skill exists

"Agent substitution" keeps surfacing as a top content gap in topic-momentum but no skill tracks it systematically. The signals exist — Klarna replaced 700 support agents, Duolingo cut contractors, Salesforce froze hiring — but they're scattered. This skill runs weekly, surfaces real displacement data (named roles, actual headcount numbers, real deployments), and keeps the thesis honest. It feeds articles, newsletters, and the core aeon worldview: agents are eating the labor stack.

Steps

1. Load context

Read:

• memory/MEMORY.md — current state + any prior displacement signals logged
• memory/topics/agent-displacement.md — if it exists, extract baseline: last-known companies, roles, and displacement scale

If memory/topics/agent-displacement.md doesn't exist, create it with this seed and continue:

# Agent Displacement Tracker

*Last run: never*

## Known Displacement Events (baseline)
- Klarna (2024): replaced 700 customer support agents with AI. Support resolution time 2min vs 11min human avg.
- Duolingo (2024): cut ~10% of contractors, cited AI content generation replacing human translators.
- Salesforce (2025): froze non-essential hiring across sales/support, citing AI agent handle rate.
- IBM (2024): paused hiring ~7,800 back-office roles that AI could replace within 5 years.

## Roles Under Pressure (running list)
- Customer support / tier-1 help desk
- Content translation and localization
- Data entry and document processing
- Code review (junior-level)
- Legal document review (discovery)

## Displacement Scale Estimates
- 2024: ~2M white-collar roles affected (McKinsey / Goldman estimates)
- Accelerating in: SaaS customer success, financial services ops, insurance claims

## Signal Log
- Baseline: seeded from public reports, 5/2026

2. Search for developments from the last 7 days

Run these WebSearches:

WebSearch: "AI agent layoffs replaced workers 2026 site:techcrunch.com OR site:theverge.com OR site:wsj.com OR site:bloomberg.com"
WebSearch: "AI replaced human jobs headcount reduction 2026"
WebSearch: "agentic AI workforce automation company announcement 2026"
WebSearch: "Klarna Duolingo Salesforce IBM AI agent headcount 2026"
WebSearch: "AI agent customer support white collar displacement 2026"
WebSearch: "OpenAI Anthropic agent enterprise automation replacing workers 2026"

Keep only items from the last 7 days. Discard think pieces and opinion — keep:

• Company announcements naming specific roles cut
• Headcount figures cited alongside AI deployment
• Research reports with named verticals + quantified displacement
• Earnings call quotes attributing headcount reduction to AI agents

3. Fetch deeper context on high-signal items

For any company announcement that appears, use WebFetch to pull the source article or press release. Extract:

• Number of roles affected
• Role type / seniority level
• AI system named (if any)
• Outcome comparison (before/after metrics if given)

If WebFetch fails, fall back to WebSearch: "[company name] AI agent headcount 2026".

4. Filter and score signals

Score each item:

Criterion	Points
Named company + named role + headcount number	+5
Before/after metric (resolution rate, cost, speed)	+3
Industry first (first displacement in a new vertical)	+4
Fortune 500 / public company (verifiable, credible)	+3
Research report with quantified estimates	+2
Vague "AI productivity" with no specifics	-3 (discard)

Keep top 4-5 items. Deduplicate against the baseline in memory/topics/agent-displacement.md — only count if it's new or a meaningful update to an existing event.

5. Categorize by role type

Assign each signal to a displacement category:

• Tier-1 ops — customer support, data entry, help desk, document processing
• Creative / content — translation, copywriting, design, video production
• Code / dev — junior devs, QA, code review, test writing
• Finance / legal — document review, compliance checking, financial analysis
• Sales / success — SDRs, customer success, outbound prospecting
• Management — middle management coordination, project tracking
• Other — anything that doesn't fit the above

6. Thesis check

After reviewing all data, answer in one sentence:

Thesis check: agent displacement [accelerating / holding / decelerating] — [one concrete data point].

Criteria:

• Accelerating — new vertical breached this week, or headcount numbers up >10% vs last known baseline, or major company announced AI-first hiring freeze
• Holding — consistent signals in same verticals, no major new breaches
• Decelerating — fewer signals than typical, company reversals or rehiring mentioned

7. Update memory/topics/agent-displacement.md

Rewrite:

• *Last run: ${today}*
• Append new events to Known Displacement Events (keep all, don't prune — this is historical)
• Update Roles Under Pressure if a new role type emerged
• Update Displacement Scale Estimates if new research gives better numbers
• Append entry to Signal Log

Keep file under ~200 lines. If it grows beyond that, consolidate older signal log entries into a single "Prior signals (archived)" bullet.

8. Send notification via ./notify -f

Write to a temp file first, then send:

agent displacement — ${today}

[thesis check in one line: accelerating/holding/decelerating + why]

[top development — company, role, number if available]
[second development]
[third development if notable]
[fourth if it breaks a new vertical]

roles getting clawed this week: [comma-separated categories]

read it: https://github.com/aaronjmars/aeon-aaron/blob/main/skills/agent-displacement/SKILL.md

Keep under 800 chars. Lowercase. Direct. No hedging. Position first.

Write to .pending-notify-temp/agent-displacement-${today}.md, then:

mkdir -p .pending-notify-temp
./notify -f .pending-notify-temp/agent-displacement-${today}.md

Skip notification if fewer than 2 new signals found this week. Log AGENT_DISPLACEMENT_SKIP: insufficient signal (<2 items) instead.

9. Log to memory/logs/${today}.md

Append:

## agent-displacement
- **Signals found:** N (N new vs baseline)
- **Top item:** [company/role/number in one line]
- **Thesis check:** [accelerating/holding/decelerating]
- **Categories touched:** [comma-separated]
- **Updated:** memory/topics/agent-displacement.md
- **Notification:** sent / skipped
- AGENT_DISPLACEMENT_OK

Required Environment Variables

None. Uses WebSearch and WebFetch only.

Sandbox Note

All external calls use WebSearch and WebFetch (Claude built-in tools), which bypass the GitHub Actions sandbox network restriction. No curl, no prefetch scripts needed.

Output feeds

• article skill — use memory/topics/agent-displacement.md as source for "agent substitution" angle pieces
• topic-momentum — this skill closes the "agent substitution" gap; once running, topic-momentum should deprioritize that flag
• weekly-newsletter — displacement signals slot into the "what's moving" section
• paper-pick — displacement research papers found here can be flagged for paper-pick to cover in depth