---
id: "lib-2026-06-00-future-of-work-in-the-age-of-automation"
type: "library"
title: "Future of Work in the Age of Automation, Augmentation, and Agentic AI (M-RCBG AWP 276)"
lang: "EN"
org: "Unknown"
source_type: "Paper"
date: "2026-06-01"
week: "2026-W23"
url: "https://nosov.ai/library/lib-2026-06-00-future-of-work-in-the-age-of-automation/"
original_url: "https://www.hks.harvard.edu/sites/default/files/2026-07/Final_AWP_276%20FINAL.pdf"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "A long Harvard Kennedy School senior-fellow working draft by practitioner Sol Rashidi that compiles others' labour-market evidence into new role frameworks and policy proposals; useful synthesis, not reviewed and with no new data."
summary_basis: "full-text"
original_minutes: "180"
tags: ["future of work", "agentic ai", "workforce preparation"]
---

# Future of Work in the Age of Automation, Augmentation, and Agentic AI (M-RCBG AWP 276)

Tier B (Useful, limited): A long Harvard Kennedy School senior-fellow working draft by practitioner Sol Rashidi that compiles others' labour-market evidence into new role frameworks and policy proposals; useful synthesis, not reviewed and with no new data.

Summary based on the full text.

## In short

A 97-page working draft (last updated 5 June 2026) from Sol Rashidi, Senior Fellow at Harvard's Mossavar-Rahmani Center (M-RCBG Associate Working Paper 276). Its thesis is that AI's main risk is a "talent formation fracture": automating entry-level work removes the path by which people become experts. It proposes an M-shaped talent model, an Orchestrator-Operator-Artisan (OOA) role typology, and a set of policy interventions.

## Key numbers

- 6-13%: employment decline for early-career workers in AI-exposed occupations, as cited from Stanford Digital Economy Lab
- 15-20%: drop in graduate-level job postings, as cited from Handshake
- 14%: decline in job-finding rates post-ChatGPT versus 2022, as cited from Massenkoff and McCrory
- 36: O*NET tasks of a Search Marketing Strategist regrouped into four families in the worked example

## What it reports

- Cited evidence on early careers: 6-13% employment decline for workers aged 22-25 in exposed jobs, 15-20% fewer graduate postings, 14% lower job-finding rates, recent-graduate unemployment at 5.6%.
- Argues the T-shaped talent model fits stable environments only and proposes an M-shaped model with several depths plus AI judgment.
- OOA typology: Orchestrators design and govern workflows, Operators supervise AI-mediated execution, Artisans supply deep expertise and tacit judgment. Archetypes describe modes of work, not job titles.
- Worked example: Search Marketing Strategist (SOC 13-1161 category, about 941,700 US workers, median wage $76,950), with its 36 O*NET tasks scored against the Eloundou exposure rubric.
- Policy proposals include an "AI Nutrition Label", an AI agent payroll tax above a workforce-impact threshold, early-career hiring tax credits, AI deployment registries, AI-ISO standards, extended antitrust, displacement notification and a capability "kill switch".
- Introduces a trademarked term, "Intellectual Atrophy", for cognitive offloading risk.

## How they got it

Literature review across labour economics, cognitive science and management research, plus framework design. Appendices give a pay-structure method and task-scoring sensitivity analysis for the worked example.

## Limits

- No original dataset; headline numbers are secondary and some mix sources and periods.
- Working draft, explicitly not formally reviewed.
- Policy section counts seven interventions in the abstract and eight in the contents.
- Trademarked concepts and framework branding point to a consulting audience.

## Why it matters for companies

The question it raises is practical for workforce planning: if AI does junior work, where do future seniors learn judgment? The OOA typology gives HR and line leaders a vocabulary for redesigning roles by task rather than by title. Use the cited numbers through their original studies.

Original: https://www.hks.harvard.edu/sites/default/files/2026-07/Final_AWP_276%20FINAL.pdf