---
title: Technology efficiency affects jobs differently
date: "2025-06-14T04:55:26Z"
lastmod: "2025-06-14T07:44:59Z"
categories:
  - visualisation
  - links
wp_id: 4141
description: "Technology can either destroy or grow jobs depending on demand elasticity, complements, regulation, and ecosystem effects, which matters for forecasting AI-era labor shifts."
keywords: ["jobs", "technology change", "efficiency", "labor economics", "AI jobs", "demand elasticity"]
---

![Technology efficiency affects jobs differently](/blog/assets/output-5.webp)

Jobs fall with technological efficiency.

- **Farmers** in the US fell from 40% (1900) to ~2.7% (1980) and ~74% drop from 1948 to 2019 despite ~175% output growth; wheat harvest efficiency rose ~75\* (300>3-4 man-hours).
- **Mechanics & repairers** grew from ~140 k (1910) to ~4.64 M (2000); machinery reliability lagged so technician demand surged over decades.
- **Construction workers** doubled from 1.66 M (1910) to 3.84 M (2000) even as labor share fell (4.3>3.0%); 5-10\* productivity gains met booming development.
- **Switchboard operators** plunged from ~1.34 M (1950) to ~40 k (1984) and ~4 k today as rotary-dial and digital switching automated call handling.
- **Travel agents** dropped >50% from ~100 k (2000) to ~45 k (2022) while travel demand rose; online booking doubled trips per agent.
- **Elevator operators** went from building-staff staple to near zero by the 1940s once automatic doors and button controls arrived.
- **Lamplighters** vanished from thousands to near zero post-1907 electrification; Edison's incandescent lamps eliminated manual lighting.

Jobs also grow with technology efficiency.

- **Software/IT workers** surged from ~450 k (1970) to 4.6 M (2014); RAM price-performance jumped >100 000\* (1 MB at $5 k>1 GB at <$0.03). 1980 IBM PC launch triggered a scramble for COBOL and BASIC coders-employers flew recruiters with cash-filled briefcases to MIT; "Y2K bounty hunters" later earned $1 k/hr inspecting two-digit dates.
- **Registered nurses** climbed from ~12 k (1900) to ~3 M (2024); medical tech (antibiotics, MRI, EHRs) boosted care per nurse >10\*. WWI field hospitals proved that trained nurses cut mortality by half; politicians returned home demanding hospital schools-enrollment tripled in one decade.
- **Wind-turbine technicians** rose from ~4.7 k (2012) to 11.4 k (2023) and head for 18.2 k (2033); turbine capacity (660 kW>4+ MW) and remote SCADA expanded roles. First U.S. "windtech" apprentice class (Minnesota West, 2004) trained atop a decommissioned 90-ft tower welded to the parking lot; grads had 100 % placement.
- **Solar-PV installers** jumped from ~4.7 k (2012) to 24.5 k (2023) as panel costs collapsed 80% and snap-in racking doubled installs per crew-day. An Arizona roofer who added PV installs in 2011 hit $1 B revenue by 2022-outselling five coal mines combined.
- **Social-media managers** grew from ~2 k (2010) to ~61 k (2024); auto-schedulers let one person handle 50+ brand channels. Oreo's "dunk in the dark" tweet (Super Bowl 2013) was crafted by a 15-person war-room-today a solo creator can replicate that reach on TikTok.

What drives head-counts growth **despite** efficiency jumps? Here's ChatGPT's guess:

- **Elastic new demand** - Cheaper output unlocks previously-priced-out customers (software for every desk, electricity from prairie winds).
- **Complementary task creation** - Tech automates **routine** sub-tasks, freeing humans for **non-routine** extensions: nurses moved from bed-making to ICU monitoring; developers shifted from punch-cards to UX design. ([nber.org][12])
- **Regulatory or safety mandates** - Every extra MRI or turbine needs certified operators; compliance can outrun automation.
- **Network effects & ecosystems** - Social platforms, app stores and open-source stacks spawn whole job families (community managers, DevRel).
- **Local installation & maintenance bias** - PV panels assembled in Asia still need boots-on-roof locally.

So in the context of LLMs, here's a guess on roles that could grow.

1. **Edge-of-frontier complements**. Prompt engineers, AI ethics leads, autonomous-fleet technicians emerge where tech leaves gaps.
2. **Orchestrators & translators**. Roles that fuse domain expertise with LLM tooling (e.g., "AI curriculum architect" in education).
3. **Ecosystem enablers**. Marketplace ops, trust & safety reviewers, fine-tuning data curators.
4. **Regtech & audit**. Assurance positions verifying model compliance and bias-often imposed by statute (EU AI Act clones).
5. **Experience designers**. As core tasks automate, differentiation shifts to narrative, community and emotion; expect growth in AI game writers, "synthetic brand" managers.

PS: The image was [vibe-coded](https://github.com/sanand0/datastories/tree/main/employment-trends) from [BLS stats](https://www.kaggle.com/datasets/bls/employment).

[LinkedIn](https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7339558335588782081)