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Computer Hardware Engineers

Computer & Mathematical
Projected change 2024–34: +7.3%
Median annual wage (2024): $155,020
Employment (2024): 77K

United States · BLS Employment Projections 2024–34 (figures for SOC 17-2061 occupational group)

AI exposure in published research

Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.

Data provider: OpenAI · "GPTs are GPTs"

Time basis: 2023 baselineex-ante estimate

Scored against GPT-4-generation capability.

Human rater basis
31.8%
GPT-4 rater basis
72.7%

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

18 rated tasks · 16 tasks with β ≥ 0.5 (88.9%)

Version:
gh-main-0471612
License:
MIT License · Copyright (c) 2024 OpenAI

Data provider: Anthropic Economic Index

Time basis: Published 2026-03-05composite index

Observed exposure
14.5%

Theoretical exposure index weighted by measured Claude usage, per the original report's definition.

17 tasks · usage observed in 4 · mean 21.3%

Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex

AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.

Version:
hf-lmi-2026-03
License:
CC BY 4.0
Observation period:
(not applicable to this release)
Model:
(not stated by the source)

2023 prediction vs observation-based index published 2026-03-05

One card (GPTs are GPTs) is a 2023 estimate of what AI could theoretically do; the other (Anthropic Economic Index) is built from observed usage and was published on 2026-03-05 — that is its publication date, not the period it observed. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.

Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.

The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.

The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.

These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.

Task Breakdown

  • Design computer hardware components and circuits
  • Test and validate hardware prototypes
  • Write technical specifications and documentation

About This Occupation

If you work as a Computer Hardware Engineer, AI is gradually augmenting your role. With an automation risk of 30/100 and overall exposure at 44%, this role faces medium transformation. BLS projects +5% growth through 2034.

ISCO-08 classification

Unit group 2152ILO official

Electronics Engineers

Definition

ILO original text (English)

Electronics engineers conduct research on, design and direct the construction functioning, maintenance and repair of electronic systems, and study and advise on technological aspects of electronic engineering materials, products or processes.

Definition & vocabulary source

Source: International Labour Organization (ILO) — ISCO-08 Structure

License: ILO CC BY 4.0

View original

Official occupational information

  • ONET17-2061.00

    Computer Hardware Engineers

    Research, design, develop, or test computer or computer-related equipment for commercial, industrial, military, or scientific use. May supervise the manufacturing and installation of computer or computer-related equipment and components.

    View original

Source: O*NET 30.2, U.S. DOL/ETA

License: CC BY 4.0

View original

Frequently Asked Questions

The Anthropic Economic Index puts observed exposure at 14.5%. The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 31.8% under human raters. Both figures are reproduced from published research without modification.

They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.

No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.