Aerospace engineering and operations technologists and technicians

AI Overlap Index
39.9 / 100
Selectively Exposed

Physical, social, or oversight-heavy work that AI augments rather than replaces.

SOC 17-3021 · Architecture And Engineering

Bureau of Labor Statistics
Median pay
$79,830/yr
Hourly
$38/hr
Jobs 2024
9,300
Projected 2034
10,100
10-yr outlook
+8% · Much faster than average
Employment change
800
Entry education
Associate's degree
SOC code
17-3021

Signal composition

how the 0-100 score is assembled

Task Automation Impact weight 60%
39.5
contribution to AOI: 23.7
Automation Potential weight 10%
50.0
contribution to AOI: 5.0
Market Pressure weight 15%
30.0
contribution to AOI: 4.5
Entry Barrier Erosion weight 15%
45.0
contribution to AOI: 6.8

By seniority

multiplicative adjustment from category curve

Entry
47.9
mult 1.20x
Mid
39.9
mult 1.00x
Senior
31.1
mult 0.78x

Entry-level roles carry the brunt because they concentrate the most automatable subset of tasks. Senior work is insulated by judgment, relationships, and accountability.

Task-level analysis

scored 0-100 for current-generation AI feasibility, weighted by BLS-stated importance

9 tasks · model: claude-sonnet-4-5-20250929
Important t5

Record data from test parts and assemblies

Recording test data is highly automatable—sensors can feed directly into databases, AI can structure and validate data formats, flag anomalies, and generate preliminary reports. This is primarily a data capture and organization task where AI excels with minimal human oversight needed.

BLS evidence: The duties section lists 'Record data from test parts and assemblies' as a key responsibility.

88
automation
Important t4

Program and run computer simulations to test new designs

AI can autonomously run simulations from defined parameters, execute test matrices, and generate initial results. Modern simulation tools with AI integration can handle routine design validation with minimal human intervention, though engineers still review outputs for critical decisions.

BLS evidence: The page states 'They also may program and run computer simulations that test the new designs.'

72
automation
Important t8

Determine causes of equipment malfunctions

AI diagnostic systems can analyze sensor data, compare against fault libraries, and suggest probable causes, significantly accelerating troubleshooting. However, physical inspection, testing hypotheses on actual equipment, and confirming root causes in complex aerospace systems still requires substantial human technical judgment and hands-on work.

BLS evidence: The page notes that technologists and technicians 'determine the causes of equipment malfunctions.'

52
automation
Important t6

Monitor and ensure quality in producing aircraft systems

AI can monitor sensor data, detect statistical anomalies, and flag deviations from specifications, but ensuring quality in aerospace production requires physical inspection, judgment calls on marginal cases, and accountability for safety-critical decisions that still require substantial human involvement.

BLS evidence: Technologists and technicians 'Monitor and ensure quality in producing systems that go into the aircraft.'

48
automation
Core t2

Operate and calibrate test equipment and computer systems for compliance with requirements

AI can assist with calibration protocols and data validation, but operating specialized aerospace test equipment requires physical manipulation, real-time judgment about equipment behavior, and hands-on adjustments that demand human presence and tactile feedback in safety-critical contexts.

BLS evidence: Technologists and technicians 'operate and calibrate computer systems so that they comply with test and manufacturing requirements' and 'calibrate test equipment, such as wind tunnels.'

38
automation
Important t7

Ensure test procedures are performed smoothly and safely

While AI can monitor procedure checklists and sensor data for anomalies, ensuring smooth and safe test execution requires real-time human judgment, physical intervention capability, and responsibility for safety in dynamic test environments where unexpected conditions arise.

BLS evidence: The duties include 'Make sure that test procedures are performed smoothly and safely.'

35
automation
Supporting t9

Meet with aerospace engineers to discuss test procedures and implications

While AI can summarize test data and draft technical reports, meetings involve collaborative problem-solving, interpreting ambiguous results, negotiating test modifications, and building shared understanding of complex technical tradeoffs—requiring human communication and real-time interactive reasoning.

BLS evidence: The duties list 'Meet with aerospace engineers to discuss details and implications of test procedures' as a responsibility.

25
automation
Core t1

Build and maintain test facilities for aircraft systems

Building and maintaining physical test facilities requires hands-on construction, installation of heavy equipment, troubleshooting mechanical/electrical systems in situ, and adapting to unpredictable physical constraints that current robotics cannot handle autonomously in aerospace environments.

BLS evidence: The duties section explicitly lists 'Build and maintain test facilities for aircraft systems' as a primary responsibility.

12
automation
Core t3

Install parts and systems into test equipment and aircraft

Installing parts into test equipment and aircraft requires precise manual dexterity, working in confined spaces with varying geometries, torque-sensitive fastening, and real-time problem-solving for fit issues—all beyond current AI+robotics capabilities in non-standardized aerospace assembly contexts.

BLS evidence: The duties include 'Make and install parts and systems to be tested in test equipment' and 'Install instruments in aircraft and spacecraft.'

8
automation

Task heatmap

automation score by task, sorted by weighted contribution

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External signals and sources

category-level priors and BLS fields that feed the four non-task signals

Automation Potential
50
karpathy 5/10
  • Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
Market Pressure
30
outlook: Much faster than average
  • BLS projected outlook: Much faster than average (8%)
  • Indeed demand signal (monthly refresh pending)
Entry Barrier Erosion
45
ed: Associate's degree
  • BLS typical entry-level education: Associate's degree
  • Credential trend signal (annual refresh)

Related in Architecture And Engineering

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