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Time Machine

Electrical and Electronic Engineering Technologists and Technicians

Scrub through 95years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

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195019752000now
Country
2026
Known today as Electrical and Electronic Engineering Technologists and Technicians (2010 SOC, current)
Latest actual · 2024
94K
BLS OEWS May 2024, as reported by the BLS Occupational Outlook Handbook and O*NET. Employment has declined approximately 34% from the 2013 OES figure of 141,150 and more than 67% from the estimated 2000 peak. The primary drivers: (1) continued offshoring of electronics manufacturing to Asia reduced the domestic production test technician workforce; (2) automated test equipment (ATE) running AI-assisted test programs reduces the headcount required per production test cell; (3) field service AI (ServiceNow FSM) reduces administrative labor content per service call; (4) the domestic semiconductor fab expansion (CHIPS Act, announced 2022) has not yet translated into technician hiring at scale. BLS projects +1% growth 2024-2034, approximately flat.
Latest actual · 2024
$77,180
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Analog bench instruments: vacuum-tube voltmeters, oscilloscopes, and signal generators (postwar era)

    The first generation of professional electronics technicians worked with purely analog measurement tools. The vacuum-tube voltmeter (VTVM), introduced in the late 1930s and mass-produced for military and industrial use from 1941, was the defining instrument of this era: it allowed technicians to measure voltage without loading the circuit under test, a critical capability for troubleshooting high-impedance radio circuits. The cathode-ray oscilloscope, produced at scale by Tektronix from 1947, gave technicians their first visual window into waveform shape and timing. Signal generators and RF millivoltmeters completed the bench. The diagnostic method was systematic: divide-and-conquer signal tracing from input to output, comparing measured voltages against schematic-specified values. A skilled technician carried a dog-eared copy of the relevant manufacturer service manual and could locate a faulty vacuum tube or resistor in a complex radio or television receiver in 30-60 minutes.

    Effect on the work

    This era established the basic skill model that would persist for decades: read a schematic, set up instruments, trace a signal, isolate a fault, replace a component, verify the repair. Employment grew rapidly throughout the 1950s as television manufacturing expanded and defense electronics budgets remained high.

    Work toolChanging equipment
  • Solid-state instruments and early digital meters (transistor oscilloscopes, DMMs)

    The transition from vacuum-tube to solid-state electronics during the 1960s transformed both the products being tested and the instruments used to test them. Tektronix released the first fully solid-state oscilloscope (the 661) in 1963; Hewlett-Packard and others introduced digital voltmeters (DVMs) that replaced analog pointer instruments with numeric readouts, reducing reading errors and enabling automated data recording. The arrival of the integrated circuit (Intel 4004, 1971; 8080, 1974) added a new dimension: logic analyzers, introduced commercially by HP and Tektronix in the early 1970s, gave technicians the ability to capture and display digital signal patterns across multiple lines simultaneously. The skill requirement shifted from reading analog waveforms to interpreting digital timing diagrams. The Keithley 610C solid-state electrometer (1967) and the HP 3400A true-RMS voltmeter pushed precision measurement into new ranges. Electronics technicians who had trained on vacuum-tube circuits now had to understand transistor biasing, TTL logic levels, and the behavior of op-amps in linear circuits.

    Effect on the work

    The shift to solid state increased the knowledge requirement for bench technicians without yet reducing employment: more complex circuits, more specialized instruments, and the rapid proliferation of new product categories (stereo hi-fi, early computers, telecommunications switching equipment) all increased demand for skilled technicians. The engineering technician workforce grew steadily through the 1960s and 1970s.

    Work toolChanging equipment
  • Automated test equipment (ATE): HP 3060A, GenRad 227x, Teradyne L200 (bed-of-nails in-circuit test)

    Automated test equipment transformed the production test side of electronics technician work during the late 1970s and 1980s. In-circuit test (ICT) systems, pioneered by GenRad (General Radio) with the 2271 in 1972 and later by Hewlett-Packard and Teradyne, used a bed-of-nails fixture to probe every component on a populated circuit board and verify its value against a programmed netlist. The ATE system replaced the bench technician who had previously performed manual point-to-point continuity and component verification checks: a human task that had taken 20-40 minutes per board could now be completed in under a minute. The technician role in ATE production environments shifted from performing the test to programming, maintaining, and troubleshooting the ATE system itself. This was the first major instance of automation reducing the labor content of a core technician task, and it drove the first structural shifts in the technician job mix away from production verification toward ATE programming and maintenance.

    Effect on the work

    ATE adoption in electronics manufacturing reduced the number of bench verification technicians required per unit of production volume, but simultaneously created demand for ATE programmers and fixture builders. The net employment effect on the overall technician workforce was modest through the 1980s because electronics manufacturing was still expanding. The structural displacement became visible only when ATE automation combined with offshore manufacturing after 2000.

    Work toolChanging equipment
  • PC-based test and SCPI instruments: HP BASIC, LabVIEW (1986), MATLAB (1984), GPIB/IEEE-488

    The personal computer fundamentally restructured the electronics technician's relationship with test instruments. Before PC-based test, automated measurement required dedicated HP or Tektronix minicomputers running HP BASIC, typically operated only by engineers. The IEEE-488 (GPIB) instrument bus, standardized in 1975, allowed any computer to control any compatible instrument; but the real democratization came with IBM PC clones in the early 1980s and National Instruments' LabVIEW graphical programming environment, introduced in 1986. LabVIEW let technicians build automated measurement routines without formal programming skills: wire together virtual instruments on a graphical block diagram and the software generated the underlying code. MATLAB (first release 1984) entered the test lab through engineer adoption but quickly became a standard analysis tool for technicians performing production data analysis and calibration uncertainty calculations. The combination of GPIB, LabVIEW, and affordable PC hardware transformed the technician workstation from a collection of independent meters into an integrated measurement system that could log, analyze, and report data automatically. This era also introduced SCPI (Standard Commands for Programmable Instruments, 1990), which standardized the command syntax for controlling instruments over GPIB, GPIB-over-Ethernet (LXI), and later USB.

    Effect on the work

    PC-based test increased the productivity of individual technicians substantially: tasks that had previously required a dedicated engineer or a specialist ATE programmer could now be accomplished by a technician with LabVIEW training. This partially offset the employment reduction from ATE automation by creating new, higher-skill roles. The era produced a two-tier technician workforce: those who could program LabVIEW and write MATLAB scripts earned significantly more than those limited to operating pre-programmed ATE systems.

    Work toolChanging equipment
  • Offshore manufacturing and the domestic technician contraction (2001-2010)

    The period 2001-2010 was the most disruptive decade in the occupation's post-WWII history. The dot-com bust eliminated large segments of the semiconductor and telecommunications equipment manufacturing workforce in 2001-2002. The broader shift of consumer electronics and contract electronics manufacturing to China, Taiwan, and Southeast Asia throughout the 2000s transferred the largest single category of technician employment (production test and quality) offshore. The US electronics manufacturing workforce, which had peaked in 2000, shed roughly 600,000 jobs between 2001 and 2010 across all occupations including technicians. For SOC 17-3023, the BLS OEWS series (starting 2003) shows the occupation declining from approximately 180,000-200,000 in the early 2000s to around 140,000 by 2010-2013. The technician roles that survived the offshoring wave were concentrated in segments the offshore model could not easily reach: defense and aerospace electronics (ITAR-controlled, requiring cleared US personnel), medical device manufacturing (FDA 21 CFR Part 820 human verification requirements), power utility and telecommunications infrastructure field service, and high-complexity R&D lab support. These surviving segments are higher-skill, higher-wage, and more defensible than the production test roles that were lost.

    Effect on the work

    Employment fell from an estimated ~280,000-300,000 at the 2000 peak to ~141,000 by the 2013 OES measurement, a decline of roughly 50% over 13 years. This is one of the steepest sustained employment contractions in any engineering-adjacent occupation in the BLS record.

    Work toolChanging equipment
  • Cloud-connected ATE, LXI instruments, and Python test automation (NI TestStand, Keysight OpenTAP)

    The second decade of the 2000s brought a new wave of test infrastructure modernization to the technician's bench. LXI (LAN eXtensions for Instrumentation), standardized in 2005 and widely adopted through the 2010s, replaced GPIB bus cabling with standard Ethernet, allowing instruments to be controlled from anywhere on the network and enabling remote test monitoring from a desktop. NI TestStand became the industry-standard test executive framework for structured production test, providing a GUI-based sequence editor that technicians (not just engineers) could use to build and maintain complex test programs. Python, once relegated to software engineering, entered the test lab as GitHub Copilot and similar tools made scripting accessible without a CS background: technicians writing Python for VISA/SCPI instrument control became common by the early 2020s. The cloud connection of ATE systems enabled centralized data collection and real-time yield monitoring across multiple production sites.

    Work toolChanging equipment
  • AI-native test tools: NI TestStand AI, MATLAB AI Copilot, GitHub Copilot for SCPI, Altium 365 AI

    Beginning in 2023-2024, AI assistance became integrated into the core tools of the electronics technician workstation in ways that directly compress the highest-skill cognitive tasks. NI TestStand 2024 added AI-assisted sequence authoring that generates test sequence scaffolding, step logic, and instrument driver calls from natural-language descriptions of test requirements. MathWorks added a Copilot to MATLAB Live Editor that generates data analysis scripts from conversational prompts, enabling technicians to build production yield dashboards without deep MATLAB programming fluency. GitHub Copilot, trained on millions of Python and C# repositories including instrument control code, generates VISA/SCPI command sequences for oscilloscopes, DMMs, and signal generators with a speed and accuracy that previously required significant scripting experience. Altium 365 AI Copilot compresses first-pass PCB design review from hours to minutes. Fluke MET/CAL AI assists calibration technicians with measurement uncertainty calculations and calibration interval optimization. The net effect: the cognitive tasks that previously differentiated the top-tier from the mid-tier technician are now accessible with AI assistance; the physical bench and field tasks that AI cannot perform remotely remain the unchallenged human moat.

    Effect on the work

    The direction of the AI-era employment effect is compressing, not eliminating, the technician workforce. Tasks that previously required a 3-5 person test engineering team can now be handled by 2 people using AI tools, which reduces headcount while increasing output quality. BLS projects only +1% growth 2024-2034, consistent with continued modest contraction balanced by semiconductor fab expansion and defense electronics modernization programs.

    AI audit toolsPattern detection
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
CHIPS and Science Act (2022) — semiconductor workforce demand signal
2030
+8%
The CHIPS and Science Act (signed August 2022) authorized $52.7 billion for US semiconductor manufacturing, research, and workforce development, with $39 billion in manufacturing incentives attracting announced fabs from Intel (Ohio, $20B), TSMC (Arizona, $40B), Samsung (Texas, $17B), and Micron (New York, $100B, partially federal incentivized). Each new wafer fab is estimated to require approximately 2,000 skilled technician-tier workers per facility, a category heavily weighted toward electrical/electronic and electro-mechanical technicians. If the announced fabs reach planned capacity by 2028-2030, the incremental demand for 17-3023-adjacent technicians could reach 10,000-20,000 positions. This projection uses a conservative +8% (approximately +7,500 positions from 93,700) as the upside scenario. The actual realized hiring will depend heavily on fab construction timelines and whether domestic training pipelines can produce credentialed technicians at the required scale.
BLS National Employment Matrix 2024-34
2034
+1%
BLS Employment Projections program, 2024-2034 cycle. Projects +1% employment change for SOC 17-3023 over the decade, approximately equivalent to +937 positions from the 93,700 base (2024). Classified as "slower than average" against an all-occupations average of +4%. About 8,400 openings projected annually, with most from replacement needs (retirements and occupational transfers) rather than net growth. The BLS methodology incorporates industry-occupation matrices and labor productivity assumptions; the projection reflects the continued gradual decline in domestic electronics manufacturing and ATE-driven productivity improvements offset by modest gains in semiconductor, defense, and clean-energy electronics sectors.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. — "GPTs are GPTs" (2023)
2028
41%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for SOC 17-3023. The Eloundou et al. analysis found a moderate LLM exposure score (beta approximately 0.407) for electrical and electronic engineering technicians. The 41% figure represents the estimated share of occupational tasks where LLMs provide meaningful capability exposure: primarily test documentation, procedure authoring, data analysis scripting, and drawing review tasks. The physical bench and field tasks (rework, probing, connector seating, on-site commissioning) score near-zero for LLM exposure. This is classified as kind: exposure rather than employment because LLM exposure is a task-level measure, not a headcount forecast. The 41% task exposure is high relative to most physical occupations but modest compared to purely cognitive roles; the split between high-exposure cognitive tasks and near-zero-exposure physical tasks makes this occupation unusually bifurcated in its AI risk profile.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereAuthor and maintain technical documentation — test procedures, calibration work instructions, assembly travelers, failure analysis reports, and field service bulletins — using AI writing assistants: draft procedure steps from bullet-point notes using ChatGPT or GitHub Copilot

Author and maintain technical documentation — test procedures, calibration work instructions, assembly travelers, failure analysis reports, and field service bulletins — using AI writing assistants: draft procedure steps from bullet-point notes using ChatGPT or GitHub Copilot; structure failure analysis reports from bench observation notes; review AI-generated procedure drafts for technical accuracy, safety warnings, and compliance with company document control format; submit for engineer review and document control approval.[12],[10]

Where your edge is

AI writing tools compress procedure authoring from hours to minutes and produce well-structured first drafts from rough notes — a genuine time multiplier for technicians who spend 10–20% of their week on documentation. The critical gap is that AI cannot verify technical accuracy: a ChatGPT-drafted calibration procedure that omits a warm-up time requirement or specifies the wrong input range will produce invalid calibration data. Before submitting any AI-drafted procedure for approval, walk through each step physically against the actual instrument or assembly to verify the sequence, safety notices, and measurement values are correct.

AI is sitting alongside you hereAnalyze production test data and identify yield trends, failure-mode clusters, and marginal units using MathWorks MATLAB with AI Copilot features or NI LabVIEW AI Assistant: load test log CSV or TDMS files

Analyze production test data and identify yield trends, failure-mode clusters, and marginal units using MathWorks MATLAB with AI Copilot features or NI LabVIEW AI Assistant: load test log CSV or TDMS files; use AI-assisted code generation in MATLAB Live Editor to write data import, statistical analysis, and visualization scripts; build control charts, parametric scatter plots, and failure-mode Pareto charts; present findings to the test engineer for design-margin or process-parameter corrective action.[6],[13]

Where your edge is

MATLAB AI Copilot and LabVIEW AI Assistant dramatically compress the scripting work required to analyze production test datasets — a technician with basic MATLAB familiarity can now produce sophisticated yield-trend dashboards that previously required an engineer or data analyst. But the AI generates code for the analysis you describe; choosing the right statistical test (Cpk vs. Ppk, Anderson-Darling normality test vs. visual histogram inspection) and interpreting the results against the design margin — not just flagging outliers — requires engineering context the AI does not have. Build working MATLAB statistical toolbox fluency alongside AI-assisted code generation so you can verify the analysis is correct rather than just accepting AI-generated output.

AI is sitting alongside you hereSet up and execute automated test sequences on electronic assemblies, PCBs, and subsystems using NI TestStand or Keysight PathWave OpenTAP: build and maintain test sequences using AI-assisted sequence generation tools

Set up and execute automated test sequences on electronic assemblies, PCBs, and subsystems using NI TestStand or Keysight PathWave OpenTAP: build and maintain test sequences using AI-assisted sequence generation tools; configure instrument drivers for DMMs, oscilloscopes, power supplies, and function generators; run production test on PCB assemblies and rack-level systems; review AI-generated pass/fail logic and limit tables against engineer-provided test specifications before deploying to production.[4],[5]

Where your edge is

NI TestStand AI and OpenTAP AI automate routine sequence scaffolding and instrument-driver boilerplate, compressing multi-day scripting tasks to hours — but the AI has no model of your specific DUT (device under test) hardware topology, RF fixture parasitics, or production test yield targets. Build deep TestStand architecture fluency (execution engine, process models, result processing callbacks) so you can verify AI-generated sequence logic correctly handles DUT initialization failures, instrument timeout recovery, and limit violations before they misclassify product in production.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Industrial Production Managers

Senior electronics technicians who develop production scheduling, quality systems, and team leadership skills are well-positioned to move into Industrial Production Manager or Test Engineering Lead roles overseeing electronics manufacturing and test operations. This pivot is particularly natural for technicians who have run ATE production cells, maintained calibration programs, and trained junior technicians — they already carry the technical credibility and operational process knowledge that makes a production manager effective in an electronics manufacturing context. Industrial Production Managers earn a BLS median of $108,790 (2024), substantially above the $67,720 technician median, and BLS projects 4% growth through 2034. The transition requires adding formal production management skills (scheduling, OEE metrics, lean manufacturing tools) and people management to the strong technical foundation.

What you'd add
  • · Production scheduling and capacity planning: MRP/ERP systems (SAP Manufacturing, Oracle Cloud Manufacturing), OEE (Overall Equipment Effectiveness) measurement and improvement
  • · Lean manufacturing: 5S, SMED (quick changeover), value stream mapping for electronics test cell operations, waste elimination in PCB assembly and test flows
  • · Quality management systems: AS9100 (aerospace), ISO 13485 (medical devices), or IATF 16949 (automotive) quality system requirements relevant to your product domain; 8D corrective action process
  • · People management: training program development for technician onboarding, performance review practices, shift coordination, contractor supervision
  • · Cost and budget management: production cost modeling, test yield improvement ROI analysis, capital equipment justification for ATE cell upgrades
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The data behind this timeline

On record since1941
Latest tracked employment93,700 (US, 2024)
Latest median pay$77,180 (2024)
Outlook+8% by 2030 (CHIPS and Science Act (2022) — semiconductor workforce demand signal)
View all 28 cited data points
YearUS employmentMedian annual paySource
195050,000n/aESTIMATE
1960115,000n/aESTIMATE
1968n/a$7,800BLS-HISTORICAL-BULLETIN
1971157,000n/aESTIMATE
1980n/a$19,500ESTIMATE
2000287,000n/aESTIMATE
2003177,940$43,650BLS-OEWS
2004178,560$46,310BLS-OEWS
2005165,850$48,040BLS-OEWS
2006166,340$50,660BLS-OEWS
2007162,460$52,140BLS-OEWS
2008162,330$53,240BLS-OEWS
2009154,050$54,820BLS-OEWS
2010147,750$56,040BLS-OEWS
2011150,020$56,900BLS-OEWS
2012144,460$57,850BLS-OEWS
2013141,150$57,200BLS-OEWS
2014137,040$59,820BLS-OEWS
2015139,080$61,130BLS-OEWS
2016134,870$62,190BLS-OEWS
2017128,320$63,660BLS-OEWS
2018126,950$64,330BLS-OEWS
2019122,550$65,260BLS-OEWS
2020115,270$67,550BLS-OEWS
2021101,450$63,640BLS-OEWS
202299,050$66,390BLS-OEWS
202397,420$72,800BLS-OEWS
202493,700$77,180BLS-OEWS
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