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Time Machine

Welders, Cutters, Solderers, and Brazers

Scrub through 155 years 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.

Carbon arc and bare metal electrode (experimental era)Carbon arc and bare metal electrode (experimental era)
SMAW shielded metal arc welding — extruded electrode coat (1927)SMAW shielded metal arc welding — extruded electrode coat (1927)
Unimate at GM (1961) — industrial welding robots begin (spot welding only)Unimate at GM (1961) — industrial welding robots begin (spot welding only)
Automotive spot welding ~95% robotic — Fanuc, Yaskawa Motoman, ABB dominateAutomotive spot welding ~95% robotic — Fanuc, Yaskawa Motoman, ABB dominate
Digital inverter welders (Lincoln PowerWave, Miller) + cobots enter fab shopsDigital inverter welders (Lincoln PowerWave, Miller) + cobots enter fab shops
AI arc tracking + orbital pipe robots + IIJA infrastructure demand (2021)AI arc tracking + orbital pipe robots + IIJA infrastructure demand (2021)
GTAW (TIG, 1941) and GMAW (MIG, 1948) — inert-gas shielded processesGTAW (TIG, 1941) and GMAW (MIG, 1948) — inert-gas shielded processes
19001925195019752000now

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2026
Known today as Welders, Cutters, Solderers, and Brazers (BLS SOC 51-4121)
US Employment
457K
BLS O*NET-sourced employment for SOC 51-4121 as of May 2024. The BLS National Employment Matrix uses 457.3 thousand as the 2024 baseline for the 2024-2034 projections cycle. Median annual wage: $51,000 ($24.52/hr) per O*NET/BLS OEWS 2024. The 45,600 projected annual openings over 2024-2034 reflect modest net employment growth (+2.2%) and replacement demand as the workforce ages.
Median Annual Wage
$51,000
Source: BLS-OEWS
AI arc tracking + orbital pipe robots + IIJA infrastructure demand (2021)Tool of the era · AI arc tracking + orbital pipe robots + IIJA infrastructure demand (2021)

The most recent era brought incremental automation into previously robot-resistant segments: orbital welding machines for pipe (a motorized head that travels around the pipe's circumference while the welder monitors parameters), adaptive arc-tracking systems that use vision or electrical feedback to follow irregular joint geometries, and AI-assisted parameter optimization. Yet the fundamental manual-weld segments remained intact. The Bipartisan Infrastructure Law (IIJA, 2021) committed $27.5 billion through the Bridge Formula Program specifically, and bridge welding — structural steel or reinforcing bar — remains a manual process. The AWS Certified Welding Inspector program (founded 1976, 100,000+ CWIs certified) certifies human inspectors, not machines. The 2023-2024 welder shortage of approximately 80,000 documented by AWS reflected this structural demand: the automatable corner of welding was automated long ago; the remaining workforce is needed precisely where robots cannot reach.

BLS projects +2.2% employment growth 2024-2034 — modest but positive, defying earlier projections of decline. The welder shortage (approximately 80,000 unfilled positions in 2023-2024) and sustained IIJA infrastructure demand suggest the occupation has stabilized at a floor defined by field welding, pipeline welding, repair welding, custom fabrication, and shipbuilding.

Projection cone · present → 2034

What credible sources project

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

AWS welder shortage / IIJA infrastructure demand scenario
2030
+8%
AWS and industry analysts documented a shortage of approximately 80,000 welders in 2023-2024 — unfilled positions representing roughly 17% of the total workforce. The shortage reflects: (a) retirement of experienced welders (median age above the national workforce average), (b) declining enrollment in welding trade programs, and (c) IIJA infrastructure demand ($27.5B Bridge Formula Program, $65B power grid upgrades, water infrastructure) requiring field-certified welders. If the shortage materializes into sustained wage pressure and program enrollment growth, employment could increase 6-10% above BLS baseline by 2030. This is the optimistic tail, contingent on infrastructure spending reaching full deployment speed and training pipelines responding.
BLS National Employment Matrix 2024-34
2034
+2%
BLS Employment Projections — industry-occupation matrix + labor productivity assumptions. The 2024-34 cycle projects SOC 51-4121 at +2.2% growth: baseline 457,300 (2024), projected 467,200 (2034), a net gain of 9,900 jobs over the decade. BLS characterizes this as "slower than average" growth. Annual job openings projected at 45,600, combining modest net growth with replacement demand from retirements and occupational transfers. The projection is notably more optimistic than Frey & Osborne's 2013 automation-risk assessment would suggest, reflecting the durability of the manual-weld segments that have resisted automation for 40+ years.
BLS Occupational Outlook Handbook 2024-34
2034
+2%
BLS OOH "slower than average" characterization for 51-4121. O*NET lists 45,600 projected annual openings 2024-2034. The OOH notes that while robots have automated much spot welding in automotive manufacturing, other industries continue to need skilled welders and the welder shortage has kept employment from declining. Median annual wage: $51,000 ($24.52/hr) as of May 2024. The OOH flags demand driven by infrastructure projects, oil and natural gas pipelines, and the construction sector.
Eloundou et al. — "GPTs are GPTs" (2023)
2028
-1%
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for 51-4121. Welders score very low on LLM exposure because the core tasks — setting up equipment, reading weld blueprints, manipulating electrodes, managing heat input, inspecting welds visually — are physical and perceptual, not text-based. The important distinction from Frey & Osborne: the displacement threat to welders is from robotics and computer vision, not from large language models. Eloundou's framework measures LLM exposure specifically; robotic welding is a different technology category. The -1% estimate represents near-term change from AI-assisted administrative tasks (weld procedure documentation, WPS/PQR generation, quality record management) rather than from the physical welding function.
Frey & Osborne (2013)
2033
-83%
Gaussian-process classifier on O*NET task features. Frey & Osborne (2013) assigned Welders, Cutters, Solderers, and Brazers a probability of computerization of approximately 0.83 — in the high-risk tier of the 702-occupation dataset, vindicating the automotive spot-welding substitution already underway. The bottleneck argument: the occupation scores low on social intelligence and creative originality, making it, by F&O's model, exposed to robotic substitution. The -83% figure represents the implied employment ceiling if the F&O probability were fully realized, which F&O did not claim. In practice, employment stabilized above 400,000 — F&O were directionally correct about the automatable portion (spot welding in automotive) but significantly overestimated the reach of automation into field, repair, and structural welding. F&O probability of 0.83 is cited from secondary literature; the exact appendix entry was not independently re-extracted from the primary PDF.
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 here

Operate safety equipment and use safe work habits.[2]

Where your edge is

AI is sitting alongside you here

Recognize, set up, and operate hand and power tools common to the welding trade, such as shielded metal arc and gas metal arc welding equipment.[2]

Where your edge is

AI is sitting alongside you here

Prepare all material surfaces to be welded, ensuring that there is no loose or thick scale, slag, rust, moisture, grease, or other foreign matter.[2]

Where your edge is

Present-day sources

Sources

Every claim on this page traces back to one of the following. Updated 2026-05-30.

  1. [1]Eloundou et al. 2024 — GPTs are GPTs (Science)· accessed 2026-05-30
  2. [2]O*NET 30.3 — US Department of Labor· accessed 2026-05-30
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