# Musical Instrument Repairers and Tuners

> Repair percussion, stringed, reed, or wind instruments. May specialize in one area, such as piano tuning.

- **SOC code:** 49-9063.00
- **Canonical URL:** https://singulariki.com/roles/role-49-9063-00
- **Also known as:** Instrument Repair Technician (Instrument Repair Tech), Luthier, Piano Technician (Piano Tech), Piano Tuner, Brass Instrument Repair Technician (Brass Instrument Repair Tech), Fretted String Instrument Repairer, Guitar Repairer, Musical Instrument Repair Technician (Musical Instrument Repair Tech)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Align pads and keys on reed or wind instruments.
- Adjust string tensions to tune instruments, using hand tools and electronic tuning devices.
- Solder posts and parts to hold them in their proper places.
- Compare instrument pitches with tuning tool pitches to tune instruments.
- Play instruments to evaluate their sound quality and to locate any defects.
- Disassemble instruments and parts for repair and adjustment.
- Repair or replace musical instrument parts and components, such as strings, bridges, felts, and keys, using hand and power tools.
- Reassemble instruments following repair, using hand tools and power tools and glue, hair, yarn, resin, or clamps, and lubricate instruments as necessary.
- Remove dents and burrs from metal instruments, using mallets and burnishing tools.
- Inspect instruments to locate defects, and to determine their value or the level of restoration required.
- Adjust felt hammers on pianos to increase tonal mellowness or brilliance, using sanding paddles, lacquer, or needles.
- Remove irregularities from tuning pins, strings, and hammers of pianos, using wood blocks or filing tools.

**Emerging tasks** (O*NET):
- Adjust the neck angle on fretted instruments.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Hearing Sensitivity _(ability)_
- Customer and Personal Service _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Manual Dexterity _(ability)_
- Finger Dexterity _(ability)_
- Near Vision _(ability)_
- Quality Control Analysis _(transferable_skill)_
- Mechanical _(knowledge)_
- Troubleshooting _(transferable_skill)_
- Repairing _(transferable_skill)_
- Control Precision _(ability)_
- Auditory Attention _(ability)_

**Skills in demand:**
- Finger Dexterity _(Common Skill)_
- Visualization _(Specialized Skill)_
- Critical Thinking _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- English Language _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_
- Time Management _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Equipment Selection _(Specialized Skill)_
- Equipment Maintenance _(Specialized Skill)_

**Tools & technology:**
- Katsura Shareware KS Strobe Tuner
- Katsura Shareware ProLevel
- Katsura Shareware SoundFrames
- Mensurix Audio
- Reyburn CyberTuner
- TonalEnergy Tuner & Metronome
- Tunable Instrument Tuner
- TuneLab
- Tunic OnlyPure
- Veritune Verituner

## AI exposure & outlook

- **AI task-overlap index:** 26th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 34th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 6th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 45th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 81st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.4% growth (About average); 0.6k annual openings; 6.2k → 6.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,320; 5,730 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-49-9063-00_