Will AI replace Railroad Brake, Signal, and Switch Operators and Locomotive Firers?
Most of the work in Railroad Brake, Signal, and Switch Operators and Locomotive Firers still leans on things AI struggles with — research rates its theoretical AI reach at only ~17%, and real-world use lower still.
O*NET-SOC 53-4022
How your 20 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~17%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~17%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 20 tasks, ratedBased on real task-level AI scores — click to collapse
- Receive oral or written instructions from yardmasters or yard conductors indicating track assignments and cars to be switched.
- Monitor trains as they go around curves to detect dragging equipment and smoking journal boxes.
- Observe tracks from left sides of locomotives to detect obstructions on tracks.
- Inspect locomotives to detect damaged or worn parts.
- Monitor oil, temperature, and pressure gauges on dashboards to determine if engines are operating safely and efficiently.
- Observe train signals along routes and verify their meanings for engineers.
- Signal locomotive engineers to start or stop trains when coupling or uncoupling cars, using hand signals, lanterns, or radio communication.
- Pull or push track switches to reroute cars.
- Observe signals from other crew members so that work activities can be coordinated.
- Inspect couplings, air hoses, journal boxes, and handbrakes to ensure that they are securely fastened and functioning properly.
- Operate locomotives in emergency situations.
- Raise levers to couple and uncouple cars for makeup and breakup of trains.
- Climb ladders to tops of cars to set brakes.
- Signal other workers to set brakes and to throw track switches when switching cars from trains to way stations.
- Check to see that trains are equipped with supplies such as fuel, water, and sand.
- Set flares, flags, lanterns, or torpedoes in front and at rear of trains during emergency stops to warn oncoming trains.
- Inspect tracks, cars, and engines for defects and to determine service needs, sending engines and cars for repairs as necessary.
- Start diesel engines to warm engines before runs.
- Make minor repairs to couplings, air hoses, and journal boxes, using hand tools.
- Connect air hoses to cars, using wrenches.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.