Evolving

Last Update: 3/13/2026

Your role’s AI Resilience Score is

69.3%

Median Score

Changing Fast

Evolving

Stable

Our confidence in this score:
Medium-high

What does this resilience result mean?

These roles are shifting as AI becomes part of everyday workflows. Expect new responsibilities and new opportunities.

AI Resilience Report for

Bicycle Repairers

They fix and maintain bicycles by checking for problems, repairing or replacing parts, and making sure everything works smoothly for safe riding.

This role is evolving

The career of a bicycle repairer is labeled as "Evolving" because, while current work relies heavily on human skills for tasks like aligning wheels and assembling bikes, there is potential for future AI tools to assist in diagnostics and other areas. Right now, there are no significant AI technologies or robots in use for bike repairs, but this could change as new tools are developed.

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Learn more about how you can thrive in this position

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Chat with Coach
Latest news
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Analysis
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News
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This role is evolving

The career of a bicycle repairer is labeled as "Evolving" because, while current work relies heavily on human skills for tasks like aligning wheels and assembling bikes, there is potential for future AI tools to assist in diagnostics and other areas. Right now, there are no significant AI technologies or robots in use for bike repairs, but this could change as new tools are developed.

Read full analysis

Contributing Sources

We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.

AI Resilience

AI Resilience Model v1.0

AI Task Resilience

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Stable iconStable

99%

99%

Microsoft's Working with AI

AI Applicability

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Evolving iconEvolving

52.9%

52.9%

Will Robots Take My Job

Automation Resilience

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Evolving iconEvolving

59.1%

59.1%

Althoff & Reichardt

Economic Growth

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Evolving iconEvolving

63.7%

63.7%

Low Demand

Labor Market Outlook

We use BLS employment projections to complement the AI-focused assessments from other sources.

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Growth Rate (2024-34):

-2.3%

Growth Percentile:

17.8%

Annual Openings:

1,600

Annual Openings Pct:

18.3%

Analysis of Current AI Resilience

Bicycle Repairers

Updated Quarterly • Last Update: 2/17/2026

Analysis
Suggested Actions
State of Automation

What's changing and what's not

Research shows that bicycle repair work still relies almost entirely on human skill. We did not find any “bike-fixing robots” or AI tools widely used in shops. The tasks listed – building frames, installing seats and gears, wrenching wheels, etc. – are all hands-on.

For example, O*NET lists “align wheels,” “assemble new bicycles,” and “install and adjust speed and gear mechanisms” as core tasks [1]. These activities involve fine motor control and custom fitting, which machines currently struggle to do automatically. Even at advanced factories like Brompton (a big UK bike maker), humans do most assembly: they use some machines for precise jobs like welding the folding hinge and robots for polishing or painting parts, but much of the bike-building is still by hand [2].

In short, outside of a few factory processes, we found no evidence of actual AI-driven bike repair or home-bicycle-service robots. (One example of AI in the cycling world is an aerodynamics app called “AiRO” that helps bike fitters tune a rider position [3], but that’s for elite training, not fixing broken gears or flat tires.)

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AI Adoption

AI in the real world

Several factors make AI adoption in bike repair slow. First, there are almost no off-the-shelf AI tools or robots for these specific tasks, so shops rely on familiar hand tools and basic software (inventory or sales systems) rather than new tech. O*NET notes repairers mostly use simple computer tools (like parts inventory databases and email) [1].

Second, most bike shops are small businesses or independents. For example, one London on-demand service employs 100 mobile mechanics who work for themselves [3]. Small shops usually can’t justify the huge cost of special robots or custom AI systems; paying skilled mechanics by the hour is still cheaper than buying and maintaining expensive machines.

Finally, bikes are relatively low-cost and each one can be different. Investing in AI pays off best when the same tasks repeat millions of times (like in car factories), which isn’t the case here. In sum, there’s little economic incentive or available technology to replace a human mechanic in most bike repairs [2] [3].

Overall, this means bicycle mechanics can feel hopeful: right now, human skills remain vital. 拢Humans are better at understanding a wiggly wheel than any AI today! Rabatt Young bike enthusiasts can take comfort that hands-on expertise and creativity – the same things that make cycling fun – are still the heart of bike repair [1] [2]. There may be helpful tools ahead (for example, apps to guide diagnostics), but for now AI is more of a distant idea than a reality on the workbench.

Managers will still trust the real person over an untested robot to fix their customer’s favorite bike.

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More Career Info

Career: Bicycle Repairers

Employment & Wage Data

Median Wage

$40,360

Jobs (2024)

13,200

Growth (2024-34)

-2.3%

Annual Openings

1,600

Education

High school diploma or equivalent

Experience

None

Source: Bureau of Labor Statistics, Employment Projections 2024-2034

Task-Level AI Resilience Scores

AI-generated estimates of task resilience over the next 3 years

1

95% ResilienceCore Task

Shape replacement parts, using bench grinders.

2

95% ResilienceSupplemental

Weld broken or cracked frames together, using oxyacetylene torches and welding rods.

3

90% ResilienceCore Task

Disassemble axles to repair, adjust, and replace defective parts, using hand tools.

4

90% ResilienceSupplemental

Repair holes in tire tubes, using scrapers and patches.

5

90% ResilienceSupplemental

Paint bicycle frames, using spray guns or brushes.

6

85% ResilienceCore Task

Align wheels.

7

80% ResilienceCore Task

Install and adjust speed and gear mechanisms.

Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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