Athletes and sports competitors: AI exposure and career outlook
Athletes and sports competitors (SOC 27-2021) sit at the 47th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 23% of tasks are already automated and 49% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 47% follows an automation pattern and 53% an augmentation pattern — close enough to even that the grouping below should be read loosely, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 53200 (Athletes), and ESDC's COPS 2024–2033 outlook for that unit group is strong risk of surplus.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 27-2021
- Canada NOC 2021
- 53200
- TEER level
- 3 — College / Apprenticeship <2 yrs
- COPS outlook
- Strong risk of Surplus
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 22% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 2nd percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 47% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 53% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 23% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 49% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
Microsoft and Anthropic place it 50 percentile points apart. The modelled figures above average the two, so for this occupation that average sits between two readings that do not agree rather than summarising a settled one. Read the two measured rows above in preference to the modelled percentages, and treat the band as provisional.
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 53200
Athletes and sports competitors map to NOC 53200 — Athletes at TEER 3 (College / Apprenticeship <2 yrs). ESDC's COPS 2024–2033 projection for this unit group is Strong risk of Surplus.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Athletes and sports competitors— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 9 of 9, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
1 of these 9statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 22% of this occupation's work activities are covered by observed AI usage, but not which ones.
Performing General Physical Activities · 5 tasks
Attend scheduled practice or training sessions.
Core task · Importance 4.2/5 (O*NET)
Maintain optimum physical fitness levels by training regularly, following nutrition plans, or consulting with health professionals.
Core task · Importance 4.0/5 (O*NET)
Participate in athletic events or competitive sports, according to established rules and regulations.
Core task · Importance 4.0/5 (O*NET)
Exercise or practice under the direction of athletic trainers or professional coaches to develop skills, improve physical condition, or prepare for competitions.
Core task · Importance 3.9/5 (O*NET)
Receive instructions from coaches or other sports staff prior to events and discuss performance afterwards.
Core task · Importance 3.9/5 (O*NET)
Judging the Qualities of Objects, Services, or People · 1 task
Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance.
Core task · Importance 4.2/5 (O*NET) · 45% of observed AI use on this task is automation-pattern
Repairing and Maintaining Electronic Equipment · 1 task
Maintain equipment used in a particular sport.
Core task · Importance 4.2/5 (O*NET)
Selling or Influencing Others · 1 task
Represent teams or professional sports clubs, performing such activities as meeting with members of the media, making speeches, or participating in charity events.
Core task · Importance 3.6/5 (O*NET)
Coaching and Developing Others · 1 task
Lead teams by serving as captain.
Core task · Importance 3.4/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Athletes and sports competitors as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Obtain technical certifications in advanced diagnostic tools and smart systems relevant to athletes and sports competitors to prepare for future technological integrations.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about athletes and sports competitors.
Sources for this occupation
Only the sources that hold data for athletes and sports competitors are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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