Athletes and Sports Competitors
27-2021.00Median wage $66,710/yr15,070 employed (US)Rank #908 of 923 scored · top 98% by substitution
Compete in athletic events.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution7
Exposure4
Augmentation44
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
9 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%3
panel mean rating 1.1/5 → substitution pressure 3/100
Technical feasibility todayw 20%6
panel mean rating 1.2/5 → substitution pressure 6/100
Cost vs. human wagew 15%3
panel mean rating 1.1/5 → substitution pressure 3/100
Adoption barriersw 20%inverted — strong barriers lower the score17
panel mean rating 4.3/5 (barrier strength) → substitution pressure 17/100
Sector adoption velocityw 10%10
panel mean rating 1.4/5 → substitution pressure 10/100
Task breakdown (9 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance.
37CI 32–42 · exposure 34 · augmentation 75 · importance 4.2/5 · click for rater detail
Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | High-resource professional and collegiate sports programs have adopted AI analytics pilots and tools, but most competitive and amateur sports still rely primarily on human coaches; adoption is concentrated in tech-forward, well-funded sectors rather than broad or deep. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Professional and elite sports have adopted analytics and video review tools substantially, though broader athlete populations still rely mainly on human coaching. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is strong and growing: video analysis tools, wearable metrics dashboards, and statistical models help coaches identify patterns and quantify performance changes faster than manual review, materially improving coaching productivity while coaches retain decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered video analysis, wearable data, and performance analytics meaningfully enhance athletes' and coaches' ability to identify strengths/weaknesses, even though humans direct the interpretation and adjustments. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze objective performance metrics (speed, distance, biomechanics) from video and sensor data, assessing performance requires contextual judgment about training readiness, psychological factors, and individualized strategy—domains where current AI cannot achieve 50% time savings at equal quality compared to expert coaches who synthesize data with lived experience. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can analyze stats and video to surface patterns, but integrating physical sensation, coaching judgment, and strategic adjustment for competitive performance remains largely human-driven.rd |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: coaches and athletes typically require hands-on, real-time feedback and customized guidance that athletes trust from human mentors; regulatory and organizational norms in competitive sports privilege human coaching expertise and interpersonal accountability in ways that deter full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted analysis, but coach-athlete trust, tacit knowledge, and the need for embodied feedback create organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI video analysis and wearables cost thousands annually, plus integration overhead, while a single coaching staff member commands comparable salary; cost per task-equivalent remains high for comprehensive performance assessment relative to human coaching labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Analytics platforms and video tools carry substantial licensing and integration costs, and human coaching expertise remains necessary, so savings versus a coach/athlete's own review are moderate at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Video analysis and biomechanics AI tools exist in production (Dartfish, Coach's Eye, AI-powered motion capture), but they primarily extract metrics rather than deliver complete performance assessment with actionable adjustment recommendations; human coaches remain essential for interpretation and decision-making. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Sports analytics products (video breakdown, biomechanics tracking, stat analysis) are deployed in professional sports today, but final performance assessment and adjustment still rely on coaches and athletes. |
Maintain equipment used in a particular sport.
13CI 10–15 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Maintain equipment used in a particular sport.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Sports organizations rely on human technicians and maintenance staff, with little to no AI or automation adoption in this domain. The physical nature and craft skill involved have resulted in slow digital transformation in equipment maintenance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Sports equipment maintenance is a physical, low-digitization task with no meaningful AI/robotics adoption trend in this space. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision could assist in equipment inspection by flagging wear or damage, but augmentation is limited. The majority of the task—actual repair and adjustment—remains purely manual, so AI's assistive value is narrow and modest. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help schedule maintenance, track equipment wear via sensors, or provide diagnostic guidance, but it offers minimal direct assistance with the physical maintenance work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Maintaining sports equipment requires physical inspection, hands-on repair, adjustment, and tactile feedback that current AI systems cannot perform. The task inherently involves manual work such as tightening, cleaning, replacing parts, and testing functional integrity—activities that fall outside the scope of AI agents today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical equipment maintenance (cleaning, adjusting, repairing gear like bats, rackets, skates, bikes) requires hands-on manual manipulation that current AI cannot perform end-to-end without robotics that don't exist for this niche use case. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Equipment maintenance does not face hard legal or licensing barriers in most sports, but there is practical friction from the requirement that someone physically present must perform the work. Organizational structures and athlete preference for familiar technicians add moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical, tactile nature of equipment care creates a practical barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Equipment maintenance is labor-intensive manual work. Current AI has no capacity to perform the actual repairs and adjustments, so there is no meaningful cost comparison; AI cannot substitute for the hands-on labor required. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physical equipment upkeep, so any AI-based approach would require robotic hardware far more costly than a human doing routine maintenance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform end-to-end equipment maintenance. While vision systems can inspect equipment, they cannot execute repairs, adjustments, or physical maintenance work, which remain entirely manual operations performed by humans or repair specialists. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical sports equipment maintenance; this remains a manual task done by athletes, equipment managers, or specialized technicians. |
Exercise or practice under the direction of athletic trainers or professional coaches to develop skills, improve physical condition, or prepare for competitions.
5CI 3–7 · exposure 0 · augmentation 75 · importance 3.9/5 · click for rater detail
Exercise or practice under the direction of athletic trainers or professional coaches to develop skills, improve physical condition, or prepare for competitions.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Although sports organizations increasingly use AI for performance analytics and injury prediction, these are augmentative tools for coaches, not replacements for athlete training and practice. The fundamental task (athlete exercising and developing) remains human-dependent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sports training incorporates AI analytics and wearables at a moderate pace, but the core physical practice activity itself sees no adoption of AI substitution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven video analysis, biometric monitoring, performance prediction, and personalized training recommendations can significantly assist coaches in optimizing training plans and helping athletes refine technique—substantially raising coaching productivity while the athlete remains the active performer. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered tools (motion analysis, wearables, performance analytics, personalized training plans) meaningfully enhance coaching feedback and training efficiency even though the human still performs the exercise. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires a human athlete's physical body and real-time, embodied performance. AI cannot execute the athletic movements, endure physical conditioning, or compete in actual sports—it can only assist coaching decisions about *how* an athlete should train. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical exertion, motor skill development, and real-time bodily performance that current AI cannot execute; AI has no physical embodiment to perform the exercise or practice itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | The task is inherently human-centered—a living athlete must perform the physical work. No automation can substitute for the biological requirement that the athlete themselves exercise, practice, and compete in sanctioned contexts. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Physical presence and embodiment are hard requirements; competitive sports rules and physiological reality prevent any non-human substitution for the athlete's own practice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems for sports analysis and coaching recommendation are optional supplements; they do not reduce the cost of employing human athletes and coaches required to deliver the core service (the athlete's training and competition). |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical practice, so cost comparison is moot—the human athlete must physically do the task regardless of AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No AI product can autonomously conduct athletic training or practice routines in the physical world. Video analysis and coaching aids exist, but they are advisory tools, not independent executors of the core task (the athlete's exercise and skill development). |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs athletic training or physical practice on behalf of an athlete; this remains entirely outside current AI product capability. |
Maintain optimum physical fitness levels by training regularly, following nutrition plans, or consulting with health professionals.
4CI 0–7 · exposure 0 · augmentation 63 · importance 4.0/5 · click for rater detail
Maintain optimum physical fitness levels by training regularly, following nutrition plans, or consulting with health professionals.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption velocity is irrelevant here, as the task cannot be automated—athletes have always and will always need to perform their own training and nutrition regardless of technological advancement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sports and athletic training sectors use wearables and AI-driven analytics for planning, but actual physical training execution remains entirely human and unautomated. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully augment this task by providing personalized workout plans, nutrition tracking, performance analytics, and scheduling via apps and wearables, assisting the athlete in optimizing their self-directed fitness work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered fitness trackers, personalized nutrition apps, and data analytics significantly enhance training planning, recovery monitoring, and health consultations, boosting athlete decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires an athlete's own physical body to perform training, consume nutrition, and engage with health professionals; no AI system can replicate the embodied execution of exercise or dietary intake that defines the core work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, embodied task requiring the athlete's own body to perform training and maintain fitness; AI cannot execute physical training or eating on a person's behalf.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | The task has absolute barriers: only the athlete's own body can train and maintain fitness, making substitution legally and physically impossible regardless of AI capability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Physical embodiment and biological reality (the athlete's own body must train) create a hard barrier beyond regulation; only the athlete can build their own fitness. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | This comparison is not applicable; there is no meaningful cost trade-off because the task cannot be performed by AI at all—the athlete themselves must physically train and eat. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical act, so cost comparison is moot; the human must do this regardless of AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI can generate fitness plans or nutrition advice through apps, the actual execution of training and maintaining fitness levels cannot be delegated to current AI systems—the task intrinsically depends on human physical action and self-discipline. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical training or nutrition adherence for a person; at most apps provide plans, not execution of the task itself. |
Receive instructions from coaches or other sports staff prior to events and discuss performance afterwards.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.9/5 · click for rater detail
Receive instructions from coaches or other sports staff prior to events and discuss performance afterwards.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for this task is essentially zero. Sports organizations continue to employ coaches specifically for pre-event instruction and performance debriefing; there is no production adoption of AI systems replacing these conversations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Professional and amateur sports coaching remains a highly physical, relationship-driven field with minimal AI adoption for this specific interpersonal task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited augmentation (e.g., video analysis, performance metrics) to inform coaching discussions, but current systems do not meaningfully assist the core task of receiving instructions and discussing performance in real time with coaches. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can support the surrounding process—analyzing performance data, generating stats summaries, or drafting talking points for coaches—which can inform these conversations, but it doesn't transform the core interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task is fundamentally about interpersonal communication, receiving coaching cues, and collaborative reflection with humans. Current AI cannot replace the two-way dialogue, contextual understanding of individual athlete needs, or the trust-building that defines effective coach-athlete communication. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an inherently human, physically embodied and interpersonal activity centered on live coaching relationships; no AI system performs this end-to-end today.9 There is no meaningful time-saving substitution possible for athletes physically receiving and discussing performance guidance. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has hard barriers: coaching and performance feedback require human judgment, trust, and accountability. Athletes and regulatory bodies (leagues, federations) mandate human coaching staff for pre-event preparation and post-performance analysis as part of standard practice. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Coaching relationships are built on trust, in-person rapport, and often contractual/organizational roles (certified coaches, team staff) that create strong structural and interpersonal barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating this task would require deploying an AI system to replace coaching staff, whose cost is already embedded in the sport's organizational structure. The marginal cost of an AI intervention would exceed savings from eliminating human coaching conversations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI alternative delivering this function, so cost comparison is moot; the human relationship is the product, not a cost to be optimized away. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task reliably. AI chatbots cannot substitute for live coaching feedback, real-time performance discussion, or the dynamic adjustment of strategy that occurs in pre-event and post-event conversations between coaches and athletes. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product replaces the coach-athlete communication loop before or after events; this remains entirely a human interaction task. |
Attend scheduled practice or training sessions.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Attend scheduled practice or training sessions.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption velocity is irrelevant since this task is inherently non-automatable and no sector can meaningfully substitute AI for athlete attendance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Sports training remains a highly physical, low-digitization activity where AI adoption pertains to analytics/coaching support, not replacing physical attendance or exertion. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist in marginal ways (e.g., providing real-time performance feedback during practice or analyzing form from video), but cannot materially augment the core act of attendance itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with training plans, performance analytics, and biomechanical feedback to improve practice quality, though it doesn't replace the physical attendance itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending physical practice sessions is inherently a human presence task that cannot be automated; a coach or trainer must physically be present to train, improving performance and technique. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending physical training sessions requires bodily presence and physical exertion; AI cannot perform an athlete's physical practice or competition preparation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | The task legally and functionally requires the athlete themselves to be physically present; no delegation or automation is possible without defeating the purpose of training. |
| Adoption barriers | claude-sonnet-5 | 5/5 | The task inherently requires the human body of the athlete; there is no substitution pathway, legal or otherwise, since the athlete must physically perform it. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | This task cannot be performed by AI at any cost since physical human attendance is the core requirement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so cost comparison is not applicable/AI is not a viable cheaper alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No AI system can physically attend a training session or substitute for an athlete's presence and participation in practice drills. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product attends or performs physical training sessions on behalf of an athlete; this is purely a physical human activity. |
Participate in athletic events or competitive sports, according to established rules and regulations.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Participate in athletic events or competitive sports, according to established rules and regulations.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | No substitution or displacement of human athletes by AI has occurred because the task is intrinsically dependent on human physical participation under established sports regulations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Professional sports competition is a physical, human-only domain with no trajectory toward AI displacement of the competitive act itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | While AI can assist athletes via training analytics, biomechanics feedback, and opponent analysis, it does not augment the actual performance of competing in athletic events, which requires human embodiment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist training, strategy analysis, and performance optimization around competition, though it does not augment the act of competing itself in real time. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Athletic performance requires physical embodiment, real-time sensorimotor adaptation, and unpredictable environmental interaction. Current AI has no physical form to compete in sports under human rules and regulations. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically competing in athletic events requires a human body performing at elite physical levels; no AI system can substitute for the physical act of competition. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Athletic competition is fundamentally bound by human-contact and physical-presence requirements; rules explicitly mandate human participants, and liability frameworks assume human agency and embodiment. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Rules of sport explicitly require human participants; robots or AI agents are categorically excluded from competing as athletes in essentially all sanctioned sports. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems lack the physical capability to perform this task at any cost, making cost comparison moot and unfavorable by definition. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so cost comparison is inapplicable/AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can physically participate in human athletic events. AI cannot execute the bodily movements, spatial reasoning, and dynamic adaptation required to compete against humans in sports. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product allows AI to physically participate in sports competitions in place of a human athlete. |
Represent teams or professional sports clubs, performing such activities as meeting with members of the media, making speeches, or participating in charity events.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Represent teams or professional sports clubs, performing such activities as meeting with members of the media, making speeches, or participating in charity events.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no adoption of AI to replace athlete representation in media, speeches, or charity events. The intrinsic value of these tasks derives from the athlete's authentic presence, making automation culturally and commercially infeasible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Professional sports representation and appearances remain entirely human-driven with no meaningful AI displacement occurring in this space. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with preparing talking points, drafting statements, or scheduling media appearances, but the core task—representing the team through personal presence—cannot be substantially augmented by AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI might help draft speech notes or prepare talking points for media interactions, but it offers minimal assistance for the core activity of physically representing and engaging as the athlete. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires authentic human presence, personal credibility, and real-time interpersonal engagement with media, audiences, and charity partners. AI cannot meaningfully replace the embodied social presence and trust that athletes bring to these representation duties. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires a real human athlete's physical presence, celebrity status, and personal relationships for media, speeches, and charity appearances; AI cannot substitute the person's identity or physical attendance. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal, contractual, and reputational barriers protect this task: teams have explicit contracts requiring specific athletes to represent them; media expect to interview the actual player; sponsors and charity events require the authentic personal presence of the athlete. Substituting AI would breach sponsorship agreements and damage brand value. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Contracts, endorsement deals, media requirements, and fan expectations require the specific athlete to appear in person; this cannot be legally or practically delegated to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of developing and deploying an AI system that could plausibly perform these representation functions—if even possible—would vastly exceed the value of replacing the athlete's personal participation, which is the entire point of the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so no cost comparison favors AI; the human's presence itself is the value being purchased. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can reliably represent a team or athlete in media interactions, speeches, or charity events as the human competitor themselves. While AI can draft statements or generate content, the actual representation and social presence cannot be automated. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs an athlete's public representation duties; this is inherently tied to a specific human's persona and physical presence. |
Lead teams by serving as captain.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Lead teams by serving as captain.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no adoption of AI as team captains because the role is inherently human-dependent. Sports organizations have shown no movement toward automating captaincy functions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Professional and amateur sports have shown no adoption of AI for in-team leadership or captaincy roles; this is a laggard area with no pilots underway. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist a captain with tactical suggestions, performance analytics, or play-calling recommendations, but such tools remain peripheral to the core leadership and authority function of the captain role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can provide data analytics, performance insights, or opponent scouting that a captain might use, but this offers only indirect, minor assistance to the leadership task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Leading a team as captain requires real-time decision-making, interpersonal authority, motivational presence, and contextual judgment that cannot be performed by AI systems. The task is fundamentally about human-to-human influence and accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | Team captaincy requires physical presence, in-game leadership, real-time interpersonal influence, and embodied trust-building that AI cannot perform; no time-saving substitution is possible.99 |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Team captaincy requires direct human authority, trust, and legal/organizational accountability. Rules of sport, team governance, and player dynamics demand a human captain by design and regulation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Team captaincy is inherently tied to being a human athlete on the roster, embedded in league rules, locker-room dynamics, and physical participation, making substitution essentially impossible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | This task cannot be meaningfully substituted by AI, so cost comparison is not applicable. A team requires a human captain; AI has no competitive cost advantage because it cannot perform the role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute offering any output for this task, so cost comparison favors the human entirely by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs team captaincy. While AI can provide tactical analysis or communication aids, no system can serve as an authoritative team leader or captain in any sport. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attempts to have an AI serve as an athletic team captain; this remains entirely outside current AI product scope. |
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How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.