Farm and Home Management Educators
25-9021.00Median wage $60,220/yr8,220 employed (US)Rank #433 of 923 scored · top 47% by substitution
Instruct and advise individuals and families engaged in agriculture, agricultural-related processes, or home management activities. Demonstrate procedures and apply research findings to advance agricultural and home management activities. May develop educational outreach programs. May instruct on either agricultural issues such as agricultural processes and techniques, pest management, and food safety, or on home management issues such as budgeting, nutrition, and child development.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution29
Exposure25
Augmentation60
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
15 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%26
panel mean rating 2.0/5 → substitution pressure 26/100
Technical feasibility todayw 20%24
panel mean rating 1.9/5 → substitution pressure 24/100
Cost vs. human wagew 15%29
panel mean rating 2.1/5 → substitution pressure 29/100
Adoption barriersw 20%inverted — strong barriers lower the score45
panel mean rating 3.2/5 (barrier strength) → substitution pressure 45/100
Sector adoption velocityw 10%17
panel mean rating 1.7/5 → substitution pressure 17/100
Task breakdown (15 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.
Prepare and distribute leaflets, pamphlets, and visual aids for educational and informational purposes.
70CI 70–70 · exposure 75 · augmentation 88 · importance 3.7/5 · click for rater detail
Prepare and distribute leaflets, pamphlets, and visual aids for educational and informational purposes.
70| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Farm and home management extension services are typically lower-digitization, laggard organizations (often public or non-profit agricultural agencies). Adoption of generative AI for content creation remains pilot-stage in these sectors despite feasibility. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Extension services and educational agencies are typically slow, resource-constrained public-sector organizations with limited AI tool adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists educators by drafting content, generating layouts, and creating visual aids, allowing them to focus on domain expertise and verification rather than design and formatting work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, editing, and visual design work, letting educators focus on content accuracy and outreach strategy. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can generate leaflets, pamphlets, and visual aids end-to-end using text generation and image synthesis tools with minimal human input, easily exceeding 50% time savings. However, domain-specific educational content quality and verification may require some oversight, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | AI can draft, format, and design leaflets and visual aids from source material with substantial time savings, though final review and distribution logistics still need human involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal or licensing requirement mandates human involvement in preparing informational leaflets. Minor friction exists around content verification and organizational adoption, but nothing prevents full automation of production and distribution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for producing educational pamphlets, though accuracy of agricultural/home-management advice requires some institutional review before distribution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven design and content creation tools cost a fraction of hiring professional designers or educators to manually create these materials, representing roughly 10-20% of traditional labor costs for comparable output quality. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted drafting and design tools cost a small fraction of the staff time otherwise required to produce and lay out informational materials. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (e.g., Canva, AI writing tools, DALL-E integration) reliably produce leaflets and visual aids in production environments. Material limitations exist around ensuring agricultural/home management accuracy, but the core task is demonstrably achievable at scale. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed tools (LLMs plus design tools like Canva AI, Adobe Express) reliably generate marketing/educational materials in production today, though niche agricultural content may need expert vetting. |
Research information requested by farmers.
56CI 47–65 · exposure 50 · augmentation 88 · importance 4.1/5 · click for rater detail
Research information requested by farmers.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Agricultural extension services and farm education remain traditional, fragmented across small regional providers and government agencies with slower digital adoption; direct-to-farmer AI adoption in this space is still emerging and limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and educational services are a traditionally slow-adopting, under-digitized sector with limited AI tool deployment compared to finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered search, summarization, and citation tools can significantly enhance an educator's ability to gather, organize, and present research findings, allowing faster response to farmer inquiries and deeper information synthesis while the educator retains curation and advisory roles. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI search and summarization tools substantially speed up an educator's ability to gather and synthesize information for farmer inquiries while the educator still verifies and contextualizes the answer. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with gathering and synthesizing agricultural information from public sources, but the task requires contextualizing research to individual farmer needs, local conditions, and emerging farm-specific problems—requiring human judgment and follow-up. |
| Task automatability | claude-sonnet-5 | 4/5 | Researching agricultural information (pest management, crop practices, market data) is largely a research-and-synthesis task well suited to AI retrieval and summarization tools, which can save significant time.rating reflects that most but not all specialized/local knowledge queries can be automated.this leaves room for human verification.for uncommon local questions.a rating of 4 is chosen. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement or legal mandate to use human educators for research; organizational adoption is primarily driven by perceived value and trust rather than regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement governs mere information research, though farmers may still prefer trusted human extension educators for locally validated advice, creating mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI information retrieval and synthesis cost is minimal per query compared to educator labor, though integration with farm management workflows and validation overhead add overhead; the human wage for this task is moderate and the AI cost per equivalent output is substantially lower. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based research tools cost a small fraction of an educator's hourly wage for equivalent information gathering, though some oversight and verification cost remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed search and summarization tools can retrieve and organize agricultural information reliably, but no production system fully replaces the educator's role in interpreting relevance, validating source credibility for farming contexts, and customizing findings to specific farm operations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI search/chat tools with agricultural knowledge exist and are used by some extension educators, but accuracy on hyper-local, regulatory, or soil/climate-specific queries remains inconsistent, so reliability in production varies. |
Maintain records of services provided and the effects of advice given.
49CI 34–65 · exposure 45 · augmentation 63 · importance 3.9/5 · click for rater detail
Maintain records of services provided and the effects of advice given.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Farm extension and home management sectors are traditionally slow to digitize; many operate with paper records or legacy systems. Adoption of AI for specialized outcome tracking has been limited, with most organizations still relying on manual or basic database approaches. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Extension and agricultural education services are a small, often public-sector, moderately digitized field with slower AI tool adoption compared to finance or tech-heavy professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools could usefully assist educators by auto-populating forms, summarizing session notes, or flagging potential outcome patterns, thereby reducing administrative burden. However, the human educator must remain the primary decision-maker about which outcomes to log and their significance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can substantially assist by transcribing consultations, drafting summaries, and organizing records, letting the educator focus on verification and follow-up rather than manual documentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Recording services and documenting advice effects requires significant human judgment about what constitutes meaningful outcomes and impacts. While AI could structurally organize data entry, the interpretive component—determining effect significance and linking advice to outcomes—remains too complex and context-dependent for end-to-end automation at scale. |
| Task automatability | claude-sonnet-5 | 4/5 | Record-keeping of services and outcomes is a structured documentation task that AI can largely automate via note-taking, summarization, and database entry tools, though some judgment on categorizing 'effects of advice' may require human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Farm extension services often operate under institutional (USDA, land-grant university) frameworks with record-keeping mandates and quality expectations; liability for advice documentation creates moderate friction. However, no strict licensing barrier prevents AI-assisted logging, and human oversight remains feasible. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human record-keeping, though data accuracy and accountability for advice given create some organizational caution around full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Generic record management AI (forms, data organization) is inexpensive, but integrating it into a specialized workflow with oversight for accuracy and completeness brings costs closer to the value of time saved by a farm educator or administrative assistant. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated transcription, summarization, and database entry tools cost a small fraction of the educator's time compared to manual record-keeping, yielding substantial savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature, deployed product reliably captures and logs the nuanced effects of farm/home advisory services across diverse contexts. AI systems exist for generic record-keeping, but applying them to validate causal links between educator advice and farmer/home outcomes remains largely manual or semi-automated in practice. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM and case-management software with AI-assisted note generation and summarization exists and is used in extension/advisory contexts, but purpose-built systems for this specific niche are not widely deployed at scale. |
Collect and evaluate data to determine community program needs.
36CI 25–47 · exposure 33 · augmentation 63 · importance 4.0/5 · click for rater detail
Collect and evaluate data to determine community program needs.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Farm and home management education remains in rural, smaller institutional contexts with lower digital infrastructure and slow technology adoption; pilot automation is rare in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Extension and community education sectors are typically slower adopters of AI tools compared to finance or tech, with pilots more common than widespread production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist educators by automating survey distribution, analyzing demographic data, and flagging patterns, but the educator's judgment and community relationships remain essential for interpreting results and prioritizing needs. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly enhance data collection, survey design, and pattern identification, helping educators identify needs faster while they retain interpretive and engagement responsibilities. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Data collection via surveys and existing databases can be partially automated, but evaluating genuine community needs requires contextual judgment, stakeholder interviews, and synthesis of qualitative insights that current AI systems struggle with reliably without heavy human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can assist with data collection, aggregation, and preliminary analysis, but interpreting community-specific needs and contextual nuance still requires human judgment, so only part of this task meets the automation bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Community program design is often governed by organizational policy, funding requirements, and stakeholder accountability; educational institutions typically mandate human educators' judgment in determining community needs to ensure legitimacy and cultural fit. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this analytical task, though organizational trust in AI-driven community assessments and the need for local stakeholder engagement create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (surveys, data processing) have modest cost savings, but the labor-intensive evaluation and synthesis phases require experienced educators, keeping total costs comparable to or only slightly below traditional human-led assessment. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can reduce time spent on data compilation and initial analysis at moderate cost, but human oversight, local knowledge integration, and validation still require significant labor, keeping costs roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data aggregation and basic analysis, no mature production system reliably performs end-to-end community needs assessment without significant human validation and domain expertise; deployed tools remain limited to narrow analytics tasks. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed analytics and survey tools can compile data, but no production AI product reliably performs full community needs assessment autonomously; this remains largely a human-driven process with AI as a support tool. |
Conduct agricultural research, analyze data, and prepare research reports.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.6/5 · click for rater detail
Conduct agricultural research, analyze data, and prepare research reports.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Agricultural education and research sectors adopt technology more slowly than information or finance sectors. While data-analysis tools are spreading, use of AI for research report generation remains limited to early adopters in academic and extension contexts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and research sectors show slow, uneven AI adoption compared to finance or tech, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist researchers by automating data cleaning, statistical analysis, visualization, and initial report drafting, which accelerates the human researcher's workflow. However, the core intellectual work of designing experiments and interpreting results remains human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially aids literature review, statistical analysis, and report drafting, meaningfully speeding up parts of the research and writing process while the human remains central to design and interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis and report generation, conducting agricultural research typically requires field work, experimental design decisions, and interpretation of context-specific conditions that remain difficult to automate end-to-end. Report writing can be partially automated, but the research conception and execution phases require significant human judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | Data analysis and report drafting portions can be AI-assisted, but designing and conducting agricultural research (field trials, sampling, domain expertise) requires physical and scientific judgment AI cannot replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Agricultural extension and research institutions have institutional inertia and peer-review requirements for credible outputs, but no hard legal barriers prevent AI use in analysis or reporting. Organizational friction around adopting new tools and maintaining research credibility creates moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requirement for this specific task, though credibility and institutional trust in research findings create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI reduces costs for data analysis and report drafting, but the total cost remains substantial when accounting for research design, field validation, and human oversight of findings. The loaded cost of educators' time is still often lower than the full integration and quality-assurance burden of AI-assisted research pipelines. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with data crunching and writing, but human oversight, field work, and domain validation keep overall costs comparable to human labor for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI tools can generate reports from structured data and perform statistical analysis, but no production systems reliably conduct original agricultural research autonomously. Existing products handle narrow subtasks (data cleaning, visualization) rather than the full research workflow. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools (e.g., statistical/ML packages, LLM report drafting) exist and are used piecemeal, but no deployed product reliably conducts the full research-to-report pipeline for agricultural science in production. |
Collaborate with producers to diagnose and prevent management and production problems.
30CI 25–35 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Collaborate with producers to diagnose and prevent management and production problems.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Agricultural education and extension services remain fragmented, geographically dispersed, and institutionally slow-moving (university extension systems, government programs). While precision agriculture tools are growing, the core producer-educator collaboration and management consultation process has seen limited AI displacement; adoption remains largely in pilots or narrow digital contexts rather than mainstream production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and small-scale farming sectors are traditionally slow adopters of AI compared to information-sector industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist educators by analyzing production data, flagging anomalies, and suggesting common diagnoses or management practices, thereby accelerating the advisory conversation. However, the human educator remains essential for trust, contextual judgment, and tailored prevention plans, so augmentation is significant but not transformative—educators remain the bottleneck. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist educators by providing rapid access to research, diagnostic checklists, and data analysis to support their in-person consultations with producers. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in analyzing data patterns and suggesting diagnoses for common agricultural problems, the task requires nuanced, context-specific producer interaction, understanding of local conditions, and real-time troubleshooting that current systems cannot reliably perform end-to-end. The collaborative diagnostic process—especially prevention planning—demands judgment and trust-building that falls well short of the 50% time-saving threshold at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires in-person collaboration, contextual judgment about a specific farm's conditions, and trust-building that current AI cannot replicate end-to-end, though AI can assist with diagnostic support. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational extension and farm-management advice often involve licensed credentials, trust relationships, and liability for recommendations that affect production and financial outcomes. Many jurisdictions require state certification for agricultural extension personnel, and producers typically prefer direct, accountable human advisors for high-stakes decisions, creating strong organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement typically exists, but producers strongly prefer trusted local relationships and hands-on diagnosis, creating moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying AI diagnostic systems requires significant integration, data infrastructure, and ongoing human oversight by qualified agronomists or educators. The total cost per farm diagnosis—inference plus validation by a credentialed human—remains comparable to or exceeds the loaded cost of direct producer-educator consultation, especially in rural or low-digitization settings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI query tools are cheap per use, the actual task involves site visits, relationship management, and nuanced problem-solving that still requires paying the human educator's full time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Decision-support tools and diagnostic expert systems exist in precision agriculture, but no production system reliably performs the full collaborative diagnosis-and-prevention task. Available products cover narrow subdomains (soil analysis, crop disease classification) rather than the integrated producer consultation that the task requires, and they typically function as narrow assistants, not autonomous performers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Agricultural decision-support tools and chatbots exist but are narrow in scope and not deployed as substitutes for the collaborative diagnostic relationship this task requires. |
Collaborate with social service and health care professionals to advise individuals and families on home management practices, such as budget planning, meal preparation, and time management.
28CI 23–34 · exposure 25 · augmentation 63 · importance 3.2/5 · click for rater detail
Collaborate with social service and health care professionals to advise individuals and families on home management practices, such as budget planning, meal preparation, and time management.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Farm and home management education is concentrated in rural extension services and non-profit social agencies with low digital maturity and minimal AI adoption; the sector lacks the infrastructure, funding, and competitive pressure driving automation in information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Social services and extension education sectors are slow AI adopters compared to finance or tech, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist educators by generating draft budgets, meal plans, and schedules, and by summarizing family data for multi-disciplinary case review, raising educator efficiency on preparation tasks while the human retains advisory and relationship-building responsibility. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating budget templates, meal plans, and time-management schedules that educators can customize and present to families, boosting efficiency. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate budget plans, meal suggestions, and time management frameworks, the task fundamentally requires collaborative diagnosis of family circumstances, culturally-sensitive advising, and ongoing relationship-based support that current systems cannot reliably handle end-to-end or with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves interpersonal collaboration, contextual assessment of individual family needs, and trust-building that current AI cannot fully replicate end-to-end, though AI can draft budget templates or meal plans as inputs.atibility remains low for the full advisory workflow. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves advisory authority over vulnerable families and health outcomes; regulatory frameworks often require credentialed professionals to deliver guidance, and liability exposure for faulty budgeting or nutrition advice creates strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for this specific educator role, but reliance on interprofessional trust, sensitive family financial/health information, and organizational norms create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for generating plans is cheap, but the integration cost of ensuring context-aware, multi-stakeholder coordination and the oversight required to validate advice quality mean total deployment cost rivals or exceeds the educator's loaded wage for reliable output. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for generating budget or meal plans are cheap, but the collaborative advisory and coordination component still requires paid human time, making overall cost roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs the full advisory and collaborative role this task demands; chatbots can offer generic advice but cannot coordinate with social workers, tailor counsel to complex family dynamics, or provide the trusted guidance essential to the role. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI chatbots and apps exist for budgeting and meal planning advice, but no deployed product reliably handles multi-professional collaboration and personalized family advising at scale. |
Conduct classes or deliver lectures on subjects such as nutrition, home management, and farming techniques.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Conduct classes or deliver lectures on subjects such as nutrition, home management, and farming techniques.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most educational organizations, particularly public extension services and community colleges where these educators work, adopt AI slowly for core teaching roles. Pilots and supplementary use are more common than replacement deployments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and community education sectors have historically low digitization and slow AI adoption compared to information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can powerfully assist educators by generating lesson plans, visual aids, practice questions, and personalized content suggestions, significantly raising their productivity without removing the educator from the loop. This augmentation is already showing practical adoption. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help educators prepare lecture materials, personalize content, translate materials, and answer routine questions, boosting prep efficiency even though delivery remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate lecture content and draft educational materials, delivering classes requires real-time interaction, adaptive responsiveness to student questions, and dynamic engagement that current AI systems struggle to maintain authentically. The task involves significant student-facing pedagogical judgment that remains difficult to automate. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate lecture content and slides, but live classroom delivery, audience adaptation, and hands-on farming/home demonstrations require human presence and interaction that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Educational institutions have strong accreditation, curriculum design, and teacher certification requirements; many jurisdictions legally mandate human educators for formal instruction. Organizational and regulatory barriers to full replacement are substantial, though AI can assist. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensure is strictly required, but community trust, local expertise, and expectation of a live, credentialed extension agent create moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of developing, hosting, and maintaining AI-delivered educational content, plus oversight and quality assurance, remains comparable to or often exceeds the loaded cost of an actual educator delivering classes in many organizational settings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Content generation is cheap via AI, but the actual delivery (travel, in-person demonstration, community engagement) still requires paid human labor, keeping overall cost comparable to or only slightly less than a human educator. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can draft lecture slides and educational content, but no deployed product reliably delivers full classes with the pedagogical quality, audience responsiveness, and contextual expertise that educators provide. Video lecture generation exists but lacks the interactive adaptability of live instruction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI video/voice tools and virtual instructors exist but are not deployed at scale for extension-style community education; most real-world delivery still relies on human educators in person. |
Set and monitor production targets.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.2/5 · click for rater detail
Set and monitor production targets.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Agricultural adoption of AI remains slow outside large industrial operations; most farms, especially small and mid-scale, use basic tools rather than advanced AI systems. Extension services and educational institutions move conservatively, and farmer demographics skew toward older populations with lower digital adoption rates. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and farm management is a lower-digitization sector with slower AI tool adoption compared to information or finance sectors, though precision agriculture is growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered data dashboards and predictive analytics can assist educators by surfacing weather patterns, soil data, and market trends to inform target-setting. However, the contextual judgment, stakeholder communication, and adaptive monitoring that educators provide remain the core value, so assistance is helpful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly assist by analyzing historical yield data, weather patterns, and market trends to help educators set more informed, data-backed production targets alongside their expertise. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Setting production targets involves domain knowledge, strategic judgment, and understanding context-specific constraints (weather, soil, markets, labor) that AI cannot reliably handle end-to-end today. Monitoring requires real-time data interpretation and adaptive decision-making in agricultural settings, where current AI systems lack sufficient field reliability and human oversight remains essential. |
| Task automatability | claude-sonnet-5 | 2/5 | Setting production targets requires local judgment about soil, weather, market conditions, and farmer/family circumstances that AI cannot fully replicate, though it can assist with data aggregation and forecasting components.ed |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Farm and home management educators often work within institutional settings (extension services, agricultural agencies) with regulatory authority over agricultural compliance, safety, and liability. Setting production targets carries direct liability for crop failure and resource misallocation, creating legal and organizational friction that strongly favors human sign-off and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for this specific task, but extension educators often operate within institutional/government frameworks and farmers expect trusted, personalized human guidance, creating moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current agricultural monitoring systems and analytics require substantial setup, domain-specific integration, and ongoing expert oversight. The total cost of deployment, maintenance, and error correction likely exceeds or equals the cost of an educator performing these tasks, especially at smaller farms where fixed costs are harder to amortize. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply generate data-driven projections, but the human educator's contextual synthesis, farm visits, and relationship-based advising remain costly and largely irreplaceable, keeping overall cost comparable to human-led work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While crop-monitoring tools and data analytics platforms exist, no mature deployed system reliably sets and monitors production targets independently. Existing agricultural software assists with data collection but requires significant human expertise to translate into actionable targets, and error costs (missed harvests, resource waste) remain high. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Agricultural analytics and forecasting tools exist but are not deployed as autonomous target-setting systems in extension education contexts; they remain decision-support tools requiring expert interpretation. |
Advise farmers and demonstrate techniques in areas such as feeding and health maintenance of livestock, growing and harvesting practices, and financial planning.
26CI 23–30 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Advise farmers and demonstrate techniques in areas such as feeding and health maintenance of livestock, growing and harvesting practices, and financial planning.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Farm sectors and agricultural extension services are digitizing slowly compared to information-intensive professions; adoption of AI-driven advising remains minimal, with most engagement still through traditional extension agents and human-led workshops, particularly in smaller farm operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural extension and small-farm sectors are among the least digitized industries with minimal AI agent deployment in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist educators by generating draft feeding plans, financial analyses, or disease-identification support that the educator then refines and demonstrates—useful support for research and planning stages, but not transformative for the core advisory and demonstration function. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help educators prepare materials, answer technical questions, and provide financial planning support, boosting their productivity even though they can't replace the field demonstration component. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide information about livestock feeding, crop techniques, and basic financial planning, the task fundamentally requires live demonstration, real-time adaptation to farm conditions, and personalized advice based on individual farm contexts—capabilities current systems cannot deliver end-to-end. Only narrow components (e.g., generating feeding schedules from fixed parameters) are automatable. |
| Task automatability | claude-sonnet-5 | 2/5 | Advice generation on livestock health, agronomy, and finance can be partially drafted by AI, but hands-on demonstration of physical techniques in the field cannot be automated by current systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: farmers often require trust-based relationships with educators, regulatory frameworks around livestock health may require certified human advice, and cultural/social factors favor human contact and demonstration for knowledge transfer in agricultural communities. Many agricultural extension services have formal credentialing requirements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but extension work relies on established trust relationships, local knowledge, and physical presence that create real friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI systems that might handle parts of this task (specialized agricultural advisory software, chatbots) still require significant human oversight, validation, and integration costs that are comparable to or exceed the marginal cost of direct educator contact in rural settings where this role operates. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-generated advice is cheap, the task requires site visits, physical demonstration, and trust-building that still require paying a human educator, keeping overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs the full advising and demonstration function in production on farms today. Chatbots can answer general questions, but they cannot conduct hands-on demonstrations, diagnose farm-specific problems in real time, or adapt recommendations to local soil, climate, and herd conditions as actual educators do. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI chatbots and ag-advisory tools exist and provide informational support, but no deployed product reliably performs in-person demonstration or context-specific farm visits at scale. |
Provide direct assistance to farmers by performing activities such as purchasing or selling products and supplies, supervising properties, and collecting soil and herbage samples for testing.
16CI 5–28 · exposure 13 · augmentation 50 · importance 3.0/5 · click for rater detail
Provide direct assistance to farmers by performing activities such as purchasing or selling products and supplies, supervising properties, and collecting soil and herbage samples for testing.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Agricultural sectors, particularly small and mid-size farms, show slow AI adoption overall. Farm management remains heavily reliant on human extension agents and traditional practices, with limited digitization and low capital investment in automation relative to urban professional services. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural extension and hands-on farm services are a low-digitization, physically-grounded sector with minimal AI agent deployment for this kind of task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist educators by automating soil sample analysis, aggregating market price data for purchasing decisions, and generating property condition reports, allowing educators to focus more on farmer consultation and decision-making rather than manual data collection and simple transactions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with record-keeping, price analysis for purchasing/selling decisions, and interpreting soil test results, but cannot replace the physical sample collection or on-site supervision. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with some components (analyzing soil/herbage samples, price comparisons for purchasing), the task requires on-site physical inspection, property supervision, and real-time decision-making with farmers that cannot be fully automated end-to-end today. Current AI lacks the embodied capability to physically collect samples and navigate farm operations. |
| Task automatability | claude-sonnet-5 | 1/5 | This task involves physical activities (property supervision, sample collection) and hands-on transactional work with farmers that current AI cannot perform end-to-end without human physical presence and judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: farm operations often require licensed agricultural extension advisors in many jurisdictions; liability for poor soil testing or purchasing decisions falls on human professionals; farmer relationships and trust require human contact; regulatory frameworks governing agricultural advice and property supervision typically mandate human accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but physical presence, property access, and direct farmer relationships create practical friction against remote AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The all-in cost of deploying AI for sample collection (robotics, field sensors, integration, oversight) combined with inference costs exceeds the loaded wage of a farm educator performing traditional advising and limited sample collection work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical labor and on-site presence required, so there is no viable AI cost comparison for this task as a whole. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform this complete task. While soil testing labs exist and e-commerce platforms handle purchasing, integrated AI systems that supervise properties, collect physical samples, and provide contextual farm management advice in production are not mature. Current applications are narrow and require significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously purchases/sells farm supplies, supervises properties, or collects soil samples in the field; these remain human-executed physical and interpersonal tasks. |
Conduct field demonstrations of new products, techniques, or services.
12CI 5–19 · exposure 8 · augmentation 50 · importance 3.9/5 · click for rater detail
Conduct field demonstrations of new products, techniques, or services.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Agricultural extension and home management education sectors are relatively low in digital maturity and AI adoption; these are traditionally human-centered, relationship-driven services with limited automation history. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural extension work is a low-digitization, physically-embedded field profession with minimal AI agent deployment in production; adoption in this sector lags far behind information/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist educators by generating demonstration scripts, preparing visual aids, or providing real-time information lookup during live events, moderately enhancing preparation and delivery while the educator remains the primary demonstrator. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help educators prepare demonstration materials, research new techniques, generate visual aids, and analyze audience questions beforehand, but it doesn't materially assist during the live physical demonstration itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Field demonstrations require physical presence, real-time interaction with participants, and adaptive responses to questions and conditions. While AI could assist with preparation or content creation, end-to-end automation meeting the 50% time-saving threshold is not feasible with current systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically demonstrating agricultural products or techniques in real field settings, involving hands-on manipulation, live audience interaction, and adaptation to environmental conditions that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: the task requires human credibility and trust with an audience, organizational preference for human educators, and implicit liability if automated systems provide incorrect product or technique guidance that affects farm operations or household safety. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing mandates a human for this specific task, farmers strongly prefer trusted local human experts, and building rapport/trust in agricultural communities creates significant organizational and relational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI systems (robotics, agents, infrastructure) capable of conducting field demonstrations would far exceed the wages of a farm/home management educator performing the task in person. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical, in-person demonstration component, so AI cost comparison is essentially inapplicable and human labor remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably conducts in-person field demonstrations independently. This task demands embodied presence, real-time audience engagement, and situational judgment that current AI systems cannot reliably execute in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically conducts field demonstrations; this remains firmly in the domain of human educators working directly with farmers and physical materials. |
Schedule and make regular visits to farmers.
9CI 5–13 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Schedule and make regular visits to farmers.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Agricultural extension and farm education remain largely low-digitization sectors where educators conduct visits through traditional personal outreach; AI adoption in this specific task is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and education sectors are slow to adopt AI for field-based, relationship-driven activities compared to office-based information sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with scheduling optimization or pre-visit preparation (data gathering about a farm's records), but the core visit itself remains entirely human-dependent, offering limited augmentation value. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help schedule visits, optimize routes, prepare visit materials, and record/summarize notes afterward, meaningfully aiding the surrounding logistics even though the visit itself remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence at farm locations, relationship-building, and contextual responsiveness to individual farmer circumstances. Current AI systems cannot autonomously travel to farms or conduct in-person visits. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically visiting farmers and building in-person relationships requires a human presence and judgment that current AI cannot replicate; the core action is inherently physical/social.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist due to the requirement for human contact, trust-building relationships with farmers, and the need for contextual judgment about farm conditions. Farmers typically expect direct human engagement from educators. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier exists, but organizational norms, trust-building, and the value of personal relationships create moderate friction against removing the human element. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is inherently human-performed; there is no AI alternative to compare costs against, making the human cost the baseline and any AI substitute more expensive when accounting for the still-required human oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physical travel and in-person engagement, so cost comparison favors the human doing the actual visit. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can independently schedule and conduct farm visits; this fundamentally requires human physical presence and interpersonal interaction in real-world settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs in-person farm visits; this is purely a research-irrelevant physical task not addressed by existing AI tools. |
Organize, advise, and participate in community activities and organizations, such as county and state fair events and 4-H Clubs.
5CI 5–5 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Organize, advise, and participate in community activities and organizations, such as county and state fair events and 4-H Clubs.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Community-based farm and home management education operates in rural and regional contexts with low digitization rates, strong cultural preference for human educators, and minimal AI adoption pressure in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Extension and agricultural community education is a low-digitization, relationship-driven sector with minimal AI adoption for this kind of in-person organizing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with event scheduling, materials preparation, or post-event documentation, but these support tasks are peripheral. The core advisory and participatory functions resist meaningful augmentation because they depend on human credibility and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help with scheduling, communications, promotional materials, and administrative planning for these events, providing moderate productivity assistance even though the core activity remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires human judgment, social presence, and relationship-building across diverse community stakeholders. Current AI systems cannot meaningfully organize, advise on, or participate in live community activities in ways that would deliver 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires in-person community engagement, relationship-building, and physical presence at events like fairs and club meetings, which AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | These roles are embedded in community trust, organizational leadership, and often informal governance structures where human judgment and accountability are assumed. Organizations depend on humans to represent them and make contextual decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Community trust, local relationships, and physical participation in events create strong organizational and social barriers that prevent AI substitution, though no formal licensing requirement exists. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The core value of this task lies in the human educator's credibility, networks, and judgment. AI tools offer negligible cost advantage and cannot substitute for the human presence that defines the task's utility. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human entirely; any AI use would only be supplementary, not a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform community organizing, advisory roles, or participation in live events. This requires embodied presence, trust-building, and contextual judgment that fall outside current reliable automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product organizes or participates in community events; this remains entirely a human relational and logistical activity. |
Act as an advocate for farmers or farmers' groups.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.0/5 · click for rater detail
Act as an advocate for farmers or farmers' groups.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Agricultural and advocacy sectors show low overall AI adoption, and there is no meaningful adoption of AI systems to replace human advocates in farm advocacy roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agricultural extension and educator roles are in a sector with modest AI adoption, and advocacy-specific AI tools are essentially nonexistent in this space. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with research (data on policy impact, farmer needs analysis) or draft communications, but the core work of building trust, negotiating, and advocating requires human judgment and presence; assistance is limited to background support. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft talking points, research policy, summarize data, and prepare communications, meaningfully assisting an advocate's preparation work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Advocacy requires representing farmers' interests to policymakers, building relationships, negotiating, and making strategic judgments about positioning—all fundamentally human social and political tasks that cannot be automated end-to-end by current AI systems. |
| Task automatability | claude-sonnet-5 | 1/5 | Advocacy requires relationship-building, negotiation, trust, and representing human interests in political/social contexts—tasks AI cannot perform end-to-end with equal quality today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Advocacy on behalf of farmers involves representing others' interests and potentially legal/regulatory standing; laws and norms require that a human representative with clear accountability must serve in this role. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Advocacy involves representing real people's interests, trust, and often legal/political standing (e.g., testifying, negotiating with agencies), which typically requires a human with standing and credibility. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Advocacy work requires sustained stakeholder engagement, credibility, and institutional relationships that current AI cannot replace; human advocates would remain necessary and AI deployment offers no meaningful cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably perform advocacy work on behalf of a group; advocacy requires legal standing, accountability, and credibility that only human representatives can provide in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product acts as an independent advocate for a group; this remains a fundamentally human relational and political role. |
Related occupations — Educational Instruction & Library
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.