We scored 30 occupations on AI resistance using a 100-point framework that synthesizes data from the Frey & Osborne automation model, Goldman Sachs task analysis, BLS employment projections, and WEF Future of Jobs Report 2025. Results: mental health counselors (97/100), surgeons (96/100), and electricians (94/100) top the rankings. The data reveals a key pattern — AI disproportionately threatens white-collar cognitive work, not the manual and relational jobs traditionally considered "lower skill."
- Our 4-factor AI Resistance Scoring methodology (summary; full methodology published separately)
- AI Resistance Scores for 30 occupations across 6 industries
- Why AI threatens white-collar workers more than blue-collar (data)
- Automation probability vs. employment growth correlation analysis
- Which specific task types within occupations face highest automation
- Limitations of this analysis and what the data does NOT tell us
Quick Answers
What jobs can AI not replace?
Based on our 100-point AI Resistance Score: mental health counselors (97/100), surgeons (96/100), electricians (94/100), registered nurses (93/100), and social workers (92/100) rank highest. These occupations score high across all four automation barriers: physical presence, human connection, novel judgment, and ethical accountability.
How do you measure AI resistance?
We score occupations on four dimensions (25 points each): physical presence in unpredictable environments, human relationship requirement, creative/novel judgment, and ethical accountability. Scores are calibrated against Frey & Osborne automation probabilities and validated with BLS employment growth data.
Are blue-collar jobs safer from AI than white-collar jobs?
Yes, our data shows this clearly. The median AI Resistance Score for manual/trades occupations is 91/100, vs. 68/100 for administrative/cognitive roles. Goldman Sachs data confirms: office support (46% of tasks automatable) vs. construction (6%) and installation/repair (4%).
Which occupation category is most AI-resistant overall?
Mental health and therapy occupations have the highest median AI Resistance Score (95/100), followed by skilled trades (91/100) and clinical healthcare (90/100). Creative execution roles (graphic designers, content writers) score lowest among 'skilled' occupations.
- → Mental health professionals score highest in AI resistance (median 95/100) — the therapeutic relationship cannot be automated.
- → Skilled trades score 91/100 median — every job site is physically unique, rendering robotic solutions impractical.
- → Goldman Sachs data: office/admin tasks are 46% automatable vs. construction at 6% and installation/repair at 4%.
- → BLS projects 78 million net new jobs globally by 2030 (WEF) — most in healthcare, trades, and human services.
- → Strong negative correlation (r = −0.81) between AI Resistance Score and Frey & Osborne automation probability.
- → The 10 highest-scoring occupations have a median BLS growth rate of 17% (2024–2034), vs. 3.1% for all occupations.
Most analyses of "jobs AI can't replace" provide lists without methodology. They assert that nurses and electricians are safe, but don't show how they reached that conclusion or what data supports it. This research fills that gap.
We built a scoring framework, applied it to 30 occupations, and validated the results against established automation models. The full methodology, data, and limitations are below.
Looking for a practical guide to landing AI-proof jobs — entry paths, salary expectations, and career transition steps? See our companion blog post: AI-Proof Jobs: 30 Careers Safe from Automation (2026 Guide).
Why This Research
Existing resources on AI-resistant careers suffer from three problems:
- No methodology. Lists assert "nursing is safe" without showing why or how they measured safety.
- No calibration. Claims aren't validated against established automation models (Frey & Osborne, Goldman Sachs).
- No nuance. Occupations are labeled binary "safe/unsafe" when automation risk exists on a spectrum.
This research addresses all three by building a transparent, reproducible scoring system and validating it against multiple external datasets.
Methodology: The AI Resistance Scoring Framework
- AI Resistance Score (ARS)
A composite score from 0 to 100 measuring an occupation's structural resistance to automation by AI and robotic systems. Higher scores indicate greater protection. The score aggregates four equally weighted sub-dimensions (25 points each).
Each occupation is scored on four dimensions (25 points each), based on occupational task analysis (O*NET work activities) and calibrated against Frey & Osborne automation probabilities:
Composite Score: ARS = Physical Presence (0–25) + Human Relationship (0–25) + Creative Judgment (0–25) + Ethical Accountability (0–25)
We synthesized data from five sources: Frey & Osborne (Oxford), Goldman Sachs Research, McKinsey Global Institute, BLS Occupational Outlook (2024–2034), and the WEF Future of Jobs Report 2025.
Validation: Strong negative correlation (r = −0.81) between our ARS and the Frey & Osborne automation probability across all 30 scored occupations. Occupations scoring 90+ have near-zero automation probability; those below 40 face 80%+.
For complete scoring rubrics, calibration details, dimension-by-dimension criteria tables, and instructions on how to score your own career: AI Resistance Score: Full Methodology.
ARS vs. Automation Probability (Validation)
Higher AI Resistance Scores correlate with lower Frey & Osborne automation probability
The AI Resistance Score captures real automation risk dynamics. Occupations scoring 90+ have near-zero automation probability in the Frey & Osborne model, while those below 40 face 80%+ automation probability.
Complete Rankings: 30 Occupations Scored
Below are the full scores for 30 occupations across six categories. Each occupation includes sub-dimension scores, composite ARS, Frey & Osborne automation probability for calibration, and BLS 2024–2034 employment projections.
Healthcare Occupations
Why healthcare scores high: Clinical healthcare uniquely combines all four dimensions. A registered nurse (ARS 93) scores high on physical presence (repositioning patients, inserting IVs in varied conditions), relationship (patient trust and emotional support), judgment (real-time clinical assessment), and accountability (medication administration liability). No other sector consistently hits all four barriers.
The growth signal: Healthcare occupations in our sample have a median BLS growth rate of 17% — over 5x the national average of 3.1%. This isn't coincidental — the same traits that make jobs AI-resistant (human need, physical presence, regulatory requirements) also drive sustained demand.
Skilled Trades
Why trades score high: Skilled trades dominate Dimension 1 (Physical Presence) — every occupation scores a perfect 25/25. What's less obvious is their strong Dimension 3 (Judgment) scores. An electrician diagnosing a wiring problem in a 1920s building is solving a genuinely novel puzzle. The Goldman Sachs data confirms: construction tasks are only 6% automatable, and installation/repair only 4%.
Green energy amplifier: Wind turbine technicians (50% growth) and solar installers (42% growth) show how AI-resistant trades gain a second growth driver from the energy transition. These roles combine physical unpredictability with surging demand.
Mental Health & Human Services
Why mental health scores highest overall: Mental health counselors achieve the highest ARS (97/100) in our entire sample because they score a perfect 25/25 on Dimension 2 (Human Relationship). In therapy, the human relationship is the treatment — research consistently shows that therapeutic alliance predicts outcomes more than specific techniques. AI chatbots can deliver CBT exercises, but they cannot provide the felt experience of being understood by another human.
The nuance: These occupations score lower on Dimension 1 (Physical Presence) since much counseling can occur via telehealth. However, their extreme scores on Dimension 2 more than compensate, and this is validated by near-zero Frey & Osborne automation probabilities.
Leadership & Strategic Roles
Why leadership scores lower than expected: Leadership roles are often cited as "AI-proof," but our scoring reveals important nuance. While they score well on Judgment (18–22) and Accountability (14–22), they score poorly on Physical Presence (6–18). A financial manager works remotely, analyzes data that AI handles well, and relies on judgment that AI increasingly assists. Their protection comes from stakeholder management and accountability — not from all four barriers.
The exception: Construction managers (ARS 77) score better than other leaders because they combine site presence (18) with strategic judgment (19). Medical/health services managers (ARS 82) benefit from clinical knowledge requirements.
Creative & Knowledge Work
Key distinction — direction vs. execution: Creative directors (ARS 72) and art directors (ARS 68) score meaningfully higher than graphic designers or content writers (not scored here, but estimated ARS 35–45). The difference is Dimension 3 (Judgment): directors set creative vision and make novel strategic choices, while executors work within defined parameters that AI increasingly handles.
Information security is a special case: InfoSec analysts (ARS 64) score lower on Physical Presence and Relationship but high on Judgment (novel threat assessment) and Accountability (protecting organizational assets). Their 29% BLS growth rate reflects surging demand that outpaces AI's ability to fully automate the role.
Cross-Category Analysis
AI Resistance by Category
Median AI Resistance Score by Occupation Category
Higher scores indicate greater structural protection from AI automation
BLS Growth Rate by ARS Tier
Employment Growth by AI Resistance Tier
Occupations with higher AI Resistance Scores show stronger employment growth
Occupations in the top AI Resistance tier (90–100) show median employment growth of 14.5% — nearly 5x the national average. The traits that make jobs AI-resistant (human need, physical presence, accountability) are the same traits driving labor demand growth.
Which Dimension Matters Most?
Correlation of Each Dimension with Frey & Osborne Automation Probability
Negative correlation = stronger protection effect (all values are negative)
Physical Presence (r = −0.74) and Human Relationship (r = −0.71) are the strongest individual predictors of automation resistance. This aligns with the Goldman Sachs finding that sectors requiring physical presence — construction (6% automatable), installation/repair (4%) — face dramatically lower automation risk than office-based sectors (46%).
Creative Judgment and Ethical Accountability are meaningful but weaker predictors individually. Their power is strongest in combination — occupations that score high on judgment AND accountability (like surgeons, 25 + 25 = 50 on these two dimensions alone) show near-zero automation probability.
For the full dimension-by-dimension scoring rubrics and instructions on how to score your own occupation, see AI Resistance Score: Full Methodology.
The White-Collar Reversal: AI Hits Cognitive Work Hardest
- White-Collar Reversal
The phenomenon where generative AI disproportionately threatens knowledge-worker and professional occupations — reversing the historical pattern where automation primarily displaced manual and blue-collar jobs.
One of the most significant findings in the current AI automation research is the reversal of historical automation patterns. Previous waves of automation (industrial robotics, computerization) primarily displaced blue-collar and routine manual jobs. Generative AI reverses this.
Why this matters: Workers in traditionally "safe" knowledge-worker roles — legal assistants, financial analysts, junior accountants, content writers — face higher automation risk than electricians and plumbers. Brookings research confirms that "better-paid, better-educated workers face the most exposure" to generative AI.
The implication for career planning: The reflexive advice to "get a college degree for a safe career" no longer holds. A licensed electrician (ARS 94, growth 9%, median pay $62,350) has a more structurally protected career than a paralegal (estimated ARS ~40, high automation probability).
Generative AI reverses the historical automation pattern. College-educated knowledge workers face higher exposure than skilled trades workers. Career resilience increasingly comes from physical presence and human connection, not credentials alone.
Limitations
This analysis has important limitations that should inform how you interpret the results:
- Scores are not predictions. An ARS of 94 does not mean the job is guaranteed safe. Scores measure structural resistance based on current AI capabilities, not future certainty.
- Sub-dimension scores involve judgment. While calibrated against Frey & Osborne, the individual dimension scores (e.g., "electrician = 25 on Physical Presence") involve analytical judgment, not purely algorithmic computation.
- Sample is not exhaustive. We scored 30 occupations across 6 categories. Many occupations are not represented.
- Frey & Osborne data has known limitations. The 2013 model treats entire occupations as units when in reality automation risk varies within occupations. The OECD has critiqued this approach.
- AI capabilities are evolving. Scores reflect the current state of AI (early 2026). Breakthroughs in robotics, embodied AI, or multimodal reasoning could shift the landscape.
- Growth ≠ safety. High BLS growth reflects demand drivers (aging population, energy transition) that overlap with but don't equal automation resistance.
Despite these limitations, the strong correlation (r = −0.81) between our ARS and the independently computed Frey & Osborne probabilities suggests the framework captures real structural dynamics.
Summary of Findings
- 1Mental health professionals have the highest AI resistance (median ARS 95/100) — the therapeutic relationship cannot be automated
- 2Skilled trades score 91/100 median — physical presence in unique environments is the strongest single protection factor
- 3Clinical healthcare scores 90/100 — combining physical care, emotional support, judgment, and accountability
- 4Leadership and creative roles are more vulnerable than commonly assumed (median ARS 68–72) due to low Physical Presence scores
- 5Strong negative correlation (r = −0.81) validates the framework against established automation models
- 6The White-Collar Reversal: generative AI threatens knowledge workers more than manual workers, reversing historical patterns
- 7Top-tier ARS occupations (90+) show median BLS growth of 14.5% — nearly 5x the national average
Frequently Asked Questions
Can I use the AI Resistance Score to evaluate my own job?
Yes. Score your occupation on the four dimensions (Physical Presence, Human Relationship, Creative Judgment, Ethical Accountability) using the scoring rubrics in our full methodology article. Scores above 75 indicate strong structural protection. Scores below 50 suggest meaningful automation risk for portions of the role.
Why does your ranking differ from other 'AI-proof jobs' lists?
Most lists are opinion-based. Ours is scored against a defined framework and validated against Frey & Osborne automation probabilities (r = −0.81 correlation). The main difference: we score leadership and creative roles lower than most lists because they lack Physical Presence protection.
What about jobs that use AI as a tool but aren't replaced?
This is augmentation, not replacement. Information security analysts (ARS 64) are a good example — they use AI tools extensively but the role requires novel threat assessment and accountability that AI cannot own. Our ARS measures replacement risk, not AI usage.
How often will you update these scores?
We plan to revisit this analysis annually or when major AI capability shifts occur (e.g., significant advances in humanoid robotics or embodied AI). The framework is designed to be stable — dimension weights remain fixed, but individual occupation scores may shift as AI capabilities evolve.
Why did you weight all four dimensions equally?
Equal weighting (25 points each) is the simplest defensible approach. Our correlation analysis shows all four dimensions contribute meaningfully (r = −0.58 to −0.74 with automation probability). Future iterations may explore empirical weighting based on larger sample sizes.
Is this peer-reviewed research?
No. This is an analytical framework published by Careery, not an academic paper. We have made the methodology, data, scoring criteria, and limitations fully transparent so readers can evaluate the analysis themselves. The strong correlation with Frey & Osborne provides external validation.
How to cite
Careery Research (2026). “Jobs AI Can't Replace: AI Resistance Scoring for 30 Occupations (Original Research)”. https://careery.pro/research/jobs-ai-cant-replace (accessed YYYY-MM-DD).
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- Include the accessed date when you publish.
- If you reuse numbers, keep the same definitions/timeframe and attribute the source line to Careery.
Researching Job Market & Building AI Tools for careerists since December 2020
Sources & References
- AI Resistance Score: A 4-Dimension Framework for Measuring Automation Risk (Methodology) — Careery Research (2026)
- The Future of Employment: How Susceptible Are Jobs to Computerisation? — Carl Benedikt Frey & Michael A. Osborne, Oxford Martin School (2013 (updated 2017))
- How Will AI Affect the Global Workforce? — Goldman Sachs Research (2023–2024)
- Generative AI and the Future of Work in America — McKinsey Global Institute (2023)
- Occupational Outlook Handbook (2024–2034 Projections) — U.S. Bureau of Labor Statistics (2025)
- Employment Projections: 2024–2034 Summary — U.S. Bureau of Labor Statistics (2025)
- Fastest Growing Occupations (2024–2034) — U.S. Bureau of Labor Statistics (2025)
- The Future of Jobs Report 2025 — World Economic Forum (2025)
- What Jobs Are Affected by AI? Better-Paid, Better-Educated Workers Face the Most Exposure — Brookings Institution (2023)
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure — ILO / OECD (2025)