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The four-chatbot experiment did not identify 10 jobs that are “safest” from AI. In Tom’s Guide’s 5 September 2026 comparison, the models were asked to rank the 10 jobs most likely to be replaced next. Their only shared choice was customer-service representative, with variants such as “Tier-1 customer service representative” and “basic customer service representatives.” That is evidence about replacement risk, not a scientific list of future-proof careers.
What the four chatbots actually agreed on
Tom’s Guide asked ChatGPT, Gemini, Claude and Perplexity the same broad question about jobs likely to be replaced by AI. All four included customer-service representative as the leading common role. The agreement reflects how much first-line support consists of standardized digital information work: answering routine questions, following scripts, checking account details and routing straightforward cases.
The article also reports one model-specific selection from each response:
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- ChatGPT: graphic designer.
- Gemini: basic market research and financial data analyst.
- Claude: broadcast announcers and radio DJs.
- Perplexity: file clerks and records-management assistants.
These are the author’s reported outputs from one prompt. The article does not publish a reproducible protocol or complete, independently verified 10-item tables, so the results should not be treated as a benchmark or forecast of employment.
Why “safe from AI” is the wrong standard
No occupation is permanently immune to automation. A more useful definition of safety is lower near-term exposure to generative AI, stronger human complementarity, or demand that may expand as AI lowers costs. A job can keep its title while its hiring volume, pay, tasks or required skills change.
OpenAI’s 2026 work framework explicitly says, “AI does not determine one inevitable future of work.” It groups occupations into four transition patterns rather than predicting that a fixed percentage of jobs will disappear:
| OpenAI category | Share reported for the framework | Meaning |
|---|---|---|
| Relatively high automation risk | About 18% | A larger share of core tasks could be automated. |
| Likely to reorganize | About 24% | People remain important, but workflows and staffing may change substantially. |
| Growth with AI | About 12% | AI may increase productivity or demand for the occupation. |
| Less immediate change | About 46% | Core work is less immediately exposed, although technology can still alter the job. |
The percentages describe categories in that framework; they are not a prediction that 18% of all jobs will vanish.
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The most defensible examples combine several characteristics: physical work in a specific place, unpredictable environments, dexterity, legal or safety accountability, trusted relationships, and tasks that cannot be completed from a text prompt alone.
Electricians
Electricians diagnose systems in varied buildings, work around live or potentially hazardous equipment, install and route hardware, and remain responsible for code compliance and safe operation. Software can help with documentation, estimates or troubleshooting references, but it does not remove the need to inspect the actual site and make accountable decisions.
Plumbers
Plumbing failures are physical, messy and highly variable. Finding a hidden leak, adapting to old construction, selecting compatible parts and completing a safe repair require on-site sensing and manual work. Scheduling, invoicing and parts research are more automatable than the repair itself.
Roofers and construction laborers
Roofing and general construction involve weather, irregular structures, changing materials and continuous safety judgments. Machines may take over selected repetitive tasks, but broad replacement is constrained by mobility, setup, dexterity and the need to coordinate work in a real location. OpenAI lists roofers and construction laborers among examples of work showing less immediate change.
Selected care, counseling and teaching roles
Care workers, counselors, tutors and advisers often handle emotion, motivation, trust, ambiguity and consequences that matter to a particular person. AI can provide practice material, administrative support or a first draft, but clients still value a responsible human who can notice context, obtain consent, escalate risk and adapt in real time. Exposure differs widely: a highly standardized intake process is more automatable than hands-on care or relationship-heavy work.
Best Value
How to judge whether any job is resilient
| Question to ask | Why it matters |
|---|---|
| How much work is physical and location-bound? | AI software cannot directly perform tasks that require moving through a building, handling tools or reaching a specific site. |
| How unpredictable are the conditions? | Irregular layouts, unusual failures and changing environments are harder to standardize. |
| Is dexterity essential? | Fine manipulation in uncontrolled settings remains difficult to automate economically. |
| Who carries legal or safety accountability? | Licensing, inspections and liability preserve a human decision-maker even when software assists. |
| How much trust or empathy is required? | People often want a relationship, reassurance and judgment, not just an answer. |
| Could AI increase demand for the service? | Lower costs can expand markets and create more work around implementation, oversight and customer needs. |
| How much of the role is standardized digital information processing? | Routine text, records, classification and scripted analysis are the most exposed components. |
What broader evidence says about exposure
Most exposed work is more likely to change than disappear
The International Labour Organization’s 2025 index estimates that one in four workers globally are in occupations with some generative-AI exposure. It places 3.3% of global employment in the highest-exposure category, while stressing that transformation is more likely than wholesale replacement. “Exposure” means that some tasks could be assisted or automated; it does not mean the whole occupation ends.
AI skills are becoming part of resilient careers
PwC reported in 2025 that job postings requiring AI skills rose 7.5% while total postings fell 11.3%. That pattern suggests resilience is not only about avoiding AI-exposed tasks. Workers who can use AI to research, draft, analyze, document or monitor quality may be better positioned than workers who refuse tools that are becoming standard in their field.
Exposure can appear first in hiring
A March 2026 paper from the Board of Governors of the Federal Reserve System found that coder employment decelerated sharply after ChatGPT’s introduction but continued to grow, more slowly than before 2022. The example shows how AI impact can appear as slower hiring or reduced growth rather than immediate occupation-wide disappearance.
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- Map the tasks, not just the job title. Separate routine digital work from physical, interpersonal, investigative and accountable work.
- Build a human advantage. Practice communication, judgment, negotiation, teaching, care, safety and problem-solving in ambiguous situations.
- Learn the relevant AI tools. Use them for low-risk drafting, search, scheduling, documentation or analysis, then verify outputs and protect confidential information.
- Add a domain credential where accountability matters. Licensing, inspections, clinical standards, building codes and regulated procedures can make expertise more durable.
- Track how your local market is changing. Demand varies by country, industry, regulation and employer; a job classified as lower exposure globally can still face disruption in a particular region.
The practical answer
Electricians, plumbers, roofers, construction laborers and relationship-heavy care, counseling, tutoring and advisory roles have features associated with less immediate generative-AI change. They are not guaranteed “AI-proof” careers. The strongest long-term position is usually a combination of real-world capability, trusted human judgment and the ability to work productively with AI.
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