Which Jobs Will Be Most Impacted by ChatGPT?

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Which Jobs Will Be Most Impacted by ChatGPT?

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Visualizing the impact of artificial intelligence on the labor market

Jobs Most Impacted by ChatGPT and Similar AI Models

On November 30, 2022, OpenAI heralded a new era of artificial intelligence (AI) by introducing ChatGPT to the world.

The AI chatbot stunned users with its human-like and thorough responses. ChatGPT could comprehend and answer a variety of different questions, make suggestions, research and write essays and briefs, and even tell jokes (amongst other tasks).

Many of these skills are used by workers in their jobs across the world, which begs the question: which jobs will be transformed, or even replaced, by generative AI in the coming future?

This infographic from Harrison Schell visualizes the March 2023 findings of OpenAI on the potential labor market impact of large language models (LLMs) and various applications of generative AI, including ChatGPT.

Methodology

The OpenAI working paper specifically examined the U.S. industries and jobs most “exposed” to large language models like GPT, which the chatbot ChatGPT operates on.

Key to the paper is the definition of what “exposed” actually means:

“A proxy for potential economic impact without distinguishing between labor-augmenting or labor-displacing effects.” – OpenAI

Thus, the results include both jobs where humans could possibly use AI to optimize their work, along with jobs that could potentially be automated altogether.

OpenAI found that 80% of the American workforce belonged to an occupation where at least 10% of their tasks can be done (or aided) by AI. One-fifth of the workforce belonged to an occupation where 50% of work tasks would be impacted by artificial intelligence.

The Jobs Most and Least at Risk of AI Disruption

Here is a list of jobs highlighted in the paper as likely to see (or already seeing) AI disruption, where AI can reduce the time to do tasks associated with the occupation by at least 50%.

Analysis was provided by a variety of human-made models as well as ChatGPT-4 models, with results from both showing below:

Jobs Categorized ByAI Exposure
AccountantsAI100%
Admin and legal assistantsAI100%
Climate change policy analystsAI100%
Reporters & journalistsAI100%
MathematiciansHuman & AI100%
Tax preparersHuman 100%
Financial analystsHuman100%
Writers & authorsHuman100%
Web designersHuman100%
Blockchain engineersAI97.1%
Court reportersAI96.4%
ProofreadersAI95.5%
Correspondence clerksAI95.2%
Survey researchersHuman84.0%
Interpreters/translatorsHuman82.4%
PR specialistsHuman80.6%
Animal scientistsHuman77.8%

Editor’s note: The paper only highlights some jobs impacted. One AI model found a list of 84 additional jobs that were “fully exposed”, but not all were listed. One human model found 15 additional “fully exposed” jobs that were not listed.

Generally, jobs that require repetitive tasks, some level of data analysis, and routine decision-making were found to face the highest risk of exposure.

Perhaps unsurprisingly, “information processing industries” that involve writing, calculating, and high-level analysis have a higher exposure to LLM-based artificial intelligence. However, science and critical-thinking jobs within those industries negatively correlate with AI exposure.

On the flipside, not every job is likely to be affected. Here’s a list of jobs that are likely least exposed to large language model AI disruption.

Jobs Least Exposed to AI
AthletesShort-order cooks
Large equipment operatorsBarbers/hair stylists
Glass installers & repairersDredge operators
Automotive mechanicsPower-line installers/repairers
Masons, carpenters, roofersOil field maintenance workers
Plumbers, painters, pipefittersServers, dishwashers, bartenders

Naturally, hands-on industries like manufacturing, mining, and agriculture were more protected, but still include information processing roles at risk.

Likewise, the in-person service industry is also expected to see minimal impact from these kinds of AI models. But, patterns are beginning to emerge for job-seekers and industries that may have to contend with artificial intelligence soon.

Artificial Intelligence Impacts on Different Levels of Jobs

OpenAI analyzed correlations between AI exposure in the labor market against a job’s requisite education level, wages, and job-training.

The paper found that jobs with higher wages have a higher exposure to LLM-based AI (though there were numerous low-wage jobs with high exposure as well).

Job ParameterAI Exposure Correlation
WagesDirect
EducationDirect
TrainingInverse

Professionals with higher education degrees also appeared to be more greatly exposed to AI impact, compared to those without.

However, occupations with a greater level of on-the-job training had the least amount of work tasks exposed, compared to those jobs with little-to-no training.

Will AI’s Impact on the Job Market Be Good or Bad?

The potential impact of ChatGPT and similar AI-driven models on individual job titles depends on several factors, including the nature of the job, the level of automation that is possible, and the exact tasks required.

However, while certain repetitive and predictable tasks can be automated, others that require intangibles like creative input, understanding cultural nuance, reading social cues, or executing good judgement cannot be fully hands-off yet.

And keep in mind that AI exposure isn’t limited to job replacement. Job transformation, with workers utilizing the AI to speed up or improve tasks output, is extremely likely in many of these scenarios. Already, there are employment ads for “AI Whisperers” who can effectively optimize automated responses from generalist AI.

As the AI arms race moves forward at a rapid pace rarely seen before in the history of technology, it likely won’t take long for us to see the full impact of ChatGPT and other LLMs on both jobs and the economy.

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This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.

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Visualizing AI vs. Human Performance in Technical Tasks

AI systems have seen rapid advancements, surpassing human performance in technical tasks such as advanced math and visual reasoning.

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A line chart showing AI vs human performance in various technical tasks

AI vs. Human Performance in Technical Tasks

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

The gap between human and machine reasoning is narrowing—and fast.

Over the past year, AI systems have continued to see rapid advancements, surpassing human performance in technical tasks where they previously fell short, such as advanced math and visual reasoning.

This graphic visualizes AI systems’ performance relative to human baselines for eight AI benchmarks measuring tasks including:

  1. Image classification
  2. Visual reasoning
  3. Medium-level reading comprehension
  4. English language understanding
  5. Multitask language understanding
  6. Competition-level mathematics
  7. PhD-level science questions
  8. Multimodal understanding and reasoning

This visualization is part of Visual Capitalist’s AI Week, sponsored by Terzo. Data comes from the Stanford University 2025 AI Index Report.

An AI benchmark is a standardized test used to evaluate the performance and capabilities of AI systems on specific tasks.

AI Models Are Surpassing Humans in Technical Tasks

Below, we show how AI models have performed relative to the human baseline in various technical tasks in recent years.

YearPerfomance relative to the human baseline (100%)Task
201289.15%Image classification
201391.42%Image classification
201496.94%Image classification
201599.47%Image classification
2016100.74%Image classification
201680.09%Visual reasoning
2017101.37%Image classification
201782.35%Medium-level reading comprehension
201786.49%Visual reasoning
2018102.85%Image classification
201896.23%Medium-level reading comprehension
201886.70%Visual reasoning
2019103.75%Image classification
201936.08%Multitask language understanding
2019103.27%Medium-level reading comprehension
201994.21%English language understanding
201990.67%Visual reasoning
2020104.11%Image classification
202060.02%Multitask language understanding
2020103.92%Medium-level reading comprehension
202099.44%English language understanding
202091.38%Visual reasoning
2021104.34%Image classification
20217.67%Competition-level mathematics
202166.82%Multitask language understanding
2021104.15%Medium-level reading comprehension
2021101.56%English language understanding
2021102.48%Visual reasoning
2022103.98%Image classification
202257.56%Competition-level mathematics
202283.74%Multitask language understanding
2022101.67%English language understanding
2022104.36%Visual reasoning
202347.78%PhD-level science questions
202393.67%Competition-level mathematics
202396.21%Multitask language understanding
202371.91%Multimodal understanding and reasoning
2024108.00%PhD-level science questions
2024108.78%Competition-level mathematics
2024102.78%Multitask language understanding
202494.67%Multimodal understanding and reasoning
2024101.78%English language understanding

From ChatGPT to Gemini, many of the world’s leading AI models are surpassing the human baseline in a range of technical tasks.

The only task where AI systems still haven’t caught up to humans is multimodal understanding and reasoning, which involves processing and reasoning across multiple formats and disciplines, such as images, charts, and diagrams.

However, the gap is closing quickly.

In 2024, OpenAI’s o1 model scored 78.2% on MMMU, a benchmark that evaluates models on multi-discipline tasks demanding college-level subject knowledge.

This was just 4.4 percentage points below the human benchmark of 82.6%. The o1 model also has one of the lowest hallucination rates out of all AI models.

This was major jump from the end of 2023, where Google Gemini scored just 59.4%, highlighting the rapid improvement of AI performance in these technical tasks.

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