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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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Ranked: AI Models With the Lowest Hallucination Rates

Hallucination rate is the frequency that an LLM generates false or unsupported information in its outputs. Which models have the lowest rates?

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AI Models With the Lowest Hallucination Rates

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.

As AI-powered tools and applications become more integrated into our daily lives, it’s important to keep in mind that models may sometimes generate incorrect information.

This phenomenon, known as “hallucinations,” is described by IBM as occurring when a large language model (LLM)—such as a generative AI chatbot or computer vision tool—detects patterns or objects that do not exist or are imperceptible to humans, leading to outputs that are inaccurate or nonsensical.

This chart visualizes the top 15 AI large language models with the lowest hallucination rates.

The hallucination rate is the frequency that an LLM generates false or unsupported information in its outputs.

The data comes from Vectara and is updated as of Dec. 11, 2024. Hallucination rates were calculated by summarizing 1,000 short documents with each LLM and using a model to detect hallucinations, yielding a percentage of factually inconsistent summaries.

Which AI Models Have the Lowest Hallucination Rates?

Below, we show the top 15 AI models with the lowest hallucination rates, their company, and their country of origin.

ModelCompanyCountryHallucination Rate
Zhipu AI GLM-4-9B-ChatZhipu AI🇨🇳 China1.3%
Google Gemini-2.0-Flash-ExpGoogle🇺🇸 United States1.3%
OpenAI-o1-miniOpenAI🇺🇸 United States1.4%
GPT-4oOpenAI🇺🇸 United States1.5%
GPT-4o-miniOpenAI🇺🇸 United States1.7%
GPT-4-TurboOpenAI🇺🇸 United States1.7%
GPT-4OpenAI🇺🇸 United States1.8%
GPT-3.5-TurboOpenAI🇺🇸 United States1.9%
DeepSeek-V2.5DeepSeek🇨🇳 China2.4%
Microsoft Orca-2-13bMicrosoft🇺🇸 United States2.5%
Microsoft Phi-3.5-MoE-instructMicrosoft🇺🇸 United States2.5%
Intel Neural-Chat-7B-v3-3Intel🇺🇸 United States2.6%
Qwen2.5-7B-InstructAlibaba Cloud🇨🇳 China2.8%
AI21 Jamba-1.5-MiniAI21 Labs🇮🇱 Israel2.9%
Snowflake-Arctic-InstructSnowflake🇺🇸 United States3.0%

Smaller or more specialized models, such as Zhipu AI GLM-4-9B-Chat, OpenAI-o1-mini, and OpenAI-4o-mini have some of the lowest hallucination rates among all models. Intel’s Neural-Chat 7B is also a smaller model.

According to Vectara, small-size models can “achieve hallucination rates comparable or even better (lower) than LLMs that are much larger in size.”

Measuring hallucination rates is becoming increasingly critical as AI systems are deployed in high-stakes applications across fields such as medicine, law, and finance.

While larger models generally outperform smaller ones and are continually scaled up for better results, they come with drawbacks like high costs, slow inference, and complexity.

Smaller models, however, are closing the gap, with many performing well on specific tasks. For example, a study showed that the smaller Mistral 8x7B model successfully reduced hallucinations in AI-generated text.

In terms of foundational models, Google’s Gemini 2.0 slightly outperforms OpenAI GPT-4 with a hallucination rate difference of just 0.2%.

However overall, several variants of GPT-4 (e.g., Turbo, Mini, Standard) fall within the 1.5%–1.8% range, highlighting a strong focus on accuracy across different tiers of the same architecture.

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To learn more about the artificial intelligence industry, check out this graphic that visualizes how much big tech giants are spending on AI data centers.

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