ChatGPT们将如何影响职业和行业 How Will Language Modelers Like ChatGPT Affect Occupations and Industries.pdf

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2023-03-22

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How will Language Modelers like ChatGPT Affect

Occupations and Industries?

Ed Felten (Princeton)

Manav Raj (University of Pennsylvania)

Robert Seamans (New York University)

1 March 2023

Abstract: Recent dramatic increases in AI language modeling capabilities has led to many

questions about the effect of these technologies on the economy. In this paper we present a

methodology to systematically assess the extent to which occupations, industries and

geographies are exposed to advances in AI language modeling capabilities. We find that the top

occupations exposed to language modeling include telemarketers and a variety of post-secondary

teachers such as English language and literature, foreign language and literature, and history

teachers. We find the top industries exposed to advances in language modeling are legal services

and securities, commodities, and investments.

Keywords: artificial intelligence, ChatGPT, language modeling, occupation, technology

1. Introduction

Artificial Intelligence (AI) will likely affect the economy in many ways, potentially boosting

economic growth and changing the way people work and play. The effect of AI on work will likely

be multi-faceted. In some cases, AI may substitute for work previously done by humans, and in

other cases AI may complement work done by humans. The effect on work will likely also vary

across industries. Recent research by Goldfarb et al (2020) document that adoption of AI is

relatively high in some industries such as information technology and finance, but low in others

such as health care and construction. Moreover, trying to understand how AI will affect work is

like trying to hit a moving target because the capabilities of AI are still advancing.

A prominent example of how AI capabilities continue to advance are the recent improvements in

AI language modeling. In particular, ChatGPT, a language modeler released by Open AI in late

2022, has garnered a huge amount of attention and controversy. Some worry about the negative

effects of tools like ChatGPT on jobs, as in the New York Post article headlined “ChatGPT could

make these jobs obsolete: ‘The wolf is at the door.’”

Others see practical and commercial promise

from language modeling. For example, Microsoft announced a $10 billion partnership with Open

AI and has linked ChatGPT with its Bing search engine.

Google felt compelled to demonstrate

its own language modeler, Bard, but mistakes during the demonstration led Google’s stock price

to drop 7%.

ChatGPT has been banned by J.P. Morgan.

However, at present, most of this is

speculation.

In order to better understand how language modelers such as ChatGPT will affect occupations,

industries and geographies, we use a methodology developed by Felten et al (2018, 2021). Felten

et al created the AI Occupational Exposure (AIOE) measure and used this measure to identify

which occupations, industries and geographies are most exposed to AI. In this paper, we describe

how the AIOE approach can be adapted to account for the recent advancement of language

modeling.

https://nypost.com/2023/01/25/chat-gpt-could-make-these-jobs-obsolete/

https://www.bloomberg.com/news/articles/2023-01-23/microsoft-makes-multibillion-dollar-investment-in-

openai

https://www.cnbc.com/2023/02/08/alphabet-shares-slip-following-googles-ai-event-.html

https://www.cbsnews.com/news/chatgpt-jpmorgan-chase-bars-workers-from-using-ai-tool/

We find that the top occupations affected include telemarketers and a variety of post-secondary

teachers such as English language and literature, foreign language and literature, and history

teachers. We also find the top industries exposed to advances in language modeling are legal

services and securities, commodities, and investments.

This article contributes to several literatures. First, by providing a systematic examination of the

effect of language modeling across occupations, industries and geographies, it contributes to a

nascent literature on the effects of ChatGPT and other language modelers on the economy (e.g.

Agarwal et al., 2022; Zarifhonarvar, 2023). More generally, the article builds on a broader set of

literature studying the effect of AI on the economy (Furman and Seamans, 2019; Goldfarb et al.,

2019). Second, the article builds on and extends a set of papers that provide systematic

methodologies for studying how AI affects occupations (e.g., Brynjolfsson et al, 2018; Frey &

Osborne, 2017; Tolan et al., 2021; Webb, 2020). The article specifically builds off and extends

the methodology described in Felten et al. (2018, 2021). In so doing, the article demonstrates the

flexibility of the original Felten et al methodology; it can be adjusted dynamically to assess the

impact of changes in AI capabilities. Finally, the article adds to a large literature on the effect of

automating technologies on labor (e.g., Acemoglu et al., 2022; Autor, 2015; Frank et al., 2019;

Genz et al., 2021).

The article proceeds as follows. Section 2 describes the AI Occupational Exposure (AIOE)

measure developed by Felten et al (2018, 2021). Section 3 extends the AIOE to account for recent

advances in language modeling. Section 4 provides results, including listing the top 20 most

affected occupations and industries. Section 5 concludes.

2. AI Occupational Exposure Methodology

According to Felten et al (2021), the AI Occupational Exposure (AIOE) is a measure of each

occupation’s “exposure” to AI. The term “exposure” is used so as to be agnostic as to the effects

of AI on the occupation, which could involve substitution or augmentation depending on various

factors associated with the occupation itself.

The AIOE measure was constructed by linking 10 AI applications (abstract strategy games, real-

time video games, image recognition, visual question answering, image generation, reading

comprehension, language modeling, translation, speech recognition, and instrumental track

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