# How Knowledge Workers Think Generative AI Will (Not) Transform Their
Industries

Allison Woodruff<EMAIL_ADDRESS>GoogleUSA ,  Renee Shelby
<EMAIL_ADDRESS>GoogleUSA ,  Patrick Gage Kelley<EMAIL_ADDRESS>GoogleUSA ,  Steven Rousso-Schindler<EMAIL_ADDRESS>California State
University, Long BeachUSA ,  Jamila Smith-Loud<EMAIL_ADDRESS>GoogleUSA and  Lauren Wilcox<EMAIL_ADDRESS>GoogleUSA

(2024)

###### Abstract.

Generative AI is expected to have transformative effects in multiple knowledge
industries. To better understand how knowledge workers expect generative AI
may affect their industries in the future, we conducted participatory research
workshops for seven different industries, with a total of 54 participants
across three US cities. We describe participants’ expectations of generative
AI’s impact, including a dominant narrative that cut across the groups’
discourse: participants largely envision generative AI as a tool to perform
menial work, under human review. Participants do not generally anticipate the
disruptive changes to knowledge industries currently projected in common media
and academic narratives. Participants do however envision generative AI may
amplify four social forces currently shaping their industries: deskilling,
dehumanization, disconnection, and disinformation. We describe these forces,
and then we provide additional detail regarding attitudes in specific
knowledge industries. We conclude with a discussion of implications and
research challenges for the HCI community.

generative AI, knowledge work, industries

††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: CHI 2024; May 11–16, 2024; Honolulu, HI††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06

##  1\. Introduction

On November 30, 2022, OpenAI released a demo of ChatGPT, a chatbot powered by
a large language model (LLM)111An LLM is a machine learning model trained on
large amounts of data (e.g., at the terabyte or petabyte scale), with model
parameter estimates in the many billions, capable of generating well-
structured and stylized natural language output in response to natural
language input.. The chatbot’s impressive ability to converse and provide
information immediately drew international attention, attracting over one
million users in just a few days (Marr, 2023). While LLMs had been under
active development for several years, and build on technologies that have been
researched and used for decades, ChatGPT’s release was heralded as a
disruptive moment in which the general population, as well as many
technologists, became aware of a significant leap in AI’s capabilities.

Since then, the rapid uptake of generative AI systems such as ChatGPT (OpenAI,
2023), Bard (AI, 2023), DALL$\cdot$E (OpenAI, 2022b), Imagen (Research, 2022),
and Midjourney (Midjourney, Inc., 2022) has been accompanied by striking
narratives. These include discussion of its increased ability to perform
complex tasks and predictions about how generative AI will disrupt knowledge
industries (industries such as law, journalism, software development, and
more, in which workers apply knowledge gained through specialized training to
engage in non-routine problem solving and develop products and services (Janz
et al., 1997)). These narratives imagine generative AI as a resource that can
automate much of the knowledge work currently done by humans, thus having
detrimental effects on labor such as eliminating significant numbers of jobs
across multiple industries (Vallance, 2023). However, while initial evidence
points to productivity gains when generative AI is used for particular tasks
(Brynjolfsson et al., 2023; Weisz et al., 2021), much remains unknown
regarding its future impact.

This unique moment in the uptake of generative AI offers a timely opportunity
to deeply consider expectations of its future use. To better understand how
people anticipate generative AI may affect knowledge work in the future, we
conducted three-hour participatory research workshops for seven different
knowledge industries, with a total of 54 participants across three US cities.
Our contributions are as follows:

  * •

We present a novel qualitative study of how knowledge workers in seven
professional fields—advertising, business communications, education,
journalism, law, mental health, and software development—expect generative AI
may affect their fields in the future. In particular, we explore perceptions
of knowledge workers not only regarding how specific tasks or jobs might be
automated or otherwise change, but also how and whether generative AI might
more broadly shift the nature and structure of work in their industries.

  * •

We describe a dominant narrative regarding generative AI that participants
across multiple knowledge industries envisioned, of generative AI as a tool to
perform menial work under human review, and we also highlight unique
perspectives from each industry.

  * •

We describe how specific social forces that participants are experiencing in
their industries—in parallel to the development of generative AI—create a
unique confluence of circumstances that frame participants’ expectations of
generative AI’s impact as it intersects with social trends surrounding its
deployment and use.

In the remainder of the paper, we review relevant background, describe our
methodology, present our findings, discuss implications and research
challenges for the HCI community, and conclude.

##  2\. Background

Our research examines knowledge workers’ perceptions of how generative AI will
impact their fields. In this section, we provide background on generative AI
technologies and knowledge work, as well as research at their intersection,
and provide an overview of generative AI narratives as well as public
perception of AI.

###  2.1. Generative AI Models

Generative AI is a type of machine learning system that generates realistic
and credibly human-like content (e.g., text, images, code, audio) in response
to an input. These systems are trained on web-scale databases and differ from
conventional machine learning (ML) systems designed to function as expert
systems, marking a shift in the purpose of ML from “problem solving to problem
finding” (Muller et al., 2022, p. 1). The concept of generative AI dates to
the 1950s when researchers began exploring the possibility of using computers
to produce new content (Knill and Young, 1997; Reynolds, 2009). However, it
was not until the 1990s and 2000s with further investment into neural
networks, interconnected nodes that process and identify patterns from large
datasets (Murphy, 2022), that the field of machine learning gained momentum.
The beginning of the so-called “deep learning revolution” (Murphy, 2022, p.
xxvii) occurred in 2014 when Alex Krizhevsky and colleagues (Krizhevsky et
al., 2012) used deep neural networks to win an ImageNet image classification
challenge. Since then, researchers developed new kinds of neural networks that
can generate novel content — including Generative Adversarial Networks
(Goodfellow et al., 2014), Recurrent Neural Networks (RNNs) (Mikolov et al.,
2010), Variational Autoencoders (VAEs) (Kingma and Welling, 2013), and
diffusion generation models (Song and Ermon, 2019) — marking technical
advances that set the stage for generative AI’s growth.

The next significant milestone occurred in 2017 with the development of the
modern transformer architecture (Vaswani et al., 2017) that allows for
parallelization in which computation is divided into smaller tasks that run
simultaneously. Transformer architectures enable generation of detailed and
realistic content, and remain the state-of–the-art for a range of generative
tasks, including text (Ramesh et al., 2021; Lewis et al., 2019), image
(Dosovitskiy et al., 2020; Liu et al., 2021), and code generation (OpenAI,
2022a); and underpin so-called “foundation models” trained on massive
datasets, such as LaMDA (Thoppilan et al., 2022), GPT (Brown et al., 2020),
and DALL$\cdot$E (Ramesh et al., 2022). The field of generative AI is rapidly
evolving; however, large language models (e.g., ChatGPT (OpenAI, 2023), Bard
(AI, 2023)) and text-to-image models (e.g., Midjourney (Midjourney, Inc.,
2022), DALL$\cdot$E 2 (OpenAI, 2022b), Stable Diffusion (AI, 2022)) are
today’s popular modalities. Large language models process and generate text
that can be used for a variety of applications, including question and answer
interactions, language translation, chatbots, code and text generation,
summarization, and language analysis. Text-to-image (T2I) systems allow users
to generate photorealistic images from free-form and open-ended text prompts.
While modalities differ, generative AI systems are marked by three key
characteristics: (1) applicability to generalized rather than specialized use
cases; (2) production of original content that is often indistinguishable from
human content; and (3) intuitive and accessible interfaces (Briggs and
Kodnani, 2023).

###  2.2. Knowledge Work

Knowledge work222The term “knowledge worker” was coined by Peter Drucker
(Drucker, 1959) in 1959. concerns specialized labor, the main capital of which
is knowledge (Galbraith, 2007); knowledge workers apply theoretical and
analytical knowledge gained through specialized training to engage in non-
routine problem solving and develop products and services (Janz et al., 1997,
p. 878). Knowledge work is a characteristic of post-industrial societies
(Castells, 2011) in which capitalist production shifts towards technical
knowledge (Bell, 1976; Pyöriä, 2005). In this way, knowledge work reflects the
reconcentration of socioeconomic and political power into institutions that
translate information resources into marketable commodities and services
(Mosco, 2008). Knowledge work thus reframes workers as a “capital asset” and
locates the responsibility for productivity on individual workers who have
autonomy to determine how a particular task should be approached within
particular boundaries (Drucker, 1999, p. 87). The most defining characteristic
of a knowledge worker is their possession of “knowledge,” not necessarily
completion of an education program (Dove, 1998), although certain roles or
fields may require licensure or credentials (e.g., attorneys, medicine). As
such, knowledge work comprises professions that use information, data, or
ideas as “raw material” for “planning, analyzing, interpreting, developing,
and creating products and services” (Heerwagen et al., 2004, p. 511).

###  2.3. Generative AI and the Future of Knowledge Work

Generative AI’s ability to enable users to easily generate new content across
generalized use cases raises questions about potential macroeconomic impacts
on knowledge labor (Eloundou et al., 2023). Unlike many previous innovations
that have impacted physical work such as manufacturing or transportation,
generative AI directly implicates professionalized knowledge work (Briggs and
Kodnani, 2023). Discourse on this topic forms a Transformative Narrative—we
discuss narratives further in Section 4.1—that suggests that generative AI
will have substantial impact on knowledge industries. For example, Briggs et
al. (Briggs and Kodnani, 2023) identify thirteen kinds of knowledge work
activities vulnerable to automation (e.g., gathering, organizing, analyzing,
and interpreting information), estimating that approximately two-thirds of US
occupations may experience disruption in how workers do their work. While more
research is needed given the rapid pace of change in the field of generative
AI, nascent work suggests potential disruption in creative work (Davenport and
Mittal, 2022), software engineering (Welsh, 2022), teaching and legal services
(Felten et al., 2023), customer service and healthcare (George and George,
2023), and marketing (Chui et al., 2023). Novice workers are likely to
disproportionately benefit from efficiency increases for particular work tasks
(Noy and Zhang, 2023). For instance, introduction of generative AI
conversational assistants in customer service roles enhanced worker
productivity, with disproportionate increases in the performance of novice
customer service agents (Brynjolfsson et al., 2023). Economists anticipate
that occupations experiencing significant automation will be offset by new job
creation (Briggs and Kodnani, 2023), such as new roles to review and manage
content created by generative AI systems (Davenport and Mittal, 2022).
However, appropriate investments in occupational change management are
required (Chui et al., 2023), including understanding worker perspectives, a
goal that directly guided our participatory research.

####  2.3.1. Generative AI Impacts on Worker Tasks

HCI scholars are beginning to examine the impacts of generative AI on
different kinds of work tasks. A dominant thread of research focuses on
different writing tasks (Gero et al., 2023; Roemmele and Gordon, 2015; Shakeri
et al., 2021; Singh et al., 2022; Yang et al., 2022; Yuan et al., 2022),
including marketing slogans (Clark et al., 2018) and storytelling (Roemmele
and Gordon, 2018). Generative AI systems can significantly influence what
topics users write about and how they are framed (Jakesch et al., 2023). For
instance, when people use a generative AI writing assistant (e.g., ChatGPT),
they write more frequently about topics suggested by the system (Poddar et
al., 2023). Writing suggestions made by generative AI systems also influence
the tone and sentiment of communications (Arnold et al., 2018; Bhat et al.,
2023), including length and how generic the text is (Arnold et al., 2020).
However, user experience of generative AI systems is not simply passive, but
also involves cognitive work to negotiate machine-in-the-loop writing
approaches (Singh et al., 2022; Dang et al., 2023). As creative work is not
necessarily outcome-focused, but process-focused, there remain gaps in our
understanding of the impacts of generative AI in explicitly commodified labor
contexts.

An emerging thread of HCI scholarship has focused on potential impacts of
generative AI on workers in professionalized settings, examining potential
professional impacts of generative AI, including how it may improve knowledge
workers’ task efficiency (Zheng et al., 2022; Arakawa et al., 2023). A limited
body of research has examined impacts on specific professional sectors,
notably creative (Inie et al., 2023; Guzdial et al., 2019; Davis et al., 2023)
and educational (Ali et al., 2021; Lu et al., 2023; Kazemitabaar et al., 2023)
domains. While some research suggests that, overall, creative professionals
are not yet worried about generative AI-related job displacement, they did
flag concerns related to worsening work quality, an erosion of the creative
process, and training data copyright concerns (Inie et al., 2023). Within the
education domain, research has focused on how generative AI can support
classroom teaching in terms of preparing course materials (Sarsa et al., 2022;
Lu et al., 2023) and student learning outcomes (Kazemitabaar et al., 2023).
However, there remains a dearth of research on generative AI’s impacts on
professional industries, particularly outside of creative contexts.

###  2.4. Sensationalized Generative AI Narratives

The rapid uptake of generative AI systems has been accompanied by a
Sensationalized Narrative about how it will disrupt society and work. Building
on previous AI narratives, it contains dramatic or even hyperbolic claims (see
critiques: (Bartholomew and Mehta, 2023; Chomsky, 2023)) about topics such as
AI sentience (e.g., (Roose, 2023)) and labor displacement (e.g., (Corfield and
Field, 2022)). Leading figures in AI have framed expectations in evocative
terms (e.g., (Metz, 2023)), for example, likening AI to “new electricity” to
emphasize the expectation that it will disrupt most or all industries (Ng,
2017). The media, futurists, and others have also explored generative AI’s
potential, pondering for example whether AI could “replace humans” (Lock,
2022). Exaggerated claims about machine intelligence envision a global
underclass of human workers supplanted by generative AI (Jaupi, 2023), with
one headline musing: “Could ChatGPT Write My Book and Feed My Kids?”
(Heritage, 2022). While such narratives may draw on claims made by AI
developers to predict a worse case scenario for knowledge work (e.g., (Meyer,
2023; Barrabi, 2023)), they often incorporate evidence in misleading ways (see
critiques: (Marcus, 2019; Omaar, 2023)). Moreover, they rarely engage the
perspectives of knowledge workers with expertise in their field.

While some argue generative AI is at the peak of inflated expectations (DeLisi
and Howley, 2023; Vynck, 2023), sensationalized AI narratives can narrow and
close public debate (Katz, 2020). In response to the hype cycle (Jesuthasan,
2023), there have been calls for more measured narratives that create space
for publics to meaningfully consider and grapple with the labor questions of
generative AI (Bartholomew and Mehta, 2023; Siegel, 2023)). This includes
consideration of the specific ways generative AI may interact with different
kinds of professional work (Joshi, 2023) and also broader knowledge industry
dynamics.

###  2.5. Public Perception of AI

Because the public’s awareness of generative AI is quite recent, little
research has been done on public perception of it. However, a fair amount of
research has been done on public perception of AI more generally, much of it
survey-based (Rainie et al., 2022; Bao et al., 2022; Blumberg Capital, 2019;
Cave et al., 2019; Edelman, 2019; Zhang and Dafoe, 2019; Northeastern
University and Gallup, 2018; Kapania et al., 2022; Selwyn et al., 2020;
YouGov, 2021; The European Commission, 2017; ARM — Northstar, 2017; Funk et
al., 2020; Lloyd’s Register Foundation, 2020; Neudert et al., 2020).
Respondents typically expect AI will have a significant impact on the future,
and often anticipate that beneficial effects are possible, with the most
favorable impressions in emerging and/or Asian markets and more negative
impressions (particularly recently) in Western countries such as the US (ARM —
Northstar, 2017; Edelman, 2019; Funk et al., 2020; Northeastern University and
Gallup, 2018; The European Commission, 2017; Lloyd’s Register Foundation,
2020; Neudert et al., 2020; YouGov, 2021). At the same time, AI is neither
interpreted as exclusively beneficial nor exclusively disadvantageous, and
public response often indicates contradictory emotions (Blumberg Capital,
2019; Kelley et al., 2021b, a; Mozilla, 2019). Job loss (especially related to
robotics and manufacturing), increased social isolation, privacy and other
social topics have been highlighted as key concerns (ARM — Northstar, 2020;
Edelman, 2019; Kelley et al., 2023, 2021b, 2021a; Smith, 2018; Rainie et al.,
2022). In fact, a recent Pew survey found that, among the 37% of US
respondents who indicated they were “more concerned than excited” about the
increased presence of AI in daily life, about one in five explicitly mentioned
job loss as the main reason for their concern (Rainie et al., 2022).

##  3\. Methodology

To explore the expected impact of generative AI applications in knowledge
industries, we conducted participatory research workshops with 54 participants
from seven knowledge industries in three US cities. While our institution does
not have an IRB, we adhere to similarly strict standards. Our research
objectives were to learn more about the following:

  * •

How do knowledge workers expect generative AI will affect their industries?

  * •

How do knowledge workers view generative AI in relationship to other changes
they anticipate in their industry?

###  3.1. Participants

We recruited 7-8 participants from each of seven industries (for 54 in total)
through Gemic, who worked with partners to arrange professional recruiting
firms in each city. We chose a wide range of industries—advertising, business
communications, education, journalism, law, mental health care, and software
development—to elicit diverse perspectives. Approximately half of the
participants in each workshop were mid-career central figures in their field,
holding roles we expected would be well positioned to speak broadly about each
industry, e.g., teachers for education, reporters for journalism, and
attorneys for law. The other half were more specialized, senior, or junior
roles in each profession to introduce a greater diversity of perspectives. See
Appendix, Table 4. Screening also aimed to recruit a balanced mix of genders
and half non-White participants in each workshop. All participants were
compensated the same amount, at industry standard. For a summary of
participant information, see Appendix, Table 5.

###  3.2. Workshops

Participatory workshops have a long methodological history in HCI, used
frequently in participatory design engagements (Till et al., 2022; Harrington
et al., 2019; Le Dantec and Fox, 2015; Rosner et al., 2016), living labs
(Dell’Era and Landoni, 2014), hackerspaces (Fox et al., 2015), and more. The
participatory workshop as a research site and method has roots in
participatory action research (Cooper et al., 2022). Inspired by participatory
action research, we used participatory workshops to engage with specific
communities of practice representing knowledge industries. Rather than
focusing on a design process or future technological possibilities, our
participatory approach centered on the elicitation of nuanced contextual
information about each industry, participant perspectives, and generation of
use cases for generative AI. This approach was grounded and situated in the
participants’ expertise and experiences, and supported by conversation with
the researchers as experts familiar with technological capabilities and
possibilities of generative AI. We thus structured our workshops to include
participant-directed educational activities and a mix of probes and
provocations to scaffold generative discussion.

We held one workshop per industry in July 2023333Workshops took place several
months after the March 2023 release of OpenAI’s GPT-4 model, which is broadly
considered to be a significant improvement over the already impressive
original November 2022 release. in Columbus, Ohio; New York City, New York;
and Oakland, California (for industries in each city, see Appendix, Table 5).
These cities were selected as centers of activity for the respective
industries, as well to represent a socio-geographic range.

Each workshop was held in person for three hours at a third-party facility.
Three to four researchers from Google and Gemic moderated each workshop to
facilitate and share information about generative AI. Participants and
researchers sat together around a large table to facilitate group discussion.
Two videographers also attended each workshop and participants were aware that
several researchers and staff were in an observation room or viewing remotely.
Participants were aware of Google’s involvement in the study. We followed
COVID-19 precautions (e.g., all participants and moderators took tests during
check-in), and refreshments were available throughout the workshops. Before
the workshops began, we asked participants to draw a map of their industry as
a short pre-work activity (Appendix: Figures 1 and 2). Workshops ran as shown
in Table 1.

_Activity_ | _Time_
---|---
Arrive, sign-in, COVID-19 testing, Industry Map |
Welcome, Introductions, Prior Experience with Generative AI | _15 minutes_
Generative AI Teaching and Q/A | _40 minutes_
Meal Break | _20 minutes_
Change Cards Activity | _45 minutes_
Break | _10 minutes_
Policy Activity and Voting | _40 minutes_
Personal Impact and Wrap-Up | _10 minutes_
Table 1. Agenda for participatory research workshops. Times are approximate.

We began with introductions and expectations for the workshop, and then
invited participants to share any prior experiences or impressions of
generative AI. We then gave a presentation to provide participants with a
common working understanding of generative AI to ground the subsequent
activities, allowing a generous amount of time to answer questions. The
presentation included a brief overview of how generative AI models learn and
generate content, how they differ from other AI models, and key
characteristics and risks of generative AI (Appendix B).

After a meal break, we introduced participants to an artifact that we designed
specifically for the workshop: a large, physical card that we term a change
card that encourages participants to reflect on important changes that could
happen in their industry in the next one to two years; changes did not need to
be related to generative AI (Appendix: Figures 3 and 4). Each participant
spent about 10 minutes filling out their individual change card(s). We then
facilitated a group discussion in which participants shared one or more of
their change card(s) and responded to each other’s ideas.

Following another short break, we invited participants to write down the broad
contours of a policy to guide the use of generative AI in their industry
(Appendix: Figures 5 and 6). Participants were again given about 10 minutes to
work individually before taking turns presenting their policies in a
facilitated discussion. We followed the discussion with an exercise in which
participants voted on which aspects of generative AI they find most helpful
and most detrimental for their industry. After a brief discussion of this
voting activity, in the final minutes of the workshop, participants were
encouraged to speak about the potential impact of generative AI outside of
their work. Throughout the workshops, we took care to encourage collaborative
interpretation, problem-solving, and discussion among participants and
moderators, and to make space for all participants to share their ideas and
opinions.

###  3.3. Analysis

All sessions were recorded and transcribed verbatim with an automated speech-
to-text service, and we then manually corrected the transcripts against the
original recordings. All artifacts (54 industry maps, 140 change cards, and 54
policies) were collected and archived. We analyzed data from the corrected
transcripts and artifacts inductively. Drawing on reflexive thematic analysis
approaches (Braun and Clarke, 2019, 2021), four authors reviewed transcripts,
in multiple configurations of paired and independent review, and one of these
authors conducted comparisons with corresponding artifacts. These four authors
engaged in deep and prolonged data immersion and discussion, independent open
coding followed by memo writing to generate themes (Birks et al., 2008), and
engaged in collaborative discussions in multiple rounds to compare their
interpretations of the data reflexively and finalize themes.

###  3.4. Limitations

Several limitations of our study methodology should be considered when
interpreting this work. First, it carries with it the standard issues
attendant with qualitative methodologies and group interviews. We conducted
the research in only three US cities with only one group per industry, and our
small sample was not statistically representative of the roles or demographics
of the professional fields we explored. Our findings should be viewed as a
deep exploration of our participants’ perspectives on their industries, but
should not be taken as generalizing to their industries as a whole. Second,
our choice of teaching content and activities, while appropriate to our
research objectives, may have influenced participants, although we tried to
minimize any effects through participant-led discussion. Finally, while we
confirmed our understanding of participants’ comments during the sessions, and
one or more of the authors have experience in most of the industries
represented, our interpretations may lack context or nuance that would have
been more readily available to members of the same professional categories.

##  4\. Findings

In this section, we present our findings. We begin by describing participants’
expectations of generative AI’s impact, including a dominant narrative that
emerged across the groups. We then turn to four current social forces that
shaped our participants’ perspectives on how generative AI will affect their
industries: deskilling, dehumanization, disconnection, and disinformation.
Finally, we describe some of the unique perspectives within particular
knowledge industries.

###  4.1. Expected Impact of Generative AI

In this subsection, we give an overview of participants’ perspectives on how
generative AI may impact their industries.444We note their perspectives were
informed by content in the workshop as well as their prior exposure. Almost
all participants had heard of generative AI or a specific generative AI
system, e.g., in the press or from colleagues, friends, or family. Many
participants also had experience using an app, especially ChatGPT or
Midjourney, at least lightly, for either personal purposes (e.g., writing a
birthday card) or professional purposes (e.g., drafting a memo). We begin by
briefly summarizing the participants’ dominant narrative regarding generative
AI, which we term the Effort-Saving Tool Narrative (see Table 2). For ease of
comparison, we also briefly summarize the Transformative Narrative and the
Sensationalized Narrative introduced in Sections 2.3 and 2.4, respectively. We
introduce these narratives to situate participant data against extant meaning-
making discourses, in alignment with reflexive thematic analysis (Braun and
Clarke, 2022, p.211). In the remainder of this subsection, we describe the
Effort-Saving Tool Narrative in more detail.

Effort-Saving Tool Narrative — Participants largely envision that it will be
realistic and desirable to use generative AI as a tool to perform menial work,
subject to human review. Further, they believe that existing guardrails in
their industries can be leveraged and augmented to perform such review. They
expect certain roles will be impacted, but for most industries they do not
anticipate substantial transformation or elimination of a wide range of jobs.
In most cases, participants did not anticipate change as broad as that
predicted in the Transformative Narrative or the Sensationalized Narrative.
---
Transformative Narrative — Typically shared in technical
reports66footnotemark: 6 or peer-reviewed papers by think tanks, consulting
firms, or academic groups, and echoed in some rigorous news reports, this
narrative takes an analytic approach to arguing that generative AI will have
broad, substantial impact across industries, jobs, and tasks. Work in this
vein often includes projections (e.g., % of tasks that can be automated by
generative AI, # of jobs affected), outlining a range of possibilities, but
usually including an exceedingly high upper bound for such estimates. While
this narrative is not as catastrophic or exaggerated as the Sensationalized
Narrative, it usually predicts transformative change.
Sensationalized Narrative — A predominant discourse in social media and many
news articles, this narrative makes dramatic or even hyperbolic claims about
how generative AI will change/replace human labor in the future. It often
draws comparisons between generative AI and previous historic innovations or
even “the big bang” (e.g., (Sahai, 2023)). In some cases this narrative
originates from leading figures in AI (Marcus, 2019) who are arguably framing
evocative messages for the lay public. However, it often misrepresents expert
opinion or incorporates evidence from sources such as the Transformative
Narrative in misleading ways and emphasizes anxiety-inducing worst case
scenarios (Omaar, 2023).

Table 2. An overview of three differing narratives regarding generative AI.

  * 5

Throughout the paper, we assign participants pseudonyms that begin with the
letter associated with their industry, i.e., A (Advertising), B (Business
Communications), E (Education), J (Journalism), L (Law), M (Mental Health),
and S (Software Development). In some cases, we have lightly cleaned the
quotes for readability, e.g., to remove filler words such as “like” or “um”.

  * 6

Due to the recency of the topic, much of the relevant literature appears in
pre-prints or white papers.

####  4.1.1. Generative AI can automate menial work: “We need to just churn it
out”

While a wide range of possible use cases were discussed, our participants’
experience, imagination, and preference centered on the use of generative AI
for menial tasks. They characterized these as routine and/or formulaic tasks
that they thought could easily be automated, or which they had actually tried
in apps like ChatGPT, such as drafting social media content, troubleshooting
tickets, leading guided breathwork sessions, or creating lesson plans.

  * “For the tasks that I find to be tedious or taking away from the bigger picture or [taking away from] what’s actually on my plate, those to-do items that I don’t really care about, but we need to just churn it out, ChatGPT all the way.”  _–A1_ 55footnotemark: 5

  * “I’ve used it to help write release notes… I’ll have this list of really boring updates or features that aren’t that sexy, and then they’re like, ‘Just make it sound fun.’ And that is really time consuming actually. And generative AI is extremely helpful for that.”  _–S5_

  * “It does seem useful when you have to sort of pound out outlines that are just very robotic.”  _–J7_

  * “Within law firms there’s some work product that you produce that takes a lot of thought and decision making and some creativity to really draft something that is unique in the way that you put it together. There’s other tasks that I do on a daily basis that are a lot more formulaic… if you’re comparing [the first] to say Citizen Kane, [the second] is more like an SVU episode where you can throw it together in 10 minutes.”  _–L1_

Participants were typically enthusiastic about offloading these tasks to
generative AI, both because they were tedious and because they felt the time
saved would allow them to focus on more meaningful, human aspects of their
work. For example, in the mental health group, participants were attracted to
the idea of generative AI taking over rote work, such as note-taking or
patient intake, thereby increasing their efficiency and freeing up more time
to focus on interpersonal work with their clients.

  * “I wish AI could help us with note-taking in any way. That’s the part of our job that I hate the most. It’s so laborious… it takes up so much of our time and if there’s anything that could support us with that, that would be amazing. I don’t know how it would do that, but if it could, I would cry for joy.”  _–M1_

Further, some greatly valued the ability to scale and handle high volumes of
work. For example, _B6_ had recently sent out 350,000 emails over a three
month period, and imagined generative AI could increase consistency and speed.

####  4.1.2. Generative AI is a useful tool: “Maybe a helpmate but certainly
not in control”

Consistent with their orientation to the use of generative AI for busywork,
participants dominantly oriented to generative AI as a tool for human workers.
For example, some participants specified that generative “AI tools should
assist” employees, but not “do” their work. Many emphasized generative AI
should not go beyond tool status and perform certain kinds of knowledge work,
e.g., generative AI should not be used for decision-making, setting strategic
direction, or forming human connections.

  * “I think to use Gen AI as a tool to assist anyone in the legal system is not necessarily a bad thing so long as it remains just that — a tool.”  _–L5 (Policy)_

  * “For tedious tasks, and time consuming aspects of the business, allow yourself to lean on an AI for support. But always push yourself and your team to be the creative ones.”  _–A1 (Policy)_

####  4.1.3. Generative AI requires human review: “You’re still gonna need
someone to follow up and review it”

Participants in all groups were concerned about generative AI’s potential to
make mistakes or produce undesirable output. For example, participants in the
advertising and business groups shared concern that generated content might
violate brand standards or copyright, and lawyers spoke of the need to attest
to the accuracy of legal documents.

  * “I don’t trust it yet because in law of course we gotta go to deep research to make certain that the sources are correct before we would ever use it.”  _–L2_

  * “You can’t trust that it’s accurate. So [that makes] me hesitant about using it going forward for anything that I take seriously.”  _–L1_

One participant recounted a news story in which an attorney submitted a legal
brief created with ChatGPT, which included “hallucinated” cases:

  * _L7_ : I haven’t used it at all. But I’m kinda wary of introducing new tech generally. Especially as a litigator, anytime you submit anything to a court, you sign it under what we call Rule 11. You’re representing it’s true and accurate and you’ve got a good faith argument for whatever you’re doing. And I think you may have heard about that [case] where a guy wrote his brief using AI and then the AI generated six cases, citing opinions that didn’t even exist apparently. Which to me is mind boggling.
_L6_ : I would’ve won so many cases if I could have done that…
_L7_ : That’s right. Exactly. And then this guy, the attorney that did it,
he’s kind of… a national joke.

Accordingly, the overwhelming sentiment in all groups was that humans would
need to check most or all of generative AI’s output to ensure its quality. In
some fields, participants stipulated that this check must be performed by a
qualified professional.

  * “The AI would produce something, and then we would say yay or nay.”  _–J5_

  * “[Generative AI code] should never be something that goes to production without human testing and review.”  _–S7_

  * “Generative AI can assist attorneys… but all its work must be proofed by an individual licensed to practice law.”  _–L7 (Policy)_

  * “A lot of this problematic stuff that we’re hypothesizing is coming out of this idea that AI could be this standalone [advertising] team doing every single thing from start to finish and releasing this ad without consent of anyone or something like that. Which I think is still pretty sci-fi…”  _–A3_

  * “I feel like you’re always gonna need some type of human oversight, right? No matter how good the technology gets, I just don’t see humans being obsolete ¡laugh¿. Like how’s that possible?”  _–A5_

####  4.1.4. Existing industry governance structures are amenable to
overseeing generative AI: “It would just be part of what we’ve already been
doing.”

Participants spoke of new roles, responsibilities, and qualifications to
enforce human oversight and monitor generative AI’s use and output in
knowledge work.

  * “Training like getting a driver’s license. Once you get the ability and understand the rules you can use [generative AI].”  _–A7 (Policy)_

Participants pointed out that many such oversight functions, while new,
integrate well in existing industry review structures and policies. For
example, lawyers review the work of paralegals or junior associates, business
communications undergo legal and compliance approvals, and software
development has code review. Participants explained:

  * “There’s oversight right now against a brand voice, tone, style, consistency. Anything that is generated has to go through an approval list… [There are] checks and balances in place. So I would feel really good about AI helping contribute to that message if it went against those checks and balances.’  _–B4_

  * “Most of our districts already have user agreements which talk about plagiarism, hate speech and those sorts of things. I think it would just fit right into most of our districts’ user agreements.”  _–E5_

####  4.1.5. Certain jobs will be downsized: “A lot of entry level positions
will be eliminated”

Participants in most industries anticipate that as generative AI streamlines
formulaic work, certain jobs will likely be eliminated. They tended to think
of the job risk as fairly narrowly scoped to specific entry-level jobs or jobs
with many rote tasks. For example, jobs such as help desk, legal assistant, or
product photography were highlighted as being at-risk.

  * “Commercial work I think is done ¡laugh¿. Commercial photoshoots cost hundreds of thousands of dollars, lighting, makeup, all that stuff. [And] now you can just type it in… instead of paying for production, AI could make it all up. They won’t need 99% of roles on set. Don’t need models, photographer, makeup, set designer, etc. Getting rid of the majority of roles is terrible.”  _–J6; J6 (Change Card)_

####  4.1.6. Need to evolve and adapt with generative AI: “Let’s evolve with
the times, right?”

While some participants were saddened by the prospect that many would lose
their jobs, others felt these changes were a natural evolution of knowledge
work, which could bring new opportunities. Those who expressed sadness told
us:

  * “I find the notion of job replacement heart-breaking.”  _–S2 (Change Card)_

  * “[The client was] trying to save money [by using generative AI], but it wouldn’t have saved money. And I also felt really sad because they pretty much outright said that their goal was to close three call centers that employed thousands and thousands of people. And it just made me really sad.”  _–S5_

While others spoke about adapting to inevitable change:

  * “Change is a constant despite humans naturally tending to try and resist it. I am all for change and constant adaptation as long as the baselines of independent thinking and learning is still held as an important skill to possess and have.”  _–L5 (Change Card)_

  * _A2_ : [Generative AI] could eventually replace headshot photography or photo shoots for certain industries…
_A8_ : Yes. I wanna wait until I can say, ‘[Generate it] in the vein of Annie
Leibovitz,’ you know? …
_A2_ : Gonna get there. That’s the thing. It’s only gonna get better. So how
do you deal with that? What happens to all those creators, all those jobs? …
I’m hopeful that there will always be a space for creative people… [but] there
will be I think a big chunk of those jobs that just kind of go away. Like the
simple headshot photographers or the person who does passport photos, things
like that. I don’t know. At this point it seems a little bleak and ambiguous,
but I could see it happening… I feel like the working jobs, the creative jobs,
the smaller ones will go away and then it’ll just be more top heavy companies
where they’re overseeing the technology.
_A5_ : Isn’t that just evolution? We went from no machines to machinery and
people lost millions of jobs. Is it just not a part of evolving? I feel like
there probably are still some human aspects that could go into AI… Is there a
human there to say, ‘Oh, this isn’t right.’? Maybe like quality control.

Some also emphasized that knowledge workers need to adapt and “reformat their
skillset” in order to stay relevant and not get left behind.

  * “[To _B3_] So you just graduated? I would learn to adapt with it and use it rather than ignore it… I guess it’s going from a horse to a car, it’s inevitable… Rather than ignoring it, just learn to drive… How [can you] do your job more efficiently and effectively by using the technology to your advantage?”  _–B4_

  * “What if there’s industries that don’t use it… then are they just no longer a part of the conversation?”  _–A1_

  * “People will adjust. We are all going to move on… I do meet a lot of people, a lot of technologists, my colleagues, coworkers, they get scared by it. But then I tell myself that if you’re scared by the AI itself, then maybe you as a person are not evolving fast enough… I think it gives us a new challenge and we should embrace it. We should accept it and then evolve faster, adopt and adjust as well.”  _–S1_

  * “I think that if you’re in the industry, keeping up with these tools and using them as an asset to enhance your work will make you irreplaceable.”  _–B3 (Change Card)_

As one concrete example, participants spoke about how upcoming changes would
require “prompt engineering” skills to elicit improved outputs from generative
AI systems. _S5_ described this as the next “big thing” and said it is an “art
form… to massage what you’re saying to ChatGPT to get the right answer in the
way that you want.”

####  4.1.7. Not embracing external narratives about disruption: “I don’t
think therapists are gonna go away.”

Overall, when compared with current expectations in the Transformative
Narrative and the Sensationalized Narrative, the Effort-Saving Tool Narrative
is a fairly limited view of expected labor impacts. Many participants expected
that even if workers in entry-level or similar roles in their industry were
affected, they personally were somewhat unlikely to experience significant
impact, feeling confident, for example, that generative AI is unlikely to be
skilled enough to replace human professionals within the timeframe of their
own careers. While some did acknowledge the potential for industry-wide
shifts, particularly in software development, advertising, or business,
sentiment such as the following was generally the exception rather than the
rule:

  * “As a communicator, it’s a little intimidating how good the bot writers can be, and I know our Bangalore teams are looking at hiring bots or putting them in place rather than hiring humans. So, there’s an excitement factor in terms of cost savings as an executive, but also an intimidation factor as a communicator to see that a whole industry of journalists or people who major in [communications] could be replaced to a degree. So, I think specific jobs, organizations, entire industries, the world will be affected by that.”  _–B4_

  * “Writers could start to become obsolete in journalism [because of generative AI]… We could potentially be pushed to the back burner and not relied upon whatsoever to produce content. To be frank, I’m very afraid and believe that I may have to change career paths soon.”  _–J5 (Change Card)_

Further, participants in most industries appeared to feel other forces are
more disruptive than generative AI. We discuss some of these in Section 4.2
and we discuss industry-specific expectations further in Section 4.3.

At the same time, participants were somewhat uncertain about both their own
projections as well as broader narratives. To bolster their reasoning, they
sometimes drew analogies to other resources such as the internet, Google
Search, or industry-specific databases, such as LexisNexis in the legal
profession.

  * “I see certain things as tools that replaced old tools. This is not really a new tool. I think it’s just an enhancement of something we already have. For example… Lexis.”  _–L7_

Relative to the Sensationalized Narrative or even the more moderate
Transformative Narrative, participants tended to take an even more tempered
view of likely changes in their respective industries. While some participants
were aware of media claims, they approached them with skepticism. In some
cases, they literally discounted these narratives by proposing adjustments to
reported statistics. At the same time, they were uncertain, and sometimes
found the claims worrying even when not fully convinced.

  * “I’ve seen it time and time again where everybody [in media companies] runs to the same goal post. Like the pivot to video is the most famous one. It’s exhausting as a journalist and we watch all these people get laid off and then a year later they say, ‘Never mind, it wasn’t the answer we thought it was, back to where we started.’ ¡ _J2_ interjects: [Remember] podcasting? ¡laugh¿¿ Yeah, exactly. We’ve all seen it. So I don’t know how ChatGPT shakes out in this whole picture, but I do think it’s probably not as insane as some people are making it in terms of the tidal changes that it’ll make to our industry. But it is probably like 60% of the way there.”  _–J3_

  * _L4_ : Goldman Sachs says that AI will take over three hundred million jobs away in the coming years.777Participants appear to be referring to a Goldman Sachs report released in March 2023 (Briggs and Kodnani, 2023) whose findings were also highlighted in a McKinsey report released in June 2023 (Chui et al., 2023). The report states that 300 million full time jobs are at risk of automation, globally. The media covered both reports, sometimes sensationally (Omaar, 2023), and coverage was often unclear about the geographic scope of the statistic.
_L7_ : Three hundred million?
_L4_ : Three hundred million.
_Moderator_ : Do you find that plausible?
_L7_ : So all of the US will be laid off.
_L4_ : Yeah. Three hundred million…
_L2_ : And some of the companies like McKinsey who have studied the
prognostications of those kinds of things find they’re wrong way more than
they’re right. But the scary part is even if it’s fifty million people, it is
significant with ripple effects throughout everything. You know, it’s amazing.
They usually aren’t right, completely. But the reality is that you get very
scared by that headline… Talk about anxiety. ‘Hello, am I in the three hundred
million who’s not gonna be employed anymore?’ Tough. Tough.
_L4_ : It was concerning when I read it.

###  4.2. Social Forces: Deskilling, Dehumanization, Disconnection, and
Disinformation

We asked participants to describe important changes coming in their industry
over the next one to two years. While some of these changes were driven by
generative AI, many were not. For example, mental health professionals expect
broader legalization of psychedelics will be transformative in their field. In
some cases, participants expected that generative AI would interact with other
changes to bring about particular impacts on their industry. Most prominently,
four existing global and national forces framed participants expectations of
generative AI’s impact. Specifically, they anticipated generative AI would
amplify the following social forces (White et al., 2019; Cox, 1987) that shape
how knowledge workers do their work in their industries: deskilling,
dehumanization, disconnection, and disinformation.

####  4.2.1. Deskilling: The “Uberfication” of Knowledge Work

Independent of generative AI, several forces are actively shifting revenue and
employment in certain knowledge industries. For example, the emergence of
remote services, such as BetterHelp and LegalZoom, was accelerated on both the
supply and demand side by COVID-19, as both providers and clients often
desired virtual sessions. Such services provide clients easier access to
mental health, legal, or other advice. While these services may reach a larger
number of people at a more affordable price, participants argued they provide
lower quality service and undermine demand for highly trained professionals.
_M6_ described how telehealth tech startups pose a greater employment threat
than generative AI:

  * “Things like BetterHelp are the biggest threat to our job security/livelihood. Because I think it’s an Uberfication of mental health care. One of the things that BetterHelp really advertises is you can text with your therapist, but their actual guidelines are you will get a text a day from your therapist and they have like 24 hours to respond. So people who are using [BetterHelp] are actually not that happy with it.”  _–M6_

The “Uberfication” of work refers to a broader shift in the political economy
of how work is “arranged through the use of digital technologies…to create
‘platform capitalism”’ (Simbürger and Neary, 2016, p. 61). A key element of
these labor conditions is supplanting employees with precarious self-employed
workers (Glöss et al., 2016). Participants, especially lawyers and
journalists, reflected on a significant shift towards contract, freelance, or
“permalance” positions (in which workers work in an extended freelance
capacity with an employer). Driven by bursty demand for expertise as well as
cost-saving measures, these positions are precarious and do not offer the same
benefits as full-time employment (Wiest, 2018).

Generative AI may interact with these extant labor trends to further deskill
industries or reduce opportunities for highly trained professionals. For
example, generative AI could propose advice that less trained human workers
could pass on to clients in cheaper online services. Some participants also
speculated that non-permanent employment would be easier to replace with
generative AI than full-time positions, since few employment protections would
be in place.

Participants also drew connections between the gig economy, generative AI, and
white collar work. _J2_ has worked as a labor journalist, previously writing
about the impact of technology on blue-collar workers. With the rise of
generative AI over the past year, his concerns have become more immediate and
personal.

  * “As a worker myself… I wasn’t too worried about [the digitization of the workspace] until now because, you know, they’re digitizing a factory or whatever… now it’s actually starting to impact my industry. And all those questions that were arising about factory workers or Uber drivers or whatever, they’re starting to be asked about us [journalists] now, to apply to us as well.”  _–J2_

Participants discussed how these forces can affect their wages and
opportunities. For example, _M6_ discussed how these changes could reduce his
wages or even undermine his investment in a specialized career, while also
reducing quality of service:

  * “I’m up to my neck in student loans still. Part of me is like, ‘Well, it’s great if BetterHelp can be affordable for people.’ But what about my job security, my ability to pay off my student loans? … And then the idea of generative AI doing chat… I do have concerns that this could be used in a way that would undermine our job security, but also I think actually not provide the level of care for the consumer as well.”  _–M6_

_S7_ shared similar concerns that generative AI might make her hard-won skills
less valuable and set back her career:

  * “I didn’t really know that I as a girl with a non-traditional background could be an engineer. And I also saw it as a way to make a lot more money than I was making. It was definitely a way for me to double, triple my salary and have access to a life that I could only dream of. And so it is kind of a bummer, even though it feels like a very first world problem, to be like, ‘Oh, now I’m back to potentially in a few years not having a very valuable skillset or a skillset that everyone can have’… It is a little disheartening to be like, ‘Oh, finally ahead. I’m finally at this place I wanted it to be,’ and now I have to play catch up again.”  _–S7_

Participants also expressed concern that, given its fitness for menial tasks,
generative AI might eliminate many entry-level positions and therefore remove
pathways into more senior roles:

  * “I would not be in my job, in the role I have today, if I didn’t start as an SDR [sales development representative]. It makes me sad to think these entry level roles may no longer be necessary… [It’s] upsetting because I think everyone has to start somewhere, and if you don’t have that basic knowledge, how do you grow from there?”  _–S6; S6 (Change Card)_

  * “A lot of help desk positions can be eliminated… I’m not too happy about that. I got my start on help desk. I didn’t go to school for computers at all. So taking a lot of those jobs away would be really sad cause they’re just a really easy entry into a good field.”  _–S4_

Participants in fields such as law and mental health also expressed concern
that clients might use generative AI themselves in a way that undermines
professional expertise, as well as revenue. Participants further expressed
concern that clients might unknowingly make poor decisions using generative AI
since they do not have appropriate expertise.

  * “Clients won’t need to hire you for their legal advice or defense if AI becomes prominent”  _–L4 (Change Card)_

  * _L5_ : A lot of administrative law is filling out paperwork and doing it for the client. The client comes for you to do it for them so they don’t have to. But ChatGPT might change that concept entirely. Amazingly, a lot of our clients that do [alcohol, gaming, food stamps], they know about [ChatGPT]. They come in and they’re like, ‘Well, I did this, I just typed it in and they gave me this. And why is yours better than mine?’ ¡laugh¿ And it’s hard to explain the nuance between why would I have a different legal perspective on this document than what you got from this database.
…
_L2_ : Bypass the expense of (legal counsel)… A person who’s not been involved
in litigation has no idea all the things we have to do to protect them… AI
can’t do that…

These issues around deskilling, menial work, the gig-economy, and more
widespread job loss all arise in the context of broader economic uncertainty
in the US. Participants, at times, speculated on how these changes to jobs
could potentially be addressed through social welfare approaches like
universal basic income, but were not confident governments or employers would
meaningfully address these labor changes, or other ethical issues related to
generative AI, due to their position operating within a capitalist
environment:

  * _S7_ : I just feel like under capitalism there can be no good AI… I’m still figuring out how I truly feel about AI. I don’t think it’s inherently evil or bad, but I think when you’re talking about using it at a company or corporation, the people at the top are always concerned about making a profit and cutting out jobs and whatnot. So I just am like, the end stage of this, no matter how good it can be, it just always feels really icky and bad.
_S5_ : Ideally it’s like the Star Trek universe.
_S7_ : Yeah.
_S5_ : I’d be into that ¡S7 laughs¿, nobody has to work. We’re all like happy.
_S7_ : That would be great. But then there’s no one at the top getting all the
profit.
_S5_ : Yeah.

####  4.2.2. Dehumanization: “Whose job will it be to find out how to
incorporate human nature into the AI?”

Replacing human workers with algorithms raises concerns about dehumanization
in which the task loses a characteristic of how humans interact with each
other (Fritts and Cabrera, 2021), becoming more sterile and impersonal
(Shortliffe, 1993). Many participants expressed concern that use of generative
AI might in various ways lead to a loss of humanity. One issue was that they
felt generative AI does not have the capacity to perform interpersonal work.
For example, business communications professionals spoke of the importance of
personal touch and authentic human communication, and mental health
professionals voiced that generative AI can not establish human-to-human
rapport, which is required for therapy to be effective.

  * “One of the most powerful things about therapeutic rapport is what we often call unconditional positive regard. I am a human here with you and I am not judging you. And I don’t get much comfort out of a computer not judging me ¡others laugh¿… Having unconditional positive regard from a robot doesn’t really change my sense of self very much. So I would say that’s one really simple thing is just the simple fact that I know that this is a human being.”  _–M6_

  * “Building rapport with someone is a huge piece to establishing effective [mental health] care. I don’t think that that can be done virtually with [generative AI]. I personally don’t think it can be. It might be able to ¡laugh¿.”  _–M3_

Participants also spoke of how the public’s aesthetic standards could shift
over time to less natural or less human content.

  * “I believe when AI created content (articles, videos, infographics, etc.) begin to seep into our feeds, we will eventually accept its strange aesthetic (which we call ‘robotic’, ‘uncanny’, etc.) as normal. This could create an entire new aesthetic of digital media as we know it. I’m concerned.”  _–J8 (Change Card)_

Participants also feel joy in performing tasks themselves, an emotional
experience not provided by using generative AI. The introduction of
algorithmic technologies into organizations in dehumanizing ways erodes
workers’ sense of autonomy (Sewell and Taskin, 2015; Newlands, 2021) and
overall job satisfaction (Lagios et al., 2021). Participants echoed these
insights in the context of the introduction of generative AI into their
industries.

  * “From the photography viewpoint for me, they’re replacing actual picture taking with just generating in AI, and the whole reason I like photography is I like holding a camera. I like framing it. I like the interaction with the person and all that. And generative AI just completely gets rid of all that. The human connection…”  _–J6_

  * “There are folks who get their joy and their sense of meaning from writing code and that’s kind of their thing… these are people that spent years mastering this trade. And I think that’s where it gets sad.”  _–S2_

  * _Moderator_ : So should [an undergraduate] go get a computer science degree where they’re going to do a lot of math and code, or should they go to some prompt engineering boot camp?
…
_S2_ : Well, it’s the same question in the context of art school. Should
someone go and learn how to paint and all the intricacies involved there? Or
is it futile because a computer can spit out that painting for you? I don’t
think there’s a right answer.
…
_S1_ : I don’t think it’s a bad time at all for a computer science degree yet.
At least for our generation or in my opinion even the next one.
…
_S2_ : If someone wants to go to school for computer science, because…
learning and tinkering is fun, they should do it and don’t let the robots stop
them.

Participants also spoke of the value of human production and creativity. Their
comments were reminiscent of the Arts & Crafts movement, which challenged the
integrity of mass produced objects, associating them with dehumanization and a
decline in production standards (Penick and Long, 2019). The Arts & Crafts
movement valued artisanal production and human craft, even though it was less
efficient than the mass production that became common in the industrial age.
Similarly, participants spoke about the value of making things by hand, and
the high quality and meaning of human-created objects.

  * “I think that there’s potential for real things outside of screens, like actual photos in an album. Like who makes those anymore? But I think things like that would become almost like sacred and similar to… the dumb phone movement straying away from smartphones and screens. I think that there’s gonna be at some point a push towards that. Like the things you could physically touch and make with your hands… Wanting the personal touch of knowing it took time and intention and purpose and thoughtfulness to create something. I think there’s real beauty in that. And I guess I don’t really see the beauty in AI.”  _–A1_

  * “I think a lot of skilled labor and artisan style jobs are going to come back into fashion and it’ll be very hard for AI to replace them. My boyfriend went to furniture school recently and so he’s a carpenter and a fine furniture maker. And I was just kind of musing about how right now I’m the breadwinner, but I think soon he might be the breadwinner because there’s something about something that’s made by hand, something that is of quality. We live in this fast fashion age where things are just mass produced. And I think there’s a real desire for something that is made with care that will last, that is creative… It takes a lot of personal skill and a lot of physical labor to make a table… and I have a hard time thinking that generative AI is really gonna be able to replace that.”  _–S7_

Some participants emphasized that people must retain critical thinking skills
as well as the ability to do the type of work that is being offloaded onto
generative AI, and that people must not become lazy.

  * “I hope my daughter never discovers [ChatGPT]… I don’t want it to take away creativity. I want my daughter to think of her own thoughts. Like when she’s doing her studies. That’s really important to me.”  _–L3_

  * _E6_ : It’s kind of like a calculator, right? They have to learn how to do the math first before they’re allowed to just do it all using the calculator.
…
_E7_ : You get this Chat thing and all this AI [that] just makes kids’ lives
easier. And people are like, ‘Well what’s wrong with that?’ There’s a lot
wrong with that ¡E2 laughs¿.

  * “I also worry about the generational effects of generative AI. Because I think at the very base, it’s another way for people to use their brains less.”  _–J8_

  * “I think you need to actively not be lazy, because it’s so helpful. You need to say, ‘Okay, I’m not gonna just let this responsibility fall onto this machine. I’m gonna still really play the biggest role here…”’  _–A1_

At the same time, some recognized a delicate line between being lazy and being
efficient, and therefore saving time for more meaningful activities.

  * _A7_ : I assume I’d be pretty lazy right now if I was in high school and I would just try to take advantage of [generative AI] as much as I could… You can literally not learn for the rest of your life if you don’t want to now.
…
_A6_ : Is it lazy or is it clever? Are you learning the long way or are you
streamlining the process?

Several participants offered a literal mechanism for retaining humanity in the
use of generative AI, proposing a specific quota, such as “at least 80% of
words, photos, everything on our site is created by human minds” or “60% [of
the job] needs to be conducted by humans.”

####  4.2.3. Disconnection: “You have to be there”

Feelings of disconnection can manifest in knowledge work, e.g., new tech-
mediated working arrangements can increase feelings of worker isolation (Vega
and Brennan, 2000) and broader social trends in perceived isolation can exert
external pressures on knowledge work practice (Erickson and Jarrahi, 2016).
Participants spoke about increased social disconnection, such as the so-called
“loneliness epidemic” (of the Surgeon General, 2023) stemming in part from
COVID-19 and the corresponding social isolation and increase in remote/virtual
work and schooling, as well as other factors, such as escalating phone and
social media use or even addiction. They were concerned that generative AI
would contribute to further disconnection from physical and social
experiences. For example, mental health professionals worried that therapeutic
uses of generative AI could have the opposite of intended effects and
exacerbate loneliness, even as patients turned to it for comfort:

  * “I hear people talking about the uses of AI [for mental health] in the chatbot model, and to a great extent, I’m wildly against it. Because I think that a big part of the reason we have the increase in people needing mental health services is because of disconnection. And I think that while it might really have helped your client in that moment to have a bot to chat to, it’s still not a replacement for human interaction. And I think that that disconnection is a lot of what is driving this [mental health] crisis.”  _–M6_

Our participants also described how good journalism requires being “on the
ground” and how the extant rise in freelance and remote work threatens the
quality of news production, a trend that could be further exacerbated by the
use of generative AI. _J8_ characterized the future of journalism with
generative AI as “just more recycled and disconnected from reality, allowing
people more to be separated from what they’re doing instead of fully immersed
in it.” Participants also expressed concern about content becoming
increasingly disconnected from reality over time as new generative AIs are
trained on layer upon layer of recycled, increasingly non-human content.

  * “As journalists, when we cover things, we are currently on the ground doing reporting, talking to human beings and things like that. And more and more it’s becoming like we’re talking to people over Zoom or we’re not being flown out to places to cover things anymore and we’re hiring freelancers… We get a bunch of footage from overseas and then we have to make something from it because we’ve hired freelancers there instead of being on the ground doing it. So it’s this gradual, very gradual disconnection from what we’re covering and what we’re thinking and the actual reality of the world.”  _–J8_

  * “[News aggregators are] a homogenization of all the stories. We lose the individual voices of the people who are witnessing or describing, you know? … [As a reporter] you still need to experience it, you need to taste it, you need to see it, you need to feel it in order to transcribe those sensations of the human experience to your readers.”  _–J1_

  * “I think it’s a lot of dumbing down and recycling that’s happening. I’m worried about the long-term effects of recycling things that have been recycled already with text and images and what that will do to ideas.”  _–J8_

####  4.2.4. Disinformation: “I think they call them hallucinations… it puts
it out so authoritatively”

Disinformation is the intentional spread of false, inaccurate, or misleading
information designed to intentionally cause public harm (HLEG, 2018), and its
production is often motivated by “ideology, money, and/or status and
attention” (Marwick and Lewis, 2017, p. 27). Participants expressed concern
about the role generative AI may play in disinformation, and more broadly in
the production of low quality content, particularly in the broader context of
US discussion of polarization, culture wars, and media bias. One concern was
that since generative AI typically incorporates internet data in its training,
its quality would be undermined by existing low-quality content on the
internet:

  * “It can’t just pull it from the internet as it is right now because there’s so much misinformation pre-AI.”  _–J7_

  * “I [am] really concerned about AI learning things all over the internet, just chaotic. But if there’s an AI that collects everything from [an authoritative source]… I’ll be really excited about it.”  _–A4_

  * “Can you tell me where all of these images came from? Can you tell me where you got this person’s blog post? Who is that person that wrote it? A lot of stuff on the internet, especially with Reddit and things, are anonymous and that’s the good and bad thing about them. And so as you put them out into the world, into this product that everyone can use, how do you even begin to really make sure it’s unbiased?”  _–S7_

Participants further expressed concern that generative AI would itself
proliferate, or would be leveraged to produce, additional low-quality content.
They were worried about misinformation and disinformation currently in the
media and online, and saw generative AI as a tool that could make it even
easier and faster to produce harmful content. They anticipate this will have
significant negative social and political repercussions:

  * “[I fear] propaganda would be so undistinguishable that no one’s gonna know what’s real… We’re not gonna be able to determine what’s real and what’s fake and it could be the doom of us.”  _–A6_

  * “It does seem like a major issue with deep fakes, especially in the realm of pornography, which is what keeps the internet running. There’s already issues with revenge porn, and the notion that you can do this with anybody’s face, anybody’s likeness… The worst human urges ¡laugh¿ and desires are gonna be generated by the generative AI.”  _–J7_

  * “Generative AI just comes at a bad time for us as Americans… and for us as journalists. We have such a problem in sensitivity around words like truth and falsehood and what is the truth and objective truth versus subjective truth.”  _–J3_

Participants were also concerned that media upheaval, caused by wide-ranging
factors such as economic or political instability, is currently driving news
aggregation and media concentration, thereby eroding the quality of
information available to the public and “dumbing down”, homogenizing, or
politicizing content. They were concerned generative AI could exacerbate these
issues. _J2_ spoke of media trends that started in Europe:

  * “[A change] that is not caused by generative AI, but that will be made worse probably by it is the further media concentration. That’s caused by billionaires buying media left and right, agglomerating them and altering and then sometimes increasing their editorial lines, to their benefit… [as] a tool for [their] political agenda… I believe generative AI might make it even worse because all of a sudden these people will be able to buy and concentrate media. If anyone has any ethics issues, well, you know, the door’s right there. And generative AI can do it for even cheaper anyway… just buy a newspaper, fire the entire newsroom… we’ll just run it by generative AI.”  _–J2_

###  4.3. Industry Perspectives

In the previous sections, we drew themes across the industries we studied. In
this section, we provide additional detail for each industry. To illustrate
the most salient points and capture the unique character of each discussion,
we created composites consistent with the content, language, and tone of
responses we received from each group (Creswell and Poth, 2018; Willis, 2019),
shown in Table 3. These composites illustrate how themes discussed in previous
sections play out across industries; for example, which roles participants
think are most likely to be affected in their industry.

Mental Health

deskilling
dehumanization
disconnection |  We work on one of society’s most important problems: mental health is a national crisis, exacerbated by loneliness. There is a huge shortage of qualified mental health professionals, and therapy requires human-to-human connection which a computer cannot provide, so we feel our jobs are extremely secure against generative AI. However, we worry about telehealth services that provide low quality service but still undermine our job security. Generative AI may be a useful tool for administrative work, simulated talk-therapy training, or as a stopgap when people can’t immediately access a human therapist.
---|---
Education

human review
dehumanization
disconnection |  We don’t expect generative AI to affect our work deeply. Students already have many ways of cheating, and generative AI is just a newer, better way that we deal with by reviewing their work. We’re worried that generative AI may make students lazy if it’s not used well. We’re also sensitive to overuse of technology and social isolation because of remote schooling during the pandemic. We’re in the middle of restructuring much of our curriculum due to standards-based grading. This gives us more latitude to design new activities, so maybe there are opportunities to work generative AI into our lessons.
Law

menial tasks
tool
human review |  Generative AI is a tool that can perform formulaic legal tasks like drafting or research, provided its work is reviewed by qualified attorneys, replacing entry-level and support positions and contributing to job loss. We’ve heard press stories about false citations, which makes us wary of using it, even with human oversight. Generative AI is unlikely to be skilled enough to replace human professionals, and it should never make decisions, or act as lawyer, judge, or jury. However, we anticipate clients will begin to use generative AI tools to do some legal work for themselves, with poor results.
Journalism

deskilling
dehumanization
disinformation |  We work in a noble profession and we are already embattled on many fronts, facing misinformation, news aggregators, decreased revenue streams, and more. Generative AI will exacerbate ongoing precarious/freelance employment, poor wages, and job loss, and the erosion of journalism as a field. Generative AI makes it easier for low-quality providers to produce mis/disinformation, with harmful effects on society. Generative AI may replace some of the work we most enjoy, like writing or photojournalism.
Business
Communications

tool
human review
need to adapt |  Generative AI can do a lot of our work, and it will inevitably be adopted in our industry. Companies and employees need to adapt, learn, and “grow with AI” in order to stay relevant and protect job security. Much of our writing is formulaic, and generative AI is a great tool to produce early drafts of routine, high-volume, and/or disposable communications like email notifications. Generative AI will need a lot of human oversight and guidance, especially to make sure it meets compliance and legal standards and is consistent with our brand voice. We can leverage our existing approval structures to check its output.
Advertising

tool
human review
lose certain jobs |  Generative AI is well suited to many tasks in the advertising industry. Sadly, certain jobs will go away, most immediately those related to product photography and video production, with layout and copywriting under heavy threat. However, creative oversight will remain in the human purview, and generative AI will be an exciting brainstorming tool. A human check will be required for most generative AI output, especially to make sure it doesn’t violate brand standards or copyright. In fact, new teams may be created to do these checks. Our industry promotes and embraces change, but we also value human craft and we often challenge the quality of digitally produced content and experiences.
Software
Development

need to adapt
deskilling
dehumanization |  Generative AI is likely to drive change across most aspects of software development. This includes, for example, the automation of menial and boring tasks, the elimination of many entry-level roles (which will limit paths for new people to enter the field), and the ability of AI to reshape low-code technology solutions and even write production-ready code within serious software companies. We feel uncertainty about the future and pressure to adapt to new advances in generative AI. Some of us worry that the time and education we invested in a lucrative career might be nullified, setting us back to where we started. Some people get into software engineering for the money, but some of us are motivated by our love of tinkering and problem solving, and generative AI may take over a lot of the work that brings us joy.
Table 3. Composites illustrating the most salient points of participants’
expectations regarding the impact of generative AI on their industry, with
industries ordered top to bottom by least to greatest expected impact. Under
the name of each industry we list the top three most salient themes (Effort-
Saving Tool Narrative–blue; social forces–red), Neither the top three themes
nor the composites comprehensively represent all points, e.g., although
deskilling was discussed extensively in Law, it was not one of the top three.

##  5\. Discussion

In this section, we build on our participants’ insights to surface key HCI
research questions at the intersection of generative AI and knowledge work.
Rather than focusing on current user practice or design recommendations for
early versions of these systems, we seek to frame larger questions about how
generative AI’s impact may be understood and shaped. Some of this work falls
squarely in the purview of HCI, but much of it likely requires broader
collaboration with other academic disciplines as well as stakeholders such as
knowledge workers, policy makers, civil society, and more.

###  5.1. Research Challenges: Human-in-the-Loop

Participants overwhelmingly favored a human-in-the-loop (HITL) approach (i.e.,
adding human review or oversight) as a necessary and sufficient remediation
for many problems with generative AI. For example, they expected that in their
industries, human reviewers could check all of generative AI’s output and
correct any inaccuracies or other quality issues. Legal scholars report that
regulators have a similar inclination to address a “panoply” of concerns about
AI with a “slap a human in it” approach (Crootof et al., 2023).

However, the HITL literature points out serious, unsolved practical problems
that were not apparent to participants. For example, humans make many errors
when reviewing algorithmic output and often override algorithms in detrimental
ways; therefore, human oversight policies can provide a false sense of
security rather than improving outcomes overall (Buçinca et al., 2021; Green,
2022). Further, research has shown that effectively configuring human-AI
coordination is extremely difficult, so handoffs are often poorly designed and
yield harmful results (Crootof et al., 2023; Elish, 2019; Green and Chen,
2019). Additionally, scholars argue that HITL approaches disproportionately
hold humans accountable, even when in practice they have very little influence
or do not have appropriate skills or time to review, leaving reviewers to
shoulder the blame for technical or structural failures (Awad et al., 2020;
Crootof et al., 2023; Douer and Meyer, 2020; Elish, 2019; Green and Chen,
2019; Wagner, 2019). This suggests two main research questions:

#1. How do we raise awareness of the limitations of HITL? We believe it is
important to exercise caution when applying HITL solutions. Those who are
making decisions about mitigations for generative AI (e.g., regulators,
decision makers in knowledge industries) should be aware of the current
limitations of HITL so they are not overly optimistic about its potential to
remediate problems.

#2. How do we make better HITL systems? Despite its current limitations, many
opportunities exist to improve HITL’s effectiveness. The advent of generative
AI particularly highlights the need for HITL approaches that will work well at
scale for review of generated text, images, and video. Also, cognitive forcing
interventions (such as asking workers to generate certain preliminary content
before being shown generative AI’s output) can engage analytical rather than
heuristic thinking (Buçinca et al., 2021). Further, reviewers could receive
specialized training in critically reviewing generative AI’s output; these
review skills might ultimately be at least as valuable as other skills like
prompt engineering. Overall, while improved solutions are unlikely to
alleviate all concerns with HITL (Green and Chen, 2019), additional research
and innovative design and development could lead to substantially better
outcomes.

###  5.2. Research Challenges: Knowledge Worker Expectations of Impact

We observed a significant gap between participants’ expectations of how
generative AI might change their field versus broader narratives of disruption
offered by the media, technologists, and academics—participants generally took
a more limited view of potential impact. This suggests the following research
questions:

#3 Why do some knowledge workers feel they will be largely unaffected by
generative AI? Are there certain workers who feel more immune to changes from
generative AI? Is this due to a failure of imagination or lack of awareness,
or do knowledge workers have particular understandings of their industries
that are not being taken into account in other narratives?

#4 How do we raise awareness among knowledge workers about the Transformative
Narrative? Improved understanding of how and why generative AI is being
positioned as capable of competently performing work at human expert levels
would prepare knowledge workers to more meaningfully provide stakeholder
input. Further, is it the case that knowledge workers underestimate the scope
of what generative AI can do and what it may be used for, and how it may
transform not just specific tasks or jobs but industries more generally? If
so, what interventions and transparency artifacts might be most meaningful?
For example, AI systems are largely not yet specialized; perhaps developing
and sharing industry-specific demos would be helpful.

###  5.3. Research Challenges: Deskilling

As technologies shift how work is performed, the necessary skill sets of
existing and new professions also shift (Acemoglu and Autor, 2011), creating a
paradox of concurrent unemployment and labor shortages (Autor, 2015).
Consistent with this, participants expressed concern that generative AI might
negatively affect the value and development of their skills. This suggests the
following research questions:

#5 What training would benefit knowledge workers, to help them reskill for
likely changes? How can we help knowledge workers adapt to the possibility of
wide-ranging transformation when there is not yet clarity? Beyond prompt
engineering, training for human review of AI output seems promising, as does
development of critical thinking skills and the ability to manage complex,
higher-level use cases.

Additionally, conventional national policy responses to labor market changes
often rely on traditional, secondary education (Collins and Halverson, 2018),
which is often inadequate against the pace of technological change (Stephany,
2021). Reskilling within individual organizations is a more cost-effective and
worker-centric strategy to evolve with technological changes in knowledge work
(Hu et al., 2021). Future work could explore how to re- or up-skill workers in
ways that minimize precarity.

#6 How can we scaffold people into higher level positions, if entry-level
pathways disappear? Who might be most disadvantaged by the potential erosion
of entry-level positions? And in industries that rely on the development of
foundational skills, how might the most talented experts arise if they do not
work through entry-level tasks? What systems might support human development
of necessary skills, even if generative AI can perform those tasks?

###  5.4. Research Challenges: Dehumanization

As a research community primarily studying the interface between humans and
computer systems, understanding where these systems are uncomfortably and
unacceptably encroaching on people’s inherent sense of humanity should be a
key research agenda. To explore dehumanization, we suggest two avenues of
inquiry to address the serious concerns raised by our participants:

#7 How can we learn about and protect tasks that bring people joy and meaning,
but generative AI can do as well or better than humans? We should understand
not just, as many economists focus on (e.g., (Zahidi, 2023; Albanesi et al.,
2023)), which tasks can be effectively be replaced by AI systems, but also
which tasks inherently bring joy and reinforce humanity. By beginning to
document these tasks that are meaningful but in some cases can be done as
well, or even at higher quality or more efficiently, by AI systems, we can
begin to design ways to protect these tasks, or at least make reasoned
decisions about when to automate them. This may involve HCI interventions to
design systems that highlight or reserve certain work or decisions for humans,
or broader questions of regulation to protect certain kinds of work across
society. While many utopian visions involve humans who are freed from work by
AI and take up new, creative, artistic endeavors, losing the craft, joy, and
humanity that exists in current work and problem-solving may have significant
downsides.

#8 How can we promote critical thinking and also prevent people from becoming
lazy? Separate from the joy and meaning inherent in certain tasks, our
participants also found innate value in performing reasoning and critical
thinking. Participants were concerned that AI could reduce or remove tasks
that challenge humans or force them to solve problems, thereby leading to a
lazy society. While considerable work within the HCI community has explored
how human and AI systems can collaborate for higher quality and more creative
outcomes (Suh et al., 2021; Verheijden and Funk, 2023; Louie et al., 2020;
Hwang, 2022; Chung et al., 2022), this question encourages research into how
these collaborations can be designed to support knowledge workers (Gero et
al., 2023), as well as the design of systems that directly engage people in
problem solving rather than simply providing answers.

###  5.5. Research Challenges: Guardrails

Responsible AI has gained significant traction, from published principles to
more actionable and/or measurable strategies. While some frequent issues, like
explainability and fairness, move closer to having concrete remediations in
system design, other broad social issues require additional work beyond
engineering and system design, including harms assessments, stakeholder
engagements, and increased focus on community outcomes. Considering the social
factors prioritized by participants in our study, current responsibility
metrics are most relevant to disinformation, and have less to offer regarding
deskilling, dehumanization, and disconnection. This leads us to the following
questions:

#9 How can we design responsible AI and governance approaches to generative AI
that embrace complex global dynamics? How can responsible AI more fully and
holistically consider impact and harm? Findings from our study highlight the
ways in which the impacts of generative AI must be considered with respect to
social forces that intersect with conditions of its development, deployment,
and use. Thus, an approach to harm or impact analysis that stops at model
evaluations (e.g., analyzing outputs with respect to pre-determined
benchmarks) will be insufficient.

#10 How can responsible AI assessments more fully incorporate stakeholder
input? Responsible AI product assessments often center on anticipating harms
from launching a product (Boyarskaya et al., 2020; Brey, 2012) through
consideration of how their contextual use may engender harms to users and
communities (Alkhatib, 2021). Many responsible AI interventions orient towards
developer-facing interventions, such as developing AI principles (Fjeld et
al., 2020), educating practitioners to foster ethical awareness (Lange et al.,
2023), and moderating generative AI systems through training data mitigations,
in-model controls (e.g., reinforcement learning), and safety classifiers that
gate outputs (Hao et al., 2023). However, limited attention has focused on
assessing communities’ desired forms and uses of AI. Meaningfully engaging
communities of practice (Wenger et al., 2002) in exploration of these
questions, such as through community-based participatory research (Hayes,
2011; Cooper et al., 2022), can inform development of practices that scaffold
community engagement into responsible AI practice.

Similarly, in a labor context, complicated questions arise regarding how to
assess the impact of AI systems that may reduce or drastically change specific
jobs. While companies may often consider the needs of an overall business, how
should they consider the needs of individual workers, particularly those whose
jobs may be cut or changed, and are there ways to directly engage them for
input before developing these AI solutions? And how can we educate or support
decision-makers in engaging stakeholders?

##  6\. Conclusions

The historical context in which technology appears influences how it is
ultimately adopted and used. Generative AI has become more available and
visible during the confluence of a global pandemic, economic uncertainty, and
more. Many knowledge workers in our study situated generative AI in this
context, highlighting generative AI as exacerbating the following four social
forces: deskilling, dehumanization, disconnection, and disinformation. In
other words, rather than seeing generative AI as an independent disruptor of
their work or their industry, they positioned generative AI as extending and
exacerbating existing forces. This framing raises important new questions and
opportunities for exploring and shaping the future impact of generative AI on
knowledge workers and their industries.

###### Acknowledgements.

We thank our participants for generously sharing their insight and expertise.
We thank our partner team at Gemic, including T.J. Foley, Roger Galvez, David
Ginsborg, Rebekah Park, and Rohini Shah for their support in the field. We are
grateful to our colleagues at Google, including Marian Croak, Jen Gennai,
Angela McKay, Anoop Sinha, and Ashley Walker, for supporting this work.

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