# The Impact of AI Innovations on U.S. Occupations

Ali Akbar Septiandri  Nokia Bell Labs, Cambridge, United Kingdom  Marios
Constantinides  Nokia Bell Labs, Cambridge, United Kingdom  Daniele Quercia

###### Abstract

AI’s impact has traditionally been assessed in terms of occupations. However,
an occupation is comprised of interconnected tasks, and it is these tasks, not
occupations themselves, that are affected by AI. To evaluate how tasks may be
impacted, previous approaches utilized manual annotations or coarse-grained
matching. Leveraging recent advancements in machine learning, we replace
coarse-grained matching with more precise deep learning approaches.
Introducing the AI Impact (AII) measure, we employ Deep Learning Natural
Language Processing to automatically identify AI patents that impact various
occupational tasks at scale. Our methodology relies on a comprehensive dataset
of 19,498 task descriptions and quantifies AI’s impact through analysis of
12,984 AI patents filed with the United States Patent and Trademark Office
(USPTO) between 2015 and 2020. Our observations reveal that the impact of AI
on occupations defies simplistic categorizations based on task complexity,
challenging the conventional belief that the dichotomy between basic and
advanced skills alone explains the effects of AI. Instead, the impact is
intricately linked to specific skills, whether basic or advanced, associated
with particular tasks. For instance, while basic skills like scanning items
may be affected, others like cooking may not. Similarly, certain advanced
skills, such as image analysis in radiology, may face impact, while skills
involving interpersonal relationships may remain unaffected. Furthermore, the
influence of AI extends beyond knowledge-centric regions. Regions in the U.S.
that heavily rely on industries susceptible to AI changes, often characterized
by economic inequality or a lack of economic diversification, will experience
notable AI impact.

##  1 Introduction

The rapid advancement of Artificial Intelligence (AI) has undeniably created
new business opportunities [1] but has also reshaped the labor market [2, 3,
4, 5], simultaneously reducing hiring in non-AI positions and altering the
skill requirements of remaining job postings [6]. Exemplifying this
phenomenon, the manufacturing sector has witnessed the automation of
previously human-intensive assembly line tasks, while chatbots and virtual
assistants have taken over routine inquiries and support functions in customer
services [7]. Recent AI advances, including generative AI, may further
(re)shape occupations over the long term, fuelling growth in certain sectors
and eroding others [8]. AI automation has not only streamlined processes but
has also generated economic benefits [9], enabling companies to allocate
resources more effectively and to redirect human capital towards higher-value,
creative, and complex tasks— _(re)skilling and upskilling_ their workforce
[10]. However, this transformation has given rise to divergent viewpoints,
with some scholars arguing for a future characterized by AI occupation
displacement and mass unemployment [11, 12], while others posit that the AI
revolution has the potential to enhance both productivity and quality of work
[13].

Previous literature on AI impact on occupations has primarily focused on two
classes of methodologies. The first measures AI impact on occupations. More
specifically, it breaks down occupations in a finite set of abilities (e.g.,
manual dexterity, persuasion) and measure the impact on those abilities. Two
examples that illustrate this approach are the Frey and Osborne’s [14] method
and the AI Occupational Exposure (AIOE) method [4]. Frey and Osborne’s method
uses 9 abilities extracted from O*NET database, covering 70 occupations that
were manually labeled and extended with a classifier to 702, including roles
like clergy, dentists, and chief executives. In contrast, AIOE uses 52
abilities derived from the O*NET database, focusing on 10 Electronic Frontier
Foundation (EFF) applications such as image and speech recognition, and
language modeling. However, both of these methods share a common limitation.
Their reliance on coarse-grained abilities in the computation of AI impact may
not fully capture the nuances of AI. Consider, for example, the ability of
information ordering. Methods based on abilities may categorize tasks that
involve organizing information in the same way, without distinguishing between
the highly structured and complex information ordering required for database
design and the simpler, routine information tasks of librarians such as
alphabetizing files [15].

The second class measures AI impact on tasks rather than occupations. This
concept is illustrated by Brynjolfsson et al.’s Suitability for Machine
Learning (SML) method [3], which measures the impact of AI using a
comprehensive set of 18,156 tasks spanning 964 different occupations. However,
SML relies on the assessments of crowdworkers to determine the suitability of
specific tasks for machine learning. This reliance may introduce subjective
biases from annotators (e.g., varying levels of expertise or cultural factors
may lead to inconsistencies), and poses challenges in terms of scalability.
Also, similar to Frey and Osborne’s and AIOE, SML is limited by the static
nature of its one-time manual labeling. As technology advances and new
capabilities emerge such as Large Language Models (LLMs), relying solely on a
fixed set of abilities or subjective assessments of tasks suitability for
automation becomes increasingly inadequate. For example, a copywriter is
likely to be impacted by LLMs [16]. However, if one were to examine
copywriters at different points in time, such as in 2010 or 2015, the impact
of AI on them would not be constant but would drastically change since
language models were not as powerful back in 2010 as they are today. To fully
capture the impact of a fast moving technology such as AI, therefore, it is
crucial for methods to be adaptable to the ever-evolving technological
advancements. To gauge the likelihood of automation, it is essential to
identify which systems are poised for construction and commercialization.
Annual business surveys serve as a source for measuring the adoption of
automation. However, they may be subject to biases (e.g., respondents may
over-report positive aspects, prioritize certain business operations,
potentially neglecting others, interpret survey questions subjectively without
standardized criteria) and are infrequently updated [17]. An alternative, more
objective source is patents. Patents are a typical source in scholarly work to
identify emerging technological innovations [18, 19, 20]. They provide
insights into emerging systems and technologies [21], leading Webb to study AI
innovations by comparing occupational task descriptions with patent titles
[2]. This method, employing a dictionary approach to identify verb-noun pairs
associated with both tasks and patent titles, has limitations though. Another
method, similar in its approach to Webb’s term matching, employs a normalized
term matching approach to determine the similarity between tasks and patents
[22], and does so to the specific area of robotics rather than AI. The method
most similar to ours was proposed in [23, 24], in which, using word embedding,
patents are matched with broad occupation categories from the American
Community Survey. However, that level of categorization is not suitable for
researching the characteristics of specific jobs. Overall, this second class
of approaches has used either term matching or word embedding. The problem is
that term matching does not capture the semantic meaning of words (e.g., it
does not distinguish between ‘bank’ as in ‘data bank’ or as in a financial
institution) [25], and word embedding does not account for word ordering
(e.g., ‘data entry and analysis’ and ‘analysis of entry data’ are considered
similar based on word embeddings yet are two different tasks). As a result,
these methods either miss relevant patents or return spurious task-patent
matches, as detailed in Tables C.1 and C.2.

To overcome these limitations, we introduced and validated the AI Impact (AII)
measure. AII utilizes 19,498 task descriptions from O*NET and assesses AI’s
impact through innovations found in 12,984 AI patents filed with the United
States Patent and Trademark Office (USPTO) from 2015 to 2020. For consistency
with prior methods, our analysis extends up to 2020 (additional analyses up to
2022 are in §D). Built on Sentence-T5 (ST5)[26], a natural language
processing-focused deep learning framework (Figure 6), the method gauges
semantic similarity between occupation task descriptions and patent
descriptions (explained in §4.1) by embedding not individual words but the
entire document (e.g., the entire patent’s abstract), allowing for considering
both semantic meaning and word ordering. The AII score is calculated in three
steps. Firstly, the method identifies the most similar patent for each task
based on maximum cosine similarity. Secondly, it categorizes a task as AI-
impacted if its similarity with the most similar patent surpasses a threshold
at the 90th percentile, as previous literature suggested [21] and this work
further empirically validated (§B). We select, for each task, the closest
patent rather than counting the number of closely linked patents. This
approach provides a more targeted understanding of the specific innovations or
solutions directly relevant to that particular task, avoiding dilution of the
analysis with potentially less pertinent or peripheral patents. Also, given
that patents can be general, our approach addresses this by using the text not
only in titles, as previous approaches did [2], but also in abstracts, which
are more likely to contain application domains or references to specific tasks
(illustrated in Table B.1). Finally, the AII score for an occupation is
computed by dividing the number of tasks impacted by AI patents by the total
tasks for that occupation. For insights into AI’s economic and spatial
ramifications, the method aggregates AII scores for each occupation at
industry sector- or regional-levels (elaborated in §4.2).

##  2 Results

###  2.1 Most- and Least-Impacted Occupations

Table 1: 20 most- and least-impacted occupations ranked by the AII (Artificial
Intelligence Impact) measure.

Rank | Most-impacted | Least-impacted
---|---|---
1 | Cardiovascular Technologists and Technicians | Pile-Driver Operators
2 | Orthodontists | Graders and Sorters, Agricultural Products
3 | Medical Records and Health Information Technicians | Floor Sanders and Finishers
4 | Numerical Tool and Process Control Programmers | Aircraft Cargo Handling Supervisors
5 | Multi-Media Artists and Animators | Dredge Operators
6 | Magnetic Resonance Imaging Technologists | Insurance Appraisers, Auto Damage
7 | Nuclear Medicine Technologists | Insurance Underwriters
8 | Software Developers, Applications | Reinforcing Iron and Rebar Workers
9 | Electro-Mechanical Technicians | Farm Labor Contractors
10 | Industrial Truck and Tractor Operators | Water and Liquid Waste Treatment Plant and System Operators
11 | Sound Engineering Technicians | Animal Scientists
12 | Computer Programmers | Brokerage Clerks
13 | Life, Physical, and Social Science Technicians, All Other | Insulation Workers, Floor, Ceiling, and Wall
14 | Earth Drillers, Except Oil and Gas | Locomotive Firers
15 | Medical Transcriptionists | Management Analysts
16 | Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic | Podiatrists
17 | Airline Pilots, Copilots, and Flight Engineers | Cooks, Short Order
18 | Physical Scientists, All Other | Shipping, Receiving, and Traffic Clerks
19 | Computer-Controlled Machine Tool Operators, Metal and Plastic | Helpers–Painters, Paperhangers, Plasterers, and Stucco Masons
20 | Biomedical Engineers | Team Assemblers

We compared the most-impacted (highest AII scores) occupations with the least-
impacted (lowest AII scores) occupations (Table 1 only reports the 20 most-
and least-impacted occupations for brevity and comparability with previous
methods), and did so by thematically analyzing the AI patents associated with
the tasks of each group’s occupations (as described in §4.5).

Most-impacted Occupations. The highest-impact occupations mainly consist of
white-collar occupations such as cardiovascular technicians, orthodontists,
health information technicians, numerical and process control programmers, and
multi-media artists. Indeed, the AII score, binned by education levels from
high school to Master of Sciences, shows that the highest impact is seen for
jobs requiring Bachelor’s degrees (Figure 1). Highest-impacted occupations are
primarily found in the healthcare, information technology, and manufacturing.
Their tasks can be completed in a very specific sequence, and the inputs and
outputs of these tasks can be expressed in a machine-readable format. To see
how, we examined the types of tasks that patents have automated, and organized
the patents into three themes: _healthcare_ , _information technology_ , and
_manufacturing_ (Table 2). For the theme of healthcare, from 2015 to 2020, 60%
of the tasks done by cardiovascular technologists and 48% of those done by
magnetic resonance imaging (MRI) technologists have been impacted by patents
automating health records’ management and analyzing MRI scans. In addition to
the advanced healthcare occupations, we observed a significant number of
patents impacting less skilled healthcare personnel including, for example,
patents recording and evaluating patient questionnaires. For the theme of
information technology, over the same five years, 47% of tasks done by
software developers and 40% of those done by computer programmers have been
impacted by patents automating programming tasks and developing workflows. For
the theme of manufacturing, over the same five years, 45% of tasks done by
truck and tractor operators and 40% of earth drillers’ tasks have been
automated. These automated tasks are planning processes such as water-well
drilling rigs and driving through electric-powered trucks. More specifically,
qualitative analyses based on ethnographic work on long-haul truck driving
(perceived as a prime target for AI displacement) have shown that truckers are
not being displaced by intelligent systems—the trucker is though still in the
cab driving [27]. Instead, they are experiencing the emergence of intelligent
systems that monitor their body directly. Such emergence has not been
instantaneous but has evolved over the years with the introduction of
positioning systems, then smartwatches, and now even more advanced health
monitoring devices [28, 29]. The problem is that previous work has assumed
that, over those five years, the impact was constant [4]. That was mainly
because they could not factor in innovations as they were coming in. By
contrast, our score is adjusted based on incoming innovations, and, as such,
changes over time.

Figure 1: AII score binned by the level of education: high school, associate
degrees from community colleges, bachelor’s degrees, and master’s degrees in
science. This binned score was obtained by averaging the scores across all
occupations in a given education category, weighted by the total employment
for those binned occupations. Table 2: The most-impacted occupations based on
AII scores. Each occupation’s score is calculated as the number of impacted
tasks divided by the total number of tasks for that occupation. For each
occupation, a task-patent pair is presented, corresponding to the most
impacted task by the patent (i.e., the patent with the highest similarity
score to the task) which is determined by calculating the textual similarity
between the task’s description and a patent’s title plus abstract.

Rank |  Occupation |  Sector |  Task |  Patent | Similarity |  # Impacted Tasks |  Total Tasks | AII
---|---|---|---|---|---|---|---|---
1 |  Cardiovascular Technologists and Technicians |  Healthcare |  Observe gauges, recorder, and video screens […] during imaging of cardiovascular system. |  Automated analysis of vasculature in coronary angiograms | 0.8753 | 15 | 25 | 0.60
2 |  Orthodontists |  Healthcare |  Study diagnostic records, such as medical or dental histories […] to develop patient treatment plans. |  Patient-Specific Therapy Planning Support Using Patient Matching | 0.8460 | 6 | 10 | 0.60
3 |  Medical Records and Health Information Technicians |  Healthcare |  Plan, develop, maintain, or operate a variety of health record indexes […] |  Dynamic health record problem list | 0.8350 | 10 | 20 | 0.50
4 |  Numerical Tool and Process Control Programmers |  Manufacturing |  Determine the sequence of machine operations, […] |  Methods and apparatuses for cutter path planning and for workpiece machining | 0.8376 | 8 | 16 | 0.50
5 |  Multi-Media Artists and Animators |  Information |  Create pen-and-paper images to be […] animated by computer. |  Line drawing generation | 0.8396 | 7 | 14 | 0.50
6 |  Magnetic Resonance Imaging Technologists |  Healthcare |  Operate optical systems to capture dynamic magnetic resonance imaging (MRI) images […] |  MRI system and method using neural network for detection of patient motion | 0.8615 | 11 | 23 | 0.48
7 |  Nuclear Medicine Technologists |  Healthcare |  Gather information on patients’ illnesses and medical history to guide the choice of diagnostic procedures for therapy. |  Patient-Specific Therapy Planning Support Using Patient Matching | 0.8630 | 8 | 17 | 0.47
8 |  Software Developers, Applications |  Information |  Design […] mathematical models to predict and measure outcome and consequences of design. |  Adaptive predictive analytics for design modification requests | 0.8295 | 7 | 15 | 0.47
9 |  Electro-Mechanical Technicians |  Manufacturing |  Train robots, using artificial intelligence software or interactive training techniques, […] |  Architectures, systems and methods for program defined entertainment state system, […] | 0.8535 | 16 | 35 | 0.46
10 |  Industrial Truck and Tractor Operators |  Manufacturing |  Move controls to drive gasoline- or electric-powered trucks, cars, or tractors and transport materials between loading, processing, and storage areas. |  Autonomous Truck Unloading for Mining and Construction Applications | 0.8301 | 5 | 11 | 0.45
11 |  Sound Engineering Technicians |  Arts and entertainment |  Record speech, music, and other sounds on recording media, using recording equipment. |  Media capture and process system | 0.8293 | 6 | 14 | 0.43
12 |  Computer Programmers |  Information |  Prepare detailed workflow charts and diagrams […] |  System and method for generating flowchart from a text document using natural language processing | 0.8443 | 7 | 17 | 0.41
13 |  Life, Physical, and Social Science Technicians, All Other |  Scientific and technical services |  Adjust remotely sensed images for optimum presentation […] |  Smart imaging using artificial intelligence | 0.8374 | 27 | 67 | 0.40
14 |  Earth Drillers, Except Oil and Gas |  Construction |  Operate water-well drilling rigs and other equipment […] |  Systems and methods for oilfield drilling operations using computer vision | 0.8280 | 12 | 30 | 0.40
15 |  Medical Transcriptionists |  Healthcare |  Transcribe dictation for a variety of medical reports […] |  Methods for improving natural language processing […] for automated generation of a clinical summarization report […] | 0.8367 | 6 | 15 | 0.40
16 |  Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic |  Manufacturing |  Move machine controls to index workpieces, and to adjust machines for pre-selected operational settings. |  Motor control apparatus | 0.8218 | 7 | 18 | 0.39
17 |  Airline Pilots, Copilots, and Flight Engineers |  Transportation |  Steer aircraft along planned routes, using autopilot and flight management computers. |  Cumulative model for scheduling and resource allocation for airline operations | 0.8367 | 10 | 26 | 0.38
18 |  Physical Scientists, All Other |  Scientific and technical services |  Develop automated routines to correct for the presence of image distorting artifacts, such as ground vegetation. |  Detection and Management of Target Vegetation Using Machine Vision | 0.8345 | 9 | 24 | 0.38
19 |  Computer-Controlled Machine Tool Operators, Metal and Plastic |  Manufacturing |  Implement changes to machine programs, and enter new specifications, using computers. |  Registering collaborative configuration changes of a network element in a blockchain ledger | 0.8370 | 10 | 27 | 0.37
20 |  Biomedical Engineers |  Healthcare |  Diagnose and interpret bioelectric data, using signal processing techniques. |  Electrocardiogram Device and Methods | 0.8622 | 7 | 19 | 0.37

Figure 2:  The number of newly impacted tasks each year for the most affected
occupations, combined with the most frequently occurring words in the patents
influencing those tasks, are organized around the themes of healthcare,
information technology, and manufacturing. These were derived qualitatively
and describe the main themes emerging from the patents. In 2016, patents
mentioning “patient,” “medical,” and “image” matched tasks in healthcare. In
2017, patents mentioning “robot,” “method,” and “module” matched tasks in
manufacturing. In 2018, patents mentioned “machine,” “path,” and “user,”
impacting tasks in IT. In 2019, patents mentioning “data,” “learning,” and
“model” matched tasks in both IT and Manufacturing.

By analyzing the annual increase in impacted tasks and examining the most
frequently occurring words in patents’ abstracts each year (Figure 2), we
identified two categories of highly impacted occupations. The first category
includes occupations that experienced a sudden impact, while the second
category includes occupations that faced continuous impact over time.

Occupations that underwent sudden impacts are predominantly within the
healthcare sector. This impact became most pronounced in 2016, when eight new
tasks were affected, gradually decreasing to just one new impacted task by
2019. In 2016, patents began to significantly impact healthcare by automating
medical imaging and diagnosis through machine learning models, devising
treatment plans and medical devices, and recording and analyzing patient data.
This impact continued into 2017 and 2018, although to a lesser degree,
focusing on predicting optimal radiation therapy doses, dental treatment
plans, and processing medical patient data.

Occupations that sustained continuous impact over time are primarily in
information technology and manufacturing. In the information technology
sector, occupations such as software developers, and in manufacturing,
occupations such as earth drillers, saw consistent increases from zero new
impacted tasks in 2016 to four new impacted tasks in 2019. In information
technology, the impact of patents became noticeable in 2017 when they began
training robots to design and execute iterative tests on chemical samples,
working on aerial and satellite imagery to create products such as land cover
maps, and implementing speech recognition and natural language processing on
audio. This impact steadily rose and extended into 2019, with patents
integrating machine learning into software systems, automating tasks such as
troubleshooting networks and code reviews. In manufacturing, patent impact
emerged in 2018, focusing on optimizing supply chain logistics and planning
material dumping operations. This impact persisted into 2019, further
supporting predictive maintenance and operational optimization such as
determining aircraft conditions.

Least-impacted Occupations. The least-impacted occupations mainly consist of
blue-collar occupations such as pile-driver operators, agricultural graders
and sorters, floor sanders, aircraft cargo handling supervisors, and dredge
operators. Indeed, the AII score, binned by education levels, shows that the
lowest impact is seen for jobs requiring a high school diploma or less (Figure
1). These occupations are primarily found in agriculture, transportation,
accommodation and food services, and construction sectors, where the core
tasks and responsibilities revolve around physical and manual labor and
typically do not require a wide range of complex mental or physical
activities, nor do they involve abstract reasoning. In agriculture, least-
impacted tasks involve food inspection and dairy management for livestock. In
transportation, least-impacted tasks involve scheduling and resource
allocation for airline operations, engine sound control, and vehicle
dispatching. In accommodation and food services, tasks include monitoring and
recording food temperatures. In construction, least-impacted tasks involve the
maintenance and operation of equipment and machinery. In addition to these
sectors, by examining occupations with nearly zero AII scores, we found
another least impacted set of occupations. Contrary to the previous least
impacted occupations which involve manual labor or dexterity, managerial
occupations typically require human interactions, having tasks ranging from
contract negotiation to proposal review to internal assessments and audits.

(a)

(b)

Figure 3: Sector-level AII scores for: (a) all sectors; (b, left) sectors with
lowest rate of change from 2015 to 2020; and (b, right) sectors with highest
rate of change.

###  2.2 Most- and Least-Impacted Industry Sectors

Having explored the disparities in the impact on occupations, our focus now
shifts to understanding the impact on industry sectors. Just as in our
previous analysis, where we compared occupations based on AII scores, we have
similarly compared industry sectors to identify the most- and least-impacted
ones (Figure 3a), and to track AI impact over the years (Figure 3b) (as
described in §4.3). By again thematically analyzing the occupations’
descriptions within each sector, we aimed to uncover potential explanations
for the most- and least-impacted sectors.

Highly-impacted sectors include healthcare, information technology, and
manufacturing, which aligns with our thematic analysis of tasks within the
most-impacted occupations. These sectors have experienced a significantly high
rate of impact. From the thematic analysis, two possible explanations emerge.

The first explanation lies in the nature of the tasks within these sectors,
which are likely to be replaced by AI-enhanced hardware. For example, in
healthcare, tasks involving the use of X-rays or MRI scans, such as those used
by radiologists, have been automated by patents on advanced medical equipment
and devices. Similarly, in manufacturing, tasks that involve the examination
of chemical or biological samples, such as those performed by food science
technicians, can now be executed by AI-enhanced hardware. The second
explanation is that the occupations within these sectors entail tasks
demanding extensive data analysis and processing. In information technology,
film editors, for example, engage in video data editing, which our patent
analysis found to have been streamlined by AI-based software. Likewise,
scientists in healthcare, information technology, or manufacturing often
handle large volumes of data, and recent patents deal with both structured and
unstructured data (e.g., using deep-learning for computational biology [30]).

Least-impacted sectors, on the other hand, include construction, accommodation
and food services, real estate, education, public administration, and finance
and insurance. These sectors have experienced a significantly low rate of
impact. From the thematic analysis, three possible explanations emerge.

The first explanation centers on the fact that occupations within certain
sectors are often associated with low-skilled or physical/manual labor. For
instance, in construction, tasks range from assembling solar panels to
maintaining pipe systems to operating various drills, all of which require
physical labor. Additionally, manual dexterity is challenging to automate. In
public administration, numerous occupations, such as police officers and
firefighters, still require manual skills.

The second explanation is that occupations within certain sectors demand basic
and non-specialized skills. For instance, in accommodation services, the tasks
of waiters and baristas typically involve minimal specialized knowledge or
vocational training, such as serving food and drinks. Similarly, in real
estate, brokers or clerks primarily require training in overseeing
transactions and handling tasks related to office operations.

The third explanation is that occupations within certain sectors often require
a high level of skill and involve extensive interpersonal interactions. For
instance, in managerial positions (e.g., CEOs), tasks typically encompass
responsibilities such as delegating tasks, attending events and meetings, and
negotiating contracts - all of which heavily rely on human interpersonal
communication. Similarly, in education, a teacher’s role primarily revolves
around delivering educational materials in person, a task that demands both
physical presence and a higher level of education and specialized knowledge.
In the legal sector, impacted occupations include those involved in drafting
legal documents or transcribing pretrial and trial proceedings, such as court
reporters, in alignment with previous qualitative analyses on legal
occupations [31]. Conversely, roles requiring the design of bespoke legal
solutions remain unaffected. Notably, client-facing tasks in the legal sector
are also not impacted, resulting in an overall expectation of limited impact
on the legal sector. Finally, in the financial sector, there are still
occupations that necessitate human interactions, such as clerks, sales agents,
and tellers.

###  2.3 Most- and Least-Impacted Geographical Regions

Figure 4: AI Impact on U.S. counties. Orange colors represent higher impact of
AI, while purple colors represent lower impact.

Just as with the occupations and industry sectors analyses, the AII measure
can be used to study the impact of AI on regional-level outcomes. In fact,
patents have been used to study the factors that influence the spread of
technology among regional areas [19], as AI is permeating in urban design and
planning [32]. Previous research has consistently shown that technology
spillovers tend to concentrate within specific geographic regions. This
concentration is often attributed to the effective transfer and dissemination
of knowledge, a process facilitated by localized interactions involving
communication, collaboration, social interactions, and the presence of a local
pool of human capital [33, 34].

By calculating the AII measure at the regional level, it became evident that
certain states, and even at a more detailed county-level granularity (Figure
4), experienced lower levels of impact compared to others. The East Coast, on
average, experiences a greater impact of AI compared to the West Coast.
However, Washington and California are exceptions to this pattern, as they are
highly impacted due to the presence of Seattle and the Bay Area.

However, the extent to which local interactions can yield significant
technological advancements depends on regional capabilities that govern
innovation processes. Income inequality emerges as a factor of concern, as it
has been found to have adverse effects on regional growth with total wages
growing superlinearly as the cities increase in size [35]. Wilkinson and
Pickett [36] argue that more equal societies, where income and wealth are
distributed more fairly among the population, tend to have better outcomes for
their citizens. We correlated the income inequality among U.S. households,
calculated as the U.S. state’s Gini coefficient (Figure A.2b), with the AII
regional-level measure (Figure 5a and Figure A.2a). We found a moderate
positive correlation (Pearson’s $r$ = 0.30), indicating that as the impact of
AI increases, so does income inequality at the state level. Contrary to this
trend, New York, Alaska, Utah, and New Hampshire stand out as outliers. On the
one hand, New York has high inequality [37]. On the other hand, Alaska, Utah,
and New Hampshire have lower inequality, partially attributed to their higher
rates of homeownership. While large urban centers grapple with widening
disparities, states such as Utah and Alaska exhibit more equitable income
distribution potentially tied to specific social and industry characteristics
[38].

(a)

(b)

(c)

Figure 5: Three socio-economic indicators as a function of region-level
($z$-scored) AII (orange dots are more than two standard deviations away): (a)
income inequality (states with higher income inequality will be the most
impacted); (b) attractiveness to the creative class (states with a larger
creative class will be the most impacted); and (c) economic diversification
(counties with the highest diversification will be the least impacted).

Another plausible explanation is that the least impacted states may not
predominantly rely on the knowledge economy. Richard Florida’s seminal work
established a link between regional economic prosperity and the presence of
the “creative class”. Florida defined the creative class as individuals
engaged in creative and knowledge-based industries such as artists, designers,
scientists, engineers, researchers, and professionals in fields reliant on
creativity and intellectual capital [39, 40]. Building upon Florida’s
conceptualization of the creative class, a creative index serves as a metric
for assessing how well cities diversify their workforce in terms of talent,
technology, and creativity [41]. His theory posits that cities and regions
with a higher concentration of the creative class are more likely to foster
innovation and economic growth. Hoyman and Faricy [42] further supported this
notion by demonstrating that states with a higher percentage of the population
aged twenty-five and over holding a bachelor’s degree or higher in
metropolitan statistical areas tend to be more economically successful.
Additionally, investments in talent and technology have been shown to predict
the retention of the creative class, ultimately contributing to state income
growth and equality [43]. Using Florida’s creativity index as a proxy for the
knowledge economy, we found a moderate positive correlation (Pearson’s $r$ =
0.40) between the AII regional-level measure and states’ creativity index
(Figure A.2c). This implies that states whose economies are primarily built
around the creative class, attracting individuals who excel in information
technology and healthcare, are more likely to be impacted by AI. Although the
presence of the creative class has previously been associated with economic
prosperity, regions that overly concentrate on knowledge-based economies are
likely to face significant challenges in upskilling and reskilling their
workforces. The state of Arkansas, however, is the sole data point exceeding
two standard deviations. Arkansas has been making strides in attracting the
creative class, an essential component of a modern and dynamic economy Reports
indicate that between 2015 and 2017, the state’s arts and cultural production
grew by 3% annually, outpacing the growth of the gross state product and
showing a significant value added to the economy by the arts and cultural
sector [44]. A key factor behind this is the formation of the Arkansas
Legislative Arts and Technology Bootcamp through Act 577 [45]. This initiative
is focused on expanding the creative economy and aims to increase revenue and
attract creative talents to the state by involving legislative leaders in the
efforts.

Table 3: Most- and least-impacted counties. The county-level AII is calculated
as the employment-weighted average of sector-level AII scores, weighted by the
importance of each economic sector in the county. For each county, we also
report the economic diversity (higher values indicate greater diversity) and
the predominant employment sector. The economic diversity values for all
counties shown are less than 2.8, which is the cutoff for the 1st quartile.
This indicates that these counties have relatively low economic diversity,
with their workforce concentrated in just a few sectors.

Rank | Most-impacted |  Economic Diversity | Most Prevalent Sector | Least-impacted |  Economic Diversity | Most Prevalent Sector
---|---|---|---|---|---|---
1 | Issaquena County, MS | 0.54 | Healthcare | Wallace County, KS | 1.55 | Finance and insurance
2 | Petroleum County, MT | 1.30 | Healthcare | Hinsdale County, CO | 1.55 | Construction
3 | Buffalo County, SD | 1.46 | Healthcare | Twiggs County, GA | 1.00 | Education
4 | Macon County, AL | 1.67 | Healthcare | Heard County, GA | 1.18 | Education
5 | Jefferson County, MS | 1.79 | Healthcare | Graham County, NC | 2.39 | Education
6 | Starr County, TX | 1.74 | Healthcare | Garfield County, MT | 1.24 | Education
7 | Iron County, MO | 1.80 | Healthcare | Storey County, NV | 1.00 | Transportation
8 | Metcalfe County, KY | 1.99 | Healthcare | Phillips County, MT | 2.64 | Education
9 | Calhoun County, AR | 0.55 | Manufacturing | Clark County, ID | 1.07 | Education
10 | Ripley County, MO | 2.27 | Healthcare | Ottawa County, KS | 2.51 | Finance and insurance
11 | Galax City, VA | 1.94 | Healthcare | Zavala County, TX | 1.39 | Education
12 | Dillingham Census Area, AK | 0.90 | Manufacturing | McPherson County, SD | 1.29 | Education
13 | Evangeline Parish, Louisiana | 2.35 | Healthcare | Red Lake County, MN | 2.46 | Education
14 | Aleutians East Borough, AK | 0.69 | Manufacturing | Judith Basin County, MT | 1.99 | Education
15 | Franklin City, VA | 2.37 | Healthcare | Gates County, NC | 1.99 | Education
16 | Sherman County, NE | 2.20 | Healthcare | Calhoun County, IL | 2.11 | Education
17 | Reynolds County, MO | 2.33 | Manufacturing | Kingman County, KS | 2.85 | Education
18 | Borden County, TX | 1.87 | Healthcare | Clay County, TX | 1.65 | Education
19 | Todd County, SD | 1.57 | Healthcare | Denali Borough, AK | 0.91 | Accommodation and food services
20 | Wayne County, KY | 2.14 | Manufacturing | Lincoln County, KS | 2.25 | Education

The third explanation lies in the fact that the least impacted states have
different regions that are more economically resilient. Previous research has
found that increased economic diversity at the regional level promotes greater
economic resilience. Davies [46] analyzed regional economies across Europe and
found that regions with more diversity in their workforce composition
exhibited higher regional stability and faster recovery from the 2008-2010
global financial crisis. Similarly, Dissart [47] found that U.S. regions with
greater diversity across sectors saw lower volatility in employment and income
growth, indicating higher regional economic resilience. The proposed mechanism
in these studies is that diverse regional economies allow for greater
adaptability and adjustment to sector-specific economic shocks. When a
recession damages an individual sector, a region with diverse industries can
absorb the shock better as workers can shift to unaffected sectors more easily
[48]. Similarly, one can hypothesize that when a sector is affected due to
automation, a region that diversifies its workforce will be more economically
resilient. In contrast, specialized regional economies centered around one or
a few dominant industries have been found to be more vulnerable to sector-
specific disruptions. Martin [49] found that during recessions, regions in
Europe with less complex, less diverse economies suffered greater rises in
unemployment rates due to their concentration in a small number of industries
that were severely impacted (e.g., manufacturing). To investigate whether
finer-grained regional differences are present, we zoomed in on our analysis
at the county level. This closer examination allowed us to delve into how
localized regional capabilities correlate with the impact of AI. In so doing,
we correlated a measure of economic diversity (computed as the entropy of
workforce distribution within a county, as detailed in §4.4 and shown in
Figure A.2d) with the AII county-level measure (Figure 5b). Two distinct
groups of counties of resilient economies emerged (Table 3). The first group
comprises counties with diversified economies where the workforce is engaged
in a wide range of industry sectors. In fact, diversified metropolitan areas
tend to experience more economic growth [18]. The second group consists of
counties where the workforce specializes in industry sectors that are least
impacted such as education. By contrast, counties specialized in most impacted
sectors such as manufacturing and healthcare suffer from such a
specialization.

##  3 Discussion

###  3.1 Consensus and Discrepancies in Previous Literature

Unlike previous methods for assessing the impact of AI on occupations, which
either rely on a finite set of abilities (e.g., manual dexterity) linked to
specific occupations [4, 14] or employ subjective evaluations to determine
tasks’ suitability for automation [3], our AII measure provides an objective
approach by leveraging patent data to capture the dynamic landscape of
technological advancements in AI.

To position our findings, we explore the consensus or lack thereof of which
occupations will be impacted by AI in the literature.

Consistent results in the literature. Frey and Osborne’s [14], AIOE [4], SML
[3], and Webb’s [2] concur that low-skilled occupations typically involving
human labor are not impacted by AI. These occupations are typically found in
industry sectors such as construction, where tasks primarily involve manual
labor and the operation of machinery. Our AII measure aligns with these
observations by categorizing such occupations as among the least impacted by
AI.

Inconsistent results in the literature. While there has been a clear consensus
in previous literature regarding manual labor occupations, we have identified
four types of occupations for which consensus has been so far lacking. These
categories include: occupations requiring basic and non-specialized skills;
occupations requiring high skills and interpersonal interaction-based
occupations; those where tasks are replaced by AI-enhanced hardware; and those
involving extensive data analysis and processing.

_Occupations that require basic and non-specialized skills (least impacted
according to our approach)._ Frey and Osborne’s findings [14] indicated that
occupations within the accommodation and food services sector (e.g., cooks,
dishwashers, waiters, bartenders) are highly-impacted. Similarly, Webb’s
findings suggest that non-routine manual occupations are highly impacted;
there are patents matching those occupations, which may well be coarse-grained
matches, as exemplified in Table C.1.

However, SML [3] and AIOE [4] found the very same occupations to be among the
least-impacted. Our approach aligns with SML and AIOE, identifying these
occupations as among the least-impacted due to the absence of AI patents
automating the manual tasks associated with them.

_Occupations that are highly-skilled and involve interpersonal interactions
(least impacted according to our approach)._ AIOE [4] found that occupations
requiring high skills and interpersonal interactions, such as those in the
education sector (e.g., teachers) or managerial positions (e.g., CEOs), are
highly-impacted. Similarly, Webb found that occupations that require
interpersonal tasks are hard to automate [2]. In contrast, SML [3] found these
occupations, particularly managerial positions, to be among the least
impacted, aligning with Frey and Osborne’s method [14]. However, in the case
of SML, there was a distinction within occupations involving interpersonal
interactions. While SML found managerial positions to be among the least
impacted, it identified occupations such as concierges, which also involve
interpersonal interactions, as more likely to be impacted by AI. Upon closer
examination of the SML method, which relies on rubrics (i.e., a type of
scoring guide for crowdworkers to assess the suitability of tasks for machine
learning, as shown in Table A.2, we noted that the corresponding definition in
the rubrics entailed a “wide range of interactions,” making it challenging to
capture their nuances. Additionally, since SML relies on crowdsourced data,
there is a potential for subjective bias or a lack of full understanding of
the nuances of interpersonal interactions. Finally, interactions were captured
by only one item out of the 23 items in the rubrics making that item’s
contribution to the overall score limited. Our approach aligns with Frey and
Osborne’s [14], Webb’s [2] and, to some extent with SML [3] as no AI patents
were found to target these occupations.

_Occupations that consist of tasks that are replaced by AI-enhanced hardware
(most impacted according to our approach)._ Frey and Osborne [14] discovered
that occupations in healthcare, such as MRI and cardiovascular technologists,
which are likely to be replaced by AI-enhanced hardware, were among the least-
impacted. In contrast, SML [3] and AIOE [4] identified these same healthcare
occupations as among the most-impacted ones. Webb [2] found that occupations
involving non-routine manual tasks (e.g., operating devices or equipment in
healthcare or manufacturing) to be among the most-impacted ones. Our approach
aligns with SML, AIOE, and Webb’s method due to the presence of AI patents
automating tasks using AI-enhanced hardware. For example, in healthcare,
patents have automated tasks such as medical imaging and diagnosis using
machine learning models, the development of treatment plans, the creation of
medical devices, the recording and analysis of patient data, and even the
prediction of optimal radiation therapy doses, dental treatment plans, and the
processing of medical data.

_Occupations that require extensive data analysis and processing (most
impacted according to our approach)._ Frey and Osborne [14] found that
occupations that require extensive data analysis and processing, such as those
in information technology or manufacturing (e.g., software developers,
chemists, aerospace engineers), were classified as among the least-impacted.
In contrast, SML [3] and AIOE [4] categorized these very same occupations as
among the most-impacted. Similarly, Webb [2] identified that occupations
involving non-routine cognitive analytic tasks to be among the most-impacted
ones. Our findings align with SML, AIOE, and Webb’s method due to the presence
of patents related to tasks that require data analysis and processing. For
example, patents about training robots for task execution, image and video
processing, speech recognition, and natural language processing, as well as
the integration of machine learning into software systems. These patents
automate tasks such as troubleshooting networks, code reviews, optimizing
supply chain logistics, planning material dumping operations, and supporting
maintenance, including determining aircraft conditions.

###  3.2 Implications

The impact of AI on occupations carries important implications for the
workforce. However, when placed our findings within the context of previous
literature, it became evident that there is a lack of consensus regarding
which occupations will be affected and which will remain unaffected. As an
initial step, we contend that achieving consensus is crucial to start
formulating effective policies to address the ongoing transformations in the
labor market. To initiate this process, with our findings in mind, we outline
three key areas in which initiatives can be developed.

Initiatives for White-Collar Workers. Policymakers and employers should launch
specific initiatives targeting white-collar occupations in sectors such as
information technology, manufacturing, and healthcare. These initiatives can
equip workers with the skills needed for high-value, creative, and complex
tasks. For example, a manufacturing worker can undergo training in robotics
programming, enabling them to effectively operate and maintain AI-driven
machinery. Similarly, healthcare professionals can acquire telemedicine and
data analytics skills to enhance patient care and diagnostics.

Initiatives for Blue-Collar Workers. Blue-collar occupations, predominantly
found in agriculture, accommodation and food services, and construction,
typically involve low-skilled work demanding physical labor. While previous
literature has suggested reallocating low-skilled workers to tasks less
susceptible to computerization [14], such as those requiring creative and
social intelligence, we argue that (re)skilling and upskilling [10] should be
approached cautiously. Our regional analysis emphasizes the importance of
striking a balance in upskilling low-skilled workers for roles demanding
creative and social intelligence. This is particularly important because
regions that excessively rely on knowledge-based economies are likely to face
significant AI impact.

Initiatives for Continuous Learning and Interdisciplinary Training. Promoting
a culture of continuous learning and skill development is essential. Employers
can encourage individuals to embrace lifelong learning through online courses,
certifications, and vocational training, enabling them to adapt to the
changing occupational landscape [50]. Similarly, encouraging interdisciplinary
training can prepare the workforce for the demands of AI-augmented
occupations. For example, blending traditional engineering with AI and machine
learning training can create a workforce capable of developing and maintaining
AI-enhanced systems across sectors.

###  3.3 Limitations and Future Work

This work has six primary limitations. First, our measure reflects whether a
patent impacts a task but cannot distinguish whether the impact means
replacing the task or augmenting it, which has been a long standing debate in
the literature [23, 51, 4, 52, 53].

Second, our analysis is based on currently existing tasks, capturing their
potential automation, and cannot be used to study the emergence of new tasks
as a result of that automation (referred to in previous work as ‘work
augmentation’ [23]).

Third, our method relies on patent abstracts to provide a finer-grained
understanding of occupational tasks, mainly from the US-focused USPTO dataset.
Future research should explore potential cultural differences in how patents
are written and used in other contexts, such as the European Union.

Fourth, it is important to acknowledge that the existing portfolio of USPTO
patents may not comprehensively cover all the innovations that may impact a
particular occupation. To address this limitation, one potential approach
could involve supplementing patent data with other sources, including research
papers and code repositories [54].

Fifth, our assumption is that the likelihood of an AI system being built is
determined by whether it is patented. While generally true, there are
exceptions. A patented system may not be built, as the patent could be
intended for defensive or offensive purposes [55, 56]. Conversely, a non-
patented system might still be constructed, with its design protected by
secrecy or trademarks [57, 58]. Furthermore, patenting rates vary among
sectors [59], and there is a lag between an innovation being patented and its
use and impacts diffusing across the economy [54].

Finally, our analysis is based solely on a concise yet comprehensive five-year
time window, spanning from 2015 to 2020, with patents from these years
accounting for 92% of the total AI patents granted until 2020. We also
repeated the analysis for 2010-2020 and found no significant difference in the
results (as shown in Table D.1 and Figure D.1). This does not capture emerging
technologies, such as Large Language Models (LLMs). Future research could
replicate our methodology to assess the impact of emerging technologies, such
as cryptocurrency, the metaverse, and LLMs by using upcoming patents in those
fields.

##  4 Materials and Methods

Figure 6: Framework automating AI Impact (AII) measure. Using the Sentence-T5
(ST5) model, we first generated two vector representations (embeddings): one
capturing the semantic meaning of an AI patent, and the other the semantic
meaning of a task description. We then computed the patent-task cosine
similarity from the embeddings on all patent-task pairs. This process was
conducted to identify which tasks were impacted by which AI patents. Finally,
for each occupation, we calculated the proportion of impacted tasks out of the
total ones, and this proportion determined the AII of that occupation.

###  4.1 Datasets

Occupation Dataset. We collected detailed task descriptions for a wide range
of occupations from the O*NET database [60], a widely used source in
occupational studies [61, 62, 3, 4, 63]. In total, we collected 773 unique
occupations and 19,498 unique tasks from O*NET 24.3 version released in May
2020. The distribution of these tasks ranges from 4 to 286, with a median of
20 tasks per occupation (Figure A.1).

Patent Dataset. To obtain a corpus of AI patents, we first retrieved 74,875
patents granted by the United States Patent and Trademark Office (USPTO)
between 2015-2020 that were classified to be about AI based on an official way
of coding patents called PATENTSCOPE AI Index [64] to then filter away patents
only tangentially related to AI. We selected the subset of patents in the
index class core AI applications (Table A.1). This resulted in a final corpus
of 12,984 AI patents.

###  4.2 Measuring AI Impact (AII) on Occupation Tasks

Sentence-Transformers. We developed a Sentence-Transformers Deep Learning
framework [65] for Natural Language Processing that uses the Sentence-T5 (ST5)
architecture [26] to convert input text into “semantic vector representations”
called “embeddings”. These embeddings capture the semantic information of the
text and allow us to mathematically compute the similarity of a pair of text
snippets.

AI Impact (AII). We defined AII as a measure of _“the exposure to AI by
measuring the extent to which an occupation’s tasks are associated with
patents”_. For each task, we identified the patent with the highest task-
patent similarity score (Equation 1) to represent the AI impact $\alpha_{t}$
on task $t$ indicating the extent to which task $t$ aligns with AI-related
innovations:

$\alpha_{t}=\max_{p}\text{sim}(v_{t},v_{p})$ (1)

where $\text{sim}(v_{t},v_{p})$ is the cosine similarity between the
embeddings of task $t$ and the embeddings of patent $p$. We computed the
impact of AI on task $t$ by taking the maximum similarity value. We took the
maximum instead of, say, the average because if the average was used,
similarity scores from patents that are not particularly relevant to the task
would be factored into the calculation, thereby diluting the AI impact score
(Figure B.1).

###  4.3 Measures of AII on Occupations, Industry Sectors, and Regions

AI Impact on Occupations. We computed the AI impact $y_{j}$ on occupation $j$
by computing the number of _AI-impacted tasks_ over the total number of $j$’s
tasks:

$y_{j}=\frac{\sum_{t\in\text{tasks}(j)}1_{\alpha_{t}>p_{90}(\alpha)}}{\sum_{t\in\text{tasks}(j)}1}$
(2)

where $p_{90}(\alpha)$ is the 90th percentile of AI impact values computed on
all occupations’ tasks, and $1_{\alpha_{t}>p_{90}(\alpha)}$ is an indicator
function whose value is 1, if $\alpha_{t}>p_{90}(\alpha)$, and 0 otherwise.
Using the 90th percentile as the threshold makes the AI impact measure more
robust to noise, which was also suggested in a previous study [21]. Given that
every task is assigned a similarity value in the previous step, the patent
deemed most similar for a specific task might still be unrelated to that task.
On the other hand, a higher 95th percentile threshold would be too strict, as
55% of the occupations would have zero impacted tasks. To further validate our
task-patent matching method, two authors independently assessed the relevance
of a patent to a task in a random sample of 100 task-patent pairs. Overall,
their agreement was nearly perfect, with a Cohen’s Kappa of 0.84.

AI Impact on Industry Sectors. To determine the impact of AI on industry
sector $s$, we calculated the mean AII score across all occupations $j$
associated with sector $s$ (Equation 3). Occupation $j$ was assigned to sector
$s$, if over 50% of workers in $j$ were employed in $s$:

$\pi_{s}=\frac{1}{N_{s}}\sum_{j\in\text{occupations}(s)}y_{j}$ (3)

where $N_{s}$ is the number of occupations associated with $s$. If more than
half of the workers in an occupation are employed in a particular sector, it
can be reasonably concluded that this occupation is primarily associated with
that sector [4]. Lower thresholds might lead to occupations being associated
with multiple sectors, making the results less specific. Conversely, a higher
threshold might be too restrictive, potentially excluding occupations with a
significant presence in a sector, even if not overwhelmingly so.

AI Impact on Regions. To determine regional AII scores, we used the National
Occupational Employment and Wage Estimates dataset published in May 2022 by
the U.S. Bureau of Labor Statistics. For each region, we calculated a weighted
average of the AII scores across sectors, weighting each sector by the number
of employees in that sector within the region.

$\Omega_{r}=\frac{\sum_{s\in\text{sectors}(r)}\pi_{s}w_{rs}}{\sum_{s\in\text{sectors}(r)}w_{rs}}$
(4)

where $w_{rs}$ is the number of employees associated with sector $s$ within
region $r$. $\Omega_{r}$ is $r$’s regional AII score that accounts for both
the AI impact on occupations in that region and the distribution of the
workforce across sectors. Sectors that employ more people in a region have a
larger influence on that region’s overall labor market dynamics. Weighting by
the number of employees ensures that sectors with a higher workforce
representation influence the regional AII score proportionately. Moreover,
some sectors might have extreme AII scores (either very high or very low). A
simple average could be skewed by these outliers. By weighting sectors based
on the number of employees, the measure ensures that the regional AII score is
not disproportionately affected by these smaller sectors. This method of
weighted averaging aligns with methodologies in previous studies [4, 3, 2].

###  4.4 Measuring Economic Diversity

To measure the economic diversity of a region, we collected its workforce
distribution data from the National Occupational Employment and Wage Estimates
dataset published in May 2022 by the U.S. Bureau of Labor Statistics. For each
region, the economic diversity is computed as the entropy of the workforce
distribution:

$H(r)=-\sum_{s\in\text{sectors}(r)}\frac{w_{rs}}{N_{r}}\log\frac{w_{rs}}{N_{r}}$
(5)

where $w_{rs}$ is the number of employees associated with sector $s$ within
region $r$ and $N_{r}$ is the total number of employees within region $r$.

###  4.5 Thematic Analysis on Occupations and Industry Sectors

Occupations. To identify emergent themes that characterize least or most
impacted occupations, we conducted a thematic analysis [66, 67] on the task-
patent pairs for all tasks associated with those occupations. This process
consists of two steps: open coding, in which textual data is broken up into
discrete parts and then coded; and axial coding, in which the researcher draws
connections between the generated codes [68]. We first applied open coding to
identify key concepts that emerged across multiple task-patent pairs;
specifically two of the authors read all task-patent pairs, and marked them
with keywords that reflected the key concepts expressed in the text. They then
used axial coding to identify relationships between the most frequent keywords
to summarize them in semantically cohesive themes (e.g., healthcare,
information technology, and manufacturing). Themes were reviewed in a
recursive manner rather than linear, by re-evaluating and adjusting them as
new task-patent pairs were parsed.

Industry Sectors. Just as with occupations, we conducted thematic analysis
[66, 67, 68] to uncover potential explanations for the most- and least-
impacted sectors. We again applied open coding to identify key concepts that
emerged on the descriptions of occupations (examples presented in Table A.3)
across the industry sectors under study, with the same two authors reading all
descriptions and marking them with keywords that reflected the key concepts
expressed in the text. We then again used axial coding to identify
relationships and potential explanations.

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## Supplementary Information

##  Appendix A Data

###  A.1 Data Collection

In a total of 12,984 AI patents granted between 2015 and 2020, the majority
contained the keyword machine learning (46%), followed by the keyword neural
network (32%), artificial intelligence (9%), and deep learning (6%) (Table
A.1).

Table A.1: Number of patents based on keywords. Keywords | Number of patents
---|---
machine learning | 6084
neural network | 4197
artificial intelligence | 1210
deep learning | 810
natural language processing | 758
planning | 595
reinforcement learning | 232
computer vision | 211
speech processing | 73
predictive analytics | 47
robotics | 35
knowledge representation | 21
control methods | 15

From 774 occupations defined in O*NET, the number of tasks for each occupation
ranges from 4 to 286, with a median of 20 tasks (Figure A.1).

Figure A.1: The distribution of the number of tasks per occupation

To compute SML scores, Brynjolfsson et al. [3] used a set of rubrics to guide
crowdworkers to assess the suitability of tasks for machine learning (Table
A.2). Note that only one item in the rubrics is about interpersonal
interactions.

Table A.2: Rubrics to compute SML scores as developed by Brynjolfsson et al.
[3].

No |  Rubric
---|---
Q1 |  Information needed to complete the task (inputs) and outputs can be explicitly specified in machine-readable format
Q2 |  Task information is recorded or recordable by computer
Q3 |  Task does not require a wide range of complex outputs (mental and/or physical)
Q4 |  Task feedback (on the success of outputs) is immediate
Q5 |  The task output is error tolerant
Q6 |  It is not important that outputs are perceived to come from a human
Q7 |  Task does not require complex, abstract reasoning
Q8 |  Task is principally concerned with matching information to concepts, labels, predictions, or actions
Q9 |  Task does not require detailed, wide-ranging conversational interaction with a customer or other person
Q10 |  Task is highly routine and repeated frequently
Q11 |  Task is describable with rules
Q12 |  There is no need to explain decisions during task execution
Q13 |  Task can be converted to answering multiple choice questions, ranking alternative options, predicting a number, or grouping similar objects
Q14 |  Long term planning is not required to successfully complete the task
Q15 |  The task requires working with text data or might require working with text in the future
Q16 |  The task requires working with image/video data or might require working with image/video data in the future
Q17 |  The task requires working with speech data or might require working with speech data in the future
Q18 |  The task requires working with other types of data (other than text, image/video, and speech)
Q19 |  Many components of the task can be completed in a second or less
Q20 |  Each instance, completion, or execution of the task is similar to the other instances in how it is done and these actions can be measured
Q21 |  Actions in the task must be completed in a very specific order, and practicing the task to get better is easy

To conduct the thematic analysis on industry sectors, we used the occupation
titles and descriptions in O*NET. For brevity, we provide 10 examples in Table
A.3 and we refer the reader to the full list in O*NET [60].

Table A.3: Examples of occupational descriptions in O*NET.

Occupation Title |  Sector |  Description
---|---|---
Cardiovascular Technologists and Technicians |  Healthcare |  Conduct tests on pulmonary or cardiovascular systems of patients for diagnostic purposes. May conduct or assist in electrocardiograms, cardiac catheterizations, pulmonary functions, lung capacity, and similar tests. Includes vascular technologists.
Orthodontists |  Healthcare |  Examine, diagnose, and treat dental malocclusions and oral cavity anomalies. Design and fabricate appliances to realign teeth and jaws to produce and maintain normal function and to improve appearance.
Medical Records and Health Information Technicians |  Healthcare |  Compile, process, and maintain medical records of hospital and clinic patients in a manner consistent with medical, administrative, ethical, legal, and regulatory requirements of the health care system. Process, maintain, compile, and report patient information for health requirements and standards in a manner consistent with the healthcare industry’s numerical coding system.
Numerical Tool and Process Control Programmers |  Manufacturing |  Develop programs to control machining or processing of metal or plastic parts by automatic machine tools, equipment, or systems.
Multi-Media Artists and Animators |  Information |  Create special effects, animation, or other visual images using film, video, computers, or other electronic tools and media for use in products or creations, such as computer games, movies, music videos, and commercials.
Magnetic Resonance Imaging Technologists |  Healthcare |  Operate Magnetic Resonance Imaging (MRI) scanners. Monitor patient safety and comfort, and view images of area being scanned to ensure quality of pictures. May administer gadolinium contrast dosage intravenously. May interview patient, explain MRI procedures, and position patient on examining table. May enter into the computer data such as patient history, anatomical area to be scanned, orientation specified, and position of entry.
Nuclear Medicine Technologists |  Healthcare |  Prepare, administer, and measure radioactive isotopes in therapeutic, diagnostic, and tracer studies using a variety of radioisotope equipment. Prepare stock solutions of radioactive materials and calculate doses to be administered by radiologists. Subject patients to radiation. Execute blood volume, red cell survival, and fat absorption studies following standard laboratory techniques.
Software Developers, Applications |  Information |  Develop, create, and modify general computer applications software or specialized utility programs. Analyze user needs and develop software solutions. Design software or customize software for client use with the aim of optimizing operational efficiency. May analyze and design databases within an application area, working individually or coordinating database development as part of a team. May supervise computer programmers.
Electro-Mechanical Technicians |  Manufacturing |  Operate, test, maintain, or calibrate unmanned, automated, servo-mechanical, or electromechanical equipment. May operate unmanned submarines, aircraft, or other equipment at worksites, such as oil rigs, deep ocean exploration, or hazardous waste removal. May assist engineers in testing and designing robotics equipment.
Industrial Truck and Tractor Operators |  Manufacturing |  Operate industrial trucks or tractors equipped to move materials around a warehouse, storage yard, factory, construction site, or similar location.

###  A.2 Summary Statistics

We checked the four variables’ distribution to ensure that they were normally
distributed before using them in our regional-level analysis. All but economic
diversity were normally distributed. Since we did not conduct any correlation
analysis on the economic diversity, a log transformation was not needed.
Instead, we plotted the quadratic fit of this variable to understand its
relationship with the AII measure computed at the regional level.

(a)

(b)

(c)

(d)

Figure A.2: Frequency distribution plots of: (a) the AII measure; (b) income
inequality; (c) creativity index; and (d) economic diversity.

##  Appendix B Task-Patent Matching

###  B.1 Validating the similarity metric used for matching

To ensure the specificity of our task-patent matching method, we plotted the
mean and max of the AII measure. We empirically found that by using the max,
we retained the most relevant patent (Figure B.1). To further validate our
task-patent matching method, we manually annotated a random sample of 100
task-patent pairs in terms of task-patent relevance; two of the authors who
did the annotations achieved a Cohen’s kappa of 0.84 (Kappa values in the
[0.81, 1] range suggest almost perfect agreement).

Figure B.1: Distributions of the task-patent similarity based on different
aggregations. By focusing only on the most relevant patents, we captured the
specificity of the AI impact measurement for each task, reducing noise, and
incorporating a wider range of AI-related advancements.

###  B.2 Matching abstracts (not only titles)

Patents tend to be general, and specific tasks may not be mentioned simply in
the patent title. To address this, our method uses the patent abstract that is
richer in text and more likely to reference a specific task or even entire
sector(s) (Table B.1). As a result, we were able to match the same patent with
occupations in multiple sectors. For example, a patent that provides methods
for radiotherapy treatment planning not only matches the task of radiation
therapists, but also counselors (abuse and behavioral disorder) who need
treatment plans. Another example is a patent for custom-fitting a service
solution to consumer requirements that is applicable to occupations from
different sectors (e.g., software developers, social workers, and solar
photovoltaic installers).

Table B.1: Example task-patent pairs matched with AII. The dash (-) sign means
that the patent is the same as the cell above it.

Occupation |  Sector |  Task |  Matching Patent Title |  Matching Patent Abstract | Similarity
---|---|---|---|---|---
Substance Abuse and Behavioral Disorder Counselors |  Healthcare |  Develop client treatment plans based on research, clinical experience, and client histories. |  Methods and systems for radiotherapy treatment planning |  Example methods for radiotherapy treatment planning are provided. One example method may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients; […] | 0.8018
Mental Health Counselors |  Healthcare |  Develop and implement treatment plans based on clinical experience and knowledge. |  - |  - | 0.8082
Radiation Therapists |  Healthcare |  Administer prescribed doses of radiation to specific body parts, using radiation therapy equipment according to established practices and standards. |  - |  - | 0.8330
Architectural and Civil Drafters |  Manufacturing |  Reproduce drawings on copy machines or trace copies of plans and drawings, using transparent paper or cloth, ink, pencil, and standard drafting instruments. |  Line drawing generation |  Computing systems and computer-implemented methods can be used for automatically generating a digital line drawing of the contents of a photograph. […] The training data set teaches the neural network to trace the edges and features of objects in the photographs, as well as which edges or features can be ignored. […] | 0.8038
Multi-Media Artists and Animators |  Information |  Create pen-and-paper images to be scanned, edited, colored, textured, or animated by computer. |  - |  - | 0.8396
Detectives and Criminal Investigators |  Public administration |  Create sketches and diagrams by hand or with computer software to depict crime scenes. |  - |  - | 0.8195
Photographic Process Workers and Processing Machine Operators |  Other services |  Operate scanners or related computer equipment to digitize negatives, photographic prints, or other images. |  - |  - | 0.8167
Software Developers, Applications |  Information |  Consult with customers about software system design and maintenance. |  System and method for custom-fitting services to consumer requirements |  Systems and methods for custom-fitting a service solution to consumer requirements are provided. […] | 0.8020
Child, Family, and School Social Workers |  Education |  Interview clients individually, in families, or in groups, assessing their situations, capabilities, and problems to determine what services are required to meet their needs. |  - |  - | 0.8010
Solar Photovoltaic Installers |  Construction |  Determine photovoltaic (PV) system designs or configurations based on factors such as customer needs, expectations, and site conditions. |  - |  - | 0.8031

##  Appendix C Previous Attempts to Link Tasks to Patents

Word-matching methods linking tasks to patents. A word-matching method employs
a dictionary approach, parsing task descriptions to identify verb-noun pairs
associated with each task [2]. This approach captures the task essence
concisely and specifically, such as the pair “install, sensor”, offering a
clear representation of the task. Using the same method, AI patent titles are
also processed to extract verb-noun pairs describing the tasks targeted by
each patent. The relative frequency of similar pairs in tasks and patent
titles determines the AI exposure score, indicating the level of task exposure
to AI.

Word-matching methods: Potential false positives. Webb method [2] identified
“elevator installers” as one of the most impacted occupations by AI. However,
he also acknowledged that this classification is an example of false positives
caused by word matching methods being coarse-grained. By applying our deep-
learning method instead, we learn that nearly all the tasks of the occupation
“elevator installers” are indeed not impacted (Table C.1): only 1 out of 20
tasks was impacted. This speaks to the robustness of a deep-learning
approaches compared to a word-matching approach.

Table C.1:  List of the the tasks for the “elevator installers and repairers”
occupation, and whether each task is impacted or not based on AII. A task is
impacted when there is a patent with a similarity exceeding the 90th
percentile threshold.

Task | Is impacted?
---|---
Locate malfunctions in brakes, motors, switches, and signal and control systems, using test equipment. | Yes
Assemble, install, repair, and maintain elevators, escalators, moving sidewalks, and dumbwaiters, using hand and power tools, and testing devices such as test lamps, ammeters, and voltmeters. | No
Test newly installed equipment to ensure that it meets specifications, such as stopping at floors for set amounts of time. | No
Check that safety regulations and building codes are met, and complete service reports verifying conformance to standards. | No
Connect electrical wiring to control panels and electric motors. | No
Adjust safety controls, counterweights, door mechanisms, and components such as valves, ratchets, seals, and brake linings. | No
Read and interpret blueprints to determine the layout of system components, frameworks, and foundations, and to select installation equipment. | No
Inspect wiring connections, control panel hookups, door installations, and alignments and clearances of cars and hoistways to ensure that equipment will operate properly. | No
Disassemble defective units, and repair or replace parts such as locks, gears, cables, and electric wiring. | No
Maintain log books that detail all repairs and checks performed. | No
Participate in additional training to keep skills up to date. | No
Attach guide shoes and rollers to minimize the lateral motion of cars as they travel through shafts. | No
Connect car frames to counterweights, using steel cables. | No
Bolt or weld steel rails to the walls of shafts to guide elevators, working from scaffolding or platforms. | No
Assemble elevator cars, installing each car’s platform, walls, and doors. | No
Install outer doors and door frames at elevator entrances on each floor of a structure. | No
Install electrical wires and controls by attaching conduit along shaft walls from floor to floor and pulling plastic-covered wires through the conduit. | No
Cut prefabricated sections of framework, rails, and other components to specified dimensions. | No
Operate elevators to determine power demands, and test power consumption to detect overload factors. | No
Assemble electrically powered stairs, steel frameworks, and tracks, and install associated motors and electrical wiring. | No

Word-matching methods: Potential false negatives. In addition to, at times,
matching incorrectly tasks and patents (false positives), word matching
methods may also fail to return genuine matches (false negatives). For
example, Webb’s paper reported a variety of tasks for which no patent was
found (Table C.2). However, by applying AII to those tasks, we instead found
that there are indeed matching patents. This discrepancy likely arises from
word-matching methods discarding multiple verb-noun pairs, and overlooking
details in patent abstracts.

Table C.2: List of tasks with zero AI Exposure Score, indicating no associated
patents [2]. In contrast, AII identifies relevant patents for those tasks.

Task | Extracted pairs | AI exposure score |  Most Similar Patent Abstract | Similarity
---|---|---|---|---
Document and maintain records of precision agriculture information. | (maintain, record) | 0.000 |  A method and system for predicting soil and/or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest. The assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and/or to an agricultural device based on acquired measurement data, particularly remote sensing data, and wherein a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure. | 0.790
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. | (apply, information) | 0.000 |  A method and system for predicting soil and/or plant condition in precision agriculture with a classification of measurement data for providing an assignment of a measurement parcel to classes of interest. The assignment is used for providing action recommendations, particularly in real time or close to real time, to a farmer and/or to an agricultural device based on acquired measurement data, particularly remote sensing data, and wherein a classification model is trained by a machine learning algorithm, e.g. relying on deep learning for supervised and/or unsupervised learning, and is potentially continuously refined and adapted thanks to a feedback procedure. | 0.813
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings. | (maintain, sensor) | 0.000 |  A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates. | 0.817

##  Appendix D Most- and Least-Impacted Occupations and Industry Sections

###  D.1 Between 2010 and 2020

We computed the AII measure on occupations and industry sectors in a larger
time window between 2010 and 2020. Similarly, we observed that the most
impacted occupations come primarily from healthcare, information technology,
and manufacturing, whereas the least impacted ones come from finance and
insurance, education, and construction.

Table D.1: 20 most- and least-impacted occupations ranked by the AII
(Artificial Intelligence Impact) measure using AI patents from 2010 to 2020.
For the 20 most- and least-impacted occupations, only 1 entry differs for
each. This is because patents in 2015-2020 account for 96% of the total
patents in 2010-2020.

Rank | Most-impacted | Least-impacted
---|---|---
1 | Orthodontists | Pile-Driver Operators
2 | Cardiovascular Technologists and Technicians | Graders and Sorters, Agricultural Products
3 | Medical Records and Health Information Technicians | Floor Sanders and Finishers
4 | Multi-Media Artists and Animators | Aircraft Cargo Handling Supervisors
5 | Electro-Mechanical Technicians | Insurance Appraisers, Auto Damage
6 | Magnetic Resonance Imaging Technologists | Insurance Underwriters
7 | Nuclear Medicine Technologists | Reinforcing Iron and Rebar Workers
8 | Software Developers, Applications | Farm Labor Contractors
9 | Industrial Truck and Tractor Operators | Water and Liquid Waste Treatment Plant and System Operators
10 | Numerical Tool and Process Control Programmers | Animal Scientists
11 | Sound Engineering Technicians | Brokerage Clerks
12 | Computer Programmers | Insulation Workers, Floor, Ceiling, and Wall
13 | Life, Physical, and Social Science Technicians, All Other | Locomotive Firers
14 | Earth Drillers, Except Oil and Gas | Management Analysts
15 | Medical Transcriptionists | Podiatrists
16 | Airline Pilots, Copilots, and Flight Engineers | Cooks, Short Order
17 | Commercial Pilots | Shipping, Receiving, and Traffic Clerks
18 | Physical Scientists, All Other | Helpers–Painters, Paperhangers, Plasterers, and Stucco Masons
19 | Computer-Controlled Machine Tool Operators, Metal and Plastic | Team Assemblers
20 | Biomedical Engineers | Ambulance Drivers and Attendants, Except Emergency Medical Technicians

Figure D.1: Overall AI impact on industry sectors from 2010-2020, ranked from
highest to lowest sector-level AII scores. A sector-level AII score represents
the mean AII across all occupations within that sector. The only difference
from the 2015-2020 version is the ordering of other services and construction
sectors.

###  D.2  Between 2010 and 2022

We computed the AII measure on occupations and industry sectors in a larger
time window between 2010 and 2022. In this extended timeframe, we again
observed that the most impacted occupations primarily come from healthcare,
information technology, and manufacturing. Additionally, we noted an
increasing impact of AI in scientific occupations.

Table D.2: 20 most- and least-impacted occupations ranked by the AII
(Artificial Intelligence Impact) measure using AI patents from 2010 to 2022.

Rank | Most-impacted | Least-impacted
---|---|---
1 | Cardiovascular Technologists and Technicians | Pile-Driver Operators
2 | Sound Engineering Technicians | Graders and Sorters, Agricultural Products
3 | Nuclear Medicine Technologists | Floor Sanders and Finishers
4 | Magnetic Resonance Imaging Technologists | Aircraft Cargo Handling Supervisors
5 | Air Traffic Controllers | Insurance Underwriters
6 | Orthodontists | Reinforcing Iron and Rebar Workers
7 | Electro-Mechanical Technicians | Farm Labor Contractors
8 | Power Distributors and Dispatchers | Rock Splitters, Quarry
9 | Industrial Truck and Tractor Operators | Brokerage Clerks
10 | Police, Fire, and Ambulance Dispatchers | Locomotive Firers
11 | Security Guards | Management Analysts
12 | Physical Scientists, All Other | Podiatrists
13 | Machinists | Cooks, Short Order
14 | Atmospheric and Space Scientists | Shipping, Receiving, and Traffic Clerks
15 | Computer-Controlled Machine Tool Operators, Metal and Plastic | Proofreaders and Copy Markers
16 | Medical Records and Health Information Technicians | Helpers–Painters, Paperhangers, Plasterers, and Stucco Masons
17 | Textile Knitting and Weaving Machine Setters, Operators, and Tenders | Team Assemblers
18 | Radio Operators | Political Scientists
19 | Medical Transcriptionists | Paralegals and Legal Assistants
20 | Physician Assistants | Telemarketers

Figure D.2: Overall AI impact on industry sectors from 2010-2022, ranked from
highest to lowest sector-level AII scores. A sector-level AII score represents
the mean AII across all occupations within that sector.