Artificial intelligence as a core of the new industrial revolution: prospects and limitations
The purpose of the article is to define prospects and limitations of artificial intelligence as a core of the new industrial revolution. The definition of the AI concept in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is...
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| Cite this: | Artificial intelligence as a core of the new industrial revolution: prospects and limitations / O.S. Vyshnevskyi, M.Yu. Anufriiev, M.S. Bozhyk, T.O. Gulchuk // Економіка промисловості. — 2024. — № 3 (107). — С. 5-21. — Бібліогр.: 24 назв. — англ. |
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| author | Vyshnevskyi, O.S. Anufriiev, M.Yu. Bozhyk, M.S. Gulchuk, T.O. |
| author_facet | Vyshnevskyi, O.S. Anufriiev, M.Yu. Bozhyk, M.S. Gulchuk, T.O. |
| citation_txt | Artificial intelligence as a core of the new industrial revolution: prospects and limitations / O.S. Vyshnevskyi, M.Yu. Anufriiev, M.S. Bozhyk, T.O. Gulchuk // Економіка промисловості. — 2024. — № 3 (107). — С. 5-21. — Бібліогр.: 24 назв. — англ. |
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| description | The purpose of the article is to define prospects and limitations of artificial intelligence as a core of the new industrial revolution.
The definition of the AI concept in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the AI concept.
Based on the analysis and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups.
Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) AI’s high energy consumption; (4) outdated pre-AI corporate culture and regulatory environment.
Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy.
AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.
Метою статті є визначення перспектив та обмежень штучного інтелекту (ШІ) як ядра нової промислової революції.
Трактування поняття ШІ в науковому співтоваристві залишається предметом гострих дискусій. Водночас у нормативно-правовій площині формується тенденція щодо його уніфікації.
Виявлено перспективи ШІ на теоретичному рівні: відчуження неявних знань від індивіда (працівника та підприємця); оптимізація системи планування; перегляд дебатів про соціалістичний розрахунок; зменшення інформаційної асиметрії; на практичному: формування нових продуктів і ринків; підвищення продуктивності праці та капіталу; масове створення нових робочих місць; оптимізація бізнес-процесів; можливість швидкого зростання для малого бізнесу та стартапів.
Обмеження, пов’язані з ШІ: тривале структурне безробіття; завищені очікування від ШІ і, як наслідок, можливе утворення спекулятивної бульбашки на світовому фондовому ринку; енергоємність ШІ; застаріла корпоративна культура та нормативне середовище, сформоване в епоху до ШІ.
Подальше вдосконалення ШІ (включаючи перехід від ШІ до загального ШІ) і розширення його використання може здійснити значний внесок у вирішення проблем, пов’язаних з економічним розрахунком і мінімізацією інформаційної асиметрії, а отже, оптимізацією трансакційних витрат в економіці.
ШІ, виступаючи локально корисним інструментом на рівні окремих підприємств та організацій, приводить до прискорення залучення коштів на фондовий ринок, що може спричинити утворення бульбашки на глобальному рівні. Якщо ця бульбашка лопне, то очікування щодо економічної ефективності штучного інтелекту будуть переглянуті, а деякі компанії, пов’язані зі штучним інтелектом, зазнають значного зниження маржі (можливо, збитків і банкрутства). Однак це, у свою чергу, започаткує наступний етап розвитку ШІ, прискорить його перехід від нинішньої вузької спеціалізації до створення повноцінного загального штучного інтелекту, який має більший потенціал для зміни економіки на всіх рівнях. У результаті ШІ стане ядром нової промислової революції.
|
| first_indexed | 2025-11-25T20:51:26Z |
| format | Article |
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–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 5
2024, № 3 (107)
UDC 004.8:338.45:338.246.83 DOI: http://doi.org/10.15407/econindustry2024.03.005
Oleksandr S. Vyshnevskyi,
Doctor of Economic Sciences, senior researcher
Institute of Industrial Economics of the NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
Е-mail: vishnevskiy_O@nas.gov.ua
https://orcid.org/0000-0002-2375-6033;
Maksym Yu. Anufriiev,
doctoral candidate
Institute of Industrial Economics of the NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
https://orcid.org/0009-0003-7954-1554;
Maryna S. Bozhyk,
postgraduate student
Institute of Industrial Economics of the NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
https://orcid.org/0009-0009-2976-6118;
Taras O. Gulchuk
postgraduate student
Institute of Industrial Economics of the NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
https://orcid.org/0009-0008-4968-0605
ARTIFICIAL INTELLIGENCE AS A CORE OF THE NEW INDUSTRIAL
REVOLUTION: PROSPECTS AND LIMITATIONS
The purpose of the article is to define prospects and limitations of artificial intelligence as a
core of the new industrial revolution.
The definition of the AI concept in the scientific community remains the subject of heated
debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unifi-
cation of the AI concept.
Based on the analysis and literary sources, the following prospects for AI can be identified
on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the
individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the
socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) for-
mation of new products and markets; (2) increasing labor and capital productivity; (3) massive cre-
ation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small
businesses and startups.
Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and,
as a consequence, the possible formation of a speculative bubble in the global stock market; (3)
AI’s high energy consumption; (4) outdated pre-AI corporate culture and regulatory environment.
Further improvement of AI (including the transition from AI to AGI) and the expansion
of its use can make a significant contribution to solving problems related to economic calcu-
МАКРОЕКОНОМІЧНІ ТА РЕГІОНАЛЬНІ ПРОБЛЕМИ
РОЗВИТКУ ПРОМИСЛОВОСТІ
© Publisher of PH "Akademperiodika"
of the National Academy of Sciences of Ukraine, 2024
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
6 ISSN 1562-109X Econ. promisl.
2024, № 3 (107)
lation and minimizing information asymmetry, and therefore optimizing transaction costs in the
economy.
AI, certainly acting as a locally useful tool at the level of individual enterprises and organi-
zations, causes the acceleration of attracting funds to the stock market, which can lead to the for-
mation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency
of AI will be revised, and some AI-related companies will experience significant margin reductions
(perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development,
will accelerate its transition from the current narrow specialization to the creation of full-fledged
general artificial intelligence (artificial general intelligence), which has a greater potential to change
the economy at all levels. As a result, AI will become established as the core of the new industrial
revolution.
Keywords: AI, artificial intelligence, industrial revolution, socialist-calculation debate, re-
ducing information asymmetry.
JEL: O4, O33, B53
Actuality
The ongoing deepening of the economy
digitalization is closely related to the use of ar-
tificial intelligence (hereinafter AI), which is
gradually penetrating all spheres of economic
activity. AI is one of the key technologies of
the Fourth Industrial Revolution. The use of AI
helps businesses increase productivity, auto-
mate routine tasks, improve customer service,
and create personalized approaches to them.
The use of AI tools allows to reduce risks and
make more informed strategic decisions.
In recent years, AI has turned from an
object of discussion by scientists, visionaries
and futurists into a significant economic factor.
Today, AI technologies are actively used in
civil and military spheres, and also affect in-
vestment flows. For example, Nvidia devel-
oped Turing and Ampere graphics processors
(GPUs), which are optimized for machine and
deep learning tasks. Thanks to this it has be-
come a leader in the field of processors for AI
and its capitalization tripled in 1 year (ex-
ceeded $3 trillion in summer 2024).
Despite the fact that the essence of the AI
concept remains the subject of debates, in 2023
there was an increase in the number of Fortune
500 companies mentioning AI in their earnings
reports compared to 2022. The number of such
companies increased from 266 to 394 (that is,
by 1.5 times), which ultimately made up almost
80% of the entire list (Artificial Intelligence In-
dex Report, 2024, p. 277).
Leading international consulting compa-
nies give very optimistic forecasts. Accor-
ding to the calculations of McKinsey, only
"generative AI could add the equivalent of
$2.6 trillion to $4.4 trillion annually" (McKin-
sey, web).
However, previous studies have shown
that at the macro level there is no statistically
significant confirmation of the positive impact
of digitalization (including Industry 4.0 and
AI) on the industry and the economy as a who-
le (Vyshnevskyi et al., 2020; Vyshnevskyi,
Amosha, Liashenko, 2019). Largely, this issue
has remained relevant. Thus, the “2024 Artifi-
cial Intelligence Index Report” notes that “over
the last five years, the growing integration of
AI into the economy has sparked hopes of
boosted productivity. However, finding relia-
ble data confirming AI’s impact on productiv-
ity has been difficult because AI integration has
historically been low" (Artificial Intelligence
Index Report, 2024, p. 272).
Analysis of the latest research
The problems of industrial development,
smart industry and Industry 4.0 technologies
are constantly receiving great attention from
the scientific and expert community. This is ev-
idenced by a significant number of scientific
and analytical publications.
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 7
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According to many researchers, the role
of AI is not just growing, but has already
achieved a fundamental impact. Thus, in the
work (Zhao et al., 2024) when analyzing the
factors influencing the level of development of
renewable energy, AI and GDP, direct foreign
investments, trade volume, population and in-
dustrial development were put on one level. So
that, AI is considered as a significant macroe-
conomic factor.
Nobel laureates in economics A. Ba-
nerjee and E. Duflo note that "unfortunately,
notwithstanding the grandiose talk about singu-
larities, the bulk of R&D resources these days
is directed toward machine learning and other
big data methods designed to automate existing
tasks, rather than the invention of new products
that would create new roles for workers, and
hence new jobs" (Banerjee and Duflo, p. 233).
Therefore, the situation when "excessive auto-
mation reduces GDP instead of contributing to
it" (Banerjee and Duflo, p. 232) is quite natural.
The problem of reconciling the risks and
opportunities of using AI remains relevant.
“Modernizing product liability rules for AI re-
quires bridging the gap between the abstract
risks that are more prominent in AI and the
concrete legal requirements and definitions that
should apply. In product liability law, estab-
lishing a clear concept of AI defects becomes
essential in assessing the responsibility of AI
manufacturers and operators for any harm or
injury caused by these systems. However, de-
termining liability in AI-related defects can be
complex due to the involvement of multiple
stakeholders. Assigning responsibility be-
comes particularly challenging when defects
arise from various sources, such as flawed
training data, algorithmic biases, or inadequate
system design.” (Buiten, 2024, p. 268) How-
ever, when these negative consequences might
occur, there may no longer be an individual or
legal entity that developed, launched or oper-
ated it.
Investigators of “The interlocks between
smart product platforming (SPP) powered by
Artificial Intelligence (AI) and Generative AI,
big data analytics, and machine learning”
(Akhtar et al., 2024, p. 1) conclude that “the
use of SPP with inherited flexibility and ad-
vanced technology – such as AI, GAI, big data
analytics, and machine learning – plays a major
role in effective and creative product design,
consequently facilitating the related manufac-
turing processes” (Akhtar et al, 2024, p. 9).
However, the assessment of the real economic
effect remains outside this study.
Baldwin and Okubo are analyzing the re-
lationship between remote work and the use of
AI, and justify the scenario according to which
“the workers who retain their jobs will be both
teleworking more and using more AI, but the
number of workers falls as AI raises the
productivity of remaining workers. But ... oc-
cupations that are the «most teleworkable are
also those that are most susceptible to automa-
tion»” (Baldwin and Okubo, 2023, p. 1550).
They confirm the growing role of AI in ena-
bling remote work and reformatting the labor
market.
“Like the steam engine, electricity, com-
puters, or the internet, which have greatly
transformed both the economy and society at
large, AI is not bound to a single specific appli-
cation but is foundational, opening up wide ar-
rays of uses.” (Davidson, 2024, p. 13). Thus,
AI itself is considered as a key element of the
new industrial revolution, becoming its sym-
bol.
Based on the analysis of previous stud-
ies, the problem of the prospects and limita-
tions of using AI as the core of the new indus-
trial revolution remains unresolved. Therefore,
the purpose of the article is to define prospects
and limitations of artificial intelligence as a
core of the new industrial revolution.
Definition of artificial intelligence
The problem of defining the concept of
"artificial intelligence", despite its wide appli-
cation, remains relevant to this day. Thus,
S. Davidson in his recent work notes that "The
term 'Artificial Intelligence' (AI) – introduced
by John McCarthy in 1956 – is surprisingly
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
8 ISSN 1562-109X Econ. promisl.
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poorly defined" (Davidson, 2024, p. 1). There
are dozens of different definitions of the AI
concept. For instance, S. Russell and P. Norvig
highlight “eight definitions of AI, laid out
along two dimensions. The definitions … are
concerned with thought processes and rea-
soning, … address behavior. The definitions …
measure success in terms of fidelity to human
performance” (Russell and Norvig, 2010,
p. 1-2). But «AI – smart machines – only
‘know’ data that can be programmed into,
or read, by a machine» (Davidson, 2024, р. 7).
In this definition, AI intelligence is limited
by the input data, but AI can be programmed
to independently search for and extract new
data.
Н. Sheikh, С. Prins and E. Schrijvers
highlight well the following problem. “It is not
surprising that AI is so difficult to define
clearly. It is, after all, an imitation or simulation
of something we do not yet fully understand
ourselves: human intelligence`” (Sheikh, Prins,
Schrijvers, 2023, p. 16). They say that “а com-
mon definition of AI is that it is a technology
that enables machines to imitate various com-
plex human skills” (Sheikh, Prins, Schrijvers,
2023, p. 15) and give the broadest and the
strictest definitions. “In its broadest definition,
AI is equated with algorithms.” (Sheikh, Prins,
Schrijvers, 2023, p. 15). It is difficult to agree
with this approach to defining AI, since it is im-
possible to move from it to a definition of ordi-
nary (human) intelligence. But “in its strictest
definition, AI stands for the imitation by com-
puters of the intelligence inherent in humans.
(Sheikh, Prins, Schrijvers, 2023, p. 15). This
definition looks more relevant to define the in-
telligence.
Given the existence of many approaches
to defining AI, we try to use the most general
and consistent one, which involves a combina-
tion of the concepts of "artificial" and "intelli-
gence". "Artificial" is defined as "made by peo-
ple, often as a copy of something natural”1. "In-
telligence" is defined as “the ability to learn,
understand, and make judgments or have opin-
ions that are based on reason”2. So that, AI is
intelligence made by people. The carrier of or-
dinary intelligence is a human. The carrier of
AI is usually something artificial. Based on
this, AI exists in an artificial body brain.
Let us try to compare “ordinary intelli-
gence” and “AI” using approach, which was of-
fer by Aristotele more than two thousand years
ago and includes consideration of the object of
study from four positions: material cause, for-
mal cause, efficient cause, final cause (tab. 1).
Table 1 – Comparison of Human and Artificial Intelligence from the Perspective of
Aristotle's Four Causes
Cause Human intelligence Artificial intelligence
Material cause Brain Hardware (physical components of a computer sys-
tem) and software.
Formal cause Structure of the mind Design or architecture (for instance, sets of data and
algorithms), which unites hardware, software and
datasets into a uniform system (AI system).
Efficient cause Society, genetics,
environment
Human intelligence (for instance, engineers), envi-
ronment (datasets).
Final cause Set goals and solve
tasks/problems
Solve tasks/problems (very often, more efficiently
and accurately than humans).
Source: made by authors.
1 https://dictionary.cambridge.org/dictionary/english/artificial
2 https://dictionary.cambridge.org/dictionary/english/intelligence?q=Intelligence
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
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Analyzing the results presented in Tab-
le 1, it becomes obvious that the simple define-
tion "artificial intelligence is intelligence cre-
ated by humans" is weakly functional. Because
it displays only one cause (efficient cause),
ignoring the essence of this phenomenon.
As a rule, before the AI has been trained
on a large dataset, it cannot yet be considered
as corresponding to modern concepts of AI.
Because here we can notice an analogy with the
religious and philosophical position of deism,
according to which God created man and the
universe, and then withdrew and no longer in-
terferes. Similarly, an engineer creates hard-
ware, a programmer creates special software,
launches "training" on datasets, then they re-
move themselves from influencing the pro-
cesses that occur inside the AI. And only after
training does the pre-AI become AI. The logic
of this process is shown in Figure 1.
Hardware Software Datasets AI
t
(time for training)
Figure 1 – logic of AI formation
Source: made by authors.
The growing role of AI in the economic
life of society necessitates its comprehensive
reflection in the sphere of legislation. So that,
for instance, regulation 2024/1689 was adopted
in the EU (13.06.2024). It harmonizes rules on
artificial intelligence and amends regulations
related to AI in EU (European Parliament,
2024). A year earlier Executive Order “Safe,
Secure, and Trustworthy Development and Use
of Artificial Intelligence” was adopted in the
USA (The president of the United States,
2023). Both these documents contain defini-
tions of AI that are close to each other (tab. 2).
The significant similarity of definitions
on different sides of the Atlantic Ocean indi-
cates the formation of a certain common posi-
tion regarding what is meant by artificial intel-
ligence. Based on analysis of Figure 1 and Ta-
ble 2 we can propose the following AI logic as
a system (fig. 2).
Table 2 – Definition of AI in official papers of USA and EU
Country Definition
Key elements
as part of system
1 2 3
EU “ ‘AI system’ means a machine-based system that is designed to
operate with varying levels of autonomy and that may exhibit
adaptiveness after deployment, and that, for explicit or implicit
objectives, infers, from the input it receives, how to generate
outputs such as predictions, content, recommendations, or deci-
sions that can influence physical or virtual environments” (Euro-
pean Parliament, 2024);
Note: “A key characteristic of AI systems is their capability to
infer” (European Parliament, 2024).
Physical basis: ma-
chine-based system;
Input: data;
Output: predictions,
content, recommen-
dations, or decisions
that can influence
physical or virtual
environments that
are relevant to exter-
nal objectives.
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The end of the table 2
1 2 3
USA “The term ‘‘artificial intelligence’’ or ‘‘AI’’ has the meaning
set forth in 15 U.S.C. 9401(3): a machine-based system that
can, for a given set of human-defined objectives, make pre-
dictions, recommendations, or decisions influencing real or
virtual environments. Artificial intelligence systems use ma-
chine- and human-based inputs to perceive real and virtual
environments; abstract such perceptions into models through
analysis in an automated manner; and use model inference to
formulate options for information or action.” (The president
of the United States, 2023, p.3).
“The term ‘‘AI system’’ means any data system, software,
hardware, application, tool, or utility that operates in whole or
in part using AI.” (The president of the United States, 2023,
p. 3)
Physical basis: ma-
chine-based system;
Input: machine- and
human-based data;
Output: predictions,
recommendations,
or decisions influ-
encing real or vir-
tual environments
that are relevant to a
given set of human-
defined objectives.
Source: made by authors.
Hardware Software
Datasets
Training
Input OutputPhysical basis
Physical or virtual environments
Human behavior
Human knowledge
Nature * * *
Information
Smart controls
Predictions
Information
Recommendations
Decisions
Machine-based system
Figure 2 – AI logic as a system
Source: made by authors.
Within the framework of this study, AI is
understood as a “machine-based system, which
based on human-defined objectives uses ma-
chine- and human-based data to solve tasks in-
fluencing real or virtual environments”.
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It is also important to note that there are
two main types of AI: narrow or weak AI and
artificial general intelligence or AGI (other
names include ‘strong AI’ and ‘full AI’). Some
examples of narrow AI: (1) virtual assistants
(Siri, Alexa, etc.), (2) chatbots, (3) image
recognition, (4) speech recognition, (5) recom-
mendation systems for selection of movies,
products, songs etc., (6) self-driving cars, (7)
medical diagnostics, (8) fraud detection, (9)
language translation; (10) manufacturing ro-
bots.
AGI has the cognitive ability to solve
complex problems in various areas, just like
humans. But AGI is only a concept, which can
become reality in future. So that examples do
not exist yet.
It is necessary to take into account that
external environments that form human intelli-
gence and artificial intelligence are two differ-
ent environments that only partially overlap
(fig. 2). At the same time, the learning area of
artificial intelligence is constantly expanding,
which can lead to the neutralization of the fac-
tor of implicit and distributed knowledge (an
important element in the Austrian school of
economic theory).
Environment for
AI training
Environment for
human training
Environment for
AI training
Environment for
human training
(A) For AI (B) For AGI
Figure 2 – Environment for AI and AGI
Source: made by authors.
Having defined the AI concept, we can
move on to considering its role in the eco-
nomy.
AI in the economy
According to various expert organiza-
tions, the further development and implemen-
tation of AI technologies has a great economic
potential, which is confirmed by a number of
empirical observations. For example, it is
claimed that "Copilot users completed tasks in
26-73% less time than other users without ac-
cess to AI" (Artificial Intelligence Index
Report, 2024, p. 272). And “a Harvard Busi-
ness School study found that consultants with
access to GPT-4 increased their productivity in
the selection of consulting tasks by 12.2%,
speed by 25.1%, and quality by 40.0%. com-
pared to a control group without AI access.
Likewise, a study by the National Bureau of
Economic Research showed that call center
agents who use AI process 14.2% more calls
per hour than those who do not use AI" (Artifi-
cial Intelligence Index Report, 2024, p. 273).
As a result, the authors of this report come to
the opinion that access to AI reduces the
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
12 ISSN 1562-109X Econ. promisl.
2024, № 3 (107)
productivity gap between low- and high-skilled
workers (Artificial Intelligence Index Report,
2024, p. 275). On average, this should increase
labor productivity.
Based on the above, it is natural that the
interest in the shares of companies whose ac-
tivities are related to the use of AI is increasing.
The most convincing example is the growth of
Nvidia's capitalization. If on June 13, 2023, the
capitalization of Nvidia reached 1 trillion US
dollars (Artificial Intelligence Index Report,
2024, p. 220), then at the beginning of June
2024 its capitalization already reached about 3
billion USD. Thus, the capitalization tripled in
a year and Nvidia secured the 3rd place in the
world (in terms of capitalization), second only
to Microsoft and Apple, whose activities are
also indirectly related to the development and
use of AI.
At the same time, indicators of the inten-
sity of AI implementation, which are related to
the labor market and the attraction of corporate
investments, are deteriorating.
For example, in the USA in 2023, the
number of vacancies related to AI decreased to
1.6% compared to 2% (of the total number) in
2022. A similar trend is observed in most other
countries. At the same time, the reduction oc-
curred in almost all sectors (waste management
and administrative support services; retail
trade; transportation and warehousing; real
estate, rental and leasing; wholesale trade;
mining, quarrying, oil and gas production;
agriculture, forestry agriculture and hunting;
management of companies and enterprises;
manufacturing; finance and insurance; pro-
fessional, scientific and technical services; in-
formation) with the exception of two (educa-
tional services, public administration) (Artifi-
cial Intelligence Index Report, 2024, p. 228).
Another important factor is the reduction
of global corporate investments in AI for two
years in a row (2022-2023) from 337.4 billion
USD in 2021 to 189.16 billion USD in 2023
(Artificial Intelligence Index Report, 2024,
p. 242).
Thus, it can be argued that a number of
contradictions has formed: (1) between busi-
ness behavior and the expectations of the ex-
pert community; (2) between the dynamics of
corporate investments and the dynamics of the
stock market. As a result, the situation that de-
veloped in the economy and the stock market
before the "dotcom crisis" of 2000 is largely re-
peated, which creates similar risks and oppor-
tunities.
It is not yet clear whether a crisis is going
to happen in the near future. In any case, the
full-scale implementation of AI will seriously
reformat the global economy. This allows us to
consider artificial superintelligence as a key
technology for the next phase of development
(tab. 3).
Table 3 – Growth mode and global economy
Growth
mode
Date began
to dominate
Doubling time of
global economy
(years)
Duration of
era (years)
Length of
era (years)
Average annual
growth rate of global
economy (%)
Hunting 2 000 000 B.C. 230 000 -2 000 000 1 995 300 0,0003
Farming 4700 B.C. 860 -4 700 6 430 0,0806
Science /
commerce
1730 A. D. 58 1 730 173 1,2023
Industry 1903 A.D. 15 1 903 127 4,7294
Superintelli-
gence?
2030 A. D.? ??? ??? ??? ???
Source: made by the authors based on (Aschenbrenner, 2024, p. 70).
–––––––––––––––––––––––––– Економіка промисловості Economy of Industry ––––––––––––––––––––––––––––––
ISSN 1562-109X Econ. promisl. 13
2024, № 3 (107)
Such a periodization seems highly debat-
able, but the main idea attracts attention. It is
worth noting that despite the acceleration of
global economic growth in recent centuries, its
slowdown has been observed in recent decades
(Vyshnevskyi et al., 2020).
To assess the universality of AI as a po-
tential core of the new industrial revolution, it
is necessary to analyze it from the standpoint of
all components of the reproduction process
(production, distribution, exchange and con-
sumption).
In the context of production, AI facili-
tates automation, optimization and customiza-
tion. AI-powered robots and machines allow to
reduce labor costs and improve product quality.
AI can analyze vast amounts of data to opti-
mize production processes, identify bottle-
necks, predict equipment failures and create
optimal plans for scheduled preventive main-
tenance. AI enables mass customization by
quickly adjusting production lines to meet spe-
cific consumer demands, such as 3D printing
based on unique specifications. It is also neces-
sary to pay attention to the expansion of the
range of goods and services, where AI is an in-
tegral part (smart homes, virtual assistants, au-
tonomous vehicles).
In the context of distribution, AI facili-
tates supply chain optimization (optimize lo-
gistics, ensuring the timely and cost-effective
delivery of goods), fraud prevention, optimiza-
tion and ensuring transparency of performance
results distribution between employees, ma-
nagement and owners (this helps to increase
staff motivation).
1 https://www.coca-colacompany.com/media-center/coca-cola-invites-digital-artists-to-create-real-magic-
using-new-ai-platform
2 https://tech.walmart.com/content/walmart-global-tech/en_us/flagship-conferences/ai-at-walmart/replay-
2024.html
3 https://www.riotinto.com/en/mn/about/innovation/smart-mining
4 https://corporate.exxonmobil.com/who-we-are/technology-and-collaborations/digital-technologies
5 https://www.ge.com/digital/blog/ai-accelerating-energy-transition-carbon-negative
6 An AI incident or hazard can be reported by one or more news articles covering the same event.
In the context of exchange, AI facilitates
an increase in its opportunities and speed (AI-
powered recommendation systems, e-com-
merce platforms, smart-contracts).
In the context of consumption, AI facili-
tates improving the quality and quantity of pur-
chases (AI-powered recommendation systems,
e-commerce platforms, AI-enabled goods and
services).
The universal nature of AI penetration
into the economy is confirmed at the level of
the world's leading companies that directly
have no connection with digital technology
sector (for instance, Coca-Cola1, Walmart2, Rio
Tinto3, ExxonMobil4 , General Electric5).
Given the specific nature of these companies'
activities, they use AI to solve a wide range of
problems related to predictive analytics, de-
mand forecasting, personalized marketing, cus-
tomer service (chatbots, virtual assistants), lo-
gistic optimization, price optimization, product
placement optimization, mining optimization,
fraud detection, predictive maintenance, wor-
ker monitoring, energy and water use optimiza-
tion.
A natural reaction to the introduction of
AI is an increase in incidents and hazards asso-
ciated with it (fig. 3). According to the OECD
AI Incidents Monitor, a sharp increase was rec-
orded in 20236.
Thus, it can be argued that a significant
part of the business and expert community
based on objective facts considers AI as a po-
tential core for the Fourth Industrial Revolu-
tion. This necessitates an analysis of prospects
and limitations AI use.
|
| id | nasplib_isofts_kiev_ua-123456789-199526 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1562-109Х |
| language | English |
| last_indexed | 2025-11-25T20:51:26Z |
| publishDate | 2024 |
| publisher | Інститут економіки промисловості НАН України |
| record_format | dspace |
| spelling | Vyshnevskyi, O.S. Anufriiev, M.Yu. Bozhyk, M.S. Gulchuk, T.O. 2024-10-14T14:32:43Z 2024-10-14T14:32:43Z 2024 Artificial intelligence as a core of the new industrial revolution: prospects and limitations / O.S. Vyshnevskyi, M.Yu. Anufriiev, M.S. Bozhyk, T.O. Gulchuk // Економіка промисловості. — 2024. — № 3 (107). — С. 5-21. — Бібліогр.: 24 назв. — англ. 1562-109Х DOI: http://doi.org/10.15407/econindustry2024.03.005 JEL: O4, O33, B53 https://nasplib.isofts.kiev.ua/handle/123456789/199526 004.8:338.45:338.246.83 The purpose of the article is to define prospects and limitations of artificial intelligence as a core of the new industrial revolution. The definition of the AI concept in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the AI concept. Based on the analysis and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) AI’s high energy consumption; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution. Метою статті є визначення перспектив та обмежень штучного інтелекту (ШІ) як ядра нової промислової революції. Трактування поняття ШІ в науковому співтоваристві залишається предметом гострих дискусій. Водночас у нормативно-правовій площині формується тенденція щодо його уніфікації. Виявлено перспективи ШІ на теоретичному рівні: відчуження неявних знань від індивіда (працівника та підприємця); оптимізація системи планування; перегляд дебатів про соціалістичний розрахунок; зменшення інформаційної асиметрії; на практичному: формування нових продуктів і ринків; підвищення продуктивності праці та капіталу; масове створення нових робочих місць; оптимізація бізнес-процесів; можливість швидкого зростання для малого бізнесу та стартапів. Обмеження, пов’язані з ШІ: тривале структурне безробіття; завищені очікування від ШІ і, як наслідок, можливе утворення спекулятивної бульбашки на світовому фондовому ринку; енергоємність ШІ; застаріла корпоративна культура та нормативне середовище, сформоване в епоху до ШІ. Подальше вдосконалення ШІ (включаючи перехід від ШІ до загального ШІ) і розширення його використання може здійснити значний внесок у вирішення проблем, пов’язаних з економічним розрахунком і мінімізацією інформаційної асиметрії, а отже, оптимізацією трансакційних витрат в економіці. ШІ, виступаючи локально корисним інструментом на рівні окремих підприємств та організацій, приводить до прискорення залучення коштів на фондовий ринок, що може спричинити утворення бульбашки на глобальному рівні. Якщо ця бульбашка лопне, то очікування щодо економічної ефективності штучного інтелекту будуть переглянуті, а деякі компанії, пов’язані зі штучним інтелектом, зазнають значного зниження маржі (можливо, збитків і банкрутства). Однак це, у свою чергу, започаткує наступний етап розвитку ШІ, прискорить його перехід від нинішньої вузької спеціалізації до створення повноцінного загального штучного інтелекту, який має більший потенціал для зміни економіки на всіх рівнях. У результаті ШІ стане ядром нової промислової революції. en Інститут економіки промисловості НАН України Економіка промисловості Макроекономічні та регіональні проблеми розвитку промисловості Artificial intelligence as a core of the new industrial revolution: prospects and limitations Штучний інтелект як ядро нової промислової революції: перспективи та обмеження Article published earlier |
| spellingShingle | Artificial intelligence as a core of the new industrial revolution: prospects and limitations Vyshnevskyi, O.S. Anufriiev, M.Yu. Bozhyk, M.S. Gulchuk, T.O. Макроекономічні та регіональні проблеми розвитку промисловості |
| title | Artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| title_alt | Штучний інтелект як ядро нової промислової революції: перспективи та обмеження |
| title_full | Artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| title_fullStr | Artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| title_full_unstemmed | Artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| title_short | Artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| title_sort | artificial intelligence as a core of the new industrial revolution: prospects and limitations |
| topic | Макроекономічні та регіональні проблеми розвитку промисловості |
| topic_facet | Макроекономічні та регіональні проблеми розвитку промисловості |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/199526 |
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