Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування
The article explores digital sovereignty for small nations in the AI era. Using General Economic Theory of Strategizing (GETS), it justifies aligning interests between governments and global platforms. Key AI factors (energy, power, big data, and algo-rithms) are analyzed, identifying data scarcity...
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| author | Vyshnevskyi, Oleksandr S. Bozhyk, Maryna S. Gulchuk, Taras O. |
| author_facet | Vyshnevskyi, Oleksandr S. Bozhyk, Maryna S. Gulchuk, Taras O. |
| author_institution_txt_mv | [
{
"author": "Oleksandr S. Vyshnevskyi",
"institution": "Institute of Industrial Economics of NAS of Ukraine"
},
{
"author": "Maryna S. Bozhyk",
"institution": "Institute of Industrial Economics of NAS of Ukraine"
},
{
"author": "Taras O. Gulchuk",
"institution": "Institute of Industrial Economics of NAS of Ukraine"
}
] |
| author_sort | Vyshnevskyi, Oleksandr S. |
| baseUrl_str | https://ojs.econindustry.org/index.php/ep/oai |
| collection | OJS |
| datestamp_date | 2026-06-15T09:49:11Z |
| description | The article explores digital sovereignty for small nations in the AI era. Using General Economic Theory of Strategizing (GETS), it justifies aligning interests between governments and global platforms. Key AI factors (energy, power, big data, and algo-rithms) are analyzed, identifying data scarcity as the primary challenge. A mechanism is proposed: trading market access for technology and investment. The study justifies regional digital alliances and local protectionism to pool resources. Scientific novelty lies in applying GETS to model state and platform-based TNCs interactions. This strategy shifts countries from pas-sive consumers to active competitors, enhancing sovereignty through proactive big data management. |
| doi_str_mv | 10.15407/econindustry2026.02.023 |
| first_indexed | 2026-06-16T01:00:25Z |
| format | Article |
| fulltext |
23ISSN 1562-109X. Економіка промисловості. 2026. № 1 (113)
ЕКОНОМІКА
ПРОМИСЛОВОСТІ
ECONOMY
OF INDUSTRY
МІЖНАРОДНІ, МАКРОЕКОНОМІЧНІ
ТА РЕГІОНАЛЬНІ ПРОБЛЕМИ
ПРОМИСЛОВОСТІ
INTERNATIONAL, MACROECONOMIC
AND REGIONAL PROBLEMS OF INDUSTRY
https://doi.org/10.15407/econindustry2026.02.023
UDC 338.2:004.8+33.011+330.34
JEL: L51, O33, O38
Oleksandr S. VYSHNEVSKYI, Doctor of Economic Sciences, senior researcher
Е-mail: vishnevskiy_O@nas.gov.ua; https://orcid.org/0000-0002-2375-6033
Maryna S. BOZHYK, postgraduate student
E-mail: bozhyk@nas.gov.ua; https://orcid.org/0009-0009-2976-6118
Taras O. GULCHUK, postgraduate student
E-mail: avto198413@ukr.net; https://orcid.org/0009-0008-4968-0605
Institute of Industrial Economics of NAS of Ukraine
2 Maria Kapnist Street, Kyiv, 03057, Ukraine
ENSURING SOVEREIGN AI FROM THE PERSPECTIVE
OF THE GENERAL ECONOMIC THEORY OF STRATEGIZING 1
The article explores digital sovereignty for small nations in the AI era. Using General Economic Theory of Strategizing (GETS),
it justifies aligning interests between governments and global platforms. Key AI factors (energy, power, big data, and algo-
rithms) are analyzed, identifying data scarcity as the primary challenge. A mechanism is proposed: trading market access for
technology and investment. The study justifies regional digital alliances and local protectionism to pool resources. Scientific
novelty lies in applying GETS to model state and platform-based TNCs interactions. This strategy shifts countries from pas-
sive consumers to active competitors, enhancing sovereignty through proactive big data management.
Keywords: General Economic Theory of Strategizing (GETS), AI, digital sovereignty, regional digital alliances, local
protectionism, platform-based TNCs.
C ite : Vyshnevskyi O. S., Bozhyk M. S., Gulchuk T. O. Ensuring sovereign AI from the perspective of the general
economic theory of strategizing. Економіка промисловості. 2026. № 2 (114). С. 23—35. https://doi.org/10.15407/
econindustry.2026.02.023
© Видавець ВД «Академперіодика» НАН України, 2026. Стаття опублікована на умовах відкритого доступу за
ліцензією CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
1
General description of the problem
and its connection with important
scientific or practical tasks
Artificial intelligence (AI), particularly large lan-
guage models (LLMs), is considered a key driver of
1 This article was prepared as part of the research projects
“The Influence of Artificial Intelligence on the Indus-
trial Economy of Ukraine” (State Registration No.
0125U002956) and “Comprehensive Scientific Study on
the Actualization of Ukraine’s Industrial Policy on the
Principles of Industry 4.0 and 5.0” (State Registration
No. 0125U003560).
economic growth and one of the major technolog-
ical innovations of the first half of the 21st century.
According to the McKinsey Global Institute
(2025) 2, generative AI could add between $2.6
trillion and $4.4 trillion to global GDP annually —
an amount equivalent to the combined economic
2 McKinsey Global Institute (2025). Superagency in the
workplace: Empowering people to unlock AI’s full po-
tential at work. 2025. https://www.mckinsey.com/capa-
bilities/mckinsey-digital/our-insights/superagency-in-
the-workplace-empowering-people-to-unlock-ais-full-
potential-at-work (Accessed 2 April 2026).
24 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
output of the United Kingdom and France. PwC
(2025) 3 forecasts that AI adoption could boost
global GDP by an additional 15 percentage points
by 2035. AI assistants such as GPT, Grok, Gemini,
and DeepSeek continue to expand their capabilities
in natural language processing, code generation,
data analysis, and business model development.
These tools are increasingly integrated into every-
day applications, ranging from virtual assistants to
autonomous systems. At the same time, humanoid
robots are becoming significantly more advanced,
largely due to progress in AI technologies.
The development of these technologies is a focus
not only for businesses but also for society, whose
interests are represented by national governments.
The issue is particularly acute for small economies
and countries with relatively small populations.
These nations have limited capacity to develop and
use AI technologies in line with their sovereign in-
terests. Therefore, the relevance of this study stems
from the need to identify strategic priorities for eco-
nomically and demographically small countries.
Analysis of recent
studies and publications
The growing role of artificial intelligence (AI) is
evident both in individual industries and in the
economy as a whole, affecting a wide range of is-
sues (Chika-Petegyrych, 2021). Recent studies in-
creasingly focus on computational power and en-
ergy supply, emphasizing that training models
such as GPT-4o requires clusters of tens of thou-
sands of GPUs and energy consumption compa-
rable to that of a medium-sized city.
Almost a decade ago, N. Srnicek (2017) 4 already
highlighted the problem of platform monopoliza-
tion and its contribution to rising inequality. If
small countries continue to rely heavily on foreign
AI services, they risk losing control over their data,
facing economic stagnation, and increasing their
geopolitical vulnerability – particularly in the
event of restricted access to critical technologies.
3 PwC (2025). AI adoption could boost global GDP by an
additional 15 percentage points by 2035. https://www.
pwc.com/gx/en/news-room/press-releases/2025/ai-
adoption-could-boost-global-gdp-by-an-additional-
15-percentage.html (Accessed 2 April 2026).
4 Srnicek, N. (2017). Platform capitalism. Cambridge, UK:
Polity Press. https://mudancatecnologicaedinamicacapi
talista.wordpress.com/wp-content/uploads/2019/02/
platform-capitalism.pdf (Accessed 4 April 2026).
Recent research 5 identifies three core cyber-
physical factors essential for AI development: (1)
energy consumption, (2) computational power,
and (3) large datasets. A fourth, more intellectual
factor can be added: (4) algorithms, which opti-
mize the combined use of computational resources
and data, including minimizing the time required
for model training and retraining.
The availability, cost, and environmental sustain-
ability of energy directly influence the scalability
and economic viability of AI development. Energy
demand is one of the main constraints on the size
and frequency of model retraining. The impor-
tance of computational power (compute) drives
the exponential growth in model performance in
line with scaling laws. As noted by Maslej et al. 6
“Model scale continues to grow rapidly—training
compute doubles every five months.” For compari-
son, in 2012 AlexNet required approximately 470
petaFLOP for training, while OpenAI’s GPT-4o
(2024) needed 38 billion petaFLOP – more than
80 million times greater 7. At the same time, the
energy efficiency of machine learning hardware
continues to improve by tens of percent annual-
ly 8, and the cost per unit of training is steadily de-
clining. However, the collection and effective use
of large datasets for AI training carry significant
risks of data exhaustion between 2026 and 2032
(with an 80 % probability) 9.
Pekny et al. (2026) provide a detailed analysis of
the interdependence between artificial intelligence
development and energy infrastructure. Wide-
spread AI adoption creates additional strain on
power grids and electricity generation. For in-
stance, data centers alone could account for up to
12 % of total electricity consumption in the United
States by 2028. The authors argue that transition-
ing to nuclear energy, particularly through small
modular reactors (SMRs), represents the optimal
solution for powering data centers. SMRs are eas-
ier to deploy, reduce investment risks, and can
supply both electricity and heat. Replacing fossil
fuels solely with renewable sources (wind and so-
lar) is considered extremely challenging due to
5 Maslej N. et al. (2025). Artificial Intelligence Index Report
2025. Stanford Institute for Human-Centered Artificial
Intelligence. https://hai.stanford.edu/assets/files/hai_ai_
index_report_2025.pdf (Accessed 4 April 2026).
6 Ibid., 4.
7 Ibid., 57.
8 Ibid., 71.
9 Ibid., 60.
25ISSN 1562-109X. Економіка промисловості. 2026. № 2 (114)
Ensuring sovereign ai from the perspective of the general economic theory of strategizing
the enormous scale of construction and capital
investment required. The study also emphasizes
that AI itself can act as a catalyst for change by ac-
celerating the energy transition — for example, by
optimizing reactor design, managing plasma in
fusion facilities, and improving resource alloca-
tion in energy networks.
The issue of digital sovereignty in the field of AI
is thoroughly examined by Krishnamurthy (2026).
The author concludes that in the current geopoliti-
cal climate, where leaders of major powers openly
disregard international law, other countries under-
standably perceive technological dependence as a
critical vulnerability. While achieving AI autono-
my requires investments of hundreds of billions of
dollars, severing economic ties simultaneously re-
moves material incentives for maintaining stability
and preventing conflicts (for example, if the Unit-
ed States and China no longer depend on Taiwan-
ese semiconductors). Instead of building “digital
fortresses”, Krishnamurthy recommends that
states focus on developing international legal
frameworks capable of distinguishing legitimate
technological influence from the illegal “weapon-
ization of interdependence,” while ensuring com-
pliance among partners. However, this approach
has a significant limitation, as it does not ade-
quately account for differences in economic and
military-political power among states.
The integration of AI and Big Data has funda-
mentally transformed business strategies, shifting
them from retrospective analysis to predictive un-
derstanding and real-time response. The success of
implementation depends not only on computa-
tional power but also on data quality, governance,
and adherence to ethical standards (transparency
and absence of bias) (Maddipatla, 2026). A strong
synergistic effect emerges from combining Big
Data with AI. Bajwa et al. (2025) demonstrate that
the integration of AI and Big Data forms the foun-
dation of Industry 4.0, enabling the transition from
traditional automation to intelligent, adaptive, and
predictive systems. Experimental evidence con-
firms that integrated AI and Big Data systems sig-
nificantly outperform the use of these technologies
separately in terms of prediction accuracy and op-
erational efficiency. Consequently, the strategic in-
tegration of AI and Big Data can substantially re-
duce market uncertainty by enhancing predictive
modeling capabilities and implementing proactive
risk management strategies (Ge, 2026).
Artificial intelligence (AI), the Internet of
Things (IoT), and Big Data are recognized not
merely as supporting tools, but as primary drivers
of innovation and economic growth (Ismaila and
Beneke, 2026).
The institutional foundations of AI develop-
ment, legal liability regimes for defective AI sys-
tems, and changes in economic calculation under
the influence of big data directly affect the capacity
of states to ensure digital sovereignty (Davidson,
2024; Buiten, 2024; Lambert and Fegley, 2023).
This is particularly important for small countries,
whose limited control over data, platforms, and
regulatory instruments increases their dependence
on external technological actors.
Formulation of the article’s aim
Thus, the unresolved and highly relevant scientific
problem is the identification of effective ways to
ensure digital sovereignty in the context of using
AI assistants as a factor in maintaining competi-
tiveness and an adequate level of economic secu-
rity. In light of this problem, the aim of this article
is to substantiate the possibilities for ensuring dig-
ital sovereignty and competitiveness of small
countries from the perspective of the general theo-
ry of economic strategizing.
Presentation of the main
research material.
The theoretical foundation of this study is the
General Economic Theory of Strategizing
(Vyshnevskyi, 2018; 2021). This theory emphasiz-
es the primacy of strategizing in economic activity
and requires the clear identification of actors, their
main goals, and the means to achieve them in a
coordinated manner. In the present research, the
key actors are national governments and global
platform companies.
All countries can be divided into three condi-
tional groups. The first group (G2) includes the
United States and China. The second group (G20)
comprises the other 20 largest economies in the
world following the G2. The final group, referred
to as NG200, includes all remaining countries that
do not belong to either G2 or G20.
A comparison of the presence of global platform
companies across these groups reveals a clear pat-
tern: the majority of truly global companies that
generate or collect massive amounts of data are
concentrated in the G2 countries (the United
26 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
States and China). The G20 group also contains a
significant number of companies that aspire to a
global scale, although most of them operate pri-
marily at the regional level. In contrast, such com-
panies are relatively rare in the NG200 group, de-
spite its large number of countries (table 1).
From the perspective of governments, the fun-
damental and self-evident goal is to ensure digital
sovereignty and national security. In contrast, the
primary interest of platform companies is profit
maximization. These two sets of goals significant-
ly influence each other. On the one hand, the ab-
sence of digital sovereignty can lead to critical ex-
ternal influence over the country’s sociocultural
and electoral sphere. This, in turn, may reshape
public opinion and economic rules in favor of for-
eign capital associated with the provision of artifi-
cial intelligence services. On the other hand, the
loss of competitiveness in high-technology sec-
tors – which are now closely linked to AI — re-
duces the state’s economic capacity, increases the
risk of economic crisis, lowers government ap-
proval ratings among voters, and ultimately weak-
ens its ability to protect the interests of domestic
businesses in international economic relations.
Table 1. Global Platform Companies Generating or Collecting Big Data
Level of Countries Macroeconomic Characteristics Global Platform Companies
Generating/Collecting Big Data
G2 (economic scale: 1) GDP (PPP) share of global GDP:
• Total: ≈ 33,7 %
• Average per country: ≈ 16,9 % GDP (current USD)
share of global GDP:
• Total: ≈ 42,3 %
• Average per country: 21,4 %
Alphabet Inc. (Google); Meta
Platforms Inc. (Facebook, Instagram,
WhatsApp); Amazon.com Inc.;
Alibaba Group; Tencent Holdings;
ByteDance Ltd. (TikTok); Baidu Inc.;
JD.com.
G20 (excluding G2)
(economic scale:
≈10–1)
GDP (PPP) share of global GDP:
• Total: ≈ 43 %
• Average per country: 2,2 % GDP (current USD)
share of global GDP:
• Total: ≈ 39,6 %
• Average per country: 2 %
SAP SE, Shopify Inc., Rakuten
Group, Reliance Jio, Samsung
Electronics, Booking.com, Vodafone
Group, Yandex, Telegram.
NG200 (countries
outside G2 and G20)
(economic scale:
<10–2)
GDP (PPP) share of global GDP:
• Total: ≈ 23,3 %
• Average per country: 0,1 % GDP (current USD)
share of global GDP:
• Total: ≈ 19,3 %
• Average per country: 0,1 %
Bolt, Spotify (Sweden)
Source: developed by the authors based on the logic presented in (Katoikos (2016, September 14). G20 – Between G2 and
G200. https://katoikos.world/editorials-op-eds/g20-between-g2-and-g200.html (Accessed 2 April 2026) and statistical
data derived from World Bank indicators (World Bank. (2024). GDP, PPP (current international $). https://data.world-
bank.org/indicator/NY.GDP.MKTP.PP.CD (Accessed 2 April 2026); World Bank. (2024). GDP (current US$). https://
data.worldbank.org/indicator/NY.GDP.MKTP.CD (Accessed 4 April 2026)).
To develop coherent policy directions in the field
of AI, it is necessary to identify the critical factors
that determine its development.
Four Critical Factors
in the Development
of AI Technologies
Analysis of previous studies shows that the devel-
opment of artificial intelligence depends on four
critical factors: (1) energy generation, (2) compu-
tational power, (3) training datasets, and (4) data
processing algorithms. Computational capacity
can be scaled through investments in hardware
such as NVIDIA GPUs or Google TPUs, while en-
ergy is supplied through traditional and renewable
sources. Plans to relocate these two factors — en-
ergy generation and data centers — into space are
already under discussion. However, access to suf-
ficiently large volumes of high-quality data re-
mains the scarcest resource and a key determinant
of competitiveness (table 2).
Big data generated by social networks, search en-
gines, Internet of Things (IoT) devices, and user
interactions have become a strategic asset, often
compared to oil in the industrial era. At the same
27ISSN 1562-109X. Економіка промисловості. 2026. № 2 (114)
Ensuring sovereign ai from the perspective of the general economic theory of strategizing
time, data rapidly become obsolete, making con-
tinuous updating essential. Thus, ensuring access
to up-to-date data is a critically important factor.
Large economies of the G2 group, such as the
United States (with platforms Google, Meta, and
X) and China (with Baidu, WeChat, and Tencent),
enjoy a significant advantage due to their ecosys-
tems that collect petabytes of data daily. Small
countries, including Ukraine, relative to the Unit-
ed States or China, risk falling into digital depen-
dence if they do not purposefully develop their
own national platforms. This dependence could
later threaten their economic sovereignty. If a sig-
nificant portion of professional activities becomes
critically dependent on foreign AI assistants, any
disruption in access (blockage, censorship, or tariff
increases) could result in catastrophic economic
losses. These risks are further amplified by ongo-
ing deglobalization and rising protectionism.
A comparison of the availability of AI develop-
ment factors on domestic and international mar-
kets (table 3) clearly shows the more limited oppor-
tunities available to countries outside the G20. The
analysis indicates that economically and demo-
graphically small countries have the least capacity
to stimulate AI development either domestically or
through imports. The most problematic area is ac-
cess to large volumes of data. All dominant AI as-
Table 2. Factors of AI Development and Mechanisms for Ensuring Technological Sovereignty for Small Economies
Factor of AI
Development
Current Problems and Potential Risks for Small Economies
(NG200) Pathways to Sovereignty
(1) Energy
consumption
1. Growth in data center energy demand will outpace
the introduction of new generation capacity
1. Creation of additional capacity. 2.
Implementation of “economic” operating
modes for data centers to smooth daily
and seasonal fluctuations (e.g., lower
prices for AI queries at night)
(2) Computational
power
2.1 Computational capacity must be adequate for the
volume of data. Conceptually, a large country with an
economy and population 100 times larger than a small
one can process 100 billion units of global big data. A
small country would require comparable computing
power to achieve similar results
2.1 Formation of a shared pool of
computational resources among groups
of small countries
(3) Large datasets 3.1 Absence of global social networks and search engines 3.1 Purchase of access to big data. 3.2
Obtaining access to global big data in
exchange for granting global companies
regulated access to the national market
(4) Algorithms
for processing big
data
4.1. Lack of laboratories conducting research on cutting-
edge algorithms for big data processing
4.1. (a) Development of national schools
for studying algorithms and their
application to big data. (b) Acquisition
of algorithms together with AI assistants
Source: generated by the authors.
sistants rely on national platforms as their primary
source of data. Countries without developed social
networks — that is, networks with a large number
of active users — cannot effectively compete in AI
training. Consequently, the greatest deficit for them
lies in general-purpose big data, which cannot real-
istically be generated domestically due to their rela-
tively small population, which serves as the ulti-
mate source of raw primary data.
This situation creates a practical objective for
governments of small countries: to actively form
and maintain access to large volumes of data. To
achieve this goal, it is necessary to examine what
such governments can offer in exchange to global
platform companies.
Since these companies are primarily motivated
by profit derived from data, it is worth examining
their business models in greater detail. The busi-
ness models of companies such as OpenAI, xAI,
Google, and DeepSeek combine limited free access
with premium subscriptions, creating a cycle of
expanded reproduction.
These companies invest substantial resources in
developing and launching free versions of their AI
systems to attract a broad audience and demon-
strate the product’s value. These investments cover
infrastructure, model training, and marketing, lay-
ing the foundation for future growth. Free access
28 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
Table 3. Availability of AI Development Resources: Domestic Production versus Imports
Factor of AI
Development
Availability through Domestic Creation Availability through Imports
G2 G20 NG200 G2 G20 NG200
1. Energy 5 5 3 5 4 3
2. Computational power 5 3 3 3 3 3
3. Large datasets 5 4 1 2 2 2
4. Algorithms 5 4 3 3 3 3
Total 20 16 10 13 12 11
Note: Scale of availability: 1 — very difficult to obtain; 2 — difficult; 3 — moderately difficult; 4 — relatively easy; 5 —
very easy.
Source: created by the authors.
enables rapid user acquisition, which is essential for
data collection and system improvement. Through
interactions with the free version, companies collect
vast amounts of data on user queries, preferences,
and behavior. This data is anonymized and analyzed
to identify patterns and refine algorithms. The con-
tinuous influx of information ensures ongoing en-
richment of the knowledge base needed for further
model training. As a result, the free version of the
AI assistant is continuously improved. Based on the
collected data, companies update and optimize the
free version by adding new features, increasing ac-
curacy, and improving speed. These enhancements
make the product more attractive, stimulating fur-
ther growth in the user base. Satisfied users of the
free version are gradually encouraged to upgrade to
premium subscriptions, which offer unlimited ac-
cess, priority processing, and additional tools. This
model generates stable revenue that offsets initial
investments. The paid version strengthens user loy-
alty and creates barriers for competitors. Profits
from premium subscriptions, along with additional
sources such as API access and enterprise solutions,
allow companies to recoup costs and generate posi-
tive cash flow. This financial stability supports fur-
ther reinvestment. A portion of the profit is directed
toward research and development (R&D), includ-
ing the creation of new AI models and expansion of
infrastructure. Scaling involves globalization of the
product, integration with other services, and adap-
tation to new markets.
As soon as companies begin large-scale commu-
nication with users and receive information from
them — which is later used to generate respons-
es — they start influencing not only the economic
but also the institutional and political sphere. At
the same time, the activities of global platform
companies are primarily regulated by the rules es-
tablished by the governments of their countries of
origin (typically G2 countries).
Thus, the critical area where the interests of gov-
ernments and platform companies clearly inter-
sect is the “collection of user data.” Satisfying local
demand often requires an understanding of local
context. For example, a model trained primarily
on users from Brazil may not provide relevant re-
sponses to users in Japan. It is also important to
note that the very process of formulating a query
itself forms part of the data collection process.
The situation described above is significantly in-
fluenced by the growing trend of deglobalization,
which manifests itself in efforts to achieve politico-
economic autarky or the creation of zones of direct
control (for example, U.S. policy toward Venezuela
or Greenland). Barriers are increasing not only in
trade (such as tariffs introduced by the United
States in 2025) but also in the use and access to
data. For instance, the European Union’s General
Data Protection Regulation (GDPR) has been in
force since 2018. These restrictions apply not only
to data but also to chips (for example, U.S. export
controls on semiconductors).
For countries outside the G2 and G20 groups, a
policy of full digital protectionism aimed at creat-
ing a completely self-sufficient national AI ecosys-
tem appears unproductive in the long term. This
can be clearly illustrated by the example of Ukraine.
Opportunities for Aligning
the Goals of Governments
and Global Platform TNCs
Within the framework of the General Economic
Theory of Strategizing (GETS) developed by
O. Vyshnevskyi, the primacy of strategizing in eco-
29ISSN 1562-109X. Економіка промисловості. 2026. № 2 (114)
Ensuring sovereign ai from the perspective of the general economic theory of strategizing
nomic activity implies not only the clear identifi-
cation of actors, their goals, and the means to
achieve them, but also the mandatory alignment
of interests among all participants to ensure a sus-
tainable and mutually beneficial outcome. In the
context of ensuring digital sovereignty for eco-
nomically and demographically small countries
(the NG200 group), the key actors are, on the one
hand, the governments of these states, representing
national interests, and, on the other hand, global
platform transnational corporations (PTNCs) such
as OpenAI, xAI, Google (Alphabet Inc.), Meta Plat-
forms Inc., Alibaba Group, Tencent Holdings, and
others that dominate the generation, collection, and
capitalization of big data for AI development.
As shown in the comparison of goals and out-
comes presented in table 4, there exists both an
objective opportunity and a necessity for aligning
the interests of these actors. Governments of small
countries pursue interrelated goals focused on pre-
serving sovereignty and competitiveness, while
PTNCs are primarily oriented toward profit maxi-
mization and market expansion. Alignment is
achieved through an exchange mechanism —
«market access in return for technology and
data” — in which each party offers something valu-
able to the other and receives adequate “payment”
in the form of resources, support, or regulatory
preferences. Each goal is examined in detail below.
The first goal of the government is to ensure eco-
nomic and political sovereignty. This is achieved
through the protection of the national information
space via regulatory measures, including data local-
ization, mandatory storage of information on nation-
al servers, and restrictions on foreign influence over
social networks and search engines. The outcome is a
protected information environment free from exter-
nal manipulation in the sociocultural and electoral
spheres. The beneficiaries are the population (as the
source of electoral legitimacy) and local businesses
(as sources of taxes and innovation). In return (“pay-
ment”), the government receives electoral support
and public approval from the population, as well as
stable tax revenues from local businesses.
Without such protection, small countries risk
losing control over their information space. As
Table 4. Alignment of Goals and Outcomes between Governments of Small Countries and Global Platform TNCs
Actor Goal
Means of Achieving the Goal
Value Proposition
(Product) Beneficiary “Payment” from the Beneficiary
Government Ensuring
economic
and political
sovereignty
(capacity for
independent
strategic
planning).
Protection of the
national information
space.
1. General
Population. 2. Local
Business
1. Electoral support and public
approval.
2. Tax revenue from citizens and
local enterprises.
Government Ensuring
economic
security and
competitiveness
via access to
advanced AI
technologies.
Facilitating access to
modern AI assistants,
search engines, and
social networks.
1. General
Population
2. Local Business
1. Electoral support and public
approval.
2. Tax revenue.
Government Technology and
data acquisition
in exchange for
market access.
Provision of domestic
market access.
PTNCs Access to advanced AI assistants
for the local population and
business community.
PTNCs Profit
maximization and
market expansion.
Provision of access to
advanced AI assistants,
search engines, and
social networks.
1. General
Population
2. Local Business
3. Government
1. Subscription fees.
2. Big Data.
3. Regulatory frameworks ensuring
legal domestic market access.
Source: created by the authors.
30 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
Krishnamurthy (2026) demonstrates, this turns
technological dependence into a critical vulnera-
bility amid deglobalization and the “weaponiza-
tion of interdependence”. Alignment with PTNCs
in this area is not achieved through a complete
ban, but through conditional access: the govern-
ment is willing to grant regulated market entry
only if national rules on data localization and algo-
rithm transparency are observed. This allows
PTNCs to gain legal access to part of the market,
while the government maintains control and re-
ceives political support from citizens who value
protection from external influence.
The second goal of the government is to ensure
economic security and competitiveness by pro-
viding access to modern AI technologies. This is
achieved by organizing access for the population
and businesses to effective AI assistants, search
engines, and social networks (such as ChatGPT,
Grok, Gemini, DeepSeek, and others). The out-
come is a technologically equipped economy in
which AI is integrated into everyday processes –
from code generation and data analysis to busi-
ness model optimization and public administra-
tion (as seen in Ukraine’s Diia ecosystem). The
beneficiaries are again the population (improved
quality of life and access to education and health-
care services) and local businesses (higher pro-
ductivity, lower costs, and access to global mar-
kets). The “payment” from beneficiaries takes the
form of electoral support, public approval, and
increased tax revenues resulting from accelerat-
ed economic growth.
Alignment with PTNCs in this area is pragmatic.
The government does not aim for full isolation
(“digital fortresses”) but offers PTNCs access to the
national user market in exchange for guaranteed
service quality, partial localization of computing
resources, and knowledge transfer (for example,
through joint R&D programs or the establishment
of local data centers). This approach minimizes the
risks of disconnection from foreign AI services,
which, as noted earlier, could cause catastrophic
economic losses for small countries with limited
domestic resources.
The third goal of the government involves at-
tracting technologies and data in exchange for
regulated access to the domestic market. For
PTNCs, the outcome is legal and predictable entry
into the national market, represented by the popu-
lation and local businesses. The beneficiary in this
case is the PTNC. In return (“payment”), the gov-
ernment and its citizens gain access for the local
population and businesses to modern AI assistants
on competitive terms.
The goal of PTNCs is to generate monetary prof-
it and expand their sales markets. This is achieved
by providing access to modern AI assistants, search
engines, and social networks. The beneficiaries are
the population, local businesses, and the govern-
ment (as regulator). The “payment” from the ben-
eficiaries includes: (1) subscriptions and payments
for premium features; (2) big data on user behav-
ior, queries, and preferences (anonymized but
highly valuable for model fine-tuning); and (3)
regulatory policies that ensure legal market access
and protection from arbitrary restrictions.
Thus, the alignment mechanism is based on a
classic exchange. The government offers PTNCs
regulated and stable access to raw national data,
while PTNCs provide AI services in return. The
government can establish “rules of the game” —
such as mandatory localization of 30–50 % of data,
joint data centers, and a minimum level of tech-
nology transfer — and receive in exchange not
only access to global models but also the ability to
fine-tune its own sovereign AI systems using com-
bined datasets (national and global).
This approach avoids extremes: full protection-
ism (which is disadvantageous for small econo-
mies due to shortages of computing power and
data) or complete openness (which threatens sov-
ereignty). Instead, it forms a pragmatic “mixed
strategy” that combines local protectionism
(Vyshnevskyi, 2023) in key areas with internation-
al cooperation among small countries to create re-
gional digital alliances capable of negotiating with
PTNCs on more equal terms.
Ultimately, aligning goals transforms a potential
conflict into synergy: the government preserves
digital sovereignty and gains tools for competitive-
ness, while PTNCs expand their markets and data
base without the risk of regulatory conflicts. Ig-
noring such alignment, as evidenced by trends in
deglobalization (tariffs, GDPR, export controls on
chips), will lead to the marginalization of small
countries in the global AI economy. In contrast,
proactive strategizing based on GETS will enable
them to achieve sustainable development amid
global competition for energy, compute, data, and
algorithms. An example of such strategic interac-
tion can be examined through the case of Ukraine.
31ISSN 1562-109X. Економіка промисловості. 2026. № 2 (114)
Ensuring sovereign ai from the perspective of the general economic theory of strategizing
Interstate Collaboration
to Overcome the Challenges
of Small Open Economies:
The Case of Ukraine
Ukraine, as a typical representative of countries
outside the G20 group, faces the full range of chal-
lenges inherent to economically and demographi-
cally small nations. These challenges are signifi-
cantly exacerbated by the consequences of pro-
longed military conflict. On the one hand, there is
a critical dependence on global platforms: Google
search services hold nearly 90 % of the market
(Statcounter Global Stats, 2026a), and their inte-
gration with Gemini is driving rapid growth in the
latter’s share, which has already taken second place
after ChatGPT and continues to strengthen month
by month (Statcounter Global Stats, 2026b). This
creates a situation in which a significant portion of
professional, educational, and administrative pro-
cesses critically depends on foreign AI assistants.
Any restrictions on access — whether through dis-
connection, censorship, or tariff increases — pose
direct risks of substantial economic losses. On the
other hand, the destruction of energy infrastruc-
ture severely limits both industrial recovery and
the construction or scaling of data centers needed
for local training and operation of AI models. At
the same time, the state, through the development
of the Diia ecosystem, has become the central plat-
form for activity, consolidating vast arrays of veri-
fied citizen data. This already represents a unique
national asset, although it is currently used pri-
marily for local administrative purposes.
At first glance, the most obvious solution to the
problem of digital sovereignty appears to be full
integration into the European digital market as
part of Ukraine’s European integration process. A
key instrument in this approach could be a pilot
project to create sovereign AI compliant with
GDPR standards, which, if successful, could later
be scaled across the entire European Union. How-
ever, this approach has fundamental limitations.
The EU is a union of states developing at signifi-
cantly different speeds: it includes powerful G20
economies (Germany, France, Italy) that possess
their own global or regional platforms and sub-
stantial computing resources, as well as other small
NG200 countries. As a result, the interests of the
latter are often diluted in pan-European initiatives
dominated by larger players. Moreover, even with-
in the GDPR framework, small countries risk re-
maining in the position of “data suppliers” without
real control over algorithms and computational
capacity. This contradicts the principles of the
GETS), which emphasize the need to align goals
and means for independent strategizing.
Therefore, a more promising alternative is a com-
bined strategy that integrates elements of local pro-
tectionism (Vyshnevskyi, 2023) with active inter-
state collaboration among small EU member states
and associated countries. In this context, local pro-
tectionism does not imply isolation (“digital for-
tresses”) but involves the use of regulatory tools to
protect the national information space. These in-
clude mandatory localization of at least 40–60 % of
data on national territory, requirements for PTNCs
to establish local data centers as a condition of mar-
ket access, and tax and administrative incentives for
investment in national infrastructure. At the same
time, cooperation with other small countries is de-
veloped to overcome the key limitations identified
in table 3 — namely, the low availability of big data
and computational power within a single country.
Given geographic, economic, and technological
proximity, regional digital alliances can be built in
stages. At the first stage, it would be advisable to cre-
ate a “core” consisting of Ukraine, Poland, Estonia,
Latvia, and Lithuania. These countries already pos-
sess mature national e-government platforms: Diia
(Ukraine), mObywatel (Poland), mRiik (Estonia),
Elektroniniai valdžios vartai (Lithuania), and Valsts
pārvaldes pakalpojumu portāls (Latvia). Each of
these platforms accumulates verified citizen data,
creating a foundation for forming a common re-
gional pool of big data large enough to enable com-
petitive fine-tuning of AI models. At the second
stage, the alliance could expand to include Czechia,
Slovakia, Austria, Hungary, Romania, Bulgaria, Slo-
venia, and Croatia, increasing the total population
coverage to 120–140 million people and generating
a critical mass for producing up-to-date data.
Within such an alliance, it becomes possible to
address all four critical factors of AI development
in practice (see table 2). For factor (3) — large da-
tasets — countries can pool their national datasets
through secure exchange mechanisms that comply
with GDPR-like standards, supplementing them
with anonymized user data from the joint plat-
form. For factor (2) — computational power — a
regional pool of GPUs/TPUs can be created based
on shared data centers financed through a com-
mon investment fund (similar to EU recovery
32 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
funds but specifically targeted at AI infrastructure).
Factor (1) — energy consumption — can be ad-
dressed through joint projects to build small modu-
lar reactors (SMRs), as recommended by Pekny et
al. (2026). This is particularly relevant for Ukraine,
given its damaged but recoverable energy sector. Fi-
nally, for factor (4) — algorithms — joint research
laboratories and specialist training schools can be
established to develop localized models that take
into account the region’s specific characteristics
(languages, legal norms, and cultural contexts).
On the alliance’s multinational platform, it would
be logical to host a unified search engine and a
common AI assistant that operates using both na-
tional and pooled data. Drawing on the successful
model of ProZorro in Ukraine, it is advisable to
clearly separate the processing (back-end) and in-
terface (front-end) components: data processing
and model training would be carried out on shared
infrastructure, while the user interface remains
national, adapted to the language and services of
each country. The existing Diia AI assistant, which
currently serves mainly local administrative func-
tions, could be significantly enhanced by fine-tun-
ing it on the regional dataset, transforming it into
a full-fledged sovereign tool capable of competing
with Gemini or Grok in terms of response rele-
vance for users in Central and Eastern Europe.
This approach substantially strengthens the ne-
gotiating position of small countries when dealing
with PTNCs. In line with the goal-alignment
mechanism (see table 4), the regional alliance acts
as a single “market” comprising the population
and businesses of several countries, offering global
companies regulated access in exchange for tech-
nology transfer, infrastructure investment, and ac-
cess to portions of global data. This prevents the
situation in which each small country individually
is forced to accept unfavorable terms. At the same
time, the alliance preserves digital sovereignty and
minimizes the risks of “weaponization of interde-
pendence” highlighted by Krishnamurthy (2026).
Ultimately, interstate collaboration transforms
the structural weakness of small open economies
into a strategic advantage. It provides not only ac-
cess to modern AI technologies and global market
competitiveness but also genuine opportunities for
independent strategizing in accordance with GETS
principles. Ignoring this path will lead to further
marginalization of NG200 countries amid deglo-
balization and technological protectionism. In
contrast, the proactive creation of regional digital
alliances will enable small countries, including
Ukraine, to move from the role of passive data
consumers to active participants in the global
competition for key AI resources — energy, com-
pute, data, and algorithms – while maintaining
their economic and political sovereignty.
Conclusions and prospects
for further research in this area
1. The problem of ensuring digital sovereignty in
the context of the rapid development of artificial
intelligence (AI) and big data is one of the most
pressing scientific and practical challenges in the
modern economy. Global competition for the key
resources of AI development — energy, computa-
tional power, large datasets, and algorithms — in-
tensifies inequality between countries and places
economically and demographically small countries
(the NG200 group) in a clearly vulnerable position.
2. From the perspective of the General Economic
Theory of Strategizing (GETS), ensuring sovereign
AI for small countries requires clear alignment of
the goals of the state (digital and economic sover-
eignty, national security) with those of global plat-
form companies (profit maximization through the
collection and capitalization of data). Without
such alignment, small countries risk losing control
over their information space, sociocultural influ-
ence, and, ultimately, political sovereignty.
3. Analysis of the critical factors of AI develop-
ment shows that the scarcest and strategically im-
portant resource for countries outside the G2 and
G20 groups is big data. Major platform companies
(primarily from the United States and China) pos-
sess ecosystems that generate petabytes of up-to-
date data daily, giving them a decisive advantage in
training and improving AI models. Small coun-
tries, including Ukraine, are unable to indepen-
dently generate comparable volumes of data due to
their limited population and the absence of global
social networks and search engines.
4. To ensure competitiveness and digital sovereign-
ty, small countries need to implement a combined
strategy that includes local protectionism — in-
volving regulatory protection of the national data
market and stimulation of the development of na-
tional or regional platforms (as exemplified by the
development of Ukraine’s Diia ecosystem) — and
33ISSN 1562-109X. Економіка промисловості. 2026. № 2 (114)
Ensuring sovereign ai from the perspective of the general economic theory of strategizing
international cooperation through the creation of
digital alliances with other small and medium-
sized countries. Such alliances should focus pri-
marily on the Central and Eastern Europe region:
Ukraine, Poland, and the Baltic states at the first
stage, followed by the Visegrád Group countries
and the Balkans at the second stage. These alliances
enable the pooling of data, computational resourc-
es, and infrastructure, as well as the joint develop-
ment of sovereign AI solutions. They also facilitate
mutually beneficial data exchange with global plat-
form companies, based on the provision of regu-
lated access to the national user market in return
for access to global datasets and technologies.
5. The development of sovereign AI should not be
reduced to complete isolation (“digital fortress-
es”). A more productive approach is to combine
elements of protectionism in the domestic market
with the active promotion of open competition
principles in third countries. This will allow na-
tional and regional platforms to expand their
presence. Particular attention should be paid to
the capitalization of big data — transforming it
from “digital waste” into a full-fledged financial
and strategic asset through reasonable “enclo-
sure,” processing, and monetization.
6. Lagging behind in the development and use of
AI carries a double risk for small countries: not
only a slowdown in economic growth compared to
global averages and technological leaders, but also
the gradual loss of digital — and subsequently po-
litical — sovereignty. In the context of accelerating
deglobalization and technological protectionism
(restrictions on chips, data, and energy infrastruc-
ture), ignoring these challenges may lead to mar-
ginalization in the global AI economy.
7. The results of the analysis of goal alignment and
interstate collaboration demonstrate that the pro-
posed combined strategy (local protectionism +
regional alliances) not only minimizes the risks of
digital dependence and the “weaponization of in-
terdependence,” but also creates real preconditions
for capitalizing big data as a full-fledged financial
and strategic asset for small countries — analogous
to oil in the industrial era. This opens new sources
of revenue and additional economic growth.
8. In the context of accelerating deglobalization and
technological protectionism, the consistent applica-
tion of the GETS in the field of AI is a necessary
condition for small NG200 countries to preserve
not only economic competitiveness but also full po-
litical sovereignty. Ignoring these mechanisms will
inevitably lead to the irreversible marginalization of
such states in the global AI economy.
9. The scientific novelty of the research lies in the
systematic application of the GETS to the issue of
ensuring digital sovereignty for small countries
(the NG200 group) in the field of artificial intelli-
gence. Unlike previous studies, which have fo-
cused primarily on technical, energy-related, or
legal aspects 10 (Krishnamurthy, 2026), this study
is the first to develop and operationalize a mecha-
nism for aligning the goals of governments and
global platform TNCs. This mechanism is based
on a detailed table comparing actors, goals, means,
outcomes, beneficiaries, and forms of “payment.”
It transforms a potential conflict of interests into
sustainable synergy through a pragmatic ex-
change: regulated access to the national market
and national big data in return for modern AI
technologies, infrastructure investment, and joint
fine-tuning of models.
Furthermore, the novelty consists in substanti-
ating a specific model of interstate collaboration
among small countries in the form of phased re-
gional digital alliances (using the example of
Ukraine together with Poland and the Baltic
states, with subsequent expansion to the countries
of Central and Eastern Europe). This model en-
ables the joint solution of all four critical factors of
AI development through resource pooling (a
shared pool of data, data centers, small modular
reactors, and algorithm laboratories), the creation
of a unified multinational platform with a clear
separation of back-end and front-end compo-
nents (following the ProZorro model in Ukraine),
and a significant strengthening of negotiating
power vis-à-vis PTNCs. This approach moves
GETS from a purely theoretical plane into a prac-
tical tool for proactive strategizing, enabling small
open economies to transition from the role of pas-
sive data consumers to active participants in the
global competition for AI resources — energy,
compute, data, and algorithms.
10 Maslej N. et al. (2025). Artificial Intelligence Index Report
2025. Stanford Institute for Human-Centered Artificial
Intelligence. https://hai.stanford.edu/assets/files/hai_ai_
index_report_2025.pdf (Accessed 4 April 2026).
34 ISSN 1562-109X Econ. promisl. 2026. № 2 (114)
O. S. Vyshnevskyi, M. S. Bozhyk, T. O. Gulchuk
LITERATURE
Вишневський О. С. Загальна теорія стратегування: від парадигми до практики використання : монографія. Київ :
Інститут економіки промисловості НАН України, 2018. 156 с. URL: https://iie.org.ua/wp-content/uploads/2019/01/
mono_Vishnevskiy_ukr_2018.pdf (дата звернення: 01.04.2026).
Вишневський О. С. Смарт-промисловість: визначення і теорія стимулювання розвитку на основі локального
протекціонізму. Економіка промисловості. 2023. № 3 (103). С. 5—27. https://doi.org/10.15407/econindustry2023.03.005
Вишневський О. С. Цифрова платформізація процесу стратегування розвитку національної економіки :
монографія. Київ : Інститут економіки промисловості НАН України, 2021. 449 с. URL: https://iie.org.ua/mono-
grafiyi/cifrovaplatformizacija-procesustrateguvannja-rozvitku-nacionalnoi-ekonomiki/ (дата звернення: 04.04.2026).
Bajwa M. et al. The impact of AI and big data integration on Industry 4.0. Spectrum of Engineering Sciences. 2025. Vol. 3,
No. 9. P. 319—332. https://doi.org/10.5281/zenodo.17111262
Buiten, M. C. Product liability for defective AI. European Journal of Law and Economics. 2024. Vol. 57. P. 239—273.
https://doi.org/10.1007/s10657-024-09794-z
Chika-Petegyrych L. The use of artificial intelligence in the implementation of the migration policy of the leading coun-
tries of the world in the context of modern global problems. Economic Innovations. 2021. Vol. 23, No. 3 (80). P. 373—
378. https://doi.org/10.31520/ei.2021.23.3(80).373-378
Davidson S. The economic institutions of artificial intelligence. Journal of Institutional Economics. 2024, Vol. 20. Art. e20.
https://doi.org/10.1017/S1744137423000395
Ge Y. L. An Empirical Study on the Impact of the Integration of AI and Big Data on Market Uncertainty in the Context
of Economic Turbulence. Economics and Public Policy. 2026. Vol. 1, No. 3. P. 1—12. https://doi.org/10.63313/EPP.2004
Ismaila B., Beneke J. D. Harnessing AI, IoT, and Big Data for social and economic growth in Africa: A Bibliometric re-
view. Acta Commercii. 2026. Vol. 26, No. 2. Art. 1526. https://doi.org/10.4102/ac.v26i2.1526
Krishnamurthy V. The Sovereign AI Myth. U of Colorado Law Legal Studies Research Paper. 2026. No. 26—3. URL:
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Lambert K. J., Fegley T. Economic Calculation in Light of Advances in Big Data and Artificial Intelligence. Journal of
Economic Behavior & Organization. 2023. Vol. 206. P. 243—250. https://doi.org/10.1016/j.jebo.2022.12.009
Maddipatla S. Big Data Analytics Applications And Opportunities With AI. Journal of International Crisis and Risk Com-
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Pekny J., Ribeiro F., Kim S., Tsoukalas L. The AI-Energy Nexus. Frontiers in Energy Research. 2026. Vol. 13. Art. 1691890.
https://doi.org/10.3389/fenrg.2025.1691890
Надійшла до редакції 10.04.2026 р.
Прийнята до друку 13.05.2026 р.
Опублікована 29.06.2026 р.
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Received: 10.04.2026
Accepted: 13.05.2026
Published: 29.06.2026
Олександр Сергійович Вишневський, д-р екон. наук, ст. досл.
E -mail: vishnevskiy_O@nas.gov.ua; https://orcid.org/0000-0002-2375-6033
Марина Сергіївна Божик, аспірантка
E-mail: bozhyk@nas.gov.ua; https://orcid.org/0009-0009-2976-6118
Тарас Олегович Гульчук, аспірант
E-mail: avto198413@ukr.net; https://orcid.org/0009-0008-4968-0605
Інститут економіки промисловості НАН України,
вул. Марії Капніст, 2, м. Київ, 03057, Україна
ЗАБЕЗПЕЧЕННЯ СУВЕРЕННОГО ШІ З ПОЗИЦІЙ
ЗАГАЛЬНОЇ ЕКОНОМІЧНОЇ ТЕОРІЇ СТРАТЕГУВАННЯ
У статті досліджено критичну проблему забезпечення цифрового суверенітету малих країн в умовах стрімкої
експансії технологій штучного інтелекту (ШІ) та економіки великих даних. На основі положень загальної еко-
номічної теорії стратегування (ЗЕТС) обґрунтовано необхідність переходу від реактивної моделі споживання
цифрових послуг до проактивного стратегування національного розвитку. Актуальність дослідження зумовле-
на глибокою інтеграцією ШІ-асистентів, таких як ChatGPT, Grok, Gemini та DeepSeek, у соціально-економічні
процеси, що створює нові виклики для держав, які не входять до групи світових лідерів (G2 та G20). Визначено
та проаналізовано чотири фундаментальних чинники розвитку ШІ: енергозабезпечення, обчислювальні по-
тужності, великі набори даних та алгоритмічну базу. Доведено, що для малих країн (поза групою G20) найбільш
дефіцитним ресурсом є саме великі дані, оскільки обмеженість населення та відсутність власних глобальних
платформ унеможливлюють самостійне навчання конкурентоспроможних моделей. Виявлено ризики «цифро-
вої залежності», яка загрожує втратою контролю за інформаційним простором й електоральними процесами.
Наукова новизна дослідження полягає в розробленні та операціоналізації механізму узгодження інтересів на-
ціональних урядів і глобальних платформних транснаціональних корпорацій. Запропоновано модель прагма-
тичного обміну: надання регульованого доступу до національного ринку в обмін на трансфер сучасних техно-
логій, спільне донавчання моделей та інвестиції в локальну інфраструктуру (зокрема центри обробки даних).
Особливу увагу приділено стратегії міждержавної співпраці малих країн. Обґрунтовано доцільність створення
регіональних цифрових альянсів, які уможливлюють об’єднання ресурсів кількох держав для досягнення ефек-
ту масштабу. На прикладі України описано поетапну модель формування такого альянсу з Польщею та країна-
ми Балтії на основі інтеграції даних національних цифрових екосистем (зокрема «Дії» та mObywatel). Це до-
зволить малим країнам створити спільний пул обчислювальних потужностей і розробити суверенні ШІ-
рішення, адаптовані до місцевого контексту. Доведено, що реалізація комбінованої стратегії, яка поєднує ро-
зумний локальний протекціонізм з активною міжнародною кооперацією, є необхідною умовою збереження
економічної конкурентоспроможності та політичної суб’єктності малих держав в умовах деглобалізації.
Ключові слова: загальна економічна теорія стратегування (ЗЕТС), ШІ, цифровий суверенітет, регіональні
цифрові альянси, локальний протекціонізм, платформні ТНК.
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| id | oai:ojs.ojs.econindustry.org:article-346 |
| institution | Economy of Industry |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-06-16T01:00:25Z |
| publishDate | 2026 |
| publisher | Institute of Industrial Economics of NAS of Ukraine |
| record_format | ojs |
| resource_txt_mv | ojseconindustryorg/54/1a45d80c8389e082565f1ce2c15f0854.pdf |
| spelling | oai:ojs.ojs.econindustry.org:article-3462026-06-15T09:49:11Z Ensuring sovereign ai from the perspective of the general economic theory of strategizing Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування Vyshnevskyi, Oleksandr S. Bozhyk, Maryna S. Gulchuk, Taras O. General Economic Theory of Strategizing (GETS), AI, digital sovereignty, regional digital alliances, local protectionism, platform-based TNCs загальна економічна теорія стратегування (ЗЕТС), ШІ, цифровий суверенітет, регіональні цифрові альянси, локальний протекціонізм, платформні ТНК The article explores digital sovereignty for small nations in the AI era. Using General Economic Theory of Strategizing (GETS), it justifies aligning interests between governments and global platforms. Key AI factors (energy, power, big data, and algo-rithms) are analyzed, identifying data scarcity as the primary challenge. A mechanism is proposed: trading market access for technology and investment. The study justifies regional digital alliances and local protectionism to pool resources. Scientific novelty lies in applying GETS to model state and platform-based TNCs interactions. This strategy shifts countries from pas-sive consumers to active competitors, enhancing sovereignty through proactive big data management. У статті досліджено критичну проблему забезпечення цифрового суверенітету малих країн в умовах стрімкої експансії технологій штучного інтелекту (ШІ) та економіки великих даних. На основі положень загальної еко-номічної теорії стратегування (ЗЕТС) обґрунтовано необхідність переходу від реактивної моделі споживання цифрових послуг до проактивного стратегування національного розвитку. Актуальність дослідження зумовле-на глибокою інтеграцією ШІ-асистентів, таких як ChatGPT, Grok, Gemini та DeepSeek, у соціально-економічні процеси, що створює нові виклики для держав, які не входять до групи світових лідерів (G2 та G20). Визначено та проаналізовано чотири фундаментальних чинники розвитку ШІ: енергозабезпечення, обчислювальні по-тужності, великі набори даних та алгоритмічну базу. Доведено, що для малих країн (поза групою G20) найбільш дефіцитним ресурсом є саме великі дані, оскільки обмеженість населення та відсутність власних глобальних платформ унеможливлюють самостійне навчання конкурентоспроможних моделей. Виявлено ризики «цифро-вої залежності», яка загрожує втратою контролю за інформаційним простором й електоральними процесами. Наукова новизна дослідження полягає в розробленні та операціоналізації механізму узгодження інтересів на-ціональних урядів і глобальних платформних транснаціональних корпорацій. Запропоновано модель прагма-тичного обміну: надання регульованого доступу до національного ринку в обмін на трансфер сучасних техно-логій, спільне донавчання моделей та інвестиції в локальну інфраструктуру (зокрема центри обробки даних). Особливу увагу приділено стратегії міждержавної співпраці малих країн. Обґрунтовано доцільність створення регіональних цифрових альянсів, які уможливлюють об’єднання ресурсів кількох держав для досягнення ефек-ту масштабу. На прикладі України описано поетапну модель формування такого альянсу з Польщею та країна-ми Балтії на основі інтеграції даних національних цифрових екосистем (зокрема «Дії» та mObywatel). Це до-зволить малим країнам створити спільний пул обчислювальних потужностей і розробити суверенні ШІ-рішення, адаптовані до місцевого контексту. Доведено, що реалізація комбінованої стратегії, яка поєднує ро-зумний локальний протекціонізм з активною міжнародною кооперацією, є необхідною умовою збереження економічної конкурентоспроможності та політичної суб’єктності малих держав в умовах деглобалізації. Institute of Industrial Economics of NAS of Ukraine 2026-06-15 Article Article application/pdf https://ojs.econindustry.org/index.php/ep/article/view/346 10.15407/econindustry2026.02.023 Экономика промышленности; No 2(114); 23-35 Economy of Industry; No 2(114); 23-35 Економіка промисловості; No 2(114); 23-35 2306-532X 1562-109X 10.15407/econindustry2026.02 en https://ojs.econindustry.org/index.php/ep/article/view/346/415 Copyright (c) 2026 Economy of Industry |
| spellingShingle | загальна економічна теорія стратегування (ЗЕТС) ШІ цифровий суверенітет регіональні цифрові альянси локальний протекціонізм платформні ТНК Vyshnevskyi, Oleksandr S. Bozhyk, Maryna S. Gulchuk, Taras O. Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title | Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title_alt | Ensuring sovereign ai from the perspective of the general economic theory of strategizing |
| title_full | Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title_fullStr | Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title_full_unstemmed | Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title_short | Забезпечення суверенного ШІ з позицій загальної економічної теорії стратегування |
| title_sort | забезпечення суверенного ші з позицій загальної економічної теорії стратегування |
| topic | загальна економічна теорія стратегування (ЗЕТС) ШІ цифровий суверенітет регіональні цифрові альянси локальний протекціонізм платформні ТНК |
| topic_facet | General Economic Theory of Strategizing (GETS) AI digital sovereignty regional digital alliances local protectionism platform-based TNCs загальна економічна теорія стратегування (ЗЕТС) ШІ цифровий суверенітет регіональні цифрові альянси локальний протекціонізм платформні ТНК |
| url | https://ojs.econindustry.org/index.php/ep/article/view/346 |
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