By Javier Surasky
We continue to seek a simple explanation of the key elements of AI to understand how this technology impacts international relations. We have already made significant progress, addressing tensions between China and the United States, regulatory disputes, computing power, human talent, and cloud computing. Now it’s time to focus on edge computing and the Internet of Things (IoT).
IoT: When the Physical
World Becomes Digital Infrastructure
We begin by
entering the space of new digital transformations through perhaps the most
familiar entry point: IoT, which is present in our daily lives, even if we
don’t always perceive it. Industrial sensors, medical devices, transportation
systems, and electrical grids are already part of the infrastructure where the
internet blends with the physical world. A simple way to think of IoT is as the
incorporation of physical objects into the digital informational ecosystem
through devices that continuously generate data, embedded in local contexts,
sensitive to the passage of time, and often representing critical segments for
personal security and privacy (Atzori et al., 2010; Gubbi et al., 2013).
Consider
smart heating systems equipped with sensors that measure temperature and the
presence of people in a home, automatically adjusting the temperature; wearable
heart monitors that constantly track heart rate in communication with a medical
platform, sending alerts when abnormal values appear; or traffic lights that
change the timing of lights based on real-time traffic. These examples
illustrate IoT and allow us to identify its three constituent elements: sensors
that capture physical world phenomena, web connectivity that enables data
transmission and reception, and the ability to make decisions and act on them.
From Local Data to the
Cloud: A Geopolitical Tension
IoT can
provide us with comfort and security, but at the cost of generating an
increasing volume of data that must travel to and from data centers, typically
in the cloud. That makes the speed of data circulation (technically known as
latency) critical to the proper functioning of these systems (Atzori et al.,
2010). The growth of IoT-connected devices poses a threat to latency, creating
bandwidth bottlenecks and exacerbating security issues related to data
transmission, as many devices handle sensitive information (remember, these
devices can monitor whether someone is in the house or how a heart is
functioning).
It is
essential to understand this point because, from a geopolitical perspective,
IoT does not create a virtual layer around physical objects; rather, it
objectifies a digital infrastructure. Data is generated in specific locations,
but its management and use, which give it value, occur in transnationally
distributed servers, generating a tension between the national and the
international. That has driven the transition from centralized to distributed
computing architectures that are “closer” to where data is produced, thereby
minimizing latency (Shi et al., 2016; Shi & Dustdar, 2016).
Edge Computing: Processing
Data Where It Is Generated
We are now
at the threshold of edge computing, defined as “any computing and network
resources along the path between data sources and cloud data centers. The logic
of edge computing is that computation should occur at the proximity of data
sources.” (Shi et al., 2016:638)
Gateways
play an important role here. What is a gateway? Let’s see: sensors typically
use short-range communication technologies, such as Bluetooth, whose signals
are received by amplifying centers that extend their range. In doing so, they
“change” the technical composition of the data to align it with the internet’s
IP language (the same one used by any computer to connect to the network). In
some cases, they also “clean” irrelevant information and execute simple rules
(if I receive X data, I must start process Y). When all this happens, the
gateway becomes an edge node. After all, a gateway is literally a “gateway”
because it connects locally produced data to the internet.
Fog and Edge: Redistributing
Decision-Making Power
Edge
computing is not alone in producing and managing locally generated data; it is
accompanied by fog computing. While similar “and they are regularly confused
with one another, there is a slight distinction between them. In fog computing,
there is just one centralized processing device responsible for handling
information from various endpoints in the systems. In edge computing, each
system participates in processing information” (Singla et al., 2021:44)
The
relationship between “internet-connected device → cloud data processing” is now
mediated by layers (edge computing and fog computing) that coordinate services,
manage available resources, and apply security enhancements to particularly
sensitive data (Shi & Dustdar, 2016). Edge and fog computing do not
compete; they work together to enhance the quality of functional
decentralization processes required by IoT.
However, as
we mentioned earlier, these models shift processing and analysis capabilities
from a central layer to several distributed layers connected through the
network, fundamentally changing the functional organization of digital
computing and directly affecting where and how automated decisions are made:
devices at the edge (either edge or fog) no longer act as mere data consumers,
but as active nodes capable of filtering information, executing computational
logic, and conditioning the operation of the system as a whole (Shi &
Dustdar, 2016). From this perspective, edge computing is a critical part of the
entire digital information infrastructure, without which the current levels of
resilience in advanced networks, particularly in contexts of high digital
dependence, low tolerance for failure, and the need for continuous operation,
could not be achieved. By acting as the first point of contact with the
emission device, the edge can isolate failures, reduce collapse points, and
sustain essential services when connectivity to large data centers is affected,
with direct impacts on areas such as security, health, or crisis management.
Standards, Corporations,
States: Who Controls the Edge
It is
precisely because of this dedicated role that edge computing standards,
including the ITU-T Recommendation X.1648 on data security in edge computing
(ITU, 2025), are set to play a fundamental role. Advances have already been
made in this area, but as we know, the establishment of standards is not purely
a technical issue or, if you prefer, not politically neutral. The setting of
standards becomes a key factor in defining interoperability patterns, which
implies strengthening some systems over others, technological dependence, and
the ability to control the flow of data in the short, medium, and long term:
those who prevail in the competition to set global edge computing standards
will have the capacity to dictate the operating guidelines for future critical
infrastructures.
Clearly,
these disputes involve not only States but primarily large tech companies and
digital infrastructure providers that design and operate the technical
equipment enabling edge and fog computing.
At the
international level, this results in alliances between States and corporations
to promote particular strategies for managing edge computing, framed in a
relationship of tension, given that the State lacks control over the
technologies and data flow processes at the edge that the company possesses.
Therefore, these priorities will not always align with the public sector’s, as
they operate within their own logics.
States seek
to balance their “capacity deficit” in the field through their normative power
to establish which data should be processed and stored locally, which should be
considered sensitive, and which can or cannot be sent to centralized processing
infrastructures located outside the national territory.
Thus,
States and companies form a partnership in which each needs the other more than
they would like.
Edge AI and Uneven
Development: Global Scenarios
The
complexity grows even further: “the convergence of edge computing and
artificial intelligence has given birth to a new research area, namely ‘edge
intelligence’ or ‘edge AI.’ Instead of relying entirely on the cloud, edge
intelligence makes full use of pervasive edge resources to gain AI insights.”
(Zhou et al., 2019:1). Technologies like autonomous vehicles, surveillance
systems, and digital health require real-time responses based on sensitive
data. Edge intelligence seeks to filter data and make decisions locally,
sending only processed information to the cloud.
This
process is linked to the “Industry 4.0” paradigm, also known as the “smart
industry,” which is based “on the use of emerging technologies to improve
manufacturing processes, machine maintenance, optimize production costs,
enhance employee training and conditions, boost customer relationships, or
create new high-quality services and products” (Rodal Montero, 2020:6). All of
this requires advanced energy systems and robust edge infrastructures,
projecting a high degree of complexity recognized by both the European Union
(European Commission, 2020 and 2025) and UNESCO (2021). The existence of these
elements, or lack thereof, becomes an international asset (OECD, 2025).
Furthermore,
edge computing and IoT do not unfold in a vacuum. The UN Development Program’s
Digital Development Compass shows how different dimensions of technology
management and human capacities combine to deploy edge computing and IoT,
clearly revealing highly unequal structures across countries and regions (UNDP,
2023).
The United
States, given its high digital capabilities and strong human capital in
technological and STEM fields, has turned edge computing into an extension of
its digital power, replicating its territorial models and exporting de facto
architectures and standards that end up conditioning the options of the
countries receiving them.
China, on
the other hand, uses its capabilities to establish edge-processing networks to
manage the enormous amount of locally generated data without relying on
external infrastructure. At the same time, it provides the government with
better tools to exercise control over its population, while also gaining
valuable experience to join the race to export edge and IoT solutions (UNDP,
2023).
Brazil is a
good case to illustrate what happens in several emerging economies. It has
significant digital infrastructure and digital economy capabilities, but these
are unevenly distributed across its territory, with hyper-concentrated zones.
As a result, it can deploy sectoral edge and IoT strategies but faces
difficulties consolidating a coherent national strategy that would allow it to
accumulate power, which means it does not substantially change its dependent
position in the global digital value chain (UNDP, 2023), even if it gains
functional autonomy in specific areas.
In Kenya,
an example of digitalization in Africa, the Digital Development Compass shows
edge computing as a possibility still subject to the consolidation of the
country’s nascent capabilities, where its deployment remains fundamentally
dependent on external actors, generating a situation of high fragility for the
government: local data processing without effective control over the
infrastructure or equitable appropriation of the value generated by that data
(UNDP, 2023).
The Edge as a New
Geopolitical Battleground
These
multiple dynamics help us understand why edge computing and IoT are
increasingly present in digital policy strategies, international cooperation,
and development. Edge not only shapes technical processes for efficient data
management but, hand in hand, raises debates about insertion into international
technological chains, the development of global digital infrastructures, the
enhancement of human capacities to manage frontier technologies within
countries, and the determination of possible margins of maneuver.
Edge
computing does not address digital inequalities; rather, depending on political
management and power relations in the global field, it can amplify existing
asymmetries. It is a technology that relies on sovereign decisions about data
but expresses inequalities in its capacity for management and exploitation. The
paradox we highlighted earlier reappears, but with different protagonists:
leading countries with high technological capacity need data from those
trailing in the race, but these, through regulatory strategies, appropriate
that data they cannot leverage unless through the systems of leading countries.
Both are called to work together in win-win relationships, but is that
practicable in a world where power inequality sets the pace?
There is no
single answer to this question. Still, there is a reality that governments and
scientists must integrate into any geopolitical analysis of digital technology
in general and AI in particular: edge computing and IoT are here to stay and
will grow in quantity and quality.
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