https://gafam.theglassroom.org/
*GAFAM Empire* is a project in which we look at all the known
acquisitions conducted by five big tech companies: Google, Amazon,
Facebook, Apple, and Microsoft, from the moment they made their first
one to the end of summer of 2022, when we stopped collecting data.
Why do we call it an empire? Our focus is on trying to understand how
these companies purchase their power and ability to grow their
respective market positions based on their information infrastructure
through the simple operation of acquiring other businesses and their
products. Our understanding of empire here is very basic: empire
controls three basic layers of knowledge: 1) collection of
data/intelligence, 2) storage of that intelligence as well as 3)
capacity of processing it. These three unique characteristics give them
powerful and unprecedented insights into how our societies operate. Why
are we not calling them monopolies? Because this framing based on the
idea of limited resources and competition for consumers frankly does not
apply - the resources are unlimited - and that is /our data and
attention/. When it comes to competition we are free to chose the ones
we like - we can use products of them all in fact. In short we call them
empires in a collonial sense, as in /digital colonialism/ and not
monopolies in a market competition sense.
All of these five companies dominate the space of collecting data
(number of users), managing and processing that data (cloud and other
infrastructures they own, including hardware) as well as processing and
analysing that data (machine learning, AI, algorithms). They all
monetize this dominant position, making them the richest tech companies
out there. How big are they, which sectors do they dominate and in which
do they compete, what futures are they trying to prepare for? It is
obvious that for them the ideal future is the one where we do everything
online, everything is digital and goes though their channels. These were
the questions driving our research and this visualization.
You can learn more about the data used here in the Methodology section,
however, we have to say that the data we acquired has significant
limits. One would hope that there is a clear way of learning how much
each of these acquisitions was worth; sadly that is not the case. This
specific data is sporadic and often unreliable. It would be great to
know the exact reasons for each of the acquisitions - this is also very
arbitrary and ephemeral - as the descriptions of purchased companies and
products often do not reflect the actual reasons of purchases: was it
buying a product, buying out a competitor, absorbing talent, skills, or
expertise, or to exploit the content (which is why GitHub users filed a
class-action lawsuit against Microsoft for training an AI tool with
their code)?
*So what is it that you can actually learn here?*
We can learn how they grew - which sectors they expanded to, what types
of know-how they absorbed, and in fact who they actually are besides who
we think they are. We, the users of their services, often associate who
they are with the most popular services we use, but looking at their
acquisitions we are looking at a very different picture - a picture
dominated by strategic purchases and political visions. In 2021 alone it
is estimated that the technology acquisition market exceeded over three
trillion US dollars. It is not insignificant what they acquire and we
should know more about why.
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