10.6092/issn.2038-3460/17578
The promises connected to emerging science and technology do not merely assist research and innovation, but are a part of it. Their diverse roles in producing hype and ensuring coordination have been extensively studied in the sociology of expectations. However, promises also circulate on a massive scale in the media sphere, as occurred with nanotechnology and artificial intelligence, recounting what the future will be like. The popularity that technoscientific visions manage to attract is less well studied and understood, although it is closely connected with how research is directed and innovation funded – and thus deserves more attention. This contribution explains why so much promising and visioneering is taking place, identifies a “regime of promising”, and discusses its implications for the relationship between science and society. Drawing on cultural and media studies to expand the sociology of expectations, it attempts to better understand the role of fiction in building socio-technical imaginaries.
application/pdf
2038-3460
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Marc Audétat
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
scenarization
Promising Technosciences in the Economy of Attention: Why Have Pessimistic Stories of Disruption and “Artificial Intelligence” Performed so Well?
Text.Serial.Journal
https://tecnoscienza.unibo.it/
13
Lectures
2023-02-19
2023-06-30
2023-02-19
2023-10-30
35-56
TS
Marc Audétat
2022
10.6092/issn.2038-3460/17578
35
University of Lausanne
2038-3460
2
scenarization
en
56
Promising Technosciences in the Economy of Attention: Why Have Pessimistic Stories of Disruption and “Artificial Intelligence” Performed so Well?
https://tecnoscienza.unibo.it/article/download/17578/16428
13
                                                                             Lecture
Promising Technosciences in the Economy
of Attention: Why Have Pessimistic Stories
of Disruption and “Artificial Intelligence”
Performed so Well?

Marc Audétat
University of Lausanne


     Abstract: The promises connected to emerging science and technology
     do not merely assist research and innovation, but are a part of it. Their di-
     verse roles in producing hype and ensuring coordination have been exten-
     sively studied in the sociology of expectations. However, promises also cir-
     culate on a massive scale in the media sphere, as occurred with nanotech-
     nology and artificial intelligence, recounting what the future will be like. The
     popularity that technoscientific visions manage to attract is less well studied
     and understood, although it is closely connected with how research is di-
     rected and innovation funded – and thus deserves more attention. This
     contribution explains why so much promising and visioneering is taking
     place, identifies a “regime of promising”, and discusses its implications for
     the relationship between science and society. Drawing on cultural and me-
     dia studies to expand the sociology of expectations, it attempts to better
     understand the role of fiction in building socio-technical imaginaries.

     Keywords: promising technosciences; credibility; popularity; economy of
     attention; counter-fiction; scenarization.

     Submitted: October 31, 2022 – Accepted: December 2, 2022
     Corresponding author: Marc Audétat, Sciences and Technologies Stu-
     dies Laboratory (STSlab), Quartier UNIL-Mouline Bâtiment Géopolis, CH-
     1015, Lausanne. Email: marc.audetat@unil.ch



1. Introduction

    Once, in 2017, when I was invited to a high school to talk about new
technology and stimulate a debate on science and society with students of
around 16, a boy who had not yet participated raised his hand and asked me:
!
TECNOSCIENZA
Italian Journal of Science & Technology Studies
13 (2) pp. 35-56 - ISSN 2038-3460
www.tecnoscienza.net                              2022
36                                                        Tecnoscienza – 13 (2)


   “Have you seen the movie WALL-E?”
   I gladly answered, “Yes I have.”
   He went on, “Well, I think we’re going to end up like that: machines
and robots will soon be able to do everything. What I’m being taught to-
day in school is going to be useless tomorrow. And I’ll be left without a
job or a place in society.”

    These pessimistic words echo the promises of “artificial intelligence”
(AI), and especially the tone used to speak of a future of machine learning
and the supposedly “disruptive” impact it will have on the job market
and society. They raise numerous questions about the “economy of tech-
noscientific promises” (Felt and Wynne 2007; Joly 2010) and its effects
beyond the circles of stakeholders in research and innovation, namely on
the public and media sphere, as well as on society and culture more
broadly. What are the ethical implications of these promises? What
should (or must) scholars try to do about them? What do they reveal
about the relationship between science and society today, as compared to
the recent past? What is the image of technosciences in society? Do peo-
ple actually believe in such bold technoscientific claims? Here, “disentan-
gling the future” challenges the sociology of expectations to broaden its
scope of analysis and extend its work “beyond an actor-centered ap-
proach” (Sand and Schneider 2017, 22). In this contribution, I intend to
explain what is meant by a “regime of promising,” arguing that in order
to fully investigate its implications, it must be understood in the context
of the economy of attention that governs the media sphere and draw on
art and literary theory to study its intensive utilization of fiction.
    Before 1990, literature in science and technology studies (STS) fo-
cused exclusively on expectations and future visions was scarce, although
the role played by promises has always been acknowledged. From a his-
torical point of view, the modern sciences have generated much anticipa-
tion, as well as many utopias and expectations, that STS – while not over-
looking – understood in terms of the dominant discourse of progress as a
technical fix. Hence, the question arises: why did the sociology of expec-
tations develop starting from the 1990s? Or rather: why have so many
promises and technoscientific future visions recently followed one anoth-
er? Appeals to the zeitgeist or the turn of the millennium are not socio-
logical explanations of this phenomenon. This contribution thus ap-
proaches promising technoscience via the sociology of expectations, at-
tempting to supplement it with missing analytical elements derived from
cultural and media studies. I hope to show that these extensions of STS
are critical for addressing what is at stake in the widely disseminated
promises of technoscience and to reflect on public engagement.1
Audétat                                                                       37

2. “History of the Future” in Different Literary Genres

    Following Francis Bacon’s New Atlantis (1626) and René Descartes’
early ideas about organisms as machines, stories intended to stimulate the
imagination and promote rational thinking became a genre in itself during
the 18th century. A whole genealogy of future visions, technological utopi-
as, and promises of progress unfolded during the 19th and 20th centuries.
Technoscientific imaginaries of the past have forged the epistemic cul-
tures of physics, chemistry, biology, and computer sciences. One example
of such anticipatory vision and imaginary is “Daedalus, or Science and
the Future” (1923, Fig. 1), a text read by the famous British geneticist J.
B. S. Haldane at the Heretics Society, in which he coined the word “ecto-
genesis” (pregnancy outside the female body) – which would become
possible not later than 1950 – and envisaged genetics as soon being able
to modify individuals, as well as to cure or eradicate certain common dis-
eases. It is this discourse which inspired the novel Brave New World, pub-
lished in 1932 by Aldous Huxley (Fig. 2), mocking the promises of his
friend (Atlan 2005). Interestingly, the fame of Haldane’s promising dis-
course was made by its “counter-story”, namely Huxley’s satirical utopia.




Figure 1. J.B.S. Haldane: Daedalus, or   Figure 2. Aldous Huxley: Brave New
Science and the Future, 1923.            World, 1932.
38                                                          Tecnoscienza – 13 (2)


    Another example can be found in the works of H.G. Wells, who, in a
1924 interview about the possibility of deriving technology from the new
atomic science and the theory of relativity, answered that it would per-
haps take centuries for something concrete to be developed. However, in
his 1914 novel of anticipation The World Set Free, Wells imagines a world
war in the middle of the coming century that comes to an end following
the mass destruction caused by a new kind of bomb developed on the ba-
sis of atomic science (Cazes 1986, 69). This novel, dedicated to physicist
F. Soddy, was read by Leo Szilard in 1933, during his flight from Germa-
ny, and it encouraged him to continue his work and to warn that an atom-
ic bomb had to be developed before the Nazi regime managed do so itself
(Cazes 1986; Guston 2012).
    These two examples indicate that the future should be approached by
comparing literary genres. Many anticipatory scientific visions have been
based on purely technical rationality, while other genres of literature mix
different types of rationality, e.g., the historical, the economic, and the
technological in some philosophical essays. But literary genres underwent
differentiation and were eventually separated out in the context of the
“two cultures” (Snow 1959). While Wells published novels as well as an-
ticipation essays, the specialization of fields of knowledge and common
opinion about literary genres came to see science fiction as the opposite
of rational scientific knowledge. The Cold War period witnessed attempts
to develop fields of knowledge that anticipated the future, such as pro-
spective science or technology assessment/technology foresight, not to
mention game theory and risk assessment. For their part, the foundations
of the social sciences (as they are still practiced today) were established by
excluding prophesizing, prediction, and fiction. The two lessons learned
from the “history of the future” are 1) that promises and fiction are con-
stitutive of modern science and 2) that the dialogue about the future of
technoscience is apparently situated in between literary genres.


3. “Fabricating Stories” for “Selling Science”
!
    In order to foster economic competitiveness and growth following the
Second World War, new layers of research funding began to be added on
top of the existing positions of disciplines in academia. In the context of
the Cold War, defense was also a driving force behind technoscientific
research. The new research-funding schemes were organized on a com-
petitive basis, on the model of the US National Science Foundation
(1950). Following a linear model conceived as a pipeline, science policy
separated “fundamental” from “applied” research, with the latter leading
to R&D. In OECD countries, for about three decades everything – the
economy, energy, consumption, and public health – was growing and get-
ting better. However, to simplify somewhat, a new era started in the
Audétat                                                                           39

1980s with the advent of emerging countries, like Japan and South Korea,
which were perceived as new competitors. The integration of Europe led
to the first Framework Programme for Research and Technological De-
velopment (1984-87), which supplemented national research funding
with European funding. OECD nation-states implemented new forms of
legitimization for additional funding, still on a competitive basis, with
terms like “strategic” and “priority” research. Competition increased be-
tween nation-states, as well as within the research system.
    The theoretical basis of science and technology studies was laid down
in the 1970s and 1980s. It was then deployed in many different directions,
leading in recent times to the development of the sociology of expecta-
tions. Two early works in this field are worth mentioning here: the editor
of the 1979 book Controversy: Politics of Technical Decisions, Dorothy
Nelkin, published Selling Science: How the Press Covers Science and Tech-
nology in 1987. Among the many different issues discussed in the book,
Nelkin paid attention to the relationship between scientific journalism
and public relations (PR). She observed that journalists and PR people
have very similar skills, interests, and occupations and that there is a
strong complicity with scientists and engineers. Scientific journalism and
PR have an osmotic relationship, in which a person passes from one do-
main to the other while doing almost the same job. Her observation still
remains completely valid today: the president of the Swiss scientific jour-
nalists’ association wrote that journalism and PR cannot be separated
(Dessibourg 2013). Moreover, looking back to the first decades of the
present century, universities and research laboratories have – for many
different reasons, including but not limited to the Shanghai ranking of
universities which has been published since 2003 – hired more and more
communication staff, including many scientific journalists. A competition
heightened on the level of communication that had not previously existed.
    In 1993, Ulrike Felt published the results of a study about high tem-
perature superconductivity (HTS) conducted between 1987 and 1989.
Widely cited, the article Fabricating scientific success stories is a compara-
tive study of four countries in which this field of physics has been estab-
lished. Press coverage in these four countries is counted up and its con-
tent analyzed. The article starts by interrogating the relationship between
science and society as they looked in 1960 and in the early 1990s:

    Science meets the public under radically altered conditions: communica-
    tion and trust, credibility and authority, support and cultural meaning are
    no longer what they used to be. (Felt 1993, 375)

    What is the point of the paper? It seemed to Felt that “no scientific
discovery [...] has given rise to such a wave of enthusiasm,” and that “sto-
ries” were told about “fundamental breakthroughs” and “technological
dreams...” (Felt 1993, 377). A physicist articulated an interpretation of
40                                                              Tecnoscienza – 13 (2)


what was happening: “he puts the blame for what he sees as misjudgment
on the part of US science policy makers and on the distortion by media-
hype...”. He explained the scientific success story as the result of a “joint
venture between some scientists and breakthrough-hungry journalists”,
considering it “an aberration” when compared to many other advances in
postwar physics (Felt 1993, 377).
    The comparative study goes on to present Felt’s insights: the domain
of physics to which HTS belonged was under “severe financial pressure,”
and “scientists hoped to gain visibility in the public domain to show the
relevance of their research,” thus “put[ting] pressure on policy makers
and funding agencies” (Felt 1993, 387). The number of press reports and
public visibility “could be turned into valuable negotiation capital when
competing with others for money.” For policy makers, in turn, fabricating
success stories brought advantages: it provided them with a better foun-
dation for setting funding priorities and legitimizing their decisions in an
increasingly competitive context. I intend to show that the relations de-
scribed here between science, the media, and science policy are on the
verge of turning into a regime whose operating speed will only increase.
    What were the reasons for the large differences in press coverage in
the nation-states compared in the study? In Switzerland, where in 1986
an important discovery in superconductivity physics was made at the IBM
lab in Zurich, in Germany, home to one of the two Nobel prize winners,
and in Austria, “there was no perceived need to inform a wider public”
(Felt 1993, 388). In these countries, the press did not at all tell the same
story, being much more interested in technical details, and “played far less
with speculations on future applications.” In the US, by contrast, “most
universities are aware [...] they get public money and therefore need the
support of a broader public.” The press in the US thematized international
competition, especially with Japan, so that stories of “technological dream
and fiction were sold.” The conclusion, evoking Nelkin’s book, is that:

     In a climate of fierce economic competition, in a time of stagnating budg-
     ets and confronted with a public that is constantly facing the consequenc-
     es of science and technology, “selling science” cannot be regarded as a
     luxury any more – it has become an obligation. (Felt 1993, 389)

    The value of this study, especially when compared to traditional me-
dia studies, which counts occurrences without considering actor net-
works, is that Felt focuses equally on the research and decision-making
milieus. Her conclusion goes against the dominant and naive view that
only journalists exaggerate scientific prospects, telling stories, while the
scientists themselves are precise and neutral.
Audétat                                                                   41

4. The Market of Promises and the Sociology of Expectations

    Arie Rip pioneered the sociology of expectations, as one of the first
researchers, at least in Europe, to pay attention to science policy and
funding agencies (Rip 1994). He directed the PhD thesis of Harro van
Lente, who is often associated with the beginnings of this field (Van Len-
te 1994). Van Lente forged the concept of the promise-requirement cycle,
and was followed by colleagues who developed other basic concepts,
mainly those of the situatedness and performativity of promises (Brown et
al. 2000; Brown 2003; Borup et al. 2006). At the end of 1990s, the sociolo-
gy of expectations literature was about to deploy. This development had to
do with the amplification of the economy (or market) of scientific promises.
    The sociology of expectations explains that hype, fiction, and stories
of distant futures are primarily aimed at stakeholders in research and in-
novation: i.e., scientific policy makers, scientists, engineers, innovators,
business managers, venture capitalists, and early users. Performativity
supposes that promises do actually influence stakeholders’ behavior to-
wards the envisaged technology and looks at the means used to achieve
influence. One privileged way of influencing is to articulate “rhetorical
devices,” for example about the inevitability of competition and progress,
and to circulate “compelling narratives” (quoted in Sand and Schneider
2017, 20) like a “forceful fiction” (Van Lente and Rip 1998).
    As a conceptual framework, the promise-requirement cycle works well
for analyzing individual promising endeavors, as well as broad emerging
domains. A future vision protects a technoscientific endeavor for a certain
time, granting it some credibility. A promise is like a shared belief, creat-
ing some space for a domain to progress and to secure research or R&D
(Ruef and Markard 2010). Eventually, however, a requirement must be
fulfilled: there must be some concrete result. It can be far from what has
been promised, as long as it is convincing enough for a new round of
funding. Cycles of funding may go on or, if no results are reached, end.
    Xeno-transplantation, gene therapy, the human genome project, bio-
fuels, stem cells, synthetic biology, personalized medicine, nanomedicine,
brain sciences, neurotechnology, wearable sensors, blockchain technolo-
gy, autonomous vehicle, cultured meat, etc., have all followed such cycles
of promise-requirement. The sociology of expectations allows us to ana-
lyze emerging science and technology in an alternative way to the popular
hype-disillusionment curve, a tool developed in the 1990s by the Gartner
company for bench-marking innovations, which, however, organizes, and
takes part in, the market of promises (Pollock and Williams 2010; 2016).
    Emerging technosciences often need a period of sustained promising
before acquiring their own impetus. Referring to biotechnology in gen-
eral, and genetically modified organisms and nanotechnology in particu-
lar, Joly states that promises pass tests of credibility (2010; 2015). These
can occur either following an R&D event or an incident on the market, or
42                                                          Tecnoscienza – 13 (2)


due to a lack of alignment between the actors, or because the technology
is contested, or simply because of the time that has passed without signif-
icant advances. Sometimes hype rebounds, while in other cases the result
is a cold shower, as happened to the bold promises of gene therapy in
2000. When concrete results do not meet expectations, the promissory
field in most cases retreats from the spotlight. Although it seldom disap-
pears completely, it attracts far less enthusiasm and money, leaving stake-
holders with their belief and lost investments in the venture. Usually,
nothing highly problematic happens in these cases, which are probably
much more numerous than those ventures that eventually reach the mar-
ket. Nobody can be held accountable for those promises that are not de-
livered on, except in rare cases where patients have been misled, for ex-
ample, or fraud is proven, as in the case of Theranos which led to the
conviction of its managers.2 This case has shed light on the methods of
promising used in Silicon Valley, as summarized by the popular positive-
thinking aphorism “fake it till you make it.”
     The sociology of expectations investigates the market of scientific
promises and highlights that future visions do not merely accompany sci-
entific and technological development, but are a part of it. They play var-
ious roles, such as creating excitement, orienting, coordinating research
efforts, and drawing road maps. Hype can be fabricated, sustained if nec-
essary, and revamped if it has vanished (Audétat et al. 2015).
     Nevertheless, the framework of the promise-requirement cycle, to-
gether with the concepts of performativity and credibility, does not al-
ways work. Indeed, there are cases of promises which do bring results,
but which are either not attractive enough for venture capitalists or both-
er vested interests, and are thus abandoned after one or two rounds of
funding (Parandian et al. 2012). Meanwhile, although they fail to bring
promised breakthroughs, credibility and money continue to be granted to
certain emerging technologies, as if there is no requirement to deliver, or
more precisely, as if that obligation could perpetually be postponed.
These expectations appear to have a second, third, or even eternal life,
although their plausibility is sometimes far from being established.
     The lesson of these cases is that finance is to some extent the master of
the game. But they also show that the game, i.e., engaging in new endeav-
ors then picking the winners, does not exactly work this way. Promise-
requirement cycles are also conditioned by how much popularity promis-
es manage to attract. Popularity here is connoted neither positively nor
negatively, in the sense of reflecting either enthusiasm or concern. Simply
put, the more popular a promise is, the more compelling it is for stake-
holders. Thus, another arena is in play, just as critical as finance, which
has to do with the performativity and popularity of promises in society,
i.e., which includes, and goes beyond, actor networks. This arena, which
is mainly located in the media sphere, connects with cultural representa-
tions and the true power of myth.
Audétat                                                                      43

5. Nanotechnology, or the “Regime of Promising”

    Promising technosciences have proven to be highly speculative and at-
tracted growing attention. A threshold was crossed when nanotechnology
started to be advertised following the adoption of the US Nanotechnolo-
gy Initiative (2001). Fantastic stories of the future, mixed with the dis-
course of transhumanism and fostering human enhancement, left many
stakeholders uneasy, especially in science (where people have informed
views about feasibility and different opinions about the ethics involved in
promising) and European science policy. The sociology of expectations
developed further, and STS specialists were “hired” by decision makers
in the EU, who did not know how to react other than by funding nano-
technology to stay competitive. Attempts to discuss with science policy
makers and stakeholders, and to engage more responsibly with the pub-
lic, resulted in a report to the EU Directorate of Research (Felt and
Wynne 2007), in which the “economy of technoscientific promises” was
subject to discussion and counterbalanced with an alternative possibility
called the “regime of collective experimentation,” in an effort to create
more commitment and engagement with European society. The EU final-
ly responded with guidelines for “responsible research and innovation,”
sidelining the 7th Framework Programme of Research (2007-2013).
    Questions were asked and the word “plausibility” was in the mouths
of many when the bold discourse about convergence at the nanoscale,
nano-machines, and connecting brains with machines and brains with
brains (Rocco and Bainbridge 2002) was promoted as an absolutely cer-
tain future, as only a matter of time (Schummer 2010). Speculation about
future technological achievement has often been found to be completely
disconnected from what was actually going on in laboratories. Technical
plausibility could be understood as an arena of negotiation of trust and
credibility, while societal plausibility opens discussion about the desirability
of certain technoscientific developments (Selin 2007; Lucivero 2016). The
concept of anticipatory governance was elaborated in order to open space
and provide methods for deliberation and assessment between stakeholders
and society (Guston 2012; Konrad et al. 2016). Nonetheless, the ethics of
promising came to be seen as completely unbound – with nanotech being
only one particular case – and matters of principle were raised for discus-
sion, for example, whether running ethical, legal, and social impact studies
(ELSI studies) on technology whose plausibility was still in question was
not granting it credibility (Nordmann 2007; Sand and Schneider 2017) and
diverting technology assessment from more concrete and urgent issues.
    With the umbrella term “nanotechnology,” another direction in the
sociology of expectations has been explored following the observation
that “struggle for meanings”3 was taking place, opposing quantum phys-
ics to chemistry, bionano, micro-mechanics, neurotechnology, and so on.
Every domain of science wanted its piece of the cake, producing “nano-
44                                                           Tecnoscienza – 13 (2)


narratives” (Milburn 2002), with the result that “folk theories” (Rip 2006)
circulated on a massive scale in a kind of competition of speculative science.4
     In short, the “economy of promises” became an issue in itself. The
term came to be used by many people to describe what they had to deal
with. Richard Jones, a British physicist involved in science policy, who
engaged as much with the scientific and technical debates about nano-
technology as with its societal implications, was among the first to talk of
an “economy of promises” (Jones 2008) and started a blog in 2004 in or-
der to promote informed debate.
     It is important to note that the claim here is not that present scientific
expectations are more fantastic than past ones. For instance, from the
1960s to 1980s (or later), it was thought certain that science would always
provide new solutions to production problems or societal challenges. En-
ergy production, which was seen as an exemplar, was said to be ever in-
creasing at ever-decreasing costs. New modes of energy production (e.g.,
nuclear fusion, anti-matter) would continue to be discovered and exploit-
ed. It was believed that this logarithmic progression would go on until the
point when an infinity of energy could be produced for free. It is hard to
believe today. At the time, though, the industrial world was at the tail end
of several decades of economic growth. This example is meant to show
that future visions of technoscience should be understood in relation to the
economics, society, and environment of their time. As such, past visions of
the future are not less or more fantasies, or phantasma, than contemporary
visions like “convergence at the nanoscale” or the “singularity.” These ex-
amples also tend to show that plausibility is perhaps less important than –
albeit a part of – the promotion of a certain technoscientific imaginary.
     A touchstone of the architecture of the sociology of expectations was
the formulation of the definition of socio-technical imaginaries: beliefs “col-
lectively held, institutionally stabilized, and publicly performed visions of
desirable futures...” (Jasanoff and Kim 2015, 6). Although “desirable fu-
tures” may not necessarily be the case, as we shall see, the value of this def-
inition is to state that the condition is to be shared across various milieus,
including in the public – meaning beyond stakeholders’ circles, beyond ac-
tors’ networks, which pushes this sociological enquiry into new areas.
     The meaning and definition of the term regime of promising was
forged by and with Arie Rip. To begin with, the word “expectation” is
perhaps not entirely convincing for the object of study. On the one hand,
it is good for potentially encompassing all kinds of horizons of expecta-
tion, including those of stakeholders and those of citizens. On the other
hand, it has a passive meaning. The term “pro-spect” might be better:
“prospecting” meaning “looking forward” in Latin, indicating an active
bet on the future. Indeed, Brown and colleagues speak of “prospective
techno-science” (2000). This is perhaps a reason why the word “promise”
came to be used more frequently. Moreover, the adjective “promissory” is
Audétat                                                                    45

a quality attributed to something, while the verb “promising” indicates
what proponents are doing and aiming at.
    We came to speak, with Arie Rip, of a regime of promising for the
speculative market, standing above research and innovation, which has
consequences for the whole research system, for academia, and from
which no discipline is protected.5 For domains of science actually not
perceived as competitive enough, the situation has often become “prom-
ise or perish” (Audétat et al. 2015). In Rip’s view, a regime is not an insti-
tution: it holds as long it is fed by various flows coming together. It fol-
lows that it can break or collapse if one flow – or all flows – diminish, if
trust disappears. Rip wrote an “STS fiction” about how this could happen
in the near future, sketching a change in science policy that now asks for
proof of concept before funding, prioritizing translational research, after
a dramatic decrease of trust in the current system of scientific reputation
and allocation of money (Rip 2015). Another proposition, coming from
French scientists disgusted with the competition and the percentage of pro-
jects granted against the total number of applications (13-17%, according
to estimates from the 2010s), would be to grant research funds by lottery.
Competition in promising would decrease; fairness would improve.
    The flows feeding the regime of promising are: 1) financial, the money
granted to research on a competitive base; 2) scientific, the number of re-
searchers applying for funding; and 3) communicational, the appetite in
the media for technoscientific breakthroughs and future visions. The re-
gime of promising technosciences results from an increase in competition
at all these different levels. Global competition for technological leader-
ship became more intense when China entered the game as a new player
around the turn of the millennium. The term of “knowledge economy”
has been used in the EU Lisbon Agenda (2000) as an answer to this per-
ceived elevation of competition. More money has been granted to com-
petitive technosciences, like the Flagship scheme, granting about a billion
euro to promising fields. The Human Brain Project, as well as Graphene,
obtained such strategic funding after, literally, campaigns of promising.
Another competition, in addition to that between nation-states, is the one
between different promising fields of technosciences, as well as within
individual fields. The third type of competition which is added to these
two takes place in the media sphere. It is perhaps this latter battlefield
that is least well understood. The sociology of expectations is mainly inter-
ested in the functioning of visions, promises, and imaginaries within science
policy and innovation systems, and less in the massive circulation, rewriting,
and popularity of socio-technical imaginaries, including their cultural
meanings. The bridge between STS and cultural studies always existed, alt-
hough the linkage of popular culture with promises and innovation, as, for
example, in Magaudda (2012), remains too rare and is to encourage.
46                                                          Tecnoscienza – 13 (2)


6. Storytelling and the Economy of Attention

    Yves Citton originally came from the field of arts and literature, later
becoming a media sociologist who caused an earthquake in this field in
the French-speaking world with Mythocratie (2010), Pour une écologie de
l’attention (2014), and Médiarchie (2017).6 Citton traces back the concept
of the economy of attention, explaining how it came to dominate the me-
dia and cultural spheres. The concept is much older than the Internet,
although it really became critical in relation to it from end of the 1990s.
The economy of attention then rapidly came to be considered critical to
work in and understand the digital transformations taking place in the
media sphere (Citton 2014).
    The basic idea of the economy of attention is that, for example, when
it comes to the number of movies you can go watch downtown or at
home in front of your television, or the number of novels published every
year, it is simply impossible to pay attention to everything: the offer very
much exceeds what you can watch or read. Any economy relies on re-
sources, and in this case, the rare and limited resource is the attention an
individual can pay each day, or week, which can by no means be in-
creased or multiplied above a certain ceiling. With the globalization of
access to the web, since the emergence of mp3s, YouTube, online video
games, social networks, and more recently streaming platforms, the in-
formation and cultural goods available have increased manyfold, whereas
the resource of attention has remained the same. As a matter of conse-
quence, attracting attention became the main struggle for any informational
or cultural product. The competition has only become fiercer and fiercer.
    A whole business developed, very much centered on advertising pro-
fessionals, which is concerned with how best to capture attention, main-
taining it, creating addiction. According to Citton, at the turn of the mil-
lennium politicians, communicators, and managers discovered a treasure:
storytelling. Storytelling came to be the privileged mode of communi-
cating anything. Recipes and toolboxes multiplied that were aimed at
helping advertisers, web managers, and business managers learn how to
attract attention, notably through storytelling.7
    As mere consumers (which we all are), it was difficult to understand
why more and more things were made available for “free,” and how so
many costly informational services became “free,” but we became accus-
tomed to accessing things for “free.” Of course, advertisement was there
to explain part of this strange new economy – although it was difficult to
understand all of its implications, until the following saying started to cir-
culate widely: “If something is free, then the real product is you.” So,
nothing is for free: we are paying with our attention. It started to become
clear that accumulating attention was equal to accumulating capital. The
way in which the economy of attention has unfolded has numerous con-
sequences, including for the print press and the public sphere as a whole.
Audétat                                                                   47

It became a matter of survival for musicians, authors, journalists, and
filmmakers. Since then, all cultural goods struggle to be noticed. Science
and technology were subjected to the economy of attention as well, and
promising conformed with these trends by producing catchy images, sto-
ries, and videos personifying desirable futures. SF writers and social sci-
entists were asked, if not hired, to contribute to creating stories and writ-
ing or filming about how life would look in the future thanks to techno-
sciences (Milburn 2002). The old scientific ethos of distancing oneself
from common sense and the media was completely turned upside down.
Even protected by its own rules, academia is subject to these trends de-
termining how to stay afloat in the ocean of available (scientific) infor-
mation. This generates bias. Take, for example, the two Stanford scholars
who, in 2017, presented themselves as whistleblowers, warning that deep
learning can now guess people’s sexual orientation by analyzing their fac-
es and thus determine if someone is gay (Baya-Laffite et al. 2018). Part of
the explanation is that breaching ethical conventions has been a way to
come to the fore, to get noticed and quoted.
    Mythocratie, the title of Citton’s book, is meant to speak to and restore
the power of myth, the power of stories. The targets of stories are desires,
values and identity – all dimensions that Citton collectively refers to as
“affects,” i.e., as what moves people. An individual’s personal story,
which facilitates identification by the audience, is central to these strate-
gies. The rules of telling a story efficiently or touching people’s emotions,
together with all the tricks to attract attention, are about mind control.
Yet, were the technosciences sheltered from the economy of attention?
By no means. In fact, promising itself has been subjected to it, and
pushed further and further by fabricating successful breakthroughs, pro-
moting champions, and visioneering. Scientific expectations require the
broadest possible popularity in order to influence stakeholders. Thus, the
regime of promising may well have been shaped by the economy of atten-
tion, eventually explaining “why so many promises.”


7. “Half of All Jobs Will be Automated by 2034”

    Recalling the classroom dialogue reported at the beginning, let us con-
sider how the story of job replacement by machines started, unfolded,
and then calmed down. Around 2012, excitement was triggered by vari-
ous mathematical paradigms and methods that were competing in the al-
gorithm industry. Neural networks – a paradigm dating back to the 1960s
that had been developed during the 1990s, before being left aside by in-
dustry in favor of statistical methods – were resurrected by computer sci-
entists prior to 2010. Neural networks allowed automatic translation to
achieve better percentages of correct matches against other more cumber-
some methods relying on databases and heavy statistical work. Research-
48                                                           Tecnoscienza – 13 (2)


ers also found that graphic cards (GPU) made for computer games can
run neural networks.8 Whether the return and take off of neural networks
represented a breakthrough or was the result of continuous improvements
remains controversial in computer science (Pasquinelli 2019). It has, how-
ever, been presented as a breakthrough and the term “deep learning” has
been popularized as a metaphor for truly abstract mathematical work.
    Just as the enormous hype about the “third spring of artificial intelli-
gence” was gaining steam, two economists at Oxford University pub-
lished a study conducted with the help of an algorithm about the impact
of AI on the job market. Their aim was to investigate the probability of
jobs being automated. They based their study on expectations concerning
the combination of AI and mobile robotics. They took into account the
prospects of algorithms being capable of equalling or exceeding humans
in performing tasks, based their estimates on the skills machine learning
was expected to attain, and took for granted applications envisaged as
likely to be industrialized, like self-driving cars or holding a conversation.
Then, they considered the many tasks included in jobs (whether routine
or non-routine) that AI could theoretically do. They took for granted that
algorithms can perform without human error and that they are by defini-
tion unbiased. They also excluded tasks entailing what they called emo-
tional, creative, or social intelligence. They then turned to a US list of 900
occupations whose description is detailed, standardized, and kept up to
date, finding that 702 were suitable for submission to the algorithmic
method of probabilistic analysis. By the way, they made the classic mis-
take of confusing work and employment, concluding that many jobs in
agriculture, industry, and especially in the service sector, like transporta-
tion, sales, call centers, accountancy, cleaning, household chores, food,
mail, healthcare, etc., were threatened by AI and robots. Their computa-
tional method resulted in an estimate that 47% of US jobs were at risk of
being automated in the near future. The resulting article was M. Osborne
and C.B. Frey, “The Future of Employment: How Susceptible Are Jobs
to Computerisation?” published in September 2013.
    It took some time for the study to hit the headlines, beginning (per-
haps) with the Huffington Post publishing a series of articles: “About a
50/50 chance a computer threatens to steal your job: paper,” and “47%
of all jobs will be automated by 2034, and no government is prepared says
economist.”9 A few weeks later, the quote appeared absolutely everywhere:
in the press, in online media, on television, and on social networks. It creat-
ed one of the biggest buzzes of all time. If we could model its diffusion day
by day over a few months and count the number of quotes, we would see a
chain reaction. The story of job replacement soon reached everybody.
    The Osborne and Frey study gave rise to escalating announcements,
predictions, and simplifications, with a particular appetite for the disrup-
tive impact AI was supposed to have on employment and society. To
name just a few examples, in 2015, Merrill Lynch Bank:
Audétat                                                                           49

    predicted that by 2025 the “annual creative disruption impact” from AI
    could amount to 14-33 trillion $, including [...] 8 trillion $ reduction in
    employment costs in manufacturing and healthcare.10

    McKinsey Global suggested that by 2030, intelligent agents and ro-
bots could replace as much as 30% of the world’s current human labor,
from 400 to 800 million jobs, and that the transformation of society is
“happening ten times faster and at 300 times the scale, or roughly 3,000
times the impact” of the (classic) Industrial Revolution. Some voiced their
belief that 99% of all jobs would disappear. A modest study came to con-
clude that there is “a 50% chance of AI outperforming humans in all
tasks in 45 years.” (Grace et al. 2017).
    With these announcements, anybody can construct his or her own
story about the future of employment and society. Indeed, many people
entered the game of prediction. For example, Yuval Harari (the author of
Sapiens) wrote in The Guardian that “by 2050 a new class of people might
emerge – the useless class, people who are not just unemployed, but un-
employable.”11 In the French media sphere, for a couple of years, a physi-
cian assumed the role of a techno-prophet announcing the darkest-
possible future. There was not a single day that he did not appear in the
media. L. Alexandre especially targeted the education system, declaring
on Swiss television that “schools are teaching pupils who will be wrecked
by AI.”12 That was a few days before I came to the high school and heard
the echo of this dark prediction in the mouth of the pupil.
    More cautious studies about the prospected impact of AI on jobs have
been conducted and published, but their voices could hardly be heard.
For example, in 2014, the Pew Research Center published an expert’s
predictions that job loss was being balanced by the job creation resulting
from AI and robotics, but its media impact was close to zero compared to
Osborne and Frey, most probably because there were no striking results
or figures. The excitement about the disruption of employment prompted
many countries, think tanks, and research institutions to conduct studies
on the same topic. Schlogl et al. examined about 200 reports about “the
future of work” published between 2013 and 2018, two-thirds issued in
2017 and 2018 (Schlogl et al. 2021), i.e., directly as a consequence of the
buzz created around AI that the study of Osborne and Frey contributed
to. Unsurprisingly, the main generators of that kind of promising are big
tech corporations. For four years, the hype around AI, which eventually
attracted investment, pressured all countries to come up with a strategy in
order to compete. It performed dominantly in the dystopian genre.
50                                                              Tecnoscienza – 13 (2)


8. Why Have Pessimistic Promising of Disruption and AI
Performed So Well?

    A phenomenon already observed in relation to promising concerning
nanotechnology (and other emerging sciences and technologies) is that
many computer scientists felt uncomfortable with storytelling that was
supposed to support their field. In the media, many experts have been
saying that all this excitement is merely speculative. But it took time be-
fore it was possible to hear the more cautious voices. Again, to name
some examples, experts stated that the idea of a “general artificial intelli-
gence,” which would make machines capable of competing with any kind
of human agency, is groundless (Pasquinelli 2019). But the idea stimulates
people (including computer scientists) to tell imaginative stories about
the future. Reacting to the Osborne and Frey study about job losses and
other prophecies, Rodney Brooks, a pioneer of AI, wrote an article enti-
tled “The Seven Deadly Sins of AI Predictions: Mistaken extrapolations,
limited imagination, and other common mistakes that distract us from
thinking more productively about the future.”13 François Blayo, a profes-
sor in computer sciences and AI at the technical university HEIG-Vaud,
in his address to a diverse audience, began by saying “Please, calm
down.”14 Zachary Lipton, professor at the machine learning department
at Carnegie Mellon University, has stated:

     […] people are afraid of the wrong things. [...] There are policy makers
     earnestly having meetings to discuss the rights of robots when they should
     be talking about discrimination in algorithmic decision making.

and concluded that:

     […] the interest in “machine learning” and “deep learning” has led to a
     deluge [...] of misreprentation of research for the purpose of generating
     retweets and clicks.15

    Although disconnected from computer science, the story of robots
taking all the jobs, that of general artificial intelligence, like that of colo-
nizing Mars, indicate that plausibility is less important than the building
of an imaginary in the population.
    In March 2018, a series of events put an end, almost overnight, to this
period of unbound hype and techno-prophecy. On March 18, an auton-
omous vehicle in trial by Uber killed a woman crossing the road at night
in Tempe, Arizona. Other lethal accidents involving the use of the autopi-
lot device in Tesla cars were then reported. The Cambridge Analytica
scandal, although already known by a few people, aired that same month in
2018, revealing the malpractice that played a role in the Brexit referendum
of June 2016, as well as in the election of Donald Trump in November of
Audétat                                                                    51

the same year. Subsequently, the promising of AI, and especially the tone of
disruption and dystopia calmed down. The number of references in the
media sphere to self-driving cars, AI, and the job market dropped. More
reasonable voices started to be heard. An OECD report that same year
came to conclude that only 9% of jobs in the US were at risk, while 32%
were at risk of important changes in relation to automation. A new study by
McKinsey conducted in December 2017 concluded that, at around 2030,
rather than a loss, there would be more likely a 14% increase in jobs.
    Explaining “why pessimistic promising of disruption performed so
well” requires us to go back to the previous decades. From the 1960s to
the mid-1990s, economic growth and competitiveness was almost the sole
justification given for funding technoscientific research, until one day this
discourse centered on economic benefits went flat, no longer performing
in a competitive environment. A need was felt to re-enchant the scientific
endeavor, which could help emerging science and technology to create
excitement. At the end of the 1990s, when struggles to attract attention
became more severe, grand stories of technoscientific future made a
comeback. Through the promises of eliminating non-infectious diseases,
curing cancer, anti-aging, convergence at the nanoscale, etc., promising
was turned toward a bright future. But after some time, it perhaps became
too bright to be credible anymore. Therefore, bright futures have been
overshadowed by more dramatic ones, futures of disruption, according to
the new vocabulary of Silicon Valley. Disruption, which goes together with
a dark future, became more attractive and more credible in the economy of
attention. Performing with pessimism became the better approach, speak-
ing of dramatic impacts on jobs, rather than repeating that technoscience
will find solutions to everything. After nanotechnology and human en-
hancement started to lose popularity around 2010, venture capitalists and
other stakeholders were glad to turn to societal disruption and AI.
    At the same time, a TV series understood better than anybody what
was happening. Black Mirror premiered in 2013 on the UK’s Channel 4.
Perhaps in opposition to nanotechnology, which hardly materialized in
daily life, the smart phone, together with the algorithms directing adver-
tisement that everybody experienced, was already making AI concrete.
The success of Black Mirror shows that creating scenarios can serve dif-
ferent ends. In the case of the TV series, writing scenarios of anticipation
that put technosciences at the center can result in very attractive and
thoughtful entertainment. The promises of technosciences and future vi-
sions are performed by and for stakeholders, but at the same time, they
acquire a life of their own in the media and cultural spheres, which then
takes part in stabilizing or contesting socio-technical imaginaries.
    The question then arises: do stakeholders and other individuals be-
lieve in the technoscientific promises they are exposed to? The verb “be-
lieving” is problematic, since it is still associated with religious truth and
revelation. To understand what is at stake here, one should turn to theory
52                                                             Tecnoscienza – 13 (2)


of representation in the arts. It is important to see, first of all, that people
are not passive receptors of promises. On the contrary, there is always an
act of reading implied. In light of differences, individuals may ignore, re-
ject, doubt, or buy a promising discourse. Stories do not merely circulate,
since many people elaborate on stories and rewrite them, scenarising fur-
ther. Yet, what happens when we read a novel (e.g., an SF novel) or
watch a movie? Citton refers to the philosopher Kendall L. Walton, the
author of Mimesis as Make-Believe: On the Foundation of the Representa-
tional Arts, published in 1990 (Citton 2010, 81). The theory of arts speaks
of “suspension of disbelief,” i.e., we suspend for a while our doubts and
skepticism about reality and credibility, in order to get into the story.
Crafting and reading stories are thus part of a game of “make-believe.”
Usually understood in relation to cultural works alone, the game of
“make-believe” may well be at work in promising science and technology.
Do people believe in some particular promising vision? In fact, people
decide whether to take it or leave it.


9. Fiction as Method

    From the lab to the market, the success of innovations is highly uncer-
tain, which is one reason why, in order to secure funding, promising is
often assertive and technoscientific futures presented in deterministic
terms. This is why scientific promises are fictions, although presented as
future facts. In Arie Rip’s words, scientific promises are to be approached
as a literary genre (personal communication). Fictions and stories are es-
sential to introducing or attempting to stabilize a socio-technical imaginary,
which in turn is paramount in order to drain investment. It explains that
technosciences need so much to play in the economy of attention, i.e., “why
so many promises”. Meanwhile, the bubbling of technoscientific promises
makes the future more opaque, exaggerated hype is misleading (Joly 2010).
Therefore, fiction has to be taken seriously, from an analytical point of
view, as well as a method of engagement – the latter in order to enlarge
space where promises and visions can be discussed, their desirability evalu-
ated or contested. The use of fiction is a method for countering this opacity
and fostering a debate about what is desirable, possible, and a priority.
    Citton forged two concepts that may be of interest for our purposes,
that of scenarisation and that of “contre-fiction,” i.e., counter-fiction (Cit-
ton 2012). He elaborates on scenarisation beyond its meaning of staging:
whereas narration is the art of telling a story, scenarisation concerns how
to meet desires, affects, values, beliefs, and ultimately how to influence
behaviors. The news are not given “reality,” but always a mix of fact and
fiction (Citton 2010; 2012), and telling stories, controlling the stories in cir-
culation, is critical for governing, making war, or preparing society for some
change. In the domain of promising technoscience, scenarisation would be
Audétat                                                                            53

the art of influencing behavior, above all of stakeholders, and preparing ac-
ceptance of technosciences throughout society. Scenarisation is to be seen
as the continuation of the analysis of the performativity of promises.
    Yet, to call into question fictions presented as future facts, one can
produce fictions of another genre, counter-fictions. Brave New World was
a counter-fiction to Haldane’s vision of ectogenesis. Arie Rip’s short story
about how the regime of promising can collapse is a counter-fiction. Black
Mirror is undoubtedly a masterpiece of counter-fiction. As Isabelle Sten-
gers explains, following author Donna Haraway (1996), “we need new
types of narratives.”16 Counter-fiction can take the form of a picture, a
story, or an essay, of a movie or a documentary. Collective participatory
scenario-building is another method. Since the social sciences have ex-
cluded thought experiments, SF literature has been the place for alterna-
tive and reflexive stories reacting to the dominant stories about techno-
science, power, economics, environment, women, and colonialism. The
SF genre is diverse, although a series of authors have openly endorsed this
commitment theorized by Citton and Haraway, such as John Brunner,
Ursula Le Guin, Norman Spinrad, Margaret Atwood, and today Alain
Damasio or Octavia Butler (to name a few). Counter-fiction does not
mean contesting the plausibility of promising technoscience, but rather
opening the deterministic boxes it is usually contained in. STS should be
able to model the diffusion of stories in the media sphere at the time of
their occurrence, to conceive counter-fictional materials and scenarios,
and to engage with the public, in order to allow a debate about what
technoscience could be, or should be, and to help disentangle the future.


Notes
    1
      For an in-depth historical, sociological, and philosophical account of the term
“technoscience,” see Bernadette Bensaude-Vincent (2009).
    2
      Le Temps, September 3, 2021; New York Times, January 4, 2022; The Guardian,
October 17, 2022.
    3
      The Struggle for Meanings: Representation and Debates in the Nanotechnology
Field, session convened by Arianna Ferrari, Andrea Lorenzet, Marina Maestrutti and
Federico Neresini, EASST Conference, Trento, September 2-4, 2010.
    4
      Nanotechnology promises were not all highly speculative. Some were in play
closely connected to laboratory work. As an example, see Crabu (2014), who analyses a
promissory object existing besides the bold promising of nanomedicine.
    5
      A secondary meaning is conveyed by the plural “regimes of promising,” referring
to particular conditions found when a broad promise is translated into the particular
conditions of a country, for example, or for differing conditions when speaking of
green electricity or personal medicine for which specific system of innovation and ac-
countability are found (Robinson et al. 2021).
54                                                                     Tecnoscienza – 13 (2)

     6
       Citton also publishes in the associative multi-journals Multitudes, revue politique,
artistique, philosophique (www.multitudes.net), which is meant as a continuation of
Futur antérieur (1990-1997) created by Toni Negri and is inspired by the Italian collec-
tive of authors known as Wu Ming. It contains several journal’s titles, where many dif-
ferent issues are approached without unnecessary disciplinary borders in the human
and social sciences.
     7
       Citton mentions as an example T. Davenport and J. Beck (2001) The Attention
Economy: Understanding the New Currency of Business.
     8
       Sussan R. (2014, December 31) Le “deep learning” pour tous?. Internetactu.net.
http://www.internetactu.net/2014/10/02/le-deep-learning-pour-tous/.
     9
      ! Strachan, M. (2013, September 14) Huffpost. https://www.huffpost.com/ en-
try/computer-jobs_n_3926922.
Rundle, M. (2014, January 17) Huffpost. https://www.huffingtonpost.co.uk/
2014/01/17/rise-of-the-machines-economist_n_4616931.html.
     10
        The Economist (2016, June 25) The return of the machinery question, p. 3.
     11
        Harari, Y.N. (2017, May 8) The meaning of life in a world without work. The
Guardian. https://www.theguardian.com/technology/2017/may/08/virtual-reality-reli-
gion-robots-sapiens-book.
     12
        RTS Info (2017, October 3) L’école forme des enfants qui vont être laminés par l’IA.
     13
        Brooks, R. (2017, October 6) The Seven Deadly Sins of AI Predictions: Mistaken ex-
trapolations, limited imagination, and other common mistakes that distract us from think-
ing more productively about the future. MIT Technology Review. https://www.techno-
logyreview.com/2017/10/06/241837/the-seven-deadly-sins-of-ai-predictions/.
     14
        Blayo F. (2018, August 23) Voyage au centre de l’IA, Numerik Games Festival,
Yverdon-les-Bains.
     15
        Quoted by O. Schwarz (2018, July 25) “The discourse is unhinged”: How the me-
dia gets AI alarmingly wrong. The Guardian.
     16
        Isabelle Stengers, professor at the Université Libre de Bruxelles, and Fabrizio
Terranova, director of the movie Donna Haraway: Story Telling for Earthly Survival,
2016, 77’, invited at the Haute école de travail social (HETSL) and the University of
Lausanne, February 6-7, 2018.


References

Atlan, H. (2005) L’Utérus artificiel, Paris, Seuil.
Audétat, M. et al. (2015) Sciences et technologies émergentes: Pourquoi tant de
    promesses?, Paris, Hermann éditions.
Baya-Laffite, N., Beaude, B. and Garrigues, J. (2018) Le deep learning au service
    de la prédiction de l’orientation sexuelle dans l’espace public: Déconstruction
    d’une alerte ambiguë, in “Réseaux”, 5(211), pp. 137-172.
Bensaude-Vincent B. (2009) Les vertiges de la technoscience, façonner le monde
    atome par atome, Paris, La Découverte.
Borup, M., Brown, N., Konrad, K. and Van Lente, H. (2006) The Sociology of Ex-
    pectations in Science and Technology, in “Technology Analysis & Strategic
    Management”, 18 (3/4), pp. 285-298.
Brown, N. (2003) Hope Against Hype: Accountability in Biopasts, Presents, and
    Futures, in “Science Studies”, (16)2, pp. 3-21.
Audétat                                                                                  55

Brown, N., Rappert, B. and Webster, A. (2000) Contested futures: A sociology of
    prospective techno-science, Aldershot, Ashgate.
Cazes, B. (1986) Histoire des Futurs: Les figures de l’avenir de saint Augutin au
    XXIe siècle, Paris, Seghers.
Citton, Y. (2010) Mythocratie, Storytelling et imaginaire de gauche, Editions Amsterdam.
Citton, Y. (2014) Pour une écologie de l’attention, Paris, Seuil.
Citton, Y. (2017) Médiarchie, Paris, Seuil.
Citton, Y. (2012) Contre-fictions: Trois modes de combat, in “Multitudes”, 1(48), pp. 72-78.
Crabu, S. (2014) Nanomedicine in the Making: Expectations, Scientific Narrations
    and Materiality, in “Tecnoscienza”, 5(1), pp. 43-66.
Dessibourg, O. (2013) Blurring lines between science journalism and communica-
    tion, in “Bulletin of Swiss Association of Science Journalism”, 3, pp. 6-7.
Felt, U. (1993) Fabrication Scientific Success Stories, in “Public understanding of
    Science”, 2, pp. 375-390.
Felt, U. and Wynne, B. (2007) Taking European Knowledge Society Seriously, Re-
    port of the Expert Group on Science and Governance, VIth Framework pro-
    gram of research, European communities.
Grace, K. et al. (2017) When will AI Exceed Human Performance? Evidence from
    AI Experts, in “Artificial Intelligence Research”, 62, pp. 729-754.
Guston, D.H. (2012) The Pumpkin or the Tiger? Michael Polanyi, Frederick Soddy,
    and Anticipating Emerging Technologies, in “Minerva”, 50(3), pp. 363-379.
Jasanoff, S. and Kim, S-H. (2015) Dreamscapes of Modernity: Sociotechnical Imagi-
    naries and the Fabrication of Power, Chicago, The University of Chicago Press.
Joly, P.-B. (2010) On the Economics of Techno-Scientific Promises, in M. Akrich,
    Y. Barthe, F. Muniesa and P. Mustar (eds.), Débordements, Mélanges offerts à
    Michel Callon, Paris, Presses des Mines, pp. 201-221.
Joly, P-B. (2015), Le régime des promesses scientifiques, in M. Audétat et al. (eds.),
    Sciences et technologies émergentes: Pourquoi tant de promesses?, pp. 31-47.
Jones, R. (2008) The Economy of Promises, in “Nature Nanotechnology”, 3, 65-66.
Konrad, K., Van Lente, H., Groves, C. and Selin, C. (2016) Performing and Gov-
    erning the Future in Science and Technology, in S. Jasanoff, G.E. Markle, J.C.
    Peterson and T. Pinch (eds.), Handbook for Science and Technology Studies,
    Cambridge, MIT Press.
Magaudda, P. (2012) Innovazione Pop: Nanotecnologie, scienzati e invenzioni nella
    popular culture, Bologna, Il Mulino.
Milburn, C. (2002) Nanotechnology in the Age of Posthuman Engineering: Science
    Fiction as Science, in “Configurations”, 10, pp. 261-295.
Nelkin, D. (1995[1987]) Selling Science: How the Press Covers Science and Tech-
    nology, New York, W.H. Freeman and Company.
Nordmann, A. (2007) If and Then: A Critique of Speculative NanoEthics, in
    “NanoEthics”, 1, pp. 31-46.
56                                                                    Tecnoscienza – 13 (2)

Parandian, A., Rip, A. and Kulve, H. (2012), Dual Dynamics of Promises, and
    Waiting Games around Emerging Nanotechnologies, in “Technology Analysis
    and Strategic Management”, 24(6), pp. 565-582.
Pasquinelli, M. (2019) How A Machine Learns And Fails, A Grammar Of Error
    For Artifical Intelligence, in “Spheres”. Available at: https://spheres-
    journal.org/contribution/how-a-machine-learns-and-fails-a-grammar-of-error-
    for-artificial-intelligence/.
Pollock, N. and Williams, R. (2010) The business of expectations: How promissory
    organisations shape technology and innovation, in “Social Studies of Science”,
    40(4), pp. 525-548.
Pollock, N. and Williams, R. (2016) How Industry Analysts Shape the Digital Fu-
    ture, Oxford, Oxford University Press.
Rip, A. (1994) The Republic of Science in the 1990s, in “Higher Education”, 28, pp. 3-23.
Rip, A. (2006) Folk Theories of Nanotechnologies, in “Science as Culture”, 5, pp. 349-365.
Robinson, D.K.R., Audétat, M., Joly P-B. and Van Lente, H. (2021) Enemies of
    the future? Questioning the regimes of promising in emerging science and tech-
    nology, in “Science and Public Policy”, 48(6), pp. 814-817.
Roco, M.C. and Bainbridge, W.S. (2002) Converging Technologies for Improving
    Human Performance: Nanotechnology, Biotechnology, Information Technology
    and Cognitive Science, NSF/DOC-sponsored report, Arlington, VA, National
    Science Foundation.
Ruef, A. and Markard, J. (2010) What Happens after a Hype? How Changing Ex-
    pectations affected Innovation Activities in the Case of Stationary Fuel Cells, in
    “Technology Analysis and Strategic Management”, 22(3), pp. 317-338.
Sand, M. and Schneider, C. (2017) Visioneering Socio-Technical Innovations: A
    Missing Piece of the Puzzle, in “Nanoethics”, 11, pp. 19-29.
Schummer J. (2010) From Nano-Convergence to NBIC-Convergence: The best way to pre-
    dict the future is to create it, in M. Kaiser, M. Kurath, S. Maasen and C. Rehmann-
    Sutter (eds.), Governing Future Technologies: Nanotechnology and the Rise of an As-
    sessment Regime, Sociology of Sciences Yearbook, Springer, pp. 55-71.
Smith, A. and Anderson, J. (2022). AI, Robotics, and the Future of Jobs, in “Pew Research
    Center: Internet, Science & Tech”. Available at: https://www.pewresearch.org/ in-
    ternet/2014/08/06/future-of-jobs/.
Van Lente, H. and Rip, A. (1998) Expectations in technological developments: An
    example of prospective structures to be filled in by agency, in C. Disco and B.
    Van der Meulen (eds.), Getting New Technologies Together: Studies in Making
    Sociotechnical Order, Berlin, Walter De Gruyter, pp. 203-231.
