1-9: automata and labor, continued
As I have stated previously, these ambivalent politics may stem from the uncanny, non-human agency of viral swarms, but they also crucially stem from our fundamental understanding of the computer virus as imminent, rather than other, to the network. For although computer viruses are commonly often associated with anarchist principles, since the 1990s virus-like software programs have become not only complicit, but necessary to the everyday functioning of network capitalism[22] - performing routine maintenance within networks, automating repetitious and dull procedures, and even performing feats of computation that would be wholly unthinkable without the emergent affordances of self-reproducing, symbiotic software agents. In fact, rather than seeing the swarm as an a priori model of resistance (ala Barlow, Rheingold, or Hardt and Negri), John Johnston notes that the emergent, swarm intelligence of many virus-like software programs are increasingly being used in different techniques of network optimization. He writes:
![[22]](media/swarm-footnote-22.jpg){kind=link}
Errors and randomness are not "bugs"; rather, they contribute very strongly to the success [of networks] by enabling them to discover and explore in addition to exploit. Self-organization feeds itself upon errors to provide the colony with flexibility and robustness (or self-repair).[23]These characteristic operations of the virus, often framed in terms of "errors" or "noise" are thus, here, subjected to a series of euphemistic transformations: viral mutation becomes emergence or innovation allowing for "flexibility," "robustness," and interoperability; just as errors become teaching tools and elements of "risk" to be pre-emptively accounted for. Sophisticated software agents which operate according to the same swarm-like principles as the virus, have also increasingly been instrumentalized in the re-routing of network traffic, making "tiny modifications" throughout the network so that through a complex process of "amplification and decay, these small contributions will either disappear or add up depending on the local state of the network."[24] Clearly, although the translation of the biological model of the swarm into an algorithmic context provides novel, emergent solutions to the symmetrical complexity of network problems, the persistent ambivalence of Johnston's language concerning the outcomes of such solutions once again underline the paradox of the networked swarm encapsulated so eloquently by Eugene Thacker. For if there is a capacity with the swarm to foster "fascination" in the absence of centralized control, and at the same time an "anxiety" in the face of this absence, there is also an equal "desire to encourage the ubiquity of networks, swarms, and multitudes along with a desire to be able to control or instrumentalize them."[25]
![[23]](media/swarm-footnote-23.jpg){kind=link}
![[24]](media/swarm-footnote-24.jpg){kind=link}
![[25]](media/swarm-footnote-25.jpg){kind=link}
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