Palantir and the Regulation of Life

Luke Munn

Luke Munn uses the body and code, objects and performances to activate relationships and responses. His projects have featured in the Kunsten Museum of Mod-

ern Art, the Centre de Cultura Contemporània de Barcelona, Fold Gallery London, Causey Contemporary Brooklyn and the Istanbul Contemporary Art Museum,

with commissions from Aotearoa Digital Arts, and TERMINAL. He is a Studio Supervisor at Whitecliffe College of Art & Design and a current PhD Candidate at the

Institute for Culture & Society, Western Sydney University.

Figure 1: Screenshot of Palantir using ‘notional’ data from a product demonstration

Introduction

On January 30 Arthur Ureche, a forty year old union dues administrator, was driving his white Chevy compact through

Los Angeles when he noticed 4 LAPD cruisers following him. Ureche’s last traf�c violation was when he was nineteen, for

driving too slow. But as he pulled over to let them pass, they stopped at a safe distance, exited their vehicles and trained

their �rearms on him. An of�cer barked out instructions using a megaphone, ordering Ureche to unlock his doors. The

SEEING WITH SOFTWARE

Studies In Control Societies

lock jammed. Ureche silently panicked, trying to comply without using any sudden movements. A helicopter whirred

overhead. The of�cers waited. Ureche’s car had been identi�ed as belonging to a wanted drug felon in California. But the

car had Colorado plates. An automatic license plate reader had misidenti�ed the vehicle. As journalist Chris Francescani

(2014) noted, “same numbers; different states.” Though this tale may be dramatic, this text is ultimately interested in this

less spectacular but more fundamental detail—exploring how the performances of software-infused systems produce

new understandings, and how, in turn, these play out in the governance of ordinary people and everyday routines.

How is life mapped, analyzed and regulated by the algorithmic? To make a beginning of any sort into this complex ques-

tion, a speci�c object might be chosen, limiting both the scope of the investigation and the claims which can be made. The

object here is Gotham, a software platform developed by the company Palantir. What does Gotham do? Essentially it pro-

vides the ability to store, query, and visualize extremely large data sets, allowing analysts to discover patterns and rela-

tionships. The concept was born from an insight at the founder’s former company of Paypal. Rather than hard-coded algo-

rithms alone, human and computational agents working together proved better at combating the “adaptive adversary” of

�nancial fraudsters (Palantir, 2016). Gotham provides both automated operations and manual tool sets: algorithms which

can be setup to �ag anomalies, graphs which visualize the relationships between entities, and the geospatial mapping of

resources and agents. These computational tools assist a human analyst in discovering the key signals in a sea of big data

noise: a link between terror cells, a transaction from a rogue trader, a location of a stolen vehicle.

Gotham seems emblematic of a shift from the sovereign to software. Granted, Gotham began life as a tool speci�cally de-

veloped for the needs of government institutions: the Department of Defense, the US Army, the NSA, and others. But the

promise of big data to provide answers and insights also seemed alluring for other actors holding massive silos of infor-

mation. The Gotham platform has now been adopted by an array of law enforcement agencies and corporations: BP, Coca

Cola, Credit Suisse, NASDAQ, GlaxoSmithKline, the City of New York, the Los Angeles Police Department. In addition,

Palantir is not a long-established Washington player, with ‘revolving door’ personnel and matching government contracts.

Rather, the company is a decidedly Silicon Valley venture with a matching company culture and long leases in Palo Alto.

With a $20 billion dollar valuation, Palantir is the fourth most highly valued tech startup, placing it directly alongside

more public companies like Uber and Airbnb (Buhr, 2015). This, then, is not a tale of shadowy intrigue and back-room

deals. Rather, I hope to demonstrate how this algorithmic control plays out not in the highly circumscribed spheres of spy-

craft or the battle�eld, but instead spills out into control mechanisms which encompass the everyday practices of ordi-

nary citizens.

 Before we begin, a brief word on meaning and method. What is an algorithm? The answers are diffuse. Perhaps this is not

surprising for a word which “has recently become a popular, if not dominant, term to refer to when describing the power

computational processes have in contemporary forms of life” (Fuller & Harwood, 2015). Speaking broadly, the algorithm

is often understood as a recipe for computation or a sequence of operations. Early on in the history of computation,

Stephen Kleene de�ned it as a performable procedure (1943, 59). Barbin et. al have since added properties of �niteness

and iteration, “distinguishing it from vaguer notions such as process, method or technique” (2012, 2). However, with its

focus on sociality and materiality, this text is interested in a notion expanded beyond the focus on abstraction in mathe-

matics or optimization in computer-science. Indeed, even within these spheres, Gurevich argues that the algorithm can-

not be “rigorously de�ned in full generality” due to a constantly expanding notion of what an algorithm can be and do

(2012, 35).

One promising de�nition of the algorithm was posited by programmer Stephen Kowalski as a combination of ‘logic+con-

trol’ (Kowalski, 1979). Logic de�nes the assumptions and goals of an algorithm, for example, to �nd a path. Control com-

prises the routines or techniques used to accomplish it, e.g. the sorting of routes carried out iteratively over time. By sep-

arating these two components, Kowalski could focus on optimization, maintaining the overall logic of a programme while

�nding faster and more accurate means of performing it. The mathematical history of the algorithm focuses on the �rst

component of ‘logic’, allowing for models which are universal and immaterial, abstracted, and idealized. However, the lat-

ter term of ‘control’ introduces notions of power, determination, restriction, and management. The term of ‘control’, also

suggests techniques and materials—the ability to manipulate something towards an objective. This leads to a crucial

question about how these operations are performed: “How does this system become a strategy in action?” (Fuller & Har-

wood 2015).

Algorithms are often understood as purely abstract procedures. But to perform work in the world, they must mobilize a

wider material composition of architectures and organizations, labors and logistics, not to mention “hardware, data, data

structures (such as lists, databases, memory, etc.), and the behaviors and actions of bodies” (Terranova 2014, 339). In oth-

er words, they are not simple, single objects but �ows of heterogeneous materials, forces, and agencies which can be

more productively analyzed as algorithmic ecologies [1]. This is an ecology of code and software, certainly, but also sur-

faces and interfaces, heat and light, bodies and labor, cables and minerals. The ethereal and ideal ‘logic’ is always twinned

with ‘control’—the particular performances and strategic con�gurations of matter necessary to carry it out.

How is the algorithm traditionally unraveled? The traditional notion is a sequential one: �rst we take this data, secondly

we loop through, third we select X from Y, and so on. Of course, this �owchart style approach can be productive for high

level understandings and big picture planning sessions. But life does not play out on a whiteboard, with users and matter

moving from one discrete state to the next. Indeed, this approach does not even mirror the architectures of present day

software production itself, which often takes the form of dozens of microservices. These are services which are highly fo-

cused, receiving particular inputs, parsing it through bespoke routines, and outputting a result at maximum ef�ciency. Mi-

croservices aim to bypass the drawbacks of the lumbering mega programme. Instead of a giant and dif�cult to maintain

codebase which performs every operation from start to �nish, they’re designed as autonomous and agile components.

Rather than the single piece of software waiting for the user to transition from state A to B, complex platforms like

Airbnb, Uber and Palantir are better understood as dispersed software services incessantly performing operations.

How might we unpack such complex ecologies of matter? Examining the entire system would appear unproductive if not

impossible— a totalization for its own sake resulting in little detail, many repetitions, and much that was irrelevant. In-

stead, this text takes up the notion of the machine theorized by Levi Bryant. A machine here should be uncoupled from its

usual connotations of metal bodies and complex circuitry. Rather, in Bryant’s ontology, all forms of life and non-life can be

productively theorized as different types of machines. To speak of the machine is simply to foreground how objects work

rather than what they are, to investigate constantly evolving operations instead of some eternally �xed essence. For

Bryant (2012), focusing on the machinic highlights questions such as “1) what �ows through the thing, 2) how does the

thing function or what processes take place in it, and 3) what is produced and how are both the �ows and machine

changed as a result of these processes?” Machines can be de�ned by their powers, by what they are capable of. Coupling

machines together thus changes not just their appearances, but their abilities—forming new things with new capacities.

Bryant explains, for example, that adding the ‘stirrup’ to the ‘horse—rider’ machine was not just a simple addition, but

rather fundamentally changed the form of warfare, providing a �rm platform which riders could exert pressure against

and thereby dramatically increasing the force behind their lances (2011). In Bryant’s ontology, machines might be corpo-

real (a bridge, a sword, a body), incorporeal (a thought, a poem, a bureaucracy), or typically combinations of the two types.

Figure 2: Relationship between a complex algorithmic ecology and subset extracted as a ‘machine’

The two machines investigated under the Palantir ecology are sub-selections of the material totality which feel strategic

and signi�cant—intersections where software and hardware, labor and nature come together to produce key algorithmic

operations. The �rst machine is composed of several Gotham tools, the ‘stack’ of hardware which powers those tools, and

the analyst which uses them to uncover patterns of life. The second machine zeroes in on a particular instance of Gotham,

examining how the analyst and license-plate data used by the LAPD comes together in the regulation of life in Los

Angeles.

Stack—Tools—Analyst machine

How is a pattern of life established? This section looks at the Stack—Tools—Analyst machine as a subset of the Palantir

Gotham ecology. It focuses on the way in which analysis is performed on big data by software or human agents in order to

de�ne a particular set of behaviors as the norm. Gotham claims to make sense of life. In order to accomplish this, it must

carry out two divergent operations which appear almost contradictory.

On the one hand, Gotham must have life. In other words, the data available and addressable within the platform must ap-

proach the richness, variety and speed of the reality ‘out there’. For this objective, messiness, ambiguity and overwhelm-

ing amounts of information are not only tolerated, but welcomed as indicators of authenticity. To this end, the layers of

backend technologies comprising the Palantir ‘stack’ enable the capture and storage of massive volumes of data which

can be queried at high velocity. This is a highly technical performance—a negotiation with scalability and servers, nodes

and tables, computation and latency. Simultaneously, however, it is also an ideological performance, supporting the vol-

ume, variety and velocity of data required to convince a user or organization that that this data represents reality. I want

to look at three requirements to make this vision rational and believable, criteria enacted in turn by three speci�c back-

end technologies embedded in the Gotham stack.

Firstly, data must approach petabyte scale. At these magnitudes, big data begins to hold out the promise of a total picture,

a set of information which can be incessantly parsed, �ltered, sorted, and searched through in order to �nd the next vul-

nerability, the latent risk, the dormant operative. This, after all, was the rationale behind the unprecedented amount of

data collected by the NSA’s mass surveillance programme, “a wide net that could �nd and isolate gossamer contacts

among suspected terrorists in an ocean of seemingly disconnected data” (quoted in Aradau 2015). That this promise is as-

ymptotic—an incessant programme of capturing ever-more information which never arrives at the horizon of the totality

—d0es nothing to diminish its power. How is data made big? One way is through the integration of additional datasets.

But disparate databases are often irreconcilable, based on multiple standards, speci�cations and formats. Another is

through the integration of unconventional data. But such information can be incomplete or imperfectly structured.

Apache Cassandra, a core component of the Gotham backend ‘stack’, is one way to address some of these issues. The tra-

ditional relational database model is comprised of rows and columns, much like Excel. In contrast, Cassandra is a so-called

NoSQL approach, a non-relational database with a much more minimal key-value model (e.g. “occupation: doctor, age:

35”). Rather than matching rows and columns perfectly between databases, this nominal ‘schema-less’ structure provides

more �exibility when merging datasets. This structure also helps with incomplete data. Rather than wasting man hours

and storage by ‘cleaning up’ data (�lling in empty cells with zeroes), the NoSQL model means that data can be ‘messier’. In

fact, Aradau points out (2015) that big data = messy data has become a new motto of sorts, characterized as “data which

comes from multiple sources and in heterogeneous formats.”

Figure 3: Screenshot from Palantir presentation, gaps in data are allowed in the ‘schema-less’ structure

Secondly, data must approach the present moment. In an elaborate presentation titled “Leveraging Palantir Gotham as a

Command and Control Platform”, a group of engineers demonstrate the capabilities of “Railgun” to an audience of gov-

ernment agencies (Palantir, 2013a). Railgun, they explain, is a layer built on top of the Gotham platform which provides it

with “the present tense” (2013a). They visualize and manage the logistics of a (notional) complex humanitarian aid project

undertaken by a Marine Corp division as it unfolds. Using real-time tracking data, they follow the progress of naval units

off the coast of Somalia, of�oading their supplies, transitioning to vehicles, getting stuck at a �ood crossing, and ultimate-

ly arriving at a Red Cross encampment (2013a). The weekly archive or even the nightly backup comes far too late to assist

in making these kinds of decisions. Rather than stable but irrelevant data, then, the engineers assert that they are work-

ing with “volatile and ephemeral data” (2013a). The focus is on data as close to the current moment as possible. So while a

long-term record might be bene�cial, this archive might be populated by “setting a rolling time horizon, beyond which

data can be �ushed out” (2013a). This constantly �uctuating data initiates a subjective shift from information to anima-

tion, from dead symbols to lively avatars. It’s this quality which allows the employees to claim that “more and more, we

are sampling reality” (2013a).

Thirdly, data must approach real-time responsiveness. It is not enough simply to have data which can be captured in the

present and stored at scale. Data must feel responsive, a quality achieved by ensuring minimal latencies between query

and response, even when operating on large datasets. Palantir addresses this by using MapReduce, a core component of

the Apache Hadoop system. As the SAS website explains, rather than a single, powerful supercomputer, Hadoop was ex-

plicitly designed to distribute processing across hundreds or thousands of consumer grade computers, “commodity hard-

ware.” The basic grouping that Hadoop establishes is the cluster, de�ned by several key nodes. MapReduce thus serves

two essential functions: “It parcels out work to various nodes within the cluster or map, and it organizes and reduces the

results from each node into a cohesive answer to a query” (Bigelow and Chu-Carroll, 2015). Mapping in this case allows a

basic job, such as word counting a million documents, to be split into batches of one hundred and ‘mapped’ to various

nodes. These batches are processed in parallel, leveraging the ef�ciencies obtained from cloud computation. The �gures

from these batch jobs are then summed by the Reduce method, which returns the total word count (Apache, 2013). While

highly technical and somewhat arcane, it’s this low-level architecture of hardware and software which transforms the ex-

perience of interacting with data. Rather than the ‘de�nitive’ SQL query which might take hours to run on a large dataset,

the low latencies afforded by MapReduce create a more conversational experience, in which feedback, iteration and ar-

ticulation become vital activities, a type of feeling out of the data. Taken together, these three backend technologies ac-

complish a subjective shift in which it appears that life itself can be exhaustively captured and incessantly interrogated.

So on the one hand, Gotham must expand, extend and encapsulate in order to legitimize its claim of sampling reality. But

on the other hand, Gotham must make sense of all of this. By itself, this sheer deluge of data tells us nothing. Information

must be worked on, either through automated processes built into the platform or through manual operations: �nding

threads, constructing sequences, and matching activities in such a way that a pattern emerges. By removing the irrele-

vant and extraneous, sorting and sifting, the analyst hopes to converge on the weak signal in the midst of overwhelming

noise. In this operation too, a kind of tipping point is reached, an accumulation of tiny indicators which slowly edge to-

wards a result. Here too we need to dive into the details, examining how patterns appear to emerge from three speci�c

tools provided by the Palantir Gotham platform.

The �rst is Search Around, a core feature evidenced by its extensive use in the �rm’s online demonstrations. As its name

suggests, Search Around can be run on any item, searching for other items which share links and visualizing them as

nodes attached in a spiderweb-like fashion (Palantir, 2013c). How are items linked together as similar? In Palantir’s

demonstrations using notional data, this took many forms: a �ight on the same plane, a shared former residence, a tele-

phone call made to the same third party, a small enough variation in IP addresses (Palantir, 2013b). Two brief points stand

out about this logic. Firstly, algorithmic proximity is not geographical proximity—persons separated by great distances

are often designated as having close-knit connections and are thus clustered tightly together on the analyst’s screen. As a

logic, searching ‘around’ an informational space operates differently than searching around physical space. The logic of

data, as Claudia Aradau reminds us, “can draw together even the most distant things” (2015, 24). The power of the visual

diagram to perform as evidence should not be overlooked in this regard. Two persons separated by a handful of pixels and

connected by a black line might easily eradicate a thousand kilometers. Secondly, these linkages are metonymic not taxo-

nomic—associations are built up by linking small tokens of information from one individual to another, rather than any

kind of obvious Linnean clustering. Undoubtedly traditional groupings like race and religion inform analysis, but they no

longer maintain their former currency. Instead, as Aradau points out, resemblances in big-data mining are primarily based

on “analogy, correspondence and similitude” (2015, 23).

Figure 4: Screenshot of the Flows plugin showing phone calls made between actors

The second tool is Flows, a plugin for Gotham which enables the visualization of material �ows. Phone calls, emails, mon-

ey, or any other material �ows understood by the system are visualized as bright dots which move from one object to an-

other over time. This tool produces an array of effects, each tied closely to its formal properties. Flows crystallizes, solidi-

fying connections between entities. Though a line already indicates an association, the bright dot moving from one point

to another ‘thickens’ this linkage, visually demonstrating the exchange of matter between one person and another. Flows

formalizes, providing a high-level understanding of often very complex networks of objects. The dots of currency or calls

often originate from a common ‘hub’ and are received by ‘spokes’, or travel between clusters before jumping to other clus-

ters. This visualization thus provides an impression of structure in the chaotic jumble of network lines—an insight into the

arrangement, groupings and hierarchies of actors. Finally Flows prioritizes, providing the analyst with the most important

agents in a network. By scaling the size of the dot to the magnitude of matter (number of phone calls, amount of money,

etc), signi�cant transactions and interactions stand out easily in the visualization and can be �agged for further

investigation.

Figure 5: Timeline tool showing time-window set to approximately one month

The third tool is the Timeline. This takes the form of date and time indicators in a module along the bottom of the screen.

The tool allows the analyst to specify a ‘time window’ of a few seconds, hours or days. This isolates the action, only visual-

izing the events or activity which occurred during that period. This window can be dragged incrementally along the Time-

line, providing the analyst with a ‘play by play’ of events as they unfolded. The key intent here, like the other tools, is to

uncover a discernable pattern, a particular signature of activity. The human analyst stands in for the algorithmic, operat-

ing according to the same logic of analogy, correspondence and similitude. Do events seem coordinated, occurring at

roughly the same times? Is there a particular sequence of behaviour which is constantly repeated? Do the seemingly ran-

dom activities of a network become cyclical or consequential over time? Conversely, is there a rupture or break in these

habitual routines which appears signi�cant? To answer these questions, Timeline is often coupled with Flows to uncover a

pattern of action. In one of Palantir’s ‘notional’ demonstrations, the analyst discovered that three operatives were receiv-

ing phone calls, then two days later were transferring �nances to a particular account, a sequence which repeated weekly.

One month later, these operatives all boarded a plane on the same day, bound for the same city of Chicago (Palantir,

2011). While the ‘insights’ discovered during these demonstrations are inherently staged, they provide a compelling vi-

sion which is taken up by a range of public and private actors; a powerful fantasy in which a pattern emerges from the

data deluge, uncovering an imminent terror threat or �nancial risk, revealing the next management optimization or con-

sumer trend.

Analyst—Thunderbird—Los Angeles machine

 How is life regulated? Following on from the pattern of life, this section examines the Analyst—Thunderbird—Los Ange-

les machine to investigate this question. Thunderbird is Palantir’s name for the automated license plate reader system in-

tegrated into the version of Gotham platform used by the Los Angeles Police Department (LAPD). While the analyst’s use

of license plate data provides the impetus for intervention, this regulation is carried out by a complex juridico-political

network of human and non-human elements: inspectors and lawyers, sensors and governors, license-plate readers and

police. This machine is performed incessantly, distributed ubiquitously, and maintained silently. Its presence, in turn, ne-

cessitates the need for subjects to self-regulate, managing their own life processes in a self-initiated performance.

Figure 6: Screenshot from Palantir presentation showing integration of license plate data into system

The Los Angeles police department was one of the �rst law enforcement agencies to adopt the Palantir platform. Indeed,

a 2013 video produced by the company uses the LAPD as an exemplary case study, a series of complimentary testimoni-

als in which the chief credits the platform with helping them “make sense of all the noise that’s out there” (2013d). In

2014, the department doubled down, spending another $2.9 million for a contract for Palantir “to furnish, con�gure, and

install a new upgraded module to LAPD’s existing platform and to incorporate new data” (Of�ce of the City, 2014). The

contract details the addition of new data modules comprising license plate data which is routinely collected, mug shots

from the local county as well as an array of information available from the Department of Motor Vehicles: home address,

home telephone number, physical/mental information, social security number, and a photograph (2016).

This expansion of accessible data and the integration of it into the uni�ed Palantir platform seeks to create a more com-

prehensive informational environment. In this way, Thunderbird exempli�es the two contrasting operations sketched out

in the previous section—voraciously expanding the scope of data capture while simultaneously providing tools and func-

tionality to converge towards a particular target. As Ian Shaw explains, “The entire ‘normal’ population must �rst be cod-

ed and modeled to geolocate the abnormal. In order to individualize, the security state must �rst totalize, effecting an in-

tensive policing of the lifeworld. The two spatial optics of urban manhunting are thus population (expansion) and person

(contraction)” (2016, 25). A key goal here is the need to ‘capture it all’, the quest towards the totalization of information

which is supported on a technical level by the Palantir stack. To be able to locate any individual, it is �rst necessary to

know every individual, entailing the representation of a mass population through data.

How does this information come together in the regulation of life? License plate data is automatically captured by dedi-

cated reader equipment manufactured by a third party, most commonly Vigilant Technologies. A �xed license plate read-

er is commonly attached to a light pole, capturing plates of cars passing beneath it and transmitting them directly back to

law enforcement headquarters. A mobile version, used heavily by the LAPD, takes the form of two cameras mounted on

top of the police cruiser. The mobile readers operate continuously, detecting plate imagery from within their visual feed,

isolating and converting it to a sequence of alphanumeric characters, and adding this to a scrolling list of plate data on a

monitor inside the car. These plates are checked against state and federal databases to match against particular activity.

The Federal Bureau of Investigation, for example, maintains a special machine-readable �le for plate reader systems

which is refreshed twice daily. The vehicle might have been reported stolen, it might be registered to a sex offender who

is violating his parole, or it might belong to a so-called scof�aw who has routinely ignored parking �nes. Once �agged, the

corresponding series of operations plays out on the owner of the vehicle—an arrest, a �ne, a warning, and so on. In this

way, every plate hides a potential crime. In fact, as Al-Jazeera reported (2016), the LAPD recently denied a Freedom of

Information Act request based on the grounds that the plate data is investigatory. In other words, all cars in Los Angeles

are under ongoing investigation.

Figure 7: Screenshot from Vigilant Technologies video showing plate scanning inside police cruiser

Critics of technology and surveillance often conjure up the nightmare scenario in order to build public support for their

stance: the global glitch, the rogue employee, the fatal error. Of course, these unforeseen situations can occur and do

matter. Their consequences often fall heaviest on those groups already marginalized or vulnerable. For example, Denise

Green, an African-American woman, was pulled over in 2009 when automatic license plate reader technology mistook a

‘3’ for a 7’, �agging her car as stolen (Winston 2014). Of�cers ordered her out of the vehicle at gunpoint, forced her to her

knees and handcuffed her while they searched her car. Green, a 50-year-old bus driver, described the experience as a

“nightmare” and had to take 2 weeks off for counseling (Winston 2014).

But these cases are anomalies. A more subtle and systemic effect occurs in those proximate to the subject and in the

wider population as a whole. As Brendan O’Connor (2016) reminds us, “a nightmare scenario of an Of�ce of Special En-

forcement inspector going rogue, stalking a colleague or creditor or lover with Palantir’s mobile technology, is certainly

conceivable. But the potential for that kind of outright abuse is less disturbing than the ways in which Palantir’s tech is al-

ready being used. The city’s embrace of Palantir, outside of law enforcement, has quietly ushered in an era of civil sur-

veillance so ubiquitous as to be invisible.” This silent regime runs as a low-level hum in the background, an undercurrent

informing (and more precisely, discouraging) a range of political practices.

‘A chilling effect’ is the term used to describe this subtle cooling, a subliminal process in which the subject self-regulates

activities which might be deemed political or controversial. In 2009, the Association of Police Chiefs commissioned a re-

port investigating the potential ethical implications caused by the automated capture of license plate data on a mass

scale. Though unsurprisingly glowing in its overall outlook, the authors did caution organizations about this potential

chilling, warning that populations exposed to the technology might become “more cautious in the exercise of their pro-

tected rights of expression, protest, association, and political participation” (Tracy, Cotter and Nagel, 7).

But is this chilling effect merely anecdotal or imaginary, an outcome simply assumed by those concerned with sur-

veillance and privacy? In 2016 Jon Penney conducted one of the �rst empirical inquiries into these effects. Penney fo-

cused on the Snowden/NSA revelations of June 2013, honing in on that moment when the world learned that the US gov-

ernment was conducting mass surveillance of their phone calls, web searches, and other everyday activities increasingly

conducted online. Contrary to many surveillance operations which remain undisclosed, the Snowden revelations were a

highly publicized bombshell which alerted a broad public that their activities were actively being monitored. Penney ana-

lyzed the traf�c of 48 ‘controversial’ Wikipedia articles—pages like ‘dirty bomb’ and ‘suicide attack’ related to terrorism

and other topics likely to raise surveillance �ags (2016, 140). Penney discovered that after the revelations in June 2013,

visitors to these pages dropped by 20%. What’s more, this was not a temporary drop-off, but part of a longer lasting ef-

fect. Penney notes, for example, that viewership of the wiki article on ‘Hamas’ was previously trending up, gaining 60,000

views per month. Post Snowden, however, this trend reversed, with 20,000 fewer people visiting the page month after

month (2016, 151). The study demonstrates that, contrary to the mantra of ‘nothing to hide, nothing to fear’, subjects un-

der surveillance do regulate their own behavior, albeit unconsciously. [2]

Of course, Palantir is not the NSA and Gotham is not the PRISM programme. We must be careful too not to overburden

this object, ascribing a whole range of overwhelming and nebulous effects to its operations. Indeed, one of the key subjec-

tivities of Palantir’s processes is just how incredibly banal they become. The functionality can be learned in a day of work-

shops (Woodman, 2016). The interface is designed to be highly intuitive. Point and Click. Drag and Drop. There’s nothing

particularly awe-inspiring here, no technology which points to its own spectacle. Rather, the whole activity becomes de-

politicized precisely to the extent to which it is deemed ordinary and procedural. At the same time, we must acknowledge

those capacities, sketched out in the previous section, which Gotham provides: the assimilation of unstructured data, the

conversational query and retrieval of information, the cross-referencing of properties and a progressive accumulation of

associations leading to the formation of an ostensibly organic pattern. Integrating license-plate data into this platform via

Thunderbird adds new capabilities: the tracking of behaviors over time and the ability to locate a subject in space. This is

a radical ampli�cation of surveillance capabilities—facilitating the targeting and interrogation of subjects on massive

scales. Gotham thus provides both a signi�cant expansion in the scope of data analysis while simultaneously facilitating

an effortlessness in their use—an economization of regulation.

For Foucault, this progression from the costly to the economic has always been the trajectory which power takes, a tra-

jectory which Jeffrey Nealon (2008) draws out through the notion of intensi�cation. For Nealon, this thread provides pow-

er with both a goal and guidance—constituting an overall objective and de�ning the transformations necessary to achieve

it. In order to be effective, power must be �exible rather than fossilized, evolving over time in particular ways. As Nealon

explains (32), this constant recon�guration precedes not randomly but logically, playing out in “the formulaic movement

of power’s intensi�cation: abstraction, lightening, extension, mobility, and increased ef�ciency.” Discipline and Punish high-

lighted a section of this trend, an evolution from the violent punishments enacted directly on the body and the brick-and-

mortar incarceration of the �esh towards a much lighter and ef�cient regime, embodied for Foucault at that time in Ben-

tham’s designs for the panoptic prison. New embodiments within this trajectory move incrementally towards a more ef-

fective performance which can be attained more ‘economically’ in every sense: materially, �nancially, temporally, and so

on.

One of the key logics here is a shift from the somatic to the systemic. Disciplinary power is often understood as a more tra-

ditional form of control exerted on the body through prisons, barracks, hospitals, and so on. But the panoptic prison antic-

ipated, even if weakly, the trajectory of power away from physical presence. As Foucault noted (1995, 255), “power has

its principle not so much in a person as in a certain concerted distribution of bodies, surfaces, lights, gazes; in an arrange-

ment whose internal mechanisms produce the relation in which individuals are caught up.” Somatic power relying on bod-

ily intervention is both expensive to maintain and constrained inherently by the corporeal—a particular body with a limit-

ed line of sight, a �nite span of attention, a �xed number of work hours, and so on. This is why Nealon (2008, 34) suggests

that intensity strives incessantly towards a more ef�cient “smearing or saturation of effects over a wide �eld,” a form of

control which can be suffused into and across a particular space. The warden can be replaced with the guard, the guard

with the dummy. In the end, the watch tower can be emptied entirely. The arrangement of cells at particular angles, the

centrality of the tower and the masked windows all constitute a system which ampli�es the disciplinary potential of vi-

sion. This system acts to distribute the effects of power equally throughout the space—the gaze is decoupled from the

warden and embedded into the walls themselves, becoming ubiquitous and ever-present.

To update this disciplinary gaze, we might posit something like an algorithmic gaze—a gaze which operates not on the body

directly, but on its data shadow—indexing the swirl of information produced by the subject and associated with him: cred-

it scores and criminal records, phone calls and chat logs, Skype calls and social media. In doing so, informational technolo-

gies maintain a diffused and largely imperceptible �eld—-a steady pressure which obliges the subject to adopt particular

practices of self-regulation. Gotham, for its part, acts as both interface and integrator for these systems—a glue to bind

together disparate data and a GUI to inspect it. While the ability of physical visibility to produce self-governing inmates

might have been overstated in Foucault’s time, the tendency of the subject towards self-governance in the hard light of

algorithmic visibility seems decidedly less so. Regulation shifts from external coercion to internal conformity, an inces-

sant performance which is both self-initiated and self-managed. As Foucault (1995, 256) reminded us, once these forces

are instantiated, “he makes them play spontaneously upon himself; he inscribes in himself the power relation in which he

simultaneously plays both roles; he becomes the principle of his own subjection.”

Despite these tendencies, power is never totalizing. There are always errors and inconsistencies present in the algorith-

mic. But the particular modalities of this power indicate that traditional framings and responses may prove relatively inef-

fective. Take, for example, the notion of ‘resistance’. Algorithmic power is not a corporeal body which oppresses and can

thus be pressed against. Rather, as our Foucauldian reading suggested, this power is highly diffuse, infused into the mech-

anisms of informational systems. In this sense, Gotham is more akin to a saturated �eld laid over a topography of subjects.

Humanity and technology are bound up intimately within this environment, interdependent and inextricable. As Verbeek

(2013, 77) asserts, “conceptualizing this relation in terms of struggle and oppression is like seeking resistance against

gravity, or language.” This is not to collapse into fatalism, but simply to recognize that the traditional language of ‘oppres-

sion’ and ‘resistance’ needs to be updated or even supplanted.

A second notion which may require updating is that of ‘refusal’, consciously opting out of particular platforms or informa-

tional systems. The extent to which this is even possible in any holistic way for those in the Global North is debatable,

though some partial non-participation is indeed feasible. Of course, refusal itself is often only feasible for those who al-

ready possess a certain degree of privilege: an established reputation, of�ine social support structures, a stable career,

and so on. This leads to one of the core reasons why refusal may be ineffective—it often seems to disenfranchise more

than it empowers, excluding the subject from life-enhancing realms of cultural, social, and �nancial exchange. In Seb

Franklin’s (2015, 136) words, “disconnection from channels of communication appear aberrant or pathological and thus

lead to expulsion from circuits of representation and inclusion.” The subject becomes cut off from vital networks, a move

which costs them greatly while effecting the system very little.

In contrast, the Analyst—Thunderbird—Los Angeles machine suggests some immanent but unexpected strategies, infor-

mational �ows which are shifted laterally by some material friction and subsequently come out somewhere different,

producing an inconsistency or inef�cacy. Several tangible examples are mentioned in a 2014 Rand report by Gierlack et

al. For instance, the report notes that the LPR camera systems are con�gured to function in both day and night settings,

necessitating the capture of both infrared and visible photos of the car plate in high de�nition. The volume of this ‘dou-

bled’ data is entirely unexpected, often overwhelming the storage systems of law enforcement agencies, forcing organiza-

tions to erase old data to free up space for new data. The result is that “these limits, rather than privacy concerns, ended

up shortening their data retention period” (2014, 68). Rather than any overt intervention from outside—government reg-

ulation or citizen activism, for example—the processes within the system itself work to undermine its own ef�cacy. In an-

other example, the complexity of the natural and built environment creates unexpected frictions which the algorithmic

fails to resolve. As the report elaborates, “The cameras also can false-read structures as license plates, as one department

found when its system kept seeing wrought-iron fences around some homes as “111-1111” plates” (2014, 80). This glitch

is generated by the disparity between the messiness of the outer world and the expected informational inputs of the code

world.

Putting these two inconsistencies together, we arrive at a �nal example. The report discloses that “drivers have beaten

the system by using black electrical tape to alter their license plates” (2014, 100). Automated license-plate reader sys-

tems all contain particular assumptions about the visual schema to be expected—darker pixels situated on the white

background of the plate itself which should resolve into a sequence of alphanumeric characters. By injecting unexpected

matter into the ecology—tape stuck between plate characters—the expected algorithmic �ow runs but is diverted or in-

terrupted. The resulting output is deemed valid by the machine but useless to humans. This practice doesn’t ‘resist’ the

system (shut down the servers?), nor ‘refuse’ it (stop driving altogether?). Rather, this practice works with the system

rather than against it, understanding the operational logics at work, playing with these processes and exposing them to

unexpected inputs. This feels like a more strategic practice—one which recognizes how entangled we are with technologi-

cal systems while at the same time instrumentalizing particular inconsistencies within these systems in order to counter-

balance their often asymmetric power structures.

Conclusion

Palantir Gotham provides a way into exploring some of the complex ways in which algorithmic operations structure sub-

jectivities today. The algorithm is not just composed of an abstracted and immaterial ‘logic’ component, but also the ‘con-

trol’ mechanisms which carry out its work in the world—an ecology of sensors and software, bodies and bureaucracy,

hardware and minerals. By investigating strategic intersections of matter as ‘machines’, speci�cities inherent in the algo-

rithmic are revealed, particular ways of understanding and acting on the world. The Tools—Stack—Analyst machine

shows several of the methods by which the algorithmic parses information in order to establish patterns of life. The back-

end ‘stack’ enables these tools with operations which support massive volumes of real-time data which can be queried re-

sponsively, operations which come together to make data analogous to life. The Analyst—Thunderbird—LosAngeles ma-

chine uses a particular Gotham instance to sketch out the algorithmic regulation of life. The automated license plate

reader data of Thunderbird initiates an informational �eld used to make correlations, track activities and locate subjects

in space. The resulting regulation often plays out as modulations of life forces, inhibiting abilities indirectly through cita-

tions, evictions, �nes and so on. This power is systematic rather than somatic, an arrangement of internal mechanisms

which acts in light and economic ways. This regulation, in turn, exerts a pressure towards self-regulation, a self-initiated

programme of governance performed incessantly. But the algorithmic is inconsistent—these errors point the way to-

wards more effective means of intervening within contemporary modes of control.

 

Notes:

[1] This term is preferred over the very general “assemblage” (DeLanda) or even the more targeted “media ecologies”

(Fuller) in that it foregrounds the logical, procedural forces of informational systems (algorithmic) which must enlist a va-

riety of bodies and surfaces in order to carry out work in the world (ecologies)

[2] The full methodology and details of Penney’s study is outside the scope of this text. The study, however, does provide

some nominal empirical support to an assumption which has been dif�cult to measure, namely that surveillance like that

carried out by Palantir’s clients has effects which diffuse into wider populations in subtle and subliminal ways, moving far

beyond those impacted ‘directly’ through violence, incarceration, deportation, and so on.

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