Shoshana Zuboff on surveillance capitalism | VPRO Documentary

vpro documentary

AI-assisted summary of Shoshana Zuboff on surveillance capitalism | VPRO Documentary — original source. Check the original for full context.

Source published: 2019-12-21

Key ideas

Surveillance capitalism monetizes involuntary 'behavioral surplus' rather than declared data, turning private human experience into raw material for behavioral prediction products. (0:06)
Hidden sensor integration and terms-of-service traps ensure continuous extraction, requiring consumers to review an estimated 1,000 privacy contracts simply to install a single Nest thermostat. (23:15)
Internal Facebook documents in Australia revealed capabilities to identify acute emotional vulnerability across 6.6 million teenagers to time commercial ad delivery. (32:24)
Current legal frameworks like the EU's GDPR fail to address surveillance capitalism because they regulate declared data ownership rather than the proprietary shadow pipeline that harvests behavioral exhaust. (44:59)

Detailed notes

## Background Published on YouTube by Dutch public broadcaster VPRO Documentary on December 21, 2019, this documentary features Harvard professor Shoshana Zuboff discussing her book "The Age of Surveillance Capitalism." It details how tech giants like Google and Facebook pioneered a predatory economic model that captures human experience as raw material to predict and modify user behavior.

## TL;DR Zuboff breaks down the mechanics of surveillance capitalism, demonstrating how early internet search exhaust was transformed into a multi-billion-dollar market in behavioral prediction products. The documentary traces how this extraction expanded beyond screens into physical movement through games like Pokémon Go, domestic spaces via smart home devices, connected automobiles, and psychological micro-targeting demonstrated by Cambridge Analytica. Zuboff emphasizes that individual privacy workarounds and conventional data-portability regulations like GDPR are insufficient against proprietary backend shadow operations, concluding that society must leverage democratic institutions to systematically outlaw behavioral exploitation just as it once restrained industrial Gilded Age monopolies.

## Atmosphere & style The film carries an intense, investigative, and sobering atmosphere driven by Zuboff's razor-sharp academic precision and moral outrage against Silicon Valley's calculated obfuscations. Striking visual contrasts—such as Amish communities setting deliberate boundaries on technology versus consumers unknowingly monitored inside their own living rooms—heighten the sense of pervasive intrusion. The documentary balances chilling disclosures about emotional manipulation and authoritarian tracking with a resolute, intellectually galvanizing finish that places faith in collective democratic resistance.

## Bulletin - [00:06](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=6s) Surveillance capitalism monetizes involuntary 'behavioral surplus' rather than declared data, turning private human experience into raw material for behavioral prediction products. - [23:15](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1395s) Hidden sensor integration and terms-of-service traps ensure continuous extraction, requiring consumers to review an estimated 1,000 privacy contracts simply to install a single Nest thermostat. - [32:24](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1944s) Internal Facebook documents in Australia revealed capabilities to identify acute emotional vulnerability across 6.6 million teenagers to time commercial ad delivery. - [44:59](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2699s) Current legal frameworks like the EU's GDPR fail to address surveillance capitalism because they regulate declared data ownership rather than the proprietary shadow pipeline that harvests behavioral exhaust.

## Key takeaways - [00:06](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=6s) Surveillance capitalism replaces natural resources with private human experience as its core raw material, functioning through operations engineered to be undetectable and indecipherable. - [03:23](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=203s) Digital exhaust shifted from system diagnostic waste into valuable 'behavioral surplus' used to train predictive machine-learning models far beyond service optimization needs. - [05:48](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=348s) Behavioral prediction operates offline and beneath conscious perception, accurately deducing sensitive personal milestones and traits from subtle habit shifts. - [08:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=522s) Biometric data harvested from everyday social media photos feeds facial recognition models licensed to foreign states and military operations for mass surveillance. - [11:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=702s) Subliminal psychological cues and augmented-reality games like Pokémon GO shifted behavioral steering into the physical world to monetize guaranteed footfall. - [19:51](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1191s) Smart-home hardware relies on undisclosed sensors and a playbook of obfuscation and habituation to penetrate private domestic spaces. - [23:15](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1395s) End-user licensing creates an infinite regress of data sharing where basic domestic hardware functionality is withheld unless consumers accept pervasive surveillance. - [26:36](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1596s) The high valuations of the 'surveillance dividend' incentivize legacy manufacturing sectors like automotive to pivot from physical craft into roving behavioral extraction networks. - [34:26](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2066s) Targeted behavioral models originally engineered for commercial ads were co-opted by political firms like Cambridge Analytica to exploit voter anxiety and anger. - [36:41](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2201s) Tech firms publicly champion personal privacy while legal counsel argues in court that platform users have forfeited any legitimate expectation of privacy. - [39:43](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2383s) Individual privacy tactics such as VPNs or search avoidance are insufficient because surveillance capitalism presents a structural challenge requiring collective action. - [42:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2562s) Failing to rein in private surveillance monopolies creates an apparatus ripe for annexation by future authoritarian governments within Western democracies. - [44:59](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2699s) GDPR and antitrust policies target declared user data and market scale, leaving the covert collection of behavioral exhaust and proprietary shadow operations untouched. - [47:44](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2864s) The past twenty years were an unregulated honeymoon akin to early industrial factory exploitation; democratic legal intervention has yet to properly begin.

## Detailed walkthrough ### [00:06](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=6s) The Architecture of Surveillance Capitalism

Harvard professor Shoshana Zuboff, whose book *The Age of Surveillance Capitalism* exposes the mechanisms of modern digital platforms, explains that the term "surveillance capitalism" is not accidental or arbitrary. Rather, it names an economic system where natural resources have been replaced by private human experience as the primary raw material for profit. These operations rely on being undetectable and indecipherable, cloaked in public-relations slogans about connecting humanity, individual empowerment, and algorithmic neutrality.

```mermaid flowchart TD accTitle: The Surveillance Capitalism Pipeline accDescr: Diagram showing how user interaction leads to digital exhaust, behavioral surplus, and ultimately behavioral prediction markets.

subgraph Collection["1. Data Harvesting"] User["User Interaction"] -->|"Generates"| Given["Declared Data (Queries, Profiles)"] User -->|"Leaves unwittingly"| Exhaust["Digital Exhaust (Typing speed, spelling errors)"] end

subgraph Processing["2. Computational Processing"] Exhaust --> Surplus["Behavioral Surplus (Exceeds service needs)"] Surplus --> Models["Machine Learning Models (Trained behavioral patterns)"] end

subgraph Monetization["3. Market Realization"] Models --> Pred["Behavioral Predictions (Future actions & emotions)"] Pred --> Markets["Sold to Business Clients (Targeting, routing, surveillance)"] end ```

The public commonly misunderstands this relationship through several rationalizations: users assume they benefit from personalized ads, believe they have nothing to hide, or assume tech companies only possess the information explicitly submitted to them. Zuboff refutes this completely, arguing that declared inputs are negligible compared to the behavioral breadcrumbs captured without conscious user consent:

> “The term surveillance capitalism is not an arbitrary term: surveillance because it must be operations that are engineered as undetectable, indecipherable, cloaked in rhetoric that aims to misdirect, obfuscate, and just downright bamboozle all of us all the time.” — Shoshana Zuboff

- Silicon Valley marketing relies on deceptive altruistic slogans like “Don’t be evil,” “Humanity,” and “The future is private.” - Tech companies monitor involuntary indicators, including typing cadence, driving habits, button color choices, and spelling mistakes.

[03:23](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=203s) Residual Data and Behavioral Surplus

In the dot-com crash era of 2000 to 2002, search and interaction metadata were treated as scrap or waste—referred to casually as "digital exhaust." Early engineers treated it merely as a diagnostic by-product. A Google representative describes how these logs were initially retained to train systems like Google's "Did you mean" spell checker, requiring more than 30 days of corrections to construct a world-class engine.

However, Silicon Valley soon realized this residual data held immense predictive power far exceeding what was necessary to optimize search results or repair tools. Zuboff terms this excess "behavioral surplus." Instead of simply returning value to the user, surplus data is redirected into large training models designed to forecast how people with specific traits will act over time.

> “This is what I call behavioral surplus: these data streams filled with these rich predictive data. Why surplus? Because right from the start, these were more data than was required to improve products and services.” — Shoshana Zuboff

[05:48](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=348s) Invisible Real-World Predictive Profiling

Once behavioral surplus aggregates across hundreds of millions of users, platforms can auction real-time predictive profiles directly to commercial clients. Zuboff illustrates this with a restaurant scenario: algorithms predict the exact food a person craves at that moment and auction that lead to local businesses, which immediately serve a targeted discount code.

Surveillance capitalism is not confined to screens or standard web advertisements, and users mistakenly believe their autonomous will renders them immune to ads. The New York Times demonstrated this deep behavioral layer by reporting on a supermarket chain whose algorithm predicted a teenage girl's pregnancy before her own father knew. The platform identified that she had swapped heavily fragranced shampoos for neutral-scented formulas to cope with the early olfactory sensitivities common in pregnancy, automatically sending baby product coupons directly to her home.

> “One of the misconceptions it's really important for us to move away from is that surveillance capitalism is something that is only manifest in our lives when we're online, or somehow it's only restricted to online targeted advertising.” — Shoshana Zuboff

- Trillions of terabytes of residual data reveal hidden personality traits, moods, sexual preferences, and political alignments without explicit disclosure. - Behavioral profiling maps unconscious habit changes to life events, extracting value beneath conscious human perception.

[08:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=522s) Facial Recognition and the Authoritarian Pipeline

When users upload ordinary family photographs to platforms like Facebook, the primary corporate asset is not the image itself, but the extracted mathematical markers. Machine learning scans the hundreds of microscopic muscles in human faces to train proprietary facial recognition engines. Platforms then commercialize these predictive capabilities with institutional buyers across the globe.

Zuboff points out that facial recognition models trained on Western consumers' social media uploads have been licensed to Chinese military operations. These models are deployed to manage open-air surveillance systems tracking the Uyghur Muslim population in China, as well as pro-democracy demonstrators in Hong Kong, effectively transforming personal family albums into enforcement infrastructure for authoritarian states.

> “They use information from our faces—which we've given billions and billions of photos to Facebook—to train models for facial recognition. Those models are then sold to military operations, some of them in China, and those Chinese operations do many things, including imprisoning the Uyghur, a subset of the Uyghur Muslim population in China, in what is rightly regarded as an open-air prison.” — Shoshana Zuboff

[11:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=702s) Psychological Contagion and Pokémon Go

The public remains unaware of these operations not due to ignorance, but because the systems are deliberately engineered to conceal their mechanics. To illustrate this, Zuboff points to Facebook’s peer-reviewed massive-scale emotional contagion experiments. Platforms deliberately injected subliminal linguistic cues into users' feeds to verify if they could manipulate real-world feelings—making users demonstrably sadder or happier—without ever alerting them to the intervention.

> “When the experimenters wrote up this work in the very prestigious scholarly journals that published the results of these experiments, they emphasized two key findings: number one, we now know that we can manipulate subliminal cues in the online context to change real-world behavior or real-world emotion; number two, we can exercise this power, these methods, while bypassing user awareness.” — Shoshana Zuboff

This behavior modification logic was carried directly into physical environments through the augmented reality game Pokémon Go, which drew over 1.3 million players in the Netherlands alone. While players gather in cities like Leeuwarden hunting digital creatures, the game functions as an experiment in directing human bodies through physical space. Pokémon Go was developed and incubated within Google under the leadership of John Hanke, representing an expansion of surveillance capitalism from the screen directly into physical geographic movement.

[15:07](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=907s) Pokémon GO and the Architecture of "Footfall"

Shoshana Zuboff traces the ancestry of augmented-reality gaming to show that commercial surveillance was embedded in its roots. It originated with an operation named Keyhole, a venture funded by the CIA and later acquired by Google to become Google Earth. When it was time to release Pokémon GO to the general public, it was consciously decoupled from Google's corporate brand. Instead, it was spun out through an unfamiliar entity called Niantic Labs, marketed as an agile, novel startup with a fun game rather than a global surveillance experiment.

Yet underneath the innocent game played by children sat a far larger, hidden framework designed precisely to mirror surveillance capitalism. In its original digital paradigm, surveillance capitalism predicted click-through rates and sold those predictive scores to advertisers whose goal was shepherding a web user toward a "Buy" button. Niantic Labs adapted this exact engine to the physical world. Instead of digital click-through, commercial clients paid Niantic for its physical equivalent: "footfall."

```mermaid flowchart LR accTitle: From Click-Through to Footfall accDescr: Diagram illustrating how digital click-through advertising evolved into real-world footfall tracking via Pokémon GO.

subgraph Online ["Online Model"] A["Residual Search Data"] --> B["Predict Click-Through"] B --> C["Online 'Buy' Button"] end

subgraph Physical ["Physical World Model"] D["Lure Modules / Gyms"] --> E["Predict Footfall"] E --> F["Brick-and-Mortar Sales"] end ```

Commercial entities purchased game infrastructure known as "lure modules" or gyms to attract crowds. Establishments like Starbucks, bars, restaurants, and ice cream parlors paid to guarantee that players' feet would tap across their floors. “Economies of action are how I guarantee that and Pokémon GO was a large-scale experiment, a global-scale experiment in economies of action using remote control means to automate behavior, to engineer behavior to fulfill others' commercial ends while you are having a great time,” Zuboff explains. The consumer believes they are enjoying a leisure activity, completely unaware that game incentives and punishments are herding them like cattle into locations that paid for their physical presence.

- Keyhole began as a CIA-invested mapping startup before Google acquired it and transformed it into Google Earth. - Niantic Labs acted as the outward vehicle for Pokémon GO so Google's direct involvement remained disguised. - Commercial sponsors purchased "lure modules" to pull players into brick-and-mortar storefronts, transforming behavioral steering into guaranteed footfall revenue.

---

[19:51](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1191s) The Smart Home and the Nest Concealment Tactic

Online tracking of searches, emails, and browsing histories eventually struck a ceiling. To sustain the accumulation of behavioral surplus, corporate logic demanded continuous expansion into every corner of unmonitored human existence: walks in the park, the inside of a commuter car, and domestic life within the home. This logic manifests aggressively in smart home hardware, such as the Nest home security system.

A tech-savvy enthusiast examining the physical hardware discovered that the Nest security system contained a built-in microphone. Curiously, this microphone was entirely absent from the product's official schematics and setup instructions. When caught, Google issued an apology, claiming the omission was an innocent oversight and that there had never been any intention to use it. Zuboff identifies this non-apology as part of a recurring corporate script: “This is their business: to obfuscate, to misdirect, to engineer our ignorance with mechanisms and methods that are undetectable and indecipherable, and if they confront you, deny it, deny it for as long as possible until they habituate.”

```mermaid flowchart TD accTitle: The Habituation and Adaptation Cycle accDescr: The tactical sequence tech companies use when hidden surveillance hardware is uncovered.

A["Deploy Undisclosed Sensor (e.g. Hidden Nest Microphone)"] --> B{"Discovered by Public?"} B -- No --> C["Extract Voice and Behavioral Surplus"] B -- Yes --> D["Plead Accidental Oversight ('Oops, so sorry')"] D --> E["Temporary Pacification / Disabling"] E --> F["Reintroduce Sensor in Next Device Iteration"] ```

The home microphone serves as a primary tool to siphon behavioral surplus out of private rooms: tracking televisions, background music, household arguments at breakfast, and visitors entering and leaving. If pushback resists habituation, the company temporarily retreats, claims the feature is disabled, and quietly reintroduces the sensor into a music player, a smart hub, or an appliance down the road.

- Nest's product documentation and consumer-facing blueprints completely omitted the installed microphone. - Capturing domestic audio yields high-value predictive signals: domestic arguments, visitor traffic, entertainment habits, and spoken interactions. - Zuboff notes the corporate playbook relies on four steps: obfuscation, stubborn denial, surface adaptation, and eventual reintroduction once scrutiny fades.

---

[23:15](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1395s) The 1,000-Contract Trap and Functionality Extortion

The legal framework of consumer consent functions as an illusion designed to compel compliance. Two legal scholars at the University of London dissected the terms of service and end-user agreements tied to a single Nest thermostat. Their research mapped out a dizzying labyrinth: the thermostat collects data, transmits it to third parties, and those third parties relay it to subsequent vendors ad infinitum, with nobody assuming legal liability for downstream use.

```mermaid flowchart LR accTitle: Infinite Regress of Smart Device Data accDescr: The endless onward transmission of consumer data through third-party supply chains.

Opting out of this endless transfer comes at a catastrophic cost. The manufacturer warns that revoking consent causes critical system updates and baseline functions to cease. The connected smoke detector may fail to trigger, and domestic heating controls may lock up, risking frozen, burst pipes. Basic domestic functionality is held hostage to sign away data. Summarizing the scholars' findings, Zuboff emphasizes: “What they conclude is that given these arrangements, any self-respecting consumer, anyone who's even a little bit vigilant about their consumption habit, should review a minimum of 1,000 privacy contracts in order to install just one single Nest thermostat in your home.”

- A single Nest thermostat ropes the homeowner into an infinite chain of downstream data-sharing partners. - Resisting data extraction triggers functional lockouts: lost software upgrades, disabled smoke alarms, and unmanaged temperatures that can freeze plumbing. - Thoroughly vetting the privacy ecosystem of one connected thermostat requires evaluating roughly 1,000 distinct legal contracts.

---

[26:36](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1596s) The Surveillance Dividend and the Automobile as Trojan Horse

The appetite for surplus demands continuous variations in qualitative inputs, moving from browser sessions into connected transport. Modern automobiles come packed with sensors, sporting at least 15 separate cameras alongside extensive diagnostic computers. Even modest access to 1% of operating cars offers near-complete real-time observation of global transit. If extracted driving records, routes, and passenger interactions eclipse the economic value of physical vehicle production, automakers could conceivably give the car away for free, monetizing total privacy forfeiture just as Google did with consumer web tools.

This reflects what Zuboff coins the "surveillance dividend." Between 2002 and 2004, Google proved that behavioral surplus generated unprecedented profit margins. Venture capital rapidly abandoned standard technology development to favor operations yielding this surplus premium. The traditional economy of building sturdy physical products has been hollowed out. A car company's board no longer wants to grind out mechanical optimizations for electric propulsion when harvesting driving metrics yields instant Wall Street market capitalizations comparable to Google and Facebook.

```mermaid flowchart TD accTitle: Ford Motor Company Surveillance Pivot accDescr: How traditional manufacturing pivots toward behavioral extraction to chase tech-firm valuations.

A["Traditional Automobile Manufacturing"] -->|"Pivot to Behavioral Extraction"| B["Ford Vehicles as Surveillance Nodes"] C["Ford Credit Financial Datasets"] --> D["Unified Driver Profile"] B --> D D -->|"Predictive Packaging"| E["Wall Street Tech Valuations ('Surveillance Dividend')"] ```

Automakers embrace this pivot openly. The CEO of Ford Motor Company pointed to Google and Facebook's stock valuations, explicitly outlining a strategy to repurpose hundreds of thousands of Ford vehicles as roving surveillance outposts. By marrying onboard telemetry with deep financial dossiers from Ford Credit, legacy manufacturers intend to mirror Silicon Valley extraction schemes. Zuboff underlines this tectonic shift: “The primary economy, what we perceive as primary economy—build a nice car, grow some nice food, build a house—means zilch in this surveillance economy.” This transformation mirrors Google's early calculus with Android: rather than merely collecting healthy margins on physical hardware like Apple, leadership realized the operating system should serve as a pervasive conduit to vacuum up human behavioral data across the globe.

- Modern vehicles operate as data nodes outfitted with upwards of 15 cameras, real-time routing trackers, and cabin monitoring. - Venture capital prioritizes companies claiming a "surveillance dividend" over traditional hardware or utility businesses. - Ford's leadership articulated plans to synthesize onboard vehicular telemetry with Ford Credit files to claim tech-sector market valuations. - The early vision within Google to sell Android simply for hardware margins was abandoned for a larger architecture of continuous, pervasive behavioral extraction.

[30:47](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1847s) Cheap Hardware, Network Drones, and the Indian Rejection

Surveillance capitalists operate on an economic logic that demands maximum raw material at minimum friction: the device itself should be as cheap as possible, or ideally free, so that behavioral surplus can stream continuously into corporate supply chains. With Google’s free mobile operating system Android commanding nearly 90% of global smartphones, the race to capture untapped populations intensified. To secure behavioral data from unconnected regions, Google launched network balloons, while Facebook deployed drones over emerging markets to offer subsidized internet bundled with the Facebook app.

```mermaid flowchart TD accTitle: Expansion of Behavioral Surplus Extraction accDescr: Big Tech uses cheap hardware and airborne internet infrastructure to pull millions of new users into behavioral supply chains.

subgraph Infrastructure["Infrastructure Capture"] A["Low-cost / Free Devices (Android OS ~90% share)"] B["Airborne Connectivity (Google Balloons & Facebook Drones)"] end

subgraph Funnel["Extraction Pipeline"] C["Millions of New Connected Users"] D["Behavioral Surplus Extraction"] E["Predictive Behavioral Models"] end

A --> C B --> C C --> D D --> E ```

When Mark Zuckerberg faced scrutiny over Facebook’s intense interest in India, he offered a standard philanthropic justification: “Our mission is to give everyone in the world the power to share what's important to them and to connect every person in the world.” Recognizing the dangers of American data extraction, India declined the offer, opting to bypass Zuckerberg’s apparent generosity.

---

[32:24](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1944s) The Australian Memo: Monetizing Peak Vulnerability

While tech firms zealously extract user data, they remain completely opaque about internal operations, leaving the public dependent on whistleblowers. Zuboff highlights an internal document penned by Facebook executives in Australia for their commercial clients. The document boasted that Facebook held data on roughly 6.6 million Australian teenagers and young adults, allowing the platform to track and anticipate acute emotional shifts.

Facebook could detect when these young users were feeling stressed, fatigued, anxious, inferior, or frightened. Advertisers were offered the ability to intervene at the precise psychological tipping point: Zuboff illustrates this with a teenager anticipating a weekend date on a Thursday evening, overwhelmed by anxiety. Facebook can signal an advertiser to push a targeted ad for a black leather jacket with immediate delivery, a discount coupon, and guaranteed arrival by morning. As Zuboff explains, “We can tell you the exact moment when they are at peak vulnerability.”

- Document scope: 6.6 million Australian teenagers and young adults monitored. - Predictable internal states: Stress, fatigue, anxiety, feelings of inferiority, fear, and low self-confidence. - Strategic objective: Trigger commercial conversions at the precise moment emotional vulnerability peaks.

---

[34:26](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2066s) Cambridge Analytica: Pivoting to the Inner Demons of Politics

The commercial exploitation of vulnerability mirrors the mechanisms of modern political manipulation. In 2018, whistleblower Christopher Wylie revealed that British firm Cambridge Analytica harvested personal data from over 87 million American Facebook users to micro-target the electorate. Zuboff emphasizes that this operation relied on the exact behavioral supply chains Facebook built for advertisers, redirected toward political engineering.

Recalling Wylie’s revelations, Zuboff notes that Cambridge Analytica mapped deep psychological vulnerabilities: “We knew so much about so many individuals that we could understand their inner demons, and we could figure out how to target those demons, how to target their fear, how to target their anger, how to target their paranoia.” By stoking these emotions, operators influenced what voters read, which groups they joined, and who they supported at the ballot box. Zuboff insists this mirrors Facebook's commercial operations: “Cambridge Analytica was nothing but a parasite on a huge host, and that host is surveillance capitalism.”

---

[36:41](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2201s) “The Future Is Private” vs. Backstage Legal Realities

Tech executives project reassurance in public while adopting ruthless legal stances in private. At Facebook’s F8 developer conference, Mark Zuckerberg announced a pivot toward personal security, asserting: “Today we are going to talk about building a privacy-focused social platform... that's why I believe that the future is private.”

```mermaid flowchart LR accTitle: Frontstage Rhetoric vs Backstage Reality accDescr: The contrast between Facebook's public marketing of privacy and its legal defense in court.

subgraph Public["Public Presentation"] direction TB F8["F8 Conference Keynote"] F8 --> Claim["'The future is private'"] end

subgraph Backstage["Backstage Reality"] direction TB Court["California Class-Action Suit (June 2019)"] Court --> LegalArg["Defense Counsel Argument"] LegalArg --> ZeroPrivacy["'No Facebook user has any legitimate expectation of privacy'"] end

Public -.->|"Contradicts"| Backstage ```

Only two months later, in June 2019, Facebook counsel confronted a class-action lawsuit in a California court brought by users demanding access to data shared with Cambridge Analytica. In open court, Facebook’s lawyer argued that anyone who shares information across a network of 100 or more people forfeits legal claims to privacy. Zuboff emphasizes the stark contrast between public marketing and the courtroom reality where a lawyer argued that “no Facebook user has any legitimate expectation of privacy.”

---

[39:43](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2383s) Individual Privacy Tactics and the Limits of Resistance

When asked how she navigates daily life, Zuboff reveals she refuses standard Google products, relying on strict operational discipline: “If there is an occasion where I feel the absolute need to use Google, I go through various layers of encryption and location reconfiguration before I go to Google search, just on principle.”

However, individual tools like VPNs, alternative search engines, and encrypted channels do not threaten surveillance capitalism’s structural power. Individual evasion leaves the wider extraction machinery completely intact: “I don't want anyone to get confused that if you forego Google search you are somehow, you know, fighting the fight, because this is a collective problem and it requires collective action.”

---

[40:43](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2443s) The Amish Alternative: Intentional Tools vs. Moral Drift

The documentary shifts to an Amish community, illustrating a social group that consciously governs technological adoption rather than submitting to market momentum. An Amish community member explains how horse-and-buggy transport deliberately slows life down to safeguard shared communal values. Their engagement with workplace technology follows strict, non-negotiable boundaries.

An Amish entrepreneur displays a desktop computer custom-built without media players, internet browsers, or external networking: it was assembled from the ground up strictly as a dedicated business tool. Similarly, his mobile phone can only make voice calls—it has no text messaging, lives at his workplace, and is never brought home. He sums up their philosophy: “We use technology as long as we use it and it doesn't get to the point where it uses us and controls us. That's the bottom line of it.”

- Hardware policy: Computers built strictly for utilitarian computation, stripped of internet capabilities and entertainment media. - Telephony boundaries: Voice-only mobile units kept strictly on business premises and barred from domestic life. - Moral framework: Communal evaluation of every tool's ethical and social impact prior to adoption.

---

[42:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2562s) The Faustian Bargain and the Authoritarian Threat

Reflecting on the Amish perspective, Zuboff rejects the idea that modern citizens must choose between full analog retreat and absolute surveillance. Society flourished and maintained deep social relationships long before these tools existed. While modern digital connectivity offers real value, surveillance capitalism forces a false choice: citizens must either surrender to tracking or exile themselves from society.

Zuboff warns that failing to curb this market logic creates dangerous precedents for state power. If authoritarian figures gain power within Western democracies, the surveillance architecture built by private monopolies will be ready for abuse: “There will come a time when, even in the West, even in our democratic societies, our government will be tempted to annex these capabilities and use them over us and against us; let's not be naive about that.” Confronting corporate surveillance now is vital to preventing democratic collapse later.

---

[44:59](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2699s) The Blind Spots of GDPR and Antitrust Law

The European Union has taken significant steps against tech monopolies, issuing multi-billion-dollar antitrust fines to Google and enacting the General Data Protection Regulation (GDPR). When asked whether these legal remedies are sufficient to protect European citizens, Zuboff argues that current frameworks only scratch the surface of the problem.

```mermaid flowchart TD accTitle: The Regulatory Scope Gap accDescr: Contrast between what current regulations govern and where real extraction happens.

subgraph Regulated["Governed by GDPR"] A["Public Operation Data"] B["Data consciously handed over by users"] C["Portability, Access, and Ownership rights"] end

subgraph Unregulated["The Hidden Machine"] D["Behavioral Surplus"] E["Unconscious digital exhaust & ambient tracking"] F["Proprietary Prediction Products & Behavioral Modification"] end

Regulated -.->|"Leaves untouched"| Unregulated ```

While praising GDPR as the furthest legal frontier and affirming the value of antitrust rules, Zuboff explains that current laws are built around older concepts of deliberate data sharing. GDPR protects data ownership, access, and portability—rights that apply only to data users consciously hand over. They completely miss the covert pipeline where companies sweep up behavioral exhaust without user consent or awareness: “Surveillance capitalism is unprecedented, and so we're going to need the laws and the regulatory regimes that respond directly to these new, unprecedented operations.”

[46:49](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2809s) The Shadow Operation and Illegitimate Profit

Shoshana Zuboff explains that surveillance capitalism extracts behavioural data from subtle, everyday human actions—from an exclamation point typed in a message to a casual walk in the park. Everyday devices and sensors pick up the cadence and timbre of our voices, converting these micro-behaviours into rich predictive signals. Because of this architecture, conventional regulatory concepts like individual data ownership, data portability, or access rights fall drastically short. The vast majority of this material lives entirely within a proprietary "shadow operation" that ordinary citizens will never be permitted to see, let alone retrieve.

Tech corporations claim these vast repositories as their own property, yet Zuboff emphasizes that they were seized directly from human lives and private experience without meaningful consent or awareness. The companies process and analyze this raw behavioral material, package it into prediction products, sell those products to commercial clients, and pocket the proceeds. Zuboff argues that this entire financial windfall is illegitimate from the ground up, as it relies on fundamentally bypassing human knowledge.

```mermaid flowchart TD accTitle: The Surveillance Extraction Pipeline accDescr: Diagram illustrating how private experience is bypassed and converted into prediction products and profit. subgraph Capture["1. Extraction"] A["Private Experience & Subtle Cues (Cadence, Voice Timbre, Walks)"] -->|"Bypasses awareness"| B["Hidden Shadow Operation"] end subgraph Monetization["2. Marketization"] B --> C["Behavioral Analysis & Modeling"] C --> D["Behavioral Prediction Products"] D --> E["Illegitimate Corporate Profit"] end ```

- Surveillance capitalism converts subtle signals—such as vocal pitch, tone, and physical movement—into behavioral prediction models. - Policy remedies centered solely on personal data portability fail because tech monopolies keep the bulk of analytical intelligence locked in hidden backend infrastructure. - As Shoshana Zuboff puts it: “Most of the data is in the shadow operation and the shadow operation is never coming to us.” - Denouncing the entire economic model, Zuboff emphasizes: “They analyzed the data. They made it into products. They sold the products and they took the profit, illegitimate profit.”

[47:44](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2864s) The Twenty-Year Honeymoon and Betting on Democracy

When asked whether citizens stand any realistic chance against the behemoth of surveillance capitalism, Zuboff offers an unexpectedly defiant historical perspective. She describes the past two decades as a twenty-year "honeymoon" for the industry, pointing out that tech corporations operated almost completely unimpeded by law simply because their methods were unprecedented. She draws a direct historical parallel to the rise of early industrial factories and child labor: when novel industrial practices first appeared, no legal frameworks existed to regulate them, and it took society decades to recognize the threat and build democratic guardrails.

Similarly, during the American Gilded Age, the monopolists who extracted natural resources and exploited workers were originally venerated as brilliant titans and wealthy gods masterfully wielding machinery and capital—only later did society recognize them as robber barons and organize against them. Zuboff stresses that society has not lost the battle against tech monopolies; rather, citizens and lawmakers have not yet begun an organized effort to outlaw and dismantle surveillance capitalism. With the economic regime existing for barely two decades while democratic institutions carry centuries of resilience, she remains steadfast in her conviction that democratic regulation will ultimately triumph.

- The first two decades of surveillance capitalism went largely unchecked because legal systems had no precedent for automated behavioral exploitation. - Historical parallels show that early industrial harms, such as factory child labor and Gilded Age monopolies, were also initially celebrated before democratic legislation stepped in to restrain them. - If societies had actively tried and failed for twenty years to restrain tech giants, despair might be justified, but systematic legal intervention has yet to be mounted. - Reflecting on our historic failure to confront the industry so far, Shoshana Zuboff notes: “We haven't even tried yet to stop it.” - Expressing her core reason for hope, Zuboff insists: “Surveillance capitalism is 20 years old. Democracy is several centuries old. I bet on democracy.”

## Notable quotes > [47:44](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2864s) Surveillance capitalism is 20 years old. Democracy is several centuries old. I bet on democracy. — Shoshana Zuboff

> [44:59](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2699s) Surveillance capitalism is unprecedented, and so we're going to need the laws and the regulatory regimes that respond directly to these new, unprecedented operations. — Shoshana Zuboff

> [00:06](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=6s) The term surveillance capitalism is not an arbitrary term: surveillance because it must be operations that are engineered as undetectable, indecipherable, cloaked in rhetoric that aims to misdirect, obfuscate, and just downright bamboozle all of us all the time. — Shoshana Zuboff

> [42:42](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2562s) There will come a time when, even in the West, even in our democratic societies, our government will be tempted to annex these capabilities and use them over us and against us; let's not be naive about that. — Shoshana Zuboff

> [26:36](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=1596s) The primary economy, what we perceive as primary economy—build a nice car, grow some nice food, build a house—means zilch in this surveillance economy. — Shoshana Zuboff

> [40:43](https://www.youtube.com/watch?v=hIXhnWUmMvw&t=2443s) We use technology as long as we use it and it doesn't get to the point where it uses us and controls us. — Amish community member

## Mentioned Shoshana Zuboff, The Age of Surveillance Capitalism, Google, Facebook, Larry Page, Sergey Brin, Mark Zuckerberg, John Hanke, Christopher Wylie, Cambridge Analytica, Niantic Labs, Pokémon GO, Keyhole, Google Earth, Nest, Ford Motor Company, Ford Credit, Android, General Data Protection Regulation (GDPR)

## Video description Harvard professor Shoshana Zuboff wrote a monumental book about the new economic order that is alarming. "The Age of Surveillance Capitalism," reveals how the biggest tech companies deal with our data. How do we regain control of our data? What is surveillance capitalism?

In this documentary, Zuboff takes the lid off Google and Facebook and reveals a merciless form of capitalism in which no natural resources, but the citizen itself, serves as a raw material. How can citizens regain control of their data?

It is 2000, and the dot.com crisis has caused deep wounds. How will startup Google survive the bursting of the internet bubble? Founders Larry Page and Sergey Brin don't know anymore how to turn the tide. By chance, Google discovers that the "residual data" that people leave behind in their searches on the internet is very precious and tradable.

This residual data can be used to predict the behavior of the internet user. Internet advertisements can, therefore, be used in a very targeted and effective way. A completely new business model is born: "surveillance capitalism."

Original title: De grote dataroof

Director: Roland Duong Research: Tom Reijner, Halil Ibrahim Özpamuk Camera: Adri Schrover Sound: Jochem Salemink Editing: Roland Duong, Paul Delput, Rinze Schuurman Production: Marie Schutgens Production assistant: Britt Bennink Image Editing: Rob Dorrestijn, Paula Witkamp Online Coordinator: Arja van den Bergh Commissioning Editors: Bregtje van der Haak, Doke Romeijn

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[Source](https://www.youtube.com/watch?v=hIXhnWUmMvw)

中文简报

肖莎娜·祖博夫论监视资本主义 | VPRO纪录片祖博夫剖析了监视资本主义的运作机制，展示了早期互联网搜索的“行为副产品”如何被转化为价值数十亿美元的行为预测产品市场。纪录片追溯了这种榨取如何突破屏幕，通过《精灵宝可梦Go》（Pokémon Go）等游戏延伸至物理移动，通过智能家居设备侵入家庭空间，进入联网汽车，并经由剑桥分析公司（Cambridge Analytica）展现的心理微靶向技术落地。祖博夫强调，个人的隐私应对手段以及诸如《通用数据保护条例》（GDPR）等常规的数据可携性法规，不足以抗衡专有的后台影子运作；她总结道，社会必须借助民主机制从制度上取缔行为剥削，正如历史上曾约束镀金时代的工业垄断那样。监视资本主义利用的是非自愿产生的“行为剩余”而非主动申报的数据来变现，将人类的私人经验转化为行为预测产品的原材料。 (0:06) 隐蔽的传感器整合与服务条款陷阱确保了持续的榨取，消费者仅仅为了安装一个Nest恒温器，估计就需要查阅多达1,000份隐私合同。 (23:15) 澳大利亚泄露的Facebook内部文件显示，其有能力识别660万名青少年的严重情绪脆弱状态，以精准把握商业广告的投放时机。 (32:24) 欧盟的GDPR等现行法律框架未能有效解决监视资本主义问题，因为它们监管的是主动申报的数据所有权，而非用于收集行为副产品的专有影子管道。 (44:59)

Source quotes

[47:44] Shoshana Zuboff: Surveillance capitalism is 20 years old. Democracy is several centuries old. I bet on democracy. [44:59] Shoshana Zuboff: Surveillance capitalism is unprecedented, and so we're going to need the laws and the regulatory regimes that respond directly to these new, unprecedented operations. [0:06] Shoshana Zuboff: The term surveillance capitalism is not an arbitrary term: surveillance because it must be operations that are engineered as undetectable, indecipherable, cloaked in rhetoric that aims to misdirect, obfuscate, and just downright bamboozle all of us all the time. [42:42] Shoshana Zuboff: There will come a time when, even in the West, even in our democratic societies, our government will be tempted to annex these capabilities and use them over us and against us; let's not be naive about that. [26:36] Shoshana Zuboff: The primary economy, what we perceive as primary economy—build a nice car, grow some nice food, build a house—means zilch in this surveillance economy. [40:43] Amish community member: We use technology as long as we use it and it doesn't get to the point where it uses us and controls us.

Original transcript

[0:06] Harvard professor Shoshana Zubov is sometimes called the Karl Marx of our time [0:11] Her monumental book the age of surveillance capitalism exposed the dubious mechanisms of our digital economy [0:19] According to Zubov our personal and private experiences have been hijacked by Silicon Valley and used as the raw material [0:27] for extremely profitable [0:29] Digital products the term surveillance capitalism is not an arbitrary term [0:35] Why? [0:36] surveillance [0:37] because it must be operations that are [0:41] engineered as [0:43] undetectable [0:45] indecipherable cloaked in rhetoric that aims to misdirect [0:52] Obfuscated and [0:54] Just downright [0:57] Bamboozled all of us all the time [1:00] Connecting them don't be evil connecting them humanity. Don't be evil [1:06] Humanity humanity, it's about empowerment of the individual. The future is privately [1:12] What really happens with your Facebook photos? Why are there hidden microphones in Google nest and [1:19] Pokemon go exposed as child molesters in [1:22] This episode of backlight Shoshanna Zubov reveals how Silicon Valley deceives us. So well [1:37] How all this comes into play when I [1:41] Buy a pair of shoes on the Internet [1:45] well [1:46] You know some people [1:48] Will say to me [1:50] But Professor Zubov, I like those targeted ads [1:54] There's they're so useful or I enjoy personalized services [1:59] sometimes people will say [2:02] You know, I have nothing to hide so I don't care what they take [2:08] Each one of these statements is a profound [2:13] Misconception of what's really going on? We think that the only personal information they have about us is [2:21] what we've given them and [2:24] we think we can exert some control over what we give them and therefore we think that our [2:30] Calculation our trade-off here is is something that is is somehow under our control that we understand it [2:37] what's really happening is that we provide personal information, but the information that we provide is [2:44] The least important part of the information that they collect about us [2:53] Thanks to their navigation and search engine Google knows where we are all the time and what we think [3:00] Facebook knows our hobbies preferences and friends [3:04] Because they retrieve a lot of information from the digital traces we leave behind unwittingly [3:10] Spelling errors in your search terms which color buttons you prefer how fast you type how fast you drive? [3:18] residual data [3:23] Way back at the beginning [3:25] Back in the year 2000 2001 2002 back in those days [3:31] These data were considered just extra data. They were considered waste material and [3:37] people called them things like digital exhauster or data exhaust [3:42] Eventually, it was understood that these so-called waste materials [3:47] harbored these rich predictive data [3:56] The search information we retain we do for quality purposes [3:59] So for example the google spell checker our did you mean feature that appears on google has been built using? [4:07] long periods of data around you know [4:11] Someone issuing a query and then issuing another corrected query rate after that and us learning [4:16] Those Corrections and it actually takes more than 30 days worth of data to build the world-class spell corrector that we have [4:25] The companies like to say we collect data so that we can improve our service and that's true [4:32] They collect data and some of it is used to improve the service to you [4:38] but even more of it is [4:42] Analyzed to train what they call models [4:46] patterns of human behavior [4:49] So once I have big training models, I can see how people with these characteristics [4:58] typically behave over time and [5:00] That allows me to fit your data right into that arc and to predict what you're likely to do [5:07] Not only now but soon and later [5:10] This is what I call behavioral [5:13] surplus these data streams filled with these rich predictive data why surplus [5:20] Because right from the start these were more data than was required to [5:25] improve products and services [5:29] Once you have the behavioral surplus the comprehensive behavioral data of hundreds of millions of people [5:35] You can start predicting the Preferences of specific groups [5:40] Think popular shoes for mail managers or the preferred restaurants of a group of people sharing the same zip code [5:48] Maybe they do a prediction of where I have dinner tonight. Well, how can I imagine what they will predict about me? [5:54] Well, you know at the simplest level [5:57] They may predict [6:01] The kind of food you're in the mood for right now [6:06] And then [6:08] sell that prediction [6:10] Auction that prediction to their business customers in the in the restaurant business who will then? [6:17] Send you a very quick ad we know you're in the mood for a delicious pasta dish tonight [6:24] We can invite you to our restaurant. Here's a discount coupon [6:28] There are some people who are saying well [6:31] It's very improbable [6:33] that those targeted ads really can achieve something because people have their own will and they will not [6:39] Buy some shoes just because there is a net in front of them [6:47] You know, I think one of the [6:50] One of the [6:52] misconceptions [6:53] it's really important for us to move away from is that [6:58] Surveillance capitalism is something that is only [7:03] Manifest in our lives when we're online [7:07] Or somehow it's only restricted to online targeted advertising [7:12] It's easy for us to say oh these these things don't affect me. The fact is this is [7:19] being conducted at a layer that is not [7:22] accessible to us [7:26] We have no idea what today's algorithms can predict about us or what behavioral data they used to do it a [7:34] Simple thing like buying a certain kind of shampoo can divulge essential information about us? [7:41] For example, the New York Times reported a case of a supermarket chain [7:45] The new a girl was pregnant even before she did who was prepared to share the news [7:51] The markets algorithms discovered that the girl switched from fragrant shampoos to more neutral smelling products [7:59] Since the olfactory senses of pregnant women becomes stronger the market algorithm assumed this girl must be pregnant [8:07] father didn't know until [8:09] He was repeatedly sent special offers for baby products [8:14] Thanks to the analysis of trillions of terabytes of behavioral data that we unwittingly leave around the digital domain [8:21] Big tech sometimes knows us better than we know ourselves [8:25] they can predict things like our personality our [8:29] emotions our [8:31] sexual orientation our political orientation a [8:35] whole range of things that we never ever intended to disclose [8:42] The predictive value the big tech can glean from residual data is huge the family photos [8:48] We post on our Facebook pages contain residual data from which vast amounts of valuable knowledge can be distilled [8:56] let's say I put my [8:59] children's birthday party photo album on the web on the Facebook pages [9:04] What we don't understand is that the most important thing there are not the photos per se [9:11] It's the predictive signals [9:14] That these companies can lift from the photos. It's not just my face [9:21] but it's [9:22] Allowing them to have the face so that they can analyze [9:26] the hundreds of muscles in my face [9:32] Uploading innocent snapshots on your Facebook page can have unforeseen [9:36] consequences our faces for example are used to train algorithms to recognize facial features and [9:43] we have absolutely no idea what that [9:46] Facial recognition software is used to all these data streams with these rich predictive signals are fed [9:53] into the new factories the computational factories [9:57] analyzed for [9:59] predictions of human behavior and [10:02] These predictions are then sold [10:05] Who are they sold to they're not sold to us. We are not the customers. They're sold to businesses to [10:13] business customers who want to [10:16] maximize our [10:18] Value to their business, whatever it may be [10:23] They use information from our faces [10:27] Which we've given [10:29] billions and billions of photos to Facebook [10:32] to train models for facial recognition [10:35] those models are then sold to military operations some of them in China and [10:41] Those Chinese operations do many things including [10:45] imprisoning the Uighur, a subset of the of the Uighur Muslim population in China in what is [10:53] Rightly regarded as an open air prison [10:57] Where they actually don't have to have people behind bars because they track and follow them constantly through facial recognition [11:06] The knowledge obtained from our residual data can be sold to anyone [11:10] Facial recognition software for instance might be sold to a Chinese company that supports the oppression of the Uighurs in China [11:17] or that helps track down advocates of democracy in Hong Kong [11:22] that way our precious family photos might be used by Facebook to facilitate authoritarian regimes and [11:30] Our privacy is guaranteed [11:32] Because it's not our faces that are sold its the residual data scraped off them [11:42] It's very difficult to have a concept of this for a for a very good reason [11:49] It's not because we're stupid [11:51] It's because these processes have been [11:55] disguised [11:57] They operate in stealth [12:03] They have been engineered to be [12:07] indecipherable to be undetectable to [12:11] Create ignorance in a vast group of all of us that they call users [12:19] Our ignorance is their bliss [12:26] There are some things that have broken through into the public view that we do know about so let's talk about Facebook's [12:34] Massive scale contagion experiments and this is where Facebook [12:41] experimented with subliminal cues [12:44] planted in its Facebook pages [12:47] That would actually influence [12:50] offline [12:51] real-world [12:52] Behavior and emotions to see if they could make people feel happier or sadder [12:58] Using subliminal cues in with language manipulation and word manipulation and so on [13:04] well [13:06] when the [13:07] Experimenters wrote up this work in the very prestigious scholarly journals that published [13:14] the results of these experiments [13:17] They emphasize two key findings [13:21] number one [13:22] We now know that we can manipulate [13:27] subliminal cues in [13:29] the online [13:31] context [13:32] to change [13:34] real-world behavior or [13:37] Real-world emotion. We know that we can be successful at doing this [13:42] Number two, we can exercise this power these methods [13:51] While bypassing user awareness [14:02] A big group of Pokemon Go players set up a meeting in Leeuwarden to stroll through the city to catch as [14:09] many Pokemon as they can. In the Netherlands there are already about 1.3 million players. [14:16] very interesting [14:17] experiment [14:19] occurs in the guise of an augmented reality game called Pokemon Go. In the game you are walking [14:27] around in the real world. The Pokemon are hiding in different places. You have to go to these places to collect them. [14:33] What are we not seeing when Pokemon Go is [14:36] introduced in our country [14:38] it's important to understand that Pokemon go is an augmented reality game that was [14:43] Incubated developed inside Google for many years Google being the first pioneer of surveillance [14:50] capitalism the inventor of surveillance capitalism [14:54] It was invented Pokemon go was invented in Google incubated there for many years developed there [15:01] led by a man named John Hanke and John Hanke invented an [15:07] operation called keyhole which was bested by the CIA and later purchased by Google and [15:13] Called Google Earth [15:15] Was a CIA startup initially? Yes. Google Earth was something called keyhole and it was a [15:21] CIA [15:22] invested startup [15:24] So it's important to understand that Pokemon go was not [15:28] Some happy little game that just got launched into the world via a toy company or something [15:37] When they [15:38] Decided to bring Pokemon go to the public. They didn't want to bring it as a Google game [15:44] They brought it to market as Niantic labs which no one had ever heard of [15:48] Just a cool startup with this cool game [15:51] So now we have this Google augmented reality game and it turns out that the big game [15:59] that is on top of the little game that the children are playing is a game that precisely [16:06] emulates the logic of surveillance capital capitalism [16:11] So in surveillance capitalism in the original version online [16:16] We predict the click-through rate and [16:19] We sell the click-through rate to the advertiser [16:22] Who pays to get clicks on on their on their website? [16:28] Clicking through ultimately to the Buy button. That's what they're hoping for [16:34] Now in the real world [16:37] Business customers [16:40] paid [16:41] Niantic labs [16:42] the Pokemon go company [16:45] paid Niantic labs [16:47] Not for click-through, but for the real-world equivalent of click-through which is called [16:55] footfall [16:57] To actually get real bodies with their real feet [17:02] into real business establishments [17:05] So that their feet would go [17:08] fall fall fall fall tap tap tap across [17:11] this store or across the restaurant or across the bar in order to [17:18] buy [17:19] Something so I could order by Niantic labs [17:23] Pokemon in my ice cream parlor for instance all of these establishments. They're buying what are called. Here's the term [17:31] lure [17:32] modules [17:34] Modules like a gym that lure people to you [17:40] Not so that they will come and be happy but so they will come and spend money in your shop or your restaurant [17:48] Starbucks [17:50] Everybody was making money [17:52] everybody was making money Niantic labs was making money and all these businesses were making money and [17:59] the [18:00] People playing the game had no idea so they used the rewards and Punishment of the game [18:07] To heard you [18:10] through the city [18:11] To the places that were paying for your body [18:17] this was the game of [18:20] Pokemon go this was the real game the shadow game [18:25] getting you [18:26] Into a place where we have predicted that you will be [18:32] So that our predictions are worth more if I can guarantee [18:36] You're actually going to be there my prediction that you're going to be there is worth a lot more [18:42] Economies of action are how I guarantee that and [18:46] Pokemon go was a [18:48] large-scale experiment a global scale experiment in economies of action [18:55] using remote control means to automate behavior to [19:01] engineer behavior to fulfill others commercial ends while [19:07] You are having a great time [19:18] You are intended to be in the feeling of being served [19:25] You are intended to be saturated with convenience so that you will not notice and you [19:32] will not complain and [19:35] all of this shadow operation will remain [19:39] Hidden because you will not ask questions [19:43] Because you're so busy being entertained [19:51] So it's no longer enough to just have what you're doing online you're browsing you're sending messages you're sending emails [19:59] We want to know about your walk in the park. We want to know about what you're doing in your car [20:05] We want to know about your home and what you're doing in your home [20:09] What if home security was different? [20:12] What if it looked different? [20:22] Well, it's a beautiful system [20:26] Some bright engineering oriented person [20:31] Discovered that the nest security system [20:34] has a [20:36] microphone [20:38] built into it that [20:41] Microphone does not figure in any schematic [20:47] When you buy the security system and it has the piece of paper that you unfold and the schematic or you go online [20:53] To learn about it and there's a schematic it does not show a microphone it [20:58] Does not discuss a microphone [21:02] Now, why would you have a microphone there? Well, remember what is our business? [21:08] our business [21:09] depends upon [21:12] Extraction of behavioral surplus [21:14] scale [21:16] scope and action [21:18] So what better device to extract? [21:24] behavioral surplus, especially new forms voices [21:29] conversations [21:31] What you're watching on television what you're listening what music you're listening to? [21:36] Who's coming in and out of your house? [21:39] Whether or not y'all you're shouting at each other over the breakfast table [21:44] All of this has tremendous predictive value [21:49] Voices are what everybody's after just like they're after faces [21:54] So now this becomes public there's a hey Google [21:58] What's up with this? [22:00] Microphone in your security system. What does Google say oops? [22:05] So sorry. Oh we didn't even know there was a microphone there. Oh [22:10] Sorry, sorry why somebody put that microphone there, but we never intended to use it [22:17] This is their business [22:20] To obfuscate [22:21] to misdirect [22:23] to engineer our ignorance [22:26] with mechanisms and methods that are undetectable and indecipherable and if they confront you [22:33] deny, it deny it for as long as possible until they habituate if [22:38] There's some element that fails to get habituated then create an adaptation [22:43] All right. We'll make sure all those microphones are [22:48] Are somehow [22:52] You know shut off so that they can't be used and [22:55] Then wait a minute and ones no one when no one is looking [22:59] Redirect it. So there will be a microphone in something else. There will be a microphone in the [23:05] You know in the home device or in the music player or whatever. It might be it will be back [23:15] What if the measure of it working was that you never had to think about it [23:25] There's two legal scholar said there at the University of London and [23:30] They analyzed the privacy policy how we give our consent to these devices, right? [23:38] So they analyzed these documents for one nest thermostat [23:45] What happens is the thermostat? [23:48] collects data it [23:50] sends those data to third parties and those third parties send data to third parties odd infinitum an [23:57] infinite regress or your data is going goodness knows where and [24:03] No company takes responsibility [24:07] For what the third parties that it's sending your data to? [24:11] May do with your data [24:14] Nest will say if you don't want us to take your data and you don't want us to send it on to third parties [24:20] that's ok, but [24:22] be aware that [24:25] Without your data we will stop [24:28] Supporting the functionality of your thermostat. We will stop upgrading the software [24:34] be aware that [24:36] The smoke detector may no longer work [24:39] Be aware that the the pipes in your home may freeze. So now the [24:45] functionality of the device is held hostage to your agreeing to the privacy contract and [24:53] They say by the way [24:55] even if you agree and [24:57] we maintain the functionality or sending it to these other third parties and [25:02] They're going to use it the way they choose and we take no responsibility for what they do with it [25:08] All right [25:09] so now these two scholars do an analysis of one nest thermostat and [25:14] What they conclude is that given these arrangements [25:18] any [25:20] self-respecting consumer [25:22] anyone who's even a little bit vigilant about their consumption habit [25:28] should review a [25:30] minimum of [25:33] 1,000 privacy contracts [25:37] In order to install just one single nest thermostat in your home [25:45] You have three minutes to exit [25:51] What if it gave you time [25:55] And what you really need from home security a sense of security [26:04] So they start extracting data at scale [26:08] First it's everything you do online and then pretty soon they need more data [26:14] But then they also discover that [26:16] We need varieties of data. It's not just volume, but it's different qualities of data [26:36] The problems in mobility are going to be the same as what we saw online with the smart phones [26:42] that you are paying with your privacy without you knowing. [26:46] Because the car knows exactly what you are doing, where you are going [26:47] and any modern car will have about 15 cameras at a minimum [26:52] and even if you have access to only 1% of all cars you know what's happening all over the world. [27:02] If the data is going to be more worth than driving the car [27:05] who knows, you might offer you a free car. You pay with your privacy [27:10] in the same way you pay with privacy for all the free services you get from Google [27:20] Well surveillance capitalism broke through in Silicon Valley [27:24] 2002, three, and four it changed the bar for investment [27:30] Now you had Google making money on the basis of what I call the surveillance dividend [27:37] Now what in what venture capitalist wants to invest in a firm? [27:43] That's you know, just making an app [27:46] When it can invest in a firm that's making an app plus a surveillance dividend. So what happened right away? [27:54] Was that the investment started flowing to the people who were making more money and that? [28:01] more came from the surveillance dividend and now that is the structure that is [28:08] flowing across the whole economy [28:11] Why bother to put all that effort into the engineers and the factories and the science? [28:17] to make a car that runs without carbon when all we need to do is sell the data from the darn vehicles and [28:25] we've got the surveillance dividend and all those investors are going to come to us and give us the kind of [28:33] Market capitalization that the leading surveillance capitalists have that's our new lease in life [28:40] That's our path forward in the 20th century [28:47] The data is worth a lot that everyone is asking for it. That's why Google want to get their products in the car not because you can make a lot of money with a car business. [28:59] You can use the car as the Trojan Horse to collect all that data. [29:07] We even now see it coming full circle back to the birthplace of mass production in the Ford Motor Company [29:13] Where the CEO of Ford Motor now says, you know, we want market capitalization like Facebook and Google [29:20] How do we get it? Well instead of figuring out how to make a car that runs carbon-free [29:27] what we're gonna do is repurpose our automobiles as surveillance vehicles, and we're going to stream data from the [29:34] 100,000 people who are driving around in Ford cars [29:38] we're gonna combine that with the data we have from Ford credit where he says, we already know everything about you and [29:45] These data sets are gonna put us on a par with the likes of the great surveillance [29:50] Capitalists and who wouldn't want to invest in Ford Motor under those circumstances? [30:07] The primary economy what we perceive as primary economy build a nice car grow some nice food build a house [30:15] Means zilch in this surveillance economy. So this began with Android [30:22] Once Google acquired the ability to make the Android phone [30:26] There were a lot of people in Google who said oh, this is fantastic [30:29] now we have a phone like Apple and we can sell a phone and we can make a great margin on that phone and [30:36] That margin is gonna fund our profits and this is this gonna make us really rich [30:41] but the the wiser heads in Google prevailed the people who already [30:47] Understood surveillance capitalism prevail and they said just the opposite [30:52] We want the phone and everything associated with the phone to be as cheap as possible [30:58] In fact if we can get the price to zero, that's even better [31:03] We want everybody to have a phone because the more they have the phone and the more they spend time on the phone [31:12] The more data we get from them the more behavioral surplus streams into our supply chains [31:18] So if we can give it away, we're gonna give it away [31:24] Google's free mobile operating system Android means Google holds the key to almost 90% of the world's smartphones in [31:32] Order to obtain as much data as possible from all these cell phones, Google [31:37] Experimented with Network balloons in those parts of the world where mobile internet is not available [31:44] Facebook not to be outdone [31:46] Flew Network drones over growing markets and offered free internet in combination with the Facebook app [31:53] And connecting them represents one of the greatest opportunities that available to humanity today [32:00] Hi mark, why are you showing so much interest in India, answer honesty? [32:06] our mission is to [32:09] Give everyone in the world the power to share what's important to them and to connect every person in the world [32:15] Aware of the potential dangers of American data robbery India politely declined the offer and may do without Zuckerberg's [32:23] generosity [32:24] In other parts of the world Facebook's true intentions are becoming clearer every day, but we need whistleblowers for that [32:32] Facebook is happy to take our data but not prepared to share information on how the company works [32:41] So this is a document written by Facebook executives in [32:45] Australia and what they told their business customers there was that we have [32:51] so much data [32:53] about [32:55] 6.6 [32:56] million [32:57] Australian young adults and teenagers as a result of that we can predict mood shifts [33:06] We can predict when they feel stressed [33:10] fatigued [33:12] anxious [33:13] inferior [33:15] frightened [33:17] all of these kinds of very personal feelings and [33:22] We can alert you [33:25] The exact moment [33:28] when they are most likely to need a confidence boost, let's say there's a [33:34] young person who's contemplating a [33:38] date over the weekend and it's now Thursday night and their [33:42] Anxiety is peaking and they need a confidence boost if you send them an ad for a sexy black leather jacket [33:49] Send it right now offer free delivery [33:52] Tell them you'll have it at their door by the time they wake up in the morning. Give them a discount [33:59] coupon [34:00] Right, you're gonna sell that black leather jacket do it now We can tell you the exact moment when they are at peak [34:08] vulnerability [34:10] That is real [34:12] that's happening and [34:15] if you don't believe me, all you have to do is just pivot a few degrees just [34:23] something that happened in [34:25] 2018 [34:26] And what is that? [34:28] That came from yet another whistleblower. His name was Chris Wiley and [34:35] He told us about Cambridge Analytica [34:40] Chris Wiley a former employee of the British company Cambridge Analytica [34:45] sounded the alarm about the methods used by this political marketing business [34:50] Cambridge Analytica used the Facebook data of more than eighteen million Americans to analyze the best ways of [34:57] manipulating American voters [35:00] One of the things that Chris Wiley said when he broke this story with the Guardian back in 2018 [35:07] Is he said we knew so much about [35:12] so many individuals [35:15] that we could understand their inner demons and [35:19] We could figure out how to target those demons [35:23] how to target their fear how to target their anger how to target their paranoia and [35:30] With those targets we could trigger [35:33] Those emotions and by triggering those emotions we could then manipulate them into [35:41] clicking on a website [35:43] Joining a group [35:45] Telling them what kind of things to read telling them what kind of people to hang out with even telling them who to vote for? [35:54] now that is absolutely [35:57] No different [35:59] Than what Facebook aim to do with these young people [36:04] innocent young people in Australia and using in New Zealand [36:09] To target their inner demons the same mechanisms the same methods only [36:16] Pivoted just a few degrees from commercial outcomes to political outcomes [36:22] Cambridge Analytica was nothing but a parasite on [36:26] A huge host and that host is surveillance capitalism [36:41] Hi Ella, hope you're settling in okay [36:46] You know how I always say everything you need is already within you [36:54] The truth is there's only so much you can do by yourself [37:01] We all need people to get where we're going ask yourself who you're going to do it with [37:24] Everyone [37:26] Welcome to f8 [37:28] Today we are going to talk about building a privacy focused social platform [37:35] So that's why I believe that the future is private [37:41] So we all good then [37:43] professor Zuboff? Well in June [37:47] 2019 [37:49] Which happened to be just a couple of months after that [37:53] That grand announcement that the future is private [37:56] there's a very interesting court case that was being tried before a California judge and [38:03] This court case was a class-action suit [38:07] brought by individuals who were demanding from Facebook [38:12] They wanted to have the data that Facebook had made available [38:18] To Cambridge Analytica [38:21] To see if they had been [38:23] Targeted and manipulated by Cambridge Analytica and this had come to a head in a California Court [38:32] the Facebook counsel who argued this case [38:38] Stood in front of the judge and said that any Facebook user [38:43] gets on the platform and [38:46] typically is sharing information with [38:49] 100 or more people in their network [38:52] once you've done that [38:54] You have no expectation of privacy [38:59] that would [39:01] make you [39:02] legitimately [39:04] able to claim any [39:06] kind of privacy right in a suit like this or any other kind of privacy oriented suit and [39:14] So here we have [39:15] Again, the public operation and the shadow operation [39:20] What's happening backstage is a lawyer standing in front of a judge saying [39:27] no Facebook user has any [39:30] legitimate expectation of privacy [39:32] This is the the next chapter for our services [39:43] You'll have some Google products you use if I may ask, no, how do you survive? [39:51] Perfectly well if there is an occasion where I feel the absolute need to use Google I go through various layers of encryption [40:00] And location reconfiguration for I go to Google search just on principle [40:07] There is a way to escape the omnipresent eyes of Google and Facebook [40:12] you can use a VPN and send all your data traffic through a secure server in another country, but [40:18] Big tech won't lose any sleep over individuals resisting the surveillance capitalists. I [40:25] Don't want anyone to get confused that if you forego Google search you are somehow, you know fighting the fight [40:32] Because this is a collective problem and it requires collective action [40:43] With us Amish we are dependent on public transportation for any distance and [40:51] Local travel we do in our slow way and I think the horse and buggy slows us down and [40:59] really helps the overall value that we're trying to create and preserve and not get carried away [41:14] Regular computer with your safety walls, you know where you can't get on porn sites and things like that now, [41:22] When our lock downs that we have that is totally locked down [41:28] And you go on the internet with this computer. No, no, absolutely not [41:33] it will not play a movie picture or [41:37] Music or anything like that. It's not a computer that was torn down. It was built from the ground up like this [41:44] Especially for Amish. Yes. Would you mind showing me your mobile phone? Okay. Yes [41:50] The only thing you can do with that is call [41:53] You cannot text do anything like that. It's just a portable phone [41:58] So this is where it stays but as far as going home, no, I don't have it [42:04] Keep it right here. That's for the business [42:08] The technology is progressing so fast it it's hard to deal with [42:14] We have to teach [42:16] What the moral impact is in being evolved in this so each individual can make better choices [42:23] for themselves [42:30] We use technology as long as we use it and it doesn't get to the point where it uses us [42:36] And controls us. That's the bottom line of it [42:42] It's kind of strange that these people who you can say are living in the past [42:49] Maybe they're living in the future. It was just a minute ago that [42:56] We didn't have many of these tools and [43:00] We were fine [43:03] We lived rich in full lives [43:06] we [43:07] Had close connections and the friends and family. So having said that I want to recognize that [43:15] there's a lot that the digital brings to our lives and [43:19] We deserve to have all of that [43:23] but we deserve to have it without paying the price of surveillance capitalism and [43:29] right now [43:31] We are in that kind of classic Faustian bargain [43:35] 21st century citizens should not have to make a choice of [43:41] either essentially, you know going analog or [43:46] living in a world where our [43:49] self-determination our [43:51] Privacy are destroyed [43:54] for the sake of [43:56] this market logic [43:58] that is unacceptable and let's also [44:02] Not be naive [44:04] you get the wrong people in charge of our [44:08] Government at any moment and they look over their shoulders at the rich [44:14] control [44:15] Possibilities offered by these new system and there will come a time one even in the West even in our democratic [44:23] Societies our government will be tempted to annex these [44:28] Capabilities and use them over us and against us let's not be naive about that [44:34] when we when we decide to [44:37] resist surveillance capitalism right now while it lives in the market dynamic [44:42] We are also [44:45] Preserving our democratic future and the kinds of checks and balances that we will need going forward [44:51] In an information civilization if we are to preserve [44:56] freedom and democracy for another generation [44:59] The European Union already has legislation with which it regularly wraps American tech businesses over the knuckles [45:07] Google was fined several billion dollars because it had abused its monopoly position in [45:13] theory European citizens are protected against data robbery by regulations for fair competition and [45:19] By the GDPR are the general data protection regulation [45:27] And do you think that the EU data protection law is a step in a good direction or is it sufficient [45:34] we have privacy laws and the gdpr is the furthest frontier of our privacy laws and [45:42] We have antitrust laws [45:44] the thing is that [45:46] as [45:47] important as these laws are [45:50] We still need more [45:53] because [45:55] surveillance capitalism is unprecedented and [45:58] So we're going to need the laws and the regulatory regimes that respond directly [46:05] to these new [46:08] unprecedented operations [46:12] GDPR talks a lot about data ownership data accessibility and data portability [46:20] These are very important things if the only data that we're talking about is the data in the public [46:27] operation [46:28] The data that we've given to the corporations [46:32] but as we have seen [46:35] most of the data that they use [46:37] To feed into the factories to produce the predictions [46:41] these are data that we haven't given or if we've given we don't even know that we've given because it came in our [46:49] exclamation points [46:50] it came in our walk in the park and [46:53] Picking up the cadence of our voice and and the the timbre of our voice all rich predictive signals [47:00] so no matter how much data ownership we claim or [47:04] accessibility or portability [47:06] Most of the data is in the shadow operation and the shadow operation is never coming to us [47:14] we will never get those data they claim them as [47:19] Data that they own they took it from our lives [47:22] They took it from our private experience without our permission [47:26] They analyzed the data. They made it into products. They sold the products and they took the profit [47:33] illegitimate [47:34] profit [47:36] Because they took it at the beginning without [47:39] Asking without our knowledge recall by passing our awareness [47:44] do you have a chance against the [47:48] Behemoth of surveillance capitalism, these twenty years have been a honeymoon [47:55] For surveillance capitalism because they have been twenty years largely [48:01] unimpeded by law [48:04] Unimpeded by law why [48:07] Well, the most important reason is that they're doing things that have never been done before so there are no laws against it [48:15] Just like factories [48:19] Employing children [48:21] You know in in a mass production factory [48:25] there hadn't been anything like that before and there were no laws against it and then took our [48:31] Societies a while to sort of wake up. We stood up against the [48:37] Extractive companies and their violence [48:40] The companies that came to be known as the Gilded Age the companies where we turned around and we call their leaders robber barons [48:49] That's not what they were called at the time at the time they were worshipped as these, you know [48:55] wealthy gods and geniuses who who who knew how to [49:02] Wield the might of machines and capital if we had been trying [49:09] To stop it to curtail it to outlaw it [49:12] For every one of these twenty years and we had utterly failed [49:17] Then I might you know, put my chin in my hand and say gosh [49:21] I'm I'm starting to feel like things might be really bad, but that's not the case. We haven't even tried yet to stop it [49:29] look [49:30] Surveillance capitalism is 20 years old [49:35] Democracy is several centuries old I [49:37] Bet on democracy. Thank you for watching [49:41] For more on this subject take a look at the playlist. You can also watch this recommended video [49:48] Don't forget to subscribe to our Channel and we'll keep you updated on our documentaries