Introduction: When Capitalism Meets Your Data
Capitalism is built on innovation, ownership, and competition but at its core, it seeks to convert human activity into economic value. Traditionally, this meant monetizing labor, land, or intellectual property. But in the digital age, human behavior itself has become the raw material. This transition gave birth to surveillance capitalism—a system that transforms everyday actions into predictive data, used for behavioral targeting and monetized at scale. Surveillance capitalism does not just observe; it extracts, predicts, and influences. While it borrows the language of free markets and convenience, it rewrites the rules of consent and autonomy in the digital world.
The Origin: Data as a Byproduct Turned Commodity
In the beginning, online companies collected some user data to make things easier like remembering your preferences or recommending products. But around 2005, something changed. Companies realized they could collect much more data than they actually needed and that this extra data could make them a lot of money.
This extra, unneeded data is called behavioral surplus. Think of it like leftover puzzle pieces from your online activity: your location, your typing speed, your sleep patterns, and companies figured out how to use it to predict your next move.
How Surveillance Capitalism Works: From Data to Dollars
- Data Collection as Raw Material
Every time you use a phone, website, app, or smart device, it collects information. Not just what you do, but also how you do it like how fast you scroll or when you pause a video. - Data Analysis and Prediction
Smart algorithms look at that data to figure out your habits. Do you tend to shop late at night? Are you likely to click on emotional stories? Do you slow down when looking at a certain kind of product? - Monetizing Behavioral Predictions
These insights are sold to advertisers or political campaigners. They pay to show you exactly what will catch your attention and influence you. - Historical Roots
This all began with online ads. Google’s AdWords and Facebook’s algorithms used to simply show relevant ads. Now, they shape what we see and when we see it to keep us online and buying. - Ethical Concerns
Most people don’t realize how much data they’re giving away. It’s not just about selling products anymore. It’s about shaping our choices, without our knowledge.
Why AI Is Promising for Data Privacy
AI’s appeal lies in its ability to handle vast datasets efficiently and intelligently. One of its most significant strengths is scalability. AI systems can monitor millions of accounts, detect anomalies, and enforce privacy policies continuously, something human teams simply cannot replicate at the same speed or volume. Furthermore, AI enables real-time threat detection. It can identify patterns of misuse, such as automated data scraping or phishing attacks, and trigger alerts before damage is done.
Another benefit is reduced subjectivity. Human reviewers may carry unconscious biases or vary in their interpretation of privacy guidelines. AI, when trained correctly, applies uniform standards and minimizes inconsistency. It also offers operational efficiency, automating time-consuming processes like data mapping, classification, and compliance logging, allowing organizations to reduce labor costs and speed up audits.
Case Study:
Cambridge Analytica and Political Manipulation
One of the most famous examples of surveillance capitalism going wrong was the Cambridge Analytica scandal. In 2018, it was revealed that millions of Facebook users had their data collected through a simple quiz app without even knowing it. That data was used to build psychological profiles to influence how people voted in the 2016 U.S. elections and the Brexit vote.
The scary part? No laws were broken at the time. This wasn’t a hack, it was the business model working as intended.
TikTok and Global Data Scrutiny
Another powerful example is the global scrutiny surrounding TikTok, the short-video platform owned by Chinese company ByteDance. With over 1.5 billion downloads globally, TikTok has faced allegations of tracking user behavior far beyond the app such as clipboard access, keystroke patterns, and even biometric identifiers like face geometry and voiceprints. In 2020, a class-action lawsuit in the U.S. accused TikTok of collecting “vast quantities of private and personally identifiable data” and sharing it with third parties, potentially including foreign governments. Countries like India, and more recently the U.S. and EU, have launched investigations or bans based on national security and privacy concerns. This case underscores how apps marketed for entertainment can function as highly sophisticated surveillance tools, monetizing youth behavior and attention while raising global compliance questions.
How Large Language Models Amplify the Problem
Today, large language models (LLMs) such as ChatGPT represent the next evolution of surveillance capitalism. These models are trained on massive datasets scraped from the public web, including user forums, social media, emails, and chat logs. While anonymized in theory, in practice, many of these training datasets contain personal expressions, biases, or even sensitive information. This leads to two risks:
(1) lack of informed consent from the data subjects
(2) the reproduction of social biases, discrimination, and misinformation. As LLMs get embedded in customer service, education, and healthcare, the line between utility and surveillance gets blurrier.
Surveillance Capitalism vs Traditional Capitalism
Unlike traditional capitalism, where goods and services are exchanged transparently for money, surveillance capitalism offers “free” services while monetizing the user behind the scenes. If you’re not paying for the product, you are the product. This model depends on constant monitoring cookies, trackers, app permissions, location data and sophisticated psychological profiling. It turns personal attention into currency. A 2022 report from the Interactive Advertising Bureau estimated the digital advertising industry in the U.S. alone to be worth over $200 billion, with the vast majority reliant on user surveillance.
Legal Pushback: GDPR, DPDPA, and Global Reforms
Recognizing the risk, global regulators have started to push back. The General Data Protection Regulation (GDPR) in the EU and India’s Digital Personal Data Protection Act (DPDPA), 2023 are landmark responses. GDPR gives users rights over their data, mandates clear consent, and imposes heavy penalties for non-compliance up to €20 million or 4% of annual global turnover, whichever is higher. DPDPA, meanwhile, introduces India-specific concepts like the Data Fiduciary and Data Principal, emphasizes purpose limitation, and mandates grievance redressal. However, enforcement and user literacy remain hurdles. Regulation is catching up, but surveillance capitalism still moves faster.
Can Ethical Business Models Compete?
Yes, but they face an uphill battle. Platforms like DuckDuckGo, Brave, and Signal are proving that privacy-focused design can still attract users. Even Apple, despite being part of Big Tech, has positioned itself as a privacy-first company by restricting third-party tracking and introducing features like App Tracking Transparency (ATT). However, these models often struggle with scale, monetization, and visibility in markets flooded by ad-driven giants. Still, their growing user base shows that the tide may be slowly turning toward privacy-conscious technology.
Conclusion: Awareness is the First Defense
Surveillance capitalism didn’t just happen, it was built. Piece by piece, code by code, it grew into a system that shapes digital life today. Its logic of extracting and monetizing human experience has reshaped economics, politics, and social behavior. As users, professionals, and citizens, the first step to fighting it is awareness. The next step is action supporting ethical platforms, demanding transparency, and enforcing laws that put people before profit. Surveillance might be the current business model, but it doesn’t have to be the future.
References:
- Cambridge Analytica Scandal – The Guardian.
The Cambridge Analytica Files - Meta’s 2023 Ad Revenue – Meta Platforms, Inc. Annual Reports.
Investor Relations - GDPR Overview – Official GDPR website.
https://gdpr.eu - India’s Digital Personal Data Protection Act, 2023 – Ministry of Electronics and Information Technology (MeitY), Government of India.
DPDPA Framework - Apple App Tracking Transparency (ATT) – Apple Newsroom.
A Day in the Life of Your Data - DuckDuckGo, Brave, Signal – Company Privacy Policies:
- DuckDuckGo Privacy
- Brave Browser
- Signal Foundation
By Mansi Sharma
