In 2024, the world generated over 120 zettabytes of data. Much of it came from you: your clicks, your voice searches, your location history, your conversations, your habits. And most of it was gathered passively, invisibly, and profitably.
Yet despite powering a trillion-dollar industry and fueling some of the most powerful AI systems in human history, the average person sees little, if any, return.
Bill Gates once proposed that we should tax robots. The idea was simple: if machines replace human workers, the companies profiting from that automation should help cover the loss of income tax revenue.
But nearly a decade later, it’s clear the real engine of automation isn’t robotic arms—it’s data. It’s not just machines doing the work—it’s information that gives those machines intelligence.
And if we’re serious about building fairer systems in the AI age, maybe it’s time to stop debating robot taxes and start talking about taxing data.
The Real Commodity: Your Digital Exhaust
AI doesn’t emerge from thin air. It learns from patterns in data—colossal volumes of it, sourced from people across the globe. Every digital footprint you leave behind is potentially training material for systems that predict your preferences, diagnose diseases, translate languages, write code, and more.
Social media platforms, search engines, health apps, online stores, smart speakers—all harvest user data, often in ways we don’t fully understand or consent to. That data is then used to develop smarter systems, more targeted ads, and highly profitable tools.
In 2023, Facebook’s average revenue per user (ARPU) in North America surpassed $60. That’s how much value your data generated annually for Meta, just from advertising. It doesn’t include the use of your data in training generative AI, biometric surveillance, or predictive behavioral systems.
If corporations can monetize this digital exhaust, shouldn’t the people producing it have a stake in its value?
From Labor to Input: Reframing Economic Contribution
Historically, we taxed labor and capital. You worked, you paid income tax. You owned a factory, you paid property tax. But today, people contribute to the economy in subtler ways—by generating data that feeds algorithms.
This isn’t traditional labor. It’s ambient, constant, and mostly unpaid. When you scroll Instagram, send a text, or swipe your metro card, you’re not “working”—but you are creating economically valuable information.
If AI systems depend on that information, then shouldn’t economic models evolve to reflect this new form of input? Should we start treating data not as an accidental byproduct but as a form of digital labor worthy of compensation?
Experiments are emerging. New models are being explored. The rise of data unions and collective bargaining platforms like PolyPoly and Salus Coop shows how communities are starting to demand greater control over how their data is used—and to negotiate a share of its value. These efforts suggest a future where people assert agency over their digital identities and advocate for collective ownership of the data economy.
But a broader solution might require something more universal: a data tax that redistributes value at scale.
Data Colonialism: A Modern-Day Extraction Economy
To understand why this matters globally, consider the concept of data colonialism.
Coined by scholars Nick Couldry and Ulises Mejias, the term refers to how powerful tech entities extract and control data from populations without fair compensation—not unlike how empires once extracted land, labor, and raw materials.
Take the 2023 controversy around Worldcoin in Kenya. The company offered cryptocurrency in exchange for iris scans, promising financial inclusion. Critics warned it was biometric exploitation—gathering sensitive data from low-income populations under the guise of innovation.
Or consider how massive language models like GPT are trained on datasets pulled from the global internet—often including data from communities that receive none of the profits or recognition for their linguistic or cultural contributions.
This is digital imperialism. The flow of data is primarily south to north, from individuals to corporations, from the Global South to the tech giants of the Global North. A data tax could begin to reverse that flow, channeling resources back to communities whose digital lives are being monetized.
Digital Sovereignty: Who Controls the Flow?
Data taxation also intersects with another critical idea: digital sovereignty. This is the principle that nations (and by extension, individuals) should control how data about them is collected, stored, and used.
Countries are taking action.
- India introduced data localization rules that require companies to store Indian data within national borders.
- The European Union’s GDPR established strong user rights around data privacy and access.
- China has gone further, asserting aggressive state control over data and algorithms.
Yet sovereignty without redistribution is only half the picture. Even with GDPR, there is no obligation for companies to share the value generated from data—only to disclose what they collect and offer opt-outs.
What if, instead of only regulating how data is collected, we began regulating who profits from it?
Would a Data Tax Actually Work?
The idea of taxing data isn’t just theoretical. It’s been proposed in policy circles, think tanks, and economic forums.
In California, Andrew Yang floated the idea of a “data dividend,” where residents receive a cut of profits made from their personal data.
In the EU, the Digital Markets Act and Data Act aim to regulate how data is shared between large and small companies—laying groundwork for economic fairness, though not explicitly a tax.
But what would implementation look like?
A data tax could function like a carbon tax—measured not by pollution, but by data volume or usage type. For example:
- Micro-taxes on every gigabyte of personal data monetized
- Tiered rates for sensitive categories like health or biometric data
- Exemptions for nonprofits and open-source research
Funds collected could support:
- Digital literacy and inclusion programs
- Public data trusts
- Universal Basic Income pilots
- AI accountability initiatives
Of course, such a system would be complex, and it would require global cooperation. But critics once said the same about digital sales tax, carbon trading, and transnational environmental regulations.
The Ethical Imperative
This conversation isn’t just about economics—it’s about justice.
When AI systems predict a person’s mental health, decide job eligibility, or flag someone to law enforcement, they do so using data extracted from people’s lives. That data becomes a proxy for identity, behavior, even humanity.
And yet the profits flow upward, while the risks—bias, surveillance, misuse—flow downward.
As author Shoshana Zuboff wrote in The Age of Surveillance Capitalism:
“Surveillance capitalism unilaterally claims human experience as free raw material for translation into behavioral data.”
A data tax wouldn’t end surveillance capitalism overnight. But it would be a step toward recognizing that human experience—our digital lives, interactions, and identities—has value.
And value should be shared.
We don’t need to imagine a sci-fi dystopia to understand what’s at stake. It’s already here: AI systems trained on unpaid human data are being patented, sold, and deployed with little regard for where that data came from.
The question isn’t whether we should regulate this new economy. It’s how.
A data tax is not a silver bullet. But it forces us to ask: Who benefits from AI? Who builds it? And who pays the price?
If we want a future where AI works for humanity, we must recognize that data is not just metadata. It is human input, and it deserves compensation, governance, and care.
Let’s stop asking whether we should tax the robot.
Let’s start asking who owns the code—and the information that feeds it.
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