Data Is Labor: The Five Degrees Of Data
In this latest phase of the artificial intelligence (AI) revolution, many people who never considered how their information was being packaged are now witnessing the significant value being generated from it. Tech's top six firms have grown by more than $8 trillion since ChapGPT's launch.
The use of AI tools has made everyone, from artists to trucking professionals, question just what or whom the inputs to AI are.
I believe business leaders need to start considering their employee's and consumers' "decisions as a product" to help understand how to price their products and compensate people in the near future.
Data Production
Recently, Meta’s Chief AI Scientist, Yann LeCun, said he expected powerful and all-knowing AI assistants within years. Today, large companies like Pepsi and Jones Lang LaSalle are reporting productivity growth based on the current generation of AI. Now that we have people’s attention, it is necessary to consider who may benefit from it and how it might impact trends in income inequality.
It is my opinion that your data is labor. It is also my opinion that your personal data is the most seminal input to any economic productivity. But let’s define productivity. The Bureau of Labor Statistics defines it "as a measure of economic performance that compares the amount of goods (output) with the amount of inputs used to produce those goods." It is normal for the owners of inputs to be compensated for their contribution to productive outputs, but so far, not in the case of AI and personal data.
Let’s also define personal data. According to the EU's General Data Protection Regulation (GDPR), "personal data is defined as any information relating to an identified or identifiable natural person," meaning any information that can be used to directly or indirectly identify an individual, including their name, email address, geolocation data and anything that can create a profile of a person.
Degrees Of Data: A Framework For Understanding Value
Over the past 25 years, people have tried to argue that personal data is intellectual property, but it is difficult to successfully argue since, under most legal frameworks, personal data generally lacks the necessary creative or inventive element required for intellectual property protection. Therefore, I think it's important to define it as a "nescient property," focusing on the lack of awareness of much of its creation, which creates labor.
Going forward, I believe we need to think about personal data as labor across five degrees. Our value when it comes to data derives from each other or, more plainly, our acknowledgment of each other. Technological institutions are naturally drawn to individual data because it is increasingly becoming essential for helping to identify and target markets.
First-Degree Data
This data results from deliberate actions and with the individual's awareness that it can be collected.
A common example of this type of data is social media industry data, but it extends far beyond that. It is voluntarily provided by individuals to platforms where it is then accessed and used by data consumers or users.
Second-Degree Data
This is data generated from intentional actions, but without the individual's awareness that it can be collected. Common examples of this type of data, and ones some may be aware of, include advertising, telecom, employee and voter data.
Individuals typically know that their information is being used and regard it as a part of an institutional or social contract normalized through practice, but they are not necessarily aware of how it is harvested and what it is for. For instance, advertising data may be based on a derivative of first-degree social media data.
Third-Degree Data
This is data generated from voluntary actions by an individual but without their intention or awareness that it can be collected.
A common understanding of this type of data includes car, health and financial data. Individuals typically contribute data knowingly in some sort of legal contract of adhesion, which requires participation. Similar to second-degree data, it is usually known that data will be harvested, but not how. As per the preceding degrees, the third-degree data can be derived through second or first-degree data.
Fourth-Degree Data
This data is generated from involuntary actions by individuals without their awareness or intention for it to be collected.
Fourth-degree data can include those related to insurance, civic and credit. This kind of data is typically extracted from an individual’s activities as a part of a known or unknown social contract. At the fourth degree, we start to consider the rights of the community, both civic and corporate, versus the rights of the human.
Fifth-Degree Data
This type of data is generated from the involuntary actions of individuals, and they are unaware or have no intention that their actions can be collected or used. This data can include genomic data. Unlike fourth-degree data, which is typically derived from more indirect, less personal sources such as aggregated behavioral patterns or environmental factors, this data is more directly linked to individual actions.
Going Forward
We should use our new understanding of "nescient property" to better distribute ownership and share the benefits of data. This can help legal frameworks, both existing and new, to differentiate between stakeholders and shareholders.
Business leaders looking for growth must remember that our economies have grown in relative peace through an expansion of property rights over multiple centuries, and I believe we should continue this trajectory toward what Michael Zakaras calls the ownership economy.
But don't just take my word for it. There are concerns from wealthy investors like Paul Tudor Jones, who says the wealth gap usually closes through "revolution, higher taxes or wars." I don't believe that all business leaders are greedy; I just don't think that we've had a modern distribution mechanism for productivity.
When it comes to data, let's share productivity with its rightful owners; remind them they are valuable and help bridge the dignity gap for humanity.
Learn more about how Data Is Labor.
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