Selective Outrage: Why Big Data’s Piracy Problem Gets a Pass
By Cliff Potts, CSO, and Editor-in-Chief of WPS News
Baybay City, Leyte, Philippines — April 21, 2026
The Double Standard Nobody Wants to Admit
For years, the United States and its allies have criticized other regions—particularly China and parts of Asia—for weak enforcement of intellectual property rights.
The argument is familiar:
- Copyright violations
- Unauthorized copying
- Lack of enforcement
These practices are labeled clearly and repeatedly:
Piracy.
But when similar behavior appears inside Western technology systems, the language changes.
It becomes:
- Innovation
- Training data
- Aggregation
- Platform optimization
The behavior does not change.
Only the description does.
What Big Data Is Actually Doing
Modern AI and data platforms operate by ingesting large volumes of human-created content.
That includes:
- Articles
- Essays
- Books
- Artwork
- Photography
This material is then:
- Processed
- Analyzed
- Reassembled into outputs
Those outputs are monetized through:
- Advertising
- Subscriptions
- Platform dominance
In many cases, the original creators:
- Are not asked for permission
- Are not compensated
- Are not even aware their work is being used
That is the functional reality.
Why This Fits the Definition of Piracy
Traditionally, piracy has meant:
The use or reproduction of copyrighted material without permission or compensation.
The current system does not always reproduce content verbatim.
But it does:
- Extract value from it
- Depend on it
- Generate revenue from it
The distinction between copying and extracting becomes less meaningful when the outcome is the same:
- The creator’s work drives value
- The creator does not share in that value
Whether the term used is “training” or “processing,” the economic effect mirrors what has historically been called piracy.
The China Comparison
Western governments frequently point to China as an example of systemic intellectual property abuse.
And in many cases, those criticisms have been valid.
But that raises a question:
Why is one form of unauthorized use treated as unacceptable, while another is normalized?
If:
- Copying a film without permission is piracy
Then:
- Using written, visual, or intellectual work to power commercial systems without compensation raises the same concerns
The inconsistency is difficult to ignore.
The Language Shield
Part of the reason this continues is language.
Terms like:
- “Machine learning”
- “Training data”
- “Model development”
Create distance from what is happening.
They make the process sound technical and abstract.
But behind that language is a simple dynamic:
- Human-created work is being used to generate value
- Without direct compensation to the people who created it
Changing the vocabulary does not change the structure.
Why This Matters Now
This issue is becoming more urgent as AI systems expand.
The more these systems rely on:
- High-quality writing
- Original reporting
- Creative work
The more they depend on the continued existence of creators.
If those creators are not supported:
- Output quality declines
- Original work becomes less sustainable
- The system weakens over time
This is not just a fairness issue.
It is a structural one.
The Likely Outcomes
There are only a few ways this resolves:
- Legal action defining limits on data use
- Licensing systems for training and summarization
- Revenue-sharing models between platforms and creators
- Or continued extraction until the supply of high-quality input declines
None of these paths avoid the core issue.
They only determine how it is addressed.
The Bottom Line
The debate is not about whether technology should advance.
It is about whether the people whose work fuels that advancement are recognized and compensated.
When value is taken without compensation, the term “piracy” has historically been used.
If the same outcome is occurring under different language, the question is not whether the term is uncomfortable.
The question is whether it applies.
If you read this and it matters, help me keep it going: https://www.patreon.com/cw/WPSNews
References
Anderson, C. W., Bell, E., & Shirky, C. (2015). Post-industrial journalism: Adapting to the present. Columbia Journalism School.
OpenAI. (2023). GPT and the future of content generation.
Google. (2023). Search Generative Experience (SGE) overview. https://blog.google/products/search/generative-ai-search
World Intellectual Property Organization. (2021). Understanding copyright and related rights. https://www.wipo.int
#ArtificialIntelligence #bigData #copyright #digitalEconomy #intellectualProperty #Technology #WPSNews
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