The following article is a guest post and opinion of Johanna Rose Cabildo, founder and CEO of Data Guardians Network (D-GN).
The illusion of infinite data
AI executes data. However, the data is increasingly unreliable, unethical and linked to legal consequences.
The growth of generated AI is not merely accelerated. It’s about devouring everything on that path. Openai reportedly faced predictions $7 billion invoice In 2024, annual revenue was $2 billion just to make the model work. All this happened while Openai and Anthropic bots wreaked havoc on their websites and raised alarm bells for massive data usage. Business Insider.
However, the problem runs deeper than the cost. AI is built on an opaque, outdated, legally compromised data pipeline. The problem of “data collapse” is real. Models trained with unvalidated, synthetic, or “old” data risks become more accurate over time, and Deficiency decisions.
Legal issues like 12 US Copyright Litigation For Openai The Legal Suffering of Humanity with the Author Media outlets are underscoring a new crisis. AI is not bottlenecked by computing. It is bottlenecked by Trustworthy Data Supply Chain.
If the composition is insufficient and the scraping does not expand
The synthetic data is band-aid. Scraping is a lawsuit waiting to happen.
Synthetic data is promising for certain use cases, but not without pitfalls. I struggle to replicate the nuances and depths of real-world situations. For example, in healthcare AI models trained on synthetic datasets can be below performance In the case of edges, it puts patient safety at risk. And in famous obstacles like Google’s Gemini model, Bias and distorted output It’s enhanced rather than fixing it.
On the other hand, not only is it a cutoff of the internet, it’s not just a responsibility, it’s a structural dead end. From the New York Times to Getty Images, lawsuits are piling up, with new regulations like the EU AI Act mandating strict data origin standards. Tesla’s infamous “Phantom Brake“The issues from 2022 show what happens when some are caused by poor training data and data sources are not checked.
The global data volume is set to exceed 200 Zettabytes by 2025. Cybersecurity venturemany of them cannot be used or verified. There is a lack of connection and understanding. And without it, trust and by this means scalability is not possible.
It is clear that a new paradigm is needed. By default, creates data that is reliable.
Data refinement with blockchain core capabilities
Blockchain is not just about tokens. This is the lacking infrastructure for the AI ​​data crisis.
So, where does blockchain fit into this story? How to resolve data chaos and prevent AI systems from being fed to billions of data points without consent
“Tokenization” captures the headline, but it’s an architecture that brings true promise. Blockchain enables three functions that AI desperately needs in the data layer. Traceability or source, immutability, verifiability. Each contributes synergistically to help rescue AI from legal issues, ethical challenges and data quality crisis.
Traceability ensures that every dataset has a verifiable origin. Just as IBM’s Food Trust validates farm-to-shelves logistics, it requires inter-source verification from a model of training data. Due to immutability, no one can manipulate records and store important information in the chain.
Finally, smart contracts automate payment flows and enforce consent. If a pre-determined event occurs and is validated, the smart contract self-executes the steps programmed on the blockchain without human interaction. 2023, Lemonade Foundation has implemented a blockchain-based parametric insurance solution For 7,000 Kenyan farmers. The system eliminated the need for manual billing processing, using smart contracts and oracles of weather data to automatically trigger payouts when predefined drought conditions are met.
This infrastructure inverts the dynamic. One option is to use gamerized tools to label and create data. Each action is recorded unchanged. Rewards are traceable. Consent is a chain. AI developers receive audit-enabled structured data using clear lineages.
Trustworthy AI needs reliable data
If the data cannot be audited, the AI ​​model cannot be audited.
When built on an invisible workforce and unverified sources, the appeal of “responsible AI” becomes flat. Humanity’s lawsuit It shows true financial risks of poor data hygiene. And the public’s distrust continues to rise, and users are investigating the user who shows them. Don’t trust AI models Trains of personal or unclear data.
This is no longer just a legal issue, it is a performance issue. McKinsey shows that a high integral dataset significantly reduces hallucinations and improves overall use cases. If you want AI to make critical decisions in finance, health or law, the training foundation must be unwavering.
If AI is the engine, then data is fuel. You don’t see people putting garbage fuel in your Ferrari.
New Data Economy: Why is it necessary now?
Tokenization grabs headlines, but blockchains can rewire the entire data value chain.
We stand at the edge of economic and social change. The company has Billions of dollars to collect data However, they have little understanding of its origins and risks. What you need is a new kind of data economy. It is built on consent, rewards and verifiability.
This is what it looks like.
First is a Consential Collection. Opt-in models like Brave’s Privacy-First AD ecosystem share data when users are respected and have an element of transparency.
The second is fair rewards. People should be properly compensated by using data to contribute to AI and annotating data. It is a service that an individual is willing or unwillingly providing, and is obtaining such data – it has inherent value to the company without approval or compensation – presents a harsh ethical argument.
Finally, accountable AI. A complete data lineage allows organizations to meet compliance requirements, reduce bias, and create more accurate models. This is a compelling advantage.
Forbes predicts that data traceability will be an industry that costs over $10 billion by 2027, so it’s not hard to see why. This is the only way Ethically.
The next AI arm race is not about who has the most GPU. It’s about who has the cleanest data.
Who will build the future?
Calculating power and model size is always important. But the real breakthrough doesn’t come from the bigger models. They will come from a better foundation.
As we are told, if the data is a new oil, you need to stop spilling, shaving, or burning it. We need to track it, cherish it, and invest in its integrity.
Clean data reduces retraining cycles, improves efficiency and reduces environmental costs. Harvard Research It shows that energy waste from AI model retraining can be comparable to emissions in small countries. Data with a blockchain-fixed data that can be verified from the start makes AI faster, faster and greener.
It allows AI innovators to build a future where they compete not only with speed and scale, but also with transparency and fairness.
Blockchain allows you to build AI that is not only powerful, but also truly ethical. Time to act is now – before another lawsuit, bias scandals or hallucinations make that choice for us.
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