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I have said for years that data is money, and that most of the self-made billionaires of the last 25 years got rich selling it. Larry Page and Sergey Brin built a multi-trillion dollar company by indexing what you want and auctioning that intent to advertisers. Zuckerberg did the same thing with who you know. The product was the data the whole time, and the men who understood that earliest now sit at the top of every rich list on earth.
So when I look at the artificial intelligence (AI) debate, I ignore the screaming and watch the data. The doom crowd runs in two directions. One camp swears everyone will be unemployed by 2030. The other camp promises abundance so total that work becomes a hobby and we all get rich by default. Both make great podcast clips. Reality will land somewhere in the middle, as it did after the printing press and the internet: real disruption, new winners, new losers, and a long, ugly transition for the people who refuse to adapt.
While everyone argues about the destination, the opportunity is sitting in the supply chain. The new money is in input data, and most of the world’s input data is being poisoned right now.
Aging fine wine?
From the 1990s until the early 2020s, almost everything published on the internet was written by a human being. Blog posts, forum flame wars, product reviews, court filings, recipes from somebody’s grandmother. That 30-year corpus is the training data behind every large language model (LLM) on the market, and it is finished. We will never get another era like it, because a growing share of what gets published today is generated by the models themselves.
When I say poison, I mean two things. The first is malicious data, planted deliberately to skew what a model believes. The second is quieter and worse: the slow dilution of derivative content, copies of copies of copies, each generation stripping out more of the diversity and flavor that made human writing worth learning from in the first place. Anyone who grew up dubbing cassette tapes already understands the physics. Every copy loses signal, and right now we are feeding the machine its own dubs at an industrial scale.
Researchers call this model collapse. Ilia Shumailov and his colleagues published the landmark paper in Nature in July 2024, showing that models trained recursively on machine-generated output degrade within a few generations. The rare ideas vanish first, then the whole distribution narrows until the output turns to confident, fluent mush. Your AI sounds sure of itself because it is averaging averages of averages, and it is lying to you with a straight face.
Atomic precedent
There is a precedent for this in the physical world. After the Trinity test in July 1945, atmospheric nuclear explosions contaminated the planet’s steel supply with radionuclides. For decades, anyone building radiation-sensitive instruments had to salvage low-background steel from pre-war shipwrecks, including the German fleet scuttled at Scapa Flow in 1919. Human writing from before ChatGPT launched in November 2022 is the low-background steel of the information age. Everything minted after the bomb went off is suspect until proven otherwise.
The opportunity
That is the trade, if you have eyes to see it. Sterile data becomes premium data. Clean corpora that have never been touched by derivative inputs, maintained like sealed archives and verified at the source, will command real prices from anyone training or running a serious model. The way to serve that market is to gate the data and let agents buy access directly: an agent hits an endpoint, pays a fraction of a cent in stablecoins, pulls exactly the records it needs, and moves on, at machine speed with per-request pricing and no human in the loop to slow anything down.
Micropayments solve access. Provenance is the other half, and it is where a scalable blockchain stops being a slogan and becomes infrastructure. Hash the content at creation and timestamp the hash on chain. Sign it with an identity that stakes its reputation on the source being clean. Now a training pipeline can verify mathematically that a document existed before a given date and came from an attested human source. Satoshi proposed a “peer-to-peer distributed timestamp server” in the introduction of the whitepaper and gave it all of section 3, and this is exactly the workload Bitcoin was designed for, at the billions-of-records scale, only a big-block chain can price honestly.
A two-tier content economy is coming. First-class content will carry proof of clean sourcing: hashes, timestamps, signatures, and a chain of custody from creation to consumption. Second-class content will be everything else, the gray slurry of unverifiable text, priced accordingly, which is to say near zero. If you create anything for a living, your future margin depends on which tier your work occupies, and the tooling to claim the first tier exists today.
Data is money. The last generation of billionaires got rich selling yours. The next one gets rich proving theirs is real. Prepare for the future.
Be good to each other. And keep your sources clean.
This opinion piece is published to encourage discussion. The author’s views are their own and do not constitute legal, procurement, or policy advice, nor do they represent the positions of CoinGeek or its partners.
In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.
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