In the early days of the AI boom, data felt infinite. OpenAI trained GPT-3 on 45 terabytes of text data, scraped from the far corners of the internet. It seemed like an inexhaustible resource. But a quiet crisis has been brewing in the world of artificial intelligence. We are running out of data.
Not just any data. We are running out of high-quality human-generated data. The kind of data that teaches an AI to reason, to write, to code. Researchers estimate that the entire corpus of high-quality text on the internet is somewhere between 10 and 20 trillion words. The latest models, like GPT-4, have already been trained on a significant fraction of that. As models get bigger, their appetite for data grows exponentially. We are consuming the internet faster than we are creating it.
This has kicked off a new kind of gold rush. Not for a yellow metal, but for the new oil of the digital age: high-quality training data. And the companies that can find and refine this resource are the ones that will build the next generation of powerful AI.
The Problem with “More Data”
The initial approach to building better AI was simple: throw more data at it. If your model isn’t smart enough, double the size of the training set. This led to a race to scrape every corner of the web—Reddit, Wikipedia, news sites, blogs, forums. The result was bigger models, but not necessarily better ones.
It turns out that quality matters far more than quantity. A model trained on a small, carefully curated dataset of high-quality text will outperform a model trained on a massive, noisy dataset scraped from the internet. A 100-gigabyte scrape of Reddit might be free, but it’s filled with slang, misinformation, and toxic content. The AI learns these bad habits. A 1-gigabyte dataset of curated, professionally written articles might cost money to acquire, but the resulting AI is more coherent, accurate, and reliable.
This is why the mantra in AI development has shifted from “more data” to “better data.”
The New Data Prospectors
In this new gold rush, a new kind of prospector has emerged. Not grizzled old men with pickaxes, but data licensing teams, API providers, and specialists in synthetic data generation. They are finding and creating the high-quality data that AI labs are desperate for.
1. The Licensing Deals
AI companies are now striking multi-million dollar deals with publishers to license their archives. OpenAI reportedly paid Axel Springer, the publisher of Business Insider and Politico, tens of millions of dollars for access to their content. Reddit is said to have signed a $60 million a year deal to provide its data to an AI company.
This is a win-win. Publishers, whose business models have been decimated by the internet, have found a new, lucrative revenue stream. AI companies get access to a stream of high-quality, human-generated content that isn’t available anywhere else.
2. The Rise of Data APIs
Scraping the web is messy and legally perilous. As a result, a new industry of data APIs has emerged. Companies like SearchCans provide a clean, structured, and legally compliant way to access web data. Instead of building their own fragile scrapers, AI companies can use an API to get a reliable stream of data from millions of websites.
These APIs do the hard work of navigating anti-bot measures, parsing messy HTML, and structuring the content into a clean, usable format like Markdown. For an AI company, using a data API is the difference between hiring a team of engineers to manage a messy scraping operation and simply getting the data they need to train their models.
3. The Synthetic Data Factories
What happens when you run out of human-generated data? You get AIs to create more data for you. This is the world of synthetic data.
An AI company might use its existing model to generate millions of examples of high-quality text—essays, articles, code—and then use that data to train its next-generation model. It’s a process of an AI pulling itself up by its own bootstraps.
This approach is powerful, but also dangerous. If not done carefully, it can lead to a kind of AI inbreeding, where models amplify their own biases and errors over successive generations. The quality of the initial seed data, and the human oversight in the generation process, is critical.
The Future of Data
The content gold rush is reshaping the internet. Content that was once created purely for human consumption now has a second, lucrative market as AI training data. This creates new economic incentives for creators, publishers, and anyone who produces high-quality text.
It also raises new questions. Who owns the data that an AI is trained on? Should creators be compensated when their work is used to train a model? How do we prevent a future where the internet is flooded with low-quality synthetic content, created by AIs for AIs?
There are no easy answers. But one thing is clear: the future of AI will be determined not by the cleverness of the algorithms, but by the quality of the data they are fed. In this new world, data isn’t just a resource. It’s the most valuable commodity there is.
The companies that understand this, that invest in building pipelines for sourcing, cleaning, and curating high-quality data, are the ones that will strike gold. The ones that continue to believe that more data is always better will be left with nothing but toxic waste.
Resources
Sourcing High-Quality Data:
- SearchCans API - Access a reliable stream of web data
- Data Quality in AI - Why quality matters
- The New Moat - Building a data advantage
Understanding the Landscape:
- Build vs. Buy - The data acquisition choice
- AI and the Web - The evolving relationship
- LLM Training Data Formats - The importance of structure
Get Started:
- Free Trial - Test our data stream
- Documentation - API reference
- Pricing - For data-intensive applications
In the AI gold rush, high-quality data is the new gold. The SearchCans API provides the clean, structured, and reliable web data you need to train powerful AI models. Stake your claim →