The invisible industry that makes ChatGPT, Stable Diffusion and every other modern AI system possible
When you use ChatGPT to write an email or generate an image with Midjourney, you rarely think about what is behind the "magic" of artificial intelligence. Yet behind every intelligent response and every generated image lies a multibillion-dollar industry that few people talk about: the AI training data market.
This industry, which according to MarketsandMarkets will reach $9.58 billion by 2029 with a growth rate of 27.7 percent annually, is the real driver of modern artificial intelligence. But how exactly does this hidden business work?
A few companies dominate in the world of AI training data that most people have never heard of:
Scale AI, the largest company in the industry with a 28 percent market share, was recently valued at $29 billion after Meta's investment. Their enterprise customers pay between $100,000 and several million dollars a year for high-quality data.
Appen, based in Australia, operates a global network of more than 1 million specialists in 170 countries who manually tag and curate data for AI. Companies such as Airbnb, John Deere and Procter & Gamble use their services to "teach" their AI models.
In parallel, there is an open source ecosystem led by organizations such as LAION (Large-scale Artificial Intelligence Open Network), a German nonprofit that created LAION-5B, the dataset of 5.85 billion image-text pairs that made Stable Diffusion possible.
Common Crawl releases monthly terabytes of raw web data used to train GPT-3, LLaMA and many other language models.
What the public does not know is how expensive it has become to train a modern AI model. According to Epoch AI, costs have increased 2-3 times per year over the past eight years.
The most surprising figure? According to AltIndex.com, AI training costs have increased by 4,300% since 2020.
One of the most controversial issues concerns the use of copyrighted material. In February 2025, the Delaware court ruled in Thomson Reuters v. ROSS Intelligence that AI training can constitute direct copyright infringement, rejecting the "fair use" defense.
The U.S. Copyright Office issued a 108-page report concluding that certain uses cannot be defended as fair use, paving the way for potentially huge licensing costs for AI companies.
An investigation by MIT Technology Review revealed that DataComp CommonPool, one of the most widely used datasets, contains millions of images of passports, credit cards, and birth certificates. With over 2 million downloads in the past two years, this raises huge privacy issues.
Experts predict that by 2028 the majority of human-generated public text available online will be used. This "peak data" scenario is driving companies toward innovative solutions:
The California AI Transparency Act will require companies to disclose datasets used for training, while the EU is implementing similar requirements in the AI Act.
For companies that want to develop AI solutions, understanding this ecosystem is critical:
The AI training data market is worth $9.58 billion and growing at 27.7 percent annually. This invisible industry is not only the engine of modern AI, but also represents one of the greatest ethical and legal challenges of our time.
In the next article we will explore how companies can concretely enter this world, with a practical guide to begin developing AI solutions using the datasets and tools available today.
For those who want to delve into it right away, we have compiled a detailed guide with implementation roadmaps, specific costs and complete tool stack - free to download with newsletter subscription.
Useful Links to Get Started Now:
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Don't wait for the "AI revolution." Create it. A month from today you may have your first working model, while others are still planning.
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