The invisible industry that makes ChatGPT, Stable Diffusion and every other modern AI system possible

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The Best Kept Secret of AI.

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.

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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 engine of modern artificial intelligence. But how exactly does this hidden business work?

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The Invisible Ecosystem that Moves Billions

The Commercial Giants

A few companies dominate in the world of AI training data that most people have never heard of:

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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.

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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.

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The Open Source World

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.

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Common Crawl releases monthly terabytes of raw web data used to train GPT-3, LLaMA and many other language models.

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The Hidden Costs of Artificial Intelligence.

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.

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Examples of Real Costs:

• Google Gemini 1.0 Ultra: about $192 million
• GPT-4: estimated over $100 million
• Future forecast: more than $1 billion by 2027

The most surprising figure? According to AltIndex.com, AI training costs have increased by 4,300% since 2020.

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The Ethical and Legal Challenges of the Sector

The Copyright Question

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.

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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.

Privacy and Personal Data

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.

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The Future: Scarcity and Innovation

The Problem of "Peak Data"

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:

• Synthetic Data: Artificial generation of training data
• License Agreements: Strategic Partnerships Like the one between OpenAI and Financial Times
• Multimodal Data: Combination of text, images, audio and video

New Regulations Coming Soon

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.

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Opportunities for Italian Companies

For companies that want to develop AI solutions, understanding this ecosystem is critical:

Budget-Friendly Options:

• Hugging Face: Over 50,000 free datasets
• Open Source Datasets: Common Crawl, LAION, MS COCO for experimental projects

Enterprise Solutions:

• AI and Appen scales for mission-critical projects
• Specialized services: Such as Nexdata for NLP or FileMarket AI for audio data

Conclusions

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.

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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.

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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.

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Useful Links to Get Started Now:

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• Development Environment: Google Colab (free with GPU)
• Open Source Datasets: Hugging Face Datasets
• Annotation Tool: Label Studio (free)
• Quick Deploy: Gradio + HF Spaces
• Hands-on courses: Fast.ai (free, hands-on)

Technical sources:

• Hugging Face Documentation
• PyTorch Tutorials
• TensorFlow Guides
• Papers With Code (SOTA models + datasets)
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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.