When AI Trains on YouTube: Who Pays the Fare?

Think about someone bunking a train ride. Nothing physical is stolen. The train still runs, the seats are still there, and the company doesn’t immediately notice. But if enough people dodge the fare — say 20% of passengers — the economics start to break. Revenues drop. Prices go up. Services get cut. Those who pay resent paying more, and more people are tempted to cheat the system. It’s a vicious cycle.

This is a useful lens for understanding how large language models (LLMs) are built. The most recognisable example is OpenAI’s ChatGPT, but there are many others — Anthropic’s Claude, Google’s Gemini, Meta’s LLaMA, to name just a few. All of them are trained on vast amounts of text and media scraped from the internet.

An LLM is essentially a system trained through a process called unsupervised pre-training. That’s a technical phrase, but the principle is straightforward: the model is fed trillions of words from books, articles, websites, and crucially, YouTube videos. The point of this phase isn’t to evaluate quality but to absorb quantity. The model learns grammar, sentence structures, and patterns of thought by seeing massive amounts of language. The bigger the dataset, the richer the patterns it can internalise.

The result is powerful. The energy and computing resources required are staggering — think more than your car will use in its lifetime, or even several lifetimes, depending on the model size. But here’s the key question: where did all that raw material come from?

YouTube as an Unpaid Data Mine

Every video uploaded to YouTube contains not just visuals but hours of language — conversations, commentary, storytelling. Millions of creators, from hobbyists to full-time professionals, have produced this content. It was intended for audiences, communities, advertisers, and perhaps even academic study. But it wasn’t created so that AI labs could quietly scrape and repurpose it into the fuel for their models.

At first, it may seem harmless — a few videos here, a few creators there. But at scale, the economics shift. If creators’ work is being consumed at massive scale by AI companies without payment, the value leaks out of the system. This is why economic sustainability, fairness, and creator recognition are not just abstract ideas but practical concerns for anyone involved in digital content.

Creators also face the challenge of standing out in an increasingly noisy environment. High-quality content can be overlooked, and creators may struggle to reach audiences who truly engage with their work. Investing in tools and strategies that respect creator effort and focus on meaningful engagement is more important than ever.

Why It Matters

Economic sustainability – YouTube’s ecosystem depends on a balance. Creators produce; advertisers pay; viewers benefit. If major consumers of YouTube’s data bypass that system, the creators see none of the upside. Over time, this undermines the incentive structure that keeps the ecosystem thriving.

Fairness and recognition – Unlike the train example, creators can’t easily “prove” that their content was bunked. There’s no fine issued after the fact. But there’s still a deep question of fairness. Should those whose work was used to train trillion-dollar AI systems get recognition or compensation?

Future access and rights – If this goes unaddressed, what happens when the next generation of models needs training? Do we simply accept that all public digital content is up for grabs, regardless of creator intent or platform rules? Or do we begin to think differently about ownership of digital expression?

Creator discovery and engagement – For marketers, researchers, and brands, understanding which creators’ content resonates and how audiences interact with it is vital. Using filters to find active YouTube channels and analysing engagement metrics helps ensure outreach and collaboration respect creators’ efforts and focus on genuine value.

No Easy Answers

We don’t claim to have a neat solution here. The broader issue of AI training on unpaid content is complex, involving legal, ethical, and economic questions. But noticing the trade-offs is the first step. Asking whether creators, platforms, and audiences are comfortable with this approach is crucial — because the future of AI and the future of online creativity are not separate tracks. They run on the same railway.

Supporting Responsible Discovery

While we can’t stop AI from consuming content at scale, there are ways to engage with creators responsibly. Tools like ChannelCrawler help those navigating the YouTube ecosystem to identify active channels and content that genuinely drives value. This kind of discovery doesn’t solve the larger AI training problem, but it can guide more thoughtful, ethical interactions between humans and creators — helping ensure that outreach respects creators’ time, effort, and rights.

Understanding which content performs best and which channels are active also adds context to discussions about fairness, recognition, and sustainable practices for the digital economy. Even if the AI “trains without paying,” humans can still act responsibly in how they connect, collaborate, and create value.