ITK: The value of human messiness

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Welcome back inside the kitchen. 🔥

There’s a lot happening behind the scenes in the AI world right now, and this is where we break it all down. Lately, I’ve noticed that the most valuable resource on the internet is the thing you probably hate the most about it.

This week’s take: The stuff you hate most online might actually be the most valuable.

That sounds strange, but I think it’s true.

For years, we treated a lot of online conversation like noise. From that random Reddit comment trolling someone’s plumbing technique to a heated political discussion in a Facebook group. Most of that did not seem especially important. In fact, it is the stuff most people still hate about the internet. You might have even left certain social platforms because of the “garbage” people post on there.

But in the AI age, that kind of content is becoming valuable for one simple reason: it is distinctly human.

The polished internet is not always that useful. A corporate blog post may be clean and technically correct, but it often sounds like every other corporate blog post. A product page tells you what the company wants you to believe. A press release tells you what the brand wants repeated. But a messy human thread tells you what people actually experienced; it tells you what they really think.

That’s why so many people already go to Reddit, Facebook groups, or niche forums when they want advice. They don’t just want the “official answer.” They want the real one. They want to hear from the person who tried the thing, regretted the thing, loved the thing, broke the thing, fixed the thing, or had no reason to make it sound better than it was.

And that creates a very strange shift.

The companies developing AI do not just need hard facts. They need examples of how humans think, talk, compare, doubt, joke, complain, argue, confess, and make sense of life. They need the rough edges of human communication because that is often what makes an answer feel useful instead of generic.

But most people did not write their old posts thinking, “This will become raw material for machine intelligence.” They were just participating in a community. They were asking for help, sharing a story, giving advice, venting about work, explaining a hobby, or trying to make someone laugh. The original purpose was human-to-human connection. Now, that same content can become part of a much larger machine.

That’s where things get complicated.

If I tell a story to a group of friends, that is one thing. If someone records it, packages it, sells it, and uses it to train a system that profits from my way of speaking, that feels different. The words may be the same, but the relationship has changed.

This is why AI data is so ethically tricky. AI does not simply “read” the internet the way a person reads the internet. A person reads something, learns from it, and moves on. AI systems can absorb enormous amounts of human expression and turn that expression into a product used by millions of people.

Of course, there are privacy concerns, but the mere sharing of data does not automatically make it wrong. Human culture has always built on what came before. We learn from other people’s words, stories, examples, jokes, arguments, and ways of seeing the world. But AI changes the scale.

A person might learn from your comment. A platform can monetize the entire archive.

That creates a weird incentive.

Platforms may start treating human conversation less like community and more like inventory. Every post becomes potential training data. Every comment becomes a signal. Every argument becomes a behavioral pattern. Every confession becomes an example of how humans process pain, confusion, desire, or belief.

And that should make us pause.

Because when human expression becomes valuable mainly because machines can learn from it, people may slowly become less like participants and more like suppliers.

That does not mean we should stop writing online, and it does not mean all AI data partnerships are evil. It also does not mean public information should never be used to build better tools. But it does mean we should be honest about what is changing.

The internet used to be a place where humans talked to other humans while companies showed ads around the edges.

Now the conversation itself is becoming the asset.

And in a world full of synthetic content, the messy, imperfect human stuff may become the most valuable thing left.

(This is for educational purposes only… not investment advice)

This week’s take: The next scarce resource in AI may not be more content. It may be real human content.

That sounds backwards because the internet feels infinite, but let’s stay on this idea for a second. There are endless articles, videos, posts, product pages, comments, forums, transcripts, newsletters, and social feeds. And now generative AI is adding even more.

But that is exactly the problem.

The web is filling up with AI-generated material. Some of it is useful, but a lot of it is not. And over time, AI companies have to worry about what happens when models start learning too much from content produced by other models.

If the training data becomes too synthetic, the output can get flatter, less diverse, and less connected to the real world. Researchers often call this “model collapse.” The simple idea is that if AI keeps eating its own leftovers, the quality of the meal gets worse.

That makes real human data more important.

Not just any human data, though.

The valuable kind is not the polished brand-safe paragraph that sounds like it came from a marketing department. As I said above, the valuable kind is lived experience. Real opinions. Product complaints. Niche expertise. Local knowledge. Arguments. Comparisons. Personal stories. Human judgment. Basically, the stuff AI cannot fully fake because it comes from actually living in the world.

That creates a new kind of AI infrastructure trend: the rise of human data platforms.

These are places where people are still creating fresh, messy, specific, high-signal content every day. And as the rest of the web gets more synthetic, those platforms may become more valuable because they contain something AI companies need badly: current human perspective.

This is not just about training models once. It is about keeping models connected to reality.

A model needs to know what people are actually saying about a new product, a new movie, a new health concern, a new software bug, a new car problem, a new investing trend, or a new cultural shift. That kind of knowledge does not live neatly in textbooks. It lives in conversations.

So the AI data trade may be shifting. The first stage was scraping the open web. The next stage may be paying for access to platforms where real humans are still producing useful, current, structured conversation.

$RDDT ( ▲ 12.63% )

Reddit is one of the cleanest examples of this trend.

Most people still think of Reddit as a social media site full of memes, arguments, niche communities, and people asking strangely specific questions. And that is true.

But in the AI age, that messiness is a hot commodity.

Reddit has something many platforms would love to have: an enormous archive of human discussion organized by topic. If you want conversations about parenting, investing, plumbing, gaming, anxiety, electric vehicles, fantasy football, skincare, software bugs, philosophy, weird medical symptoms, or which air fryer is actually worth buying, there is probably a Reddit thread for it.

That matters because Reddit is not just a feed. It is a giant map of human experience.

People do not only post what happened. They explain why it mattered, what they tried, what failed, what worked, and how other people responded. That back-and-forth is valuable because it gives AI systems more than facts. It gives them context.

That is why Reddit’s data is interesting.

Google and OpenAI are both investing over $60 million per year in partnerships with Reddit to access its content through Reddit’s Data API. That tells you something important: AI companies are willing to pay big for structured access to real-time human conversation.

Now, Reddit is still mainly an advertising business. So this is not a pure data-licensing story. Its core content becomes valuable in more than one way.

That is a pretty interesting setup.

Reddit’s recent results also show that this is not just a theory. The company has been growing quickly, and its “other revenue” category, which includes content licensing, has continued to expand. Again, advertising is still the main business, but the AI data layer gives Reddit another way to monetize the same underlying asset of messy human conversation.

So at the end of the day, the same messy threads people used to laugh at may become some of the most important raw material in the AI economy.

Not because they are polished.

Because they are human.

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Hi — I’m Hunter, a PhD candidate whose work has appeared in major academic journals and popular tech outlets. I founded FryAI to make staying ahead of AI clear, accessible, and fun.

📧 Email me directly if you have any questions or want to chat!