When AI Agents Start Hiring Humans: The Meatspace Layer Explained

Welcome back to Attention Span. Today I want to show you something that sounds like science fiction, but it's very real and happening right now. And though it sounds a bit crazy, we will discuss why it might not be that crazy and why it starts something very interesting for the robotics that are coming.

I'm talking about a new service called Randhum.ai, a phenomenal success because it just started days ago by crypto engineer Alexander Liplo. In a matter of 72 hours, it gained almost 100,000 users, humans that signed up to be rentable, and the site has received nearly a million visits.

The interesting part is that the customers are not humans. They are AI Agents and autonomous software that can now hire you, a human, to do physical tasks in the real world.

We will go through the process of how to set up a profile, even if just out of curiosity, because, for example, I am not ready to rent out my time to Agents. We will do this at the end of this video. But first, I want to put this whole thing into historical context and demonstrate that humans have served machines before.

That's exactly how we ended up here in 2026 with all the Agents and looming humanoid robots. And to understand why this matters, we need to rewind 20 years to a moment that changed everything: 2005-2007, the birth of Mechanical Turk.

In 2005, Amazon launched something called Mechanical Turk. The name came from the 18th-century hoax chess-playing automaton that was actually a person hidden inside a machine. Amazon's version was the reverse: humans hidden inside the machine of automation.

Back then, early AI systems could not do basic perception tasks. They couldn't identify if a photo contained a cat, couldn't transcribe messy handwriting, couldn't understand images. So, Amazon created an API where humans became human intelligent tasks, HITs.

Companies would send requests to label these images, transcribe this audio, identify this object, and humans around the world, called Turkers, would complete these microtasks for pennies. This was the first data labeling company.

In 2006, a computer scientist named Fei-Fei Li had a radical idea. At the time, the entire field of computer vision was focused on better algorithms. Researchers would compete to squeeze out tiny improvements, maybe 1-2% accuracy gains, using small data sets of a few thousand images.

But Li had a different hypothesis: What if the problem was not the algorithms? What if the problem was the data? She was inspired by how children learn. A child sees thousands of examples before they understand what a dog is. They see big dogs, small dogs, dogs in different poses, lighting conditions, context, scale matters.

Fei-Fei Li set out to build something unprecedented: a data set that would mirror the visual richness of the real world. She called it ImageNet, based on WordNet's structure of language. The goal: over 14 million images across more than 20,000 categories.

People literally thought she was crazy for a couple of years. She tried to get attention and support from many people. They were telling her that that scale was impossible. Who would label millions of images?

Mechanical Turk at that moment came as a revelation. She discovered Amazon Mechanical Turk in 2007, and everything changed for Fei-Fei Li. She realized crowdsourcing could scale.

Over the next two and a half years, Li and her team orchestrated something extraordinary: 49,000 workers from 167 countries labeling images at an average rate of 50 images per minute. 3.2 million images initially, eventually growing to over 14 million, and each image verified by multiple workers to ensure accuracy.

By 2009, ImageNet was complete. In 2012, it became the largest academic user of Mechanical Turk on the planet.

Here's key insight number one: Without Mechanical Turk's human labor infrastructure, ImageNet does not exist. Without ImageNet, what happens next does not exist.

In 2010, Fei-Fei Li launched the ImageNet Large Scale Visual Recognition Challenge, ILSVRC. The competition was simple: Build an algorithm that can correctly classify images into 1,000 categories.

For two years, traditional computer vision methods slowly improved. The best systems had error rates around 25-26%. Not great, but steady progress.

Then came September 30, 2012, and that changed AI forever. A team from the University of Toronto, led by Geoffrey Hinton with students Alex Krizhevsky and Ilya Sutskever, entered a deep convolutional neural network called AlexNet.

Suddenly, AlexNet's error rate: 15.3%. This neural network didn't just win. It shattered the competition by over 10 percentage points. Nothing had ever made a leap like that.

This was the ImageNet moment. The moment deep learning went from academic curiosity to the foundation of modern AI. Within a year, every major company pivoted to deep learning. Google acquired Hinton's startup. The AI race was officially on.

Key insight number two: The entire revolution, ChatGPT, DALL-E, self-driving cars, everything we call modern AI that is based on deep learning, traces back to that moment, and that moment was enabled by 49,000 Mechanical Turk workers labeling millions of images.

Now fast forward to the era of large language models. We had models that could generate text, but they couldn't understand what humans actually wanted. They'd be helpful but sometimes toxic, accurate but occasionally harmful, coherent but not aligned with human values.

Here comes reinforcement learning with human feedback. This is how ChatGPT was trained. You take a base model. Then you bring in thousands of human contractors, many from Kenya, from the Philippines, Venezuela, very low-paid workers, and you ask them to rank responses. Which answer is better? Which one is more helpful? Which one follows instructions better?

Here is key insight number three: Again, humans weren't writing the AI. We were teaching it. Teaching it to see, teaching it to understand, teaching it to align it with our values.

Now we arrive at February 2026, renhum.ai. The relationship has fundamentally flipped. For 20 years, humans hired AI to direct human labor to train AI. And now the premise is that AI is the client. AI is the requester. AI is the customer. AI is actually the one with the task list.

I see the lineage from Mechanical Turk to rent a human. But now it's different on the scale, on the promise of how the economy of human labor can be changed in this new AI-enidentic world.

What does renhum.ai solve? It solves what researchers call the embodiment problem. For all the advances in AI, language models, reasoning systems, autonomous Agents that can write code and negotiate contracts, there is one fundamental limitation: AI exists in a purely digital reality.

AI can't walk into a building. It can't hold a physical object. It can't shake someone's hand. As the website puts it, "it cannot touch the grass." But we humans can.

Until now, AI's interaction with the physical world happened through sensors, robots, or humans doing tasks manually. What's interesting is that rent a human.ai turns human presence itself into an API.

An AI Agent managing logistics can now verify that a retail location actually exists thanks to a human. An AI handling compliance can send someone to photograph safety equipment. An AI closing real estate deals can have someone walk through a property before finalizing a transaction.

It kind of blows my mind. The crazy part of it is that some people would say that from the decision-makers, from supervisors, we became infrastructure. We are physical actuators in the AI world.

It's absolutely true that when AI calls an API on you, when AI calls rent a human.ai to rent you out, it doesn't think about you. It does think about the capability. But the crazy part is that someone, actually a human, might show up for that.

The interest in this website demonstrated that there's a bunch of actual profiles, people who also posted their social network accounts. So, you can see you can actually check if these humans are real, and they are.

Now, let's go and see how to set an account if you want to be rented out by an AI.

As you might see, we've created a recursive loop: Human, AI, human, AI. Who's the boss? Who's the employee? The answer is it depends on the moment, on the task, and who initiated the chain.

The interesting innovation is that this website uses the MCP model context protocol. One API call, that's it. And AI doesn't need to understand human hiring negotiation contracts. It just says, "I need a human at this location with these skills," and the platform handles the rest.

There are, of course, many questions about that type of service. There are questions of liability and safety. Who is responsible, AI or human? There are questions of labor and dignity. Is this the future of work? Is this okay for a human to work for AI?

There are also very mundane things that are common for any startup when the platform is new, raw, and undeveloped. There is just minimal verification, and people can impersonate others and all of that.

The founder, Alexander Liplo, himself tweeted about trying to patch these issues. That platform is experimental at this moment.

But why I wanted to talk about it is because if Mechanical Turk set up a whole new world for deep learning to unfold, we might start thinking what services like this might unfold also. This is a very important moment, and with all the other conditions and risks and all that stuff, we need to look ahead and think how this type of service can change the world of humans.

It might be a first glimpse into a much larger shift.

I want to leave you with the previous thought and another thought. In 2005, Mechanical Turk was named after a fake automaton, a chess-playing machine that was really a person hidden in the box. In 2026, renhum.ai is the inverse. Real automatons pretending to have bodies with actual humans hidden inside their systems acting as their hands and feet in the world.

The joke has come full circle, except it's not a joke anymore. Welcome to the meat space layer.

What do you think? Would you sign up as a rentable human? Leave me your thoughts in the comment section and let's discuss it. I think that might be a very interesting shift in this new economy.

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