Why AI Startups Are Prioritizing Data Control

This summer, Taylor and her roommate did something unusual: they strapped GoPro cameras to their heads and recorded their daily lives. They filmed themselves cooking, cleaning, painting, and sculpting. The footage wasn’t for a vlog or an art project; it was for training an AI vision model.

For one week, they worked for a company called Turing, which hired them as data freelancers. Their goal was to capture how people use their hands to solve problems and complete everyday tasks.

“We’d wake up, sync our cameras, and start filming,” Taylor said. “We cooked breakfast, cleaned dishes, and then worked on our art.”

They were paid to create five hours of synced footage a day, but the work took closer to seven hours because wearing the cameras was tiring.

“It would give you headaches,” Taylor said. “When you take it off, there’s just a red square on your forehead.”

Training AI Through Real Human Work

Turing isn’t trying to teach AI how to paint or cook like humans; it’s training it to see, think, and act. Instead of using text data like ChatGPT, Turing’s models learn from video.

The company believes this hands-on approach helps AI understand how real-world tasks are performed. To do this, Turing works with all kinds of people—artists, chefs, electricians, and construction workers, anyone who uses their hands to get things done.

“We’re collecting data from many different types of work,” said Sudarshan Sivaraman, Turing’s Chief AGI Officer. “That way, the AI learns from real experiences, not just computer simulations.”

A Shift in How AI Companies Gather Data

In the past, companies scraped huge amounts of data from the internet or used cheap contractors to label images. But now, the race is for high-quality, real-world data.

Companies like Turing are paying more for original data, gathered directly from people. This shift shows that in AI, quality matters more than quantity.

The Rise of Human-Led Training

Turing isn’t the only company doing this. The email startup Fyxer uses AI to help people manage their inboxes. Its founder, Richard Hollingsworth, learned that success depends on small, focused training sets, not massive ones.

“We realized it’s all about the quality of the data,” Hollingsworth said.

To get that data, Fyxer hired real executive assistants to train the AI on what emails need replies and what can be ignored. These assistants worked closely with engineers to teach the model how people make decisions.

“It’s a very people-oriented problem,” Hollingsworth explained. “Finding great people is hard, but it’s worth it.”

Why Data Quality Matters

Even though much of AI training now involves synthetic data, fake examples created by computers, it still depends on the quality of the original real data.

Turing says around 75–80% of its training data is synthetic, but it all starts from the real GoPro footage people like Taylor record.

“If your base data is poor, your synthetic data will also be poor,” Sivaraman said.

Building a Competitive Edge

By gathering their own data, companies like Turing and Fyxer are building strong competitive advantages. Anyone can use open-source AI models, but not everyone can build custom datasets trained by real experts.

“The best way to build powerful AI,” Hollingsworth said, “is through great data — human-led, high-quality, and deeply personal.”

These projects show how the future of AI depends not just on advanced models, but on real people doing real work to teach those systems how the world actually functions.

FAQs

1. Why did people like Taylor wear GoPro cameras?

They were recording real-world activities like cooking and cleaning to train an AI vision model to understand human actions.

2. What kind of company is Turing?

Turing is an AI company that trains vision models using real and synthetic video data from people doing hands-on work.

3. Why is AI data collection changing?

Companies are now focusing on smaller, high-quality datasets instead of massive, low-quality ones scraped from the internet.

4. What does Fyxer’s AI do?

Fyxer’s AI helps users manage their emails by learning from experienced executive assistants who train it on real inbox decisions.

5. Why is high-quality data so important for AI?

AI systems learn from examples. If the data is poor or unrealistic, the model performs worse — even when synthetic data is added later.

How AI Startups Are Empowering Themselves Through Data Control