We mapped 1,297 AI labs, data vendors and work platforms in 64 countries, read the filings and logged 117 pay offers. Here is what the market looks like.
Thanks for reading our first Glasp Research report.
One question we could not answer from public data: what do people actually earn on these platforms, per hour, after unpaid tests and rejected tasks? If you have done this work, we would love to hear your experience in the comments (no personal details needed).
And if you work at an AI lab, a data vendor or a platform named here and see something we got wrong, tell us and we will fix it.
What struck me most here is how uneven and opaque this market still is despite its size and growth. The report makes it clear that human data work now sits at the core of AI progress, yet basic facts like total market size and typical worker earnings remain fuzzy or missing. I found the contrast between the huge run rates at private vendors and the thin evidence on what individual contributors actually take home especially troubling. The language and regional pay gaps, plus the heavy dependence on a few big customers, suggest a fragile ecosystem where both workers and smaller vendors carry most of the risk. It feels like we urgently need better transparency standards on both sides so people can make informed choices about participating in this new kind of labor.
This is a fascinating and much-needed look at the human side of AI development. What stands out most is the gap between the enormous value human data creates for AI companies and the limited transparency around what contributors actually earn. As AI systems become more advanced, expert judgment, real-world experience, and human feedback will remain essential. I also think the regional pay differences and dependence on a few major customers raise important questions about fairness and sustainability. Greater transparency around compensation, working conditions, and data rights would benefit both contributors and companies. The future of AI should recognize not only technological progress but also the people who make it possible.
Thank you for sharing this with us. It’s really well written and a very comprehensive analysis — it’s been incredibly inspiring and thought-provoking for our team. Many thanks also for citing my report; I truly appreciate it.
We warmly welcome the opportunity for more collaboration between us going forward. Looking forward to working together on more projects ahead!
Thank you so much, and thank you for your analysis of China's data collection centers. It filled a gap we could not cover from English sources. Glad the piece was useful for your team.
Thanks for reading our first Glasp Research report.
One question we could not answer from public data: what do people actually earn on these platforms, per hour, after unpaid tests and rejected tasks? If you have done this work, we would love to hear your experience in the comments (no personal details needed).
And if you work at an AI lab, a data vendor or a platform named here and see something we got wrong, tell us and we will fix it.
What struck me most here is how uneven and opaque this market still is despite its size and growth. The report makes it clear that human data work now sits at the core of AI progress, yet basic facts like total market size and typical worker earnings remain fuzzy or missing. I found the contrast between the huge run rates at private vendors and the thin evidence on what individual contributors actually take home especially troubling. The language and regional pay gaps, plus the heavy dependence on a few big customers, suggest a fragile ecosystem where both workers and smaller vendors carry most of the risk. It feels like we urgently need better transparency standards on both sides so people can make informed choices about participating in this new kind of labor.
This is a fascinating and much-needed look at the human side of AI development. What stands out most is the gap between the enormous value human data creates for AI companies and the limited transparency around what contributors actually earn. As AI systems become more advanced, expert judgment, real-world experience, and human feedback will remain essential. I also think the regional pay differences and dependence on a few major customers raise important questions about fairness and sustainability. Greater transparency around compensation, working conditions, and data rights would benefit both contributors and companies. The future of AI should recognize not only technological progress but also the people who make it possible.
Thank you for sharing this with us. It’s really well written and a very comprehensive analysis — it’s been incredibly inspiring and thought-provoking for our team. Many thanks also for citing my report; I truly appreciate it.
We warmly welcome the opportunity for more collaboration between us going forward. Looking forward to working together on more projects ahead!
Thank you so much, and thank you for your analysis of China's data collection centers. It filled a gap we could not cover from English sources. Glad the piece was useful for your team.