Micro1’s rapid climb over the past two years has pushed it into a cohort of AI companies scaling at breakneck speed. The three-year-old startup, which helps AI labs recruit and manage human experts for training data, started the year with roughly $7 million in annual recurring revenue (ARR).
Today, it claims to have surpassed $100 million in ARR, founder and CEO Ali Ansari told TechCrunch. That figure is also more than double the revenue Micro1 reported in September when it announced its $35 million Series A at a $500 million valuation.
Ansari, 24, said then that Micro1 works with leading AI labs, including Microsoft, as well as Fortune 100 companies racing to improve large language models through post-training and reinforcement learning. Their demand for top-tier human data has fueled a fast-expanding market that Ansari believes will grow from $10-15 billion today to nearly $100 billion within two years.
Micro1’s rise, and that of larger competitors such as Mercor and Surge, accelerated after OpenAI and Google DeepMind reportedly cut ties with Scale AI following Meta’s $14 billion investment in the vendor and its decision to hire Scale’s CEO.
While Micro1’s ARR is growing fast, according to the founder, it hasn’t yet matched its rivals: Mercor’s more than $450 million, sources told TechCrunch, and Surge’s reported $1.2 billion in 2024.
Ansari attributes Micro1’s growth to its ability to recruit and evaluate domain experts quickly. Like Mercor, Micro1 began as an AI recruiter called Zara, matching engineering talent with software roles before pivoting into the data-training market. That tool now interviews and vets applicants seeking expert roles on the platform.
Beyond supplying expert-level data to leading AI labs, Ansari says two new segments, still barely visible today, are on track to reshape the economics of human data.
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The first involves non-AI-native Fortune 1000 enterprises that will begin building AI agents for internal workflows, support operations, finance, and industry-specific tasks.
Developing these agents requires systematic evaluation: testing frontier models, grading their output, choosing winners, fine-tuning them, and continuously validating performance in production. Ansari argues this cycle depends heavily on human experts evaluating AI behavior at scale.
The second is robotics pre-training, which requires high-quality, human-generated demonstrations of everyday physical tasks. Micro1 is already building what Ansari calls the world’s largest robotics pre-training dataset, collecting demonstrations from hundreds of generalists recording object interactions in their homes. Robotics companies will need vast volumes of this data before their systems can reliably operate in homes and offices, he said.
“We anticipate that a good portion of the product budgets at non-AI-native enterprises will go towards evals and human data, moving from 0% to at least 25% of product budgets,” said the CEO, who founded Micro1 while at UC Berkeley. “We’re also helping robotics labs create robotics data; these two areas will account for a massive share of that $100 billion-a-year market.”
Even as new markets emerge, Micro1’s current growth still comes primarily from elite AI labs and AI-heavy enterprises. The startup is scaling its work with these labs on reinforcement learning, the feedback loop to test and improve model behavior.
Micro1 hopes its early move into robotics data and enterprise agent development, in addition to scaling its specialized RL environments, will help it capture additional market share as the data wars intensify.
For now, Ansari says the company is focused on scaling responsibly, paying experts well, and keeping people at the center of an industry built on training machines.
The company currently manages thousands of experts across hundreds of domains, ranging from highly technical fields to surprisingly offline disciplines. Many earn close to $100 an hour, according to Ansari.
“There are Harvard professors and Stanford PhDs spending half their week training AI through Micro1,” Ansari said. “But the bigger shift is in the sheer volume and range of roles. It’s expanding into areas you wouldn’t expect to matter for language model training, including offline and less technical fields. We’re very optimistic about where this is heading.”
