Micro1’s rapid rise over the past two years has propelled it into a group of AI companies scaling at breakneck speed. The three-year-old startup, which helps AI labs hire and manage human experts for training data, started the year with about $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 number is also more than double the revenue Micro1 reported in September, when it announced a $35 million Series A at a $500 million valuation.
Ansari, 24, said at the time that Micro1 was working with leading AI labs, including Microsoft, as well as Fortune 100 companies struggling to improve large language models through retraining and reinforcement learning. Their demand for top-tier human data has fueled a fast-growing 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 has 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 caught up with its rivals: Mercor is worth more than $450 million, sources told TechCrunch and the Surge’s was mentioned $1.2 billion in 2024.
Ansari attributes Micro1’s growth to its ability to quickly hire and evaluate experts in the field. Like Mercor, Micro1 started as an AI recruiter called Zara, combining engineering talent with software roles before turning to the data training market. This tool now interviews and vets applicants seeking specialist roles on the platform.
Beyond providing expert-level data to leading AI labs, Ansari says two new divisions, still barely visible today, are on track to reshape the human data economy.
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The first includes non-native AI Fortune 1000 companies that will begin building AI agents for internal workflows, support functions, finance and industry-specific tasks.
The development of these agents requires systematic evaluation: testing frontier models, grading their output, selecting winners, adapting them, and continuously validating performance in production. Ansari argues that this cycle is highly dependent on human experts evaluating AI behavior at scale.
The second is pre-training in robotics, which requires high-quality, human-made 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 massive amounts of that data before their systems can work reliably in homes and offices, he said.
“We predict that a large portion of product budgets in non-native businesses will go to metrics and human data, moving from 0% to at least 25% of product budgets,” said the CEO, who founded Micro1 while at UC Berkeley. “We also help robotics labs generate robotics data; these two areas will represent a huge share of this $100 billion a year market.”
Even as new markets emerge, Micro1’s current growth still comes primarily from elite AI labs and heavy AI enterprises. The startup is scaling its work with these reinforcement learning labs, the feedback loop to test and improve the model’s behavior.
Micro1 hopes that its early move into robotics data and business 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.
Currently, the company manages thousands of experts in hundreds of fields, ranging from highly technical fields to amazingly offline industries. Many earn close to $100 an hour, according to Ansari.
“There are Harvard professors and Stanford PhDs who spend half their week training artificial intelligence through Micro1,” Ansari said. “But the biggest change is in the sheer volume and scope of the roles. It’s expanding into areas you wouldn’t expect to be important for training language models, including offline and less technical fields. We’re very optimistic about where it’s going.”
