For this edition of Founders’ Corner, we chatted with Ali Ansari, Founder & CEO of micro1. micro1 is an AI-powered hiring and workforce platform that provides the infrastructure to source, vet, and manage human feedback used in AI model training and fine-tuning. The platform automates how contractors are recruited, screened, and assigned to specialized tasks, ensuring high-quality data and annotations across complex AI workflows. Under Ali’s leadership, micro1 is helping frontier AI labs and enterprise research teams scale model development with trusted human expertise.
The real inflection point came in early 2025 when we closed a major frontier AI lab as a customer. After working with them closely, we built trust very quickly. They began ramping usage and became one of our largest customers almost immediately.
That success created momentum. Soon after, we signed essentially every frontier lab and now work with a large portion of the Magnificent Seven and several Fortune 100 companies.
What made that possible was the work we had done in the two to three years prior. We built an AI recruiter capable of sourcing and vetting expertise across virtually any domain. When that first lab asked us to recruit dozens of finance experts and later hundreds of doctors and surgeons around the world, they quickly saw the power of the platform. From there, we became a go-to provider of human data for training and evaluation pipelines.
We are still in the very early days of data spend in AI, and I believe that spending will follow an exponential curve. Today, most model training relies on complex questions and answers generated by domain experts – lawyers, doctors, and engineers – who design prompts and define what high-quality responses should look like. But the real opportunity emerges as models begin acting autonomously and executing multi-step tasks over longer time horizons.
To enable that shift, models require dramatically larger and more sophisticated data. As the use cases expand, so does the need for expert-generated data, which creates an enormous opportunity for companies like micro1.
I actually disagree with that completely, for two reasons. First, the argument usually centers around synthetic data – the idea that as models improve, they’ll be able to generate their own training data and eventually replace human input. But synthetic data isn’t something that suddenly gets “turned on.” It has always existed and improves gradually as models improve.
In practice, synthetic data amplifies human data rather than replacing it. Every data pipeline still begins with ground-truth human data. Domain experts generate prompts, evaluate outputs, and define what a correct response looks like. Synthetic data then multiplies the value of that human data by expanding and iterating on it.
Over time, that multiplier increases, making each human data point more valuable, not less.
There’s also a basic economic principle at play – Jevons Paradox. When something becomes more efficient or more useful, spending doesn’t decline; it increases. As models become more capable and synthetic data improves the efficiency of training pipelines, labs will expand into more domains and attempt longer-horizon tasks. Data becomes cheaper and more powerful, so overall demand rises.
The second proof point is coding. Coding is probably the area where AI is working best today, and demand for coding data has exploded. Labs have a huge appetite for it, and we’re actively hiring researchers focused specifically on coding data pipelines. It’s a great example of a use case where models are improving rapidly, and the demand for high-quality human data is increasing at the same time.
Synthetic data doesn’t replace human data – it amplifies it. As models improve, every human data point becomes more valuable.
