By Contributor,Daniel Warner,Ray Ravaglia

Copyright forbes

While most debates focus on how AI will educate people, Micro1 is hiring elite educators to educate AI. Pictures are Chief Revenue Officer William Almeida and Chief Executive Officer Ali Ansari.

In education circles, discussions about AI usually focus on how these systems can assist with tutoring students, grading papers, or customizing learning, rather than just being a magic box that produces average essays with a click of a button. Micro1 shifts that perspective. The company enlists professors and PhDs to teach AI models like students, first identifying what the model doesn’t understand, then guiding it toward mastery. “We are building Micro1, the AI platform for human intelligence,” CEO Ali Ansari tells me. He explains that the business sources expert educators, assesses their impact, and coordinates the data they generate to improve models at specialized tasks.

From “AI Interviewer” To Human-Data Operations

Micro1 didn’t start in education at all. Ansari first built an AI screening tool while still in college to interview software candidates for contract technical jobs. That internal tool became a product, and then the market shifted the company’s focus entirely: top labs began asking Micro1 to recruit expert educators for post-training models. In other words, the bottleneck wasn’t in finding people who could create the models; it was in finding teachers who could ensure the models understood their tasks.

Today, Micro1 presents its work across three pillars. First, an AI recruitment engine that sources and vets professors and PhDs from top institutions in various subject domains. Second, talent performance management that tracks the KPIs relevant to human-in-the-loop training. Third, a data platform where instruction, data creation, evaluation, and iteration occur.

When The Model Is The Student

What does it mean to “teach” a model? Micro1’s experts start by trying to “stomp the model,” a technical term for identifying failure modes with carefully designed prompts. Then they develop evaluation criteria which specify what should and should not appear in a correct answer, similar to the way that is done in academic courses. These evaluation criteria help educators assess semantic quality even when a model’s wording varies, and they can also enforce domain-specific formatting when it’s relevant. While a journalistic article may allow for stylistic variety, a legal email must follow a very specific format.

This approach becomes especially important in esoteric subjects, such as advanced mathematics, or specialized fields like finance or medicine, where the internet lacks reliable, well-labeled examples. In such cases, you can’t simply gather large datasets from the web and hope for the best. Instead, you need real teachers to create genuine problems that highlight gaps in understanding and assessments that track progress, just as you would in a traditional classroom.

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Why This Flips The Education Narrative

In human education, good teachers assess prior knowledge and avoid reteaching what students already understand; they sequence tasks to hit the just-hard-enough zone to keep students engaged and maximize learning. Micro1 is applying those pedagogical best practices to model training. The result is not just improved AI, but a new perspective on the AI-education discussion: sometimes the most impactful “classroom” is the one where humans teach the models.

One such educator is Mark Esposito, Chief Economist at Micro1 and a Professor at Harvard’s Center for International Development and Berkman Klein Center for Internet & Society. He describes the work this way:

Working at Micro1 has been one of the most stimulating and intellectually rewarding experiences of my career. Training models that will influence millions of users carries both privilege and responsibility. The privilege lies in being part of a small group entrusted with codifying knowledge ethically; the responsibility is ensuring that by advancing model performance in our domains of expertise, we help steer the moral arc of technology toward the right side of history.

His words capture the double-edged nature of this new academic frontier: professors are not just sharpening models, but also shaping the ethical compass of technology itself. As these models increasingly become the teachers and advanced knowledge workers of the future, this training closely mirrors the type of training that faculty provide to graduate students, and the implications of the work are comparable in terms of the downstream impact they will have on the global state of knowledge.

This point about the nature of expert contributions is echoed by Daniel Warner, Micro1 Chief Marketing Officer, who says “The mission of professors and PhDs has always been to disseminate knowledge. In the AI era, the most scalable way to do that is by training the models that will go on to teach and support millions of people.”

Business Footing And A Look Ahead

Micro1 is roughly three and a half years old and recently announced a $35 million raise at a $500 million valuation led by 01A. The company also highlights early work beyond language models in areas such as robotics. As Joshua Browder, Micro1 board member and CEO of AI company DoNotPay, said, “with robotics, there are no internet-scale corpus of robot demonstrations. The only way to build capable systems is to capture human performance, from simple manipulation tasks to more complex routines such as folding laundry, by using detailed cameras and sensors.”

The Emergence Of A New Job Category

Fears that AI will overshadow human ingenuity overlook an important fact: the best systems are increasingly shaped by human creativity. “Human brilliance is needed more than ever,” Ansari told me. The emerging job category of teaching models is expanding at a faster rate than any other. If he and Esposito are correct, universities and educators may soon discover that their most scalable classroom is the model-training pipeline, where their expertise not only reaches a cohort of students but also helps millions through the systems that those students and the wider world will eventually use.

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