Utkarsh Amitabh did not plan to add one more role to his already full schedule when 2025 began. He was running Network Capital, teaching at universities, writing, and working toward a Ph.D. at Oxford. He also had a newborn at home. But when micro1, a data-labeling and AI-training company, approached him, he agreed to take a closer look.
What drew him in was not the title or the money. It was curiosity. “Intellectual curiosity drew me in,” he told CNBC. Training enterprise AI systems, he felt, aligned with the work he had done for years across technology, strategy and research.
Amitabh calls himself a “deep generalist”, and his background supports that claim. He studied mechanical engineering. Then moral philosophy. He spent more than six years at Microsoft working on cloud and AI partnerships.
He founded a global career platform. He wrote a book on side hustles. His research focuses on the future of achievement in an AI-driven world.
For someone with such a varied journey, the micro1 assignment fit naturally. The role is part-time. He works late at night, around three and a half hours after his daughter sleeps.
The compensation, $200 an hour, has earned attention, but Amitabh says the work mattered more than the money. What he liked most was that the job demanded expertise and precision.
His work at micro1 involves breaking down complex business problems and designing prompts that AI models can understand.
If a model misunderstands a question or responds incorrectly, he diagnoses the gap. Sometimes the machine misses a step. Sometimes the instruction needs sharper language. Every correction adds to the dataset that trains the model.
This work is meticulous. “You need to have immense attention to detail,” he said. He describes long hours spent tracing a single mistake, checking whether it was made by the AI or by the human who guided it. The job has its own rhythm: break the problem, test the model, study the failure, start again.
Amitabh says the work is demanding because the models change quickly. As the systems learn, the experts have to adapt.
That tension between human knowledge and machine learning keeps the role interesting. He describes the process as seeing whether the “machine and human” can improve one another.
The broader question, whether this same technology will replace workers, is one he doesn’t avoid.
He calls it “the trillion-dollar question.” Jobs will be lost, he admits. But he also believes new ones will be created. He places himself between optimism and realism, convinced that humans will continue to work with machines, not against them.
His view is simple: knowledge is not fixed. It grows. And AI, instead of ending careers, could push people to learn faster and work smarter. As he puts it, the fear around AI could “empower us to learn better” and rethink how we build skills.
For now, Amitabh continues to balance work, research and family.
His day job is still Network Capital. His nights are spent improving AI systems. His approach reflects something unusual in today’s tech world, steady curiosity instead of hype, and a belief that humans and machines will move forward together.
