FigureAsia 35 Under 35 · AI
Adarsh Hiremath
Age 22 · Technical founder and operating leader · United States; distributed specialist network serving frontier laboratories and enterprises
Organising Human Expertise for Frontier Artificial Intelligence
- Age at the edition eligibility date
- 22
- Field
- Human-expertise infrastructure, model evaluation and enterprise agents
- Country or region
- United States; distributed specialist network serving frontier laboratories and enterprises
- FigureAsia U35 Assessment
- 87.8 / 100
Profile
Career and documented record
Adarsh Hiremath helped turn Mercor from an AI recruitment experiment into an infrastructure company connecting specialist human knowledge with model training, evaluation and enterprise systems. His 2025–2026 work combines benchmark design, organisational context and the difficult operational task of verifying expertise at scale.
Adarsh Hiremath co-founded Mercor with Brendan Foody and Surya Midha while still in his early twenties. As founding chief technology officer, he helped build the company’s initial semantic-search and automated-interview systems. The company subsequently moved beyond conventional recruitment, supplying domain experts for model training, reinforcement learning and evaluation. By 2026, Hiremath and Foody were serving as co-chief executives. Hiremath’s most attributable recent work is unusually concrete. In March 2026 he co-authored APEX-SWE, a benchmark developed with Cognition to test models on production software-engineering tasks rather than isolated coding exercises. At release, its leading system achieved 41.5% pass@1, indicating substantial remaining limitations. Two days later, Hiremath personally introduced Mercor Enterprise AI, a proposed context layer for helping organisations identify, configure and assess internal agents. The company reported rapid financial and contractor growth, including more than US$1 billion in annualised revenue by May 2026 and over 30,000 contractors paid weekly. These are company statements, not audited public-company disclosures. Rapid expansion also brought material control challenges: independent reporting in April 2026 described employee fraud and identity-verification incidents, including suspected North Korean operatives. Those issues do not erase the contribution, but they form part of its record.
FigureAsia selection
Why Adarsh Hiremath is on the list
FigureAsia selected Hiremath because human expertise has become a critical but poorly understood layer of frontier AI. His company has helped formalise how specialists are found, compensated and engaged in training and evaluating models, while APEX-SWE contributes a more economically grounded measure of capability. The selection recognises Hiremath’s attributable technical and strategic work, not the company’s valuation. It also weighs governance seriously: identity verification, workforce integrity and contractor protections are central tests of whether this infrastructure can scale responsibly.
Verified work
The 2025–26 record
Verified contribution 01
In October 2025, the company announced a US$350 million Series C at a US$10 billion valuation.
Verified contribution 02
In March 2026, Hiremath co-authored APEX-SWE, a benchmark built around production software-engineering tasks.
Verified contribution 03
In March 2026, he authored Mercor Enterprise AI, proposing an organisational context graph for configuring and evaluating enterprise agents.
Verified contribution 04
In May 2026, the company reported exceeding US$1 billion in annualised revenue, paying more than US$2 million daily to over 30,000 weekly contractors.
Field context
The work in its field
The company connects geographically distributed specialists with frontier laboratories and enterprises. Its reported contractor and payout figures indicate substantial reach, but it does not publish a fully audited country-by-country breakdown of participants, clients or economic outcomes.
Hiremath’s documented family connection to India and the company’s distributed expert model connect Asia’s technical and professional talent pools with international model development and evaluation.
FigureAsia U35 Assessment
Assessment breakdown
87.8out of 100
Defining contribution
22 / 25
A completed piece of work, institution or system that materially changes what the field can do.
Demonstrated impact and reach
18.2 / 20
Observable adoption, scientific use, policy consequence or operational reach, with self-reported metrics labelled as such.
Personal agency and attribution
13.8 / 15
Evidence that the individual shaped the result, separated from team, employer and investor halo.
Technical or institutional originality
13.5 / 15
A new method, product form, research direction, governance mechanism or deployment model.
Durability and field-shaping influence
8.5 / 10
The likelihood that the contribution will remain useful beyond a single news cycle or model release.
Evidence integrity and responsible practice
7.3 / 10
The quality of the record, the precision of claims and the seriousness with which limitations and harms are addressed.
Asia–world relevance
4.5 / 5
A documented connection to Asia, impact on Asian systems, or clear importance to the region’s place in the international field.