Hashed CEO Kim Seo-jun: Humans No Longer Execute; Agents Are the Workforce
Speaking at the Nitro Track Day event in Seoul, Kim Seo-jun proposed that AI-native organizations should shift from 'human-in-the-loop' to 'human-on-the-loop': agents autonomously execute and self-correct, while humans design structures, set goals, reflect intent, and assume responsibility. Hashed has already implemented this model internally, with AI automatically submitting over a hundred pull requests on weekends.
Kim Seo-jun is the CEO of Hashed, a South Korean crypto venture capital firm. At the 'Nitro Track Day' event held in Seoul, he put forward a clear organizational thesis: AI-native teams should not remain in the 'human-in-the-loop' (Human-in-the-loop) mode—where AI executes tasks and humans approve them step-by-step—but should instead transition to 'human-on-the-loop' (Human-on-the-loop). In this model, agents autonomously complete tasks and self-correct errors, while humans are responsible for designing work structures, setting intent, and taking responsibility for outcomes. He stated, 'Humans should no longer perform execution work; agents do the work, while humans design the structure, reflect intent, and take responsibility.'
Hashed operates internally according to this model. According to Kim Seo-jun, while employees were resting on the weekend, AI automatically submitted over 100 Pull Requests via Telegram. The development team for V, an Asian optimization agent service developed by Hashed using a venture-building approach, generated over 4,100 PRs in nearly one month. Within the team, individual and team-level agents communicate with each other to handle tasks such as schedule management and meeting invitation creation.
Lee Jae-hong, CEO of Across, an AI search optimization company, added a perspective on resource efficiency: not all tasks require high-performance models. 'You don't need to bring a cannon or nuclear weapon to solve something that can be handled with a pistol,' he said. The key is to identify the capability boundaries of each model and select them according to the scenario.