In a stunning career pivot that underscores the growing intersection between elite engineering and artificial intelligence, Bercan Kilic has secured $55 million in seed funding for microagi, his Munich-based startup teaching factory robots to perform complex tasks by analyzing videos of humans doing everyday chores.
What Happened
Kilic’s journey began at Red Bull Racing in 2023, where he held what many engineers consider a dream position designing aerodynamics for the Formula 1 juggernaut during its championship-winning streak. Despite the prestige and technical brilliance of the work, Kilic found himself questioning the broader impact of his contributions. This existential moment prompted him to leave motorsports and channel his engineering expertise toward a problem with tangible real-world applications: training industrial robots.
microagi’s novel approach leverages computer vision and machine learning to extract actionable insights from video footage of people performing manual labor tasks. Rather than relying on traditional hand-coded programming or expensive sensor installations, the platform learns robot behaviors by observing human movements, significantly reducing implementation costs and timelines for manufacturers.
The funding round, led by Hummingbird with participation from Northzone and other prominent investors, marks the largest seed investment ever raised by a German startup. The capital injection validates a market thesis that industrial automation remains one of the most lucrative opportunities in enterprise AI.
Key Points
The timing of microagi’s emergence couldn’t be more strategic. Manufacturing facilities worldwide grapple with labor shortages, aging workforces, and the need to upskill quickly in competitive markets. Traditional robot programming requires specialized engineers and months of implementation—a barrier that keeps automation out of reach for small and mid-sized manufacturers.
Kilic’s background in aerodynamic modeling translates surprisingly well to this challenge. Both domains demand deep understanding of physical systems, computational efficiency, and the ability to abstract complex real-world phenomena into mathematical models. The skills that optimize airflow around a racing vehicle apply equally to optimizing manufacturing workflows.
The video-learning approach also addresses a critical pain point in AI adoption: data collection. Rather than requiring manufacturers to generate synthetic training data or invest in specialized hardware, microagi can work with existing CCTV footage and smartphone videos—accelerating deployment significantly.
What This Means
microagi’s success signals that European AI startups are increasingly competing at venture capital scales previously dominated by US and Chinese companies. For manufacturers, the implications are profound: industrial robots may finally become accessible to the broader market, not just automotive giants and mega-factories.
As labor costs rise globally and supply chain resilience becomes paramount, companies betting on AI-powered automation will likely outpace competitors relying on traditional hiring. Kilic’s decision to trade F1 glory for industrial impact reflects a broader realization that transformative technology often emerges not from prestige industries, but from solving unglamorous, universal problems.
