Over the past few months, I’ve been exploring and reflecting on two things: AI and the kind of impact I want my work to have.
I’ve explored everything from prototyping applications and running models locally to studying academic research and the foundation models driving today’s AI wave. It feels fair to say I’ve spent time across much of the AI stack.
What I Learned
The pace of progress over the last few years has been extraordinary. What excites me most is not specific products, but the underlying capabilities and the structural shifts in how intelligent systems are trained, scaled, and deployed. These advances have moved AI from experimentation to foundational infrastructure — with enormous potential for societal impact, especially when we move beyond the typical “developers building for developers” trap.
I’ve also spent time reflecting on the kind of impact I want to make through my career. This was a long-overdue exercise, and I didn’t want to rush it.
I am under no illusions about what AI can and cannot do. Still, I see substantial potential — not only in future advances, but in the technology as it exists today. The underlying methods have crossed the threshold of being unquestionably useful at scale. In my view, this represents one of the most significant shifts in computing since the Internet — a deep technological inflection point that is too consequential to simply observe from the sidelines.
What’s Next
As a deep-tech systems thinker with a broad perspective, I’m increasingly drawn to building and contributing at the frontier — particularly where advanced AI systems intersect with the life of humans and real-world impact.
- How can frontier AI make a difference in high-stakes domains such as health, science, and energy?
- How do we design systems where human judgment and machine reasoning compound rather than compete?
- How can we responsibly design AI systems capable of operating independently in complex environments without continuous human supervision?
- How can we improve training efficiency, robustness, and decentralization without defaulting to ever-larger monolithic systems?
- What technical and organizational structures are required to ensure trust, accountability, and long-term societal benefit?
- How do we translate deep technical progress into tangible improvements in human well-being?
I’m based near Aarhus, Denmark, and open to roles or early-stage collaborations nearby or remotely with limited travel on a semi-regular basis.
If you’re building serious deep-tech systems at this intersection — where frontier AI meets meaningful real-world impact — I’d welcome a conversation.