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Intrinsic motivation and curiosity are the most desirable traits in new hires, often outweighing top academic marks. While this drive cannot be taught directly, educators can spark it by connecting coursework to exciting real-world applications. Students are advised to find what they truly enjoy, as passion leads to better performance and career satisfaction.
When spotting latent talent, look beyond existing skills. The most promising individuals are those who act like 'sponges,' demonstrating an insatiable openness to absorb new perspectives and challenge their own methods. This attitude is a stronger indicator of future growth.
Instead of being a deterrent, having a genuinely hard scientific problem is a powerful recruiting tool. It attracts curious, convention-challenging people who are motivated by solving what others cannot and are willing to work through ambiguity to achieve a breakthrough.
In a field as complex as AI for science, even top experts know only a fraction of what's needed. Periodic Labs prioritizes intense curiosity and mission alignment over advanced degrees, recognizing that everyone, regardless of background, faces a steep learning curve to grasp the full picture.
Prioritize hiring generalist "athletes"—people who are intelligent, driven, and coachable—over candidates with deep domain expertise. Core traits like Persistence, Heart, and Desire (a "PhD") cannot be taught, but a smart athlete can always learn the product.
When hiring, a candidate with high passion for the subject matter but low experience is more valuable than an experienced candidate with low passion. Skills are teachable, but genuine enthusiasm for the mission is not. This framework helps resolve the common hiring dilemma between potential and polish.
Passion isn't just about enjoyment; it's about an innate drive to learn. The best indicator that you've found your calling is when the process of acquiring new skills and knowledge in that field feels like a hobby, not work.
The most promising junior candidates are those who demonstrate self-learning by creating things they weren't asked to do, like a weekend app project. This signal of intrinsic motivation is more valuable than perfectly completed assignments.
As AI automates tasks and transforms industries, fixed skills have a shorter shelf life. The defining characteristic for success will be curiosity—the intrinsic motivation to explore, ask questions, and learn continuously. It's the engine that enables adaptation and discovery.
When hiring, Brookfield seeks people who are "nerdy" in their intellectual curiosity. The firm values individuals intrinsically motivated to dissect and solve complex problems that others have failed to crack, prioritizing this trait over any specific background or stereotype.
For cutting-edge AI problems, innate curiosity and learning speed ("velocity") are more important than existing domain knowledge. Echoing Karpathy, a candidate with a track record of diving deep into complex topics, regardless of field, will outperform a skilled but less-driven specialist.