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.

When building core AI technology, prioritize hiring 'AI-native' recent graduates over seasoned veterans. These individuals often possess a fearless execution mindset and a foundational understanding of new paradigms that is critical for building from the ground up, countering the traditional wisdom of hiring for experience.

Early in a technology cycle like the web or AI, successful founders must be technical geniuses to build necessary infrastructure. As the ecosystem matures with tools like AWS or open-source models, the advantage shifts to product geniuses who can build great user experiences without deep technical expertise.

Don't default to hiring people who have "done the job before," even at another startup. Unconventional hires from different backgrounds (e.g., archaeologists in customer success) can create unique creativity. The priority should be finding the right fit for your company's specific stage and needs, not just checking an experience box.

Perplexity's talent strategy bypasses the hyper-competitive market for AI researchers who build foundational models. Instead, it focuses on recruiting "AI application engineers" who excel at implementing existing models. This approach allows startups to build valuable products without engaging in the exorbitant salary wars for pre-training specialists.

In rapidly evolving fields like AI, pre-existing experience can be a liability. The highest performers often possess high agency, energy, and learning speed, allowing them to adapt without needing to unlearn outdated habits.

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.

Periods of intense technological disruption, like the current AI wave, destabilize established hierarchies and biases. This creates a unique opportunity for founders from non-traditional backgrounds who may be more resilient and can identify market needs overlooked by incumbents.

Contrary to the belief that distribution is the new moat, the crucial differentiator in AI is talent. Building a truly exceptional AI product is incredibly nuanced and complex, requiring a rare skill set. The scarcity of people who can build off models in an intelligent, tasteful way is the real technological moat, not just access to data or customers.