Since modern AI is so new, no one has more than a few years of relevant experience. This levels the playing field. The best hiring strategy is to prioritize young, AI-native talent with a steep learning curve over senior engineers whose experience may be less relevant. Dynamism and adaptability trump tenure.

For roles leveraging new technologies like AI, where tools are nascent and constantly changing, competency is a fleeting metric. Instead, hire for curiosity. A curious mind will adapt, learn, and master new tools as they emerge, making them a more valuable long-term asset.

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.

In the current AI wave, young founders possess a unique advantage: they never learned the 'old way' of doing things. This lack of pre-AI mental baggage allows them to rethink entire workflows from first principles, giving them a speed and innovation edge over experienced operators who must first 'unlearn' old habits.

AI tools act as a 'superpower' for high-agency generalists who possess good taste and deep customer understanding but may lack deep technical specialization. This could reverse the long-standing corporate trend of valuing specialists, making these empowered generalists the most impactful players in a company.

Since AI tools are new and their use is often restricted at legacy companies, prior experience is a poor predictor of success. Artemis prioritizes a candidate's eagerness to learn and operate at the cutting edge, teaching them their intensive, multi-instance workflows upon joining.

Contrary to the belief that AI architecture is only for senior staff, Atlassian finds that "AI native" junior employees are often more effective. They are unburdened by old workflows and naturally think in terms of AI-powered systems. Senior staff can struggle with the required behavioral change, making junior hires a key vector for innovation.

Young people may understand new AI tools but lack the context to apply them for business value. The opportunity lies in pairing their tech fluency with business process knowledge, teaching them how to generate actual ROI from AI—a critical skill gap across the entire workforce.

Instead of trying to master every new AI model, a more effective learning strategy is to analyze an existing professional workflow. By identifying which steps AI can handle versus which require human oversight, one builds deeper, more applicable skills.