The long-term career value of AI isn't in getting answers, but in asking deeper questions. Professionals who use AI tools to simply get the output will be commoditized. Those who use them with deep curiosity—to understand the underlying principles of their field—will be the ones who innovate and lead.

With AI tools changing weekly, the most critical skill for designers is no longer mastery of a specific tool but a deep sense of curiosity. This drives the continuous process of asking questions, experimenting, and adapting to a rapidly evolving landscape.

A top VC's most important interview question is now "How have you used AI in your daily life this week?" The key is identifying individuals who are running towards the new technology and embracing change. This mindset is uncorrelated with age or seniority, making it the most critical hiring signal.

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.

Sendbird updated its job descriptions for 'AI-first' roles to de-emphasize years of experience. Instead, they screen for high curiosity, agency, and energy, believing these traits are better predictors of success for employees who must constantly learn and build with new tools.

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.

AI capabilities are evolving so rapidly that specific tool expertise is fleeting. The durable skill is a mindset of playful curiosity: consistently testing the newest models on your own work problems to discover their emerging capabilities and how they can extend your powers.

As AI makes implementation trivial, the primary differentiator for knowledge workers will be their curiosity and agency. The ability to envision new projects and ask novel questions becomes more valuable than the technical skill to execute, which can be delegated to AI agents.

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.