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.

Sending a resume is now an outdated and ineffective way to get noticed by AI startups. The proven strategy is to demonstrate high agency by building a relevant prototype or feature improvement and emailing it directly to the founders. This approach has led to key hires at companies like Suno and Micro One.

Instead of searching for new "AI" job titles, non-coders should focus on applying AI capabilities to traditional roles like marketing or sales. Companies are prioritizing existing positions but now require AI fluency, such as building custom GPTs or using AI assistants, as a core competency.

Formal AI competency frameworks are still emerging. In their place, innovative companies are assessing employee AI skills with concrete, activity-based targets like "build three custom GPTs for your role" or completing specific certifications, directly linking these achievements to performance reviews.

Don't hire based on today's job description. Proactively run AI impact assessments to project how a role will evolve over the next 12-18 months. This allows you to hire for durable, human-centric skills and plan how to reallocate the 30%+ of their future capacity that will be freed up by AI agents.

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.

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.

Snowflake's hiring philosophy for the AI era prioritizes adaptability over specific, perishable skills. Recognizing that today's tools will be obsolete tomorrow, they screen for lifelong learners by asking questions like, 'How do you advance your craft?' rather than focusing on current tool proficiency.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.