AI safety organizations struggle to hire despite funding because their bar is exceptionally high. They need candidates who can quickly become research leads or managers, not just possess technical skills. This creates a bottleneck where many interested applicants with moderate experience can't make the cut.

Sending a resume is now an outdated and ineffective way to get noticed by AI startups.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.

Theoretical knowledge is now just a prerequisite, not the key to getting hired in AI. Companies demand candidates who can demonstrate practical, day-one skills in building, deploying, and maintaining real, scalable AI systems. The ability to build is the new currency.

The MATS program demonstrates a high success rate in transitioning participants into the AI safety ecosystem. A remarkable 80% of its 446 alumni have secured permanent jobs in the field, including roles as independent researchers, highlighting the program's effectiveness as a career launchpad.

Eleven Labs bypasses traditional hiring signals by looking for talent based on demonstrated skill. They hired one of their most brilliant researchers, who was working in a call center, after discovering his incredible open-source text-to-speech model. This underscores the value of looking beyond resumes.

There's a significant disconnect between interest in AI safety and available roles. Applications to programs like MATS are growing over 1.5x annually, and intro courses see 370% yearly growth, while the field itself grows at a much slower 25% per year, creating an increasingly competitive entry funnel.

Contrary to the perception that AI safety is dominated by seasoned PhDs, the talent pipeline is diverse in age and credentials. The MATS program's median fellow is 27, and a significant portion (20%) are undergraduates, while only 15% hold PhDs, indicating multiple entry points into the field.

Employers now value practical skills over academic scores. In response, students are creating "parallel curriculums" through hackathons, certifications, and open-source contributions. A demonstrable portfolio of what they've built is now more critical than their GPA for getting hired.

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.