In an era where AI can assist with coding challenges, 10X's solution is to make their take-home assignments exceptionally difficult. This approach immediately filters out 50% of candidates who don't even respond, allowing for a much faster and more focused interview process for the elite few who pass.
Related Insights
To prevent engineers from gaming output-based pay, 10X assigns a "Technical Strategist" to each project. The engineer is paid for output, but the strategist is incentivized by client retention and account growth (NRR), creating a healthy tension that ensures high-quality work is delivered.
High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.
Traditional hourly billing for engineers is obsolete when AI creates 10x productivity. 10X compensates engineers based on output (story points), aligning incentives with speed and efficiency. This model allows top engineers to potentially earn over a million dollars in cash compensation annually.
Dr. Fei-Fei Li states she won't hire any software engineer who doesn't embrace AI collaborative tools. This isn't about the tools' perfection, but what their adoption signals: a candidate's open-mindedness, ability to grow with new toolkits, and potential to "superpower" their own work.
In AI PM interviews, 'vibe coding' isn't a technical test. Interviewers evaluate your product thinking through how you structure prompts, the user insights you bring to iterations, and your ability to define feedback loops, not your ability to write code.
To scale hiring efficiently, eliminate ambiguity. Each interviewer must make a definitive 'yes' or 'no' decision. If an interviewer is 'not sure' after their session, they are the problem, not the candidate. This prevents endless interview loops and forces clear, decisive judgment.
For high-level leadership roles, skip hypothetical case studies. Instead, present candidates with your company's actual, current problems. The worst-case scenario is free, high-quality consulting. The best case is finding someone who can not only devise a solution but also implement it, making the interview process far more valuable.
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.
Traditional hiring assessments that ban modern tools are obsolete. A better approach is to give candidates access to AI tools and ask them to complete a complex task in an hour. This tests their ability to leverage technology for productivity, not their ability to memorize information.
