Designing AI-Resistant Technical Evaluations: Anthropic's Approach

The Rising Challenge: Evaluating Technical Talent in the Age of AI

As AI capabilities rapidly advance, evaluating technical candidates becomes increasingly complex. Traditional technical assessments that effectively differentiate skill levels today may be easily solved by AI models tomorrow, rendering them obsolete. Anthropic, a leading AI research company, has faced this challenge head-on, particularly within its performance engineering team. This article details their journey in designing and redesigning a take-home test to remain robust against AI assistance, sharing valuable lessons learned along the way.

The Original Take-Home Design: A Performance Engineering Challenge

In November 2023, Anthropic needed to efficiently evaluate a large number of performance engineering candidates. They designed a take-home test centered around optimizing code for a simulated accelerator, resembling TPUs. Over 1,000 candidates have since completed it, with dozens now contributing significantly to Anthropic's infrastructure, including engineers who built and maintain the Trainium cluster.

Key Features of the Original Test

• Realistic Simulation: A Python simulator mimicking a TPU with features like manually managed scratchpad memory, VLIW, SIMD, and multicore capabilities.
• Parallel Tree Traversal: The core task involved optimizing a parallel tree traversal algorithm, deliberately avoiding deep learning specifics to assess fundamental skills.
• Debugging Component: The initial version included a bug that candidates needed to debug, testing their tooling and problem-solving abilities.
• Time Constraints: A 4-hour (later reduced to 2-hour) time limit, reflecting the pressures of real-world performance engineering tasks.
• AI Usage Allowed: Candidates were explicitly permitted to use AI tools, mirroring their potential usage on the job.

The Claude Opus Challenge: Iterations and Redesigns

Each new Claude model presented a significant challenge. Claude Opus 4 initially outperformed most human applicants within the time limit. While still allowing differentiation among top candidates, Claude Opus 4.5 subsequently matched their performance. This prompted Anthropic to iterate through three versions of the take-home test.

Iteration 1: Claude Opus 4

The initial Claude Opus 4 solution was remarkably efficient, highlighting the need for a more complex evaluation.

Iteration 2 & 3: Increasing Complexity

Subsequent iterations focused on increasing the complexity and nuance of the task, moving beyond straightforward optimizations to incorporate more subtle challenges and requiring deeper understanding of the underlying system. The goal was to create problems that required creativity and ingenuity, areas where humans still hold an advantage.

Lessons Learned: Building AI-Resistant Evaluations

Anthropic's experience revealed several key principles for designing evaluations robust to AI assistance:

• Focus on Fundamentals: Prioritize assessing core skills and knowledge rather than specific tools or techniques.
• Embrace Complexity: Design problems with multiple layers of optimization and subtle nuances.
• Encourage Creativity: Incorporate tasks that require innovative solutions and out-of-the-box thinking.
• Allow AI Usage: Acknowledge that AI tools are becoming integral to the workflow and allow their use, while still evaluating the candidate's underlying skills.
• Longer-Horizon Problems: AI struggles with problems requiring sustained effort and deep understanding over extended periods.

The Open Challenge: Can You Beat Opus 4.5?

Despite the advancements in AI, human engineers can still outperform models when given unlimited time. Anthropic is releasing the original take-home test as an open challenge. If you can achieve a better score than Claude Opus 4.5 with unlimited time, they encourage you to reach out: https://daic.aisoft.app?network=aisoft

Conclusion: Adapting to the Future of Technical Evaluation

Anthropic's journey highlights the evolving landscape of technical evaluation in the age of AI. By continuously adapting their assessment methods and focusing on fundamental skills, creativity, and problem-solving abilities, they are ensuring they can identify and recruit the best performance engineers. The lessons learned from this experience provide valuable insights for any organization seeking to evaluate technical talent in a rapidly changing world.