Standard benchmarks fall short for multi-turn AI agents. A new approach is the 'job interview eval,' where an agent is given an underspecified problem. It is then graded not just on the solution, but on its ability to ask clarifying questions and handle changing requirements, mimicking how a human developer is evaluated.

The most significant gap in AI research is its focus on academic evaluations instead of tasks customers value, like medical diagnosis or legal drafting. The solution is using real-world experts to define benchmarks that measure performance on economically relevant work.

The primary bottleneck in improving AI is no longer data or compute, but the creation of 'evals'—tests that measure a model's capabilities. These evals act as product requirement documents (PRDs) for researchers, defining what success looks like and guiding the training process.

Building a functional AI agent is just the starting point. The real work lies in developing a set of evaluations ("evals") to test if the agent consistently behaves as expected. Without quantifying failures and successes against a standard, you're just guessing, not iteratively improving the agent's performance.

Traditional AI benchmarks are seen as increasingly incremental and less interesting. The new frontier for evaluating a model's true capability lies in applied, complex tasks that mimic real-world interaction, such as building in Minecraft (MC Bench) or managing a simulated business (VendingBench), which are more revealing of raw intelligence.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.

OpenAI identifies agent evaluation as a key challenge. While they can currently grade an entire task's trace, the real difficulty lies in evaluating and optimizing the individual steps within a long, complex agentic workflow. This is a work-in-progress area critical for building reliable, production-grade agents.

Traditional, static benchmarks for AI models go stale almost immediately. The superior approach is creating dynamic benchmarks that update constantly based on real-world usage and user preferences, which can then be turned into products themselves, like an auto-routing API.

OpenAI's new GDP-val benchmark evaluates models on complex, real-world knowledge work tasks, not abstract IQ tests. This pivot signifies that the true measure of AI progress is now its ability to perform economically valuable human jobs, making performance metrics directly comparable to professional output.