How well can top AI models do these jobs?

One of the biggest fears fueling the public’s apprehension toward AI is that the technology will eventually take their jobs.

We’ve already seen evidence that some roles like entry-level software development, customer service, and marketing are feeling the effects of automation powered by generative AI. Being able to track the real-world work capabilities of AI models will become increasingly important as models get more and more powerful.

To that end, OpenAI has created a new AI benchmark called “GDPval” that aims to measure just how well leading AI models can do realistic tasks for a variety of “economically valuable” jobs.

OpenAI describes the benchmark as an evolutionary step away from the first wave of benchmarks that followed a more academic, exam-style model:

“[GDPval] measures model performance on tasks drawn directly from the real-world knowledge work of experienced professionals across a wide range of occupations and sectors, providing a clearer picture on how models perform on economically valuable tasks. Evaluating models on realistic occupational tasks helps us understand not just how well they perform in the lab, but how they might support people in the work they do every day.”

Working with experienced industry professionals, the researchers created a dataset of 220 realistic tasks from 44 occupations that someone might do in the course of their work in a particular role.

Here’s an example of one of the tasks in the benchmark’s training data for a real estate broker:

We went through the data and picked a few common jobs from the benchmark’s results. Unsurprisingly, software developers were the most impacted job, with Anthropic’s Claude model getting an average 70% win rate on the test, which was then compared to a human in that role. For example, a score of 50% would put the model on par with a human expert. Audio and video technicians should feel that their job is secure (for now), as the models executed those tasks with very low scores.

OpenAI acknowledges there are limitations with this benchmark. For instance, currently, each task comes with some background materials that are required to do the task — but generating those background materials itself requires complex work and the benchmark doesn't assess current model's ability to complete those necessary preparatory tasks. Instead that work is done by the humans testing the AI. The paper also notes that this is a small dataset, and the current jobs tested are mainly those of “knowledge workers” that can be performed on a computer.

Maybe a future version will be used to test how well a robot can scrub your toilet.