OpenAI Scholars 2018: Final projects

OpenAI wrapped up its inaugural Scholars program this year, and the ten participants in that first class have now published their final projects. The setup was simple in concept: give people from underrepresented backgrounds in AI a stipend, a mentor from OpenAI's research staff, and roughly three months to go from foundational study to an original piece of work. No prior machine learning credentials required, just curiosity and the willingness to grind through the math.

What's notable isn't just that the program existed, but what it produced. The scholars didn't just replicate tutorials or rehash existing papers. They tackled real open problems across reinforcement learning, generative models, and interpretability, then wrote up their findings the way any research lab would, with code, results, and honest discussion of what didn't work. For people who months earlier might not have had a foot in the door of an OpenAI-style lab, that's a meaningful leap.

The timing matters too. Back in 2018, the conversation around AI's talent pipeline was heavy on the fact that the field was drawing from an extremely narrow slice of people, mostly funneled through a handful of elite computer science programs. Initiatives like this were a direct, if modest, response to that. Rather than waiting for universities to fix the pipeline, OpenAI built a short, intensive side door directly into applied research.

It's worth remembering that this was a first cohort, which means mistakes were made and lessons were learned in real time. Mentorship structures, project scoping, and the balance between structured coursework and independent research were all things the team had to figure out on the fly. OpenAI treated the wrap-up not as a victory lap but as a data point for how to run the next one better.