"Whatever work I identify is always based on the current iteration of the model. If I say the model isn't good at X and my solution helps fix X, it is based on that version of the model. If I miss the deadline, I don't know whether the next version will have the same issues or not."
"What we're limited by in these foundational labs is compute. It's not like Big Tech or other places where you can keep hiring a bunch of people and give them small pieces of a task to do. Everyone needs compute to actually do something, and as soon as you have a lot of people, the compute gets divided, so no one will be able to do anything."
"The work is super dynamic. Sometimes you think something is super easy and you'll get it done in a day. Other times, it's the opposite - because there are so many unknowns, it might take a week."
Working at frontier AI labs like Meta Superintelligence Labs and OpenAI involves highly dynamic project cycles centered around major milestones spanning months. Researchers identify work based on current model iterations, with tight deadlines creating intense periods. The work is unpredictable—tasks estimated for one day may take a week due to numerous unknowns. Frontier labs differ significantly from Big Tech companies; compute availability is the primary constraint rather than hiring capacity. Because compute resources must be distributed among team members, large teams become inefficient. High-bandwidth communication between stakeholders is essential, requiring flat organizational structures without excessive hierarchical layers.
Read at Business Insider
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