I agree. “I worked really hard on it” is neither necessary nor sufficient for research quality. We already know that lots of careful-looking, labor-intensive, neatly written work can still be wrong or non-replicable. Meanwhile, some valuable insights emerge from relatively simple “aha” moments, and some deep ideas are developed more clearly outside the formal journal pipeline (ex: The Bitter Lesson).
Instead of reverting back to the old imperfect proof-of-work proxy for truth, we should try figuring out how to use these new AI tools to help assess research merit more efficiently.
Granted, some research work will require expensive experiments or other forms of “hard work”, in which case proof-of-work can still function as a useful initial filter.
I agree. “I worked really hard on it” is neither necessary nor sufficient for research quality. We already know that lots of careful-looking, labor-intensive, neatly written work can still be wrong or non-replicable. Meanwhile, some valuable insights emerge from relatively simple “aha” moments, and some deep ideas are developed more clearly outside the formal journal pipeline (ex: The Bitter Lesson).
Instead of reverting back to the old imperfect proof-of-work proxy for truth, we should try figuring out how to use these new AI tools to help assess research merit more efficiently.
Granted, some research work will require expensive experiments or other forms of “hard work”, in which case proof-of-work can still function as a useful initial filter.