[D] Can we stop glazing big labs and universities?

A viral post on the r/MachineLearning subreddit is sparking a critical conversation about how credit is assigned in AI research. The author argues against the common practice of attributing a paper's findings solely to a prestigious institution like Google or MIT simply because one of the many co-authors—often a student intern or a non-lead researcher—is affiliated there. This 'glazing' of big labs, they contend, obscures the actual work done by the lead authors, who may be researchers at less elite universities.

The post emphasizes that in academic publishing, the first author typically performs the majority of the work, while the last author is usually the supervising principal investigator. Crediting the institution of a middle author distorts this established framework and can create an unhealthy feedback loop. The concern is that this bias leads to 'crummy' papers from big names receiving undue attention while potentially major advances from smaller, less-connected teams are overlooked.

Drawing a cautionary parallel, the discussion warns that machine learning could follow the path of fields like biology, where publishing in top journals like Nature is often gatekept by a small set of elite institutions. The core argument is that ML's strength lies in its relatively open culture where 'advances can come from anyone.' To preserve this, the community must consciously judge research on its intrinsic merit rather than the prestige of affiliated brands, ensuring credit is assigned fairly to the individuals who do the work.