Not necessarily, not in some of the most useful ways of simply having a significantly more capable way of automating research & experimentation of many sorts. Anyone with a dataset and a laptop with a decent gpu, or pretty reasonably priced API or subscription to access models that don't even need to be frontier level, they can setup a loop for intelligent iteration through countless analytical methods, subset analysis, enriching the data with other sources, search and retrieval of research literature to cross reference results of analysis with other findings and write a basic summary for the person to review, look for alternative methods for analysis.

Yes, not everyone will be able to launch a swarm of agents and a few billion tokens, but scientific research has always had areas where the most advanced tools were beyond the reach of every scientist who might like to have access.

Only for about five more years. In 7-10 years the price of memory and compute should drop by 80-90%. In ten years the bleeding edge 14,000 token/s machines and 4TB memory is going to look quaint, "that stuff can't even bootstrap a kernel and entire (usable) posix-compliant OS in less than a week" vs the future of "the computer updates it's firmware in real time to meet the needs of the current task"

Not only resources being controlled by a few organizations, but the noise of all of the people who have used AI to “discover” something novel, when they don’t understand the domain in the first place. When everyone is discovering something, but what they are publishing is either incorrect or already known, how do you wade through the slop to find the gems (maybe academic publishing was already filled with human slop, though).