It is naive and incorrect to believe LLMs do not have biases. Of course they do, they are all trained on biased content. There are plenty of articles on the subject.

> Would you jump into the ocean and start trying to talk with them, or would you look up what is already discovered?

Why resort to straw men arguments? Of course anyone would start by looking up what has already been discovered, that doesn’t immediately mean reaching for and blindly trusting any random LLM. The first thing you should do, in fact, is figure out which prior research is important and reliable. There are too many studies out there which are obviously subpar or outright lies.

But I think you're right to describe it as research in the headline, because a lot of people will relate more to that term. But perhaps describe it as conducting a literature review further down.

First, AI systems absolutely embody cognitive biases - they're just different from human ones. These systems inherit biases from:

  - Their training data (which reflects human biases and knowledge cutoffs)
  - Architectural decisions made by engineers  
  - Optimization criteria and reinforcement learning objectives  
  - The specific prompting and context provided by users

Second: You frame AI as a "power suit" for distilling information faster. While speed has its place, a core value of doing research isn't just arriving at a final summary. It's the process of engaging with a vast, often messy, diversity of information, facts, opinions, and even flawed arguments. Grappling with that breadth, identifying conflicting viewpoints, and synthesizing them _yourself_ is where deep understanding and critical thinking are truly built.

Skipping straight to the "distilled information," as useful as it might be for some tasks, feels like reading an incredibly abridged version of Lord of the Rings: A small man finds a powerful ring once owned by an evil God, makes some friends and ends up destroying the ring in a volcano. The end. You miss all the nuance, context, and struggle that creates real meaning and understanding.

Following on from that, you suggest that this AI-driven distillation then "allows for another level, experiments, surveys, etc to uncover things even further." I'd argue the opposite is more likely. These tools are bypassing the very cognitive effort that develops critical thinking in the first place. The essential practice for building those skills involves precisely the tasks these tools aim to automate: navigating contradictory information, assessing source reliability, weighing arguments, and constructing a reasoned conclusion yourself. By offloading this fundamental intellectual work, we remove the necessary exercise. We're unfortunately already seeing glimpses of this, with people resorting to shortcuts like asking "@Grok is this true???" on Twitter instead of engaging critically with the information presented to them.

Tools like this might offer helpful starting points or quick summaries, but they can't replicate the cognitive and critical thinking benefits of the research journey itself. They aren't a substitute for the human mind actively wrestling with information to achieve genuine understanding, which is the foundation required before one can effectively design meaningful experiments or surveys.