The AI Discourse in Academia is Toxic. It Doesn’t have to be.

You are allowed to have a nuanced opinion about AI

Photo by Vida Huang on Unsplash

On Wednesday I shared a paper on Bluesky. Nothing out of the ordinary; sharing and learning about new papers is one of my favorite aspects of social media, so when I saw a paper I thought was interesting published in Nature that day I shared it with a short note. The paper, Miao et al., 2026, describes a framework that converts a scientific paper into an MCP server that an AI agent can query. I think this is a promising approach: it may force authors to package their code, data, and metadata into a runnable pipeline, rather than the static PDF in addition to a half-working GitHub repo, which could improve reproducibility. It might also give readers a way to interrogate the methods interactively, which can be especially useful in fields like genomics, where methods sections can be many dozens of dense technical pages and are often hard to reproduce from the text alone. So I posted about the paper without thinking too much.

The response was jarring. Hundreds of vitriolic replies and quotes, with a level of collective incandescence I completely did not anticipate.

But considering the state of AI discourse in academia, maybe I should have?

To be fair, my post was over-enthusiastic. The realistic case for tools like this is more measured than what I wrote, and if I could edit the post I would definitely soften the “future of scientific papers” framing. But this is not what drove the response volume: the most-endorsed replies, with hundreds of likes, framed my post as an endorsement of a system that teaches students not to read; this interpretation is not what the paper (or my post) proposed at all, but once it took hold everything downstream was a response to the strawman rather than to the actual paper. A minority of the responses were substantive; some were just dismissive. But many were personal attacks, including curses, bullying, xenophobia, calls to take away my funding (LOL), and telling me that I should quit my job (don’t tempt me).

I am not writing this to complain about the nasty personal responses to this post (I have been called worse by peer reviewers). I am writing because this response fits a broader pattern in how academia now reacts to anything AI-adjacent: a toxic, knee-jerk hostility to AI in which the specific tool, application, or question all disappear into a general presumption that the whole category is terrible. This is a common occurrence; for example, the political scientist Alexander Kustov wrote a measured AI-positive piece last year and in return received “professional threats, ad hominem, and coordinated pile-ons,” including calls to his employer to fire him. This is also not a Bluesky-specific problem¹ or a social media-specific problem. The anti-AI issue in academia is much broader. You can see it in grant reviews, hallway conversations, paper reviews, Q&A sessions. Mentions of AI are often met with suspicion, with little care about the specific methodological question or model.

And I can understand why. AI has real limitations. The environmental cost may be substantial, the over-claiming from industry leaders is embarrassing, and there are legitimate problems with the actual technology: hallucinations, brittleness in unfamiliar domains, uneven distribution of benefits, unresolved questions about the consent of training-data use. I have also argued before that AI on its own will not speed up research in biology. At the same time, AlphaFold is a remarkable scientific advance that changed the entire field of structural biology; language assistance has lowered the barrier for scientists whose first language is not English; code generation has compressed a lot of routine analysis work. Importantly, “AI” is not one thing: an LLM drafting a paragraph is not the same technology as a variational autoencoder learning a latent representation of single-cell data, which is not the same as AlphaFold. Applications, users, and purposes vary just as widely. Having a single opinion about “AI” is like having a single opinion about “software.” The only serious position on AI in science is nuanced and case by case; any answer to “is AI good for science” that neatly fits into a single yes or no is unserious.

But, unfortunately, somehow AI has been sorted into the same tribal buckets as every other contested topic in public life. It functions less as a technical subject and more as an identity marker; which side you are on tells people who you are, before they hear what you think about any specific system. This is clearly, at least in part, due to social media, which rewards affect over analysis and surfaces the highest-arousal takes. People whose brains have been fried by spending hours a day marinating in the most extreme framings end up reacting to “AI” the way people react to other loaded political words: with a visceral response that precedes any specific claim. A software tool for pattern recognition has been recoded into a morality test.

This is bad for science.

Scientific communities used to be places, at least on their better days, where a specific technical claim could be evaluated on the specific technical merits. We can go back to this. We can treat AI as a normal technology for doing science, and evaluate specific AI methods on their merit, which is what science does for every other tool. For example, an MCP wrapper of genomics papers can improve accessibility and reproducibility of data analysis; it does not mean people are not going to be allowed to read papers. An AI-based preprint ranking system I reviewed recently reproduces the prestige hierarchies it claims to escape; but it does not mean AI is going to replace scientific journals. It’s possible to review specific AI systems without a claim about the global societal impact of AI.

Importantly, when the response to a mildly AI-positive share is a toxic pileup, people learn: they stop posting about AI, or never join the platform to begin with. The thread that started this is a good example: there were no actual AI scientists anywhere in the discussion, and the loudest amplified responses came from scholars in humanities with no knowledge in genomics, or from political accounts with no scientific affiliation at all. These hostile reactions have essentially eliminated any scientific AI content from the platform.

You might think, “OK, stopping the AI discussion is actually good for science!” But your toxic reaction to anything AI does not stop the conversation from happening. It only takes you out of it. The conversation continues without you elsewhere. Science is still moving forward, building AI tools, using AI, discussing AI. The only thing changed by your reaction is whether your voice is included.

So here is an invitation to join the real discussion: you are allowed to have a nuanced opinion about AI.

1. As an aside, this experience made it clear to me that, as I’ve suspected for a while now, Bluesky is not a place to have serious academic discussion. Any post that veers even slightly outside the Bluesky mainstream (which is left politically, anti-AI scientifically) will get ignored or piled on; I have yet to see a real nuanced scientific discussion in the comments about a paper. Also, there is an idea that Bluesky is a much kinder, more inclusive place compared to X. This is definitely not the case. The cesspool is just as bad, only smaller.