Accessibility, Except Here

When Twitter got taken over by Musk a couple of years back, something akin to an intellectual exodus of sorts happened, and lots of communities left Twitter for other, less ideologically captured alternative platforms, with the majority of these being part of the Fediverse.One might ask if these platforms are not equally politically charged though.A rather recent alternative is Bluesky, which is often simplistically described as a leftist variant of Twitter. Today it can be reasonably said it is the stronghold of anti-AI ideologies out of all popular social media platforms, but people on the Fediverse are arguably at least equally opposed to it.

What makes this particularly interesting is the gap in technical literacy between the two. Bluesky has a rather broad user base, while the Fediverse, Mastodon and its sibling platforms, is home to some of the most technically capable people across the broader internet. People doing programming language research or developing novel rendering techniques, the kind of deeply principled craftspeople whose departure from Twitter marked the end of what once made it genuinely valuable. You would expect this level of technical depth to produce a more nuanced engagement with LLMs. Instead, the criticism voiced across both platforms is largely indistinguishable, and the very people you would expect to apply careful technical analysis are instead relying on the same ideological reflexes to the emergence of this new technology.

While I agree with lots of the criticism regarding the negative externalities of LLMs and especially how this technology is forcefully being pushed onto our industry by people in roles of power, I think dismissing it in its entirety solely on ideological grounds is the wrong answer and, with regards to the goals of the people that voice such criticism, a quite unproductive one at that. While much has been said on the purported merits of LLMs and their value as tools in software engineering, there is another angle that has not yet been addressed enough: how LLMs democratize access to technical education.

It is true that one can use LLMs in ways that stand in opposition to pedagogical goals. But they are a remarkably effective tool for studying technical topics nevertheless. The usual caveats regarding their probabilistic output still apply, but this does not invalidate what else they offer as an interlocutor. Using natural language to communicate with an LLM can feel close to talking through a more knowledgeable version of oneself. The interactive nature and the targeted feedback that they provide are not replaceable by more traditional and established forms of media like a textbook. Which is not to say either renders the other useless. To the contrary, the two work rather well together. Being able to ask questions without end about the contents of a textbook enables everyone, regardless of their background, to study technical topics in depth.

Unfortunately, that this aligns with the values of accessibility and equality so central to these communities seems to go entirely unexamined.The most obvious objection is economic: cost. But open-source models lag behind their commercial counterparts by half a year at most, and that gap is narrowing. Advances in optimization paired with increasingly capable consumer hardware mean that running a highly competent model locally is not some distant utopian fantasy. The economic barrier, while genuine, is eroding faster than most critics seem to acknowledge.