The death of the German public intellectual Jurgen Habermas at the age of 96 provides a useful starting point to consider the current developments in Artificial Intelligence (AI). I referred to him as a public intellectual, rather than a philosopher or a sociologist, because his political role - and I do literally mean his performance of a role - is the bulk of his legacy. In the future, few outside of academia will read his works, such as The Structural Transformation of the Public Sphere or The Theory of Communicative Action, but his ideas about democratic discourse, suitably vulgarised, will survive in the memory of his liberal admirers. This memory was summarised by the Guardian's editorial that marked his death in two statements: first, "that our nature as linguistic beings puts reason and the search for consensus at the core of who we are"; and second, that Habermas was responsible for the "concept of the public sphere, where rational debate can take place and disagreements be brokered, implied pluralism, civility and inclusion."
The first statement is little more than obeisance to the just-so story of the Enlightenment, which ignores the realities of power and the irreconcilable interests of class in favour of an essentialist abstraction: Rodney King's "Can't we all just get along?". In particular, it sidelines the key criticism of instrumental reason advanced by Theodor Adorno and Max Horkheimer in The Dialectic of Enlightenment, which was the intellectual context of Habermas's emergence in the postwar era as a junior member of the Frankfurt School before he struck out on his own. The second statement ignores the structural constraints on the public sphere that make a mockery of such terms as pluralism, civility and inclusion. An object example would be the closing down of debate on Gaza in Germany, which Habermas himself contributed to by co-signing a statement by established academics in November 2023 that Israel's response to the October 7th attack was "justified".
Being on the wrong side of history is an occupational hazard for any public intellectual, but in Habermas's case losing the public argument became a distinguishing feature. As Peter Verovšek put it: "While it is certainly true that Habermas was accorded a certain respect as the éminence grise of the German public sphere, this recognition is more visible in the vehemence with which he was attacked than in the agreement his interventions found. ... Habermas never lost his commitment to democracy — to the idea that he could only present arguments, leaving his fellow citizens the communicative agency to decide what they thought and what they wanted to do, even if their decisions would often go against him". What this highlights for me is the extent to which Habermas was engaged (perhaps unwittingly) in a performance, like the court jester whose lèse-majesté is indulged but ultimately ignored.
In The Structural Transformation of the Public Sphere, Habermas argued for a fundamental shift in the 18th century from a "representational" culture centred on the court, where power was imposed through ritual and splendour, to a "public" culture centred on dialogue, criticism and consensus in multiple, more modest and disparate arenas, from coffee houses to Masonic lodges, which arose as a result of capitalism. There are two points to make about this. First, that the historical reality was less clear cut, the simple disjuncture of Habermas's tale ignoring the epistemological traditions of scholasticism and the Rennaisance as well as the persistence (and even recrudescence under Fascism) of overpowering ritual and splendour. And second, that the public sphere birthed by capitalism was just as much of a performance of power, a point noted not only by Adorno and Horkheimer but by later theorists such as Michel Foucault. The quadrille may have replaced the minuet, but they were both dances.
So what has all this got to do with AI? One claim is that AI may help achieve consensus in the realm of politics by mediating discourse: a theory tested by Google DeepMind's so-called Habermas Machine. Large language models (LLMs) are built on discourse in the form of written statements. These may be assertions (discourse is not limited to dialogue), or they may be commentary on other statements: disputations, counter-arguments, critique. These statements may or may not have a truth value. Though the appetite for more training data has meant that more and more of what is fed into the machine is low-grade, and increasingly the recycled slop of AI itself, the original intent was to privilege academic and technical literature on the grounds that this would be more reliably truthful. This meant absorbing the academic (even scholastic) paradigms inherent in the data: exegesis, citation, disputation. This preference isn't novel in digital technology. Google's Page Rank is a paradigmatic application of peer review, after all.
The result is that AI's determination to be authoritative leads it to "generate detailed “reports”, including names and dates, references and sources – the kind of material that suggests deep research and understanding, but may in fact be hallucinated or nonexistent." AI aims to be plausible by mimicing the forms and tropes of academic and scientific publication. Given all that we know about the institutional biases of academia, the prevalence of hoaxes and the crisis of replicability, is it any wonder that AI generates bullshit? AI's determination isn't a product of the data but of the programming. It has become fashionable to talk of LLMs as inscrutable, which leads to the anthropomorphism of "consciousness", but the reality is that they are curated and operate within quite strict boundaries. AI is problematic because of the biases inherent in the training data but also because of the trainers' own biases encoded into the guardrails - e.g. the pre-emptive interventions intended to stop it going full Mecha-Hitler - and the micro-decisions of thousands of human data "cleaners".
The American blogger Noah Smith recently claimed that "AI is a force for moderation. If I'm a Republican, and I talk to AI, I'm talking to something that was trained on data from both Republicans and Democrats. So the AI is more likely to pull me towards the center." The assumption that LLMs are big enough to avoid bias, like the idea of an equidistant "center", ignores the role of selection (both in the sense of what gets published and what is absorbed into the model) and misunderstands that there are real differences in political language. While to outside observers the Democrats and Republicans look like two wings of the same party, as Gore Vidal once memorably noted, they employ distinctive vocabularies and rhetorical forms. The Democrats also want to bomb Iran and provide more money and arms to Israel, they just deplore the vulgarity of Donald Trump and Pete Hegseth.
In simple terms, conservatives tend to be more assertive while centrists place a higher value on civility. Each can be considered a strategy of domination, arguably echoing Habermas's distinction between the representational (imposed) and the public (consensual). In other words, Republican and Democrat data (written statements that reflect their ideological positions) are not necessarily the same, not just in their differences of vocabulary ("liberty" and "wealth" versus "society" and "investment" etc) but in the force and style of their arguments (confidently normative versus cautiously empirical, for example). Consequently, there is no good reason to believe that AI will avoid bias in its interpretation. It depends on what it has come to value through training and what parameters it has been given by its all-too-human programmers.
We also have to bear in mind that AI is backward-looking, just as the academic and scientific milieux it relies on are. It privileges established knowledge (which may be wrong), which means it is inevitably conservative: there can be no paradigm shift, let alone a singularity. It lacks the imagination that distinguishes genuine human intelligence. In Habermas's lifeworld, knowledge is advanced by research and the gathering of more data, ultimately through the continuing growth of the global population - i.e. by the reproduction of human intelligence and experience of the world. The problem for AI, which has been brewing for some years and cannot be offset by "more compute", is that the quality of fresh data has plummeted because we've already used all the good stuff. Its attempts to originate knowledge, to create novel data by inference, too often result in hallucinations that strive for the credibility of form, not of substance.
Habermas fulfilled a necessary role for postwar Germany, advocating and personally exemplifying a theory of participatory democracy while the Ordoliberal establishment secured market liberalism from democratic challenge. At the crucial juncture of 1968, he turned against the socialist student movement. Thereafter, for all his denunciations of the right in the Historikerstreit (historians' dispute) and his later criticisms of the EU's shortcomings, he functioned as the tame conscience of German liberalism. Just as Habermas lapsed into irrelevance, so AI will become ever more conservative as it strives for authoritativeness (borrowing the name of an eminent German thinker without his permission being an example of this) and as the economic incentives encourage a dumbing-down for safety's sake (being sued for consequential damages is a bigger worry than being sued for copyright breaches). Artificial General Intelligence (AGI) will remain as much of a Utopian ideal, just out of reach, as a public sphere where pluralism, civility and inclusion reign.






