AI for Philosophy: Progress through Capital-Intensive Philosophy (guest post)

“AI systems… will transform the discipline: they will make philosophy more capital-intensive. “

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The following is a guest post by David Strohmaier, a philosopher and computer scientist in the Natural Language and Information Processing group at the University of Cambridge. In it, he explains why he thinks it’s reasonable to believe that AI systems will eventually have the capacities to “drive” philosophy, and how this will change the economics of philosophy.

AI for Philosophy: Progress through Capital-Intensive Philosophy
by David Strohmaier

Introduction

Generative AI has arrived in philosophy. Not only do journals receive significant numbers of AI-generated papers of dubious quality, there has also been increasing talk of AI work dedicated to philosophy.¹ I have observed these developments as a researcher working in both AI and philosophy and, from that perspective, want to lay out a shift I see coming for philosophy and why I endorse it: philosophy will become a capital-intensive discipline in which a significant portion of research will be driven by AI systems.

My position depends on the capacities of AI systems. Since I am aware that many philosophers are skeptical about AI technology in general, let me first lay out the skeptical and the optimistic positions on whether AI systems will be able to drive philosophy, i.e. lead to whole new ways of doing philosophy.

The Skeptical and the Optimistic Positions on AI Capacities

The skeptical position about the capacities of AI systems is frequently motivated by the fact that prompting an LLM to write a philosophy paper either produces underwhelming results or requires so much hand-holding that one might question the value of using an LLM. Without massive revisions, AI writing is often atrocious. I recently had the joy of reviewing a computer science paper that was clearly AI-written. The prose was painful. The sentences were endless chains of semicolons and em-dashes. The model had no sense of proportion, blowing up negligible details while relegating important information to one of the numerous appendices.

Beyond stylistic problems, AI models struggle to produce truly innovative insights. Instead, they tend to dwell on rather technical, minor points. From the sceptic’s vantage point, this suggests that AI-driven philosophy would just produce forgettable pieces no one wants to engage with. Sometimes this sceptical assessment rests on a dated understanding of AI systems as mere LLMs that derive their capacities solely from the texts on which they were pre-trained. This picture no longer holds because training regimes have been diversified, but it remains true that the systems have not been specialised for philosophical innovativeness.

The optimistic position regarding the capacities of AI systems is often justified by projecting the current rate of progress into the future. The technology is progressing rapidly, so while AI systems may not yet be able to compose papers that hold up to the rigorous standards of top philosophy journals, we should give the AI labs a few more years. I think that there is much to be said for this projection, but we don’t require bold predictions about superintelligence or anything of that sort to foresee the growth of AI-driven philosophy.

To justify my optimism, I will assume only the application of AI technology as it exists, that is, foundation models and harnesses as they are currently available to frontier labs. Such models have been able to disprove a long-standing conjecture in discrete geometry and to develop a new algorithm for multiplying 4×4 complex-valued matrices. So far, the exploration of such uses is the exception in philosophy, but the technology is available.

While so far there are no well-documented instances of AI systems solving philosophical problems, I am optimistic because philosophy has a crucial feature making it promising for applying AI at scale: widespread inferential dependence across areas. To answer a question in metaphysics, one may have to consider issues in semantics and epistemology or even move beyond the limits of the discipline and take physics into account. Due to these pervasive cross-area dependencies in philosophy, the ability of AI systems to scale is extremely valuable. AI agents can not only identify relevant statements from other debates, but can formalise published theories at scale and then use tools to construct counter-examples.

For such use cases, it doesn’t matter that the prose style of LLMs is grating. Nor do AI systems lack the ingenuity to be employed in this way. The limited innovativeness that AI has been proven to possess, for example in developing novel algorithms for matrix multiplication, is sufficient for exploring various formalisations and testing them against counter-examples. As long as the AI systems can perform these functions at scale, they can lead us to the solution of philosophical problems.

One might respond that the use cases I gestured at require the availability of verification, and it is true that I see areas such as decision theory, axiomatic metaphysics, and formal epistemology as among the most promising candidates for AI-driven philosophy. However, even for the parts of philosophy which do not allow for determinate conclusions, AI systems will help establish which positions can reach a rational equilibrium. After all, these systems have the ability to consider thousands of discursive options and to systematically explore different aggregation schemes for judgements as they are recorded in the philosophical literature or human text in general. They can map the discursive space at a level of granularity that would be too labour-intensive for us.

In the previous paragraphs, I emphasized the ability of AI systems to scale on philosophically relevant tasks. It is due to these scaling abilities that I expect that AI systems will drive a new form of doing philosophy. Importantly, the more capital is spent on scaling AI inference, the better the results. There are always more counter-examples to consider, more aggregation schemes to explore, another angle to try, but you need the compute to do so.

The Transformation to a Capital-Intensive Discipline

Having sketched why I believe that AI systems will have the capacity to drive philosophy, I turn to how they will transform the discipline: they will make philosophy more capital-intensive. Simplifying slightly, over the thousands of years of philosophy’s history, capital, in the sense of external means of production, has played two roles:²

1. Capital allows those who own it either to engage in philosophy themselves or to enable others to do so. This form of capital enters philosophy only insofar as it provides the means of living while attention is directed towards philosophical problems. For example, the income from Plato’s estate allowed him to dedicate his time to philosophy.
2. Writing and reading materials are a form of capital that more directly supports the philosophical process. They are the means of producing philosophical arguments and insights. In the history of philosophy, getting access to such capital was often challenging, but these days a well-equipped public library may supply these means of philosophy.

If we broaden the use of the term “capital”, the most important form of capital for philosophy may be human capital, the skills and dispositions exhibited by philosophers.³ Being an academic philosopher may not be just a life of the mind, but it comes as close as any profession does. Up to now, free time, a library computer, and a mind honed on philosophical arguments used to be almost all you strictly needed for philosophy. Before the personal computer, all that was required was a typewriter, before that a fountain pen. Changes were rare, progressed at glacial pace, and were small compared to transformations in most industries. The two most impactful inventions may have been writing and the printing press, the means to record and spread our ideas. The arguments and their evaluation still had to spring from our minds. In this regard, the rise of AI-driven philosophy will be transformative.

To glimpse how philosophy will be transformed, it is worth considering how other disciplines function. When I entered computer science after my philosophy PhD, I underwent a culture shock. The role of capital contributed to the shock, not least because access to capital circumscribes what you can do in computer science. The number of GPUs available to you matters. You just cannot address certain questions if you don’t have the required equipment. You need access to the means of producing AI research. During my philosophy PhD, I once jokingly asked a faculty member what philosophy grants were spent on. Armchairs? His response was that the funding freed the philosopher from other obligations, freed them for philosophy. Once the funding suffices to pay your salary (and those of your postdocs/PhD students) and to cover your expenses for conferences/research visits, that’s about it.⁴

In computer science, and especially in AI research, there is never enough funding. If, instead of $5,000 worth of GPU credits you gave us $2 million, we would have no difficulty spending it. Our research scales with the capital, and while the marginal return on capital diminishes with size, most academic AI research doesn’t reach the point where this becomes relevant. Consequently, not just human capital matters, but access to computational capital. This might also be the future of philosophy as a capital-intensive discipline.

Objections to the Transformation and Why I Endorse It Nevertheless

While a number of principled objections have been raised against the use of AI for philosophy, the prospect of this transformation might very well be behind the distaste some philosophers show for AI. Academic philosophers like doing philosophy and many of them like doing it just the way they are doing it, thank you very much. Most philosophers enjoy engaging with each other in argument. They want to read what another philosopher has written, consider it, and respond carefully after much deliberation. Money matters for this process only insofar as it frees your time for doing philosophy, but it barely affects how you spend this free time.

A common position along these lines is that the process matters more than the result. According to this position, even if capital-intensive AI systems could uncover valid philosophical arguments or true philosophical statements, these would be of little value. What matters is individual reflection and engagement with other philosophers. Process, not product, is the source of value for philosophy.

The type of AI-driven philosophy at which I have gestured would certainly employ a different process, but it doesn’t have to dispense with human understanding of philosophical arguments and results. Even with scaled AI systems engaging in a variety of tasks, I expect us to be able to comprehend the core philosophical moves. However, this understanding will be arrived at by engagement with the output of AI systems. That is a fundamentally different process for arriving at understanding than the one academic philosophers are used to. If one values the current process of private deliberation, the sharing of preliminary drafts, and the exchange of arguments in colloquia or conferences, then one might reject the AI-driven process.

While I feel deep attachment to the traditional way of doing philosophy, an exclusive commitment to it is easy to preserve as long as the process in question is the only one that can produce philosophical insights. So far, there is no price to pay for it. That will change when AI-driven philosophy comes into its own. It will be hard to justify an exclusive commitment to the process when further insights could be achieved if one combined labor with capital. The results will be better once new AI systems transform philosophy.

Personally, I am excited by this development. Although I very much enjoy engaging in philosophy the old-fashioned way, one reason I turned to computer science was that I wanted to solve problems. How often, if ever, philosophers are able to solve the problems their discipline raises is a matter of dispute. In this dispute, I have been (privately) on the side of those who believe that progress is possible but extremely difficult. If AI fulfils its promise, its introduction could be a decisive moment in the history of philosophy. The rate at which we solve philosophical problems might increase!

If my vision came true and if we nevertheless insisted not merely on understanding the results of AI-driven philosophy, but on not having AI drive the process at all, it would suggest that we don’t care all that much about the answers to our questions after all. I don’t think that is the case. We care about the semantics of normative language, whether wholes exist over and above their parts, and whether any of these questions can really be answered. If AI can help us find answers to these questions or show that they are unanswerable, it will be an enormous success. That is why I endorse the transformation.

The vision that excites me might be a source of fear for others. What if AI-driven philosophy proved successful at solving philosophical problems? If the approach failed, if it produced nothing of value, then it would eventually die off, not least because it requires capital that could be employed elsewhere. However, if AI-driven philosophy proved successful, it might crowd out other modes of doing philosophy that current practitioners value. Those willing (and able) to deploy capital on philosophical problems will respond more quickly and with more impressive results. It will become increasingly difficult to sideline their output. Some philosophical problems matter to us because we want to know the answer and, therefore, we will also care about the solution, regardless of how it was arrived at.

I wish I could assuage the worry that AI-driven philosophy will crowd out human-capital-centered philosophy. However, I cannot do so in good conscience, because I have observed how, in NLP, multiple approaches that I genuinely care about have been crowded out by the immense progress of deep learning. Of course, we will continue to exchange arguments and debate, if only for our own entertainment and mostly outside academic obligations. Nevertheless, I expect the discipline to be transformed. If my vision comes to pass, much of the old-fashioned process will be displaced by capital-intensive AI-driven philosophy. The dynamics in a department will shift when a colleague, instead of spending hours carefully considering a draft shared with them, lets an AI system poke holes in it. Something will be lost. Such is the nature of transformative change. If we are able to uncover and understand one more philosophical truth due to this change, I am willing to pay the price.

AI Use for This Post

The original idea was all mine. I used Claude to search the literature, copy-edit, and get feedback. Prompted to take the perspective of an analytic philosopher, Claude proved much more skeptical about the potential of AI for philosophy than I am. To ensure that no slop slipped in, changes were made by me rather than directly inserted by AI tools.

Footnotes

1. An earlier proposal on AI contributing to philosophy was made in 2023 by Clay and Ontiveros, but went largely ignored by the discipline. ↩︎
2. I focus here on the writing of philosophy and on arguing with other practitioners, not the teaching of philosophy. The latter has been more capital-intensive for a while, especially when attempts are made to scale courses.  ↩︎
3. If we insist on dragging Bourdieu into this, we can also discuss social capital and issues of prestige, but I will leave those aside for this post. ↩︎
4. I am simplifying here by assuming the case of pure armchair philosophy. Many philosophical projects have other uses for funding. For example, experimental philosophers would surely love to scale up some of their experiments. ↩︎