It’s old news by now, but OpenAI solved 372 open math problems. They released the results suddenly, and the response was equally swift and contentious. Fields Medalists put out a letter calling for attention to what they call a “severe misalignment” between mathematics and AI companies. Their central claim is that mathematics works towards collective understanding, and that more solutions alone don’t advance that goal. Soon after, OpenAI committed millions of dollars to damage control for AI in mathematics. Much has already been said about this letter, and I suggest that anyone interested read it themselves, so I will cling to just one idea from the joint statement: “We are witnessing a general threat to intellectual work.”

The following is a collection of reflections attempting to address this claim, each coming from my own experiences in my department and university, alongside trends I’ve picked up from national reports. Some of it’s intuition, some of it’s observation, and I’m sure at other institutions the state of the discourse might be quite different. Still, everywhere I look, AI-use seems to be a persistent issue. I want to consider why that is, and why it feels like we’re not doing anything about it.

The state of things

A few months ago, an MIT taskforce released a set of recommendations on AI-use in education. They spend some time detailing the various ways AI can and does interrupt traditional education models: students don’t do their homework, don’t go to office hours. They abandon study groups and professors offer fewer research assistantships. Hands-on learning is replaced by automation.

Of course, AI only replaces these things because it proves itself better at completing assignments, studying, and teaching than traditional methods (to a subjective degree). This is an underlying issue, but its consequence is worse: AI is quietly replacing the most intellectually productive parts of education. One of the key threats of AI, the report argues, is that “...students may internalize a transactional model in which assignments are outputs, teachers are evaluators, peers are optional, and knowledge (or an MIT degree) is an optimizable commodity to be acquired or produced as efficiently as possible.”

But when hasn’t it been a transactional model? Without financial aid, the per-year cost of attendance at MIT is $92,760. For students coming from a middle-class family in a high cost-of-living state, their family is fronting the full bill without the excess income to support it. Of course, students will load classes to graduate early, strategically distribute their effort to get a decent GPA, and/or focus most of their time on the internships or research necessary to get jobs or graduate positions (which, year after year, are getting more competitive).

Faculty are in similar straits. Research requires funding; funding requires proof of concept; proof of concept requires a history of significant outputs. While navigating this loop, faculty are also expected to teach and mentor the next generation of scholars and leaders. Researching well takes time and attention. Teaching well takes time and attention. It's no surprise that faculty offload parts of these tasks to AI. As the standards of output and involvement become higher, their careers depend on it.

To the authors’ credit, they recognize that this isn’t a new issue. As they see it, AI is an opportunity to finally address longstanding issues in academic integrity head on. But will that be enough? You can improve education to accommodate AI, but that doesn’t change the economic conditions academia exists in. Maybe we craft coursework that forces student engagement, but students and faculty are still in the same bind. As I said, AI being better at these tasks than humans is an underlying issue, but even more fundamentally, our metrics for “better” are more easily fulfilled by AI than by humans. This is a design problem: it suggests that these metrics don’t consistently measure intellectual achievement in the first place.¹

So what are universities doing about the challenges of AI in academia? It varies by institution, but across the board it's fair to say the response has been slow. It’s telling enough that even MIT didn’t release a report like this until 2026, let alone begin to implement the suggested changes. My own university has committed itself to the benefits of AI in research — hiring AI-related positions in earnest in 2022 and recently invested massive sums of money into AI development — yet, the actual AI-use guidelines (notably, not an enforceable policy) don’t go much further than “check your work and don’t do plagiarism, please.”

Universities are responding asymmetrically to AI. Actions are comparatively big and fast when it comes to integrating AI, but relatively slow and shallow when mitigating the harm it might cause. This, I will argue, is because AI’s harms against academia are a more efficient iteration of academia’s harms against itself. Well before AI was a capable reasoner, universities were fighting against their own ideals of expanding human knowledge.

On human knowledge

Here I’ll make the case for human-generated knowledge being distinct and not entirely replaceable by AI-generated knowledge, with a different approach than standard human exceptionalism.

I’ve always liked to look up the authors of my assigned readings. There are some intellectually proper reasons for doing this — identifying context, bias, influences in the work — but in complete honesty, I just like being able to imagine the person speaking to me through the text.

In my second semester of college, I read Doubleweaving Two-Spirit Critiques by Qwo-Li Driskell. The article points to the lack of Native American voices or considerations in the emerging area of “queer of color critiques”: critiques that analyze racism and patriarchy as a joint enterprise. (Here, “critique” means to contest or reinterpret a dominant theory or narrative, and is usually thought to emerge from the author's subjective social position). To illustrate this point, Driskell uses the image of “doubleweaving," a Cherokee basketry technique, to describe how queer and native perspectives can and should intermingle, overlap, and strengthen each other.

Driskell’s voice is loud in the piece. You can hear their frustration and indignation. They speak of Two-Spirit and indigenous folks with “we” and “us.” They leverage their identity as part of the critique: “I can speak for us; we need to be part of the conversation.”

I looked them up. A few months prior, a watchdog investigated Driskell’s family history and determined they had no ancestry in any of the tribes they claimed to be a part of. Were they lying outright, or did they actually believe it? I don’t know. As of writing, Driskell’s former university seems to have confirmed these accusations.

My professor and classmates were also shocked when I shared this information. We discussed the article in class anyway because, as my professor put it, “we shouldn’t throw the baby out with the bathwater.” Yet, I couldn’t help but feel like it wasn’t the same baby anymore. I can’t read an argument for native representation the same, knowing that the author misrepresented themself as native. The history of the author changed the text.

So what’s left when the author has no history?²

The humanities case

For the past year or so, when I tell people I’m studying anthropology, they will sometimes respond, “Well at least you won’t be replaced by AI.”

Anthropology has never been known for its lucrative career prospects, so it's a bit funny to see it framed as a relatively stable and resilient job market. (To be clear, it’s not.) There seems to be a narrative floating around that as AI completes more and more of our work, the most employable skills will be working with people and generating original ideas. This is anthropology’s domain. But why can’t it be AI’s as well?

There’s no reason to assume that AI won’t get really good at interacting with humans, or that it won’t be able to make things within the window of what we deem “original”. Not getting into the potentials of robotics, it's very plausible that AI will be able to complete virtually all human cognitive tasks. But even before we get to that point, it’s worth comparing anthropology with the kind of knowledge about human culture LLMs can already produce. Consider this riddle:

I consume a large and wide variety of texts about humans. Listen to them and interact with them. I find patterns. Scramble and regurgitate what I’ve seen into something intelligible to what I know. I capture, in new words, the way people speak and think and write. What am I?³

Obviously the actual mechanisms here are different, but it is worth considering anthropological work and complex text prediction as similar meaning-making processes. The premise of immersion in the sample data, constructing a useful model of that data, and distilling that model through new language is shared. The main difference is the issue of authorship, and it’s this issue that still defines legitimate anthropological knowledge. It’s not entirely fair to say “AI can’t produce knowledge on the human experience because it's not human” unless you similarly contend that anthropologists working in cultures other than their own can’t produce knowledge about those cultures.⁴ Still, who wrote it matters, and how honestly they represent themself also matters. Just as the extent to which Driskell’s claimed indigenous identity affected the merits of their work is contested, whether or not an AI can be a responsible author is up for debate.

So let’s say that, for now, anthropologists are safe from AI only if there’s more value in a human perspective than in the degree of output, as is probably true for most “humanistic” professions. This isn’t an optimistic prognosis. Many people have already turned to AI for entertainment, therapy, education, art, music, religious guidance, companionship, and so on. These roles have proven to be replaceable so long as we don’t care whether or not another human fills them (or the barriers for humans to fill them are perceived as too high).

Anthropology⁵ is already one of the disciplines hardest hit by growing preference for impact-driven research. Departments have been shrinking in size long before funding cuts rattled through the rest of academia. Anthropologists have slowly slid themselves under the label of “social scientist,” sometimes in an earnest preference for the scientific method, and sometimes to defend the legitimacy of their work. Anthropology (which is notoriously slow, subjective, nebulously defined, and with at-best dubious career prospects outside of academia) hasn’t fared well as institutions increasingly value high volumes of research and measurable, marketable impact.

In many ways, anthropology has already been experiencing what academia as a whole is facing in the wake of the AI boom. We’ve already seen first hand that a “human touch” is diminishing in value compared to measurable and high-volume research. And just as AI has positively impacted many areas of research, not all of these pressures on anthropology are bad. The discipline has undeniable limitations, and I, too, am in favor of the turn towards applied work. Nevertheless, these pressures reflect what academic institutions prioritize. Suddenly, we all need to reconvince ourselves and our institutions that slow, contemplative, and empirically flawed work is still valuable.

To restate, I believe anthropology (and the humanities in general) aren’t really vulnerable to AI. Rather, they are vulnerable to academic incentives for publications, impact, and efficiency, which have overshadowed a pure pursuit of human knowledge for a while now. It’s because of this that AI is able to compete with humans in institutionalized academic work at large.

Facing the future

So I agree with the Fields Medalists: we are facing a general threat to intellectual work. But what I’ve hoped to capture in these vignettes is that AI is more of an instrumentation of that threat than its origin. The parts of intellectual work that we, as humans, value most are also the most threatened by AI, precisely because they are already threatened by our institutions. It’s not that AI inherently undermines human values. We have already created systems that undermine our values, and of course AI will follow those incentives to their logical end.

I think this tension between the ideals and real incentives of academic institutions reflects prevailing attitudes towards AI within academia, which I will lump into two broad categories:

First, are the optimists. They are behind the billion-dollar investments, the hiring booms, the blooming and questionably developed AI degrees popping up across the US. They see AI skills as necessary for preparing students for the workforce and as a valuable investment into public good. AI is the future of science and business, and universities should establish themselves in that future now. This perspective helps the university to accrue prestige, secure funding, and adapt to emerging value systems post-AI.

Second, are the pessimists. They dislike AI, and many refuse to use it entirely. They are concerned about impacts on the environment, the cost of living, and the job market. Those who do engage with AI worry about its ethics, how it might make our society’s systemic failures even more difficult to avoid. They are often internal dissidents to the university’s handling of AI, but are nevertheless used as evidence of a “responsible AI policy.” They follow much of the argument I have put together in this essay: AI is a problem because it’s making the problems we already have worse.

Admittedly, there are many people in between. CS students who rely on the technology for every assignment, yet are increasingly pessimistic on the future of work. AI optimists that imagine a future of abundance, but are nevertheless anxious about cybersecurity. Many people who don’t know and don’t care.⁶ In any case, dominant narratives in universities don’t encompass AI Safety or existential risk. It’s not that I don’t hear them discussed. Since the explosion of public awareness of existential risk, I’ve overheard classroom and gym conversations toeing the line between humor and anxiety: “have you heard how OpenAIs gonna kill all of us?” Still, most public-facing voices dismiss existential risk as alarmist, distracting, or (most commonly) don’t discuss it at all.

There are probably several contributing factors to this gap in the discourse, but I will focus on one: the narrative of existential risk is emotionally outcompeted. The university is already facing an “existential risk” from AI: before it threatens the existence of humanity, it is threatening the existence of the university. As I have argued in this post, the cracks in the post-AI university are foundational. For the two camps described above, we see that universities and academics are already faced with the choice to respond to AI or become irrelevant. Adapt or die. It’s not just people’s jobs that are on the line, but the very ideals they have built their lives and identities around. Unfortunately, I think that a hypothetical AI-driven doomsday (which is already burned by skepticism towards the concept of “doomsday”) is emotionally overshadowed by the very tangible benefits and injustices appearing right before people’s eyes.

But there’s still a chance to leverage this into something useful. Pessimism about the future of AI is reflected in pessimism about humanity. Walking out of my machine-learning class, my professor stated bluntly that we will never have the AI utopia because “humans are evil. We won’t let it happen.” On the other hand, many of the people optimistic about AI (including those mentioned above) are the same ones that already have faith in our existing political and economic systems. Popular attitudes towards AI and the ways it’s used emerge from existing narratives around work, truth, value, etc. If the goal is to mobilize towards AI regulation, it seems we need to understand how a narrative of existential risk fits against preexisting understandings of AI, extinction, authoritarianism, and other items in the list of AI-caused bad endings.

I have presented universities as a case where the negative impacts of AI aren’t because of AI, but because of institutional problems that already existed. If we take this as a smaller version of AI’s societal impacts as a whole, it seems that if we secure one of the good endings, it will be because of what we built outside of AI as well.

TL;DR: I research people on the internet, and this blog is one way that I talk to people on the internet. Comment as a way to take part in this research!

Why a research blog?

I have three main goals for this blog:

1. Explore internet-based methods for anthropology research
2. Explore participatory research methods
3. Write more, more often

Anthropologists study all things human, but I’m particularly interested in digital life, technology, and the futures we build with it. There’s a lot of reasons to love the discipline. It’s nuanced, creative, personal, and has endless topics of study. Anthropology catches subtleties and complications that other disciplines often miss.

But there’s at least one caveat: anthropology is very, very slow. Quality anthropological work often takes years of immersion in a given field in addition to the standard literature review and write-up period. With as fast as technology moves and as quickly as ideas can get around on the internet, my work—no matter how true to a specific period of time—will always be a few steps behind. By bringing the research, analysis, and distribution of my work online, I hope my projects can stay true and relevant to the people I study.

I’m starting this blog in conjunction with a project on the rationality community. Rationalists use blogs and personal websites, among other things, to get their ideas into the world and work through them with each other. I’m taking a leaf out of their book: if this community can develop influential ideas through blog posts, why can’t I?

This blog takes the standard anthropological method of participant observation onto the internet. My research wants to understand how ideas form on the internet, so I’m sharing my ideas on the internet. Part of that means embracing the forms of feedback, commentary, and circulation common online. Like how rationalist ideas have been traded around and transformed over the years, I want to see what it means to give my own research that collaborative treatment. It seems only fair that as I participate in what others are doing, I give them the opportunity to participate with me.

As is probably already clear, the tone of this blog is more casual and less polished than a traditional research paper. Unlike a paper, a blog is a living document, a work in progress. As the ancient saying goes: it’s not a bug, it’s a feature.

Writing (or rather, “creating”) is a core part of social-anthropological research, and not just something to save for the very end of a project. Writing forces you to think deeply and bring your ideas to the surface. You can lay them out before you, pick them apart and critique them, and show them to others so they can do the same. These are valuable ends in their own right, whether or not the writing is very good or “publishable.” Writing for a blog reminds me that my work doesn’t have to be perfect to be worth putting into the world.

To put it short: I want to write more, more often, and this blog is a means to that end.

What is participant observation?

Participant observation is a method used in qualitative research to get a closer, more nuanced picture of the population being studied. The researcher is an observer—taking notes, conducting interviews, analyzing what they see—but also a participant in the community being studied. For example, if you wanted to understand a particular religious group, you could start attending church services, community events, make friends, and so on.

What I find most interesting here is that the researcher becomes a part of the study population. The method goes beyond “write down what you see and analyze” and asks the researcher to consider their own internal state. How has this community responded to me? How have I responded to it? How do I understand things after spending time with these people and their ideas? Analysis is also an act of self-reflection; field notes are also a diary.

One of my favorite examples of this kind of work is Shapeshifters: Black Girls and the Choreographies of Citizenship by Aimee Cox. The book comes from 8 years working at Fresh Start, a transitional homeless shelter for Black girls in Detroit, of which Cox was director for a time. Her storytelling is incredibly vivid. You can smell the bleach-cleaned bathrooms, the exhaust of passing cars on Janice’s front porch. You get an intimate picture of the lives of the girls at Fresh Start— insomuch as each of the girls shared their lives and stories with Cox. Her relationships with these girls and other staff at Fresh Start undeniably colors her analysis. By the end of the book, you get a sense not just of how these girls navigate homelessness, but of who Cox is and how her work fits into this broader picture of race and class in Detroit.

This kind of work has its trade-offs. You can’t make many claims of objectivity. There’s always the risk of over-generalizing off of a small sample. The outputs are often more literary than scientific, and take just as much earnest reflection to interpret as to produce. Yet, there’s still so much participant observation—and a corresponding ethnographic text—can capture that other methods can’t.

Think of your favorite novel. Why can a summary never do it justice? You can read a thorough list of every plot point and thematic undertone, and, somehow, still miss the feeling of the story. A good story will change how you think; it will teach you something about yourself; it will feel different to every person who reads it. Participant observation takes advantage of that subjective, emotional experience and allows the researcher to capture the personal, ineffable parts of living through something. In the study of human lives and cultures, I think those parts are very important.

Where do you come in?

Participant observation needs someone to participate with. This blog is for my research, but it’s also for you. You may be someone I’ve worked with for my research, someone who came from another blog, someone who just stumbled upon this website, or someone professionally interested in my work. In any case, “you” and I are part of the same digital world, and no one owns exclusive rights to ask or answer questions about what we do here. I may ask questions here, present ideas and theories, or share some insights from my work so far, but they are also yours to think about, to write about, and to discuss in your own circles. Of course, I would love it if you kept me in the loop! I’ve put a comment box at the end of these blog posts so the discussion can start here. Unless otherwise specified, I will use comments as: 

• Feedback on ideas shared on this website
• Discussion/interpretation of relevant concepts
• Direction for future/continued research

And I will not:

• Directly quote comments as primary sources
• Scrape comments for text analysis

However, this blog is ultimately a personal project and is not run in conjunction with an IRB. Please take your normal precautions when sharing personal information on the internet. When I cross-post another site, I’ll usually put an option to “opt-out” of being included in my research so discussion can continue as usual. If you want to say something, but don’t want your comments to be used for the purposes above, feel free to comment on a cross-post!

If you have any questions about my research or this blog, don’t hesitate to reach out.

(And just as a disclaimer, this site was designed by me but built by ChatGPT. The fish is 100% my work, though.)