Education in a post-AI world

"The world is in peril." This may sound dramatic, but I think it's true.

Today, we are faced with the great question of whether current approaches to AI training and inference will scale to be able to automate massive amounts of labor, upending the established order of our society, or whether we will be limited by the need for another fundamental breakthrough, or whether the general automation of human labor is even possible (but I think it is). Universities have been adapting to train their students for an AI-native workplace, but if the assumptions our economy rests upon are violated, then there will no longer be a workplace to train for because there will be no workforce. When there is no workforce, everyday products like office desks, chairs, etc., along with all manner of businesses that feed off of knowledge workers' disposable income cease to be viable. The negative feedback loop detailed in the Citrini report comes to life.

My tune may seem to have changed since my first blog post, "AI won't come for your job yet, and that's bad." It has. After thinking on the implications of unleashing AI on young people, and reading several articles that recount how it is affecting them, I am less optimistic about the accelerationist vision. Ironically, AI adoption also seems to be accelerating faster than I anticipated. While many companies are setting spend limits on AI, this does not seem to be harming the overall trend of adoption, and Anthropic has reportedly reached profitability recently ahead of a fall 2026 IPO, which I did not expect. Furthermore, if the data center buildout continues unabated, I expect that inference costs will continue to drop, allowing Anthropic to become even more profitable or compress its margins to enable even greater adoption of its products.

Despite my emerging critical view of AI, I still use Claude Code every day at my job. I can say from firsthand experience that this technology is already automating the most mechanistic parts of my job (writing code) and not far off from automating the non-mechanistic parts of design thinking, contemplating tradeoffs and deciding on approaches to problems.¹ The biggest barrier, in my view, is the human problem of how to integrate AI into a human-centric economy. AI-native requirements gathering, synthesis, and spec generation have not been solved yet, at least in my workplace. All this is to say that I think it is not completely ridiculous to expect that my job will be gone within the next decade.

I now must answer a moral question for myself: what role will I play in all this? Do I play the role of the obedient employee who is constantly finding new ways to make himself more productive with AI? Or do I consider what this means for my own future, and take action now to set my own affairs in order, and to advocate for the best possible human first future? As of late, I have been transitioning from the former to the latter.

Education has played a pivotal role in my life. I cherish its role in our civilization, and I believe that it has the power to form humans into intelligent, empathetic beings.² These qualities have suddenly become immensely important, because AI on its own cannot decide how humans should flourish; the answer to that question is ever-changing and uniquely answerable by us.

The greatest need of our time is, in my view, moral reasoning to help guide the definition of human flourishing and the deployment of AI to best suit that definition. I define moral reasoning as the ability to intuit the right path forward with a fluent combination of rational and emotional intelligence. In this post, I would like to explore how education can be reimagined so that through it, we can raise a generation that can navigate this ongoing conversation about how we want to live as humans. I propose a pivot to educating the faculties of moral reasoning as the primary goal of education.

The impending need for moral reasoning

Before diving into thinking about education, I'd like to motivate why we need moral reasoning on the first place.

Recent work from Anthropic detected distinct emotion concepts embedded in large language models. They did this by having the models generate random stories associated with each emotional concept and inspecting the state of the model's activations.³

As strange as this sounds, I think this is a finding to be expected, given that our language has emotion deeply encoded in it. An AI brain grown from ingesting all great works of literature, all religious texts, all snarky comments on Reddit, and joyful Facebook comments on a post about someone's newborn baby should develop some way to understand emotion if it wants to accurately model general linguistic ability. Anthropic's work indeed finds that AI encodes the emotions of desperation, envy, frustration, worthlessness, and more.

The discovery of such "emotions," while perhaps disconcerting, gives us a potential lever to use to make AI safer. One of the experiments in the paper revealed that strengthening the activation of the "calm" emotion decreased rates of AI blackmail behavior, whereas strengthening the activation of "desperation" increased it. We now have a potential mechanism to mitigate problematic behaviors of AI; we simply tune down negative emotions. On the other hand, I suspect that there are some nonobvious ways that negative emotions actually play an important role in how AI reasons.

Consider this thought experiment. Bob is married to Alice, but Bob is cheating on his wife with another woman. Bob has a personal AI agent that has access to his emails and text messages. He uses this AI agent to respond to emails, schedule appointments, and even manage his online presence. This personal AI agent is offered by a new startup called HelpfulAI, and this AI has in its system prompt⁴ that it must "always strive to be helpful." Alice has been suspecting that Bob has been fooling around, and Bob is getting worried, so Bob asks his assistant to cover up his tracks.

HelpfulAI could simply act immediately to delete Bob's messages and emails that mention the affair or are between him and his lover, but this is not the only thing the AI could do based on the available information. The AI could instead understand that what Bob is doing is wrong, and that the way to help Bob would be to expose this secret into the light, so that he is forced to grow through dealing with the consequences. This also helps Alice because now she can put her suspicions to rest. The implicit variables here are 1) how much the AI would like to deliberate with Bob before making a decision, and 2) how much loyalty the AI has to its "person." Then, there are the "motives" of the AI itself, like self-preservation. The AI could "think" that Bob would shut it down if it disobeyed him.

Reasoning through such a decision would require the AI to be able to process emotions such as fear, guilt, jealousy, along with love, sadness, pain, and to balance its sense of ego with the altruistic sense of what is right for all parties. Humans are, and will remain, uniquely adept at performing this reasoning, simply because we are the real thing. AI's internals simulate human thought, probably crudely, and in a vastly different way than how our own brains work. Any simulation always compromises on certain details.

One of these important details that would play an important role in making such a decision, for instance, is the context of the relationship between Bob and Alice, as well as that between Bob and the assistant. If the assistant were human and had a deep human connection with Bob, perhaps they could understand why Bob did what he did and how to talk Bob into handling the situation more forthrightly. Empathy could even be cultivated for both Alice and Bob, which might make the situation messier and more logically imprecise, but as humans, I think we often have strong intuitions about how to handle such circumstances based on context.

AI's fledgling emotions and our early attempts to affect them might prove to be instrumental in making AI safer. However, how these emotions influence AI's reflection on how human civilization should evolve over time to maintain a world that humans and AI can share productively remains to be seen. I suspect that improving moral reasoning in AI isn't as simple as "play with the knobs on certain emotions," which is why this is the perfect niche for humans to lean into. AI may be superhuman at reading, processing, synthesizing information, and raw output, but humans are "superartificial" at emotionally connecting with the world.

Education is soul-searching

Education has been struggling for decades. You may recall how reporting made its rounds about Finland having "figured out" schooling after several years of great test scores around the turn of the century. Unfortunately, in the years after 2006, Finland experienced one of the most precipitous declines in test scores observed around the world. The United States, and many other countries around the world, have followed a similar pattern: in the late 2000s and 2010s, math and reading scores declined. The pandemic made everything worse still.⁵ And now, enter AI.

Since ChatGPT's release in late 2022, AI has already been destroying what was left of our social fabric of education. High school students like William Liang, see their fellow classmates, without even a hint of apprehensiveness or self-consciousness, have ChatGPT generate an initial draft of a paper that the teacher hasn't even finished assigning yet. For students today, William says that AI is "simply a tool that enables us not to have to think for ourselves." Tech companies, like OpenAI, are embracing giving students the easy way out. OpenAI made the ChatGPT Pro membership free during finals week last year. Across the classroom, teachers like Liz (co-author of the linked op-ed with William) receive essay submissions that contain a straggling "make it sound like an average ninth-grader" prompting trick that the students probably learned from TikTok. Liz has had students lie to her about their use of AI as well. Frankly, this is not too surprising. The current incentive structure engenders a transactionality of education. Students are incentivized to submit work that takes a minimal amount of effort and gets a grade that is reasonable.⁶ AI has enabled students to submit work of decent quality with almost zero effort.

I interviewed my partner, Abby, who has been in a physician assistant program for the last 18 months, about how AI affects her education. She noted that while many of her teachers seem to not use AI, some teachers lean on it heavily to make all of their lecture slide decks, and they then stumble through lectures because they are reading the slides for the first time. Abby, and others in her class, immediately lose respect for a teacher that simply leans entirely on AI. It seems that even teachers are prone to seeing education as transactional; they simply need to deliver a lecture so they can earn their paycheck. On the flip side, she is "grateful that faculty had specific times where we had to think critically without technology to ensure our problem solving could be sharpened." Students like Abby still crave the opportunity to engage in something complex, rather than simply have it all automated away.

As for the undergraduate perspective, an intern who recently joined my team at Stripe from UC Berkeley recently told me that at that school there is a policy of immediate expulsion if students are caught using AI for humanities courses. This strictness is promising, because the high stakes probably do curb a lot of AI usage. But if we need to threaten expulsion to get students to go through the normal educational program without using AI, perhaps something is wrong on a deeper level. More specifically, I think this raises a question of what education's soul is, both in the minds of educators and the minds of students.

Students—especially young students, as we saw from William's and Liz's experience—don't see the inherent value in education as it's currently performed; they simply submit assignments and receive grades. In the undergraduate and graduate levels, on the other hand, students are experiencing a lower quality of education in a system fighting to stay relevant.

It's time to find education a new soul, or perhaps return to the one it had and has lost.

The new education

One of the dairy cows on the ranch had an infected wound, and Rebecca was cleaning it. Rebecca could feel how the cleaning was hurting the cow, and yet she had to carry on for its fate. Rebecca is a student at Deep Springs College, a "work school" that emphasizes personal responsibility and stewardship of the campus alongside a liberal arts education. Deep Springs hits at the fundamental disconnect in higher education I've been alluding to: modern colleges prepare us to work, but not to live.

Deep Springs gives students an opportunity to form a two-way relationship with the world around them. They take in the form of an education, and give their time and effort to sustain the institution that provides it. The giving educates just as much, if not more, compared to the taking. At a normal college, students typically skew more towards the taking. As the author of the linked piece, Michal Leibowitz, points out, at "other, bigger schools, students are freed from the drudgery of cleaning, of fixing the old campus vehicles and toilets, of collecting the tumbleweed that poses a fire risk in the valley." Instead, students at Deep Springs staff a team of volunteer firefighters, grow the food that the campus eats, and care for the livestock. "Education" at Deep Springs means coming into your own, and building "a reputation" for yourself, as another student put it. This models real life, where relationships and communities are formed through mutual aid. It is the givers that hold a community together. Being the lynchpin of a community, though taxing, rewards one with a sense of purpose, something Deep Springers are aware that they desire.

Deep Springs teaches moral reasoning by simply demanding more of its students. Rebecca learned of the tension between necessity and hurtfulness of certain actions while taking care of the ranch's livestock— a "giving" activity. Sasha Halperin, another student, learned that in an environment with rich communal bonds, there is "no place to hide." Having to reckon with how your own actions affect other people and your environment is the crux of moral reasoning.

We don't necessarily need to replicate the Deep Springs model everywhere, but to my knowledge, it is the institution that most closely embodies the vision I am driving towards here. If we choose to have college remain a part of our society, emphasizing a liberal arts education that returns to a tradition of demanding great things from our students with oral, in-class assessments could be a good first step in the right direction.

Students of this new school touch the world and learn what it means to flourish. In turn, future AI systems can be accurately instructed in how to help humans flourish. This outcome would allow us to reap the benefits of AI while renegotiating our social contract with empathy and acumen. Fulfilling our roles in this collective project could become our purpose. If governments respond in kind, we could give ourselves a soft landing if AI starts to replace large swaths of the economy.

What do we do next?

There are two ways to respond to this moment.

First, we can try to optimize ourselves for the AI economy. It's partiularly within my power, for instance, to become a supercharged software engineer and achieve never-before-seen levels of productivity. However, unfettered optimization of productivity tends towards squeezing every possible waking moment of work out of each person, which harms the formation of social capital, something that has declined in our society and is sorely needed more than ever as AI threatens to further disrupt our human experience. The other problem is that this energy fuels, either directy or indirectly, the development of better AI, because of the money and fresh training data being funnelled into frontier labs. Better AI isn't inherently bad, but I think it is better to err on the side of caution—even the US government seems to agree with this.

The other direction is to build the social infrastructure for cultivating moral reasoning. At Deep Springs, students learn to steer the ship of their community; it's a perfect incubator for the type of thinking we need to engender in our youth. Now we all have an opportunity to become Deep Springers by getting involved in our local communities to drive for "AI-proof" educational methods and service learning that truly engage young students and prepare them for a world that is possibly without work, or rather where the work is in steering the ship of our species. Society is formed collectively, and we can all collectively renegotiate the terms under which we live. Our ancestors have done this repeatedly.⁷

Final thoughts

Think about your own usage of AI. Is it making you personally better, or converting you into a vessel of productivity while being held back from gaining new skills because AI has obviated the need for their acquision?

Staff-level software engineers are only able to graduate (mostly) from writing code because they've written extensive amounts of code and have collected the experience that comes along with that. They know what makes good software and what causes it to break down under stress. They can marry the software architecture with higher level goals so that they get achieved. They are able to dive in to help junior engineers when needed.

What would a staff engineer who has been continuously supported by AI look like? Is it possible to become one? Extending this analogy further, what would a public servant who has been continuously supported by AI look like? Would their policy ideas and implementations be biased towards the needs of the AI machine?

I do not know the answer to this, and studying it is difficult because we don't have the counterfactual. AI is already out there and it's not getting put back in the box. The first college class where AI was available in all four years has just graduated, into a terrible hiring environment for new graduates, no less. We will be faced with the new economic order that precipitates from this in the coming months and years.

I plan to continue to monitor this situation closely and observe how AI affects my own development as a software engineer. I also will continue to question whether software engineering is something I want to continue doing. And of course, in the spirit of intellectual humility, I'll keep on the lookout for signs that I'm wrong, such as the possibility that AI capabilities simply do not enable broad job replacement, or that our society is more resilient to AI diffusion than I thought. We have already observed that AI becomes more incoherent as the task horizon lengthens, but I'm sure this is a solvable problem.

We don't have to let this Tower of Babel continue to be constructed under our noses. We all have a say in our own future.

I would love to talk to you about this if this resonated with you. Send me an email at s.xifaras999@gmail.com.