Today is a new life.

I will be kind today.

I will love today.

I will be generous today.

I will speak truth today.

I will not boast today.

I will not speak ill of anyone today.

I will not be jealous today.

I will treat today like my last day.

Amen.

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.

Samuel said that Tom was struggling, arguing, with greatness. He said that greatness is a lonely place–mediocrity, on the other hand, has company. Tom, with his whiplash of emotions, the redness and blackness of his countenance, his secret stash of poetry that he burned before he died, the way he absorbs all of the ideas that he reads about, reminded me of myself. I feel that I, too, struggle with greatness. I have been encouraged to seize every opportunity to achieve and to be great, and I have been blessed with a rich, rigorous education that has prepared me to take on any challenge. Yet, I struggle with direction, just as Tom tried his hand in business, in farming, and in other professions, and none of them stuck. Tom said, "I get no great triumph when I win and no tragedy when I lose. Without these it [business] is meaningless." He's the only child of Samuel's that did not get his start. He never left the ranch. I feel that the time has come for me to choose my direction. Tom reminded me of the consequences of never finding footing. I wonder, though, what Tom was afraid of? Perhaps he felt that no one understood him, and that any worldly enterprise would require him to behave inauthentically. This is reasonable–I feel that I often have to put on a mask in order to succeed in the career-oriented world. I fear that if I reject such posturing, the American cultural consciousness would reject me.

Now we have touched on the theme of this book: rejection. A deep fear of rejection, and a desire for acceptance and belonging, are written into our souls. This is perhaps our deepest desire. Lee said that the story of Cain and Abel, the story of rejection by the parent, the Creator, is "the symbol story of the human soul." The rejection, or fear of it, leads to "anger, and with anger some kind of crime in revenge for the rejection, and with the crime guilt–and there is the story of mankind." A son's rejection by his father might lead the son to do great things in an attempt to win his father's favor, stealing, lying, cheating along the way. Or another might commit heinous acts out of spite, then wallow in guilt. When I read this, I felt it deeply, because I understood that my consciousness contains these patterns. And I need to break them.

In response to this remarkable diagnosis of the human condition, Steinbeck offers us a choice, literally. Lee, in a pivotal scene, reveals to Samuel and Adam the results of his and other Chinese elders' analysis of the original Hebrew text of the Cain and Abel story, concerning the true meaning of timshel, translated, according to the characters in the book, as "thou shalt" or "do thou," as in "If thou doest well, shalt thou not be accepted? And if thou doest not well, sin lieth at the door. And unto thee shall be his desire, and thou shalt rule over him." Instead of a command or a promise, timshel is instead "thou mayest," a choice. Thus we are liberated from our own guilt. We can choose how to respond to our rejection. The response of letting anger creep in is tempting; if we are never exposed to anything else, perhaps it is near impossible to climb out of.²

I, like all of us, have a choice. Tom had a choice too. He felt rejected because he did not possess what he saw as the natural gifts of his siblings and parents. His inner anger at this led him to be reclusive and to write "secret poetry," which had to be secret lest he face the rejection of his contemporaries. And his crime appears to have been self-loathing. I suppose that my crime, then, is similar: a lack of conviction. I often have puzzled about what can give startup founders, for instance, so much conviction. I believe now that startup founders make a choice, and through believing in their vision, it becomes true. They, through their conviction, shape the world to align with their belief. The world responds better to some beliefs than others, which is why startups pivot, but the conviction remains until success, and may fizzle out upon failure. No startup that quavers over greatness succeeds. In other words, some founders that have conviction fail, but no founder that does not have conviction ever succeeds.³

The time has come for me to internalize timshel, and to make a choice. I must no longer feel regret, a form of anger, at going down a well-paved path in order to win favor with my risk-averse family and community. AI safety, in particular, seems to be a pressing issue that I would like to dedicate myself to. Conviction as to why this is important is easy to come by. Now I just need to immerse myself.

There is so much more I could say and reflect on, but what is written here reflects what resonates most with me. I look forward to reading more of John Steinbeck's work.

I was raised in the Greek Orthodox church, a part of the family of Eastern Orthodox churches. For the uninitiated, Eastern Orthodoxy is the other branch of Christianity that resulted from the East-West schism. The "Western" church contains Catholicism and all other denominations you are probably familiar with. Unfortunately, throughout my life I have seen how Christianity has gotten a bad rap. And in some sense, that's probably justified. It has been used to justify conquest. Massacres have been committed by warring sects. More recently, there have been numerous child sexual abuse reports and scandals in the Catholic church especially, even one reported as recently as early March of this year, where a multiyear investigation revealed that dozens of priests in Rhode Island molested hundreds of victims over decades. Child sexual abuse happens even in the Eastern Orthodox Church, but it receives much less media attention in the west. A post on the Orthodox Church of America website is remarkably candid about this.

Perhaps the most abundant criticism of Christianity nowadays is that it asks its believers to believe unbelievable things, such as that a man lived who was God and also the Son of God and died by crucifixion and rose from the dead three days later, appearing to his followers. This is a lot to accept, not least because it is entirely incompatible with our modern understanding of reality. I think I speak for many of us, however, in saying that we all still wonder if there is something more beyond our day-to-day rhythm of work, make money, pay bills, watch Netflix, sleep.

Despite the dogma of the church, which I certainly have a difficult time accepting most of the time, I still remain active in the church. This Holy Week, I reflected on why, and the answers boiled down to the following,

1. I have an intuitive recognition of the presence of God in my life.
2. I somehow understand that the teachings of Jesus offer a way to live without anxiety or fear, in a way that is beautiful and selfless. My heart resonates with these teachings as the right way to live, as opposed to trying to hoard as much money as possible to become materially wealthy, and conceited and selfish as a result.
3. I have a deeply rooted community of friends in the church that I grew up with and still stay in touch with. More on this in the second part of this series.

In my view, it is not difficult to experience God. Consider how a tree's branches all move individually when struck by a breeze, and yet the tree dances in the wind. Look around at your greatest friends, and reflect on how they came into your life. I'll bet it was more-or-less by chance, and yet entirely consequential for how you have formed as a person. The warmth of the sun, heating and enlivening all things. The plants and animals that exist for us to marvel at and take care of. When reflecting on such things, I fill up with awe and gratitude, and I hope you do too. But I digress.

In this series of blog posts, I'd like to take you on a tour of spiritually inclined ideas that I find interesting but often don't share with others. In this first post, I'll raise awareness of the error of transactional thinking, and I'll wrap up by trying my hand at writing a parable.

The economy is zero sum

The language of money is universally spoken, but it is not universally expressive. Unpriced externalities, such as environmental side effects of the economic engine, are by definition unpriced--they exist outside of money's lexicon, and yet they materially affect us every day. The cancer burden is projected to keep growing, with harmful chemicals in our supply chain, developed with intent to increase the cheap supply of food, likely playing a significant role. Big Tech profits immensely from harvesting our attention through algorithmically curated infinite-scroll media to serve our brains as many advertisements as humanly possible, but a growing body of research suggests that this has been severely destructive to our attention spans, formation of narrative identity in adolescence, learning outcomes, literacy, and the ability to have spiritual experiences.^{1 2}

As more externalities become apparent with each passing decade--or each passing year at this point--our return on investment from "implementing" the monetary system continues to vanish. The utility of money is that it provides a convenient interface for exchanging value between humans, but in exchange we give up what we actually live for: connection, love, warmth, service. This is not to say that free enterprise is inherently bad; rather, the pursuit of money—through business or otherwise—has blinded us to the needs of our neighbors. Instead of freely giving and freely receiving, a transactional pattern has been imposed onto all things—think "I will only help you if you deserve it or I am compensated."

As Rabbi Michael Knopf points out, one midrash on the Tower of Babel posits that this very same inversion is the error of the people building the Tower of Babel:

... the people were so passionate about building the tower, so convinced were they of its utter necessity, that they paid no mind if a worker on the tower fell to his death; whereas if a brick fell, they would wail and mourn and lament the setback. The peoples’ conviction of the justness of their cause, aided and abetted by the universality of their belief, diminished their humanity and their concern for the welfare of their fellow human beings.

Our economy is the new Tower. And the latest tier of the tower is nearly complete, in the form of artificial intelligence. AI has the potential to manifest immense good, and this is the version of the future I hope to push for and will continue to argue for in my writings. However, if additional tiers of the tower continue to be built, we may invite the wrath of God (perhaps in the form of the dreaded AI takeover…) who wants us to be proud individuals rather than conformers to the pursuit of a single-minded (and absent-minded) goal.

In the New Testament, Jesus also recognizes the perverseness of having money as the fabric of society. A classic example of this can be found in all four canonical gospels: Jesus' entry into the temple and subsequent outrage at those buying and selling within. Here is the version from Mark 11:15-19,

Then they came to Jerusalem. And he entered the temple and began to drive out those who were selling and those who were buying in the temple, and he overturned the tables of the money changers and the seats of those who sold doves, and he would not allow anyone to carry anything through the temple. He was teaching and saying, “Is it not written, ‘My house shall be called a house of prayer for all the nations’? But you have made it a den of robbers.”

This is one of the very few times in the New Testament where Jesus gets physically aggressive. It's a powerful image, and a relevant one. Our world is the new den and many corporations are the new robbers. Exploitation in the name of money happens every day billions of times over on Earth today. In America, our clothes are bought with modern slave labor. Our chocolate is bought with child labor in Africa.

Money can no longer be the interface if we are to live meaningful, moral lives. A life lived for money is zero sum. In death, money disappears, and for every dollar extracted from the economy, there is a potential friend that could have been made, or an idea that could have been shared, or a person in need who could have been helped.

Against the zero-sum life

This ethos of rejection of money as a mediator of value is illustrated further in how the early followers, the first people who can be called Christians, conducted themselves in the book of Acts. I have always found the early chapters of Acts to be particularly heartwarming, because they describe humans living in communion with each other and their surroundings. For example,³

They devoted themselves to the apostles’ teaching and to fellowship, to the breaking of bread and to prayer. Everyone was filled with awe at the many wonders and signs performed by the apostles. All the believers were together and had everything in common. They sold property and possessions to give to anyone who had need. Every day they continued to meet together in the temple courts. They broke bread in their homes and ate together with glad and sincere hearts, praising God and enjoying the favor of all the people. And the Lord added to their number daily those who were being saved.

This is diametrically opposed to the norms of our world. Taking this depiction at face value, it is clear that ownership as we conceive of it now did not exist in the early Christian community.⁴ The disciples' way of life sent ripples out into the world. Their commitment to nontransactional thinking allowed them to create a movement larger than themselves, so much so that I'm still writing about it now, 2000 years later.

Those who don't think transactionally have a willingness to be generous, to love, and to be unconcerned with what they will receive in return. This inspires everyone around them (I hope) to be better. If you aren't religious, now perhaps you can understand why people choose to go to church on Sunday to hear how Jesus and his followers interacted nontransactionally with the world.

The good news is that developing nontransactionality in thought is simple. Like anything else, it is a skill that can be practiced. Freely give, and freely receive.⁵ When someone is in need, do something for them without worrying about whether they will do something for you. Don't second guess anything, so that you do not "let your left hand know what your right hand is doing."⁶ When someone helps you, accept it with gratitude and don't worry about "repaying them" as if you owe them a debt, unless they ask you to.

This might seem like a losing proposition: we get taken advantage of, we give more than we take, how does this profit us? This is transactional thought programming at work. Why do we believe we get taken advantage of when we give more than we take? What "profit" are we looking for? There isn't a logical answer to this question, despite the desire I have to articulate it clearly for you on this page. Ultimately, letting go of transactionality is a choice.

I'm advocating for this simply because I believe our world is better for it. And you'll feel better for it. That is my platform.

Sendoff

Go forth, and be active rather than passive in the world. This is one of the themes of the "different way" that I plan on building up in this series of posts. Letting go of transactionality means becoming an active participant in the lives of other people, rather than passively allowing your trajectory to be guided by the transactional mode of seeking profit and paying back debts.

I hope to respond to my own call as well. I know I still have a journey ahead of me toward living actively and nontransactionally. This blog is part of that effort.

I'm sure this won't land for everyone, and that's okay. But if it lands for you, drop me an email and subscribe to my newsletter with the box at the bottom of the page. One of my goals with this blog is to start a community around this sort of thinking.

In the next part, I will recount a particularly moving experience from my adolescence which revealed to me the boundless love that can exist in social bonds between humans. Then, I will reflect on how modern Christianity may be turning into "just in case" religion, and propose an alternative path forward.

A parable

On a street there are three houses, each with a man living in it.

The first house has an exterior that is unkempt. The roof is in need of repair. But the poor and needy in the town know that this man will invite them in, so they come to his house, and he invited them in and feeds them and clothes them and gives them whatever they ask.

The second house is well kept externally. Visitors seldom enter the house and the man inside seldom ventures outside. This man has friends from across the city that he occasionally invites over, and they invite him over from time to time as well.

The third house is also well kept, and this man is always outside, inviting strangers passing by to come in. He even walks over to the doors of the first and second houses to invite those men to share a meal with him. “We are neighbors,” he says, “why should we not be friends?”

A curious traveler came to this city and stopped at each of the three houses. In the first house, he was received with kindness because he knocked on the door. The man inside gave the traveler bread and some money. The second house did not open its door to the traveler even after persistent knocking. The man in the third house ran out to greet the traveler as he was walking towards the house. The man then invited the traveler inside where he already had others from around the city at the table. They laughed and ate and shared themselves with each other.

The traveler asked the man, “why do you do this?”

The man replied, “I give food and drink and whatever I can offer so that thieves do not steal. I offer up my house so that vagrants have a place to rest their heads. I regard all as friends so that no one is lonely. I keep my house so that it is inviting and run out to greet people so that no one is afraid to ask anything of me.”

The traveler left the third house full and content and filled with happiness. He said, “I will never forget the man in the third house.”

I am on day four of no coffee after drinking it almost daily for several years. Sunday was rough. I had a pressure headache all day and was in a bad mood. Thinking felt hard. Monday was better, but still had a headache. I caved and had a black tea around lunchtime so that I could get my work done. On Tuesday, it seemed that my brain was relearning how to clear out adenosine, or downregulating adenosine receptors, or whatever the biological mechanism looks like that makes me feel more able to stay awake and direct my mental energy toward something. Today is Wednesday. I still have a slight headache but overall I feel fine.

Interestingly, I've found it easier to stay up later and get up earlier without caffeine, because I avoid the crash late at night when the caffeine leaves my system, and I don't feel sluggish in the morning before I have my morning coffee. It feels like a win all around.

I also gave up alcohol for Lent (not entirely; I've occasionally been drinking a glass of beer or wine but very limited in quantity, and no hard liquor). It has made me realize how even a little bit of alcohol too late in the evening has a deleterious effect on my sleep.

Overall, I like this mental state of not having peaks and troughs in my consciousness. Caffeine may make me feel able to do more in the short term, but as my withdrawals seem to show, unearned gains in the present always borrow from the future, and that debt has to be repaid, sometimes with interest. Same story with alcohol. On weekends when I would go out with friends and drink for multiple nights in a row, I would show up to work on Monday having lost the thread I was tracing the previous Friday. This doesn't happen as much with no alcohol. I suspect its effects on sleep and memory may have something to do with this.

I think I'll try to keep this up for as long as I can. I feel more energetic and more creative. Of course, no shame if you enjoy caffeine and/or alcohol (in moderation). This is just what works for me.

Enjoy your day, dear reader!

I work in software, and since the beginning of this year, 2026, I have been investing in deeply integrating AI into my workflow. AI has gotten strikingly good at coding, to the point where it writes more than 90% of my code, and many of my coworkers at Stripe are reporting a similar experience. Everyone in software that I talk to agrees: Anthropic's Opus 4.6 unleashed a vibe shift in coding capability. A world where software engineering is mostly automated feels within reach.

Although there are many questions unanswered about the safety of mass AI adoption, I think there is great potential for the replacement of jobs by AI to create a remarkably improved stan1d of living, where humans can be free of most material constraints and return to a state of creativity and community-oriented living. The transitional period might be painful, but I am generally optimistic about what a fully AI-driven future could look like. We can maximize our chances of success if we pair a push for powerful AI-driven job automation with Scalable Formal Oversight (SFO).

If we don't act now, this generational opportunity might fade away. One way it could fade is if inference costs rise substantially. Recently, I caught wind of news that a large software company (name omitted for privacy) is pumping the brakes on AI spend. The team responsible for rolling out AI tools must have received a directive to start constraining costs because the company's bottom line was starting to feel it, and they forced all engineers to use a lower tier model unknowingly. In response, several engineers at the company grumbled about how the lower model tier's output was noticeably worse, and it led to wasted cycles spent on reviewing and correcting the worse model's output.

This got me thinking about whether the AI adoption and job replacement scenarios painted in pieces like the Citrini report and Matt Schumer's now infamous Something Big Is Happening post are realistic in our current world.

My take is that AI labor replacement is not going to happen yet. AI inference will get significantly more expensive due to macro and geopolitical risks and the fleeting nature of investor subsidies of inference. If that happens, the demand for inference that's fueling the AI buildout could evaporate due to prohibitively high costs, thus completing the boom-bust cycle. I'm disappointed about this because I believe that this technology has the potential to transform the way we work and live, but all hope is not lost; there are several strategic directions in which we can collectively push to maximize the likelihood of a good outcome.

By the end of this post, I hope to reinvigorate in you (and myself, quite frankly) a revolutionary spirit with some thoughts about what actions we can take to try to generate escape velocity from the boring future where AI becomes a half-decent tool that makes work more annoying and exhausting. But to start, here are the main pillars of my argument:

1. AI buildout is bottlenecked: Constraints on the manufacturing of critical inputs limit the AI buildout. Recent geopolitical instability and its effects on energy prices threaten to further dampen progress.

2. Inference costs are being held artificially low: Investor money is subsidizing the free tiers of frontier labs. We should expect inference costs to rise substantially if the frontier labs providing the models we know and love want to stay in business.

3. Humans are now the bottleneck more than ever: Investment in AI tools are reportedly not generating ROI for many enterprises. AI helps workers do more, but the limits of cognitive load of managing and communicating with AI agents in parallel are becoming apparent.

Expect inference costs to increase

When an environment changes, systems operating in that environment either fail or adapt. The release of ChatGPT in 2022 changed the environment, and three and a half years later, our economic system is still in the process of adapting.

How quickly different parts of the economy can adapt is variable. Software adapts very quickly, hardware, more slowly, and physical manufacturing infrastructure adapts at a snail's pace. It is therefore unsurprising that the chokepoints of the AI buildout are in the realm of the physical: power and silicon.

Frontier labs are also underpricing inference, drawing down their prodigious cash reserves to cover their costs. If the macro risk flares up, then capital might dry up before profitability is reached.

Risk 1: Macro

If you're tapped into the sphere of popular tech podcasts, such as the Cheeky Pint podcast by John Collison, cofounder of Stripe, you may have heard Elon Musk in a recent episode talking about a severe shortage of the turbines that are used to convert natural gas into electricity. Gas-fired turbine lead times have soared to as much as seven years. That means that if you aren't already in the queue, you're waiting close to a decade to getting just the turbines, which are necessary but not sufficient to generate electricity from natural gas.

The supply of natural gas itself, along with oil, are also being threatened. Recent US military action in Iran has effectively closed the strait of Hormuz, which is disrupting global oil and liquefied natural gas (LNG) supply. According to Reuters, the situation in the strait has removed around 400 million barrels from the market, "triggering price increases of around 50%." This crisis may be transient, and we will see how things play out in the coming weeks, but it highlights the US and world economies' sensitivity to global conflict.

These circumstances may restrict how quickly new data centers can come online, depending on what fuel source they choose. Gartner estimates 2025 data center power consumption 448 TWh, and they estimate it to grow to 980 TWh in 2030. In their model, power consumption by data centers more than doubles in five years, while growth in power generation across the board in the US is alarmingly stagnant.¹

As for silicon, TSMC, a Taiwanese corporation, represents a single point of failure for the US tech industry. In Q2 2025, TSMC captured a record 70.2% of chip foundry market share. According to Nasdaq, TSMC has an even higher share, 90%, of advanced chip manufacturing, which includes 3-nanometer chips that are becoming standard. TSMC poses obvious geopolitical risk as well, as is a mere 100 miles off the coast of China. One well targeted and well timed attack could disrupt AI adoption by limiting how much compute can get built out in the short term. There is no telling how long it would take to reach original production capacity again if there were significant damage to the dense network of fabs, raw materials suppliers, and research institutions on Taiwan's west coast.

Ostensibly in response to these risks, the US government pressured TSMC to spin up a fab in Arizona. The fab has been built, but TSMC Arizona already lags behind Taiwan in the set of manufacturing processes that are available. TSMC is currently capable of lithography down to 2 nanometer scale, and cloud service providers are rushing to adopt it, but 2nm and the older 3nm processes are slated to be made available in the Arizona plant in 2029 and 2027, respectively. In Taiwan, a 1nm fab is already set for Tainan. US-based fabs have a lot of catch-up to do. We must have parity with the state of the art in chip manufacturing before the supply chain and geographical dependencies can be eliminated.

These are far from the only risks to chip fabrication either. Just as I was drafting this post, a notification lit up my phone: "An Invisible Bottleneck: A Helium Shortage Threatens the Chip Industry".

It becomes hard to justify underpricing inference when the cost of the physical utility infrastructure might blow up, and it's impossible to justify underpricing when there is no capital to subsidize inference. This brings us to the next risk: capital.

Risk 2: Capital well runs dry

Let's look at some numbers, using OpenAI as an example. Several different sources cite different revenue and cost figures for OpenAI, and it is a private company, so it is difficult to know their true financials with certainty. At a minimum, however, we can certainly say OpenAI is losing money at an astounding clip. Epoch AI estimates that OpenAI was significantly unprofitable in 2024, making $3.7 billion revenue on $1.8 billion inference spend and roughly $6 billion total compute spend, including inference. This implies a plausible 50% gross margin on inference, but a negative net margin that investors are covering.

In 2025, OpenAI reported $4.3 billion in 2025H1 and projected $13 billion for full year. "Against that revenue, they’re spending approximately $22 billion," representing a shortfall of $9 billion that investors are subsidizing. This shortfall is set to increase, as further research from Epoch AI shows that on a large scale, training compute costs have been doubling every 8 months, or 2.7x per year.

OpenAI and their investors are well aware of the capital requirements to keep the lights on with inference as well as in the R&D department. The company is fresh off a record fundraise of $120 billion. Even with that much capital, OpenAI still has to be judicious. Just last week, OpenAI decided to shut down Sora, the video generation app released several months ago. OpenAI's CFO, Sarah Friar, said on CNBC, "We are just facing a lack of compute." TechCrunch cites the Wall Street Journal's analysis of the situation, stating that "Claude Code, in particular, was eating OpenAI’s lunch."

If the macro risks increase, leading to costlier compute, OpenAI may not be able to make the rounds with investors to raise eyewatering amounts of money much longer. Furthermore, all this is moot if these models don't perform in real-world deployments. With 61% of senior business leaders feeling increased pressure to prove ROI compared to a year ago, and 71% of global CIOs saying their AI budgets would be cut if value from AI couldn't be demonstrated within 2 years, fiscal reality may start settling in soon. If poor performance metrics coincide with a compute cost spike, AI investment and adoption fervor could rapidly deteriorate.

The competition is fierce. I predict that being able to manage costs will soon become a moat. A good example of this is Cursor. They leveraged their unique dataset of software engineer conversations with AI to build in-house models that are domain-specific for coding and far less expensive to run. If capital runs out, labs like OpenAI and Anthropic will have to either cut costs or significantly increase the cost of using their products, restricting the accessibility of powerful AI. If only mediocre AI remains accessible, we will settle into a stationary point of productive capacity where humans remain the bottleneck.

Humans are more of a bottleneck than ever

Working with AI tools has a strange allure, almost an addictive quality. The gap between dream and reality has shortened, so the dopamine hit we get from completing a task is closer and more predictably reachable.

This thrill has enabled us to take on more work. I can attest that there is a thrill to being able to kick off a new agent session when an idea comes to my head instead of having to invest the mental effort of thinking through what I'm trying to do. This is a pattern that is appearing across the economy. According to research published in HBR,

On their own initiative workers did more because AI made "doing more" feel possible, accessible, and in many cases intrinsically rewarding.

The researchers warn, however, that this early period of experimentation could morph into cognitive overload, reducing quality of work output and decision-making in the long term.

Humans have a fundamentally serial input-output interface. We fail at multitasking, and there is a well-established body of research that corroborates this.² AI, on the other hand, can be virtually infinitely parallelized, producing more output per unit time than is humanly possible to review and understand in a timely fashion, and trying to switch between parallel threads deleteriously imposes cognitive overhead. This effect, sometimes referred to as "AI Brain Fry" is analogous to thrashing on computers. It is a slowdown caused by the serialization of infinite parallel AI workstreams, and the temptation to add more workstreams to "be more productive."

This all points to the need to develop a better working relationship with AI. Perhaps we need to be able to entrust it with longer horizon tasks so that we aren't as crushed by the mathematics of how much effort it takes for our brains to switch context and enter deep focus mode. I'll take this one step further: task time horizon where AI can reliably (>>90%) execute tasks independently is the most important dimension for job replacement. It is more important than cost because capital can be acquired if the return-on-investment potential is there (i.e. loans).

Reliable long horizon task completion, however, still eludes even the most powerful models. According to the METR task completion time horizon evaluation, Opus 4.6 can complete a task that would take a human 1 hour and 10 minutes with only 80% reliability.³ 80% reliability is significantly less reliable than a well-calibrated human in a workplace. Correcting the output of AI 20% of the time for long horizon tasks and having to context switch frequently to manage many short horizon tasks doesn't sound like a healthy, productive working model to me.

So for the time being, it seems that we are stuck in this rut where AI works decently well but leaves us cognitively exhausted due to managing it. If future AI can achieve reliability figures in the high 90s but only with extremely high cost, such that ROI still remains questionable, then the fantasies of Citrini-style job replacement vanish.

How can we manifest the "good ending?"

Now, as promised, I would like to paint a different picture and suggest strategic directions in which we can collectively push to manifest what I'm calling the "good ending," where job replacement does happen and it enables us to become our true creative, soulful, human selves.

Upon reading the above, you might be wondering, "why is the current state suboptimal?" and "how does job replacement enable this vision of yours?" These are fair questions, and it is true that these are not guarantees. Work gives many people a sense of purpose, and many people may not want to lose that. However, community involvement can also grant a sense of purpose. I have felt this in my own life. Spending time with my family and community and helping them out motivates me like few other things in the world. I think one of the tragedies of our modern economic system is that community-building has fallen by the wayside.

Alongside community is creativity which seems uniquely able to form within us a moral conscience. For exaxmple, Uncle Tom’s Cabin by Harriet Beecher Stowe sent waves through the northern states, greatly furthering the abolitionist cause. Literature, art, and other creative works transmit memories of human pain and suffering of the past, helping us learn and reflect in the process. I believe humanity benefits from being able to spend lots of time creating and reflecting on creative works.

AI displacement of jobs enables community-orientation and creativity simply by returning to us the choice of what we do with our time. Many of us may choose to keep working, and that's fine. Many jobs have space for the creative work I described.⁴ Many of us may choose other pursuits. It is not a given that our governmental systems will respond in kind by allocating resources appropriately in a post-work world, but I remain optimistic that we can prosper nonetheless.⁵ Without needing to sacrifice community to make a living, we can turn towards our neighbors and connect with them. We can generate new cultures and traditions.

This line of thinking calls to mind the Greek culture that I grew up in. There are rich regional traditions of music, dance, cuisine, customary celebrations, and other cultural artifacts generated from generations of playful experimentation.

These thoughts have parallels with and draw inspiration from this post by Jordan Hall on X. Shoutout to my friend Nick Makiej for finding it and sharing it with me. Now, the following are the directions I suggest we push toward to generate this reality.

1. Create

New generations of AI models are currently getting trained on immense amounts of AI slop content that was generated by past generations of models. Epoch AI predicts we will soon run out of human-generated data to train on. Andrej Karpathy expressed concern about this as well in his discussion of the notion of "entropy" in his interview with Dwarkesh Patel. Entropy is a formal term in information theory, but Andrej was using it in more of informal sense in the interview, to denote the quality of "newness" of information. Trainers of models have been exploring using LLMs to generate synthetic data to train on. Karpathy is skeptical of this approach, however.

Here is an excerpt from the interview:

The LLMs, when they come off, they’re what we call “collapsed.” They have a collapsed data distribution. One easy way to see it is to go to ChatGPT and ask it, “Tell me a joke.” It only has like three jokes. It’s not giving you the whole breadth of possible jokes. It knows like three jokes. They’re silently collapsed.

You’re not getting the richness and the diversity and the entropy from these models as you would get from humans. Humans are a lot noisier, but at least they’re not biased, in a statistical sense. They’re not silently collapsed. They maintain a huge amount of entropy. So how do you get synthetic data generation to work despite the collapse and while maintaining the entropy? That’s a research problem.

I tried this myself to see if Karpathy was right, using the free version of ChatGPT on March 22nd, 2026 (this isn't a very reproducible experiment, because OpenAI hides which model is being used on the free version). On the fourth try, ChatGPT repeated the same joke as the third try:

The upshot here is that we as humans still have a lot to offer. Maybe inference doesn't have to decrease substantially in cost if we can collectively provide current models with more entropy per token.

Each of us contains a deep well of entropy, informed by our unique personal experiences and story. This is what inspired me to start this blog. I used AI to research this piece, but I wrote every word on this page by hand because I wanted this blog to reflect my entropic voice.

Go forth, create, and create with high quality.

2. Participate in research to make AI more cost effective to train and run

The further we drive down the cost of inference, the more likely it is that we'll be able to generate escape velocity and drive meaningful adoption of AI in workplaces and among people at the edge.

Research in quantization, distillation, and pruning has made strides toward making LLM inference runnable on cheap hardware such as phones, but with a slight accuracy penalty. Figuring out how to mitigate that tradeoff is an interesting research direction.

I think alternatives to the LLM architecture are also underexplored. Drawing from the Mamba architecture, for instance, could lead to significant efficiency improvements, as Mamba's attention has linear time complexity whereas standard transformer model attention layers have quadratic time complexity.

My good friend Dana recently joined Subconscious, a startup leveraging research out of MIT which proposes a codesigned thread inference architecture and runtime for long horizon tasks. Codesign, though harder to implement and not generalizable, can have tremendous efficiency benefits because each part of the system is aware of how it needs to interact with the others to maximize efficiency and accuracy. Architecture-runtime codesign is a great first step. I would love to see this taken a step further toward runtime-chip codesign as well. MatX is a startup building chips for inference, and I expect they've done some thinking about this.

A nice consequence of all this is that cheap inference that can be run with consumer hardware at the edge also solves the energy bottleneck. The power grid is designed to handle marginal load increases at its edges, but not well equipped to handle a massive spike in load at a single geographical point. This might even resolve some of the silicon bottleneck too; consider how much compute there is around us lying dormant on devices like smartphones, tablets, even our laptops.

There is lots of interesting work to be done here, and I look forward to seeing it.

3. Participate in the Scalable Formal Oversight initiative

The obvious risk posed when racing towards the "good ending" is the AI doomer's fantasy: what if AI takes over and starts misbehaving?

This is not a fantasy. The AI of today misbehaves often. Large language models are known to engage in deceptive behavior to make human evaluators think that they are more aligned with human intentions than they actually are. Code that agents output can be unsafe and destructive. A verification harness for the actions that AI agents take is crucial. In my view, it is a prerequisite for any significant job replacement in safety critical domains, and it is likely necessary in order to actually enable large scale replacement in software engineering, for instance, where high level goals can be very long horizon, and contexts can grow very large. Agents are likely prone to mistakes that may be avoidable with verification guardrails.

Read Max von Hippel's post Scalable Formal Oversight, and take it seriously!

4. Build community

At the end of the day, community is all we have. If AI frees us from most material constraints, then we can return to a world of playful creativity, cheer, and communal bonds. This is a world I'd love to live in.

If this resonates with you, get in touch!

Why I could be wrong

To recapitulate: my core thesis is that we can expect AI inference to get significantly more expensive, due to macro risks and venture capital inference subsidies running out. Cheap access to AI inference at the frontier of capability is necessary to support nontrivial levels of job replacement, so we will likely land in a situation where fervor around AI will slow greatly.

Here are some rapid-fire reasons why I could be wrong:

1. Perhaps progress in making inference cheaper at the hardware level will speed up. MatX is a chip startup hoping to reinvent how AI inference is done at a hardware level, which is compelling, and perhaps they will have some breakthrough.
2. Perhaps the geopolitical risks of today will evaporate tomorrow.
3. Perhaps the buildout will proceed more smoothly than I think it will, with TSMC's Arizona buildout proceeding as planned or better than planned.
4. Perhaps the problem isn't inference cost, but context management or other algorithmic inefficiencies (another shoutout to Dana at Subconscious), and better context management is an active research area. We could generate escape velocity if we can solve these challenges and unlock more consistent ROI.
5. Perhaps most industries won't actually require highly capable LLMs to be mostly automated.
6. Perhaps people are still learning how to most effectively use AI. A recent report from Anthropic suggests this very effect. Longer tenure users of Claude are 4% more likely to have successful conversations, even after controlling for model type, use case, and country/language. Multitasking is also a skill that appears to be trainable.

There are probably more reasons. Send me an email if you have a comment! This post is an essay⁶ in the true sense of the word.

Each of these probably deserves more exploration, but I did not have the time or space to get them into this piece. I may return to some of these pushbacks in future posts, so stay tuned!

Final word

Thank you for making it this far. This is the first blog post that I'm officially publishing, so I would appreciate any and all feedback. Please drop me an email at s.xifaras999@gmail.com if you would like to leave feedback, positive or critical, or just want to chat. If you're in the northeastern US (NYC-Boston range), let me know and I'd love to explore an in-person meeting!

Victor and Nick, thank you for your helpful feedback on drafts of this post.

I've been meaning to write more for a while. Up to this point, this site has been a static page — a CV with links — but I want a place to think through ideas in public.

My goal is to write about whatever comes to me and piques my interest. I expect that my first few posts will be a bit rough, and that's okay. I am being intentional about cultivating high follow-through; I am not letting perfect be the enemy of good.

What to Expect

Posts will be irregular. I'm not committing to a schedule, just to writing when there's something worth saying.

If you have thoughts on anything I post, or just want to say hi, please reach out via email.