2026-11-12 19:23:11
Cosmophagous AI (my name for AI that races desperately at near-light speeds to grab that last galaxy, that last star, that last planet before it forever recedes over the cosmological horizon) is sometimes invoked as a counterargument to the idea that even an ASI that is mostly indifferent to our fate would keep us around if the cost of doing so were sufficiently low.
For example, Ryan Greenblatt makes the following point in Notes on fatalities from AI takeover:
[T]he costs of keeping physical humans alive are extremely low, probably it only slows the industrial expansion by a small amount (and this slowdown is probably the dominant effect from a resource perspective), probably by less than 1 month and very likely less than 1 year. Delaying this long costs a pretty tiny fraction of long-run cosmic resources, probably well less than one billionth of the resources that will ultimately be accessible to the AIs in aggregate.
Thus, extremely small amounts of motivation to keep humans alive could suffice for avoiding large fractions of humans dying due to industrial expansion (depending on the exact preferences of the AI).
Writing in the comment section, Tanner Cook counters:
Framing the cost of a delay as "a billionth" of expected return, then reasoning that because a billionth is a small fraction it is a negligible cost feels like sloppy reasoning.
So let's put actual numbers to it. A delay of a month represents the loss of about 26 billion stars and their respective planets. Suddenly the assumption that preserving humans is worth losing 26 billion solar systems seems a lot less sure. A delay of a day costs 850 million solar systems.
In this post, I argue that our observational evidence and, indeed, our very existence is a strong argument against the existence of cosmophagous AI.
The oldest metal-rich stars (population I), which are capable of supporting star systems comparable to our own, are estimated to have appeared roughly 10 billion years ago. Even if we assume the earliest spacefaring civilisations took about the same time to evolve as human civilisation has on Earth (a conservative estimate), i.e. 4.5 billion years from the formation of their planets, that still gives these civilisations a 5.5-billion-year headstart on us.
At a rather modest velocity of 0.1 c (I say "modest" because many proponents of the idea expect cosmophagous AI to travel at near-light speed in its frantic race to the last atom in the Universe), this means the earliest possible cosmophagous AIs would have been able to devour everything within a bubble spanning 550 million light years.
For perspective, the Virgo Supercluster, the 47,000-galaxy supercluster we are part of, is "only" 147 million light-years across. Even if only a single cosmophagous AI had appeared in this structure, and even if we were lucky enough that it had evolved at exactly the opposite edge of the supercluster, it would have had enough time to catch us and consume us several times over.
And yet, here we are. Not only do our observations see no evidence of distant stars blinking out of the sky as something harvests them for energy (which could be handwaved away by "any sufficiently advanced technology is indistinguishable from magic"), but the fact that we ourselves are still here constitutes prima facie evidence that no cosmophagous AI evolved inside the Virgo Supercluster within the first 8.5 billion years after the first population I stars.
Right now, the atoms in your body are reading these words, processing the information they encode, and using your mouse to scroll down the page. They have not been repurposed into pure computronium or turned into Dyson sphere construction materials!
There are multiple potential explanations for this (non-exhaustive list, in no particular order):
The why is less important than the what, though: the point is cosmophagous AI is not a thing in the Virgo Supercluster, and any ASI that we build is overwhelmingly likely to come to the same conclusion as any other ASIs developed inside the supercluster within the last 10 billion years: chasing the cosmological horizon is not feasible, desirable and/or necessary to accomplish its goals.
Such an ASI would not operate on an ultra-tight time schedule in which every month of delay costs it millions upon millions of star systems: the Virgo Supercluster will still be accessible in tens of billions of years and can be taken at a leisurely pace, while the Local Group, containing more than a trillion stars, will basically remain accessible forever. If it has even a tiny sliver of care for humans, as Greenblatt suggests, then it could easily afford to delay its expansion by a month, a year or a decade without sacrificing anything meaningful.
One could counter with an extreme version of the rare Earth hypothesis, claiming that intelligent life is so mind-bogglingly rare in the Universe that, statistically, we shouldn't expect to have met cosmophagous AI (or other forms of grabby aliens) yet.
This proposition is already barely tenable within the confines of our Milky Way, which contains 100 to 400 billion stars. But the probability of us being the only spacefaring species in the Virgo Supercluster, with up to 200 trillion stars in it, is so vanishingly low that at this point we would be better served by considering more exotic (e.g. simulation theory, quantum immortality) or even theological explanations (e.g. "[a] God exists and made Creation for us and us alone").
None of this is meant to play down fears of misaligned ASI. To be clear, misaligned ASI could mean a very bad time even if it didn't specifically take the shape of a cosmophagous AI! But I do think it's worthwhile to establish which misalignment scenarios make up a large slice of the probability space, and which ones don't, so we can direct our energy and attention to the former.
2026-11-12 17:51:22
It's important to distinguish between two different takeaways you could have from the recent METR report:
1. Shorter timelines: AI will soon fully automate AI R&D
2. Intelligence explosion more likely: Once AI fully automates AI R&D, AI progress will accelerate until we get superintelligence
I will walk through my tentative views on each.
I think this is pretty clear. AI is already much better at AI research than I had realized and it is improving at a faster rate than I had realized.
So we're less far to go to full automation than I thought and we're moving towards it more quickly.
This one is more complicated.
The key questions is about whether the feedback loop of AI improving AI is strong enough to accelerate over time, even holding compute fixed.
We can break this feedback loop down as follows:
more ECI -> more R&D labour -> more total R&D effort -> more ECI -> ...
Now, let's assess what we can learn about each stage of this feedback loop.
When you increase ECI, how much more quality adjusted labour do you get at AI R&D?
Well, recently the amount of quality-adjusted labour increased pretty fast! A moderate increase in ECI led to a big increase in labour.
So stage 1 is stronger than I used to think.
Can improved labour have the same effect as increasing both compute and labour? Or does more labour not make much difference because compute experiments become a big bottleneck?
Well, we've already had a ~2x speed from more labour without getting bottlenecked by compute. That rules out strong "bottleneck" views that I've seen economists endorse.[1]
But it doesn't rule out compute bottlenecks biting a bit later on. In my opinion, these were always the most plausible bottleneck views.
It also doesn't speak to the possibility that much algorithmic progress depends on scale-dependent algorithms. If so, alg progress might be much slower if we couldn't increase the scale of training compute and benefit from new algorithms that only emerge at new scales. (Though METR's claim of "no foreseeable bottlenecks" is evidence against this.)
So for me there is only minor update on the strength of stage 2. Compute bottlenecks are still plausible. IE far from inevitable.
Some of the speed-up in recent progress wasn't driven by increasing R&D efforts, but just by it being easier to make AI progress than before. Eg the speed-up in progress from RL.
This is evidence that stage 3 is more powerful. Doubling R&D inputs led to more progress than we thought. (Though only a little bit more, if you think most of the acceleration was due to AI increasing labour inputs.)
The more sceptical you are of stage 1 - that AI has driven the recent faster progress - the more you should update about stage 3. Once you accept that progress has indeed become faster, you have to update upwards on one of the stages!
So, to summarise how my views on each stage have changed:
- more ECI -> more R&D labour: much stronger than I thought
- more R&D labour -> more total R&D effort: stronger than I thought, but compute bottlenecks still plausible
- more total R&D effort -> more ECI: a little bit stronger than I thought
Overall, this is definitely evidence that there will accelerating progress. But the evidence doesn't feel overwhelming at all. Still could go either way. I feel less bullish than AIFP seem to be.
And remember, even without accelerating progress, AI progress will still become 2-3x faster if we automate AI R&D. And we can't handle that.
[1] On a strong bottleneck view, if one the growth of input R&D input accelerates but the other doesn't, progress is bottlenecked by the latter. Compute growth hasn't accelerated, only labour. Strong bottleneck views predict that the labour acceleration won't make much difference because of the compute bottleneck.
2026-11-12 14:41:09
In the experience machine thought experiment, you choose to either continue your life as you are currently living it, or spend the rest of it inside a machine that cultivates a world perfectly fitted to you. It was introduced to argue against hedonism, but we can imagine the machine pushing for any metric we care about—it doesn’t necessarily have to be pleasure. For example, we can consider a non-hedonistic experience machine which makes you feel a perfect balance of happy, challenged, loved, fulfilled, empowered, or anything else you care about.
The philosophical tension in this formulation is supposed to be between any experience-based ethics and some intuitive feeling that the experience machine is inherently deceptive. The thing that feels wrong is that the experiences don’t seem real in some important way. Robert Nozick, the creator of the original thought experiment, says “we want to do certain things, and not just have the experience of doing them.” This allows us to ask: Does Nozick's objection[1] make sense? Can we explain what realness is or why it matters to begin with?
Interestingly, I have found that many people are unable to understand what this thought experiment is getting at. They say things like “I wouldn’t want to go into the experience machine because part of what makes life enjoyable is the fact that you sometimes struggle, feel pain, etc.” But if this is true, our non-hedonistic experience machine would account for it! By construction, we are considering an experience machine which is simulating the ideal world for you. Still, people think we cannot artificially construct a world better than the one we already have. I try to explain, “Well, imagine your life, with nothing changed, but without kidney stones, broken AC in the summer, etc. Surely there are some adjustments to your life which would make you happier?” I think people do think the world can be better but not in a way an experience machine could create. These people claim the experience machine can’t have anything to offer in a way that makes me feel like they fundamentally don’t understand what we are attempting to talk about.
In early October of this year, Jacob Coxon appeared on The Daily Show to warn Jon Stewart's audience of AI danger. It’s a very interesting interview. I suggest people watch it in full.
Many of the YouTube comments were positive. I think that much of Stewart’s audience left feeling like this was not merely a troubling interview but a highly important one. It seems like Coxon communicated effectively how humanity is at risk, and the audience was largely receptive.
Now here’s what Coxon himself wrote on Twitter after the interview:
In this interview I tried to argue that a post-scarcity society will benefit working people. It was largely a rhetorical failure. People don’t trust that the benefits will be distributed, and tech optimists have to fix this communications issue.
When Coxon discussed how he thinks aligned AI could usher in a kind of AI utopia, the audience wasn't with him. He doesn't get into too much detail, but Coxon's good AI future is roughly "No jobs. Universal high income."
This leaves Stewart appalled, saying, "I think for the most part, even when we're doing jobs we don't particularly like, that humans like to feel relevant." He also feels like the AI companies aren't giving the outside world a choice. That Coxon and SF have a utopia in mind, that is probably a dystopia, but the outside world isn't allowed to object. Stewart's responses are met with thunderous applause from the studio audience.
Utopias feel like the mask an evil thing wears to appear friendly. People have heard too-good-to-be-true promises ending in tragedy before and are on their toes.
So it’s hard to talk about utopia, but it’s especially hard when you are Jacob Coxon. It’s probably fair to say there is a widespread suspicion of the young, successful tech guy who wants to dramatically change a bunch of stuff and thinks everyone is going to really, really like it. One comment under the YouTube video of the interview reads, “This has the feeling of John asking an Alien representative what the Aliens are about to do to humanity.” Importantly, Coxon doesn't fit the classic tech optimist caricature! He quit his job warning of tech-induced extinction! But the tech optimist character is strong enough in the public consciousness that Coxon is easily pattern-matched to it, and therefore is subjected to a bunch of assumptions about what is really going on here.
At one point, Stewart almost says the quiet part out loud. He explains to Coxon, with a gentle paternal tone, that the AI industry “lives in a bubble of its own making. And that they should really get out and hug some people because in the real world, that’s not reality.” I think that this is Stewart’s way of saying “Candidly, I think you are weird. I don’t trust that you really understand people. Your utopian vision is fundamentally antisocial in a way you may not be fully capable of understanding.” To be clear, I don't think that this is fair, but I believe this undertone is present throughout the interview.
Through this lens, Coxon's statements take on a certain meaning. When Coxon says "No jobs. Universal high income," I understand him as saying:
Look. AI superintelligence is going to basically be magic and could transform the world in any way it wanted to. The set of possibilities is so vast. It's hard to imagine that traditional jobs and the 40-hour workweek are literally the best of all those possibilities. I don't think machines should just replace humans. But I imagine that if the superintelligence was on our side, it would look more like every human is empowered to build whatever life they want, and wouldn't be forced to work a job they don't find fulfilling just to pay the bills. In simple language: "No jobs. Universal high income."
But this is not what is heard. I think what is heard is something closer to:
You people hate your jobs! And you have to work them because you are poor. My AI god could help with that. It can replace your human job so you can focus on other stuff. It would solve your other problems too so you can just hang out and be happy.
The public can have a difficult time imagining a bad AI future. But I think it can be equally difficult for people outside of the EA/rationalist/AI safety sphere to understand what we mean by a good AI future. It's not only that they don't want it, but they don't even understand what's being proposed.
No matter how I describe my non-hedonistic experience machine, the assumption is that the utopia it promises is really a hedonistic hell. In the same way, when someone says AI could make the future radically better, I think the public is imagining that AI will solve all of our problems for us and there won't be any purpose for humans, leaving us with a WALL-E-style dystopia.
In WALL-E, humans leave an uninhabitable planet Earth to just drink soda and watch TV and stuff.
To be clear, I'm not claiming that the experience machine is good or equivalent to what Coxon is proposing. I am more trying to say that it produces the same kind of confusion. When Coxon says, "AI can transform our world to be radically better!" the audience hears "READY OR NOT, IM BUILDING THE FUCKING EXPERIENCE MACHINE LETS GOOOOOO!!!! GET READY FOR YOUR JOBLESS FUTURE!"
I think there is something generalizable here. Painting in broad strokes, the public has a set of expectations for what the future will look like, and it's hard to picture something different. Perhaps a different set of biases also makes it hard for optimists to imagine AI dystopia.
In my favorite moment of the interview, Stewart asks Coxon about his time at OpenAI and Anthropic: "I don't mean this in the wrong way, but what did you think you were doing? Like honestly, you're there for four years and all of a sudden you're like 'these motherfuckers[2] are smart!'" Coxon responds, "That's completely fair. It was always in the long term. There was this big scary thing in the future. But man. I struggle to think too far into the future and now it's just right there on the doorstep."
He continues, "So now it's right there. It's actually scary. In the past ... it's just hard to visualize a decade into the future." Indeed, when you have a certain future in mind, it's difficult to imagine what another may hold.
It's worth noting that this isn't his only objection, but it highlights what I consider to be the most obvious, interesting tension.
He means the AIs.
2026-11-12 14:36:43
The simplest interesting player in MIRI's provability bot prisoner's dilemma tournament was FairBot, which cooperates if it can prove its opponent cooperates. It cooperates with another FairBot despite the apparent infinite regress, by Löb's theorem (some might say magic).
A related line of work was how to represent statements like "if I take this action, it will have that consequence" in provability logic. For example there's these old slides by Vladimir Slepnev that I came across on the MIRI website.
A post by James Payor brings these together. Well, maybe he didn't think of it like that. But I think of it as alternate FairBots, which look like different formalizations of the consequence of an action. Like, a bot that cooperates if and only if it can prove its cooperation results in its opponent's cooperation. Such bots are equivalent to FairBot conditional on Löb's theorem, but the idea was to cut off the infinite regress without Löb's theorem.
I'm writing this post to share some formula manipulations, simple substitutions really. It turns out it's very easy to see that these bots are equivalent, assuming Löb's theorem. But more importantly, we can clearly see the machinery by which Löb's theorem cuts off the infinite regress, and how these alternate formulations accomplish the same thing.
I'll start by just showing you the substitutions I have in mind.
FairBot:
where
For the first substitution, we'll need Löb's theorem and its trivial converse:
Substituting:
Then, substituting with the FairBot definition:
This is Payor's "Idea #1". It's also what you would get by straightforwardly applying the formula in the Slepnev slides to formalize "I'll cooperate if that results in my opponent's cooperation".
What about Payor's "Idea #2", the subject of Critch's post? That says
To get that, we use this theorem of provability logic (by which I mean the system GL):
Using that instead of Löb's theorem, we obtain Idea #2 in the same way as Idea #1.
In this section, I'm going to consider the consequences of
If I'm FairBot, it seems consistent that I defect because I can't prove you cooperate, but that's just because you defect because you can't prove I cooperate, because...
We'll start by describing this infinite regress in modal logic formulas instead of in English. Then we'll see how Löb's theorem cuts it off, and how the alternate formulations accomplish the same thing.
FairBot again:
Since we're asking "what if I defect?", we'll negate both sides:
Since the opponent is also a FairBot:
To see the infinite regress, let's get back to substituting.
Substituting out
And we can keep going:
We defect on each other all the way down in these possible worlds, however deep we want to look.
Well, let's look at the first substitution we did, using Löb's theorem:
Negating both sides:
I defect if... it's possible that you defect, even though there's a proof you cooperate?
I found this confusing. But youzicha on Tumblr interpreted it as so: consider the last world in which you defect. Of course, as we saw previously, there is no last world.
To see that mechanistically, we can try substituting, using the ordinary FairBot formulas (
The right-hand side can be refuted in provability logic (that is, its negation is a theorem of GL).
The
Repeating Idea #1:
Negating both sides:
Actually I don't see it. Really I want to get to the next one,
which was
Negating both sides,
I've previously called this condition "betrayal": I defect if it's possible that you betray me, that is, defect even though you can prove I cooperate. That was in my post trying to walk through the reasoning in plain English.
Back to modal logic.
We only need one substitution with
Again, the right-hand side is refutable;
Idea #2 was exciting to me because this "I imagine you imagining me" reasoning cuts off after just a few steps. It was my first hint that cooperation in provability logic might not be so mysterious.
Now I get that you can cut off infinite regresses with the contrapositive of Löb's theorem:
In provability logic with Löb's theorem, these reformulations simply follow from there.
Of course the point of the reformulations is that they don't require Löb's theorem. It's like we "already used" Löb's theorem in rewriting the condition, and no longer need it to prove cooperation.
I don't know the ultimate significance of that. But I've at least shared something of personal significance. Once I learned why every question "bottoms out" in provability logic, I had to bring it back around to the first time I saw something like that.
2026-11-12 13:38:56
Yesterday, Goodfire released the companion technical report to Nick Cammarata’s viral interpretability essay "Neural Networks Are Already Clean", but it seems to have gotten buried in all the recent crazy AI news.
The technical report is titled: "Neurons as an Interpretable Basis". Goodfire's Twitter announcement for it can be found here.
Like Nick Cammarata's essay, it's full of inventive, oft-interactive data displays. Don't just feed this to an AI agent and assume they can grok the post by pulling a PDF.
Quoting from the Abstract: (emphasis mine)
We study the neurons of Qwen3.6-35B-A3B using sparsification methods, attribution tools, and research agents, and find three properties that make neurons interpretable units of computation:
First, they are already functionally sparse. Ablating the 92.2% weakest MoE neuron activations for each forward pass has only modest effects on model capabilities – a 2.9% increase in language model loss.
Second, neurons have relatively few semantic groupings (modes) of computational functions, and we can identify these and describe them concisely. The majority of neurons have either one mode only or a single mode that is overwhelmingly causally significant. In cases where multiple causal modes are present, the number is small and legible to our methods.
Finally, we can use agent-driven experimentation to generate neuron descriptions in natural language, and these descriptions allow us to predict, simulate, and activate the neuron. Providing the description to a judge model allows us to (1) predict whether the neuron will fire in a given passage of text with 95% accuracy vs random text; (2) simulate its token-by-token behavior in high-activation regimes sufficiently to recover 73% of the damage (as a proportion of a ceiling of damage restorable) from the neuron’s ablation; and (3) write text that causes the neurons to fire more strongly than anything observed in a 10B-token corpus.
Overall, we argue that neurons are meaningful units of computation that can be effectively used for interpretability in their own right, letting us study the model on its own terms rather than through a learned proxy.
You may also recall the saga of the Colorado neuron that also activated somewhat on the Tornado Python library.
Did that show neurons really are dirty after all? The paper includes an appendix which analyzes that neuron further. They find:
About 98.8% of the neuron's causal effect comes from text about Colorado, other mountainous places, or other locations, decreasing as places become less Colorado-like.
(...)
Tornado is not one of a large set of hidden secondary functions. (...) On a code corpus, 98% of strong firings were on Tornado or code that calls it.
The two computations interact very little. Tornado predictions run mostly through code and web-library neurons. (...) Colorado and Tornado route 2% of their relevance through shared neurons, comparable to what Colorado shares with unrelated web libraries.(...)
We do not know why the Tornado association exists. We suspect Qwen3-32B was initialized by copying layers of Qwen3-14B, since many neurons align semantically 24 layers apart.
(...)
We consider this a benign form of overlap. The two modes are easy to identify and separate: clustering the neuron's firings by their upstream inputs separates Colorado from Tornado perfectly, and the two computations meet only in a narrow pathway around layer 14 and its copy. Tornado also has its own representation among code neurons, so an analysis starting there would reach the overlap quickly.
There is much more to digest here, but I think this work continues to look like a promising direction for interpretability.
2026-11-12 13:35:32
The lowest point of my life was the summer of 2019.
Waking up at 4AM every day to go to work as a janitor, and never getting more than five hours of sleep, dark bags permanently lined my eyes. My diet of cereal and frozen pizza grew my gut day after day, and peppered my face with acne. After enduring an injury and not being able to exercise, my body was atrophying and falling apart. Every day was pain.
And that was just the physical side of things. Mentally, I was getting pushed closer to the edge by a mental disorder that wouldn't get diagnosed for another six years. On top of that, my anger issues pushed away anyone that tried to get close to me.
Emotionally, I was a man on a deserted island—all alone. No one I could turn to for support. So I turned inward, numbing myself and the outside world, by escaping into the online world. I spent all my waking hours listening to podcasts while at work, and watching YouTube, porn, and playing video games while at home.
Needless to say, romance was not part of my life. I dreamed of having a girlfriend, but one look in the mirror and I was scared of what I saw: a penniless, tired, weak man, on the brink of physical, emotional, and spiritual collapse.
What rescued me from my abyss was reading the book 12 Rules for Life by Jordan Peterson. The fourth rule is: “Compare yourself to who you were yesterday, not to who someone else is today.”
The author discusses how envy can block you from bettering yourself, poisoning your thoughts, by reminding you that there's always someone out there who is better than you at something. But that doesn’t matter, because you can always improve yourself, as long as you are willing to aim low enough:
Pay attention. Focus on your surroundings, physical and psychological. Notice something that bothers you, that concerns you, that will not let you be, which you could fix, that you would fix. You can find such somethings by asking yourself (as if you genuinely want to know) three questions: “What is it that is bothering me?” “Is this something I could fix?” and “Would I actually be willing to fix it?” If you find that the answer is “no”, to any or all of the questions, then look elsewhere. Aim lower. Search until you find something that bothers you, that you could fix, that you would fix, and then fix it. That might be enough for the day.
After reading that paragraph, I went to the bathroom and looked at myself in the mirror: pimples and acne scars all over my face.
“It’s something that bothers me,” I thought, “and it's something I could probably fix, but would I actually be willing to fix it? Ugh, but skin care is so feminine! And besides, I don't know the first thing about how to start.” The next paragraph in the book acknowledged my frustrations:
Maybe there is a stack of paper on your desk, and you have been avoiding it. You won’t even really look at it when you walk into your room. There are terrible things lurking there: tax forms, and bills and letters from people wanting things you aren’t sure you can deliver. Notice your fear, and have some sympathy for it. Maybe there are snakes in that pile of paper. Maybe you’ll get bitten. Maybe there are even hydras lurking there. You’ll cut off one head, and seven more will grow. How could you possibly cope with that?
“Exactly!” I thought, “If I delve into skin care, then I'll have to do all this research on the topic, which will probably force me to change my diet, and fix my sleeping patterns, and reconsider my job as a janitor, and then I'll have to take my career seriously and do research there, and…and…and…fuck!! It's too much! Too many hydras appear when I try to tackle one small fucking thing!”
Fortunately, the next paragraph provided an answer to my pleas:
You could ask yourself, “Is there anything at all that I might be willing to do about that pile of paper? Would I look, maybe, at one part of it? For twenty minutes?” Maybe the answer will be, “No!” But you might look for ten, or even five (and if not that, for one). Start there. You will soon find that the entire pile shrinks in significance, merely because you have looked at part of it. And you’ll find that the whole thing is made of parts. What if you allowed yourself a glass of wine with dinner, or curled up on the sofa and read, or watched a stupid movie, as a reward?
Ask yourself what you would require to be motivated to undertake the job, honestly, and listen to the answer. Don’t tell yourself, “I shouldn’t need to do that to motivate myself.” What do you know about yourself? You are, on the one hand, the most complex thing in the entire universe, and on the other, someone who can’t even set the clock on your microwave. Don’t over-estimate your self-knowledge.
I contemplated this for a long, long time. Combative and self-defeating thoughts percolated on the edges of my subconscious, trying to keep me stuck in my old ways. But I kept returning to this idea: keep aiming lower until there's something small enough that I would be willing to fix (even if doing so makes me feel pathetic).
I stared at my acne-riddled face in the mirror. Self-pitying and contemptuous eyes stared back at me.
Grimacing, I pulled my phone out and googled “Basic skin care routine.” Two things came up: face wash and lotion. I shook my head, feeling embarrassed from my pathetic lack of knowledge, and drove to the general store.
Once back home, I washed my face for the first time. Staring at my wet and shiny face in the mirror, a voice in my head asked in the quietest way possible, “What next?”
Intense and overwhelming thoughts flooded my brain: “You are pathetic for even trying!” and “You've got too many problems—you're hopeless!” A poisonous swirl of feelings gnawed at my resolve. Panicking, I returned to the book for guidance on what I should do next:
Let the tasks for the day announce themselves for your contemplation. Maybe you can do this in the morning, as you sit on the edge of your bed. Maybe you can try, the night before, when you are preparing to sleep. Ask yourself for a voluntary contribution. If you ask nicely, and listen carefully, and don’t try any treachery, you might be offered one. Do this every day, for a while. Then do it for the rest of your life. Soon you will find yourself in a different situation. Now you will be asking yourself, habitually, “What could I do, that I would do, to make life a little better?”
“Okay,” I reassured myself, “just keep aiming low, and reward myself with every effort I make, no matter how small.”
I sat on my bed and thought deeply. “What’s the next easiest thing I could do to ameliorate my suffering?” I looked over at my pillow, which my acne-marked face laid upon every night. I went and changed the pillowcase. After that I stopped. I had done enough for one night. I didn't want to overwhelm myself by doing too much.
As the days went on, my thoughts and feelings tormented me, proclaiming that I was a loser and that I should stop trying. Like Sisyphus pushing the boulder up the hill, I struggled mightily, but didn’t stop. I continued to aim low, and remained begrudgingly patient with my miniscule progress.
At an embarrassingly slow rate, little by little, my life changed. Over the months, then years, things started getting better.
With every pathetic enemy I defeated (e.g., washing my face daily; going to bed at a reasonable hour; learning to cook for myself), I slowly built momentum. I eventually ran out of low-level tasks, and developed the confidence to confront mid-sized tasks (e.g., learning how to socialize; graduating college; affording and moving into my first apartment).
Over the years, my ethos became: If I don't know what to do in life right now, I might as well do something good for myself.
And I've created a good life by following this, until…
…until something new happened that shook me to my core…
…I fell in love for the first time…
“The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes.” — Marcel Proust
After falling in love, I looked back on my years-long self-improvement journey like I was Odysseus going to war in Troy—seeking glory and fame by proving myself. I don't regret that time I spent, but I do see it with new eyes: that I've been playing the game of life for a long time by myself. Alone.
My eagerness to achieve came not from a place of love, but from a place of spite. I had to show the world that it can hurt me all that it wants, and that I can take it. I will stand, alone, against anything it throws at me. The final two lines of this poem used to deeply resonate with me:
Do not go gentle into that good night.
Rage, rage against the dying of the light.
My whole life I’ve felt alone. The pain was unbearable, so I subconsciously suppressed that terrible feeling, and replaced it with self-improvement—all fueled by a quiet rage and contempt for the world.
But then something different happened. Someone dared to love me.
I didn't believe it at first. I couldn't. How could anyone love a wretch like me? Even with all my improvements, at my core, I felt unlovable.
It was as if I had a small, hurt, betrayed, sulking child hidden in the castle walls of my heart, ordering me to develop myself further, to build my castle walls a little bit higher.
But then, a quiet knocking came at my castle door, accompanied by a tiny voice that said, “I love you.”
The child, sitting deep within the castle walls, quit his sulking for a moment, thinking that his ears deceived him. But then came the knocking again, and the voice, muffled from behind the castle door, repeated, “I love you.”
This time the child heard it, but he did not believe it, “Lies! I've heard that before, and it wasn't true. Go away!”
The child’s cries reverberated across the walls, clashing against the deafening silence that normally suffocates the castle. Then, once more, “I love you.”
“But how can you?!” the child wailed, “Look at me, I'm pathetic and small and… and… nobody could ever love a person like me!”
Once more, but louder, “I love you.”
Sitting on the ground with his head between his knees, tears streaming down the child's face, he looked up in disbelief, “…really?”
“Yes, really. I love you. Will you let me in?”
Sitting on the ground, the child couldn’t even remember where the castle door was. Hesitating, he stood up slowly, with his head down and arms crossed, and shuffled towards the sound of the voice, which kept repeating, “I love you,” to help guide him.
Upon reaching the castle door, the child hesitated once more, then asked, “…do you really mean it?”
The voice on the other side, now quieter, said, “Yes. And I'll show you. Will you let me in?”
The child, with stress coursing through his veins from being hurt before, opened the door.
On the other side stood a small girl. She smiled.
She said, “I see you!” And the boy ran into her arms, hugging her tightly, never wanting to let go.
Being deeply seen and heard, for the first time in my life, was a profoundly transformative experience.
It taught me that I didn't have to hide behind my castle walls anymore. It made me realize that while all my self-improvement was great, it was a journey that I took all alone, and that company makes travel better.
After that experience, Robin Williams' speech in the movie Good Will Hunting took on a whole new meaning for me:
If I asked you about women, you’d probably give me a syllabus of your personal favorites. You may even have been laid a few times. But you can’t tell me what it feels like to wake up next to a woman and feel truly happy.
And if I asked you about love, you’d probably quote me a sonnet. But you’ve never looked at a woman and been totally vulnerable. Known someone who could level you with her eyes. Feeling like God put an angel on earth just for you…who could rescue you from the depths of hell.
And you wouldn’t know what it’s like to be her angel, to have that love for her, to be there forever. Through anything. Through cancer. And you wouldn’t know about sleeping sitting up in a hospital room for two months holding her hand, because the doctors could see in your eyes that the terms “visiting hours” don’t apply to you. You don’t know about real loss, because that only occurs when you love something more than you love yourself.
I found the courage to lower my castle walls, to love someone more than I loved myself, and I'm a better man for it. After that experience, my guiding ethos changed. I stopped going through life doing things solely for myself, by myself.
“The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes.” — Marcel Proust
Instead, I departed on a new voyage, and began living with a new guiding principle: If I don’t know what to do in life right now, what can I do to help my community and the people I love?