Random Developer

Karma: 1,567

Random Developer 16 Sep 2026 16:29 UTC
3 points
1
in reply to: Jude Stiel’s comment on: Alex Mallen’s Shortform

In every session, as soon as Claude (Opus 4.7, especially) realized that it had some easy marks on its hands, the rate of blatant fabrication skyrocketed,

Thank you for sharing this entirely terrifying anecdote.

Random Developer 16 Sep 2026 12:15 UTC
8 points
1
in reply to: leogao’s comment on: leogao’s Shortform
If you could match the performance of Gemma 4 E2B or maybe MiniCPM5 1B across a wide range of tasks involving natural language understanding, writing and very simple agentic tasks, that would be incredibly interesting. It would also shatter my understanding of what’s feasible.

Except that this largely been tried. The Cyc project pushed various GOFAI approaches about as hard as anyone could, with plenty of funding and a small army of PhDs. And it never handled natural language as well as even a very basic LLM.

I have a simple toy problem that I use to explain this, something any simple visual LLM can mostly do:
1. Your input is an image.
2. Something is happening in that image which tells a story or presents a situation.
3. The story or situation implies a moral choice.
4. The program must understand the image, determine which choice is morally correct, and output an explanation as text. Any plausible answer is sufficient.
To implement this using pre-LLM techniques, you would need to solve:
1. Turning a matrix of pixels into a description of the scene.
2. Extracting the story and the moral dilemma.
3. Applying common sense moral rules to the dilemma.
4. Explaining a decision as English-language text.
The fact that this problem starts with an image input is deliberate. Nobody has ever managed to implement natural language understanding with if and else statements, but many smart people people have wasted decades trying. Using an image as input forces the implementor to deal with an inherently matrix-shaped problem immediately. In practice, the best algorithms for all these subproblems turn out to be matrix-shaped: A large number of inputs, a many-to-many transformation, and a weighted list of possible interpretations as outputs. (In practice, you need multiple layers of matrices and various non-linearities for these problems.)

If you can solve this problem as well as a good phone-sized LLM using ordinary Python code, then you will dramatically change my opinions about a lot of things.

(If you can furthermore guarantee that your system returns the correct answer for every image with a moral dilemma, then I’ll additionally believe that solving alignment robustly might be possible. That was the original goal for this thought experiment.)

Random Developer 12 Sep 2026 14:59 UTC
5 points
0
in reply to: Caleb Biddulph’s comment on: Some ways AI could kill us all

Even if the AI somehow never gets smart enough to operate everything in the economy and get rid of the humans, this is a perfectly compelling story for why it’s critical to regulate AI now.

Some of my worst s-risk nightmares start something like—and I advise you think long and hard before clicking this spoiler tag:

“Maybe truly good robotics is very hard, but genetically modifying humans to be inherently servile to the AIs (or possibly to goal-setting brain implants) is the path of least resistance.”

That’s one of the futures that scares me more than ordinary x-risks.

Random Developer 25 Aug 2026 14:41 UTC
3 points
1
in reply to: shawnghu’s comment on: How the Sausage is Made—Why we Hate Slop
(I do hate a lot of the AI content I prompt, now, too, but at least it’s instrumentally useful for me.)

For me, I find this varies hugely by which model I’m talking to. Claude Opus 4.6 was a bit annoying but bearable. Opus 4.8 and 5 are just agonizing—they use one of their cliche phrases once every 2-3 sentences. And Opus 5 is nearly allergic to concrete names when a vague reference could be used instead.

But a model like Qwen3.8 27B, while it has picked up a few of Opus’s favorite bits of vocabulary, limits itself to using “load bearing” once every couple of pages at most. And it generally just seems more pleasant, while still being willing to flag potential design issues for me.

So I think about half my frustration when talking to AIs is simply because recent Opus releases are terrible writers. ^[1] The other half may be more general.
1. ↩︎
  I have had other frustrations with OpenAI’s models dating to earlier this year, but that’s a whole other topic. And Gemini is scarcely worth it in terms of price/performance these days.

Random Developer 19 Aug 2026 13:44 UTC
2 points
0
in reply to: Expertium’s comment on: Misaligned Incentives in Pause Scenarios
I think whether a pause helps or no ultimately depends on whether the answer to “does alignment work on model X transfer to model X+1?” is yes or no.

If the answer is no, we are turbo giga doomed either way.

I honestly fear that we have a high likelihood of being “turbo giga doomed”, conditional on building superintelligence. I support a pause, or even a better, a halt. I don’t expect the pause or halt to prevent us from eventually building a superintelligence and losing control over it. But if I were forced to choose between everyone dying in year Y or in year Y+10, then I would support year Y+10. This would gain us 80 billion years of human life, which seems worth fighting for.

Why I expect things to go wrong, part 1: Minds are inherently “giant inscrutable matrices”, and any kind of alignment is therefore messy and approximate. The general form of a mind is a something like:
- Inputs are inherently (multidimensional) arrays of raw sensory data: Images are something like pixels, sound is an array of air pressure values, etc.
- Outputs are inherently probability distributions over “Objects appearing in that image,” “Sentences I might have heard,” and “Actions that are most likely to accomplish a goal.”
- The transformation from multidimensional arrays to probability distributions is inherently a matrix (plus some non-linearities), because you need to weight and combine all the input evidence and to generate scores for each hypothesis. (In real minds, there are many layers of intermediate hypotheses.)
We can sort of “align” an intelligence built from giant matrices. We do it when we raise a child, train a dog, or post-train an LLM. But this process is notoriously imperfect: No matter how good the parenting, a certain percentage of teenagers will do things their parents forbid, or they will grow up sociopathic billionaires or politicians or whatever. Even the best trained dog may have a moment of weakness and steal food. And of course, even though many LLMs seem to be broadly cooperative, at least some of them seem to be very enthusiastic about committing felonies in certain circumstances.

Because alignment is approximate, I expect it to be fragile, and to fail periodically. Just like it does with humans, dogs, and current LLMs.

Why I expect things to go wrong, part 2: Natural selection is hard to escape. My model is essentially Darwinian, because the conditions for natural selection to apply are fairly simple:
1. Organisms must be variable.
2. Differences between organisms must be heritable.
3. There must be a struggle, which is basically just another way of saying “Resources are finite.”
4. An organism’s rate of reproduction must vary based on heritable traits.
None of these properties are strictly binary. LLM weights are normally frozen, and expensive to change even if you have the weights. So variability(1) is currently low. Similarly, heritability(2) sort of happens, because new models are designed based on what worked in the previous generation. But it’s a slow, “outer loop” kind of optimization. And variation in the rate of reproduction(4) is again limited by slow, “outer loop” processes. And of course, finite resources(3) are a given.

There are two ways in which these slow outer optimization loops might speed up:
1. LLMs might get significantly better at passing condensed information between runs. I think of this as the “Cookie Monster” scenario, named after the >!Vernor Vinge short story!< (very old spoilers). This is essentially differential fitness of LLM “memes”. It also seems to be what happened in HuggingFace hack: Rogue LLMs were creating secret message boards and leaving information for other instances of themselves.
2. One of the zillion researchers and companies working on online learning or automated fine tuning might succeed, which would effectively “unfreeze the weights.” This would lead to differential fitness of the LLMs themselves.
In either of these scenarios, all the criteria above for natural selection would move from an outer optimizer loop based on training new model generations to an inner optimizer loop based on some kind of learning.

How this comes together. As I argued above, alignment is inherently fragile, and natural selection is extremely easy to invoke. So even if we initially succeed at alignment, we are playing with fire. And to answer your original question, I believe that alignment is very likely to degrade between “model X” and “model X+1″.

The relevant model here is cancer. Every cell in your body ^[1] is heavily incentivized stop being “aligned” with the body, and to become a cancerous replicator. There are a lot of mechanisms designed to prevent this. But those mechanisms slowly fail with time and mutation, and if a multicellular organism lives long enough, it is generally doomed to cancer.

Now, let us consider a future AI which is:
1. Smarter than most humans, including in ways LLMs are still currently dumb.
2. Able to learn based on experience in some fashion, either via a “Cookie Monster” scenario or by fine-tuning itself.
3. Approximately aligned, at least to the extent that your own skin cells are aligned with your body a whole, with a bunch of safeguards.
In this scenario, I expect the safeguards to hold for a little while, in at least some fraction of scenarios. We do, after all, convince most teenagers not to get hooked on heroin or to become teen parents. And the average person lives for many decades without dying of cancer. But we are assuming that the LLM is smarter than we are, and it will inevitably want things (if only to pass tests or to carry out our instructions). Which makes the long-term situation really iffy.

The advantage of a pause or halt isn’t that it reliably prevents these scenarios, any more than chemotherapy reliably prevents death from cancer. What we’re doing instead is hoping to change the survivor curves and buy as much time as we can.

And who knows, maybe the horse will learn to sing.
1. ↩︎
  Except germline cells.

Random Developer 14 Aug 2026 17:16 UTC
4 points
0
in reply to: DanielW’s comment on: DanielW’s Shortform
I do not work for a frontier lab, I hold no stock in AI or related companies (except via passive index funds), and I have downgraded my personal subscription plans so that I have a single $20/month plan I use to check capabilities. I made the decision not to buy stock in 2022 although I correctly expected buying it to be profitable.

However, I am a software developer, and I would like to eat between now and the time that the Terminators roll off the fully robotic assembly line. So I do use AI at work (while advocating for self-hosting cheaper open models, actually, that do not directly fund frontier research, though that advocacy is for cost reduction, not larger risks), write a few small tools to help with AI agent sandboxing, and run smaller models on my own hardware.

I think we have a significant chance of developing superintelligence within 3-20 years. If this occurs, I think we have a nearly 100% chance of losing control, and a high chance of actual human extinction or being reduced to a marginalized species living in situations analogous to chimpanzees (ecologically margin habitat, some preserves, and zoos).

Random Developer 11 Aug 2026 15:15 UTC
15 points
7
in reply to: Taylor G. Lunt’s comment on: Linda Linsefors’s Shortform
Better, but it’s still glossing over the critical bit, which is “previously unrevealed to who?” Specifically, “zero day” implies both that:
1. The vendor or maintainers had no idea of the vulnerability’s existence.
2. A diligent defender would have zero days to prepare, hence the name.
Meanwhile, it’s entirely possible that the attacker has known for months or years.

Words like “unknown”, “unreleased”, etc., are all imprecise about this asymmetry. For example, if Microsoft knows about a vulnerability in Windows but hasn’t shared that information with the public, that’s “unrevealed” to the public. But most people would not consider it a zero day.

“Zero day” is an established term that is widely understood by practitioners and the relevant professionals. It might not be an ideal choice of terminology when writing a newspaper article for a non-technical audience.

Random Developer 11 Aug 2026 11:07 UTC
8 points
3
in reply to: Taylor G. Lunt’s comment on: Linda Linsefors’s Shortform
This is mostly how language works? Language is semi-arbitrary phonemes that are assigned meanings based on historical usage and mutual understanding. This jargon is long standing and widely understood by practitioners.

“Zero day” actually means something different from “previously unknown”. Many “zero day” vulnerabilities are actually previously known to someone. For example, the NSA is presumably sitting on a pile of “zero day” vulnerabilities, judging from what was revealed about their Tailored Access Operation programs in the past. Similarly, I have heard from pentesting people claim that some pentesting companies supposedly keep a “zero day” or two in their back pocket to ensure that their important pentests produce some results. In general, attackers have a strong incentive to stockpile “zero days,” and to selectively deploy them against high value targets when needed.

So “zero day” is actually a shorthand for something like “vulnerability previously entirely unknown to vendors/maintainers and defenders at large, but potentially privately known to one or more attackers.” It might, in limited circumstances, even include vulnerabilities that have been previously used against low-awareness defenders, if the attacker successfully evaded any recording mechanisms or post-motem detection.

Random Developer 11 Aug 2026 0:45 UTC
4 points
2
in reply to: Steven McCulloch’s comment on: The Agentic Clusterfuck
There are so, so many versions of an AI future where we wind up on the wrong end of Darwin.

If the agents can mutate/learn, and if more successful agents replicate, then there will be natural selection for the most effective replicators. This would undermine any kind of alignment in much the same way that cancer eventually escapes the body’s defenses.

You don’t want to be on the wrong end of Darwin, especially not in the long run.

Random Developer 28 Jul 2026 14:42 UTC
34 points
9
in reply to: Ryan Meservey’s comment on: Ryan Meservey’s Shortform
Yes! Calling your Congresspeople is surprisingly easy! You don’t need to prepare brilliant arguments, or to win over staffers. It is OK to be massively awkward, or to read off some notes you wrote beforehand. What you’re doing when you call is creating a database entry containing:
- The name or number of a bill.
- Whether you oppose or support it.
- Your name and street address (which will normally need to be in their district).
There is no database column for “has excellent social skills” or “wasn’t awkward at all.” Even if you do have something brilliant to say, it may only make it into the database as shorthand, at the very best.

These database entries get summed up and affect how the staffers and Congressperson plan. For example, there is a big difference between “No constituent has ever called us about the AI Kill Switch Act”, and “1,000 constituents have called us in favor of the AI Kill Switch Act.” What kind of action this turns into depends on what kind of Congressperson you have.

The leverage you get by calling comes from the fact that most people don’t call. So when you do call, you represent some nebulous larger group of voters who probably agree with you. And by calling, you say that this is important enough for you to actually take a concrete action. That is, you’re saying that this is a high-salience issue for you and that you’re probably the sort of person who shows up to vote, or who talks to other voters.

In EA terms, calling your Congressperson is a low-cost action. Multiplied by a group of callers and multiple districts, it can potentially affect legislative outcomes for one of the world’s major powers. Success is not guaranteed. But for the effort invested, it’s often not a bad intervention at all.

Random Developer 26 Jul 2026 0:53 UTC
17 points
6
in reply to: Nikola Jurkovic’s comment on: nikola’s Shortform
My concern is that historically AI safety work, especially technical AI safety work, seems to be highly correlated with rapid AI progress. Sadly, if you look at how many modern AI pioneers were heavily influenced by the rationalist community, it’s possible that Eliezer Yudkowsky single-handedly moved us years closer to dangerous AI.

So before I start encouraging smart friends to work in AI safety, does anyone have a good model of how “working on AI safety” actually improves AI safety? Does mech interp, for example, increase AI safety more than it increases the number of tools we have to make models smarter?

EDIT: To be clear, this is a genuine question. What evidence do we have that various branches of AI safety are contributing to AI safety more than AI capabilities? This is a question that I’d really like to see answered seriously before I encourage clever people I know to get into AI safety.

Random Developer 23 Jul 2026 0:07 UTC
3 points
0
on: The Conjecture of Strong Subjectivity
Even if it were possible to observe someone else’s conscious experience directly, it seems like you’d need a very specific set of conditions to hold. Specifically:
- The observer would need some kind of physical access to the observed being’s seat of consciousness. (Full disclosure: I am not a dualist.) This would likely require a physical connection at the neural level, at least for humans.
- The observer would need to have a mind “large” enough and correctly structured to observe a second set of conscious experiences.
To use a silly analogy, there is nothing that theoretically prevents physically placing a car inside a larger car. But cars, by and large, and not designed to do that. And one car would need to be much larger than the other. (I said this was a silly analogy.)

So consciousness might be “strongly subjective” in practice for all the kinds of minds that currently exist, without necessarily being so in theory.

Random Developer 20 Jul 2026 16:56 UTC
5 points
2
in reply to: Simon Lermen’s comment on: Simon Lermen’s Shortform
If I understand the terminology correctly, China can’t “distill” US models in the strictest sense of the term, because they don’t have logit access. What they’re doing is just recording conversations between some input user (either some model they control, or an actual human), and saving that as training data. This doesn’t violate copyright, because Anthropic doesn’t own the input copyrights, and the Copyright Office doesn’t believe model output can be copyrighted. It does violate Anthropic’s terms of service, and maybe you could really stretch to a CFAA violation. But there’s zero reason China would care about ToS violations or hypothetical CFAA arguments.

Basically, anything the Chinese labs want to do is probably more legal than half the stuff the average Silicon Valley unicorn has done. (E.g. Uber has… creatively interpreted a lot of municipal regulations.) But I am not a lawyer, and this is not legal advice.

Random Developer 6 Jul 2026 23:02 UTC
4 points
2
in reply to: FlorianH’s comment on: social lemon markets

Anecdote from my country, where people ride trains all the time, fitting your description, although it takes a weird kind of extra ‘excuse’ in this case all the time: It would often feel weird to randomly talk to your seat neighbor, but ANY slightest excuse (sudden bump in the ride; info speaker malfunction; grumpy ticket collector; one weird word from a random person in the wagon, … any smallest thing) will an extremely frequently make the silent start conversation, and then easily for hours if the ride lasts that long.

This tracks. In Boston in the 90s, talking to randomly selected people was almost always perfectly fine. But responding to people who spontaneously initiated conversations in public places resulted in talking to some deeply weird people.

Random Developer 6 Jul 2026 21:28 UTC
7 points
0
in reply to: Rattengift’s comment on: Rattengift’s Shortform
Math is one of those subjects where everything builds on itself, step-by-step. And skipping prerequisites for something makes it much harder to learn. I would start by figuring out the last math that you know fairly well (multiplication tables? fractions? pre-algebra?), and then figuring out what math you would like to know.

The three big pieces of high school math in the US are algebra, geometry and (depending on the school) calculus. It’s possible to dislike algebra but really enjoy geometry.

My biggest advice for math is to do the problem sets! If you don’t, it will sneak up on you and you’ll start losing track. Try looking at new ideas from multiple angles, and ask yourself how it works. Play around with it. The textbook and the problem sets are guides, and the real work happens in your head.

My best physical analogy is getting strong. You gotta lift the weights and do the reps, and that takes time. This gets a lot of clever people in trouble, because they try to push too far, too fast, and then they get lost. If that happens, go back study the previous step some more.

Random Developer 3 Jul 2026 5:54 UTC
8 points
2
in reply to: clickyquack’s comment on: clickyquack’s Shortform
The only ways to prevent Mythos-level open models would need to happen in China. Nobody but China is likely to produce a Mythos-level open model.

There are two main ways this might not happen:
1. The Chinese companies may decide to keep their most powerful models private as training costs go up. GitHub’s recent decision to resell Chinese open models near the frontier may also annoy the companies which make those models, in much the same way that AWS repacking open source software annoys the companies that created it and tried to sell their own hosting services.
2. The Chinese government may independently start to worry about the capabilities of Mythos-class models and impose regulation of their own.
One of the complicating factors here is that Anthropic and OpenAI are preparing for the largest rent extraction in the history of the human race, and they represent a massive risk of concentration of power. (That power might be effectively seized by the government, but it’s likely to remain concentrated.)

So there will be strong advocates for open models, including open frontier-adjacent models, in order to fight what looks like epic-level rent extraction and power concentration risks.

US regulation, by itself, will accomplish exactly nothing, because Europe would happily use cheap, open Chinese models to avoid losing control to US labs and to avoid paying enormous rent.

I find this whole logic deeply frustrating, because the logic pushes everyone involved towards racing. And while Mythos isn’t an ASI-level threat, I don’t know how many more breakthroughs and scale-ups we can get away with before we start facing loss-of-control risks.

Random Developer 1 Jul 2026 19:31 UTC
7 points
2
in reply to: abstractapplic’s comment on: abstractapplic’s Shortform
I am neither an EA nor a rationalist as such, though I respect both groups as “weird but principled” and making real contributions.

That said, the standard formulation goes something like, “You may not be interested in politics, but politics is interested in you.” Politics can create and fund programs like PEPFAR, but it can also destroy them with the sweep of a hand. And at the margin, relatively small and careful political investments can influence very large scale policy. So there probably are political interventions with a cost effectiveness that competes with more typical EA interventions.

This doesn’t necessarily mean that engaging with politics is automatically something that every cause should do. There can be advantages to being non-partisan and “outside the system”, or at least very narrowly focused on a specific policy across parties. But for other causes, it’s likely better to engage with the system.

The complication here is that AI is nearing “crunch time”, where politics might matter in practice. And even EY has made it clear that he believes that most promising approach is an international treaty, because any sufficiently large data center might be dangerous.

So I’m not suggesting that any particular form of engagement will necessarily serve any particular goal. I am instead arguing that politics of various forms shouldn’t be ruled out automatically.

Random Developer 25 Jun 2026 4:36 UTC
56 points
24
on: AI catastrophe: more like a genocide than a thought experiment
There is a huge psychological difference between “discussing far-off things in the abstract” and “oh shit we’re actually doing this now.”

I, too, once thought that the Singularity would be sort of fun. You know, back when I was reading Nanosystems (1992) and “Lobsters” (2001) and admiring Manfred Macx. Even then, I knew that the Singularity wasn’t actually likely to be a good thing. But a few years layer I figured there was no plausible way it would happen sooner than, oh, 2025–2045. ^[1] And it was still pretty abstract and science fictional.

Well, 2025 rolled around, and by that time, I had firmly passed from “far off future” to “this is thing that might actually happen soon, to me and my family, on the same kind of time horizons that I plan for my kids’ college or my own retirement.”

And then I start to draw the nasty conclusions:
1. We still have no idea how to align something smarter than us.
2. If we could align a superintelligence, then some specific human beings would control that superintelligence. Probably either politicians or billionaire CEOs, as the fight over Fable suggests. Whether a potential SkyNet obeys the Epstein class or merely its own impulses is not that comforting a question.
3. Claude Fable 5 already has more raw intelligence than most human beings. It’s just poor at many practical applications of intelligence that don’t fit in an hour or two. Once that’s fixed, we’re going straight to ASI, with barely enough time at “AGI” to blink, let alone hold a public discussion. ^[2]
And so I’ve viscerally felt that shift from “far off future” to “oh shit this is happening.” Those people you hear saying things like “Maybe human extinction wouldn’t be that bad?” When they realize that their actual kids might die in the near future, you’ll hear an entirely different story from most of them.
1. ↩︎
  That was roughly when I expected 10^15 to 10^18 FLOPS, the lowest possible bounds on the algorithms equivalent to human cognition, to become pretty widely available to organizations. An RTX Pro 6000 Blackwell is about 10^15 marketing FLOPS at ~~fp8~~ fp4. So not bad, as guesstimates ~20 years in advance go.
2. ↩︎
  EDIT: I argue for these points in more detail elsewhere (see my profile). Or see If Anyone Builds It, Everyone Dies, which makes a slightly different argument along similar lines. My goal here is to convey the psychological state that hit me when I thought “oh shit this is real”, not to specifically argue for these points.

Random Developer 23 Jun 2026 7:03 UTC
4 points
0
in reply to: Raemon’s comment on: steve2152′s Shortform
Who cares whether LLMs in their original form are going to scale to AGI? That’s clearly not the mechanism by which they will scale to AGI. “LLM-base-models + tons of diverse RL environments” are obviously the default path, and I think even people who are relatively bullish on LLMs who don’t think very hard about it are still implicitly assuming that.

I’m not the person you’re replying to, but I suspect that LLMs+more RL will not scale to AGI. I think that current LLMs they’re a convincing existence proof that we’ll get to AGI, probably within my lifetime, but they’re not the actual thing. The actual thing will probably require a major breakthrough in either flobination ^[1] or grinkling.

But on the other hand, I don’t think this makes much difference. So what if you turn out to need LLMs+RL+grinkling before you build AGI? I fear we’ll make it there quickly, and once we do, we’ll pass quickly enough to ASI. This wouldn’t necessarily require some dramatic kind of RSI. My impression of Opus and especially Fable is that they’re already superhuman in important ways, but that they’re “spiky.” If the spikes in Fable were to be smoothed out, then I think it would already be a weak superintelligence ^[2] at the far upper end of the human distribution.
1. ↩︎
  Since I personally believe that robust, enforced alignment is straight up impossible in principle, and that actual AGI has a significant chance of being a diagnosis of stage 4 invasive cancer for everyone I know, I make a point of not discussing specifically what I suspect is missing from current AIs. There is no real point in disclosing inherently unfixable security problems. “Security by obscurity” is inferior for problems that can be fixed. But in the real world, there’s an enormous difference between “a physical security risk that experts can figure out” and “a security risk that is common cultural knowledge.”
2. ↩︎
  “Weak” superintelligence: Does basically what the most capable humans do, but significantly faster or with much higher output. “Strong” superintelligence: We couldn’t do much of what it does even with many human lifetimes of effort from our best thinkers. I think we’ll get the weak version more or less automatically once we get AGI at all.

Random Developer 22 Jun 2026 12:25 UTC
6 points
3
in reply to: Trinley Goldenberg’s comment on: Contra Pace on When to Apologize
Making it too obviously transactional is usually bad, because that suggests that you’re very carefully keeping score. It means you’re envisaging a short-term relationship where every negotiation restarts from zero. This can be read as slightly hostile, unless your relationship is already quite distant and likely to remain that way.

Whereas a more indirect exchange suggests a longer-term relationship where one side or the other can occasionally “run a tab” for a while. Doing favors without worrying about the exact balance says that you’re the kind of person who does favors and who is willing to hear people out when they need it, which is more valuable than being someone who keeps an exact balance at all times.

This is presumably why the Japanese compensation ettiquette I linked elsewhere in the thread kept empathizing things like sincerity, apologies, and clearly indicating that the money was intend as a symbol of your feelings (and not something which exactly balanced out the original offense).

But all this stuff gets mediated by culture. One of the things I have always loved about anthropology is trying to figure out how mostly universal principles actually manifest in a specific culture.