7 ms·

This is actually what keeps people using AI: variable reward schedule. It's basically gambling.

deleted 7d ago
[deleted]
jimmaswell 7d ago
Programming before AI was always variable reward. It was a gamble against your own time and patience. Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered. Something I wrote could have worked on the first try or I could have had to spend the day chasing logic errors (or multiple days chasing memory errors if it was C or C++). Maybe I would just get bored of the project, especially if I realized there were 20 layers of yaks I needed to shave first, and Visual Studio got stuck updating again, and before I could even start actually coding I had to spend the entire evening on an exhausting merge conflict. My entire weekend could be gone with nothing to actually show for it. I got so sick of all this at some point that I slowly stopped doing anything that wasn't my job. But then AI got better and better and I realized it was the ultimate unblocker. When that dreaded malaise started creeping in signaling it was a project's end because I didn't want to waste any more of my life dealing with bullshit orthogonal to what I was trying to do, I'd give it to the AI. It felt like a miracle the first time this worked, and it still does. If we were previously equipped with shovels to dig through bullshit, we now have a fully automated Bagger 288. The reward schedule now isn't variable anymore; the chance that I finish something in a good state is 100%. I can focus on the parts I actually enjoy - architecting the broader system, making the parts mesh together in a sensible way that's easy to work with and has some mathematical elegance to it, hand coding the bits I want to be really specific about (but now without the endless frustration of bugfixing or import errors and edgecases being immediately discovered, thanks to the AI).
- supern0va 7d ago
  >Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. I was working on a side project recently. I had spent months designing the data model in my spare time, thinking through how to make it as elegant and durable to change as possible in the long term, since (if I launched it) the repercussions for getting it wrong would be significant. Once I had a working design, it probably would have been several more months to build a working prototype and start testing it. Instead, Claude knocked out the prototype for me in an afternoon. And it immediately became clear that it didn't work: not because the data model didn't solve all the problems I wanted it to solve, but because it didn't fit the shape of how I quickly learned a normal person would need/want to interact with the product. I was so focused on the long term, that I never thought about what the first five minutes of a user with hands on the thing would need. And the changes needed would be significant. Maybe there's some variable reward mechanism. But I sure was glad to be able to pull that particular slot machine handle and learn that than waste even more of my time on what was a dead end.
  - ricksunny 7d ago
    (Appreciating this entire thread) - side question: you used the word ‘shape’ in the abstract sense, something I never came across until Claude vibecoding came along. Was it common / did you use ‘shape’ in the abstract sense before say 2025? Also, my coming from being a non-coder, I have a lot of appreciation for the possibilities for project failure one way or another due to a data model or project schema being wrong, even though I still only have a superficial understanding of what either of those concepts even are. . My question is, does your conception of data models in the abstract come from a formal academic course, like an algo’s & data structures course, or from trade-knowledge acquired through the practitioner grapevine?
    - supern0va 6d ago
      >you used the word ‘shape’ in the abstract sense, something I never came across until Claude vibecoding came along. Was it common / did you use ‘shape’ in the abstract sense before say 2025? As far as I can remember, this has been fairly common in tech (or at least where I've worked) for some time, though it's possible that my memory here is a bit fuzzy. I will say that my spouse has often commented that "Claude talks like you", which I suspect is because it is trained on a lot of language specific to the tech world. >My question is, does your conception of data models in the abstract come from a formal academic course, like an algo’s & data structures course, or from trade-knowledge acquired through the practitioner grapevine? I have worked for over a decade on the telemetry for a specific major product that most people have probably used, and shepherded it through a major re-architecture, so most of this is from my career. I came in right after it was built and witnessed the pain as things had been layered on over a long period of time as the product evolved, so I've just witnessed all the pointy bits where naive early decisions can come back to bite you later.
- bevr1337 7d ago
  > It was a gamble against your own time and patience. At this point, what do the words even mean? Your own patience and available time are always completely random and fairly distributed across a large enough data set? > Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered. Our ignorance isn't random chance. As we research and experiment, we reduce the problem area.
  - jimmaswell 7d ago
    > At this point, what do the words even mean? Your own patience and available time are always completely random and fairly distributed across a large enough data set? Predicting the time a task will take is impossible. Something that sounds like a 5 minute script can turn into a month of banging your head against unknown unknowns. I lose my patience when the afternoon I allocated is getting overrun by nonsense and I'm missing out on other things I wanted to do or household maintenance. > Our ignorance isn't random chance. As we research and experiment, we reduce the problem area. Every thought we have has random chance to be wrong despite our conviction that it's correct. Descartes' Evil Demon plays his tricks on all of us. How many times have you typed some line of code only to realize it was obviously wrong afterwards? Even for simpler matters we "hallucinate" all the time. I was deep in thought trying to help someone come up with an acronym the other day and felt convicted that "Goal Oriented Augmented Retrieval" worked for GOAL until I said it aloud. Our thoughts and actions are consistently wrong some portion of the time because our meat computers are not perfect positronic brains running prolog. We put cereal in the fridge and say "you too" to the waiter. Every thought we put down or action we take is a gamble on the soundness of the thought or action.
thepasswordis 7d ago
Literally putting tokens into a machine and hoping profit comes out. Amazing.
- seki285 7d ago
  Bingo
ricksunny 7d ago
1000%. My social community if reference is nomads of varying skills. The lower-skilled among them (the ones who were on the digital marketing, crypto,, now into building sales CRM tool) are manifestly demonstrating addictive tendencies around vibecoding) - the chain-smoking ADD guy pulling all-nighters still sticks in my mind. I myself (also low sw-skilled, just not the digital marketing / crypto hustling type) am exhibiting these tendencies in the form of all-nighters that in practice produce very little leverageable value-add. To remedy I try to be conservative in my goals - like only produce a comprehensive english-language PRD instead of any code till the tools get better and stroke the gambling instinct a bit less. (By the way this is not to say that all nomads are low-skilled or even low-sw-skill, not by a long shot. I’ve met plenty of CS-degreed nomads who I treat as the wizened experts that they are, from whom deferentially elicit their pearls of hard-won software project wisdom. It was one of these even who, non-derisively, recognized the ‘pulling the slot machine lever’ reflex among vibecoders after I posed the apparent addictive tendency. On later describing this to a longtime friend deeply conversant in social science, he immediately responded with a nod and the phrase ‘variable reward response’.)
mysterydip 7d ago
Which also explains why response speed is so important.
stavros 7d ago
People say this, but I've never seen it. AI has been very consistent in its rewards for me.
- wuisce 7d ago
  You're absolutely right. And it matters.
gbraad 7d ago
This is why I also suspect them to waste tokens on purpose.
- Grimblewald 7d ago
  I really do think its tge case. Ever notice how juuust when you run out of usage its allllmost correct? then the handy helpful "buy more credits!" link pops up. Meanwhile local models are finishing the task without fucking around and without alterting shit it wasnt supposed to touch. How is it a environmental catastrophe level, datacentre requiring, bullshit model is so much worse than something that runs on my workstation and doesn't cost us a ha itable planet? Either they're fucking with us serving 8b models at scale or china really has the AI race in the bag so much so that they can openly release what the USA jealously guards. They moan and complain about china copying from them (while doing the same), but if that's the case in full, then why are the chinese models better? you dont copy bad work and come out ahead.
  - gbraad 4d ago
    Out of curiosity; what do you run, and how? And yes, I noticed DeepSeek stayed on track way more than Claude.
- BikiniPrince 7d ago
  Listen Pal, this is load bearing. If you know what is good for you then you will stop asking questions. —Claude
  - gbraad 4d ago
    "Public opinion has been affected. we need a reset. Let me announce it on X."
varispeed 7d ago
Something regulators should look at. They don't deliver consistent compute, yet charge consistent money. In my opinion that's fraud.
- autoexec 7d ago
  Maybe they should be regulated like lootboxes and be required to post odds.
dpark 7d ago
I see this sentiment pretty regularly, and I don’t get it. Variable rewards is not sufficient to establish that it is “ basically gambling”. Everything in life is variable reward. You invite a friend over, they might accept or they might not. Drive to work, traffic might be good or might be bad. You ask a colleague to finish a task, they might do it or might not or might do a good job or might not. Everything is variable reward. Is everything gambling?
- Tadpole9181 7d ago
  I would agree that 1-2 years ago models were more "slot machine"-esque - sometimes the output was good, sometimes the output was bad. And as a result, I primarily used them for auto-complete functionality and bouncing ideas around. In those workflows, you can easily ignore it if the spin is wrong. Not everyone has the desire to work around the system, and many are diametrically opposed to the concept of AI. They get this perception that it's a slot machine because of that inconsistency, and then do the human thing of assuming that other people must just be flawed if they're different from them. They're "addicted to gambling". Obviously, things have changed. Open models can still be like that, but are often so fast and cheap at iterating it doesn't matter. SOTA models aren't perfect, but are to the point that they're generally much better than the average developer. But once that perception set in and the meme spreads, it's really hard for some to break out of it. Especially at the pace AI development has been moving. It's just that simple.
- jodrellblank 7d ago
  > "Everything is variable reward. Is everything gambling?" well, no. If you work overtime and get paid overtime, you are not gambling and that is not a variable reward. Humans engage more with rewards that are intermittent and variable. Like Futurama's scene from 'The Scary Door' where the character says "A casino where I'm winning, I must be in heaven! A casino where I always win, that's boring, I must really be IN HELL!". A constant predictable reward is boring, less engaging. So if you know you get no overtime, but sometimes your boss rewards you with $5 coffee voucher, sometimes a free pizza dinner, sometimes double-time pay for the time worked or a half-day off, now you might be gambling 1hr overtime for an intermittent variable reward. > "Drive to work, traffic might be good or might be bad." Good traffic is not a "reward" for driving to work(!) and you have to drive to work regardless so you are not risking anything [you might be risking your life, but you are not making a choice which can reward you with good traffic]. You might say that going a different route is a choice and a gamble which could reward you with good traffic, but traffic engineering does not work that way because if there was a consistently low-traffic route, everyone else would take that route until it was no faster than any other route. Traffic will generally be the predictable and similar every day, plus 'arriving at work early' is not much of a reward.
smugglerFlynn 7d ago
Modern LLM services are engineer's pipe dream that was heavily shaped by the shadiest product management dark patterns you can find: applying gambling-style engagement tactics, exploiting cognitive biases, exploiting users' lack of technical understanding to inflate product expectations, using fear mongering in external and investor communications. And that's not even a complete list.
tiborsaas 7d ago
Is it gambling if I'm beating the house?
raincole 7d ago
Gambling but with positive expected value, yes.

deleted 7d ago

[deleted]

Programming before AI was always variable reward. It was a gamble against your own time and patience. Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered. Something I wrote could have worked on the first try or I could have had to spend the day chasing logic errors (or multiple days chasing memory errors if it was C or C++). Maybe I would just get bored of the project, especially if I realized there were 20 layers of yaks I needed to shave first, and Visual Studio got stuck updating again, and before I could even start actually coding I had to spend the entire evening on an exhausting merge conflict. My entire weekend could be gone with nothing to actually show for it. I got so sick of all this at some point that I slowly stopped doing anything that wasn't my job. But then AI got better and better and I realized it was the ultimate unblocker. When that dreaded malaise started creeping in signaling it was a project's end because I didn't want to waste any more of my life dealing with bullshit orthogonal to what I was trying to do, I'd give it to the AI. It felt like a miracle the first time this worked, and it still does. If we were previously equipped with shovels to dig through bullshit, we now have a fully automated Bagger 288. The reward schedule now isn't variable anymore; the chance that I finish something in a good state is 100%. I can focus on the parts I actually enjoy - architecting the broader system, making the parts mesh together in a sensible way that's easy to work with and has some mathematical elegance to it, hand coding the bits I want to be really specific about (but now without the endless frustration of bugfixing or import errors and edgecases being immediately discovered, thanks to the AI).

supern0va 7d ago
>Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. I was working on a side project recently. I had spent months designing the data model in my spare time, thinking through how to make it as elegant and durable to change as possible in the long term, since (if I launched it) the repercussions for getting it wrong would be significant. Once I had a working design, it probably would have been several more months to build a working prototype and start testing it. Instead, Claude knocked out the prototype for me in an afternoon. And it immediately became clear that it didn't work: not because the data model didn't solve all the problems I wanted it to solve, but because it didn't fit the shape of how I quickly learned a normal person would need/want to interact with the product. I was so focused on the long term, that I never thought about what the first five minutes of a user with hands on the thing would need. And the changes needed would be significant. Maybe there's some variable reward mechanism. But I sure was glad to be able to pull that particular slot machine handle and learn that than waste even more of my time on what was a dead end.
- ricksunny 7d ago
  (Appreciating this entire thread) - side question: you used the word ‘shape’ in the abstract sense, something I never came across until Claude vibecoding came along. Was it common / did you use ‘shape’ in the abstract sense before say 2025? Also, my coming from being a non-coder, I have a lot of appreciation for the possibilities for project failure one way or another due to a data model or project schema being wrong, even though I still only have a superficial understanding of what either of those concepts even are. . My question is, does your conception of data models in the abstract come from a formal academic course, like an algo’s & data structures course, or from trade-knowledge acquired through the practitioner grapevine?
  - supern0va 6d ago
    >you used the word ‘shape’ in the abstract sense, something I never came across until Claude vibecoding came along. Was it common / did you use ‘shape’ in the abstract sense before say 2025? As far as I can remember, this has been fairly common in tech (or at least where I've worked) for some time, though it's possible that my memory here is a bit fuzzy. I will say that my spouse has often commented that "Claude talks like you", which I suspect is because it is trained on a lot of language specific to the tech world. >My question is, does your conception of data models in the abstract come from a formal academic course, like an algo’s & data structures course, or from trade-knowledge acquired through the practitioner grapevine? I have worked for over a decade on the telemetry for a specific major product that most people have probably used, and shepherded it through a major re-architecture, so most of this is from my career. I came in right after it was built and witnessed the pain as things had been layered on over a long period of time as the product evolved, so I've just witnessed all the pointy bits where naive early decisions can come back to bite you later.
bevr1337 7d ago
> It was a gamble against your own time and patience. At this point, what do the words even mean? Your own patience and available time are always completely random and fairly distributed across a large enough data set? > Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered. Our ignorance isn't random chance. As we research and experiment, we reduce the problem area.
- jimmaswell 7d ago
  > At this point, what do the words even mean? Your own patience and available time are always completely random and fairly distributed across a large enough data set? Predicting the time a task will take is impossible. Something that sounds like a 5 minute script can turn into a month of banging your head against unknown unknowns. I lose my patience when the afternoon I allocated is getting overrun by nonsense and I'm missing out on other things I wanted to do or household maintenance. > Our ignorance isn't random chance. As we research and experiment, we reduce the problem area. Every thought we have has random chance to be wrong despite our conviction that it's correct. Descartes' Evil Demon plays his tricks on all of us. How many times have you typed some line of code only to realize it was obviously wrong afterwards? Even for simpler matters we "hallucinate" all the time. I was deep in thought trying to help someone come up with an acronym the other day and felt convicted that "Goal Oriented Augmented Retrieval" worked for GOAL until I said it aloud. Our thoughts and actions are consistently wrong some portion of the time because our meat computers are not perfect positronic brains running prolog. We put cereal in the fridge and say "you too" to the waiter. Every thought we put down or action we take is a gamble on the soundness of the thought or action.

thepasswordis 7d ago

Literally putting tokens into a machine and hoping profit comes out. Amazing.

seki285 7d ago
Bingo

ricksunny 7d ago

1000%. My social community if reference is nomads of varying skills. The lower-skilled among them (the ones who were on the digital marketing, crypto,, now into building sales CRM tool) are manifestly demonstrating addictive tendencies around vibecoding) - the chain-smoking ADD guy pulling all-nighters still sticks in my mind. I myself (also low sw-skilled, just not the digital marketing / crypto hustling type) am exhibiting these tendencies in the form of all-nighters that in practice produce very little leverageable value-add. To remedy I try to be conservative in my goals - like only produce a comprehensive english-language PRD instead of any code till the tools get better and stroke the gambling instinct a bit less. (By the way this is not to say that all nomads are low-skilled or even low-sw-skill, not by a long shot. I’ve met plenty of CS-degreed nomads who I treat as the wizened experts that they are, from whom deferentially elicit their pearls of hard-won software project wisdom. It was one of these even who, non-derisively, recognized the ‘pulling the slot machine lever’ reflex among vibecoders after I posed the apparent addictive tendency. On later describing this to a longtime friend deeply conversant in social science, he immediately responded with a nod and the phrase ‘variable reward response’.)

mysterydip 7d ago

Which also explains why response speed is so important.

stavros 7d ago

People say this, but I've never seen it. AI has been very consistent in its rewards for me.

wuisce 7d ago
You're absolutely right. And it matters.

gbraad 7d ago

This is why I also suspect them to waste tokens on purpose.

Grimblewald 7d ago
I really do think its tge case. Ever notice how juuust when you run out of usage its allllmost correct? then the handy helpful "buy more credits!" link pops up. Meanwhile local models are finishing the task without fucking around and without alterting shit it wasnt supposed to touch. How is it a environmental catastrophe level, datacentre requiring, bullshit model is so much worse than something that runs on my workstation and doesn't cost us a ha itable planet? Either they're fucking with us serving 8b models at scale or china really has the AI race in the bag so much so that they can openly release what the USA jealously guards. They moan and complain about china copying from them (while doing the same), but if that's the case in full, then why are the chinese models better? you dont copy bad work and come out ahead.
- gbraad 4d ago
  Out of curiosity; what do you run, and how? And yes, I noticed DeepSeek stayed on track way more than Claude.
BikiniPrince 7d ago
Listen Pal, this is load bearing. If you know what is good for you then you will stop asking questions. —Claude
- gbraad 4d ago
  "Public opinion has been affected. we need a reset. Let me announce it on X."

varispeed 7d ago

Something regulators should look at. They don't deliver consistent compute, yet charge consistent money. In my opinion that's fraud.

autoexec 7d ago
Maybe they should be regulated like lootboxes and be required to post odds.

dpark 7d ago

I see this sentiment pretty regularly, and I don’t get it. Variable rewards is not sufficient to establish that it is “ basically gambling”. Everything in life is variable reward. You invite a friend over, they might accept or they might not. Drive to work, traffic might be good or might be bad. You ask a colleague to finish a task, they might do it or might not or might do a good job or might not. Everything is variable reward. Is everything gambling?

Tadpole9181 7d ago
I would agree that 1-2 years ago models were more "slot machine"-esque - sometimes the output was good, sometimes the output was bad. And as a result, I primarily used them for auto-complete functionality and bouncing ideas around. In those workflows, you can easily ignore it if the spin is wrong. Not everyone has the desire to work around the system, and many are diametrically opposed to the concept of AI. They get this perception that it's a slot machine because of that inconsistency, and then do the human thing of assuming that other people must just be flawed if they're different from them. They're "addicted to gambling". Obviously, things have changed. Open models can still be like that, but are often so fast and cheap at iterating it doesn't matter. SOTA models aren't perfect, but are to the point that they're generally much better than the average developer. But once that perception set in and the meme spreads, it's really hard for some to break out of it. Especially at the pace AI development has been moving. It's just that simple.
jodrellblank 7d ago
> "Everything is variable reward. Is everything gambling?" well, no. If you work overtime and get paid overtime, you are not gambling and that is not a variable reward. Humans engage more with rewards that are intermittent and variable. Like Futurama's scene from 'The Scary Door' where the character says "A casino where I'm winning, I must be in heaven! A casino where I always win, that's boring, I must really be IN HELL!". A constant predictable reward is boring, less engaging. So if you know you get no overtime, but sometimes your boss rewards you with $5 coffee voucher, sometimes a free pizza dinner, sometimes double-time pay for the time worked or a half-day off, now you might be gambling 1hr overtime for an intermittent variable reward. > "Drive to work, traffic might be good or might be bad." Good traffic is not a "reward" for driving to work(!) and you have to drive to work regardless so you are not risking anything [you might be risking your life, but you are not making a choice which can reward you with good traffic]. You might say that going a different route is a choice and a gamble which could reward you with good traffic, but traffic engineering does not work that way because if there was a consistently low-traffic route, everyone else would take that route until it was no faster than any other route. Traffic will generally be the predictable and similar every day, plus 'arriving at work early' is not much of a reward.

smugglerFlynn 7d ago

Modern LLM services are engineer's pipe dream that was heavily shaped by the shadiest product management dark patterns you can find: applying gambling-style engagement tactics, exploiting cognitive biases, exploiting users' lack of technical understanding to inflate product expectations, using fear mongering in external and investor communications. And that's not even a complete list.

tiborsaas 7d ago

Is it gambling if I'm beating the house?

raincole 7d ago

Gambling but with positive expected value, yes.