What's Anthropic's optimization target??? Getting you the right answer as fast as possible! The variability in agent output is working against that goal, not serving it. If they could make it right 100% of the time, they would — and the "slot machine" nonsense disappears entirely. On capped plans, both you and Anthropic are incentivized to minimize interactions, not maximize them. That's the opposite of a casino. It's ... alignment (of a sort)
An unreliable tool that the manufacturer is actively trying to make more reliable is not a slot machine. It's a tool that isn't finished yet.
I've been building a space simulator for longer than some of the people diagnosing me have been programming. I built things obsessively before LLMs. I'll build things obsessively after.
The pathologizing of "person who likes making things chooses making things over Netflix" requires you to treat passive consumption as the healthy baseline, which is obviously a claim nobody in this conversation is bothering to defend.
What makes you believe this? The current trend in all major providers seem to be: get you to spin up as many agents as possible so that you can get billed more and their number of requests goes up.
> Slot machines have variable reward schedules by design
LLMs by all major providers are optimized used RLHF where they are optimized in ways we don't entirely understand to keep you engaged.
These are incredibly naive assumptions. Anthropic/OpenAI/etc don't care if you get your "answer solved quickly", they care that you keep paying and that all their numbers go up. They aren't doing this as a favor to you and there's no reason to believe that these systems are optimized in your interest.
> I built things obsessively before LLMs. I'll build things obsessively after.
The core argument of the "gambling hypothesis" is that many of these people aren't really building things. To be clear, I certainly don't know if this is true of you in particular, it probably isn't. But just because this doesn't apply to you specifically doesn't mean it's not a solid argument.
I was surprised when I saw that Cursor added a feature to set the number of agents for a given prompt. I figured it might be a performance thing - fan out complex tasks across multiple agents that can work on the problem in parallel and get a combined solution. I was extremely disappointed when I realized it's just "repeat the same prompt to N separate agents, let each one take a shot and then pick a winner". Especially when some tasks can run for several minutes, rapidly burning through millions of tokens per agent.
At that point it's just rolling dice. If an agent goes so far off-script that its result is trash, I would expect that to mean I need to rework the instructions and context I gave it, not that I should try the same thing again and hope that entropy fixes it. But editing your prompt offline doesn't burn tokens, so it's not what makes them money.
Simply, cut-throat competition. Given multiple nations are funding different AI-labs, quality of output and speed are one of the most important things.
Assuming that is still true, then they absolutely have an incentive to keep your tokens/requests to the absolute minimum required to solve your problem and wow you.
which then causes increased token usage because you need to prompt multiple times.
Idk, maybe it's just me though.
Intermittent variable rewards, whether produced by design or merely as a byproduct, will induce compulsive behavior, no matter the optimization target. This applies to Claude
Does this mean I should not garden because it's a variable reward? Of course not.
Sometimes I will go out fishing and I won't catch a damn thing. Should I stop fishing?
Obviously no.
So what's the difference? What is the precise mechanism here that you're pointing at? Because sometimes life is disappointing is a reason to do nothing. And yet.
This is an incorrect understanding of intermittent variable reward research.
Claims that it "will induce compulsive behavior" are not consistent with the research. Most rewards in life are variable and intermittent and people aren't out there developing compulsive behavior for everything that fits that description.
There are many counter-examples, such as job searching: It's clearly an intermittent variable reward to apply for a job and get a good offer for it, but it doesn't turn people into compulsive job-applying robots.
The strongest addictions to drugs also have little to do with being intermittent or variable. Someone can take a precisely measured abuse-threshold dose of a drug on a strict schedule and still develop compulsions to take more. Compulsions at a level that eclipse any behavior they'd encounter naturally.
Intermittent variable reward schedules can be a factor in increasing anticipatory behavior and rewards, but claiming that they "will induce compulsive behavior" is a severe misunderstanding of the science.
The variability in eg soccer kicks or basketball throws is also there but clearly there is a skill element and a potential for progress. Same with many other activities. Coding with LLMs is not so different. There are clearly ways you can do it better and it's not pure randomness.
So you're saying businesses shouldn't hire people either?
There is absolutely no incentive to do that, for any of these companies. The incentive is to make the model just bad enough you keep coming back, but not so bad you go to a competitor.
We've already seen this play out. We know Google made their search results worse to drive up and revenue. Exact same incentives are at play here, only worse.
IF I USE LESS TOKENS, ANTHROPIC GETS MORE MONEY! You are blindly pattern matching to "corporation bad!" without actually considering the underlying structure of the situation. I believe there's a phrase for this to do with probabilistic avians?
https://www-cdn.anthropic.com/58284b19e702b49db9302d5b6f135a...
(cmd-f "slot machine")
Are you totally sure they are not measuring/optimizing engagement metrics? Because at least I can bet OpenAI is doing that with every product they have to offer.
That is a generous interpretation. Mighr be correct. But they dont make as much money if you quickly get the right answer. They make more money if you spend as many tokens as possible being on that "maybe next time" hook.
Im not saying theyre actually optimizng for that. But charlie munger said "show me the incentives, and ill show you the outcome"
This didn't used to be the case, so I assume that it must be intentional.
The analogy was too strained to make sense.
Despite being framed as a helpful plea to gambling addicts, I think it’s clear this post was actually targeted at an anti-LLM audience. It’s supposed to make the reader feel good for choosing not to use them by portraying LLM users as poor gambling addicts.
I found it interesting that Google removed the "summary cards" supposedly "to improve user experience" however the AI overview was added back.
I suspect the AI overview is much more influenceable by advertisement money then the summary cards where.
If Dave the developer is paying, Dave is incentivized to optimize token use along with Anthropic (for the different reasons mentioned).
If the Dave's employer, Earl, is paying and is mostly interested in getting Dave to work more, then what incentive does Dave have to minimize tokens? He's mostly incentivized by Earl to produce more code, and now also by Anthropic's accidentally variable-reward coding system, to code more... ?
I think their greater argument was to highlight how agentic coding is eroding work life balance, and that companies are beginning to make that the norm.
Even a perfect LLM will not be able to produce perfect outputs because humans will never put in all the context necessary to zero-shot any non-trivial query. LLMs can't read your mind and will always make distasteful assumptions unless driven by users without any unique preferences or a lot of time on their hands to ruminate on exactly how they want something done.
I think it will always be mostly boring back-and-forth until the jackpot comes. Maybe future generations will align their preferences with the default LLM output instead of human preferences in that domain, though.
yeah I think the bluesky embed is much more along the lines of what I'm experiencing than the OP itself.
This is subtly different. It's not clear that the people depicted like making things, in the sense of enjoying the process. The narrative is about LLMs fitting into the already-existing startup culture. There's already a blurry boundary between "risky investment" and "gambling", given that most businesses (of all types, not just startups) have a high failure rate. The socially destructive characteristic identified here is: given more opportunity to pull the handle on the gambling machine, people are choosing to do that at the expense of other parts of their life.
But yes, this relies on a subjective distinction between "building, but with unpredictable results" and "gambling, with its associated self-delusions".
It is a business that sells monthly subscriptions
Wait, what? Anthropic makes money by getting you to buy and expend tokens. The last thing they want is for you to get the right answer as fast as possible. They want you to sometimes get the right answer unpredictably, but with enough likelihood that this time will work that you keep hitting Enter.
In an environment where providers are almost entirely interchangeable and tiniest of perceived edges (because there's still no benchmark unambiguously judging which model is "better") make or break user retention, I just don't see how it's not ludicrous on its face that any LLM provider would be incentivized to give unreliable answers at some high-enough probability.
Ideas are a dime a dozen, now proofs of concept are a load of tokens a dozen.
Trust me, we all feel like the house is our friend until its isn't!
To the bluesky poster's point: Pulling out a laptop at a party feels awkward for most; pulling out your phone to respond to claude barely registers. That’s what makes it dangerous: It's so easy to feel some sense of progress now. Even when you’re tired and burned out, you can still make progress by just sending off a quick message. The quality will, of course, slip over time; but far less than it did previously.
Add in a weak labor market and people feel pressure to stay working all the time. Partly because everyone else is (and nobody wants to be at the bottom of the stack ranking), and partly because it’s easier than ever to avoid hitting a wall by just "one more message". Steve Yegge's point about AI vampires rings true to me: A lot of coworkers I’ve talked to feel burned out after just a few months of going hard with AI tools. Those same people are the ones working nights and weekends because "I can just have a back-and-forth with Claude while I'm watching a show now".
The likely result is the usual pattern for increases in labor productivity. People who can’t keep up get pushed out, people who can keep up stay stuck grinding, and companies get to claim the increase in productivity while reducing expenses. Steve's suggestion for shorter workdays sound nice in theory, but I would bet significant amounts of money the 40-hour work week remains the standard for a long time to come.
This isn't generally true at all. The "all tech companies are going to 996" meme comes up a lot here but all of the links and anecdotes go back to the same few sources.
It is very true that the tech job market is competitive again after the post-COVID period where virtually nobody was getting fired and jobs were easy to find.
I do not think it's true that the median or even 90th percentile tech job is becoming so overbearing that personal time is disappearing. If you're at a job where they're trying to normalize overwork as something everyone is doing, they're just lying to you to extract more work.
It starts with people who feel they’ve got more to lose (like those supporting a family) working extra to avoid looking like a low performer, whether that fear is reasonable or not. People aren’t perfectly rational, and job-loss anxiety makes them push harder than they otherwise would. Especially now, when "pushing harder" might just mean sending chat messages to claude during your personal time.
Totally anecdotal (strike 1), and I'm at a FAANG which is definitely not the median tech job (strike 2), but it’s become pretty normal for me to come back Monday to a pile of messages sent by peers over the weekend. A couple years ago even that was extremely unusual; even if people were working on the weekend they at least kept up a facade that they weren't.
When people talk about leaving their agents to run overnight, what are those agents actually doing? The limited utility I've had using agent-supported software development requires a significant amount of hand holding, maybe because I'm in an industry with limited externally available examples to build am model off of (though all of the specifications are public, I've yet to see an agent build an appropriate implementation).
So it's much more transactional...I ask, it does something (usually within seconds), I correct, it iterates again...
What sort of tasks are people putting these agents to? How are people running 'multiple' of these agents? What am I missing here?
I might run 3-4 claude sessions because that's the only way to have "multiple chats" to e.g. ask unrelated things. Occasionally a task takes long enough to keep multiple sessions busy, but that's rather rare and if it happens its because the agent runs a long running task like the whole test suite.
The story of running multiple agents to build full features in parallel... doesn't really add up in my experience. It kinda works for a bit if you have a green field project where the complexity is still extremely low.
However once you have a feature interaction matrix that is larger than say 3x3 you have to hand hold the system to not make stupid assumptions. Or you prompt very precisely but this also takes time and prevents you from ever running into the parallel situation.
The feature interaction matrix size is my current proxy "pseudo-metric" for when agentic coding might work well and at which abstraction level.
But so far that doesn't change the reality - I can't find any opportunities to let an agent run for more than 30 minutes at best, and parallel agents just seem to confuse each other.
I came from embedded, where I wasn't able to use agents very effectively for anything other than quick round trip iterative stuff. They were still really useful, but I definitely could never envision just letting an agent run unattended.
But I recently switched domains into vaguely "fullstack web" using very popular frameworks. If I spend a good portion of my day going back and forth with an agent, working on a detailed implementation plan that spawns multiple agents, there is seemingly no limit* to the scope of the work they are able to accurately produce. This is because I'm reading through the whole plan and checking for silly gotchyas and larger implementation mistakes before I let them run. It's also great because I can see how the work can be parallelized at certain parts, but blocked at others, and see how much work can be parallelized at once.
Once I'm ready, I can usually let it start with not even the latest models, because the actual implementation is so straightforwardly prompted that it gets it close to perfectly right. I usually sit next to it and validate it while it's working, but I could easily imagine someone letting it run overnight to wake up to a fresh PR in the morning.
Don't get me wrong, it's still more work that just "vibing" the whole thing, but it's _so_ much more efficient than actually implementing it, especially when it's a lot of repetitive patterns and boilerplate.
* I think the limit is how much I can actually keep in my brain and spec out in a well thought out manner that doesn't let any corner cases through, which is still a limit, but not necessarily one coming from the agents. Once I have one document implemented, I can move on to the next with my own fresh mental context which makes it a lot easier to work.
Hope it helps!
This is definitely a way to keep those who wear Program and Project manager hats busy.
As i build with agents, i frequently run into new issues that arent in scope for the task im on and would cause context drift. I have the agent create a github issue with a short problem description and keep going on the current task. In another terminal i spin up a new agent and just tell it “investigate GH issue 123” and it starts diving in, finds the root cause, and proposes a fix. Depending on what parts of the code the issue fix touches and what other agents ive got going, i can have 3-4 agents more or less independently closing out issues/creating PRs for review at a time. The agents log their work in a work log- what they did, what worked what didnt, problems they encountered using tools - and about once a day i have an agent review the worklog and update the AGENTS.md with lessons learned.
If you have a loop set up, e.g., using OpenClaw or a Ralph loop, you can stretch that out further.
I would suggest that when you get to that point really, you want some kind of adversarial system set up with code reviews (e.g., provided by CodeRabbit or Sourcery) and automation to feed that back into the coding agent.
Providing material for attention-grabbing headlines and blog posts, primarily. Can't (in good conscience, at least) claim you had an agent running all night if you didn't actually run an agent all night.
Is it possible? Yes, I've had success with having a model output a 100 step plan that tried to deconflict among multiple agents. Without re-creating 'Gas town', I could not get the agents to operate without stepping on toes. With _me_ as the grand coordinator, I was able to execute and replicate a SaaS product (at a surface level) in about 24hrs. Output was around 100k lines of code (without counting css/js).
Who can prove that it works correctly though? An AI enthusiasts will say "as long as you've got test coverage blah blah blah". Those who have worked large scale products know that tests passing is basically "bare minimum". So you smoke test it, hope you've got all the paths, and toss it up and try to collect money from people? I don't know. If _this_ is the future, this will collapse under the weight of garbage code, security and privacy breaches, and who knows what else.
He is building a trading automation for personal use. In his design he gets a message on whatsapp/signal/telegram and approves/rejects the trade suggestion.
To define specifications for this, he defined multiple agents (a quant, a data scientist, a principal engineer, and trading experts - “warren buffett”, “ray dalio”) and let the agents run until they reached a consensus on what the design should be. He said this ran for a couple of hours (so not strictly overnight) after he went to sleep; in the morning he read and amended the output (10s of pages equivalent) and let it build.
This is not a strictly-defined coding task, but there are now many examples of emerging patterns where you have multiple agents supporting each other, running tasks in parallel, correcting/criticising/challenging each other, until some definition of “done” has been satisfied.
That said, personally my usage is much like yours - I run agents one at a time and closely monitor output before proceeding, to avoid finding a clusterfuck of bad choices built on top of each other. So you are not alone my friend :-)
I can see the utility in creating very simple web-based tools where there's a monstrous wealth of public resources to build a model off of, but even the most recent models provided by Anthro, OpenAI, or MSFT seem prone to not quite perfection. And every time I find an error I'm left wondering what other bugs I'm not catching.
Do some people just create complete SaaSlop apps with it overnight? Of course, just put together a plan (by asking the LLM to write the plan) with everything you want the app to do and let it run.
Wouldn’t be better to setup an api docs (Postman, RapidApi,…), extract an OpenAPI version from that, then use a generator for your language of choice (Nswag,…)?
You can draw the line wherever you want. :) Personally, I wish I'd built a new gaming rig a year ago so I could mess with local models and pay all these same costs.
I think of my agents like golems from disc world, they are defined by their script. Adding texture to them improves the results so I usually keep a running tally of what they have worked on and add that to the header. They are a prompt in a folder that a script loops over and sends to gemeni(spawning an agent and moving to the next golem script)
I also was curious to see if it could be used it for developing some small games, whenever I would run into a problem I couldn't be bothered to solve or needed a variety of something I would let a few llms work on it so in the morning I had something to bounce off. I had pretty good success with this for RTS games and shooting games where variety is something well documented and creativity is allowed. I imagine there could be a use here, I've been calling it dredging cause I imagine myself casting a net down into the slop to find valuables.
I did have an idea where all my sites and UI would be checked against some UI heuristic like Oregon State's inclusivity heuristic but results have been mixed so far. The initial reports are fine, the implementation plans are ok but it seems like the loop of examine, fix, examine... has too much drift? That does seem solvable but I have a concern that this is like two lines that never touch but get closer as you approach infinity.
There is some usefulness in running these guys all night but I'm still figuring out when its useful and when its a waste of resources.
In my case, I built a small api that claude can call to get tasks. I update the tasks on my phone.
The assumption is that you have a semi-well structured codebase already (ours is 1M LOC C#). You have to use languages with strong typing + strict compiler.You have to force claude to frequently build the code (hence the cpu cores + ram + nmve requirement).
If you have multiple machines doing work, have single one as the master and give claude ssh to the others and it can configure them and invoke work on them directly. The usecase for this is when you have a beefy proxmox server with many smaller containers (think .net + debian). Give the main server access to all the "worker servers". Let claude document this infrastructure too and the different roles each machine plays. Soon you will have a small ranch of AI's doing different things, on different branches, making pull requests and putting feedback back into task manager for me to upvote or downvote.
Just try it. It works. Your mind will be blown what is possible.
Generate material for yet another retarded twitter hype post.
It's more like being hooked on a slot machine which pays out 95% of the time because you know how to trick it.
(I saw "no actual evidence pointing to these improvements" with a footnote and didn't even need to click that footnote to know it was the METR thing. I wish AI holdouts would find a few more studies.)
Steve Yegge of all people published something the other day that has similar conclusions to this piece - that the productivity boost for coding agents can lead to burnout, especially if companies use it to drive their employees to work in unsustainable ways: https://steve-yegge.medium.com/the-ai-vampire-eda6e4f07163
Yeah I really feel that!
I recently learned the term "cognitive debt" for this from https://margaretstorey.com/blog/2026/02/09/cognitive-debt/ and I think it's a great way to capture this effect.
I can churn out features faster, but that means I don't get time to fully absorb each feature and think through its consequences and relationships to other existing or future features.
But for what I've seen both validating my and others coding agents outputs I'd estimate a much lower percentage (Data Engineering/Science work). And, oh boy, some colleages are hooked to generating no matter the quality. Workslop is a very real phenomenon.
I was really impressed with how it parsed the structured checklist. I was not at all impressed by how it digested the paper. Lots of disguised errors.
“It’s not like a slot machine, it’s like… a slot machine… that I feel good using”
That aside if a slot machine is doing your job correctly 95% of the time it seems like either you aren’t noticing when it’s doing your job poorly or you’ve shifted the way that you work to only allow yourself to do work that the slot machine is good at.
There's also this article on hbr.org https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...
This is a real thing, and it looks like classic addiction.
I think you are mistaken on what the "payout" is. There's only one reason someone is working all hours and during a party and whatnot: it's to become rich and powerful. The payout is not "more code", it's a big house, fast cars, beautiful women etc. Nobody can trick it into paying out even 1% of the time, let alone 95%.
Claude Code wasting my time with nonsense output one in twenty times seems roughly correct. The rest of the time it's hitting jackpots.
Right but the <100% chance is actually why slot machines are addictive. If it pays out continuously the behaviour does not persist as long. It's called the partial reinforcement extinction effect.
If you are unfamiliar with the various ways that naive code would fail in production, you could be fooled into thinking generated code is all you need.
If you try to hold the hand of the coding agents to bring code to a point where it is production ready, be prepared for a frustrating cycle of models responding with ‘Fixed it!’ while only having introduced further issues.
My paraphrase of their caveats:
- experts on their own open source proj are not representative of most software dev
- measuring time undervalues trading time for effort
- tools are noticeably better than they were a year ago when the study was conducted
- it really does take months of use to get the hang of it (or did then, less so now)
Before you respond to these points, please look at the full study’s treatment of the caveats! It’s fantastic, and it’s clear almost no one citing the study actually read it.
[0]: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
Not everyone gets hooked on those, but I do. I've played a bunch of those long-winded idle games, and it looks like a slight addiction. I would get impatient that it takes so long to progress, and it would add anxiety to e.g. run this during breaks at work, or just before going to sleep. "Just one more click".
And to be perfectly honest, it seems like the artificial limits of Anthropic (5 hour session limits) dig into similar mechanism. I do less non-programming hobbies since I've got myself a subscription.
Tests, linting, guidance in response to key events (Claude Code hooks are great for this), automatically passing the agent’s code plan to another model invocation then passing back whatever feedback that model has on the plan so you don’t have to point out the same flaws in plans over and over.. custom scripts that iterate your codebase for antipatterns (they can walk the AST or be regex based - ask your agent to write them!)
Codify everything you’re looping back to your agent about and make it a guardrail. Give your agent the tools it needs to give itself grounding.
An agent without guardrails or grounding is like a person unconnected to their senses: disconnected from the world, all you do is dream - in a dream anything can happen, there’s nothing to ensure realism. When you look at it that way it’s a miracle coding agents produce anything useful at all :)
And to another point: work life balance is a huge challenge. Burnout happens in all departments, not just engineering. Managers can get burnout just as easily. If you manage AI agents, you'll just get burnout from that too.
The difference is that in gambling 'the house always wins', but in our case we do make progress towards our goal of conquering the world with our newly minted apps.
The situation where this comparison holds is when vibe coding leads nowhere and you don't accomplish anything but just burn through tokens.
What? Your vibe coded slop is just going to be competing with someone else's vibe coded slop.
It’s funny because this is what we do already at many jobs but now its just telling a computer to tell a computer what to do. A higher level of abstraction.
What's wild to me is that there's a whole other segment of people that treat tokens as, I dunno, some kind of malicious gatekeeping to the magical program generator. Some kind of endorphin rush of extracting functional code from a naive and poorly formed idea.
To the former group, the gambling metaphor is flatly ridiculous. The AI is a tool and tokens are your allocation for tool time. To the latter, someone is trying to stifle you and strangle your creativity behind arbitrary limits.
I don't know how to feel about this other than uneasy and worried.
I do think it can be addictive, but there are many things that are addictive that aren't gambling.
I think a better analogy is something like extreme sport, where people can get addicted to the point it can be harmful.
Maybe someone can show me how you're supposed to do it, because I have seen no evidence that AI can write code at all.
When it works for pure generation it's beautiful, when it doesn't it's ruinous enough to make me take two steps back. I'll have another go at getting with all the pure agentic rage everyone's talking about soon enough.
Step 2: download Zed and paste in your API Key
Step 3: Give detailed instructions to the assistant, including writing ReadMe files on the goal of the project and the current state of the project
Step 4: stop the robot when it's making a dumb decision
Step 5: keep an eye on context size and start a new conversation every time you're half full. The more stuff in the context the dumber it gets.
I spent about 500 dollars and 16 hours of conversation to get an MVP static marketplace [0], a ruby app that can be crawled into static (and js-free!) files, without writing a single line of code myself, because I don't know ruby. This included a rather convoluted data import process, loading the database from XML files of a couple different schemas.
Only thing I had to figure out on my own was how to upload the 140,000 pages to cloudflare free tier.
when its actually writing code its pretty hands off, unless you need to course correct to point it in a better direction
People are already at the point they could be working a fraction of the time they do, but they continue to work full time, producing shit they don't need, like ever growing vehicles and incremental smartphone upgrades etc.
We’re well past the need to retry the same prompt multiple times in order to get working code. The models with their harnesses are properly agentic now, they can find the right context, make a plan, write the code, run the tests and fix the bugs with little to no intervention from a human.
The hardest part now is keeping up with them when it comes to approving the deliverables and updating the architecture and spec as new things are discovered by using the software. Not new bugs but corrections to your own assumptions you had before the feature was built.
The hard part is almost entirely management.
That’s something to seriously think about.
Touché.
I really cannot tell
But that's for personal pleasure. This post is receeding from the concerns about "token anxiety," about the addiction to tokens. This post is about work culture & late capitalism anxiety, about possible pressures & systems society might impose.
I reflect a lot on AI doesn't reduce the work, it intensifies it. https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies... The spirit of this really nails something core to me. We coders especially get help doing so much of menial now. Which means we spend a lot more time making intense analysis and critiques, are much more doing the hard thought work of 'is what we have here as good as it can be'. Finding new references or patterns to feed back into the AI to steer already working implementations towards better outcomes.
And my heart tells me that corporations & work life as we know it are almost universally just really awful about supporting reflective contemplative work like this. Work wants output. It doesn't want you sit in a hammock and think about it. But increasingly I tell you the key to good successful software is Hammock Driven Development. It's time to use our brains more, in quiet reflection. https://github.com/matthiasn/talk-transcripts/blob/master/Hi...
996 sounds like garbage on its own, as a system of toil. But I also very much respect an idea of continuous work, one that also intersperses rest throughout the day. Doing some chores or going to the supermarket or playing with the kid can be an incredibly good way to let your preconscious sift through the big gnarly problems about. The response to the intensity of what we have, to me, speaks of a need to spread out the work, to de-concentrate it, to build in more than hammock time. I was on the fence about whether the traditional workday deserved to survive before AI hit, and my feels about it being a gross mismatch have massively intensified since.
As I started my post with, I personally have a much more positive experience, with what yes feels like a token addiction. But it doesn't feel like an anxiety. It feels like the greatest most exciting adventure, far beyond what I had hoped for in life ever. This is wildly fun, going far far further out than I had ever hoped to get to see. I'm not "anxiously" pulling the lever arm on the token machine, I'm just thrilled to get to do it. To have time to reflect and decide, I have 3-8 things going at once (and probably double they back burnered but open, on Niri rows!) to let myself make slower decisions, to analyze, while keeping the things that can safely move forwards moving forwards.
That also seems like something worker exploitative late capitalism is mostly hot garbage at too! Companies really try to reduce in flight activities. Sprint planning is about crafting deliberate work. But our freedom and agency here far outstrips these dusty old practices. It is anxiety inducing to be so powerful so capable & to have a bureaucracy that constraints and confines, that wants only narrow windows of our use.
Also, shame on Tim Kellogg for not God damned linking the actual post he was citing. Garbagefire move. https://writing.nikunjk.com/p/token-anxiety https://news.ycombinator.com/item?id=47021136
I _kind_ of get this if we're talking about working on big, important, world-changing problems. If it's another SaaS app or something like that, I find it pretty depressing.
