The New AI Superpowers: Focus and Followthrough | Hacker News Reader

The New AI Superpowers: Focus and Followthrough

rickmanelius.com

220 pointsmooreds78 comments

This seems very related to a trend I’m seeing as my company goes all in on AI: everyone thinks that every problem is “a couple hours” with AI now, and they all want zero external dependencies because they can move faster alone. As a result, we’re now in an even worse “yet-another-…” age where everyone has built approximately the same (but somehow incompatible) versions of all the same beginner-level software, and (ironically) while they want no external dependencies they’re also pushing for org-level mandates to require everyone else to use their solution. Meanwhile, no one wants to do the slow/bottleneck part that cant easily be automated or scaled; they just throw an “agent” at it and call it done—but there’s nothing _there_. You can trust the agent on easy tasks and you can’t trust it on hard ones, but you can’t tell which ones are easy or hard. Improvements in foundation model tech move thresholds of the problem but can’t eliminate it.

Long story short, I think we’re in a phase where the organizational value function is lagging behind the tech. A “proof of concept” used to be correlated with “proof of work” and some amount of domain understanding, but I think now what we need is a focus on “proof of understanding” or else you’re probably just wasting tokens on a baby version of the problem. A decent proxy right now is that if you have zero external dependencies then your solution is probably a toy.

problem is that no one knows what to build and there are way too many ppl employed

I’m seeing kinda the opposite. At some point it becomes obvious what to build and there’s no friction to building it anymore. So you get a flood of low effort copies of the same thing and no one wants to depend on anyone else, and the old value function was that ownership matters. I think the new value function needs to be much more focused on outcomes—and ownership isn’t an outcome.

The result of this is huge wastage in terms of effort and tokens.

This is huge... At least from what I've seen, many companies (even if they don't know it) are largely sloppy R&D shops. Most people are on projects that are highly speculative and unlikely to generate revenue. But it often isn't framed that way. Everyone believes their project is the obvious way forward.

> they’re also pushing for org-level mandates to require everyone else to use their solution.

That's how you get promotions when you don't do anything valuable lol ok done with the cynicism

Not cynicism, incentives. Everything is shaped by incentives. We make movies about heroes and martyrs because they're 0.001% of us.

No disagreement here, the incentives for software have always sucked. People complain constantly about how everything is enshittefied, that's just corporate incentives manifested.

Do this long enough and you really believe it's good, after all the years of being promoted for strong-arming people.

But hey, I do it. I prebake my own tools to work within the system, a new person joins, and I show them the tools as an example. Instead of learning what it does and why, they just learn how to use the tool (now via claude).

AI completely broke some of the signals management used as proxies for team competence. In the past, teams that moved quickly and had a proof of concept to show management were valued for the speed of their execution. Usually a team that could execute quickly had some competent developers who could also quickly execute on the challenges of the implementation when it scaled up to real world problems.

Now anyone with enough LLM tokens can move quickly and have a pretty proof of concept in front of management in days or maybe hours. There's a land grab to get things in front of management so you can stake your claim on something. The implementation details are assumed to be solvable by enough Claude sessions and ChatGPT prompts.

Where this falls apart is that the real difficult problems don't scale well with LLM brute force techniques. You need people who understand the problem and know how to drive the LLM agents around properly, as well as when to set them aside and go look at the code and data themselves.

The teams who try to brute force everything with LLM tokens can get a little farther every few months, but for complex problems I've seen enough variations of teams hitting a wall of complexity and tech debt with a team that doesn't know much about how their system works because they've just been asking agents to solve every problem as it comes up. With each iteration it takes a little longer for the agents to come to a solution and the system becomes a little more brittle. Even with our current mega-context-window LLMs you can reach a point where every other change to your big ball of code breaks something else, which requires another agent session to diagnose and fix, which might then break another thing down the line. The refactors to try to clean things up end up breaking even more. Having a zillion tests starts to backfire when the team can't tell which tests are testing valid results and which are baking in incorrect results.

Maybe there's an end game where agents get so good that none of this matters, but I think it's more likely that we're going to have to build up better intuitions about what good execution looks like. The old signals of moving fast are meaningly and potentially even negative now.

> AI completely broke some of the signals management used as proxies for team competence.

This is very similar to how LLMs were already breaking signals in writing. It was (is?) an enormously important proxy (however imperfect) for a bunch of things like: "put effort into this" or "is smart" or "is interested and engaged" or "has an emotion" or "has carefully considered the idea."

Heck, even basic awareness of an issue/detail gets forged, mentioned in the text but never in the mind of the sender.

In a way, LLMs have made it much more difficult to be believed. On the one hand, my inner paranoiac feels vindicated -- preparing for the worst case, over a lifetime, feels more prescient than it should. On the other hand, making decisions, specifically about how much faith to put into another unfamiliar human, becomes even less tractable.

The work can no longer speak for the human. A nightmare for the introvert.

The work, even before LLMs, struggled to speak for the introvert (and even the extrovert) -- a unique enough work usually results in puzzlement, a questioning of motives, and sometimes even overt ridicule.

Even for writing, it was a struggle. How many times did pieces of writing get ignored (or rapidly skimmed) because they were "TLDR"?

When I was very young (a teen), I wondered (after seeing the films The Matrix and Fight Club) to what degree other people were a dream-like hallucination (basically, those films made the concept of p-zombies accessible to a kid). That childish daydream, is now a reality. My inner paranoiac squeals with delight. Ah, the perversity. Even skill at deception itself can be faked, now. Even the beautiful can treated as if it were hideous.

I wonder if anyone ever made an episode of Star Trek, where one crew-member pranks another (possibly inebriated) crew-member, by having them wake up in a perfect holodeck reconstruction of their ship and crew. How long before they would notice? Weeks could go by, before a red alert dissolves the illusion.

It's after 3am, and I am not inebriated, but fuck I need to sleep. Sometimes I hope I drift into a coma, so that I can deal with things that are, on the one hand hallucinations, but on the other hand _not_ deceptions. There is a universe out there, where _their_ Matrix (film), is Vanilla Sky (amusingly, its inspiration was the film Abre los Ojos, released one year before The Matrix -- maybe in this universe, the Spanish Armada reached England).

Good night, you doomed, sad people.

This is exactly what I’m talking about. I’m seeing the same pattern. And the most useful proxy I’ve found so far for “we don’t understand the problem” is taking zero dependencies. It means that you haven’t bothered to understand what the library does and you think it can be replaced with something AI one-shots.

Given the limits of LLM's regarding context, why is zero dependencies the goal ?

Shouldn't something like maximum reuse/abstraction, minimal written code, should be a better goal ?

Possible leading to more llm-scalable systems ?

Two things.

1. Deps are an organizational hassle. They need review, get flagged as security and legal risks, and (mostly incorrectly imo) have been somewhat tarnished by the idea that deps are all just "leftpad" - simple things that are over abstracted. This is the "incorrect" argument that I don't really think should carry much weight, but often does win the discussion internally in organizations.

2. Deps can be misaligned with your precise needs, and you often end up "owning" them anyways the second you patch/fork/modify it in any way. This one is legit. It's a real thing that happens and is painful. Sometimes being honest that you should just be responsible for the thing in the first place is the right call. This one I buy as an argument, but it just doesn't apply to that many deps.

But the combination of the two makes larger organizations somewhat adverse to dependencies on average.

Zero dependencies should not be the goal. It’s just a modern retelling of “not invented here” syndrome. It’s an anti-pattern because now you own maintenance on something that is not your core problem. In every case I’ve seen so far, the problem is that the person running the LLM doesn’t understand what problems the library solves and so they erroneously think the dependency and their generated solution are equivalent.

In my side hustle time, I use coding agents to explore what if projects I would otherwise never get to build.

At my day job, I use AI to fix config, container, and installation issues so I can spend my time coding instead of struggling to fix mostly other people's mistakes. And really, in this age of python wheels, containers, virtual environments, and fragmented APIs, the cognitive load of doing this by hand would shut me down for the day.

1.5 years in and I haven't burnt out. If anything, I'm ramping up. I'm no longer afraid of adding a web UI, or insane numbers of unit tests, or deploying other people's software.

What I'm not doing is using it to build products, but instead just doing so to augment the supporting stack. All of this is subject to change as the AI landscape evolves.

Ya this is exactly how I use it, the LLM does everything i don't want to do. Which is basically everything except the exact code im writing. Its very freeing to not have to worry or touch a jira ticket ever again. The LLM just does it for me.

That's exactly what the article highlight might actually put your "competitors" (if you see others like that) in front of you, as focus and "what you actually did" becomes more important. Might be that while you shipped and experimented with 50 different APIs, your "competitor"/peer just did 3, but they were more carefully considered so the payoff ended up bigger/better, in whatever way that could be.

That's how I understood the article at least.

My biggest struggle is UI. I can make functional, but my design skills couldn't sell water to someone in death valley

Pick a website you like, and tell claude to use it for inspiration. I do this all the time and the results are pretty impressive and different than what you get out of the box prompting.

> Pick a website you like, and tell claude to use it for inspiration.

I believe you misspelled "rip it off."

I guess I shouldn't be surprised that people are using the tools built by ripping off the collective creative works of humanity to rip off individual things, too.

When other people emulate your behavior because they appreciate the results you got, we used to call that leadership. Something has gone awry that we now call it a ripoff.

Emulating your _behavior_ is completely different from imitating your _output_.

Steve Jobs said something about good and great artists.

> Steve Jobs said something about good and great artists.

Picasso said that, Jobs quoted it (and to his credit, gave attribution... which I doubt is happening with all of these claude-cloned designs).

Brilliant irony!

im not ripping it off, or at least, i wouldnt consider that. a perfect example is artsy. i like the clean style of the website, and the colors, so if im building a prototype instead of getting the standard shadcn look i get something a bit different. im not building a competing product to artsy, i built a piece of software i run locally to aggregate my taxes everything year.

Same, honestly. I've dabbled in trying to come up with some UI stuff with LLMs, but I'm still not at the point where I'm totally comfortable. At my day job, luckily I have people much more talented than me at design who can provide feedback and guidance. For my personal side projects, I've had some modest success inducing the agent to provide the feedback from a suitable "persona", for lack of a better term (e.g. "Is there any existing standard on how to make this component accessible?", "How intuitive would you expect a non-technical user to find interaction with this?").

I read somewhere that Claude is better at UI than Codex. I haven't tested it yet. I am currently using Codex for a couple of native application projects with Avalonia. The UI isn't really good at all and I can fix it by hand far better than it does with prompts. For example, a table to view database data ended up with huge spaces between rows and columns and it can't seem to grasp how to format a table nicely. So I fixed it by hand, no big deal.

> At my day job, I use AI to fix config, container, and installation issues so I can spend my time coding instead of struggling to fix mostly other people's mistakes. And really, in this age of python wheels, containers, virtual environments, and fragmented APIs, the cognitive load of doing this by hand would shut me down for the day.

I think it was a mistake to add this much complexity to the world of software in the first place, so I'd view AI as a bandaid to a problem rather than addressing the underyling issue. But on the whole, the cost of all this extra complexity, data centers to run AI to fix layers of crud and buildup don't seem to provide much of a net benefit to society (except to those who are at the top and need to see growth to pad their retirement portfolios).

Oh no disagreement that it was a mistake, but as Ambassador Kosh once stated "The avalanche has started, it is too late for the pebbles to vote."

I would suggest not concluding the end of this movie from the first 5 minutes of the story. But then, I really fought against complexity over my career and lost hard. One can either check out as a fossil of the good old days or adapt to the new reality. 6 months ago, AI was about to replace all the engineers and then reality slapped the C-suite hard on that subject as just one recent plot twist.

It's not 5 minutes into this story, I think. It's been going on for a long time, but really started to become a "conscious entity" with computers - and I do think computers were a mistake.

Your job would be so much harder without those supposedly complex tools. Most of those listed help solve issues with multiple environments. What simple solution is there to that.

AI as an expensive bandaid

At least in Enterprise IT, where I work, the challenge is not how you scale technology, but how you scale trust. Big IT problems are always trust problems. That messy CRM systems everyone complains about? The messiness is likely due to low organizational trust, departments working in silos, and disagreements on definitions (what is a customer?).

LLMs can scale technology, but they are terrible with scaling trust. You know what scales trust in Enterprise? Good leaders, good culture, good conversations, good meetings, good narratives, good insights, good decisions. Exactly the things that scaled trust 50 years ago, and exactly the things LLMs cannot directly impact.

But now see what is happening. Rather than doing the hard work of gaining trust and consent, employees are using LLMs to build their localized agents. Of course they are happy! But the Enterprise, as a whole, is not better off, the real problems have not been addressed, the consensus has not been reached.

I came close to burning out around two years ago. It wasn't even the workload so much as the uncertainty of being able to solve hard problems and feature requests on time. I was in a constant state of stress and fear, thinking about those problems in the evening, at night, and in the morning.

With AI—or coding agents, to be more specific—I'm perhaps working even more than before, yet I feel considerably more relaxed. It feels like the variance in task difficulty and uncertainty has flattened out. If I had a difficult network-related issue before, it might have taken me hours of research and debugging before even being able to tell what was even going on and how long it would take to fix. Now I can just spawn an agent with the right keywords, in the right environment, and with the right permissions, and get feedback within minutes while focusing on other stuff in the meantime.

While I was pessimistic about where software development was heading just a year ago, I now feel more optimistic and happy about how things are going. My only concern, however, is whether it will reduce the demand for software engineers in the long run.

I was getting frothy and feeling stress due to juggling sessions. I switched to managing my backlog (Obsidian base), writing specs, launching agents in the background. I review and make changes,merge and release. It's a fixed cycle and I optimize to maintain relaxed engagement. Rate of features and fixes went up a lot.

I'm actually not feeling the 'burnout', though I too have a bunch of projects sitting at various levels of vibe-completeness. Mostly one just kind of yanks on my attention a bit more than the others and so they end up getting a little neglected. I'm fine with it though, I love having all these little forever projects of mine finally coming to life. My worry is kindof the opposite, what happens when I've finished all the things. Will I run out of ideas? I hope not, but already some of my pet projects have reached a level of completeness where I just cannot see anymore to add, for me they have reached a level of done. That frees me up for the next project but what if I run out of projects!

Get another hobby like gardening?

Then just set aside some time to be bored. People generate some of their best ideas during this time.

The one thing I miss about driving to work.

Felt like the article could have benefitted from more focus and follow through tbh. Relatable problem, but it didn't say anything beyond exactly what you would have gotten from reading the title.

> So logically, if AI can help us complete tasks 2-100x faster than before, we should be UNDERWORKED and experiencing ZERO burnout, right?

Got to admit, I stopped reading here.

In my experience, burnout isn't related to the amount of work, but to your confidence in being able to do your job and if you find your job meaningful (like, does it even matter?).

I'm probably doing twice as much work now compared to the period just before I burned out, but I'm also nowhere near a burnout. Part of that is general experience, but it's also because I feel like I know what I'm doing (so, no imposter syndrome) and because I've chosen my job based on what I find meaningful to do (is it ethical? does it make a difference in society? is it interesting?).

I understand why LLM assisted development can lead to burnout. You now have a machine that "does most of the work for you". It's a gateway to questions like "how good are you really at your job?" and "anyone could push these buttons, couldn't they?" and "is this what the rest of my life should be?". It probably also reduces how interesting or fulfilling the job is for many people. You've been reduced to a cog in the machine, and you might be questioning your ability to be anything but.

I mostly agree with your observations, but in my experience, if you're spread very thin, you may be nearing the sudden burnout onset.

You feel motivated, full of energy, your juggle millions things at once, and then stumble once, then another time. Then your mistakes become more frequent. And you don't even realize you make them, others do... Until eventually you no longer operate efficiently.

Can take a few years to develop.

Take some rest. Especially if you feel productive like never before. It's gonna be hard to shut down for the first few days, so it should be at least a week of rest. Better two.

Chances are: after coming back you'll immediately notice a dozen of stupid decisions you've made.

Thank you :)

I think I'm handling this as well, though. I have my own personal retrospective every once in a while where I evaluate the different things I work on and if they're worth continuing, or if there are other things (or just rest) I need to replace one of them with.

I agree with your points here - I don't know if I feel more burnt out, but I definitely feel like my work has become less meaningful and fulfilling despite doing more work and iterating faster on a daily basis. I miss the satisfaction that comes with solving a problem on my own after wrestling with it for a bit.

It raises the uncomfortable question of, if so much of my work could be automated, was it ever really that important to begin with? It definitely has me asking a lot of questions about what I'm doing and where my life is headed.

One optimistic view is that AI could free up time for people who see their job as just a job, allowing them to invest more in other parts of their lives... but who knows how far down the line that is, and whether it'll even reach that point.

I will never believe the premise of 100x boost from AI, why are we still pushing this narative? Yes, AI is amazing in small, constrained, focused pieces of code but the code is nowhere fit for production and the last 10% needed to ship shows that the 90% that's already been done by AI is absolutely trash. Hacks on top of hacks.

Is no one trying to ship polished things to customers anymore, will each of us have a hacky, bugged version of the same thing with different quirks?

My first ever software project even before I worked as a swe had less bugs and frictions than all recent projects where AI was used. Some people like mitchellh seem to know what they are doing (I have not taken a look at ghostty's codebase as it's in zig) so I sometimes get the feeling I'm holding it wrong but in the end everyone around me seems to have similar problems with shipping.

I think the trick is to learn exactly what things AI can give a 100x speed boost with, and use it for those tasks, and only those.

The tasks where there's little-to-no speed boost at all -- do those yourself. You'll feel better and happier.

The best thing is you'll have much more time to work on those things now, because AI is doing all the stupid/boring/dumb/mundane/I-hate-this-so-much/I-don't-ever-want-to-think-about-this tasks for you, the ones that normally make your brain dribble out your earholes.

Good points.

First use of AI code generation only recently - I think the key is to throw away code until you can one-shot the app. You wouldn't employ a dentist and tell them half-way through what they must do next.

That's just my first impression.

Interestingly, I started with Gemini and switched to ChatGPT using the initial creativity of Gemini as the starting point.

So like search results, why go to page 7 if you can just retry/rewrite repeatedly and never move passed page 1 because at one point, you'll find the right page at the top. As they say "too many cooks spoil the broth" so just get the menu selection right and the perfect meal will be delivered, and if it's not, don't send it back, throw it away and reorder! The cook won't care - it's a robot - no feelings.

If you can't one-shot it, or almost one-shot it - then you f##### up, not it.

> I had something like 40 “proof of concept” projects going on

Right. That's the problem. In the non-coding world it's the idea that your pile of crap expands into the available space. So, if you get a garage that is twice as large, it will eventually be just as overloaded and piled-up with crap as the first, smaller, space.

The secret to happiness is to control how much crap you put on your pile. It can be that simple. Or, live with the crap you have and maybe a little bit more, but not 40x more.

It's wild we're seeing people rediscover the same solution to the same problems we've had forever: being precious about your darlings.

Code generators reduced the friction for implementing every shitty idea someone has ever had, but because developers seem to have never experienced throwing shitty ideas away (because if it was written in code it _has_ to be important right?) they treat everything coming out of the Codegen as a precious idea that eventually builds up to clutter one's brain leading to Implementation Fatigue the author experienced. The solution? Go through that same process but focus on one idea at a time and make it good.

It does make me wonder if the people experiencing this had hobbies outside of software engineering (let's say, anything creative) they'd be able to spot the signs earlier.

Funny. In writing, the advice is to "kill your darlings". Don't let a particular paragraph that you love a lot stop the whole story because it doesnt fit.

I think that's still the same idea. In his example the darling would be the entire project, in your example it's the paragraph.

Advocating for killing a single paragraph for the sake of the book is like abandoning a single feature for the sake of the project, which is his central point.

The only difference is if you're referring to the darling as the whole or as one paragraph/feature

I am going through this burnout cycle since the COVID-19 days. I noticed that the origin of my burnout is not just due to my job, but also several other factors. Though my job constitutes 80% of it. Within that 80% I noticed that doing a monotonous job contribute to 80% and remaining due to orgn. policies, boss and coworkers.

When I first started using AI agents to automate my work tasks I felt liberated for a few months, but after that I again started feeling burnout. There were days when I tried to compete with AI Agents to complete my tasks earlier like zooming in a race car which added to my burnout.

The quest to learn or gain knowledge will alleviate your burnout and the moment you start unlearning your burnout reappears. Thus I stick to HN for most of the day to kill my burnout though it is overwhelming.

I'm not sure that AI helps us with that last 1%. Just the 99% which is why, instead of having a backlog of 0% projects, we have a backlog of 99% projects. My hope is that we're more effective prioritizing a backlog of 99% projects than 0% projects.

Turns out the last 1% is 90% of the work

But also you can make money by doing 80% of the work and managing the last 20% as it comes up.

Appreciate the quote of the Essentialism book by Greg McKweon, which I am currently reading. Funnily enough I decided the book when I found out (on Imprint app) that one of the key values of the “essentialist” is playfulness. I was sold.

I launched 1 side project that could potentially help my main project every week, for the last couple of months. To me, it’s not draining at all. I love that the main barrier, time, has been removed. The nagging feeling that I am missing out, did not try, should be working on something, seeing competitors launch my idea. All gone.

I love it. Will any of them succeed? It’s early days, but seeing potentially life changing growth in multiple areas at once gives me a lot of energy.

Keep in mind that if a large mass of people experience a boost in activity and energy to explore new topics, those at the top will simultaneously want to use up all that energy by making us work harder as well. The end result only seems like a benefit to you because the other side of the equation hasn't caught up.

The myth you fell into is, assuming that the AI is here to increase your personal productivity. No. Think of AI as a vendor who can provide you with contractors that can code for you. 20 years back, if you are promoted as a manager and approved with a budget to hire 10 contractors, what would you do? You would hire 6 coders, 3 testers and a project manager. Your would mostly interact with the PM giving feature specs, and checking out the progress. You might also ask someone to do the code reviews. You won't sit with every coder and go through the code every day.

AI gives you coders, not some "tool" that you can use to make yourself a faster coder.

But AI is only able to get you through those already easy first 90-99%. The hard and most time-consuming part of getting to 100% is still there, and AI does not help or make us more productive achieving that, in my opinion.

I totally agreed with Rick. A.I is a rabbit hole revealer. And it encourages me to go down them with its "Great Idea" spam.

Edit: Reveler vs Revealer

It's good that you clearly see the "Great Idea" spam for what it is. That comes from the reinforcement-learning human-feedback (RLHF) that is used to keep you coming back for one more pull.

> Now that AI makes things 2-100x more efficient

What things, why, and how did you measure this?

op probly sucked at writing code in 2022 and now has the illusion that he is suddenly able to.

thing is that writing software wasnt that hard even in 2022 with all the cloud, frameworks and stuff

> So logically, if AI can help us complete tasks 2-100x faster than before, we should be UNDERWORKED and experiencing ZERO burnout, right?

No. you got lied to by AI bros. 100x times the code (that if you burn enough tokens that you could actually hire someone to go thru your ideas and make them...) is not 100x times the productivity

That's not the reason, the reason is that you get paid for eight hours, and that's how much you'll work, regardless of the number of tasks you complete in those hours.

Ah, you're not on salary in the US, I take it.

I dunno about in the US, but being on salary in Canada I bet I've averaged well over 40 hours a week in my career. I know the usual trope is people on salary work as little as possible but it's not true for me. And that's not a flex. I'd much prefer to work less, but deadlines are deadlines and they're always calibrated to cost me my evenings and weekends, not cause the customer any delays

> I know the usual trope is people on salary work as little as possible but it's not true for me.

Yeah. It hasn't been that way for 25y or more in the US. Lots of 60-80h weeks.

There are three kinds of deadlines:

* the business season (whether holidays or otherwise) - you lose actual bags of money any time it is missed. Alternative versions is "run out of VC money" and "get jumped over by competition" deadlines, those are real

* the "someone decided to promise it to customer" - Missing it over and over might get you to lose a customer but generally after missing one people responsible for setting them make them long enough to have some leeway

* The "gantt chart fitting" deadline - somewhere somewhere plans a thing he had no idea about, just gathered various random estimates and put them in a chart. Then stuff changes, estimates are missed, and somehow the guys at end of the chart had to catch up for guys at start of the chart taking too long. Missing it doesn't matter, it's self inflicted pain by managers to managers.

Majority of deadlines are in third category. Small part is in second. Vanishingly small part is in third. And without pain the system won't learn to not be too "optimistic" with them. And so enabling delivering miscalculated deadlines just makes it worse.

Do not get me wrong, sometimes shit does hit the fan and you burn midnight oil to fix it, but shit hitting the fan often enough all of the walls are brown is management's, not worker problem

> shit hitting the fan often enough all of the walls are brown is management's, not worker problem

Sure, but as a worker how do I actually convince my bosses to hold themselves or each other accountable? Shit flows downhill and I'm close enough to the bottom it lands on me, not my bosses

I'm not speaking in hypotheticals. I've received the "not a team player" speech a number of times in my career for refusing to do continuous overtime

I love building software, I love writing code, but man am I increasingly convinced I'm not cut out for the industry

Not having an exact number of hours works in both directions; there's no lower or upper bound on how much you're expected to work, and with most tech jobs being at-will employment, it's not like they couldn't fire you with virtually no notice if they decided you weren't working enough. "Work as little as you want, if you think you can get away with it" is not a game of chicken that most people will end up being super confident in playing.

"AI speed up your work". Really? How? I still have my 40 hours contract. I still work 40 hours a week.

AI slop.

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