> Here's a term for what I think is happening: the human reward function problem. In machine learning, a reward function tells an agent what good looks like. Writing code by hand was never easy, but it was full of small rewards. Solving a problem in your head. Understanding a gnarly bit of logic. Watching the code compile. The feeling of control. LLM-assisted programming has automated much of the work that generated those dopamine hits and replaced it with the cognitive load of review and supervision. The satisfying part shrank. The exhausting part grew. And there are no new rewards to fill the gap.

Say what you will about the Claudisms in this piece, this bit certainly rings true for me. With old school coding, there was always a reward at the end, the harder it was, the more satisfying it felt.

With agentic coding, I really doesn’t feel like that, at least not in the same way. It feels more like continually riding a wave of productivity, where small features or huge features have similar levels of interaction required. And that’s exciting in the beginning but quickly becomes very tiring.

> a Berkeley Haas study which describes how AI usage increases the intensity of work

The marketing around this 'study' has been impressive. They published the HBR article [0] and other promotional material [1] over 5 months ago, but as far as I can tell, the actual study itself (even a preprint version) still isn't available anywhere. Hard to evaluate how believable the results are without being able to actually see the details of how the study was done. For now, all we know is that it's based on ~40 qualitative interviews from a single company in a single industry.

[0] https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...

[1] https://newsroom.haas.berkeley.edu/ai-promised-to-free-up-wo...

I’m finding you can still get into flow state, but you need to find that sweet spot where the context switching is pushing you right up to your limits but not too far.

If one of your agents is working with you on something which requires deep focus, that naturally requires more of your mental resources. So it’s all a dynamic scheduling problem. It exercises your ability to recall contexts very rapidly and I’m enjoying growing that capacity.

We've started calling it "Human on the hook" instead of human in the loop in work.

It is more accurate, in terms of, it only matters when something goes wrong.

Edit:"On the hook" is a general expression that means if something goes wrong it lands on you as the responsibile party, generally in a negative way. As in, if it goes right, you don't get kudos, if it goes wrong, you're on the hook for it

unlike the op, I've been having a wonderful time using claude, both at work and for my own personal projects, so I will share what has worked for me, just in case it resonates with anyone else.

my anecdotal advice is to avoid the entire "agent" temptation, and treat the LLM as a code generator. have a single session running at a time. come up with a plan, iterate on it until you are satisfied, then tell it to execute the plan, and watch it. not necessarily to the extent of reading the scroll (though I sometimes do do that too!) but as it finishes each step look over what it has done, suggest improvements and course corrections, and then let it go on to the next step. at the end you will have a pretty good grasp of the state of the code, and the overall time it will take you isn't really any longer than trying to churn out reams of code and then go through it all at once.

the other option if you want something closer to a one shot workflow is to go into far more detail during the planning stage, have it describe not just architectural details but actual code (if you're a senior engineer especially you probably know what the key pieces of code that will drive a lot of other decisions mechanically are likely to be).

also refactoring is cheaper than it has ever been, if something feels hard to grasp to you stop and work with the LLM until you like the looks of it better.

and again, the key bit is to have one LLM doing one thing at a time, and to stay engaged in the process while it does so.

> Yes the code (sorta) writes itself, but the human reviewing, directing, and course-correcting feels worse, not better.

I noticed the opposite. When reviewing and directing a colleague or subordinate, I spend probably 30% of my brain cycles, and 70% of my activation energy, to weigh the technical merit of my feedback against the human impact it will make: bruised egos, differing architectural convictions, correct and polite tone of comments, additional workload for the colleague. The dread of potentially seeing that the code is not good at all, and needing to decide _what to do in that situatuon_, trading off technical debt in the future vs team dynamics and psychological impact right now.

LLM does not care about any of that. It is so much easier.

> I felt that one in my bones. I was up until nearly 2am recently, prompting, because I was so close to getting a plan right. Or so I thought. [...] And it's addictive in a way that makes the isolation worse.

Right, it's more like pulling the lever on slot machine. Oooh, 677, bad luck, do a ritual and try again, and maybe this time...

Sure, regular programming also has a feedback loop, but normal errors are--as much as possible and by design--things that happen consistently for reasons, reasons that force you to engage you mind to discern them and then eliminate them (hopefully) forever. Experienced developers don't just try something random, hope it works, and if it works you just dismiss it as unknowable.

> But the bottleneck was never the code. It was always the human attention, the engineering judgment, the ability to hold a coherent vision for a system. We just didn't notice because writing code felt like the hard part.

Unless, perhaps, you were already fatigued trying to deal with many stakeholders who can't agree what the system even is. :p

> But the bottleneck was never the code. It was always the human attention, the engineering judgment, the ability to hold a coherent vision for a system. We just didn't notice because writing code felt like the hard part.

I keep wondering what I’m missing in the AI enthusiasm, and maybe this is a big part of it? Writing code has never felt like the hard part to me.

In my 20s, I was excited about using a computer. AIM trained fast touch typing. I learned modal editing with vim. I learned all the common Unix commands to transform text files and filesystems in myriad ways. I learned to script and to create my own productivity keyboard shortcuts. I ran Gentoo Linux at home. Then I started my software career.

There, I learned git inside and out. I learned that IDEs all have vim keybindings, so you can have seamless language integration alongside speed-of-thought text manipulation. I became an expert in Java.

When I’m programming, if I know what I’m building, I’m moving at maximum speed. I’m not thinking about typing or syntax or using my mouse much. I’m learning the shape of the code I’m changing. I’m figuring out the right changes to make for myself and future work. When I pause, I’m pausing to think. Sometimes I realize the entire approach won’t work, but I learned something valuable, and I restart the work in a better direction with fewer pauses.

The code was never the bottleneck. Coding never feels like the hard part. When it does feel hard, I build a better abstraction or use IDE refactoring tools or craft a gnarly Unix pipeline with one or more sed invocations.

But this AI excitement is making me think perhaps this combination of skills is unusual. Maybe a lot of devs haven’t been exposed to great tooling or mastered the tools. If I put myself in those shoes, then coding seems much harder, and AI coding seems like a bigger win.

If I were in my 20s today, I might not spend so much time mastering the skills I take for granted. In that context, AI would feel like a magic productivity boost. For my part, though, I got excited about software engineering when I truly grasped that none of it was magic.

While I appreciate and agree with the key points of the post, Claude's writing style fingerprints are all over it and I guess it's even more exhausting to read someone's AI written article.

Reminds me of "The Animal is Tired" (2021) (https://www.robinhobb.com/blog/archives/2021-05)

> I came to the formalisms of software engineering through painful experience rather than academic instruction. If anything, that made me take those principles more seriously once I understood them.

Not related to the article, but I've seen this thought before and I think its wrong.

This isn't what good academic instructions gets you. Instead, they provide a systematic approach to learning foundational/core formalisms which let you recognize other problems as being of the same kind.

An academic background should let the person reason from a place of pre-explored essential complexity, instead of first having to rediscover & deconstruct the accidental complexity.

Building scar tissue about why things are a certain way is practical experience for (non)academics alike.

>It's also, frankly, quite lonely. Programming with an LLM is an intensely solitary activity. > You and the machine, going back and forth, refining and prompting and reviewing.

I just want to comment on this. Maybe im part of some spectrum, but building stuff with AI in that "solitary mode" ive found it really enjoyable. It takes me too the times 30 years ago when I was a 14 year old writing my own games on Basic and C++ with Allegro.

I had nobody but tutorials and books. And the hky of building, compiling and seeing the result for myself was very enticing.

Maybe it's because I found peers my age uninteresting. I lived in a small Mexican town where 14 year olds where thinking in bullying someone, and unfortunately that someone was usually me.

If someone remembers The Hackers Manifesto (The Conscience of a Hacker) I feel that again after so many years, with AI. Edit: particularly this part:

---

I made a discovery today. I found a computer. Wait a second, this is cool. It does what I want it to. If it makes a mistake, it's because I screwed it up. Not because it doesn't like me...

Or feels threatened by me...

Or thinks I'm a smart ass...

Or doesn't like teaching and shouldn't be here...

There are different kinds of developer. Some will find their joy through building fast, they tend to love LLMs. Some love the art of writing code; they don't tend to love LLMs. And there are others too. Those that enjoy fully understanding a piece of code can find the process deeply draining, while those that look at things from a system level find the LLM frees them from the details.

All kinds are needed for different types of work, and it's not discussed enough that LLMs make some developer archetypes more effective and others more exhausted. Great article.

Funny I made some very similar points awhile back in a blog post, thinking of it in terms of mode collapse: https://tonyalicea.dev/blog/single-mode-burnout/

You are right to push back on that.

Are you tired?

Tell us why.

http://tired.com

> The temptation to delegate the review itself to an AI was enormous. But, as he put it: "at that point, what am I still doing here?".

Sadly, this is a question for his boss, not him. It’s not existential. It’s economical.

the irony of an article about human fatigue being detected as AI-written by half the comments is doing more for the argument than the article itself

Pinch to zoom on an early iphone navigating those fixed-width sites worked surprisingly well.

I still prefer it to the responsive pages where stuff moves unpredictably and annoyingly. Before you never had that feeling that the webpage was fighting you.

I sometimes wonder if there is an equivalent loss for this new AI world and one that I've noticed is a kind of sameness that is slowly spreading across the internet.

> He described waking up to thirty PRs every morning, each one pulled overnight by someone's AI, and needing to make snap judgment calls on every single one. The temptation to delegate the review itself to an AI was enormous. But, as he put it: "at that point, what am I still doing here?".

It's so funny and somber to see programmers having an existential crisis when they get a glimpse of what work is like for business managers, the demographics many programmers detest.

I am also guilty of holding the business majors in contempt back in college, and now here I am, doing what they are doing in office in a much more indifferent and unenjoyable manner. At least I don't get into trouble with HR from calling my agents a stupid fuck (yet).

> The honest truth

> That loss is real and it's worth naming

I think I will not heed the first sentence and bear with this. What motivates people to do this? What do they get out of prompting Claude for some vapid "thought piece" and spamming it on the internet?

I don't understand how people are using AI.

A lot of the time, what I want to build, doesn't have a succinct English sentence to describe it. If I describe the user requirement I just get a Fisher-Price toy thing that kind of ignores most of the adjectives and adverbs in my requirement. So I'd have to prompt with a big list of specs and algorithms for the specific thing I want. Then what's the point?

It stresses me out for some reason and I'm just working on a hobby project.

Shame. There is something thoughtful in the post, I am sure, but I am so tired of reading Claude that I couldn't get myself to engage.

One could hope that the author is making a meta-point.

It's sad to have to unearth the very human line of thought in this article from the very foamy LLM slop.

The 2 a.m. prompting bogs down to FOMO of not being fast enough with ideas, and that some other people will implement it faster and one won't make the share of money they envisioned.

I certainly don't have this problem. Even with LLM assistance, my hobby projects experience slow, steady growth, but it's done on my terms. I code when I have a mood, with or without LLM.

Recently I bought a Claude subscription only to use it for 3 days to speed up some coding. Then I cancelled it and stopped. My creative days ended, and I got to other stuff. I know I'll lose 27 days of possibilities, but I couldn't care less. If I'm in the mood to code with AI, I'll buy another month, maybe only for another couple of days.

People, stop accelerating at full throttle, find some real joy in life. It's not about the amount of lines, features or products shipped, let alone about amount of dollars you brag about, if they need this much effort and sacrifice.

Should we not get to work less if Ai is increasing productivity so much while also making us exhausted more quickly?

Perhaps on the way to UBI and the end of labor, we could get a 32 and 24h work wweek with lots more vacation, my hope at least

Relatable.

> with my colleague Douwe

Wait, meltano Douwe? Small world. Glad to see you're doing well. I always liked meltano.

> In an era when anyone can produce reasonable-looking UI

Identical looking slop? Every Claude-based vibe coded app looks identical.

> The fear of skill rot is legitimate. And the fear that if you don't go fast enough you'll be left behind is — while often overstated — not entirely unfounded.

You know what, that's OK. I just hit "OK" on LLM Scala code I _actually_ think is awful. It works. It's probably faster than the "pure" code I'd write by hand. The code I would write - as a FP and Scala/Elm/Haskell/... enjoyer - would actually be maintainable for humans, but LLMs struggle with it. But LLMs writing code for LLMs? Sure, have at it. Objectively lower barrier of entry.

> So if you're feeling overwhelmed, destabilized, simultaneously more productive and less happy, know that you're not alone.

But yes, I am indeed simultaneously more productive and less happy.

https://skaldmaps.com, my little side project, was only possible _because_ I was able to feed my real world knowledge about real estate, combined with GIS and SWE knowledge into various torment nexus... pardon me, LLM prompts.

Since I don't have the _time_ to write boilerplate react code (it's pepper and tomato season in Georgia, which _actually_ brings me joy), telling Claude/Codex/... how to write dbt models saves me time and I objectively get a lot more done, but it's not fun.

I guess that's also why I still enjoy blogging. You can't use LLMs for blogs without people noticing immediately. Shameless plug: https://chollinger.com/blog/

Enjoy my entirely human typos, since that's clearly rare these days.

>When you've earned your opinions about architecture and code quality the hard way, they feel less like textbook rules and more like scar tissue.

I don't think it's common for any compsci programs to (competently at least) teach architecture and code quality.

>The honest truth is that in the last few months, there have been days when I have spent close to two full days writing a plan for an LLM to execute: obsessively clarifying, specifying, re-specifying, only to have it still do something inexplicably stupid.

It's because LLMs are actually taking us back in time to the pre-agile days where there was a career path (architect) that involved almost nothing but painstaking spec authoring and endless meetings to review and course correct the work of the engineers whose job was to implement what you designed as closely as possible. I have to emphasize that this was a different career path than what we think of as a senior engineer today. Not everyone likes this.

The tiredness isn't from being in the loop. It's from what the loop hands you. Reading a wall of an agent's prose to check if it understood a screen is exhausting. The same check as a marked element with coordinates takes a second. It's not a human-attention problem, it's format problem. Models emit prose because it's cheap to generate, not cheap to review.

I feel the opposite, AI is making me less tired at the end of a working day even though I get much more done.

What used to tire me: being forced to have a sharp eye for syntax errors when programming, or simply the effort of all the typing and navigating through source files. Trying to visualize details of the codebase I was changing, while at the same time keeping a high level picture in my head of the feature I was changing.

With AI, I can focus on the high level picture. I can focus on the steps to get there and the steps to verify that it works. I don't have to focus on syntax anymore and there is much less need to visualize large parts of my code base. With AI, work is still tiring but much less, and in a different way.