Turning my brain off actually works really well when I'm doing something more creative, and while I know this isn't exactly what he's talking about, I wonder if we are just offloading things we shouldn't have to think about to have more space to think about other things.

Of course, this becomes a lot more difficult when you're unemployed.

To be fair, that is often a reasonable tradeoff when there’s higher priority things the business needs to focus on.

I sometimes suggest timeboxing investigations for this reason. Spend 4h on this - if root cause is not found, just throw more hardware at it. Although I expect a writeup of what was investigated and ruled out, so that it can be used when someone returns to it.

All is possible with good blackbox testing.

It pays 16 craploads of money per year.

Interesting thought. In a way this is already the case, to some extent.

But wouldn’t it be way more economical to have some sort of AI-insurance service? i.e. protection against AI fucking things up?

Or a different way around, Asimov's The Machine That Won The War, 1961: https://xpressenglish.com/our-stories/machine-that-won-the-w...

Moral Crumple Zones https://ferd.ca/notes/paper-moral-crumple-zones.html

More commonly known as an "accountability sink". These existed before but it is so much worse with AI.

Yeah this is the closest I've found to explaining my own discomfort. Why I've been almost entirely an individual contributor my whole career and why I may opt-out and retire.

I have limited tolerance to take demeaning workload that I know "the boss" could do himself. I think my pride and satisfaction is in my intellectual contribution, not my ability to relay messages nor do repetitive slop work.

But the real revulsion is being a scapegoat. I will not rubber stamp and take responsibility for things not under my control. I have limited capacity to review and sign off work I've delegated to someone else. It is usually easier for me to do it myself and know it inside-out than to delegate and then review enough to have the requisite confidence in the result.

When the new plan is to increase throughput via delegation to these generators, I can't possibly review to my standards. So, I refuse to accept any of it as my work.

It also freaks me out a bit to think of the collective mess that is developing across society, with so many people apparently ready and willing to shovel and rubber-stamp. I think the "move fast and break everything" cult is going too far.

But, primarily, I just need to protect my own sanity and live to my own standards. And hope that I can back away far enough to avoid some of the splash damage when entropy catches up...

You're telling me none of the 730,000 people laid off over the last four years were fully engaged employees?

The challenge here is that the business world doesn't really recognize the concept of cheating.

In particular, tech is very open to people cheating and if billions of dollars could be had by somehow communicating the chess engine's work to a human at chess, the world of business would cheat at it.

Tech has a number of large startups that basically started with the assumption of "what if we assumed the law had no teeth?"

In the world of business and money, you lose if you don't cheat because everyone else is cheating.

I’ve pondered this too. I was in a group meeting with a consultant who is upfront with clients that he has started to use Claude heavily to speed up his output. There are probably a lot of clients who would still pay for his services because maybe they still appreciate his input or the relationship and don’t want to go through the pain of figuring out how to replace him with an LLM. It has been my experience that some teams would rather just subscribe to some software or platform even if there’s a free alternative.

That sounds miserable.

Welcome to our future that was already here years ago.

https://en.wikipedia.org/wiki/Bullshit_Jobs

Fulfilling work disappeared a long time ago, the book bringing this up was from 2018 before chatbots were really a thing. We are just experiencing the acceleration to all jobs being bullshit jobs.

No one should be planning on being a software engineer in about 3-5 years, all need to plan for alternative income sources or early retirement. I think, at least.

Interesting. I recall Geoffrey Hinton saying something similar about Radiologists.

“People should stop training radiologists now” "it is just completely obvious that within 5 years deep learning will do better than radiologists”

https://youtu.be/2HMPRXstSvQ?si=prlQ_uGkCrWAZwgU&t=22

I'm starting to thing AI is the baseline of cognition and it matches the worst parts of humanity. Its just, we trained on the better parts so it's not so obvious.

Work in support for most of your life and you come to the conclusion that there are a whole lot of meat proxies out there. In fact everyone does the meat proxy somewhere in their life.

I started seeing people turn off their brain as they use LLMs

I mean, this was true for computers. "Computer says no"[0] was thing long, long before LLMs. I've had well educated, seemingly very intelligent people break when they couldn't log in steadfastly ignoring the [caps lock key is on] error message. This becomes even more "fun" and "interesting" when a person is being a meat proxy for some horrifically dangerous industrial process involving high pressures, excited chemicals, and enough rotational inertia to send god flying.

[0] https://en.wikipedia.org/wiki/Computer_says_no

You can farm without knowing how crops work and drive without knowing how engines work

I think you missed the point entierly. The problem is not people turning their brains off, the problem is brains wony be needed anymore in the near future, not even meat proxy brains

The human brain is a filter that seeks novelty. All the repetitious junk gets compressed down to something like key frames. This is why things like alert fatigue are so dangerous. Any signal that only very occasionally means danger will eventually be ignored.

This ignores the reality that many people are, unfortunately, taking that one iPhone picture, sending it to everyone in their contacts list as if it’s art, and then moving on to the next slop shoot.

Fuji also sells an Instax printer so you can "print your smartphone images in seconds."

https://www.instax.com/printer/

Also the AI powered instax Up! app...

Have even more fun with the instax UP!™ app. With the introduction of AI, you can now scan an instax™ photo regardless of the background or angle, or even in your hand.

However, for writing software with AI the analogy still works. Just as the photographer needs to pick a shot worth capturing, you have to figure out software worth building. That often also involves some leg work: you need to understand the problem and user needs. Then you need discernment to figure out where the software works well, and which parts need improvements

Well yeah and then you can be liberated by the fact that maybe getting to the landscape and experiencing it and not taking the photo was the goal all along.

You can be creative and take the photo for your sake but at the same time accept that you can generate 1000s of them and many of the generated ones will be more liked by the general public than your creative one.

Here is why it works in more than one way:

In the past few could afford to be photographers due to time and money. Many were not the most artistic or talented minds in the universe on the subject (you can’t find those until you give everyone a camera) but just people who enjoyed it and could make money off of it. Those who gained experience with the old tech were essentially AI models trained on statistically good probabilities of getting good shot. You had smaller selection and the model / brain quality determined what % of the best that photo was between 0 and 100% for any sunset. In modern days more people get a camera - more people with bad taste and more people with good taste. You also get more attempts at the shot - more photos taken, more tries. More tries means you are now preserving more of that moment from more angles and extending the time your taste people have to pick the best shot of all possible.

Your plethora of statistically average photos happen if you don’t have anyone filtering the ginormously increased output. If the problem is really valuable, someone will filter it. Possibly a model.

The real filter is value, not capability anymore. Do you have the empathy and taste to know what matters? The AI tokens/cost lets everyone make and become interested in making of someone hasn’t solved their problem. And then if search isn’t improved - everyone will just repeat the making of the same thing over and over. So if you don’t want repeated slop. fix search.

Or do you copy everything and everyone into making more of nothing.

There used to be no such thing as a woodworker. It used to be a number of different professions, such as the jointer, the cabinet maker, the planer, etc. now with modern tools it’s merged into one job

They don't think of those terms. However they generally are looking at their photos and so get a sense of what "looks nice" - which is what those terms are all getting at.

Now if you know and analyze your photos in those terms you can get good faster. There is no shortcut to the value of quantity.

What we have never done here is define how much effort the "professional" has put into getting better. I've known a lot over the years that know the terms and have the "best" equipment, but they just take a measurement and put it into the tools without understanding the whole and so take really bad pictures. They consider composition as a checklist - which works okay (only okay) in a studio when it means put the chair here - but they can't handle a sunset where they can't control the scene.

Award winning photography means get those details perfect. If you have the details right a phone is often more than good enough to take the right shot - and if it can take more photos faster than is worth a lot more than the best equipment limited to one shot (or a few) and hope that you get hit the shutter at the right moment. If you can get the right moment the better equipment is better, but that is one of the least important parts of the shot.

"look at this list of subjective things"

What is more interesting to me, the most beautiful photo of a sunset taken by an iPhone ever, or a mundane quality photo of my daughter taking her first steps?

Even in the late 19th century "bracketing" was already a photographic technique where a photographer would take several shots of the same subject from various angles and with various settings. Digital cameras may make this faster and cheaper, but the one who employs thoughtful practices in their photography is far more likely to win that competition than the one who just (to paraphrase your example) spins to win.

Once upon a time, in Switzerland, there was a "chronometry contest" putting the best watchmakers of Switzerland against each other to make the most accurate mechanical watch and have it certified by the observatory of Neuchâtel (observatories were at the time the entities in charge of giving the most accurate time, based on reference star observations).

It worked for a time, and manufactures employed experienced, legendary even, watchmakers and specialized "watch setters" to create and tune to the best possible extent a few experimental watch movements. They would be sold as collection pieces, the firm would gain boasting rights in its advertising, and the experience informed the design and tuning of more mainline movements.

By the end of the history of (the revival* of) this concours, the winner was always Tissot, a second-rate company. What hides behind Tissot is ETA, a huge watch movement manufacturer for all of the Swatch group (dozens of brands, from Omega to the worst no-name stuff you can find).

What they were doing to win was just to pick the best watch movements out of the millions of basic, bland, uninteresting "workhorse" ones they were producing each year. Just by chance, there was always one combination of the hundreds of components, each with their manufacturing tolerances and variations, that would fit all together to make an exceptionally accurate movement.

In the end, yes, Tissot won repeatedly. But the concours is dead. And Tissot is still a second-rate brand making second rate watches. Nobody is interested by their watch movements, no collector, no historian, no customer.

The thinking guy with the one approach will likely lose a photo competition to one of the accidentally better shots of the person who tried a lot more.

If that were the case then photo competitions would have been upended by now by tourists who accidentally took masterpieces with their phones.

I can take as many thousands of pictures as I want but, without taking the time to critically review them to see what works and what doesn't, I will rarely produce a prize-worthy picture. And then there's the risk that I'll discard it anyway because I don't have the eye to separate the prize-winning picture from the Instagram shot all my contacts are publishing.

Maybe so, but for the good shots by the thinking guy, I suspect he would derive longer term emotional gratification from how well they came out. Effort and investment adds to the experience.

Making things easy is good as long as it is not too easy, then it's Spotify muzak.

That’s what AI does.

Except it sometimes shows you a photo of something completely different than what you're looking at for or alters the landscape to "improve" it. Maybe that's not a good analogy.

That's an interesting analogy, because there's still a spectrum. Photography's big advancement was the move to digital. Prior to that, you had to know what an F stop is, different developer chemicals, manual focus. These days, yes, an iPhone is actually quite good at taking pictures. Apple invested billions for that to happen, it wasn't an accident. SLR cameras are still there though, you still have wedding photographers. You don't have the mall photographer for once a year photoshoots so much anymore, however. Everyone just takes pictures everywhere as they need. Software is the same now. I need a program to do a thing, I'll just have AI whip one up instead of looking for software that does something that but has 100 more features I don't need. Gone are the days where customers would come to the mall to get photos done and buy software. (Though that world's been gone for a while.) Software is now only a means to an end, so better find the customers that have needs and embed with them instead of having them look for Photoshop.

Hell yea, I want this power.

I think this analogy works with respect to stuff like coding, engineering, etc, where the AI is capable of validating the success of its own efforts and where the result just has to "work", regardless of its elegance or aesthetic quality.

I also think the analogy works if we're talking about average photographs, or even very good photographs, that are conventionally well-composed, balanced, etc, but lack the special spark that we associate with true art.

But if the photographer on the hill is a real artist, it's hard for me to imagine a current frontier LLM winning the contest. I suppose it may stumble upon a winning photo sooner than a roomful of monkeys would, but I believe the number of attempts would still be so vast that it would be more efficient for the human to make one single attempt at taking a photo, rather than dealing with prompts and sorting through the millions of AI-generated options.

EDIT: I also think it's very easy to underestimate the size of the nearly limitless set of possible "solutions" to the photograph, and also easy to overlook the constant feedback loop in the artist's mind as he considers each option and looks through the viewfinder, while the LLM is shooting blind the entire time.

This analogy seems strained to me.

The credulous LLM users Dan Luu is complaining about don't take the equivalent of thousands of photos of the same sunset. They take a couple photos at most, say "LGTM", and move on to taking a photo of something else, even if their couple of photos of the sunset aren't great.

As someone who will often compare different things, I think doing that usually takes time. I think LLMs can help reduce the time (especially if it's programming, not necessarily so much in other areas), but I'm not seeing many people use LLMs to try a large number of independent approaches and compare them.

That's the smallest dan luu post I've seen.

Train conductors are valuable IF they have first aid and CPR training; maybe riot training. There are probably a few other things along those lines as well - but none of them have anything to do with running a train: all the train related tasks can be better done automated.

There are still some things in an airplane that an autopilot cannot do. There is nothing on a train in normal operation (as opposed to maintenance) that a computer cannot do - as evidenced by multiple trains around the world running without humans operators since the 1990s!

I don't see how cutting pay for conductors is going to make anyone's lives better

It's going to make passengers' lives better by making their train rides more affordable, allowing them to improve their lives by leveraging those funds.

Or, it's going to make shareholders' lives better by increasing their profits.

Either way, some people will benefit from it. The question is -- what is the real, practical tradeoff, and is the tradeoff worth it to the participants in the system.

Money, as a limited resource, means that money not spent on the conductor's wages can be spent elsewhere. In this hypothetical, fully automated trains can be run more frequently, so the money not spent on conductor wages means that the train comes every 5 minutes instead of every 15, and more stations.

It's going to make shareholder's lives better. And you better believe we're replacing conductors with lowest-bidder slop, that's the whole point. That's shareholder capitalism's playbook, it hasn't changed for the last 50 years.

What, you don't want to save a few bucks on a train ticket? Come on, you'll love it!

The long island economy is split between well paid public servants, NYC commuters, and the circular economy of driveway contractors and tile contractors in an arms race for the nicest walking surface.

There are no other jobs on the island that pay decently. Cutting edge engineering work will make you half of what it will in either the DC or Boston areas.

The conductors are highly paid in part because it's the local custom, and part because a third of the economy would collapse if that train stops running.

I'm interpreting this as justification for sysadmins making a comeback, which I would absolutely love. We lost so much as a society when they were taken behind the shed.

When throughput is an issue, you have to think judiciously. I'm definitely not saying you shouldn't think but you have to apply human oversight effectively. For me that means front-loading through dialectic planning, and maximizing guardrails.

It's an interesting idea. I'm not sure I agree.

Here's the thing I'm finding - LLMs allow me to do more coding when my brain isn't firing on all six cylinders. It means I get more code written because I don't have to be "in the zone" to exhaustively hunt down every piece of API and dependency and have my flow continuously broken up by missing information. The code written in that state is lower quality, but I can patch up five-cylinder-brain code faster than I can synthesize code from a blank page outside of my flow state.

What reason is there for the company to employ the meat proxy? The company can just run the LLM in a loop and lay off the employee. There's no point at which this methodology will work for the employee.

Company is pleased! Cost savings!

Alas, the joy doesn't last long as pretty soon after the company realizes that after an LLM replaces almost all their employees, the much easier task of replacing the entire company begins, because the company itself has just become a much thinner meat proxy for getting an LLM to do whatever it is the customer relies on the company for.

Customer is pleased! Cost savings!

Alas, the joy doesn't last long as whatever savings they have from replacing the company with their own LLM loop is offset by whatever it is they do to earn income being similarly impacted.

All of the moats degrade together though they may fully give out at slightly different speeds.

Opus in particular seems to somehow combine pretentious intellectualism with sheer idiocy.

Not a big fan of him,but he was a dev before and it was a nightmare working with him as a dev.

I don't know how they eval me. Maybe they eval usage and maybe they don't. I just know I feel uncomfortable to sit next to him sometimes

Even if I agree that you have to use your brain, when my boss is literally telling me to stop questioning whether AI automation is the right answer in team meetings/chats I'm just going to shut up and give them what they want.

Some would tell you that you need to find a different job with a better boss, but I'd say that this is quickly becoming something very hard to escape from. Investors want line to go up, and they all talk to each other about this "AI" thing that lets them cut staff and keep productivity, so naturally we're all getting it crammed down our throats.

Suppose I do something myself. I reap all the benefits, but it takes a lot of time, I may not have the expertise to do a good job, and I get all the blame if something goes wrong. Suppose I go to the park and find one of several guys who would do it for free. I get all the of benefits, he might do an excellent job, but, because there's no contract, I still take all the blame.

Suppose I actually hire a professional to do it. I receive fewer of the benefits. However, he's much more likely to do a good job, and the legal system provides a variety of ways to hold him accountable for the work.

Suppose I hire a professional, but then insist that he use legions of guys from the park to do the actual work, since that's going to be some much faster and cheaper per quantum of work. I also tell him that if he's unwilling to do that, I'll find someone who will. I also tell him that, since he has so much more unoccupied time now that the original work is in the hands of all those other dudes, he gets to supervise still more work done by other swarms of guys from the park.

At this point it should be clear that I'm not interested in quality work, but in having someone poised to absorb blame. I want a patsy.

Now replace "guys at the park" with LLMs, and replace "free" with "nearly free, but with no warranty."

I think it might be appropriate if what we're talking about is a human connecting an LLM to something it can't natively access. Like something in the physical world - a piece of hardware, a different human, software the LLM can't access. As the human in the loop your role might be to mostly just shuffle information back and forth from this external resource. But depending on the situation there could still be quite a lot of interpretation and analysis performed by the human in the loop.

I don't think that's quite the problem the OP is pointing out. A corollary of "meat proxy" is "cognitive surrender." [1] In both cases, blindly pasting AI output and being an "Enter key presser", you have surrendered your cognition. In the latter case, you're in the loop but there's not a lot of reason for you to be.

Your underlying supposition is right, though. If you are truly thinking and in the loop, you are by definition not a meat proxy. I think those are the people who are successful with AI coding tools, but it takes discipline and it's still hard to find the joy and reward when you're not the one solving the problems.

[1] https://swiftrocks.com/using-ai-without-losing-critical-thin...

I would like to argue opposite.

Developers like to "turn their brain off" during "super hard debuging" sessions. Mental flow state is a well documented as pleasurable experience. It is like a drug!

Auditing and reviewing autogenerated commits, is hard gruelling mental labour, compared to that. You do not even get a credit as an author! You can not get into groove to get your fix!

trends like these remind me very much of how true some of the predictions in Douglas Hofstadter's "Anti-Intellectualism in American Life" were: https://ia802903.us.archive.org/6/items/richard-hofstadter-a...

he was writing in reaction to the McCarthy era which, arguably, is very similar to kind of bombastic nativism we're seeing now from our political elites. his argument essentially being that there are cultural and institutionalized forces that are driving people to, well, turn their brains off - specifically to no longer be skeptical of what they consume, to not want to refine and improve their toolbox of analysis, to not want to understand how things work for the sake of knowing

and that these acts were deliberate - a populace is so much easier to control when it's complacent and unknowing, who believes, unskeptically, that Oceania has always been at war with Eastasia, unquestioning about who was writing the PR missives that feed the news cycle (see also: https://www.commondreams.org/opinion/what-is-copaganda), how verbatim they are, and what the real intent of the authors were

like, I get that you don't want to put in effort for a job you don't like. but that effort also makes you better at your work. it makes it easier on you, it lets you create less incidents, it empowers you to be more agent in your own life, to, who knows, have the guts to start a consulting gig because your skill-sets actually are better than the average

there's so many benefits from just being more diligent, longterm, proactive benefits. but that's not everybody's extrinsic motivator and it's so much easier to just be Skinner-boxed by social media companies into doomscrolling, I suppose

Thats not the problem of LLM/AI.

The problem is, that digital work, the new frontier of employment for a lot of epeople, got and will continue to get A LOT cheaper, faster and more accessable.

The PO who always complains about someone? They will just vibe stuff. It might even be shit but it will work good enough until LLMs/AI/AGI/ASI is good enough or better anyway than an avg developer.

Man i have seen so so much shitty code even in software companies.

You know when your HR software takes seconds to load? Yeah an LLM can do that today too

I will take my chances. Software is part of my identity and I won't abandon it that quickly.

Are we going to make a new job title for that? Should we call that job a programmer?

Works for me! The most popular programming languages in 2030 will be English and Mandarin. You don't have to like it, you just have to deal with it.

There are certain parts of my job that can be mediocre and I'm happy to offload them to models. Those are, however, not the parts that count.

Good designs tend to have simple mental models. Some of these mental models are generic and apply across many projects. Or are even written into standards. I wouldn’t be too dogmatic about impossibility of understanding before building. Sometimes knowledge beats experience, and having an intuition for when can save a lot of time.

I somewhat agree with your point, but also I find that the major agent harnesses work really hard at tricking you into leaning back and not letting a proper understanding of what you are building to develop.

This isn't a new problem. I had it when working with outsource developers, too. In both cases, it seemed that just sitting back and describing what I wanted in plain English prevented me from properly wrapping my head around the problem. Which then meant I failed to develop the insight needed to find the really good improvements. Those usually consisted of identifying and removing the bits that were making things worse.[1] It's incredibly difficult for me to think of anything other than just adding more stuff on top of what's already there if I'm not getting my own hands dirty.

So I've been moving away from plan mode and letting the spin away on building large chunks, and back toward working in small increments and typing out the important code with my own hands. That seems to be the sweet spot. I'm still getting a productivity boost because there's more than enough boilerplate and trivial function definitions to farm out. (Easy prompt, too: "Implement `someFunctionStub`.") But I'm also not letting Plan Mode drag me back into the waterfall development pit of insanity.

1: https://en.wikipedia.org/wiki/No_Silver_Bullet

That temptation is akin to giving in to maladaptive (day)dreaming. The problem is the dreaming, the noticing, the getting annoyed, and the feeling drained - all of these are happening at one place - being done by one entity - your brain. So how to train it to not to do that? Well, again, that same brain has to do it. What I am saying here is - our brains are extremely good at getting "used to". Because it "feels good" to the brain - at least in that instant. So any work that's happening on its own, feels good for most people. More the time passed in that "got/getting used to" state unchecked, more difficult it is to try and bring it back to not getting used to. Or control it, in an attempt to strike a fine balance.

I still don't get who is this guy and why anyone should care.

The obligatory XKCD, panel 2: https://xkcd.com/568/

Heh, do you see how mad some people here on HN get when Claude tells them no. They must all be in management. It's a very delicate dance.

I think this is the dissonance that has been making me feel insecure / depressed around the state of AI coding. On the one hand I am way more productive. But on the other hand it is almost requiring more of my brain than before, because I am having to determine what is good output and what is slop. But that's not the part of my brain that I particularly like engaging — I like understanding something deeply. But in this case, that is just not possible. I like to think that as the human-in-the-loop, I am supplying the requisite _taste_ to the design and overall structure of the program. But at what point does my taste get taken over by the LLM as models improve?

Another problem is that when something breaks then I am very limited in my ability to reason through the breakage. It's necessary to go back to the LLM and ask it why it broke. At which point, I'm able to follow the reasoning — but not in any durable manner where I'll be able to remember it. To me, it's sort of like the difference between memorizing a passage and having a ready reference at hand at all time. We had this same problem with information before — now it's come for our reasoning.

Maybe, but the parts of my brain I'm still using are a lot easier to use.

I was dreaming to be promoted to a cushy management role where I could talk bs for a living.

I managed to attain 'hands-on team lead' and later an ill-fated 'bootstrapped cryptocurrency CTO' role... Nobody wanted to pay me to talk as a career. Now they do!

So I'm very happy about that.

I feel contempt for all the people who had it easy and 'chose' to be engineers voluntarily.

There's some software that really doesn't require my brain. I gave Astra an ssh key for root on a little Pi Zero with a microphone attached and asked it to make recordings of a siren going off in our neighbourhood, based on a couple of sirens. This would have been a great deal of work a couple of years ago, now it's a few tokens, and honestly, it doesn't really matter if it's shitty code, I just want the timestamps.

Then there's the software that I write for a living, and almost my entire day is now spent making hard decisions. /grilling gives me difficult technical design decisions endlessly and I am forever doing product management on features from agents. My brain is back in CTO mode, which isn't what I'd planned for myself after I'd left that particular cursed path, but fine. I am doing the opposite of turning my brain off, because I no longer can just chill out and mindlessly write code.

Both these states can exist simultaneously. But my brain work has shifted into something different, while getting no less intense.

There are a lot of programmers who are working with chatbots and watching YouTube simultaneously for the first time, simply matching the rest of the economy for the nature of work.

If you can do your job while watching TV your brain is turned off.

The more I see posts like this, the more I realize that the problems people have with AI are no different than the problems we had with outsourcing (development to low-cost countries) 20 years ago. The results and challenges are the same; except now the feedback loop is within minutes instead of overnight.

Let's say that happens. What reason is there for the company to employ the meat proxy? The company can just run the LLM in a loop and lay off the employee.

Why do you think management is so gung-ho about LLMs? Employees are expensive, and laying them off is therefore extremely appealing.

The entire, explicitly stated goal of the AI movement is to produce an ASI, which is better across every metric than a human. Even if you refuse to turn off your brain, the economics of paying you to keep the lights on upstairs aren't going to work out.

This was never going to work out well for the human.

I'm not sure what else to say. You listened to these people speak, what the heck did you expect?

I think you may need to update your priors a bit. AI is already replacing professionals. Developers are already losing jobs. LLMs and agents are already running in the hot loops of our technological civilization, unsupervised, with root. Institutional knowledge and skill are already being lost.

Yeah, I experience this a lot. I call it “zombie mode.” It’s like pulling a slot machine, though. Sometimes AI can go a few turns just with encouragement. But when I don’t maintain conceptual engagement, drift is inevitable.

My solution is saying “gnite” and coming back in a new terminal window with a clear restatement of my purpose. Or “what are you working on and why” can help.

Terminal windows are not great for keeping context; waiting for better design. It is possible.

I've noticed some tasks (80-90%) can be accomplished by quickly typing out a feature/bug fix, accepting plans with ~10% brain power, and moving on. But there are other tasks where the LLMs struggle, and it requires significantly more time and mental effort to think through the architecture and requirements before it's to a point where the LLMs can process it.

What's difficult, is I now spend most of my time on these high-throughput tasks, where my brain isn't exactly off, but focused on quantity over quality. It's often difficult to identify when a task requires "real" brain-power, and when it does, it adds a lot of friction to dig into the code and orient myself. So it's hard to know when it's the right time to do that.