ES version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
52% Positive
Analyzed from 12829 words in the discussion.
Trending Topics
#code#more#don#system#actually#problem#coding#things#still#something
Discussion (189 Comments)Read Original on HackerNews
The code change itself doesn't specifically matter. But suffice to say, it was about an AI feature in one of our products.
The code was stamped by Claude driven by a prompt. The prompt was for a ticket generated with the Atlassian AI integration. Atlassian had digested docs made with AI. The docs came from strategy memos I'm 90% sure were written entirely by Claude.
The strategy was chosen by management at the urging of exec leadership. The execs now communicate mostly via AI written memos. I do not know how they make decisions, but they reference tech influencers, market conditions, customer expectations.
This gave me pause. Who had actually made the decision then? Arguably there has been several layers of human review, but the actual source of the decision was hard to pin down.
We were not building the feature because we wanted it. We were building it because we thought other people expected it.
Perhaps reflecting on the state of the market, I thought, could indicate who was actually in control.
Where do investor and customer expectations come from in 2026? It is very murky, at least in tech. There appears to be hype. Some hype comes from true believers, some comes from cynics. But both respond to market incentives that reward bigger and bigger claims.
Where does the market's "action" come from? What is the driver?
Investors do not really seem to understand what the tech is or its limitations. Some are passive operators. Others are just responding to the overall froth and speculation in the market - which becomes a runaway feedback cycle.
This left me lost.
Nobody in this ecosystem, I thought, is actually in control here.
Nobody is actually orienting work and action to real, concrete goals. It's all based on speculation and anxiety about the future.
So it is not only that nobody understands what the code does. It is that we cannot, or at least I cannot, explain the motivation. There doesn't seem to "be" any form of "intention" in this environment.
It has all been hollowed out, replaced either be inscrutable machines, or inscrutable incentives.
Ironically it rather resembles the kind of "misaligned" superintelligence we are supposed to be avoiding.
This is a long winded way of saying "people made up clever/sensible sounding stuff". Now it's easier to do it with AI so the problem is worse. However, I'm not sure what you were looking for was ever really there - the "inscrutable machines" and "inscrutable incentives" were always quite inscrutable.
"Product" studies this data and tells engineers what features they want. I am rarely instructed to A/B anything...excepting when it's defensive, to ensure the first rule of "don't interrupt the flow of money" is upheld, if at all. Most of the features are obvious improvements anyway (determined from plain conceptual planning, manual testing, and personal usage).
ofc I don't understand the customer or market or anything else. I'm paid with the expectation that I'm not going to share an opinion about it, especially since am never exposed to the raw data and inner circle decisioning except during a quarterly meeting...maybe. This is part of why AI is so successful. Humans in large organizations are specialized with little creative input and lots of mechanical process. Coding is largely a mechanical black box (turing machine) from the outside looking in. It works seamlessly because I don't need to know about the things product wants, so the AI doesn't know and everyone carries on faster than we were before.
The hyperbole feels like it's been contrived to fit into the classic AI counterpoint: But humans do this too!
The reply was always "It's hard, and there are always going to be bugs."
But it's not just about bugs. It's about systems that are hard to use, poorly designed, and opaque. And sometimes the opacity is deliberate. There are dark patterns, outright lies about what happens to data, and more or less obvious grifts.
Was any of this truly great before AI arrived?
Is it an accident that you can't kill an MS365 subscription if you have more than X amount of GB on OneDrive, but the actual usage includes all your spam and email attachments, and it doesn't show up unless you know where to find it, and the location is very non-obvious, and so is the cleanup and deletion process?
Or that the eBay fee structure for international sales is utterly incomprehensible without automated help?
Or that if you select Subscribe and Save on Amazon your cancellation date is something like two weeks before the next delivery?
Or that if you sign up to Discord you need a mobile and an email for activation, but the site doesn't tell you this, and nor do tech support, who insist that a mobile is optional?
And software quality - services down, records lost, records stolen, photos and documents deleted - is a whole other layer on top of that.
AI is a moderately good solution to the first set of problems, because it can search and assemble information far more quickly than you can. So IME it's pretty damn good at tech support - not perfect, but better than DIY in many cases.
Software quality? We'll see what happens. If tech stacks collapse over the next couple of years we'll know AI was terrible thing.
I'm in the 'Too early to tell' camp. There's a fair chance they might. But if so it will be because of poor testing and design, not because of code review or lack of hand-rolled code. And it's not completely clear that quality levels wouldn't have dropped anyway.
Data can describe to you what exists. But it can’t tell you what you value.
What you describe is people who can’t tell the difference, and who let the machine (data) make the value judgments.
The parent comment is interesting, but ultimately I think in this case, the AI is actually revealing something about the true nature about their place of employment, a nature that has always been there versus some mutation caused by the prevalence of the AI itself.
A lot of money can be made purely algorithmically. Think market markers or other algorithmic trading. The ought vs is divide is quite narrow here. It's not a moral question, the "value" is in the money to be made. It's actually not a great example to invoke Hume's problem. Many companies essentially are chasing a similar spread, its just less obvious. Few people ever ask what "ought" to exist. If the power of AI makes more businesses operate more reactively and algorithmically, because of more data or processing power or w/e that really is probably in keeping with their alignment and goals. Because the ultimate ought for a company is we ought to be making more money. So, in many ways the ought is really not that interesting, the is is satisfactory provided the return on whatever their version of a spread is keeps improving.
The number of companies that actually "invent" useful things and thus ask even vaguely meaningful "ought" questions are extremely slim. The vast majority of employees are, at best, accessories to these questions, even in software where even before AI many of us were not doing very interesting work. There is a lot of essentially rebuilding your competitors same layers on top of common libraries and standards where the actual interesting work is done. Really not unlike asking AI to cook you up a boilerplate by leveraging the vast work of a fraction of SWEs who maintain OSS tools. It's the same pattern and the same sort of behavior, just now your "layering" is becoming automated to the point of irrelevance.
What the parent misses - the real promise of AI is paradoxically, that it will allow more people to ask actually interesting ought questions as AI owns more of the spreads. In the same way a human does not compete with an algorithmic trader, and at some level, really doesn't care. The more algorithmic your business becomes, the less any individual human "value judgement" matters. And really this is desirable, because again, most companies are not asking interesting value questions anyway. The end state of this you are missing is these companies are going to cease to exist. In the optimistic case this will free you up to ask more interesting value questions - like how do I value all my UBI enabled free time. In the less optimistic case your value judgments will be more dire - like who do I sacrifice given the Terminators are at the door and we only have x quantity of supplies left.
This is the other paradox. When questions of what ought to happen are of paramount importance, you are probably finding yourself in a very undesirable situation. It's easy to valorize the ought problem from a distance, it is much much harder to actually engage with it when it actually matters. In many ways, the relative luxuries of society and civilization are derived from taking such questions out of most of our hands. This is (perhaps surprisingly) true even as you climb the ladder of power:
I used to think that if there was reincarnation, I wanted to come back as the President or the Pope or as a .400 baseball hitter. But now I would like to come back as the bond market.
And the complement is credit/provenance laundering. These are things being misappropriated.
The grift often does both of these with the same sleight of hand, and this is what gets accelerated with the new tools and cavalier culture around everything.
It bothers me to no end when I get an AI written response, especially from the executive team or any one of my co-workers
Yet everyone assumes that, like before, there must be a reason.
Worse, we can't tell apart real decisions choices from the dream-machine side effects.
Question from someone written in AI? Just answer it in AI and send it back. What was it actually about - who cares? Bug comes in? Post the jira link in claude and don't even bother prompting anything else. If something critically breaks - well, eh, we'll deal with it then. FIRE (early retirement), a prediction their layoff is inevitable, and investing aggressively so you can finally escape actually working are often invoked in the same breath. Everyone feels like they're just trying to punch the drywall and grab as much copper wire out of the walls as they can until they're finally let go and/or the whole company fails.
There's a great deal of nihilism and cynicism in the industry currently, and it feels like LLMs are just greatly enabling it. Where you would've done a halfassed job previously, you'd now do an unchecked AI job.
It probably is. Where I work it has been made clear, as in actually stated by leadership, that any process that does not include AI input is to be considered broken and needing to be fixed. It doesn't matter if it works, if it is 100% human made and maintained, it is broken and needs to be fixed by injecting AI in a critical place. You should be in a position that if you lost access to the AI tools, you are unable to proceed.
This is the step towards replacing people. You don't need highly paid people in that process, you just need someone who can speak a language the AI tools can transcribe. This is no different than moving from a codebase or process that is tightly coupled to a specific technology to a more generic process so that you can easily and quickly change the backend tech on a whim. The technology being removed is the people. The AI is important, you are not.
If you try to do good work, you won't be able to keep the pace with the slopmaxxers, which will mean you get laid off earlier. You will also be swamped in slop review.
If you get called out on some issue or shitty implementation, you can just make Claude abstract it away behind more complexity to the point where people have a hard time doubting you because they don't have time to get into the details and verify things.
Grab as much as you can, invest in immovable property and other shit that has value after a stock crash and enjoy the ride
If that happens on a large enough scale those investments won’t be worth much.
That’s very graphic.
The thing is, I already felt a bit like that before AI. It’s just money. This quarter’s. The rest doesn’t exist. Make flashy features faster and you’ll be promoted. Make things carefully so that they last and can be maintained easily, and you are be ignored.
I say this not as an engineer that tried to write maintainable software and now is butthurt, but as an (ex)manager who tried to promote such people. And now is butthurt. I found myself telling my engineers that if they wanted to get promotions they had to prioritize the shiny and skip the rest.
I hated it.
Now I am back to Engineering. I do use AI heavily at work because no one cares but if my output is “too slow” I will look bad. I at least try to raise concerns when I see them. The answer tends to be “yeah, we can’t afford to do that properly now”.
I use AI way more sparingly for my personal open source stuff. Because I care.
> Ironically it rather resembles the kind of "misaligned" superintelligence we are supposed to be avoiding.
yes, and it is the exact same system that is producing the "misaligned" superintelligence. Funny how that works, and begs the question: exactly how are you supposed to avoid building "misaligned" superintelligence?
At some point deferring all decisions to AI will be the competitive thing to do, regardless if its aligned or not.
Was there never a developer in the loop? I hope my org won't give up control of their business to an AI like this soon, sounds like a nightmare to figure out what's going on.
Who's in control? Everyone is, to some extent. And no one is: when you're hungry for food, are "you" in control of that? You can consciously repress your impulses to go eat something, but your mind didn't create those impulses.
Human societies develop impulses and minds of their own, emerging (weakly) from the impulses and minds that comprise them, and they make decisions in mysterious ways.
Of course, it sure is nice when we can come up with a compelling story for the motivations behind something. Easier said than done…
>Perhaps reflecting on the state of the market, I thought, could indicate who was actually in control. Where do investor and customer expectations come from in 2026?
This looks like ill-fated Gartner driven development on crack. Where does customer expectations come from in 2026? How about from your customers?
If you're doing enterprise software and talk to your users, you'll end up learning that the actual users of your software don't use 80% of your features. You might even learn they don't use your software at all, and that the software was mandated top-down from the C-suites or that the execs in charge forgot what purpose your software served but are too insecure to question it, unless and until there is a sudden pressure to cut costs.
the simulation has become simulacra! cosmic horrors abound.
To your point, AI can drive itself; it's just that the fashion in which control occurs is distributed. Which is to say, the process (e.g. a feature being implemented, in some way, or at all) is not spontaneously occurring: it's emergent from the mesh of AI automation.
Niceties aside, it's pretty clear that humans are not in the loop in a meaningful way, much of the time. What emerges may hence not well be not aligned with business or technical needs. What it is aligned to may be impossible for we humans to discern.
Who controls the way a forest grows?
Ask a tree, get an answer, but don't forget, that's not the answer.
Except for the enterprise customer and at enterprise prices.
AI has become the thing you do, and what you do it with, to achieve it. It's a self-fulfilling chicken that is an egg that is a chicken.
It boggles me we completely forgot that the world operated like this just 4 years ago
You can still write anything by doing it either small steps, or at once followed by a lot of refactors while skimming over code and asking tons of questions / making refinements via prompts, guidelines, test guardrails. A team can still reason and whitepaper about the same things. Devs who were previously shy to ask some specific details (to not seem dumb) can now confidently survey big codebases and get insights in whatever style they can swallow.
The bigger problem I see is that all this requires a lot of communication, and most importantly writing skills, something that disappears in thin air in the last decades.
Unfortunately I learned that not everybody thinks this way. Some orgs do imperfectly fine without good engineering discipline, and that has been the case before AI... AI has only made it easier to give the appearance of good engineering, which is exactly the pre-AI goal of many orgs
And I don't think it was ever necessary to go to the point where people just gave up authorship. These were choices made by adopting the "I'll do everything for you" agentic "harness" model that shipped with Claude Code but it was never inevitable.
e.g. we completely dropped fill-in-the-middle completion OG CoPilot auto completion model. That combined AI authorship with a human always in the mix and I actually really enjoyed it. It's just that the models involved were pretty stupid. We totally could have had IDE / shell / tooling integration that kept people in the driver's seat while automating parts of the drudgery away. Instead what we got was a simple chat loop with "oh, whatever, you go do it" being the ultimate result. Cuz, you'll totally review everything after and understand it, right?
The things should end by quizzing you on what was just made and if you don't pass, just throw it away. That'd be funny to watch.
One is:
1) Complex IDE integration where our AI model is called to assist users in making decisions, assisting them to do the things they already intended to do. It appears more as a function of the IDE rather than something separate. It shows off the intelligence of the model but doesn't show off any autonomy.
2) A separate tool that can be branded Anthropic. It takes over completely. It advances the narrative that we've already been spreading that AI is going to make some human jobs obsolete. It looks to employers (the actual people who spend money) like it replaces an expensive developer even if at first it requires one. It has minimal to no integration point.
Which do you think they'll approve. I think it has less to do with the user motivation and more with the producer's.
The issue of course is that if you do invest the time, then you're no longer saving time by using AI. You're just spending it reading and trying to understand something you didn't write. And that can be unpleasant in its own way.
My hot take is that for parts of a system that can be considered its core, forming a deep understanding is almost always important, and so is knowing how the different business domains integrate and where the connection points are. For many others, a high level understanding is sufficient. The difference is that now, with AI, you can make that choice. Before, you had to write everything yourself, and for any sufficiently complex and long-lived system it became impossible to hold all of it in your head.
I believe that this is the most important thing... and something that people are afraid of. I've got dozens of personal projects and more than a few branches in my employers repo of things that didn't work... things that I tried, figured out it wasn't going anywhere and went to try some other approach.
I suspect that there's a bit of sunk cost fallacy going on elsewhere. "If I don't know how to do it, I'm not even going to try" and "I got this far, I'll keep doing it despite it being wrong."
As a programmer, I am often disappointed at the lack of curiosity in the language and how things could be done. My example would be people writing Java code as if it was still Java 7 - no streams, no Optionals... The fear of having to go back and do it again if using something new doesn't work they'll be in a worse position than if they did it the old way and not realizing that learning what doesn't work or seeing how things that didn't work in this situation may be the right thing for some other future problem.
I'd think that's what they call paradigm shift, and this probably repeated across generations from the introduction of the printing press, PC, the wheel, the internet to stochastic parrots that reduced what we still stubbornly insist require our special neurons to mere statistical modelling that can be aggressively scaled.
Now in the age of AI... we adopted another system from a team and when we asked for knowledge sharing to prep for the handover the answer we got was "do we still do that? Just ask Claude".
It's now been a few months. We've shipped features in this new system. I still have no idea how it works. Okay I kind of know, but only at a very shallow level.
AI is the worst thing to happen to our industry.
And having AI code to review is no different than any other code that ever was to review, so the review tooling is - as it necessitates - also benefited by lugubrious application of .. more AI. But: all AI is human reviewed.
So it's not a big impact. We just don't ship code that isn't 100% human reviewed, If that's insurmountable: you're doing it wrong. Use AI to make code readable again.
And then, also, put AI back in its box. Don't give devs 100% full-time API access to subscriptions: give them actual hardware to use, to go 100% local.
Local AI is, thus, the best AI, folks. Don't use more than you can run locally, is a great way to keep AI code properly maintainable.
The industry will prove this, itself, sooner or later: If you can't put your AI in its box for safe-keeping, you're doing it wrong, anyway... and should've already learned this practice as a habit, decades ago, vis a vis future-proof tooling... (See also: not logging everything you do with an AI? Big fail.)
Sure, the absolutely intoxicating addiction of Big Metal AI™ is going to put a lot of consumers in a deep, deep pit of Neo-Illiteracy - however: 'good' AI code is actually just good code.
> And having AI code to review is no different than any other code that ever was to review
If your company is sticking to "everything needs (human) review" then you wont run into most of this. This issue is that a lot of companies are using AI as an excuse to remove that review process (either partially or entirely).
Everyone can write code these days. Trouble is, all code has to be SIL-4 code now, because, human, you will never know if your compiler trusts your AI until you trust your compiler. This rule will be true for decades into the future, I'm willing to wager...
But, ultimately, software has to follow certain rules, or it just doesn't work. Proper workflows - involving review - are needed. Because security is pretty much over, otherwise.
Folks are finding it easier to make their own software now, too - rather than use others. I predict an end to the app stores - or at least, the primary interface is going to end up being "describe the app you want to use today" instead of picking words from a list ..
Edit: Since I seem to have touched a nerve - I've been working on a project to solve this: https://www.archme.io if you want to know my thoughts on the right abstraction
I have strong disagreement because it sounds like, by analogy or proxy, we have also "solved writing"
Just to make this clear: if you can define a really good PRD and sophisticated technical specs, and a strong set of tests cases to pass, at the right level of architectural granularity, plus adversarial code review processes that triangulate and weed out most mistakes, SOTA agents can write the code autonomously, at or above the quality level of most human coding teams. I call that "solved" but only if you meet those context requirements. Which is still hard, not solved, at that layer.
Solving writing is not a good analogy IMO. Writing is for human consumption, and cannot be wrapped in objective requirements and verification processes. Certain forms of writing perhaps could be (can't think of one at the moment but I don't doubt some exist), and those forms might be good analogies for being "solvable" or "solved."
I'm not typing keys, but I am very much still concerned about the quality and nature of the code. Coding to me is more than pushing keys
> if you can define a really good PRD and sophisticated technical specs
I still believe we cannot waterfall software, the idea seems like taking a step backwards. How often do we learn about an unforeseen complexity only after getting into the implementation?
In my experience with agents, it's better to be iterative and in-the-loop. Start with a decent description, have them research the code/issue, write up an initial plan/design, work iteratively on writing code and updating design doc, review and finalize the code and markdown. Then future agents will have some resources to shortcut understanding the code base.
Saying that LLMs have "reduced the cost of coding" would be boring. And using your analogy, pencils, typewriters and computers have all reduced the cost of writing, but writers are still around.
The problem is, without PR reviews & strict oversight, we're losing knowledge, system design & control of our codebases & products. Which is why, IMO, the coding is solved but the other parts which used to be so tightly coupled to programming are cropping up as their own issues.
You might still need to nudge the LLM in the right direction or stop it from going off weird tangents, but none of that involves touching actual code yourself.
Today, human-language outlines / briefs / prompts are “compiled” to code which is itself then adapted to hardware. We are stretching less and less across the divide, doing less and less work on the terms of the machine. Now the farthest we’ll stretch is often formatted markdown - the most basic application of machine-parseable structure to very organic human thinking. Because we’re given the chance to be less precise, coherence suffers.
At least that's how I experience it. In the before times each non-trivial code change had a real opportunity cost as it would easily consume two days until I could even estimate whether this is worth looking deeper into.
I think "solved coding" is taking it too far, but for many projects, the mechanical aspect of it has been removed or reduced greatly.
LLMs will have a much harder time "solving writing", because they cannot develop their own style and so are severely limited, creatively. This is less important for coding.
I still think they produce shoddy or sus code too often, an artifact of the current generation's training to try anything and everything until it "completes the task". They have a hard time even with that concept, which is part of "coding" imo
They haven't solved coding.
Programming is an art form. And the better you get at it, the better kinds of ideas (abstractions) you can create.
This is something today's AI cannot do.
If everyone were to permanently switch to AI for software development, software innovation would cease.
For example, nobody on our team writes manual code anymore, we have basically set up a harness where an engineer types up the requirements for a change, the system implements it given certain constraints, we have automated unit and integration tests that are ran, and if any errors pop up, they get fed back into the loop until fixed.
But to do that, you need to actually know what you are doing - you have to have good instructions to keep the agents in check and not start making mods outside of their bounds especially when the issue is with a dependant service that is causing errors.
To solve something, there must be a defined problem, what is the problem that was solved. Or perhaps it is just "coding is solved" is the turn of phrase de jour be ause we haven't yet found a more succinct and accurate way to describe the paradigm shift
When it comes to non technical people using Ai to build things on code, the outcomes are on average pretty poor, which i see as evidence that the driver and their expertise behind the Ai matters a lot. A notable example is Terence Tao's conversation with ChatGPT, us math normies could never have done that. The same applies to coding agents ime
Except a little worse, since they were raised alone in a library, act mostly the same, and have harsh limits on personal growth.
My team recently spent two weeks on a wild goose chase trying to figure out why TensorFlow Lite was generating nonsensical OpenCL kernels. Well it turns out that LLVM had a few bugs in the RISC-V assembly for our platform that was leading to silent garbage. It took combing through assembly dumps, hexdumps, a lot of pain staking debugging, and going through the TensorFlow Lite source code to to track this down.
In your opinion, if code is the wrong abstraction to be working at, how do you approach this scenario?
To your point, it's not the wrong abstraction for solving code level bugs. Just like python is not the right abstraction for solving memory corruption or pointer mis-alignments.
Which is really the same problem with coding.
The agentic model of it just taking over and doing everything is poisonous to effective long term team work.
We're well past the point where it's about the quality of the work they produce. It's the way they integrate (or rather, don't) into human practices.
This time I add another definition "when you can own it".
If you can really get a good set of requirements, go and write all of your test cases out, and then throw it at an AI that will one shot it. Its perfect.
I've tried this out. Even with relatively small applications and with spending hours reviewing the spec documents, there was always something I missed or something that wasn't quite right when seeing it live.
But after 2 months it starts to backfire me. I still know nothing. I have some understanding of the system design and core components but I have zero clue about how certain things are done under the hood. Because AI read code for me and code for me and I take it as my own understanding.
In last week I end up limiting my AI usage and forcing myself (it is really hard) to read and code at least a bit by myself to start having any idea about what is going on here.
But once you do, working with AI is like having a very capable team of programmers knowing all programming languages you might need and understanding your goals.
Here's an anecdote about a way to do this wrong.
I have found with AI coding methods that there's a line where it becomes a hail-mary (in the American Football sense).
A hail-mary is when you throw the ball to the end zone and just pray someone catches it. This is almost always at the end of the game.
This moment with AI code is indicative that you can't put together a coherent plan so you just tell the agent to "make it good". It used to be that the results here would suck, but now the agents are really competent, so the results might be good.
But at that moment, that's your cue to back up. Because as soon as you take a solution that's so far detached from your understanding, you're underwater. The hail mary is not part of a larger game plan. It's the last play of the game. There's nothing after.
So as soon as you reach that moment in your coding, you're signaling that you're done understanding not just the code, but even the way it works at a high level. If you're still going to work with this code after, then back up and work with the AI to get more understanding of the problem.
So that's why that Andy Weir's book was named that!
The architecture and the intent don't matter to business folks, it never has, and with LLMs it matters less than ever.
Vibe code into production is satisfactory regardless of architectural understanding or intent. If there are issues, just have the LLM spin up some agents to play whackamole until the issues are pushed beyond visibility.
Ultimately, the idea that we can hang onto fleeting engineering disciplines misunderstands where the industry is going, regardless of any assessment of the LLMs capabilities.
Aside: this reminds me of running a “negative split” in a long distance race, where you aim to run the second half faster than the first. It’s very hard to do this because you have to be willing to let everyone else in your pace group pull waaay ahead, and running above race pace in the beginning feels “free” with all of the adrenaline. But if you do manage to stay disciplined, it’s a fantastic feeling to reach half-way with gas in the tank, and then start to reel in all those runners who sped by in the beginning.
well as you said, the market will decide in the end. You may be even further behind in years 2 and 3. btw, we're already in "year 2 or 3" territory for some, i wonder how those companies are doing vs their competitors who adopted AI full throttle?
Of course things can change drastically in the next years, as they already have in the last few.
Only thing I know is that it is really stressful working on this rat race
And of course, this is very much dependent on the switching costs. Which sucks, because tech companies are great at making this as high as possible; the EU is trying to do something about this with their data portability legislation.
Anecdote: I worked at a company providing infrastructure as service. We lost some bids the first time around, likely for a variety of reasons (price, features, ...). And, some customers came "back" to us after using our competitors' offerings and being burned by their reliability. Yes, switching costs were real, but the pain of losing their customers was even higher.
You can still know things and get force multiplication out of LLMs, if you are disciplined and caring enough. In practice, most people won't be. And you can't force other people to be. But you can force yourself to be.
> You can still know things and get force multiplication out of StackOverflow, if you are disciplined and caring enough. In practice, most people won't be. And you can't force other people to be. But you can force yourself to be.
Do you need all these layers of abstraction when the human is no longer looking at the code?
Best analogy is forgetting how to use a slide rule following the advent of calculators. The former was made to make hand-calculation of logarithms easy. The latter does these calculations directly (obviating the need for a slide rule at all).
I think what humans still need to learn are the theory and domain fundamentals for their industry. If that industry is computer science, that means algorithms, calculus, linear algebra, etc. I think the future of CS is then (a) theoretical human-drive design and (b) prompt engineering to implement and verify that design.
It would also be helpful to have domain knowledge outside of CS as having the skills to build something is nearly commoditized (outside of the above fundamentals).
Even hygenic macros still end up being there primarily to increase legibility and ergonomics. Good ones, like core.async, make it easy to understand how threads and the like are glued together, but ultimately it's just syntax-sugar on steroids.
If the goal is not for humans to read the code at all, I'm not entirely sure of the point of syntax macros; the LLM could just generate the expanded code.
Dealing with legacy messes I used to get frustrated and bored of making the improvements, now it's easy to clean up code bases and write loads of tests. I asked Astra to come up with a plan for Playwright testing the whole App, I have not built it yet but the flows suggested were fantastic as was the ephemeral database we plan to create for CI.
I built my friends portfolio website almost entirely vibe coded in 4 hours and it looks unbelievable, we added so much slickness (he's a designer) just prompting together. I used a CMS I had never used once before and it was so so easy to do without any of the usual need to read docs about everything.
I've done so much devops now I'm actually fairly confident that me and an AI can do anything you want in terms of deployment/infra and scaling from AWS to Terraform to whatever.
Anyway my main concern about this technology is not that it is crap at coding it's that the improvements in how it codes and thinks are absolutely dramatic which is extremely scary - it has come so far in a year I wonder what the next year will bring.
While these criticisms were technically true, they were stated mostly out of a sense of insecurity from people whose jobs were basically spending years and years just glueing code together and mixing APIs to display some CRUD apps rather than out of a genuine concern for whether LLMs were actually producing poor products.
Nowadays those concrete criticisms don't really work anymore, LLMs are pretty good now and surpass most developers when it comes to writing the majority of shovelware that people have been employed, and so the narrative is changing from concrete criticisms about how LLMs were genuinely not capable of writing software... to these kinds of abstract and philosophical arguments that are really hard to argue against because they make no concrete claims.
If you say an LLM can't implement a feature, we'll we can test that claim concretely and LLMs are getting much better with every new release. If you say its code is slower, buggier, or less maintainable, those claims too can be measured and once again they're getting really good at these. If you say it takes longer to complete a task or requires more human intervention, we can compare it and measure etc...
But now the objection has shifted not to LLMs are incapable, but people are now incapable and LLMs represent a degradation of the "craft". And here there is nothing left to test or falsify. The argument has stopped being about whether the LLMs work, because that's verifiable and they are now at a point where it's hard to argue against their ability to actually produce functioning software, so now the argument is about whether people are morally, culturally, or intellectually permitted to use it.
Whatever the path is, regular engineers (90% of the people around here) will get screwed up one way or another. But hey, playing with LLM agents is cool!
Are you suggesting not using the tools? A kind of technical version of the Amish way of life?
I dunno I think my guidance and testing is very important and I make sure not to ship things with bugs and code that is really awful. I'm still just about necessary for now.
We have both the tools and the skills.
Later, we will loose the skills because of AI and the depletion of natural resources will lead to the scarcity of the tools.
Scheduled a quick call to align me on what he expects - normally he wouldn't do that but he has attached a big agenda written by Claude what the presentation could show, and invited two other product colleagues of mine.
I came to a Miro board of the Claude Agenda, put into Miro using the MCP.
Honestly, just tiring. Asked my colleagues if they would just put the Claude agenda into Miro with Claude, what they need me for when an AI could just narrate it.
Enough, already.
[1] https://github.com/jackyzha0/quartz [2] https://github.com/sspaeti/second-brain-public
The beige coloring with the bright green "recently updated" label somehow triggered my "this template is made by Claude" instinct.
We all interact with systems through mental models, but if many devs are just prompting claude when something doesn't work, they might read what claude found, but lose out on the exploration, debugging, and work that builds and reinforces the correct mental model and discourages the wrong one. And if devs are missing out on the mental models, will they actually be capable of driving efficient solutions to problems as the mental models get worse.
This is also freeing up time to explore things that before you wouldn't have been able to even start. New fields in tech are opening up. It's all about the model, compute, plugins, third parties... and more to come!
And this is not limited to code, but also how the world works. It's all being abstracted into prompts. Funnily enough, I'm also learning that way...just taking less time to get to the point. But this is not the first time we go through this. Eg. Google vs a library. And like anything, if no one knows anything anymore how do we distinguish from one another? There's a level of wanting to understand in order to distinguish ourselves from the rest in the serendipity of everyday life.
Call me naive, but something tells me we're going to start being much more open to just exploring the world with all this time we just bought ourselves thanks to technology. We were always gonna get to this point and there'll undeniably be bumps ahead.
Does anyone single person understand what’s happening when compiling a large C++ code base? Meaning, can anyone track the basket cast C++ language constructs down to the Clang IR to the optimized machine instructions? From there can any one person follow those machine instructions all the way through to the actual registers etc to actually running the code?
So, if you're pooping out code, and committing it because tests still pass, and that's all you know, you're in for a treat. When an executive wants to know why a b0rked feature lost their department millions of dollars, guess who will have to answer for it, and its not the LLM.
My advice is to find ways to keep on top of how it all works, and if you're the only one who cares, well, then, that makes you even more valuable, not less.
In a lot of shops right now, this leads to an accountablility-authority gap. Where AI changes are merged in quickly without review and without my input, how can I be responsible for understanding the system? I can't. Its the same with reliability.
A lot of good engineers take it a a personal duty to understand the system and keep it up. The way a lot of businesses are using AI makes that impossible. And your job might just be cranking out features, with regard to little else.
I think this is what is stressing a lot of engineers out. It might be worth having a conversation with your boss about what you are actually accountable for. If your boss agrees you are not responsible for uptime, reliability, security, or even understanding the system you might find yourself much happier. If you your boss wants you to be accountable for those things, then you should feel empowered to ask for authority over the things that give you control of the outcome.
> You may not write the code by hand but you understand it enough to investigate and fix it when it fails. It is how I think we should leverage AI instead of becoming a meat proxy.
[0]: https://raahelbaig.com/entry/responsible-human-in-the-loop/
Not only that, but even if we assume best effort on the engineer, the business pressures don't often allow that. I'm under constant pressure to deliver more, faster with less people. The performance eval ladder at my company was just reworked to double the amount of deliverable features expected per job level per year. Our CEO told us we should be able to deliver what took us the past decade to deliver in a quarter, every quarter going forward.
How could you possibly have a human anywhere near that loop with those demands?
Its like saying you can keep fluent level of French as a foreign language to you by just reading it. Nope you won't - do it for 2 years solidly and you will have problems forming basic sentences correctly if you don't use language actively, forming foreign language constructs in your mind.
Those few of us who are largely apart from current llm craze (due to my mega corporation and its obscure rules and procedures) are not missing much I see. I still happily code changes by hand and at most consult basic available llms via prompt, its beautiful process to create fix to a problem which seems cryptic at the beginning, and knowing it all came just from my mind. By far the best part of my corporate job, will keep hanging to it for as long as possible.
Even with unlimited spend, it seems immensely beneficial to dig into the code base and fix a certain amount of bugs oneself. Oftentimes this is ends up being quicker than having to type out a detailed explanation of an issue in plain language, with the added benefit of maintaining intimate knowledge of the code base.
It absolutely won't be long until product managers are the only humans who actually need to be involves with the software development process.
Tail risks have always existed in software development. The tail risk of a bug introduced by some dev who quit five years ago is similar to the tail risk of a bug introduced by Claude six months ago. Deal with it by building better visibility into how your systems work. Demand that your agents write good documentation to accompany their code-writing.
If you’re doing it right these days, it means you’re thinking of a much bigger picture and containing downside risks as boldly as you’re expanding the frontier of upside opportunities.
As a person who's a solid generalist with over 30 years in various roles, I am completely and utterly shocked at how little foundational knowledge people in "senior" roles possess across a wide variety of technical fields. I'm often treated like some wizard or oracle for knowing things that everybody in the field used to know, I just haven't retired yet.
AI didn't create this phenomenon, it's just the latest (and probably the fastest) iteration of it. I recall another particularly large iteration happened when Windows NT 4.0 Server saw mass adoption. Suddenly, people who were effectively IT technicians were now sysadmins.
Edit: grammar
Unarguably, the world's technical capabilities have increased by this shift (while decreasing the required technical understanding required of the people managing it). Albeit, with some security implications.
https://youtu.be/JrBdYmStZJ4
Looking at an architecture, module boundaries, and comparing the organization to the domain is the part LLMs don't do well and is unironically difficult for humans. The less you care about the domain (ie having a mental model), the harder it is.
At the end of the day I'm not paying an engineer to send me claude all day. I'm paying someone to become an expert on a system, even if AI assisted. The product will be better if i have someone that deeply understands the system and where to point AI to. Someone that can understand the full big picture can also anticipate future needs - something AI cannot do at all.
I'm in EE/embedded/FPGA work and you can make an absolute hell of a mess with AI in that world, so maybe everyones opinions are more based around front end software or something. I think people forget there are fields that do not have the huge data training base that frontend/backend software does. a large majority of good designs in FPGA are proprietary at big defense corps, not on stackoverflow and github.
https://www.youtube.com/watch?v=Jt0OoXluC8g at 4:08:
I don't think you need to review every line of code, but you absolutely do need to be able to describe how the system works and its high level structure.
As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here. You are responsible for a large system that has been worked on by multiple different collaborator (both human and agentic). You need to be able to make smart, informed decisions about that system, and talk with credibility to other stakeholders about what it can and cannot do and sensible next steps for the project.
We have never had as abundant a supply of tools to help us learn our craft. I expect that many people will thrive.
People who are a bit lazy and prone to cheating will be able to hurt themselves even more.
Ai can help you learn if you are intentional about it.
I just don't see how you can truly reason about a system without delving into code. Tests aren't enough, running the software isn't enough, high level system architecture isn't enough.
>As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here ... You need to be able to make smart, informed decisions about that system
In my experience, engineering managers are too detached from the system to accurately reason about the system. Tech leads on the other hand usually can given enough time, but they tend to defer judgement to senior ICs on the team who are more familiar with the code.
Point is: there's no way to have your cake and eat it to. You either read the fucking code and keep a mental model of how the system works in your head, or you have an overstated confidence in your ability to reason about the system (and this has been a problem well before LLMs).
I know this is being hyperbolic but I thought this was an odd post to include. I've met plenty of data engineers that don't have great knowledge of the business/product and SWEs that do have that. ¯\_(ツ)_/¯
I am gonna appeal to Occam’s razor here and say, the integral variable here is AI, and the only variable you need to know is AI. The problem is AI.
Really? Seems like they're not making proper use of the tool. I've been reading MORE not less, and also learning more along the way. Just hitting "enter" is a choice, these tools are so powerful if you invest your curiosity, time and experience.
Sounds like they don't care about what they're doing in the first place, writing code by hand won't fix that.
Always has been, always will be.
I am actually hopeful that AI will finally break the industry and force a reckoning around this. Some of it goes to our economic system. New builds are usually capitalizable, flashy, and a great way to get promoted.
Doing ten to fifteen years of thankless maintenance, keeping a critical system alive with high quality? Usually nobody cares, and it's OPEX, not sexy.
As I was reading the OP I kept thinking..well this sounds exactly like what used to happen before AI.
The more things change...
The point about PMs is I think illustrative of this issue. Yes, great PMs who actually understand the product they want to build and can then turn that into something using engineering teams as a black box system exist. But by and large, no they don't. A massive part of why waterfall fell out of fashion is because it turns out most orgs don't have and can't find PMs/PM adjacent people who can build that knowledge, and you can see how agile attempts to correct for this by turning a pipeline into an OODA loop. If you can prevent pipeline flushes, it will beat that OODA loop, but it turns out as an industry we can't actually prevent enough pipeline flushes for that to be the case and we can (for the most part) use that OODA loop to eventually iterate into something a paying customer might give us money for.
Part of the issue is that we're treating the AI like it's just another compiler or static code generator. It's not. The reason they're not is because they require a complete specification of what to output, ie the code. That level of specificity is what allows engineers to mostly stop caring about the assembler and just stay in their higher level code (the main exception being extremely hot loops in places too complex for the compiler to optimize well) and legitimately be able to say they understand the code without ever looking at the compiler/linker output. The entire point of AI for code generation is taking an incomplete specification and turning it into a completed one. If you review and understand every line of output the way almost no one did even before AI was a thing, then you're not going to fall into the trap of not knowing anything about your system anymore. But at the same time, you're not really going to get much benefit from AI either, because you're just replacing time spent coding with time spent reviewing code. It might even take you longer to review than it would to just code it yourself (i think every senior engineer who's mentored a junior dev knows this exact feeling).
To actually get the benefit from AI you have to let go of looking at the code output at all, because looking at that output is the slow part and because just looking and reviewing still won't give you the same knowledge as actually implementing. The passenger aviation industry handles the second issue by preferring/requiring manual control in critical areas (takeoffs and landing) and using simulators to extensively train for manual recovery outside of the abilities of autopilot. For developers the second bit is actually why reviewing AI output is a fool's errand - it will give you the false confidence in your understanding of the system. That doesn't mean we have to embrace vibe-coding or accept slop. It means changing the engineering process from one that deals with a deterministic system to a stochastic one. As an aside, I think this is why upper level managers and executive are leaning into AI code generation so hard - engineering was already a stochastic black box system to them.
This shit is blatantly obvious. And if people keep pushing and pushing for LLMs to generate things that are actually used directly, the problem will grow so large that most people will barely remember a time when their job was something that could be understood.
This is why generative AI sucks.
I can't read the source code since it's 8 million lines of code and written in a programming language I don't know and in a language I don't speak.
^ This is the real world. Some comments talked about how maintainability is king and you just can't keep a mental model together of what the LLM produced. In real life software there is no single person with a mental model of how the system works end to end. In the most ideal scenario you have an architecture diagram, some readme's, and a runbook of how to use the system or get it running in a dev environment. Everything else is manually tracing through mountains of code of wildly varying quality.
coding harnesses are god sent tools when it comes to analysis and maintainability of existing code bases (including code they have produced).
The labs saw this early, and thus many roles at the labs are "Member of Technical Staff". That's the future for every software team. You're not a software engineer anymore, but you're also not a PM, nor a designer. Think horizontal slices, not vertical: Every human's responsibility is to leverage AI to be an expert on everything necessary to deliver some vertical slice of the business.
Product Managers have been claiming this for years now, but put an AI in front of them and they also just start asking it to do their product management job for them.
Worst ones I witnessed were just using it to hallucinate tickets. I saw one even fired because of that. Endless mountains of text going absolutely nowhere, both me and the CPO thought we were going insane from reading so much Claude-ish.
Best one I know used Copilot to code a "Notion to Jira" exporter so it copies requests from business people into Jira. Automated their own work, did nothing else other than monitoring the Python script daily and running ceremonies.
They can pontificate all they want about taste but: modern apps are all copycats of others, work badly, monetization strategies are spaghetti against the wall...
Product management will be replaced by AI way sooner than engineers.
PMs have always lived in the world of dealing with hazy abstraction in both directions: Unclear requirements coming in, turned into unclear system capability whom they have to rely on the engineers to parse. This is where Engineers will have to get comfortable living now: Unclear requirements coming in, unclear code coming out. Its clear that many engineers aren't ready for this, and I don't blame us; it SUCKS. If I had ten dollars for every time I've heard a PM say "no one has any idea what's going on" over the past fifteen years, I wouldn't have to work anymore.
Engineers are probably still the role most suited to adapt to this new world, as you say, but I think people are still vastly underestimating how much they will be personally impacted by the changing industry. If you hate your job now, for reasons like those the article communicates, you'll hate it ten times more in a year.
I was in a Hackathon for students which quite a few staff, like myself, infiltrated. The results were completely unfair, staff and teams with staff (like mine) cleaned up the awards.
In my case I was working with a student who was much better at writing platformers in Unity than I was and an another student who could draw the art we needed even if she'd been trained to think every problem we had interacting with each other had something to do with "the patriarchy".
Myself I'd been in many startups where the game was make a half-baked demo that you could demo on stage and get people excited about it. So everything from presenting broken software on stage and making it look not just perfect but enticing and developing software that has the qualities it takes to present it that way was routine for me, the bit that isn't routine is onboarding unexperienced people to this life in two days.
The more things are unprecedented, the more you need a longer view with more experience.
Who is teaching these young girls such foolish things?