When the work is equal to the knowledge and judgment of the painter, it is a bad sign; and when it surpasses the judgment, it is still worse, as is the case with those who wonder at having succeeded so well. But when the judgment surpasses the work, it is a perfectly good sign ; and the young painter who possesses that rare disposition, will, no doubt, arrive at great perfection. He will produce few works, but they will be such as to fix the admiration of every beholder.
Leonardo da Vinci, "A Treatise on Painting.", p. 225
This echoes my experience with Claude Code. The bottleneck isn't the code generation itself—it's two critical judgment tasks:
1. Problem decomposition: Taking a vague idea and breaking it down into well-defined, context-bounded issues that I can effectively communicate to the AI
2. Code review: Carefully evaluating the generated code to ensure it meets quality standards and integrates properly
Both of these require deep understanding of the domain, the codebase, and good software engineering principles. Ironically, while I can use AI to help with these tasks too, they remain fundamentally human judgment problems that sit squarely on the critical path to quality software.
The technical skill of writing code has been largely commoditized, but the judgment to know what to build and how to validate it remains as important as ever.
TaupeRanger · 36m ago
That's a narrow view of the issue described in the blog post. You're coming at this from the perspective of a software engineer, which is understandable given the website we're posting on, but the post is really focusing on something higher level - the ability to decide whether the problems you're decomposing and the code you're reviewing is for something "good" or "worthwhile" in the first place. Claude could "decompose problems" and "review code" 10x better than it currently does, but if the thing it's making is useless, awkward, or otherwise bad (because of prompts given by people without the qualities in the blog post), it won't matter.
steveBK123 · 2h ago
So really the same two skills that a senior engineer needs to delegate tasks to juniors & review the results..
skydhash · 1h ago
Nope, dealing with juniors is way less frustrating because they learn. So overtime, you can increase the complexity of their tasks until they're no longer junior.
AndrewKemendo · 58m ago
Disagree. Some learn, not all and decreasing numbers career to learn
Also most juniors have no idea how to write tests, plan for data scale, know which IPC-RPC combo is best for prototyping vs production
Etc…
90% of software is architecture and juniors don’t architect
macNchz · 29m ago
> Disagree. Some learn, not all and decreasing numbers career to learn
This is an organizational issue then—someone who is operating at a junior level who demonstrates that they don’t care to learn should be let go.
steveBK123 · 1h ago
Agreed on that point, and my question for a lot of the AI bros has been "what would you actually do with unlimited interns who never improve much?"
For me, not much! Others may differ.
In my own experience interns are a net drag. New college hires flip positive after 3-6 months.. if they are really good. Many takes upwards of a year.
skydhash · 57m ago
> In my own experience interns are a net drag. New college hires flip positive after 3-6 months.. if they are really good. Many takes upwards of a year.
And mostly their output is not really about incorrect code, but more likely incorrect approaches. By reviewing their code, you find gaps in their knowledge which you can then correct. They're here to learn, not to produce huge amount of code. The tasks are more for practice and evaluation than things you critically need.
I don't want to work with a junior, but I'm more than happy to guide them to be someone I can work with.
strgcmc · 1h ago
I do agree that "unlimited interns who don't improve much" is less practically useful than it might seem at first, but OTOH "never improve much" seems unrealistic, given the insane progress of the field in the last 3ish years (or think back 5 years and tell me who was realistically predicting tools like Claude Code to even exist by 2025).
Also, there's a decently large subset of small startups where there's 1 technical founder and a team of contract labor, trying to build that first MVP or cranking out early features in a huge rush to stay alive, where yeah, cheap unlimited interns might actually be meaningfully useful or economically more attractive than whatever they're doing now. Founders kind of have a perverse incentive, where a CTO doesn't need to solo code the first MVP, and also doesn't need to share/hand-out equity or make early hires quittteee as early, if unlimited interns can scale that CTO's solo productivity for a bit longer than the before-times.
lolinder · 18m ago
> but OTOH "never improve much" seems unrealistic, given the insane progress of the field in the last 3ish years
The point is that no one should hire an intern or a junior because they think it will improve their team's productivity. You hire interns and juniors because there's a causal link between "I hired an intern and spent money training them" and "they joined my company full time and a year later are now productive, contributing members of the team". It's an investment in the future, not a productivity boost today.
There is no causal link between "I aggressively adopted Claude Code in 2025" and "Claude Code in 2026 functions as a full software engineer without babysitting". If I sit around and wait a year without adopting Claude Code that will have no measurable impact on Claude Code's 2026 performance, so why would I adopt it now if it's still at intern- or junior-level skill?
If we accept that Claude is a junior-level contribution then the rational move is to wait and watch for now and only adopt it in earnest if and when it uplevels.
skydhash · 47m ago
> Also, there's a decently large subset of small startups where there's 1 technical founder and a team of contract labor, trying to build that first MVP or cranking out early features in a huge rush to stay alive, where yeah, cheap unlimited interns might actually be meaningfully useful or economically more attractive than whatever they're doing now
That's when experienced developers are a huge plus. They know how to cut corners in a way that will not hurt that much in the long term. It's more often intern level that are proposing stuff like next.js, kubernetes, cloud-native,... that will grind you to a halt once the first bugs appear.
A very small team of good engineers will get you much further than any army of intern level coders.
AndrewKemendo · 1h ago
This is exactly how to use it and exactly why it’s a huge deal
In my experience so far, the people that aren’t getting value out of LLM code assistants, fundamentally like the process of writing code and using the tooling
All of my senior, staff, principals love it because we can make something faster than having to deal with a junior because it’s trivial to write the spec/requirement for Claude etc…
gherkinnn · 7h ago
That matches my experience.
Decomposing a problem so that it is solvable with ease is what I enjoy most about programming and I am fine with no longer having to write as much code myself, but resent having to review so much more.
Now, how do we solve the problem of people blindly accepting what an LLM spat out based on a bad prompt. This applies universally [0] and is not a technological problem.
Agreed on the review burden being frustrating. Two strategies I've found helpful for managing the cognitive load:
1. Tight issue scoping: Making sure each issue is narrowly defined so the resulting PRs are small and focused. Easier to reason about a 50-line change than a 500-line one.
2. Parallel PR workflow: Using git worktrees to have multiple small PRs open simultaneously against the same repo. This lets me break work into digestible chunks while maintaining momentum across different features.
The key insight is that smaller, well-bounded changes are exponentially easier to review thoroughly. When each PR has a single, clear purpose, it's much easier to catch issues and verify correctness.
Im finding these workflow practices help because they force me to engage meaningfully with each small piece rather than rubber-stamping large, complex changes.
prmph · 2h ago
What the heck, the code generation _is_ absolutely still a bottle-neck.
I dare anyone who making these arguments that LLMs have removed the need for actual programming skill, for example, to share in a virtual pair programming session with me, and I will demonstrate their basic inability to do _any_ moderately complex coding in short order. Yes, I think that's the only way to resolve this controversy. If they have some magic sauce for prompting, they should post a session or chat that can be verified by other (even if not exactly repeatable).
Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet. Having to hand hold it every step of the way, continually keep correcting basic type and logic errors (even ones I had corrected previously in the same session), and in the end it just could solve the challenge I gave it.
I have to be cynical and believe those shouting about LLMs taking over technical skill must have lots of stock in the AI companies.
coffeefirst · 2h ago
Indeed.
All this “productivity” has not resulted in one meaningful open source PR or one interesting indie app launch, and I can’t square my own experience with the hype machine.
If it’s not all hat and no cattle, someone should be able to show me some cows.
DontchaKnowit · 24m ago
I find this hard to believe- how would you even know if someone used AI in producing a PR or indie product? Are you omniscient?
Further, there are articles here on HN all the time about people using AI for actual serious work. Heres a pretty significant example :
Why do that when they can ignore you and keep living in their bubble?
sgarland · 42m ago
> Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet
I have been extremely cynical about LLMs up until Claude 4. For the specific project I've been using it on, it's done spectacularly well at specific asks - namely, performance and memory optimization in C code used as a Python library.
sokoloff · 2h ago
I don’t think AI marks the end of software engineers, but it absolutely can grind out code for well specified, well scoped problem statements in quarter-minutes that would take a human an hour or so.
To me, this makes my exploration workflow vastly different. Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”
I think that gets to a better outcome faster in perhaps 10-25% of software engineering work. That’s huge and today is the least capable these AI assistants will ever be.
Even just the human/social/mind-meld aspects will be meaningful. If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out. (This might even turn out to be half the benefit of productivity enhancing tools.)
skydhash · 1h ago
> Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”
What? Software engineering is about problem solving, not finding the first thing that works and called it a day. More often than not, you have too many solutions and the one that's implemented is the result of a list of decisions you've taken.
> If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out.
You should really read the mythical man month.
numpad0 · 1h ago
Last week I was like, I might as well vibe code with free Gemini and steal his credit than researching something destined to be horrible as Android Camera2 API, and found out that at least me using this version of Gemini do better if I prompt it in a... casual language.
"ok now i want xyz for pqr using stu can you make code that do" rather than "I'm wondering if...", with lowercase I and zero softening languages. So as far as my experience goes, tiny details in prompting matter and said details can be unexpected ones.
I mean, please someone just downvote and tell me it's MY skill issue.
dgb23 · 6h ago
I want to add something to this which is rarely discussed.
I personally value focus and flow extremely highly when I'm programming. Code assistance often breaks and prevents that in subtle ways. Which is why I've been turning it off much more frequently.
In an ironic way, using assistance more regularly helped me realize little inefficiencies, distractions and bad habits and potential improvements while programming:
I mean that in a very broad sense, including mindset, tooling, taking notes, operationalizing, code navigation, recognizing when to switch from thinking/design to programming/prototyping, code organization... There are many little things that I could improve, practice and streamline.
So I disagree with this statement at a fundamental level:
> The technical skill of writing code has been largely commoditized (...)
In some cases, I find setting yourself up to get into a flow or just high focus state and then writing code very effective, because there's a stronger connection with the program, my inner mental model of how it works in a more intricate manner.
To me there are two important things to learn at the moment: Recognizing what type of approach I should be using when and setting myself up to use each of them more effectively.
thrwthsnw · 1h ago
Just move up an abstraction level and put that flow into planning the features and decomposing them into well defined tasks that can be assigned to agents. Could also write really polished example code to communicate the style and architectural patterns and add full test coverage for it.
I do notice the same lack of flow when using an agent since you have to wait for it to finish but as others have suggested if you set up a few worktrees and have a really good implementation plan you can use that time to get another agent started or review the code of a separate run and that might lend itself to a type of flow where you’re keeping the whole design of the project in your head and rapidly iterating on it.
physicsguy · 6h ago
A similar debate has happened in education where people seem to think that having ability to critically analyse texts is more important than knowledge. and to some degree that’s true but personally I think that without building on some decent level of foundational level of knowledge and having a mental model of a subject, you can’t tackle thorny questions because you don’t have enough to draw upon as examples and counterpoints about how to proceed.
My current employer is currently going on a top down driven “one tech” mission and trying to rationalise the technology stacks across diverse product lines. Which is all fine but the judgement is a poor one because the biggest developer bottleneck that comes up in internal developer surveys is the corporate mandated IT things and a relatively hostile setup without even local admin rights, which make sense for general office workers and don’t make sense at all for software developers.
Wololooo · 6h ago
Reminds me of that concept that I saw pop up in HEP in recent years between "users" and "experts".
This distinction in that case is so dumb I cannot wrap my head around it: You first encounter the code, are unfamiliar with it but very quickly you become expert in order to solve your problem and advance the thing forward.
It does not matter which codebase you start on, what matters is that you understand what the actual stack does and what is involved in there because people are supposed to understand deeply what they are doing.
But this comes from the "corporatisation" of every single entity, where random metrics are used in order to assess performance instead of asking the simple question of "does it work" or "does it need fixing" or "will this thing break".
There is a clear disconnect between the manager type people that are removed from the work and the managers still doing things practically, which understand what the stressors are and where some work of deep understanding and extra contextualisation of the systems, is required, in order to not mess the whole thing up.
This being said, this is coming from a very peculiar perspective and with a very specific tech stack which is and is not industry standard at many levels...
kragen · 5h ago
High-energy physics?
Wololooo · 4h ago
Yes, sorry I should not have abbreviated that... Professional deformation...
kragen · 4h ago
Pas de problème, merci beaucoup!
mihaaly · 4h ago
Education itself is supposed to teach us learning, not the mere facts/methods, not just the hard knowledge. Hard knowledge comes with it anyway as some sort of 'side product'. You cant learn on nothing, something will be used for it, that something forms the hard knowledge eventually. Typically broad set but shallow hard knowledge.
Ironicly, this is what I feel chipping away in modern collaborative developments. The appreciation of learning capability. In the self interest of the organization (short term self interest, long term is too unpredictable, so does not exists in the practice) specific technical knowledge parcticed individuals are sought out for the purpose of easy replacement: not to be dependent on personnel, have it like a plug and play component. The ability to learn is not valuable while inside the organization. Should be practiced enough for years beforehand and applied intensely after joined. For the sake of claiming evolving organization the teaching may be outsourced in a very limited time to some sort of external enterprise making money on disseminating hard knowledge with made up examples or generic (artificial) applicability, instead of doing it in the actual context of the organization. Be part of the organization. Daily. Application of the new hard knowledge in the specific context of the organization will be casual by the random enthusiast. If they can break through of the company policy and established ways of management. Eventually the policies and practicies must be rigid as well, shouldn't they, so the personnel working in the management could be as easily replacable as the foot soldiers of code. For the sake of the organization. Call this approach the Organization Oriented Development.
physicsguy · 2h ago
> You cant learn on nothing, something will be used for it, that something forms the hard knowledge eventually. Typically broad set but shallow hard knowledge.
As a counterpoint though, the way things have gone in the U.K. is to go deep on niche topics without building up appreciation of the broad strokes. To give an example, there’s a GCSE History course for 14-16 year olds where the syllabus is effectively “medicine through time” and “the American West” without ever going near the British Empire, colonialism, the Tudor or Elizabethan periods, the reformation, the Industrial Revolution, Irish home rule and independence, etc. etc. any one of which gives much more insight into the formation of the state and cultural affairs as it stands today.
To my mind it’s too narrow a focus at too young an age when teaching a subject that a lot of children take. It also means there are constantly “we don’t even teach that at school” debates.
lordnacho · 4h ago
Judgement and technical skill go hand in hand. Technology merely moves the boundary of what is considered judgement, and what is considered technical skill.
I know someone who wrote programs in the punch-card era. Back then, technical skill meant being diligent and thoughtful enough that you avoided most bugs when writing the program. If you screwed this up, you had to wait for another time slot. What does this mean for the complexity of programs you could write? Well, it means you are quite limited. You can't build judgement about things above what is now considered a very basic program.
I learned to program before the AI era that seems to be nascent. Technical skill means things like being able to write programs in python and c++, getting many computers to work together, being able to find hints when something goes wrong, and so on. Judgement now covers things like how a large swarm of programs interact, which was not really in scope for punch-card guy.
Now AI arrives, and it appears that we are free from technical skill problems. Indeed, it does fix a lot of my little syntax issues, but actually it just moves the goalposts. There's soon going to be no excuse for spending time working out the syntax for a lambda function, you'll be expected to generate a much more complicated product, for which you will need an even higher overview to say you are providing judgement.
mehulashah · 8h ago
I would argue that this is already true in roles where one supervises the work of another person with skill. Great leaders, for example, were once practitioners. Over time their skills may fade, but their judgment makes them effective and able to the scale their impact.
drewcoo · 6h ago
In software, we promote good engineers to management, effectively accelerating the Peter Principle.
It doesn't have to be that way. Management skills are not an outgrowth of the skills of the managed, but orthogonal to them. This is similar to the lesson many PhD candidates I've known learn: expertise in their field is not pedagogical expertise. Companies who promoted from within used to provide training for new managers.
apwell23 · 4h ago
> In software, we promote good engineers to management
i've not seen this. Infact its the opposite.
paulluuk · 26m ago
You've seen good managers promoted to engineers? ;)
I have seen this happening, usually the engineers with the best technical AND people skills are first made lead developer, and eventually "team lead". After team lead they can climb the corporate ladder with titles like "junior vice president" or "senior director".
TrackerFF · 6h ago
AI works great for providing you a starting point, and giving a big picture view of how certain things work, and how you should structure them.
Sometimes, even if you're a really seasoned software engineer, you'll encounter something you haven't seen before. Maybe to the point that you don't really even know what to search for to get started. So instead of spending half a day scrounging various forums, e-books, etc. you can ask the model, in somewhat vague terms, what you're looking for - and some of the LLMs are quite good at just that.
Now, the implementation of such things, not quite there yet. My experience has been that the more obscure the problems you deal with, the more obsolete code the model will spit out - with dead and unsupported libraries etc.
CleverLikeAnOx · 7h ago
This blog post shows how not to use AI. The author would have been unlikely to write such a uselessly redundant conclusion if they had to type it themselves.
Edit: I like the post, but it didn't need to be padded with fluff.
wiseowise · 1h ago
Where do you think LLMs learn to write “uselessly redundant conclusion”?
Veen · 6h ago
We shouldn't be too quick to jump to "AI did it." People write redundant paragraphs and sentences in articles all the time because they're led to believe that every article needs a conclusion that sums up what's already been said. Ironically, including one in this article showed a lack of good judgment, which isn't confined to AIs.
QuadmasterXLII · 21m ago
It does show an interesting second order downside of publicly using an LLM for anything: it raises everyone else’s suspicion that the rest of your work is LLM generated.
red_admiral · 4h ago
And what is that judgement based on? Jobs that an AI can't do yet, like designing a system architecture and drawing boundaries (which features go in the same service), need someone with experience.
We can apply this to all points in the Future of Work section. Even the conclusion "What should you do, and why?" is basically a disguised "What domain-specific knowledge do you have to make an informed opinion on the 'why' anyway?"
0x445442 · 1h ago
Writing musical notation doesn't strike me as technically difficult but I'm unaware of any musical composers who weren't proficient in at least one instrument.
Good judgement is only accessible to those who've invested considerable time in the rudiments.
layer8 · 5h ago
When your judgement tells you “this is wrong”, you may need the technical skill to know what instead is right.
The real question is when AI will surpass the average human in both judgement and technical skill.
wg0 · 6h ago
The problem is - you can't judge if you're not skilled.
So still, get skilled. Learn everything first hand. Try to master it.
That's how our species prevailed in the first place.
hinkley · 5h ago
CERT advisories are evidence that skill is necessary but insufficient. There’s a lot of code. We get ping ponged between various sections of the code every few weeks. Other people are contributing. There’s a ton of ways code can look like it’s probably correct and not be. There are non obvious bugs everywhere, and there’s an element of luck to whether you’re in the right headspace to catch them all.
overfl0w · 5h ago
This reminds me of Asimov's Jokester story where the same themes are explored - there is an all-knowing computer but someone needs to ask the correct questions.
"Early in the history of Multivac, it had become apparent
that the bottleneck was the questioning procedure. Multivac
could answer the problem of humanity, all the problems, if it
were asked meaningful questions. But as knowledge accumulated
at an ever-faster rate, it became ever more difficult
to locate those meaningful questions.
Reason alone wouldn't do. What was needed was a rare
type of intuition; the same faculty of mind (only much more
intensified) that made a grand master at chess. A mind was
needed of the sort that could see through the quadrillions of
chess patterns to find the one best move, and do it in a matter
of minutes."
stopthe · 1h ago
That chess metaphor didn't age well
roenxi · 8h ago
Human judgement is like a house built on sand, it is basically provably feeble [0]. I've literally never in practice seen a human update their beliefs using Bayes' formula. I suspect we'll find that at some point fairly soon AIs will just have better judgement than us because they can be programmed to incorporate formal statistical concepts while humans have to rely on evolving grey goop which we haven't quite mastered. I imagine it'll be almost comical watching human experts going up against a system that can actually intuit the difference between a 60% and 70% chance of something happening in their risk calculations.
Humans will still have a role expressing preferences and subjective questions though. Questions like "how much risk do you want your investments to take?" or "does this look good?" ultimately can't be answered by AIs because they depend on the internal state of a human.
[0] See also, the academic field of psychology
bsder · 7h ago
> I've literally never in practice seen a human update their beliefs using Bayes' formula.
Then you've never debugged anything genuinely difficult.
Moving from "Where did I screw up in my code?" to "Is this library broken?" to "Wait, that's not possible. Let's look at the compiler output on Godbolt." to "Are you kidding me? The SPI system returns garbage in the last bit for transactions of 8n+1 bits?" (BTW, Espressif, please fix that in the C6. Kthxbye.) is all about establishing ground truth and adjusting your Bayesian priors as you gather evidence.
Corey_ · 6h ago
Humans may not update like Bayesians, but we read context, shift priorities, and act under pressure. Judgment isn't just math — it's lived experience, intuition, and meaning in motion. That’s still hard to replicate.
dwoldrich · 5h ago
AI enables me to gold plate _everything_ I do, which feels exhilarating, if a bit exhausting. Having decent taste and being able to continuously test and verify my work allows me to smooth over the occasional hallucinations and elicit towering, mind-bending solutions.
AI's no replacement for experience; garbage in-garbage out.
When AI gets too good, I figure people will cloister to stop feeding the beast. It can only lead to ignorance and misery, I fear.
kragen · 5h ago
Can you elaborate? What kinds of things are you doing, maybe something related to programming? What part of the job do you delegate?
dwoldrich · 4h ago
Not delegating, just tricking everything I develop out far more than I would have in the past. I'm on yet another hodgepodge project in a looong, decades long series of hodge and podge. AI is letting me begin to answer to my own satisfaction, "what does it look like to do everything to the best of my ability?"
In my current gig, I have an on-prem database and legacy application that is human-powered software, where parts of the business process never touch the computer and a human does the work (mostly support stuff), (and for no good reason other than this system never had real engineering support.) So, I joined the team, and started to wrangle the system.
First thing I was asked to do was get their database code and schema into source control with managed releases. The gold plating process that I never would have entertained in the past led me to get a migration tool installed, get a unit test engine installed in the database and writing new code with tests, figure out even how to refactor the big ball of mud and coming up with patterns there, doing github workflows to run the tests and deploy to multiple environments, linters, Slack alerts.
It's not that I wasn't aware of all these things, I just never would have done all of them _to the extent_ that I did because the time needed to research it all traditionally and spike the solutions would have been too great. And I documented it all!
After the databases were basically under control and I had gained the team's trust, I moved the team to start automating the human-powered parts of the software. We started an admin console webapp project. Again, I was heavy into AI all along the way, even during requirements elicitation. Our data is a rube goldberg machine of cloud and on-prem, but the majority of what we need to get under control is legacy/on-prem. We want the webapp to eventually be hosted in the cloud, but to be close to our databases and not have to fuss with private links, we decided for starters to deploy the webapp on-prem next to them.
So, that meant figuring out how to get our github builds deployed on-prem. There was this huge saga in figuring out how to provision an on-prem GitHub Runner and use Powershell Remoting to fan out our deployments from there to all of the on-prem servers. Never EVER would I have been able to figure out the permissions and powershell provisioning steps needed to pull that off. It's all very gross, Windows is gross, but what we've built works dependably and is secure. I probably would have just used Samba or some other cheesy way to move files around and trigger deployments if I didn't have AI to bounce all these ideas off of.
Another example: we wanted our BFF microservices to eventually deploy as Azure Functions, so gold plating meant we had to figure out how to build and deploy functions on-prem. It ended up being very productive, but again I would have never entertained doing such a thing unless I could bounce my ideas off AI and get credible directions on how to proceed. Instead, I would have written the service as trusty/crusty old Express 4.x and ported the code to Functions once we made the jump to cloud. I am saving future me a ton of work and heartburn!
At every step AI is giving me the latitude to ask, given whatever nasty situation I'm in, what would be the best code/most secure/nicest architecture in that case? It's arduous to continually pepper it with questions and spend many days zeroing in on a final solution with it. But, it beats the guessing game of searching DuckDuckGo, StackOverflow, and software vendors' documentation - those are now the _last_ places I look for answers. (For ill, I'm sure.)
layer8 · 5h ago
What do you mean by gold-plating? What does not gold-plating look like?
westcoast49 · 7h ago
Music was never an issue of skill. You might have needed skill in order to get to the point where you could use your judgement, but skill was never the deciding factor. So, I don't agree with Brian Eno's point that there has been some kind of seismic shift when it comes to this. Rather, it's just a matter of a shift in the type of skill that you need to have. The same is probably true when it comes to AI tools within the field of programming.
drewcoo · 6h ago
Getting a seat in a symphony involves skill. Brian Eno didn't just do rock.
westcoast49 · 3h ago
I'm just arguing that there has been no qualitative shift. The skills you need to produce pop music today are different than the skills that you needed before. Now you need to be able to operate a MIDI sequencer or a program like Ableton, which is not necessarily easier in and off itself, than for instance learning how to play a guitar. I think it's the same with AI tools and programming. We're just talking about a different set of skills that you need in order to be competitive. I don't think there has been any real shift from "skill needed" to "judgement needed". I think this relationship remains the same, both for pop music and programming, but also for the example of playing an instrument in a symphony orchestra.
Tarq0n · 4h ago
Ever seen a photoshop expert at work? Every human endeavor has a skill distribution. Hammering in a nail takes skill.
k__ · 4h ago
I think, the argument would make more sense if software like Cubase came with unlicenced samples from all songs out of the box. Artists sued and won when someone used their samples without permission.
If you use AI to create art, it's like that.
CuriouslyC · 4h ago
There's a difference between a sample and something inspired by something but also significantly different. The copyright laws around music are kind of draconian so it's not a good analogy for code anyhow, imagine a world where a fundamental do while loop had 90 year copyright protection, and that's the sort of world we'd be living in if code copyright was like music copyright.
k__ · 3h ago
If you see the generated content as "the music that's created with unlicenced samples" that's true.
However, if you see the trained models as "the music that's created with unlicenced samples" it isn't true.
crabl · 9h ago
As the marginal cost of writing code decreases, the opportunity cost associated with writing the "right" code increases dramatically
Fr3dd1 · 8h ago
Today, if someone uses LLMs for code generation, he/she will probably question the generated code and will put his own judgement above it. I am curious how fast that will change, especially for juniors. When will they start to question their own judgement and just go with the generated code becuase its "more safe"?
chasd00 · 1h ago
LLMs are very good at sounding right. I’m sure the code generated from them are rarely reviewed by junior developers. Even if they did question it i bet they give the LLM the benefit of the doubt. “Well the computer said this so it must be right otherwise it would be a bug and I bet Anthropic has caught all the bugs…”
kusokurae · 8h ago
At present, when juniors do this at my company, they usually get fired within the month. The onboarding docs now explicitly state that though code review is a joint-responsibility process, you as the submitter are responsible for understanding it, ensuring it all works, and being aware of the broader scope and consequences. Maybe many companies have placed more responsibility on the reviewier to catch problems in the past?
Fr3dd1 · 7h ago
I would go a step further and dont let juniors use LLMs for code generation. The purpose and your role as a junior is to not only work but also to learn. When using genrated code, you miss a lot of opportunities to do so. Of course you could learn of some other methods or stuff from the frameworks you are using but imho thats not that big of an advantage.
Animats · 6h ago
Probably when the generated code is, on average, better than human-generated code. Somewhere between 1 and 10 years out.
bravesoul2 · 8h ago
Does the technical skill give you better judgement though? Can a masterchef make better Star Trek replicator meals?
kusokurae · 8h ago
I think the reality is that, this notion of "democratising" various technical mediums is used to gloss over that, as countless studies have now evidenced, humans primarily learn and remember well thing by doing things, not merely passively consuming them. Fine-precision decision-making will likely always be the domain of dedicated tooling designed to better correspond with the particular medium or task -- that is, manual testing and experimentation, not relying on logocentric prompt idolatry.
When we get into literature, visual art etc. it becomes more of a problem. You can't get Cormac McCarthies or Mars Voltas from software designed to give you perfect statistical 50% grey, and people who try and hack it without doing the reading, are going to end up writing gibberish. People who actually enjoy and like art, music whatever are going to grow Very bored with the overwhelming majority of work reliant of primarily generative methods, save for those who already have discretion learned through experience of many tools and other means of expression.
12112521312 · 7h ago
Yes, really good pianist often compose music that is interesting, that other composers don't make ( e.g. Listz, Alkan ).
In jazz, nothing can replace practicing your improv skills.
drewhk · 6h ago
Also, judgement alone might not be enough. Judgement can take you to "something is off", but not necessarily further. I mix music as a hobby and it takes a good amount of practice to step up from recognizing the presence of a problem to actually know where and how to fix it. If you don't know where you should look, you just aimlessly try various things, and it is not unusual to make the problem worse.
Eventually you learn to properly recognize the problems, not just their presence, but their actual nature and implications. But this takes practice.
financypants · 36m ago
Have people noticed the ai-assisted code "creep"? Cursor now by default applies its changes before you've even hit accept, and the tab autocomplete is getting out of control. Sometimes I'll have my cursor resting on some block of code, then suddenly Cursor suggests I delete the whole thing.
giordanol · 3h ago
The tooling problem is 90% solved. The new technical bottleneck is human judgment.
4b11b4 · 4h ago
Yeah but, you can't make a judgement in these technical areas without the technical skill... No?
z3t4 · 2h ago
It takes skill to see the beauty
somewhereoutth · 2h ago
"Anyone with access to AI tools can now produce work that, _at least superficially_, resembles professional output."
Key quote, emphasis mine.
monero-xmr · 8h ago
Nothing stopped anyone from hiring 10 to 100 offshore devs for every American software dev for the last 20 years. Yet Google, Amazon, Microsoft, and so on paid top dollar for the Americans.
And American business still pays top dollar. Even more than before. The judgement was always the problem. If the issue was bodies in seats the problem was already solved.
The #1 cause of layoffs in America is offshoring caused by Zoom and other telework tools perfected during COVID. AI is a convenient excuse.
Pop music is mostly not about music quality - hits are always passable - but about celebrity. The rare song that elevates a new artist quickly converts them into celebrity, which converts future songs in their style into further hits 100,000x easier than before. Maybe even 1 billion times easier than before given the amount of songs created every year. Yet AI is supposedly an expert at generating music, and images, and video, and code, and on and on.
I’m not seeing the evidence of layoffs from AI. I’m seeing evidence of better productivity from existing employees, which is the same result of every groundbreaking technology since all time.
CuriouslyC · 8h ago
The thing that stopped people from hiring 10-100 offshore devs per American dev lead/architect was communication primarily, with quality floor being an additional factor. Plenty of American companies have "hired 10-100 offshore devs" per American lead/architect, but they call it a foreign office, they solve the communication problem by having on-site managers that can serve as proxies, and they solve the quality floor problem by having the office in countries where the locals have strong expertise.
If AI can solve the communication and quality floor problems (it's pretty close), having 100 agents per dev lead/architect becomes perfectly viable.
aleph_minus_one · 7h ago
> they solve the communication problem by having on-site managers that can serve as proxies, and they solve the quality floor problem by having the office in countries where the locals have strong expertise.
I think this problem goes deeper: there exist lots of countries where people are strong in programming, but from my work experience, the whole "programming culture" (how to approach problems; how to structure the program; ...) differs quite a bit between countries. So, from other countries you can get great programs, but the style can differ quite a bit from what you are used to.
MangoToupe · 8h ago
> Pop music is mostly not about music quality - hits are always passable - but about celebrity.
Interesting and bold statement. How do you distinguish the two?
kusokurae · 8h ago
Worth mentioning here that historically a lot of the famous pop hits have had plenty of interesting technical decisions typically only a mature composer would come up with. Weird short bars, time signature stuff, temporary key changes, weird jazz chords etc. You don't notice them because they're refined choices.
simianwords · 6h ago
Empirically: by randomly asking N participants to rate pop music made by a celeb and by an indie band and observing if these participants were able to identify the celeb statistically.
monero-xmr · 8h ago
Whatever schlock Taylor Swift manufactures next will be a global hit. Doesn’t matter the quality
smitty1e · 8h ago
Grok3: "The phrase "There is no royal road to geometry" is attributed to the ancient Greek mathematician Euclid. According to historical accounts, particularly from the philosopher Proclus, Euclid reportedly said this to Ptolemy I Soter, the ruler of Egypt, when the king asked if there was a shorter or easier way to learn geometry. Euclid's response emphasized that geometry, like any rigorous discipline, requires effort and dedication, with no shortcuts even for royalty."
You can use AI as a royal road, but it may or may not prove an effective substitute for the learning required to provide judgement.
When the work is equal to the knowledge and judgment of the painter, it is a bad sign; and when it surpasses the judgment, it is still worse, as is the case with those who wonder at having succeeded so well. But when the judgment surpasses the work, it is a perfectly good sign ; and the young painter who possesses that rare disposition, will, no doubt, arrive at great perfection. He will produce few works, but they will be such as to fix the admiration of every beholder.
Leonardo da Vinci, "A Treatise on Painting.", p. 225
https://archive.org/details/davincionpainting00leon/page/224...
1. Problem decomposition: Taking a vague idea and breaking it down into well-defined, context-bounded issues that I can effectively communicate to the AI
2. Code review: Carefully evaluating the generated code to ensure it meets quality standards and integrates properly
Both of these require deep understanding of the domain, the codebase, and good software engineering principles. Ironically, while I can use AI to help with these tasks too, they remain fundamentally human judgment problems that sit squarely on the critical path to quality software.
The technical skill of writing code has been largely commoditized, but the judgment to know what to build and how to validate it remains as important as ever.
Also most juniors have no idea how to write tests, plan for data scale, know which IPC-RPC combo is best for prototyping vs production
Etc…
90% of software is architecture and juniors don’t architect
This is an organizational issue then—someone who is operating at a junior level who demonstrates that they don’t care to learn should be let go.
For me, not much! Others may differ.
In my own experience interns are a net drag. New college hires flip positive after 3-6 months.. if they are really good. Many takes upwards of a year.
And mostly their output is not really about incorrect code, but more likely incorrect approaches. By reviewing their code, you find gaps in their knowledge which you can then correct. They're here to learn, not to produce huge amount of code. The tasks are more for practice and evaluation than things you critically need.
I don't want to work with a junior, but I'm more than happy to guide them to be someone I can work with.
Also, there's a decently large subset of small startups where there's 1 technical founder and a team of contract labor, trying to build that first MVP or cranking out early features in a huge rush to stay alive, where yeah, cheap unlimited interns might actually be meaningfully useful or economically more attractive than whatever they're doing now. Founders kind of have a perverse incentive, where a CTO doesn't need to solo code the first MVP, and also doesn't need to share/hand-out equity or make early hires quittteee as early, if unlimited interns can scale that CTO's solo productivity for a bit longer than the before-times.
The point is that no one should hire an intern or a junior because they think it will improve their team's productivity. You hire interns and juniors because there's a causal link between "I hired an intern and spent money training them" and "they joined my company full time and a year later are now productive, contributing members of the team". It's an investment in the future, not a productivity boost today.
There is no causal link between "I aggressively adopted Claude Code in 2025" and "Claude Code in 2026 functions as a full software engineer without babysitting". If I sit around and wait a year without adopting Claude Code that will have no measurable impact on Claude Code's 2026 performance, so why would I adopt it now if it's still at intern- or junior-level skill?
If we accept that Claude is a junior-level contribution then the rational move is to wait and watch for now and only adopt it in earnest if and when it uplevels.
That's when experienced developers are a huge plus. They know how to cut corners in a way that will not hurt that much in the long term. It's more often intern level that are proposing stuff like next.js, kubernetes, cloud-native,... that will grind you to a halt once the first bugs appear.
A very small team of good engineers will get you much further than any army of intern level coders.
In my experience so far, the people that aren’t getting value out of LLM code assistants, fundamentally like the process of writing code and using the tooling
All of my senior, staff, principals love it because we can make something faster than having to deal with a junior because it’s trivial to write the spec/requirement for Claude etc…
Decomposing a problem so that it is solvable with ease is what I enjoy most about programming and I am fine with no longer having to write as much code myself, but resent having to review so much more.
Now, how do we solve the problem of people blindly accepting what an LLM spat out based on a bad prompt. This applies universally [0] and is not a technological problem.
0 - https://www.theverge.com/policy/677373/lawyers-chatgpt-hallu...
1. Tight issue scoping: Making sure each issue is narrowly defined so the resulting PRs are small and focused. Easier to reason about a 50-line change than a 500-line one.
2. Parallel PR workflow: Using git worktrees to have multiple small PRs open simultaneously against the same repo. This lets me break work into digestible chunks while maintaining momentum across different features.
The key insight is that smaller, well-bounded changes are exponentially easier to review thoroughly. When each PR has a single, clear purpose, it's much easier to catch issues and verify correctness.
Im finding these workflow practices help because they force me to engage meaningfully with each small piece rather than rubber-stamping large, complex changes.
I dare anyone who making these arguments that LLMs have removed the need for actual programming skill, for example, to share in a virtual pair programming session with me, and I will demonstrate their basic inability to do _any_ moderately complex coding in short order. Yes, I think that's the only way to resolve this controversy. If they have some magic sauce for prompting, they should post a session or chat that can be verified by other (even if not exactly repeatable).
Yesterday almost my whole day was wasted because I chose to attack a problem primarily by using Claude 4 Sonnet. Having to hand hold it every step of the way, continually keep correcting basic type and logic errors (even ones I had corrected previously in the same session), and in the end it just could solve the challenge I gave it.
I have to be cynical and believe those shouting about LLMs taking over technical skill must have lots of stock in the AI companies.
All this “productivity” has not resulted in one meaningful open source PR or one interesting indie app launch, and I can’t square my own experience with the hype machine.
If it’s not all hat and no cattle, someone should be able to show me some cows.
Further, there are articles here on HN all the time about people using AI for actual serious work. Heres a pretty significant example :
https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-...
I have been extremely cynical about LLMs up until Claude 4. For the specific project I've been using it on, it's done spectacularly well at specific asks - namely, performance and memory optimization in C code used as a Python library.
To me, this makes my exploration workflow vastly different. Instead of stopping at the first thing that isn’t obviously broken, I can now explore nearby “what if it was slightly different in this way?”
I think that gets to a better outcome faster in perhaps 10-25% of software engineering work. That’s huge and today is the least capable these AI assistants will ever be.
Even just the human/social/mind-meld aspects will be meaningful. If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out. (This might even turn out to be half the benefit of productivity enhancing tools.)
What? Software engineering is about problem solving, not finding the first thing that works and called it a day. More often than not, you have too many solutions and the one that's implemented is the result of a list of decisions you've taken.
> If it can make a dev team of 7 capable of making the thing that used to take a dev team of 8, that's around 15% less human coordination needed overall to get the product out.
You should really read the mythical man month.
"ok now i want xyz for pqr using stu can you make code that do" rather than "I'm wondering if...", with lowercase I and zero softening languages. So as far as my experience goes, tiny details in prompting matter and said details can be unexpected ones.
I mean, please someone just downvote and tell me it's MY skill issue.
I personally value focus and flow extremely highly when I'm programming. Code assistance often breaks and prevents that in subtle ways. Which is why I've been turning it off much more frequently.
In an ironic way, using assistance more regularly helped me realize little inefficiencies, distractions and bad habits and potential improvements while programming:
I mean that in a very broad sense, including mindset, tooling, taking notes, operationalizing, code navigation, recognizing when to switch from thinking/design to programming/prototyping, code organization... There are many little things that I could improve, practice and streamline.
So I disagree with this statement at a fundamental level:
> The technical skill of writing code has been largely commoditized (...)
In some cases, I find setting yourself up to get into a flow or just high focus state and then writing code very effective, because there's a stronger connection with the program, my inner mental model of how it works in a more intricate manner.
To me there are two important things to learn at the moment: Recognizing what type of approach I should be using when and setting myself up to use each of them more effectively.
I do notice the same lack of flow when using an agent since you have to wait for it to finish but as others have suggested if you set up a few worktrees and have a really good implementation plan you can use that time to get another agent started or review the code of a separate run and that might lend itself to a type of flow where you’re keeping the whole design of the project in your head and rapidly iterating on it.
My current employer is currently going on a top down driven “one tech” mission and trying to rationalise the technology stacks across diverse product lines. Which is all fine but the judgement is a poor one because the biggest developer bottleneck that comes up in internal developer surveys is the corporate mandated IT things and a relatively hostile setup without even local admin rights, which make sense for general office workers and don’t make sense at all for software developers.
This distinction in that case is so dumb I cannot wrap my head around it: You first encounter the code, are unfamiliar with it but very quickly you become expert in order to solve your problem and advance the thing forward.
It does not matter which codebase you start on, what matters is that you understand what the actual stack does and what is involved in there because people are supposed to understand deeply what they are doing.
But this comes from the "corporatisation" of every single entity, where random metrics are used in order to assess performance instead of asking the simple question of "does it work" or "does it need fixing" or "will this thing break".
There is a clear disconnect between the manager type people that are removed from the work and the managers still doing things practically, which understand what the stressors are and where some work of deep understanding and extra contextualisation of the systems, is required, in order to not mess the whole thing up.
This being said, this is coming from a very peculiar perspective and with a very specific tech stack which is and is not industry standard at many levels...
Ironicly, this is what I feel chipping away in modern collaborative developments. The appreciation of learning capability. In the self interest of the organization (short term self interest, long term is too unpredictable, so does not exists in the practice) specific technical knowledge parcticed individuals are sought out for the purpose of easy replacement: not to be dependent on personnel, have it like a plug and play component. The ability to learn is not valuable while inside the organization. Should be practiced enough for years beforehand and applied intensely after joined. For the sake of claiming evolving organization the teaching may be outsourced in a very limited time to some sort of external enterprise making money on disseminating hard knowledge with made up examples or generic (artificial) applicability, instead of doing it in the actual context of the organization. Be part of the organization. Daily. Application of the new hard knowledge in the specific context of the organization will be casual by the random enthusiast. If they can break through of the company policy and established ways of management. Eventually the policies and practicies must be rigid as well, shouldn't they, so the personnel working in the management could be as easily replacable as the foot soldiers of code. For the sake of the organization. Call this approach the Organization Oriented Development.
As a counterpoint though, the way things have gone in the U.K. is to go deep on niche topics without building up appreciation of the broad strokes. To give an example, there’s a GCSE History course for 14-16 year olds where the syllabus is effectively “medicine through time” and “the American West” without ever going near the British Empire, colonialism, the Tudor or Elizabethan periods, the reformation, the Industrial Revolution, Irish home rule and independence, etc. etc. any one of which gives much more insight into the formation of the state and cultural affairs as it stands today.
To my mind it’s too narrow a focus at too young an age when teaching a subject that a lot of children take. It also means there are constantly “we don’t even teach that at school” debates.
I know someone who wrote programs in the punch-card era. Back then, technical skill meant being diligent and thoughtful enough that you avoided most bugs when writing the program. If you screwed this up, you had to wait for another time slot. What does this mean for the complexity of programs you could write? Well, it means you are quite limited. You can't build judgement about things above what is now considered a very basic program.
I learned to program before the AI era that seems to be nascent. Technical skill means things like being able to write programs in python and c++, getting many computers to work together, being able to find hints when something goes wrong, and so on. Judgement now covers things like how a large swarm of programs interact, which was not really in scope for punch-card guy.
Now AI arrives, and it appears that we are free from technical skill problems. Indeed, it does fix a lot of my little syntax issues, but actually it just moves the goalposts. There's soon going to be no excuse for spending time working out the syntax for a lambda function, you'll be expected to generate a much more complicated product, for which you will need an even higher overview to say you are providing judgement.
It doesn't have to be that way. Management skills are not an outgrowth of the skills of the managed, but orthogonal to them. This is similar to the lesson many PhD candidates I've known learn: expertise in their field is not pedagogical expertise. Companies who promoted from within used to provide training for new managers.
i've not seen this. Infact its the opposite.
Sometimes, even if you're a really seasoned software engineer, you'll encounter something you haven't seen before. Maybe to the point that you don't really even know what to search for to get started. So instead of spending half a day scrounging various forums, e-books, etc. you can ask the model, in somewhat vague terms, what you're looking for - and some of the LLMs are quite good at just that.
Now, the implementation of such things, not quite there yet. My experience has been that the more obscure the problems you deal with, the more obsolete code the model will spit out - with dead and unsupported libraries etc.
Edit: I like the post, but it didn't need to be padded with fluff.
We can apply this to all points in the Future of Work section. Even the conclusion "What should you do, and why?" is basically a disguised "What domain-specific knowledge do you have to make an informed opinion on the 'why' anyway?"
Good judgement is only accessible to those who've invested considerable time in the rudiments.
The real question is when AI will surpass the average human in both judgement and technical skill.
So still, get skilled. Learn everything first hand. Try to master it.
That's how our species prevailed in the first place.
"Early in the history of Multivac, it had become apparent that the bottleneck was the questioning procedure. Multivac could answer the problem of humanity, all the problems, if it were asked meaningful questions. But as knowledge accumulated at an ever-faster rate, it became ever more difficult to locate those meaningful questions. Reason alone wouldn't do. What was needed was a rare type of intuition; the same faculty of mind (only much more intensified) that made a grand master at chess. A mind was needed of the sort that could see through the quadrillions of chess patterns to find the one best move, and do it in a matter of minutes."
Humans will still have a role expressing preferences and subjective questions though. Questions like "how much risk do you want your investments to take?" or "does this look good?" ultimately can't be answered by AIs because they depend on the internal state of a human.
[0] See also, the academic field of psychology
Then you've never debugged anything genuinely difficult.
Moving from "Where did I screw up in my code?" to "Is this library broken?" to "Wait, that's not possible. Let's look at the compiler output on Godbolt." to "Are you kidding me? The SPI system returns garbage in the last bit for transactions of 8n+1 bits?" (BTW, Espressif, please fix that in the C6. Kthxbye.) is all about establishing ground truth and adjusting your Bayesian priors as you gather evidence.
AI's no replacement for experience; garbage in-garbage out.
When AI gets too good, I figure people will cloister to stop feeding the beast. It can only lead to ignorance and misery, I fear.
In my current gig, I have an on-prem database and legacy application that is human-powered software, where parts of the business process never touch the computer and a human does the work (mostly support stuff), (and for no good reason other than this system never had real engineering support.) So, I joined the team, and started to wrangle the system.
First thing I was asked to do was get their database code and schema into source control with managed releases. The gold plating process that I never would have entertained in the past led me to get a migration tool installed, get a unit test engine installed in the database and writing new code with tests, figure out even how to refactor the big ball of mud and coming up with patterns there, doing github workflows to run the tests and deploy to multiple environments, linters, Slack alerts.
It's not that I wasn't aware of all these things, I just never would have done all of them _to the extent_ that I did because the time needed to research it all traditionally and spike the solutions would have been too great. And I documented it all!
After the databases were basically under control and I had gained the team's trust, I moved the team to start automating the human-powered parts of the software. We started an admin console webapp project. Again, I was heavy into AI all along the way, even during requirements elicitation. Our data is a rube goldberg machine of cloud and on-prem, but the majority of what we need to get under control is legacy/on-prem. We want the webapp to eventually be hosted in the cloud, but to be close to our databases and not have to fuss with private links, we decided for starters to deploy the webapp on-prem next to them.
So, that meant figuring out how to get our github builds deployed on-prem. There was this huge saga in figuring out how to provision an on-prem GitHub Runner and use Powershell Remoting to fan out our deployments from there to all of the on-prem servers. Never EVER would I have been able to figure out the permissions and powershell provisioning steps needed to pull that off. It's all very gross, Windows is gross, but what we've built works dependably and is secure. I probably would have just used Samba or some other cheesy way to move files around and trigger deployments if I didn't have AI to bounce all these ideas off of.
Another example: we wanted our BFF microservices to eventually deploy as Azure Functions, so gold plating meant we had to figure out how to build and deploy functions on-prem. It ended up being very productive, but again I would have never entertained doing such a thing unless I could bounce my ideas off AI and get credible directions on how to proceed. Instead, I would have written the service as trusty/crusty old Express 4.x and ported the code to Functions once we made the jump to cloud. I am saving future me a ton of work and heartburn!
At every step AI is giving me the latitude to ask, given whatever nasty situation I'm in, what would be the best code/most secure/nicest architecture in that case? It's arduous to continually pepper it with questions and spend many days zeroing in on a final solution with it. But, it beats the guessing game of searching DuckDuckGo, StackOverflow, and software vendors' documentation - those are now the _last_ places I look for answers. (For ill, I'm sure.)
If you use AI to create art, it's like that.
However, if you see the trained models as "the music that's created with unlicenced samples" it isn't true.
When we get into literature, visual art etc. it becomes more of a problem. You can't get Cormac McCarthies or Mars Voltas from software designed to give you perfect statistical 50% grey, and people who try and hack it without doing the reading, are going to end up writing gibberish. People who actually enjoy and like art, music whatever are going to grow Very bored with the overwhelming majority of work reliant of primarily generative methods, save for those who already have discretion learned through experience of many tools and other means of expression.
In jazz, nothing can replace practicing your improv skills.
Eventually you learn to properly recognize the problems, not just their presence, but their actual nature and implications. But this takes practice.
Key quote, emphasis mine.
And American business still pays top dollar. Even more than before. The judgement was always the problem. If the issue was bodies in seats the problem was already solved.
The #1 cause of layoffs in America is offshoring caused by Zoom and other telework tools perfected during COVID. AI is a convenient excuse.
Pop music is mostly not about music quality - hits are always passable - but about celebrity. The rare song that elevates a new artist quickly converts them into celebrity, which converts future songs in their style into further hits 100,000x easier than before. Maybe even 1 billion times easier than before given the amount of songs created every year. Yet AI is supposedly an expert at generating music, and images, and video, and code, and on and on.
I’m not seeing the evidence of layoffs from AI. I’m seeing evidence of better productivity from existing employees, which is the same result of every groundbreaking technology since all time.
If AI can solve the communication and quality floor problems (it's pretty close), having 100 agents per dev lead/architect becomes perfectly viable.
I think this problem goes deeper: there exist lots of countries where people are strong in programming, but from my work experience, the whole "programming culture" (how to approach problems; how to structure the program; ...) differs quite a bit between countries. So, from other countries you can get great programs, but the style can differ quite a bit from what you are used to.
Interesting and bold statement. How do you distinguish the two?
You can use AI as a royal road, but it may or may not prove an effective substitute for the learning required to provide judgement.