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Navigating customer requirements and building something that satisfies both market's needs and company strategy can be an incredibly difficult and frustrating problem to solve. Especially if you need to also oversee the execution of the strategy. So not only you have to predict what they want or know the domain deeply enough to understand what they say they want is not what they really want, you also have to come up with a plan for executing your solution in a corporate environment.
There is a reason that books like "the staff engineer's path" cover topics such as local maximums, communication, establishing support for executing a plan or creating alignment on big efforts. In large corporate environments with multiple international customers, code is most of the time not the hardest problem.
Just yet another case of people seeing only the extremes and not the entire spectrum
You nailed it: in a corporate setting, code is definitely not the hardest part and it’s why companies can sometimes make do with a skeleton crew of offshore engineers who make $20-$30 bucks an hour
The way harder part is building the right thing and just designing the thing soundly to begin with
This type of software is where AI absolutely kills it - problems with tons of forum posts, writing code for systems with a ton of various kinds of quality developer documentation
On the other hand, if you’re doing something novel or sending a $10bn machine to mars, you probably don’t want to yolo it with AI
Why would they cover programming? That's what all the books on programming are for.
Also I see no need for signal processing to make programming a hard problem. Writing correct code is hard. Writing code that makes incorrect code easy to spot and hard to write is hard.
You can be great at local maximums, communication, establishing support for executing a plan or creating alignment on big efforts, and proceeded to still create a ball of mud. Bug ridden, hard to read, hard to debug.
Because programmers have generally been forced to wear additional, invisible hats that are essential to making the code happen in the first place.
Writing code is not hard. Writing correct code is. Knowing what is correct in a setting with paying customers generally involves interacting with those customers. Either directly or worse. The gigantic salaries paid to the most prolific employees is not due to their ability to write code. It is due to their ability to interrogate the shit out of the customer until they finally reveal the true requirements.
That's like saying "building a car is not hard, building a real car that you can use and that passes regulation is".
IOW, writing code is hard in every reasonable context.
(In interesting ways this is programming’s greatest boon and curse: if we treated it more like building bridges or cars, the world would be a very different place.)
I mean, this was drilled into me at uni — that software was not likely to escape regulation forever and that you can't know with certainty how all the code you're writing will be used when you're not observing the use.
For example, under what constraint regime should the calculator app bundled with an OS be written? It's just a little bundled toy app. Until someone under pressure uses it to calculate a medicine dose, expecting it to be a calculator like it says.
Perhaps this gives away my age more than anything else.
Now add the time dimension - keeping code correct as the business and the people in it change.
That's how I explain to people that LLMs will not replace us developers.
Timestamp is: 36:42-39:55
I believe Graeber perfectly predicts the problems, in 2019, with vibe coding creating immediate "value" from production, but failing to produce true value through maintaining the system (like one continually washes a cup to give it value over time).
The few people who manage to write good quality production apps with AI are developers whether they like it or not and that number isn't high enough to obsolete existing developers.
This is worth saying precisely because of this difference - people see huge amounts of bad code generated by LLMs and think it is equivalent to carefully written code because the results look similar at first glance and they don’t bother to read it.
That said, it's still reasonable to think that once (and only once) the hard part is done, then the easy part is easy. It would just be wrong to think that you can do the whole thing without having to do hard parts. I think the argument here is that with AI this is more possible—that the tasks are more separable. Even if it's the same one person doing the hard stuff and then passing what they learned to the AI to do the easy stuff.
Two reasons they might not separate well (there are others):
- If in a company's product development it's hard(er) to have one person doing H and another doing E, then you're generally going to have one person doing H and E. More or less, this means a person can only do E easily if they do H beforehand. So hard things are required no matter what.
- People come in whole persons. If people skilled/educated to do H tend to be the same people skilled/educated to do E (can be because of how programming is educated, but also can be because there aren't that many programmers), then you're always going to be plugging programmers who have both H and E into roles and it'll probably be more efficient to plug them into roles requiring both rather than just H or just E. You could, within that population, determine who is comparatively advantaged (and we do do this mildly with senior vs junior or with "architects"), but plugging a person into an E-only role is going to involve that person questioning what happened during the H part beforehand. In part because they're good enough at H to question it, but more importantly because they're implementing the H such that they're aware when their E might not be as easy as it could be.
Both of these seem like they might be less true now with AI (and also perhaps because there are more programmers).
While it is true that a programmer's job is a lot more than writing code, I also wonder to what extent that businesses will actually be able to tell a good programmer from a bad one. For example, a lot of folks at big companies can honestly get away with being a ticket-taking code monkey because so much of the responsibility has been abstracted away so that they don't actually get punished for not caring about the customer. It's sort of similar to how many schools realistically wouldn't care to distinguish between a teacher who puts in extra effort into their classroom versus one who clocks in and clocks out as long as some bare minimums were being met.
I think strong programmers will get rewarded in the right companies that need them, but it's still an open question as to how many companies exist that have their bottom lines actually depend on a programmer doing a good job at wearing all those extra hats.
Bit of both probably. I've seen really awful code in my time, so would say "actually coding well" is indeed one of the hard parts.
But knowing what the real problem to be solved is, is indeed important. (Isn't that what sales is? Working with the customer to tease out the real thing they need solving?)
I go half the speed of a junior developer, but the code I write lasts five years to a decade with an order of magnitude or two fewer bugs and long-term maintenance burden.
If you have shop where you have the best product-owner in the world, and exact clarity on how you want to build something, how all failures are handled, all the tradeoffs, all the implementation details, all the risks, then product is simple, then your company absolutely can get away with hiring a less than top-tier engineer.
However if you're combining all of those skills/roles into one individual (a staff+ engineer) then of course it's going to be expensive.
Dude.
Writing correct code is the whole point of the process of writing code. If you're defining coding without care for correctness, of course you can write it off as not the hard part.
Any coder who thinks "correct" ends there is hopelessly misinformed.
Correct code is that which does what is required of it at whatever level of correctness you are examining.
Wow, some "the killer is calling from inside the house" vibes right there. But I totally agree that the game of telephone has always been _an_ issue - maybe not _the_ issue but certainly a big one.
That doesn’t describe any devs I know, especially not in the old days. Interrogating customers and bringing back requirements was the job of management (who got big salaries, too.)
Writing code has a minimum IQ requirement; a significant fraction of the population will essentially never be able to code by themselves. That labor supply limitation is what's kept programmer salaries relatively high.
Average IQ of Electrical Engineers: 121
https://www.iqcareerlab.com/tools/iq-for-profession/electric...
...which is the top 10% of the population.
https://www.desperateminds.com/blog/iq-120.html
However, where we agree, is that there is more to engineering than writing code. It's problem solving. Even if the product team, the c-suite, the board, the investors, et al, are all in on a product that they believe customers want, doesn't mean the real problem of bringing that idea to life at scale has been solved.
coding is always the hard part, always. whenever I was on any project and we had more work than resource we never hired “people to wear other hats” - we hired people to write code, that’s it. thats the fucking job.
you ever see a leet-code-for-understanding-requirements? yea, me either…
... as long as the buyers could never discern
Development is essentially becoming management.
Even with wrangling agents, you’re really just making them right the “correct code”. Something EMs don’t do, or at least the good ones don’t do with their ICs.
Only now instead of local or offshore devs, it is agents.
Now you tell someone else your idea and have to hope they get it. Otherwise you have to argue, rephrase, start all over again.
We are back to the tree-swing project management, but we added another layer
The real conclusion is that programming is such a high-leverage activity that even technically trivial, low-quality programming is immensely valuable economically. That's not going anywhere, but maybe LLMs are going to make it all that much cheaper. (Which is mostly great! But I really don't look forward to the painful debugging and maintenance that reams of shit code will push down on programmers.)
But also, there still is a ton of programming that is fundamentally difficult. That's not going anywhere either. And LLMs are useful there too, but they're currently nowhere near replacing the expertise needed to do novel and non-trivial technical work.
In an ideal world, making mediocre code cheaper should leave more room for taking on harder technical challenges. In reality, this has always been dictated far more by non-technical factors—culture, leadership, trust, risk tolerance...—than by anything intrinsic to programming. But, at least for now, we can use the LLM hype to motivate the kind of deeper technical work that always made sense but was too uncertain or too open-ended or too long-term for non-technical leadership.
And we should also drop the bullshit "code was never the hard part" framing.
Encoding your ideas into a programming language is easy. Understanding that your ideas are bad is hard.
You have clients with multiple devices connecting to your backend simultaneously, while you mediate their interactions with your partner systems. Their versions might not be up to date. It's a distributed system. When was the last time you cracked open a distributed systems textbook?
When was the last time you built a system and stared reality right in face, that is: - can't trust your clocks - pick 2/3 of CAP - exactly-once delivery impossible - the code will need to be altered and released without downtime - hackers will try to exploit you for fun and profit - your manager doesn't want you wasting time getting the above right
Coding is the easy bit.
Either move up the ladder or leave.
The essential complexity can be easily resolved by talking to domain experts. You will get a nice requirements document afterwards. That’s when the engineering and management concerns appear.
Because they're not the ones deciding it, they just provide input for the Heads/VPs/managers who make the strategic decisions. And they are paid pretty well for this.
The hard part has always been how to solve x problem. Coding is the last piece of that part, which while not easy is not the hardest.
The art of computer programming books are not about coding they are about computer science, i.e. figuring out how to compute solutions to problems.
Figuring out what to build is definitely not the hard part though.
The degrees, the books, that’s not the supposedly easy part. That’s the HARD PART, and it’s actually the “what you build” thing. What you build is the code architecture, knowing how to conjure objects, methods, modules, lambdas out of thin air in a way that faithfully represents a real-world problem. Literally the shape of the resulting code. It’s not product or CS.
The easy part is supposed to be actually typing out the code, putting the methods together, remembering method names and syntax quirks.
It was a very useful contraction in the right senior circles where there was a pretty good understanding of the meaning and decently reliable assumed consensus.
I eventually stoped using the phrase because it had started leaking deeper into the team and the impact on earlier career or less confident programmers was often no longer positive, it could be misinterpreted in lots of different ways but the most harmful was when it would further decimate confidence and discourage requests for help when something wasn’t obvious to the ultimate author.
As with almost every attempt to generalize in software engineering the repetition or extrapolation beyond the context in which it was intended can have negative side effects, it doesn’t matter if it’s a simple notion like “dry”, or a comment like “code was never the hard part”. None of these phrases survive context loss and still retain efficacy at general receivers.
The point isn't that labor isn't "valuable" it's that "value" here is a moral position. This same thesis could have been said about basically any mechanized industry.
There is no putting the genie back in the bottle. You can't un-invent the nuclear bomb, or the printing press, or the steam engine. We need to find a politically stabilizing way forward, and that means we need political coalitions that don't consume themselves with infighting.
Right now we can't even work together to build housing for young people... how the hell are we going to get through this mess without actually trying to build something bigger by making sacrifices.
Well-engineered code is hard. It still is. Code that is reliable, extensible, maintainable, scalable, legible, understandable is hard. Code where the specs and the "why" behind it were pressure-tested via thought and good intuition.
Real engineering has always been about talking with the multiple parties, organising architecture meetings, taking down requirements, if no infra team available setup the whole CI/CD pipeline, and so on.
Programming could be done in whatever language, or low code/no code tool, solved the business problem.
Now what will remain to humans is a big question.
https://www.nair.sh/guides-and-opinions/communicating-your-e...
Then naturally seniority teaches office politics, and what to actually invest time on.
Of course he is an AI consultant among other things.
Edit to add: instead of "adapt" (which would imply "embrace AI", which I didn't say), my intent was more along the lines of "be adaptable."
You must be anti ai among other things.
Yes, there always will be artisanal weavers and a smaller number of them get paid a lot more money to do this by people who can afford it. Everyone else either started operating a loom or did something else.
Automated looms are now making mass amounts of textiles but the artisans are no longer doing the physical act of weaving. The artisans come up with cool designs, get feedback from customers, solve people's problems and outsource the rest (physical labor).
We are in the loom moment. Are you designing things people want? Are you doing the weaving? These two paths can coexist but they are diverging disciplines with diverging difficulties and diverging value.
Typing code into a computer is rapidly becoming physical labor now. The layer of creativity and problem solving is quickly rising to a level above the code since the code is a fluid now that comes and goes easily.
There's probably a lot wrong with the "coding was never the hard part" take, but this quote right here shows that the author is not willing to engage with the actual idea that folks who say this are espousing. Because if the author was discussing these ideas on good faith, he'd know that the answer is obvious: there's much, much more to a software engineer's job than just coding, and that other stuff is very hard to do well, and people pay for that.
Again, I'm not saying the "coding was never the hard part" folks are right, but I really, really hate straw men.
"Code was never the hardest part."
There you go. Doesn't imply that coding is easy.
Writing code that's logical and easy to follow, that can be extended in several likely dimensions without major plumbing work, and that doesn't contain "gotchas" for the maintainer, is not at all easy.
Typing code is indeed not the hard part, programming is.
Probably is a question of terms: there is two things: -design- and -coding- (but could be anything as: building, drilling, turning, traveling..) so to make things without a good design is the regal way to problems, many times is a problem left by others, and in a world more and more complex and fast-changing can only be more and more worst. Hard to say what part is really the hardest, but starting with a bad design is no good.
And now probably LLM will solve some problems, but for sure these tech will create more and more new ones.
https://people.inf.ethz.ch/wirth/Articles/LeanSoftware.pdf
Figuring out what code to write and what not to write was always hard.
The code would come out easier the more time I spent thinking and designing and talking about it with others.
of course, YMMV
As computing systems become increasingly capable and encroach in our territory that distinguishes us and lead to our success as a species, intelligence (whatever that is or isn’t), we redefine the problem and handwave away the new capabilities.
It’s getting increasingly more difficult to do that in knowledge domains with current frontier agentic systems. They’re not AGI, but they start to make it increasingly difficult to move the goal posts for many people’s comfort.
We really need a lot more philosophers, sociologists, and frankly economists working on this problem: in an era where physical needs were mechanized away and increasingly aspects of the knowledge economy are shifting away, what does it look like in modernity? How do we sustain or adapt our current economic models? What new models may be needed? Do we need to continue to enforce this whole work to survive in an environment where much work is disappearing or at the very least shifting around.
No, we’re not there yet. You still need experts to guide things around, but it’s becoming increasingly easier to do more in this space with less humans. That’s not a trivial change in the US where we put most our eggs in this whole knowledge economy basket.
Well, this is also an insult.
Cleanest way I read this is seeing how basically none of it makes any sense for someone coding as a hobby, or in any non-business context.
This is not an article about coding, it’s a promotional piece for business stakeholders.
No matter how long you spend on architecture, no matter how carefully you plan your features, if you are an actually good developer there are choices that emerge only from the first draft of the code — things that you could do better, broader ideas that suddenly emerge and change your view of your own work, abstractions that become possible once you internalise the project through writing it, realisations that a requirement is unscalable or unsafe, etc.
Nobody ever finds those things entirely in the planning stage in any piece of code of consequential size or functionality: if it was easy, we'd all be doing waterfall development like 1970s consultants or using StP like 90s consultants, and none of those other ideas about coding would ever have emerged.
LLMs will just write the code. They will never have the rest of that experience. And I think any coder who doesn't have a visceral feel for what I said above is just bad at it.
"The code was never the hard part" is just edgelord AI evangelists masking denial with a pithy mantra. The code, its capabilities, the tooling choices, it's all indivisible from all the other hard parts.
But then these are often also the people who think they can use AI song or image generators to do the bulk of the work and "add the finishing touches". They also think "taste is all that is left" when the thing that gets us paid is not just our taste, it's our responsibility for and to our work.
The arguments are couched in questions… which all either have ready answers or imply strawman arguments that few are making.
I suspect it’s emotional and indirect because the author understands how poor its arguments are. That leaves open the question of why do the blog post at all, but I guess bloggers have to blog, whether or not they’ve got anything to say. An angry, emotional, vague post probably gets a nice amount of views.
> Those decades spent fighting memory bugs in C or C++, with the scars to prove it, are worthless in the age of Rust, Go, Python and JavaScript.
For most people sure. I’ve had GC kill a service in production, or even just tank p99. And I think a decade of C gives you a massive head start with rust. Less screaming “WTF WHY?” at the compiler anyway.
nothing has changed since then.
The comparison does not imply that coding is an easy thing to do, just that it's easier than being really, really good at the bigger picture.
What Carmack did wasn't hard because writing C is hard.
Isn't the real argument that "writing code is not the hard part"? As in, reading and understanding is the hard part. Figuring out what and how to change is the hard part.
Writing is the last 1% that happens after you have already finished the 99% of talking to people, figuring out what needs to be built, building up context about the codebase and surrounding infrastructure in your heard, planning the actual changes.
Managers and execs aren’t saying this it’s the programmers and coders themselves making the claim that coding was never the hard part.
It is still an insult though. It’s an insult to themselves. It’s the lie all programmers including me tell themselves as reality itself insults us. Coding WAS the hard part.
That’s exactly what we were good at. Now our skills are getting owned by automation. How do we face reality shitting in our faces? We lie. We fabricate a reality that’s more acceptable. We frame our environment in a way that still validates our existence. If AI has invalidated all of my programming skill then I need to find something else to support my identity.
https://www.kalzumeus.com/2011/10/28/dont-call-yourself-a-pr...
Besides, there was a whole back-and-forth among multiple bloggers and comment sites (including here: https://news.ycombinator.com/item?id=3170766) from back then in response that agreed or disagreed with that patio11 post. Here's one: https://web.archive.org/web/20111126183459/http://www.jacque... Here's another (though a couple years later): https://yosefk.com/blog/do-call-yourself-a-programmer-and-ot... From the second one's conclusion:
> When I introduce myself, I usually call myself a programmer, regardless of my current work on chip architecture and management and stuff. I got into programming for the money, so it's not like I'm overflowing with pride when uttering "programmer". I just think programming is a great career and the right thing to call myself for me.
> There's an alternative approach where you program, but you don't call it that, and you use programming as a starting point from which you transition to some form of being involved in business as directly as possible.
> It sounds a bit roundabout to me – why not just get an MBA instead? – but maybe it's the right path for some (especially considering that some prestigious MBA programs want you to have industry experience before you can even enroll.)
> The important thing is to choose the path that suits your preferences, follow it consistently, and realize where your approach is most likely to succeed. Because where I work, someone applying for a programming position and not calling himself a programmer will not make a good impression.
Sometimes calling yourself a "Software Engineer", or focusing on "$X company revenue definitely attributed to my efforts" rather than the technical details, is the right thing to do. Sometimes it's not. In any case I'll continue explaining to outsiders that "software engineer" is mostly just a fancy term for "programmer", and to programmers to call themselves whatever they think will best give them a chance at working where, on what, and for how much money they desire.
Occasionally I see a tech person in SV upset about AI automating away jobs. My dude, your whole job is to automate away jobs.
Good riddance to the overpaid coders.
And hello cheap replacements!
The bottom line has improved. And that's good for business. Which was the only thing that mattered. Regardless of any reactionary sentimentalism.
Side note: I so forgot about the "Don't make me think" book! Thanks for reminding this exists. I submit this should be part of "Software Development 101", right there alongside SICP.
This is a specific attitude I try to beat out of juniors. You will not be dismissive of the point of this exercise.
Debugging is hard.
To be precise, it depends on the domain. The people who could actually write algorithms or core implementations were always a minority. Programmers like me mostly did copy-paste from Stack Overflow or assembled libraries.
It's not that code wasn't difficult—it really was.
In CRUD apps, about 70~80%of the work was building the same thing over and over, so once you got familiar with it, most of it was repetitive practice. But the number of people who could actually create something new was always small.
Most business programs had issues that arose in the application stage, the application layer. In this application layer, only a very small portion involved difficult logic. Most of it was just applied.
The problem is that people often romanticize the lower layers beyond their own, compilers and low level systems, calling that 'real programming,' and in doing so, they make programming seem harder than it is. In reality, the coding that most people make money from is mostly at the abstracted layers. The infrastructure beneath those layers is owned by giant corporations. If you work at one of those giants, that's fine. But beneath them are countless consumers paying those giants, and the coding that targets those consumers isn't that difficult.
In the end, whether coding was difficult or easy depends entirely on which layer you're working in.
What's certain is that coding was difficult, and it still is.
I’ve met a lot of programmers where those concepts where only words and not something they have understood. A snippet of code is either something they have to learn or copy, it’s not something they can fluently manipulate. It’s the difference between having to use a dictionary and sample phrases and speaking the language fluently. The former is a chore, while you don’t even notice the latter.
Software consists of various layers, and everyone has their own specific areas of strength. For instance, because I am an application programmer, I often need to write code that prevents the program from halting—aligning with recent programming trends that involve preserving the computation context using monads. In other words, my strengths lie in the overall architecture and interface design. This fundamentally relates to cohesion and coupling. I excel in this area, particularly when dealing with codebases around the 60,000-line mark. This is a realm where books like Clean Code are quite effective (many people dislike it, but it is actually a well-written book). Put differently, I possess the ability to mass-produce software (regardless of absolute quality). Over the course of 7 years, I have built CRUD applications for 43 companies across 16 different domains (ranging from drones and golf simulators to tax SaaS and supermarket POS systems). Therefore, I believe I have at least an average, solid capability in this regard. The primary area where I actually made money was PLC, so while I may not have deep academic expertise, I certainly do not think I lack capability.
In Korea, the profession known as SI (System Integration) is a field where you enter contracts on a "project" basis. In that environment, I have encountered a wide variety of people. From those experiences, my takeaway is that programming is divided into quite several distinct layers.
To speak of algorithms first: I learned basic algorithms and fundamental data structures in university. However, in the field where I worked, there were many people who struggled to implement those basic algorithms, yet they still built a large number of applications. Why is that? There was even someone who made an amount of money I could never dream of touching in my lifetime. Why did he make so much money when he didn't even know how to implement basic algorithms?
The answer is quite simple. It is because the "implementation model" and the "contract and cost model" are different. The contract and cost model is a self-contained body of knowledge. It is the ability to know exactly where to fit a given piece into the puzzle. Implementation is simply the ability to build that piece from scratch. In fact, mostly due to issues like employee turnover and the organization's future maintenance capabilities, many teams (specifically, organizations with lower implementation capabilities) decide on an open-source library and design their architecture based on its API. In these cases, the primary technical challenge becomes how to connect those components based on the performance of that library.
Yet, people tend to think that only those who can implement from scratch are capable of programming. A person who can take someone else's implementation and piece it together to fulfill their own contract is also a programmer, but people frequently forget this. Depending on which layer you exist in, certain knowledge requires you to implement it yourself, while other knowledge only requires you to understand the contract. I believe this is the core of programming.
Those on the side that must design and build libraries or frameworks naturally have things they must know about implementation, as they are creating the SDKs. However, I have seen quite a few cases where these very people have no idea how their work is actually utilized in the upper layers. And these types of knowledge are highly fragmented.
In my case, I am familiar with quite a few paradigms. On my personal homepage wiki, I can differentiate between OOP, DOD (Data-Oriented Design), and others, and in the context of relational databases, I know exactly where the ORM impedance mismatch occurs. However, this is largely an area intertwined with architecture, and its essence is closely linked to David Parnas's theory of information hiding. In other words: to what extent do we hide the internals, and where do we expose them to minimize the contact surface area and ensure a safe connection?
For example, I can't implement PostgreSQL's B-tree. But I can design a business system using PostgreSQL. I only know the name of TCP congestion control—I don't know how to implement it. But I can build networked applications.
That's what an 'industry' really is. The ability to trust the contracts of other people's implementations and assemble them. The ability to trust others.
In that regard, I agree with many of your points. However, I actually believe the software industry needs more of those "average" people. I think the very definition of an "industry" should premise that average people can maintain their livelihoods simply by dedicating themselves to a single specific field. From that perspective, when building one's expertise within this fragmented landscape of knowledge, it is perfectly natural not to know much outside of your own specific layer.
p.s https://www.makonea.com/en-US/casual/cargo-cult-programming-...
>If coding is easy, how come programmers were in high demand, and have demanded large salaries for years (even before ZIRP)?
There is more to those roles than just coding. In fact the more expensive "programmers" often do not code themselves.
>Why was there so much stress, overwork and burnout even before AI started churning out 5000-line PRs?
Something being time consuming is different from something being hard. There are simple factory jobs that also demand overworking.
>Why did companies seek 10x ninja rockstar coders and subject them to leetcode interviews—surely, a junior fresh out of college could churn out something if it's so easy?
Building software takes time and since velocity is important companies wanted people who could increase velocity.
>If coding is easy, why do we have doorstoppers like Clean Code and The Pragmatic Programmer? Is The Art of Computer Programming a light summer read? Is SICP a coffee-table book? Why do we have bootcamps or even whole college degrees dedicated to it?
So authors can make money? Programming is learned by a ton of kids on their own there is no need for boot camps or college degrees just for the benefit of being able to program.
If coding is easy, was Carmack just at the right place at the right time? Why do we consider Fabrice Bellard a genius?
The earlier you are to a field the easier it is to have your impact recorded. In markets with first movie advantage being earlier also helps a lot. A lot of people were able to program so what made them earlier than others was not just being able to program.
>If coding is easy, why are people angry at AI (or anyone else) copying their code? Why do they act like they've poured their sweat, soul, and copious amounts of time into something so trivial?
Again something being time consuming doesn't mean it was hard.
>If coding is easy, why do many now feel like their identity and professional purpose are being stripped away from them?
When you spend a big percentage of your life doing something it becomes part of your identity regardless of difficulty.
>If coding is easy, why is software so damn buggy?
Because making bug free software takes a lot of time and resources. Those resources have a higher return on investment elsewhere.
>If deciding what to build is the hard part, why do so many product managers seem clueless? Why aren't there rigorous 10-step interviews for them? Why aren't they getting paid more than the developers?
People are clueless because it is hard. Pay is not based off of difficulty.
>If deciding what to build is the hard part, why aren't market researchers, usability experts and—hell, customer success—considered rockstars in a software company? If “understanding the customer” is harder, why are business analysts looked down on as pencil pushers?
Because the company finds it cheaper to outsource? A ton of companies have their employees setting up and using telemetry to understand their customers so it's not a one dimensional thing.
>If implementation is easy and finding demand is harder, why are programmers upset when the salespeople promise a new feature to a customer to close the sale? They've found a genuine demand, something people will pay for!
Programming takes time and resources. These may have a higher return on investment elsewhere than this niche feature. It may make maintaining the entire product take more resources to support a niche feature.
>If coding is easy, why doesn't everyone just build ten variations of a thing and see which pans out?
Again building entire products takes a lot of resources. And 10x the cost of building every product is not going to be competitive in the market.
The phrase "X is the hard part" means that X is the hardest part, not that all the other parts are easy.
It is all the parts of maintaining the software with time and engineering with all the moving parts (not the coding) is the point of why software engineering exists.
Now there is hard-er coding. Novel algorithms, performance sensitive stuff, and so on.
Claude writes 90% of my code but the bottlenecks always were and still are:
* getting clear requirements from product
* getting the damn code reviewed so I can merge it
Neither of these are fixed. Frankly, overuse of AI has made both of these worse. Claude brained product owners going hog wild with Claude Design are a nightmare to deal with, and the volume of absolute trash quality code being submitted for review is soul crushing.
No, using AI to automate code reviews is not acceptable. Code Review isn’t about a systematic checklist (though they can help) it’s about making sure people understand the actual changes being made to the system because it’s people who are accountable for what happens in production. LLMs can be part of the process of reviewing code but they suffer from the same issues as any other chatbot based tools (hallucinations, context confusion or not enough context, getting bogged down in impossible code paths or other minutia, etc…) so you have to review that review carefully too. Human judgment is still king.
These days my job is primarily reviewing offshore slop and making sure it’s in a good enough state to merge. I’m doing merge and release management way more than actually coding (and it sucks btw because I actually enjoy coding with or without agents). If the quality of the code turned in for me to review and merge is any indication, engineers/system architects are going to have their hands full.
The divide between product managers and developers mimics the artificial divide between humanities and STEM.
You divide workers into competing groups, then make they outperform each other.
In reality, practically all humans can both become excellent coders and acquire deep product skills as well. We can also learn a wide variety of other skills in a single lifetime. The only blockers on that are social and psychological, you're meant to not believe that is possible.
All this talk about code in this adversarial role with product comes from that, and all of it dissolves under almost any valid critical angle. The engagement with this kind of discussion takes place exclusively in that aforementioned social layer.
TL;DR weak bait
There's still some skill involved, but the skill is mostly in manual testing, and accurately phrasing what went wrong. The AI is better at debugging than people are, and the code isn't great, but perfectly adequate for pretty much everything. And it's even fine at system design these days.
It's interesting realizing how mind numbingly thoughtless my job has become.
Don't get me wrong, I wouldn't mind it if this was skilled work, but it just isn't.
How long does it take? Does it find the issue? In more or less time than the engineers took?
Ask it to review your code for design and cohesiveness issues. How does it do?
> Making software is no longer very hard. It's becoming a few steps up from burger flipping. Maybe somewhere around line chef.
You did acknowledge that "some" skill was required:
> There's still some skill involved, but the skill is mostly in manual testing, and accurately phrasing what went wrong. The AI is better at debugging than people are, and the code isn't great, but perfectly adequate for pretty much everything. And it's even fine at system design these days.
All I asked was for you to back this up, since the burden of proof is on you to prove your claim, not on me to prove it for you.