Grant me the serenity to accept the bad code i shouldn't fix, the courage to change the code I can, and the wisdom to know the difference.
Well, It's really early in the morning and I've got the quote of the day already
And I think there is a place for perl, just like there is a place for bash one-liners.
The authors example is personal software. The things we write to scratch our own little itches, that do not need to be shared or developed together with other people.
Fantastic
I think this kind of work is constantly misunderstood and undervalued. I don't really see it as a binary thing, more like a complex skill that most people are terrible at, some are good at, and a handful of giants use to be just ridiculously productive in their field.
It reminds me of hedgehogs and foxes - foxes tend to be bad at making one off progress on their own, but are critical for accretive work.
Also I was reading a textbook the other day and thinking wow, it is absurd how much more valuable these things can be than other resources, and it's exactly because they canonize. It would be a massive loss if they stop getting written.
The core truth of it is that a massive amount, possibly most, of the world's software is not a carefully hand-crafted application in that lives in Github written by expert software developers. It's a heap of Excel functions in an XSLX file, with no tests, no source control, no PRs, and no real planning behind it. And it works for that one specific task that the person who built it needed at the time.
AI vibe-coding is probably filling in the middle-ground between that stuff and 'real' code - it does more than just building somehting to complete today's task, and it is accretive in the sense that someone can build on top of it, but it doesn't really look that way to someone used to working on 'proper' software.
[1] Further reading if you're interested - https://news.ycombinator.com/item?id=27048672
To me 'accretive work' means something you do at a lower level than your task at hand which by itself doesn't count progress, but rather lay the groundwork for it so it's compounding from there on. AI has nothing to do with this.
I think the main argument that the billions spent are not going to be recouped is accurate, but I strongly suspect the cost of producing high quality code will remain the same -just being produced faster (speed, cost, quality - you still only get to pick two)
If one Steve Yegge can burn tokens in “Gas Town” that cost as much as me and ten others then you have saved my salary but spent it on Steve’s token use for roughly the same code quality as me steve and ten others would have produced in three months - just it took steve three days
Same price, faster delivery. Is that a win ? I suspect that facebooks recent announcement (“we cannot think of enough things to do with software so who wants our GPUs?” Might suggest that it’s a business model problem more than a software probkem
- on the one hand, time-to-market is super important. Getting to the right place faster is obviously better.
- on the other hand, figuring out right product/right fit is hard, and if a business spends that much cost every 3 days chasing every idea (most of which may be bad ideas), they’ve probably wasted a lot of money.
Obviously token costs are cheaper than developers, and local models would reduce costs still further. But the thought I keep coming to is: maybe there’s a benefit to slowing down and not jumping to implement?
I usually hear the opposite side (better to implement 10 things and throw out 9 of them, easier to react to prototypes, etc.). But I also think the infinity of possible ideas doesn’t get smaller when you throw more engineers or compute at it. You just end up exploring more, possibly bad ideas. This works out if exploring more of the space builds a greater understanding of the problem and increases the likelihood that one of your choices pans out. But the cost of exploring the space isn’t $0 and 0 time.
And it's certainly not the same price!
What products are you talking about? Because I see smaller teams or one man bands putting out low quality prototypes, but not teams of 10 delivering a years work in a sprint.
Everyone else in the team is now just aware of what's happening, and understand the architecture from the meeting to review / discuss it. But implementation and rollout is fast and just by the 2 of them.
The lead told me maintaining the quality was so much easier for the 2 of them with the right AGENTS.md lines, as he didn't have to spend time fixing guiding many people to do the right thing in PR reviews.
The closest I can explain this phenomenon to thos who are surpised was by the LLM variance section in this recent blog post:
I find these neologisms helpful as they quite precisely capture the intended meaning and are easy to remember. Doctorow is an impressive and entertaining communicator, and being an author he needs to market himself and his work, so fair play to him for trying to score a hit follow up to "enshittification".
The earliest use of "centaur" in this sort of context I know of is Kasparov's advanced chess idea from the late 1990s: "a bad (chess) player with a good computer program will always beat a good player with a bad program". How far we travelled since then...
The programs I've taken apart and looked at, even ones running in real industrial settings and large corporate factories, 90% of them are terrible.
Most code is just 'Today's Task.' The people who deny this are probably those working at IT service companies, because they build around maintainability and scalability.
But as you go down into hardware, there's an additional pressure: 'We don't know when this hardware will reach end-of-life.' The centaur metaphor is a simplified dichotomy. 'Centaur is good, reverse-centaur is bad.' But in reality, the vast majority of programs end up as disposable one-off code.
These days, AI related articles just seem to amplify whatever values people want to believe, turning into tribal warfare posts. Realistically speaking, you can write maintainable code with AI too. In fact, the 'Canonization' mentioned in this post is essentially pattern-templating, which AI does better.
The fundamental problem with AI code is that as the input prompt gets deeper, it introduces enterprise level complexity rather than the depth the program actually needs. I don't think that's the core issue here.
The advantage of human written code is that it can be complex when it needs to be and simple when it doesn't, but AI code tries to apply the same level of complexity everywhere. Honestly, the most widely used things in the world are CRUD, and I don't think they require that much complexity.
A good programmer applies the right level of complexity to the situation.
Even human written code leaks abstractions depending on requirements.
Take ORM as an example. Can you see the query count? Is there a rule to prevent N+1? Conditions like these keep getting added. It's just a matter of explicitly adding a layer to handle them.
These days, I see a lot of AI articles filled with nostalgia about how things were different in the past, and it catches me off guard. I'm not sure if that's really how Western programming culture was, or if where I am, the vast majority of work has always been just 'get it done.'
In my opinion, good programming is about choosing the right level of complexity based on the code's expected lifespan, likelihood of change, cost of failure, and transferability. I don't think everything needs to be maintainable.
The value you can extract from a tool depends on your skill in using it, and knowing when not using it.
These AI companies have stumbled upon the new cigarette. Did you know athletes in the 1920s would smoke cigarettes because they thought it improved performance? Cigarettes are just a tool, right? Of course, we could never be as stupid as they were...
I'm not sure that this is the only way, just the way that selfish, sloppy, or impatient actors within business often work. If more wealth is created, more efficiencies found, more problems fixed, new jobs created, these would also bring the returns desired.
It's mostly about why some people enjoy working with AI ("I get to build things I can use, that I couldn't build otherwise!)" and others don't ("This code is all slop and nobody understands it, and it makes me sad")
It touches a little bit about those two perspectives in general, which he calls centaurs (in charge of the work) and reverse centaurs (the work is in charge of them)
Some commentators are unknown for good reason, or otherwise not worth the effort to get to know. Cory Doctorow is not one of those.
A blog post loosely summarized as "HEY REMEMBER WHEN I COINED THAT TERM HERE ARE THE LINKS TO ALL THE TIMES I USED THE TERM AND HERES A NEW ANECODOTE ABOUT THE TERM" screams that its trying to force the use, and therefore the posters relevance.
